Wind power forecasting has gained significant attention due to advances in wind energy generation in power frameworks and the uncertain nature of wind. In this manner, to maintain an affordable, reliable, economical, and dependable power supply, accurately predicting wind power is important. In recent years, several investigations and studies have been conducted in this field. Unfortunately, these examinations disregarded the significance of data preprocessing and the impact of various missing values, thereby resulting in poor performance in forecasting. However, long short-term memory (LSTM) network, a kind of recurrent neural network (RNN), can predict and process the time-series data at moderately long intervals and time delays, thereby producing good forecasting results using time-series data. This article recommends a hybrid forecasting model for forecasting wind power to improve the performance of the prediction. An improved long short-term memory network-enhanced forget-gate network (LSTM-EFG) model, whose appropriate parameters are optimized using cuckoo search optimization algorithm (CSO), is used to forecast the subseries data that is extracted using ensemble empirical mode decomposition (EEMD). The experimental results show that the proposed forecasting model overcomes the limitations of traditional forecasting models and efficiently improves forecasting accuracy. Furthermore, it serves as an operational tool for wind power plants management.
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During the past few decades it has become clear that microbial communities can be found in the most diverse conditions, including extremes of temperature, pressure, salinity and pH. Enzymes such cellulases, pectinases and chitinases produced from actinomycetes play an important role in food, fermentation, textile and paper industries. Extremophilic actinomycetes have a remarkable enzymatic potential but till now natural resources due to the difficulties in isolation and maintenance of pure culture explored only up to a extend. For this purpose, 42 desert soil samples collected from different governorates; Minia, Assuit, sohag, Qena, and Luxor in Upper Egypt were investigated for the occurrence of alkalithermophilic actinomycetes. Isolation was done using actinomycetes isolation agar (AIA), starch nitrate agar and glycerol yeast extract agar at 550C. and pH 12. A total of 15 actinomycetes cultures were isolated, and purified at the same conditions then evaluated for their enzymatic activities of cellulase, pectinase and chitinase through primary screening on solid media. Among these, 13 isolates (86.6%) were found to be extracellular cellulase producers; 10 isolates (66.6%) were chitinase producers; 8 isolates (53.3%) were pectinase producers. Based on these results, we suggest that the extremophilic actinomycetes, which are a part of the biodiversity of the soils from different localities in Upper Egypt, are promising sources for novel enzymes and hence open exciting avenues in the field of biotechnology and biomedical research.
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Artificial neural networks (ANN), adaptive neural fuzzy inference system (ANFISSC) and support vector machine models were used to determine total dissolved solids (TDS) of the Zayendehrood River in Iran. Intotal, nine parameters (Ca2+, SO42-, Na+, Cl-,EC, pH, HCO3-, Mg2+ and SAR) were utilizedto estimate the TDS of the river at a monthly time scale. Statistical data were categorized into low-flow and wet periods based on river discharge. Principal component analysis (PCA) was used in order to determine the input of the models. The results indicate that the PCA method, in both wet and low-flow periods, performed suitably based on the evaluation criteria for all models. The parameters of the first component included Ca2+, SO42-, Cl-, EC, Mg2+ and SAR in both periods. In contrast, the parameters pH and HCO3- of the second component provided unacceptable precision. The ANFIS-SC model was more precise than the other two models, with an RMSE value of 12.33 for the first component in the low-flow period. However, the ANN model was most precise in the wet period, with a calculated RMSE value of 13.87.
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It is an unknown fact that many skin diseases have similar type of shape, size and symptoms. Hence, it is a cumbersome task to recognize and classify these diseases by the doctors. So, for the correct identification of skin disorders, doctors need to check the patient’s history alongside certain laboratory testing and physical examinations. But all these processes are time consuming and also costlier for a common man. Hence, this paper discusses a MATLAB based software system introduced to reduce the complexity and thereby providing accurate results. This system includes image preprocessing, features extraction and classification for prediction of the type of skin disorders. Besides feature extraction, the paper mainly focusses on the classification based on three classifiers—SVM (Support vector machine), KNN (Knearest neighborhood) and NB (Naïve Bias classifier)— and provides a comparative result based on various parameters. It can be concluded from the comparison tables that among the three classifiers, SVM provides the highest accuracy of 98.73% while KNN with 93.67& and NB with 84.81%. This classification helps a doctor to achieve the exactness of the type of skin disorder. In this system the patient needs to provide the image of the infected portion as input and the proposed system shall detect the disease.
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Analysis of learning behavior of MOOC enthusiasts has become a posed challenge in the Learning Analytics field, which is especially related to video lecture data, since most learners watch the same online lecture videos. It helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns for learners and predict their performance by MOOC courses video. This paper exploits a temporal sequential classification problem by analyzing video clickstream data and predict learner performance, which is a vital decision making problem, by addressing their issues and improving the educational process. This paper employs a deep neural network (LSTM) on a set of implicit features extracted from video clickstreams data to predict learners’ weekly performance and enable instructors to set measures for timely intervention. Results show that accuracy rate of the proposed model is 82%–93% throughout course weeks. The proposed LSTM model outperforms baseline ANNs, Super Vector Machine (SVM) and Logistic Regression by an accuracy of 93% in real used course’ datasets.
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Industrial insider threat detection has consistently been a popular field of research. To help detect potential insider threats, the emotional states of humans are identified through a wide range of physiological signals including the galvanic skin response, electrocardiogram, and electroencephalogram (EEG). This paper presents an insider risk assessment system as a fitness for duty security evaluation using EEG brainwave signals with explainable deep learning and machine learning algorithms to classify abnormal EEG signals indicating a potential insider threat and evaluating fitness for duty. The system is designed to be cost effective by using an Emotiv Insight EEG device with five electrodes. In this study, the data from 17 people in different emotional states were collected. The different levels of emotions were mapped and classified into four risk levels, namely low, normal, medium, and high. The data were collected while the subjects were presented with different images from the scientific international affective picture system. The collected EEG signals were preprocessed to eliminate noise from physical movements and blinking. The data were then used to train self-feature learning of two- and one-dimensional convolutional neural networks, Adaptive Boosting, random forest, and K-nearest neighbor’s models; the proposed method yielded classification accuracies of 96, 75, 97, 94 and 81%, respectively.
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The paper presents the research results of reinforcing phase formation mechanism as noticed at the grain boundaries of sintered Beryllium during hot forming of powders. It is shown that when the powders are heated, the amorphous Beryllium oxide film covering the metallic Beryllium particles degrades by a devitrification and crystallization of into discrete strengthening particles. It has been established that depending on the content and ratio of low-melting impurities this mechanism can have both a homogeneous and a heterogeneous character, which determines the "dispersion-grain-boundary" hardening effect. The results are obtained in the graphical and analytical dependences form characterizing the range of possibilities for controlling the strength properties of sintered Beryllium of TIP-56 grade obtained by hot isostatic pressing.
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Over the years, the use of carbon fiber in the automotive, aerospace, and energy industries has generated an increase in the global demand for these materials, causing problems with the management of the waste produced. The objective of this work is to compare the recycling process by pyrolysis with a microwave thermolysis process, and then to nano-reinforce the recycled fibers with carbon nanotubes using the Poptube technique. To determine the effectiveness of these methods, thermogravimetric analysis (TGA), Raman spectroscopy, infrared spectroscopy (FT-IR), X-ray diffraction (XRD), atomic force microscopy (AFM), and scanning electron microscopy (SEM). The carbon fiber is successfully recovered using both methods, the results obtained indicate that excess temperature and power generate damage to the surface of the fibers, increase the ratio in the D and G Raman bands (12% for microwaves and 24% for pyrolysis), in addition to the fact that there is an increase in surface roughness as the processes become more aggressive (15% for microwaves and 3% for pyrolysis). Although at a morphological level microwave thermolysis generates a greater change in the fiber surface, it is a process 50% faster than pyrolysis and requires more research. Regarding the growth of nanotubes using the poptube technique, in both recycled fibers the growth of these is observed, being reflected in the Raman spectrum with the appearance of the G 'band, however, it is necessary to improve the homogeneity and dispersion on the surface of the fibers.
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Burnt clay brick is one of the oldest and widely used construction materials. Its production with various waste materials can reduce the environmental hazards and improve brick performance at low manufacturing costs, thereby leading towards more sustainable construction. This research aimed to evaluate the effect of using waste marble powder (WMP) in varying percentages, i.e., 0, 3, 6, 9, 12, and 15%, by weight of clay in an industrial brick kiln plant. This study performed a range of mechanical and durability tests on the raw material, i.e., clay, WMP, and bricks, to quantify their performance. The results indicate that incorporating WMP resulted in a reduced unit weight of the bricks, making the structure lighter in weight. Furthermore, compressive strength and freeze-thaw test results for all the brick specimens and sulfate tests for the brick specimens with 12% WMP addition were within the Building Code of Pakistan and ASTM prescribed limits. Moreover, sulfate test results of brick specimens having 12% WMP were also within specified code limits. Finally, it can be concluded that WMP up to 12%, by weight of clay, can be incorporated to prepare clay bricks, reducing the environmental waste to achieve sustainability and economy for the brick industry.
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Speech signals recorded in far-field or with a far receiver typically comprise additive noise and reverberation, which cause degradation and distortion in the reliability and intelligibility of speech signal, and the recognition performance of speaker recognition systems, with severe consequences in a wide range of real applications. Channel equalization, i.e. the removal or reduction or other cleaning methods of the channel effects, to some extent, mitigates the mismatching problem at the cost of added distortions to the vulnerable speech signal themselves, and therefore, its effectiveness is limited. Recent research indicates that a new speaker feature, Gammatone frequency cepstral coefficients (GFCC), exhibits superior noise and reverberation robustness than other features. This paper proposed two methods to combat the effect of reverberation on speaker verification performance. The first method is using GFCC features as a robust feature to alleviate the effect of reverberation on system performance. While the second method is using multi training to combat the reverberation effect. Speaker verification experiments in the artificial and real reverberant conditions show the efficiency of the proposed methods in terms of decreased equal error rate EER and detection error trade-off DET.in terms of decreased equal error rate EER and detection error trade-off DET.
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In the present study TiO2-based nanomaterial doped with nickel (Ni) and praseodymium (Pr) ions were prepared using a facile and cost-efective method. Diferent techniques were used to characterize the nanoparticles. The crystalline nature of nanoparticles was confrmed by X-Rays Difraction analysis (XRD). The results revealed that with the introduction of Pr and Ni ions in TiO2 lattice the particle size decrease from 18.01 to 9.02 nm accomplished by the enhancement in surface area of the nanoparticles. The results obtained UV– Vis difused-refectance (DRS) spectroscopy of co-doped nanoparticles demonstrated a reduction in the bandgap compared with the pure TiO2. The variation in surface defects and oxygen vacancies were evaluated using Photoluminescence (PL) studies, which exhibited a gradual reduction in PL intensity upon the addition of dopants in the TiO2 Lattice. Furthermore, the exciton recombination was also investigated using electrochemical impedance spectroscopy (EIS), which showed the reduction in the recombination rate in doped nanoparticles. The photocatalytic activity of prepared nanoparticles was tested on three azo dyes (reactive blue-19 (RB-19), reactive orange-13 (RO-13), and reactive blue-13 (RB-13)). The experimental results showed that 99.0% of RB-19, 85.0% for RB-13, and 70.0% RO-13 were degraded after a small interval of irradiation. The adsorption of dye on the photo-catalytic support was checked by performing the experiments under diferent conditions by varying the pH values and temperature. Furthermore, the catalysts also exhibit remarkable activity for the overall water splitting in an alkaline solution with NiPr-3 catalyst being the best, which recorded onset overpotential of 330 mV and a Tafel slope of 76 mV dec−1 for Oxygen evolution reaction (OER). It is also clear that Ni–Pr-3 (with 5% nickel and 3% Pr) content is more active in the OER process showing comparatively lower over-potential value at 10 mA/cm2 than Ni-5 and TiO2.
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Artificial intelligence systems (AIS) are increasingly penetrating the most intimate spheres of our lives. Although this technology is promising in many ways, the ethical and social issues associated with its use are numerous. Despite their ubiquity, AIS are in most jurisdictions only subject to a minimal normative framework. The many legislative reforms that have taken place and those that are underway, mainly oriented towards the protection of personal and biometric data, are nevertheless insufficient to provide an adequate framework for this disruptive technology. Over the last ten years, publications in AI ethics have also multiplied. Whether they come from the academic world, public bodies or industry, these statements and other guidelines, guides and codes of ethics aim to orient the design and use of AIS. These documents therefore aspire to play a normative role in AI-related practices, but as they lack binding mechanisms and enforceability, many consider them inadequate. For this reason, AI ethics initiatives are even sometimes greeted with suspicion. Indeed, faced with the increasing misuse of AI ethics for purposes of what is commonly characterized as ethics washing, i.e. a public relations strategy designed to discourage more restrictive legislative initiatives, many believe that ethics has no place in the normative framework of AI and that it should once and for all give way to state law. While it is impossible to deny the existence of this instrumentalization of AI ethics, it would certainly be wrong to reduce AI ethics to these misuses. Moreover, the disqualification of ethics on the basis of its lack of binding force shows a misunderstanding of what ethics really is. In order to understand the particular function that ethics can play in the normative framework of AI, it is important to distinguish it from law and to situate it in relation to it. In this presentation, I will propose to reflect on these questions by using conceptual tools from the theoretical framework of information ethics developed by Luciano Floridi. I will argue that Floridi's distinction between what he calls hard ethics and soft ethics is particularly useful for distinguishing two functions of AI ethics and for thinking about their relationship to law. In the presentation, ethics and law will be analysed as distinct but complementary normative sources from the perspective of normative pluralism.
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Cancer remains leading cause of death worldwide. Several therapeutic modalities are available for the treatment of cancer however, these therapies are completely effective only when cancer is detected in its early stage. Early detection of ailment requires a noninvasive, easy to handle, fast, cost effective and reliable technology for which results are easy to interpret. A plethora of studies have confirmed the association of altered mitochondrial enzyme activities with cancer development and progression. Mutations and defective function of mitochondrial dehydrogenases i.e. succinate dehydrogenase (SDH), Isocitrate dehydrogenase (IDH), and alpha ketoglutarate dehydrogenase (KDH) are considered the cause of cancer progression and tumorigenesis. The activity of mitochondrial enzymes in patient’s sample/bodily fluids such as in blood, urine, tissue and cells may act as biomarkers for the early detection and better prognosis of various oncological disorders. The aim of present research work is to develop a noninvasive, rapid, reliable, cost effective and less laborious and simple method based on biochemical criteria for assessment of defects in mitochondrial enzyme systems for the early diagnosis of cancer. Methods: The blood samples of 50 cancer patients and 50 healthy individuals were collected for the study. The mitochondrial dehydrogenase enzyme activity of leucocytes, was estimated using an optimized colorimetric assay. The optimized protocol included the RBC lysis of blood sample and digitonin detergent mediated leucocyte permeabilization to release cellular enzymes in to buffer. The nitro blue tetrezolium dye was oxidized by mitochondrial dehydrogenases in to farmazan crystals. The dehydrogenase activity was measured by taking optical density of DMSO dissolved farmazan crystals using spectrophotometer. Enzymatic activity was estimated in pmol/mg of protein present in sample. Results: The dehydrogenase activity in cancer patients was found to be significantly higher (p<0.0001) than healthy control subjects. Statistical significance was measured by student’s ‘t’ test. Our results revealed that cancer patients have two folds (1.31±0.52) of enzymatic activity than healthy individuals (0.58±0.24). Conclusion: We have developed dehydrogenase enzyme activity based colorimetric assay, which is a noninvasive, reliable, fast and inexpensive approach for preliminary diagnosis of oncological disorders.
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In this work, we have done a numerical simulation for convective flows laminar and stationary for (cooling the electronic components) by using water (base fluid) and Cu, Ag, and Diamond Nanoparticles with a volume fraction of 0.02 to improve heat exchanges. For this reason, we have carried out three studies in the form of geometry. The first study was divided into small channels of 10 channels and 11 fins. The second study concerns the effect of the addition of the pie shape ribs and parallelogram ribs in micro channels on thermal performance. The third study concerns the effect of three different types of Nanoparticles, Nanoparticles volume concentration and types of cooler metals on heat transfer in a mini-channel. Through these studies, we can conclude: • The micro-channels with parallel ribs were more efficient at transferring heat compared to micro-channels with pie shaped ribs. • The increase of the fraction volumetric of Nano-particles in basic fluid (water) allows ameliorating the heat transfer coefficient in a mini channels cooler, especially with the decrease of the Nanoparticles diameter. • For the two cooler metal types, the copper cooler is better in the reduction of the temperature.
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In this work, we investigate the cumulative effects of laser and spin orbit interaction (SOI) on the thermodynamic properties of a quantum pseudodot using the Tsallis formalism. Through the evaluation of the energy we derive some thermodynamic properties at the accessible states. From the results obtained, we found that contrary to the SOI, laser field is a suitable external field to reduce the rate of entropy (disorder) in quantum system, this allows to have a control upon the spin alignment, increase the number of accessible states but stabilizing our system at the same time (reduce the rate of entropy) due to the great effect of laser field. Therefore, the combined effects of laser field and SOI are an important parameter to enhance the thermodynamic quantities and more define the spin alignment of quantum system.
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Nowadays, researchers are of great interest to innovate new low cost materials for solar cells. Several research works focused on the Pyrite (FeS2, FeSe2,..etc) films fabrication because of their low cost. The used synthesis technique was the simple spray pyrolysis followed by a heat treatment. Indeed, this technique is efficient for obtaining pyrite thin films having good properties for several applications. In a first step, the simple and non-toxic technique: spray pyrolysis, was used for growing amorphous iron oxide thin films; which were heat treated under sulfur or selenium at several temperatures, in a second step. But, unconvincing results were obtained. FeS2-Pyrite, for example, is a promising candidate for absorption and photocatalysis. Furthermore, it is of great interest in applications of renewable energy conversion due to its high optical absorption coefficient, but to use it in this area, improving its optical and electrical properties is greatly necessary. However, researchers thought about its alloying/doping in the aim to improve its optical and electrical properties to make it more effective as a photovoltaic material. Different alloying elements/dopants were chosen. In this work, the ruthenium has been treated as a good candidate for alloying the FeS2 and FeSe2 thin films, in the aim to improve their properties since they were chosen among the low cost materials for solar cells. Hence, an aqueous solution of FeCl3.6H2O (0.03 M) was sprayed for 5 min onto glass substrats, pre-heated at 350°C. On which, was srayed, immeadiatly, for 1 min, an aqueous solution of RuCl3.3H2O (0.03M). Blackish amorphous layers were obtained, that were heat treated under sulfur or selenium atmosphere (~10- 4Pa) at various temperatures. The obtained thin films presented a high absorption coefficient and direct band gaps corresponding to desired photon energies values for the estimated applications. The obtained results are an interesting plus in this domain.
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Based on a look-back analysis of the obtained research results and a comparison of these results with those in available publications the following conclusions were made: 1. The morphology of corrosion products formed on the inside surfaces of NPP systems has a four-layer structure. a. A layer of solid corrosion deposits tightly bonded with the surface is formed above the oxide film. b. Tightly bonded deposits are under loosely bonded («loose» or dissipative) corrosive deposits layers that are dynamically balanced with the corrosion products particles dispersed in the water coolant. 2. Phase composition of solid corrosion products depends on the presence of ferrous (II) and ferric (III) iron oxide-hydroxide compounds whose ratio depends on water chemistry conditions: Under reducing water chemistry conditions, the phase composition of all corrosion products is determined by a spinel structure of magnetite (Fe3O4). Under oxidizing water chemistry conditions, the partial oxidation of ferrous (II) iron ions results in a defect structure of nonstoichiometric magnetite FeА3+ [Fe2+1-хFe3+]BO4-х, where А and B are two non-equivalent positions in magnetite structure. At х = 1 a nonstoichiometric magnetite structure changes into hematite α-Fe2O3 or maghemite γ-Fe2O3 structure. It is noted that the mathematical models nowadays used for description of mass exchange and mass transfer of corrosion products in NPP primary systems do not consider physico-chemical processes leading to the formation of such complex (phase, disperse, chemical, radionuclide) composition of corrosion products. The paper presents a diffusion model for corrosion products mass exchange and mass-transfer in the “steel/water coolant” system as an alternative to the electrochemical model for general corrosion. The diffusion model provided better insight into understanding how the phase, disperse, chemical, and radionuclide composition of steel corrosion products is formed in the coolant of NPP primary system.
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In this work, we propose the implementation of three carbon sponges, generated from the carbonization of melamine-formaldehyde sponges coated with different HKUST-type metal-organic frameworks (MOFs) in different thermal conditions. The employed MOF precursors were trimesic acid (BTC), nickel and cobalt salts. The monometallic HKUST type MOFs were synthesized using a simple method of controlled precipitation, which starts from two precursor solutions. It was revealed that the carbon sponges can selectively absorb oil in the water/oil mixture, possessing magnetic and enhanced hydrophobic and superhydrophobic properties. All the pyrolyzed carbon sponges, obtained at 500 and 700°C, were not the most optimal since they had absorption capacities of around 25 g/g and only supported up to 4 absorption cycles. On the other hand, the carbon sponges, obtained at 300°C, had absorption capacities greater than 40 g/g.
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The minimum amount of tooth preparation that can be fully controlled is crucial in achieving long-term, stable, and effective aesthetic restoration, which is also a major difficulty in aesthetic restoration. The tooth preparation can be imple- mented efficiently and accurately through digital technology based on the fixed-deep hole guiding technology. Prior the actual tooth preparation, the technology first designs the virtual contour, layering, and virtual occlusion of the prosthesis on the computer. Then, virtual tooth preparation is carried out by cutting back according to the virtual prosthesis. Next, the virtual drilling operation plan is designed according to the shape of the virtual tooth preparation and the contour of the abutment tooth. Finally, the tooth preparation guide plate is designed and printed in 3D. It realizes the whole process of quantitative and precise guidance of dental preparation, visualizes the restoration space, reduces the clinical operation time, and guarantees the quality of dental preparation. It also promotes the improvement of the teaching quality of digital practical exercises.
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The aggregation of nanoparticles is a natural phenomenon caused by a variety of biological and physical conditions, yet these aggregates are extremely unstable. If the aggregates had been stable, they may have been valuable for various biological applications. For the optical characteristics and photothermal heat generation of stable aggregates of metal nanoparticles, a forced synthesis of aggregates of tiny gold nanorods was carried in a mixture of Dulbecco's Modified Eagle's medium (DMEM) and Bovine Serum Albumin (BSA). To investigate the photothermal characteristics of hexadecyltrimethylammonium bromide (CTAB) coated gold nanorods, they were transformed into stable aggregates. The absorption spectra of aggregates revealed a redshift when compared to monodispersive gold nanorods, confirming that the form and size of the aggregate rely on the quantity of BSA in DMEM as well as the concentration of CTAB in the stock solution of the gold nanorods suspension. Greater redshift is caused by a higher concentration of BSA in DMEM and a lower concentration of CTAB in monodispersive gold nanorod solution. The well-defined plasmonic absorption resonance peaks of gold nanorod aggregates were therefore tailored up to the second biological therapeutic window. Gold nanorod aggregates were shown to be stable for at least one week after being synthesized. These aggregates were also photothermally studied utilizing a high power broadband near-infrared light source. Aggregates were also photothermally stable, implying that they can be used in similar applications repeatedly. The photothermal conversion efficiency of these stable gold nanorod aggregates was better than that of the nanorods in their monodispersive state. Stable aggregates' optical and photothermal capabilities might be beneficial for biological therapeutic applications, sensing, deep tissue light penetration for imaging, and other scientific applications by redshifting the plasmonic absorption resonance peak and enhancing the photothermal effect.
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The study aimed to evaluate the pro-, antioxidant and biological activities of newly synthesized nanocomposites of RGO and its combinations with silver and copper by luminescent and microbiological assays. The antimicrobial activity was tested during 24h against E.coli and St.aureus. The Gram-positive bacteria were more resistant than the Gram-negative. Strongest antibacterial effect was demonstrated by the graphene nanocomposite decorated with silver and copper. The cubic silver nanoclusters size 30nm showed better bacteriostatic effect than the spherical nanoparticles on reduced graphene sheets with 43nm diameter. The eukaryotic cell cytotoxicity effect was evaluated with two cell lines - MDCK-kidney-epithelium-noncancerous-cells and A549- lung-cancerous-epithelium-cells, tested for 24h. Our results showed that RGO Ag:Cu had stronger cytotoxic effect on eukaryotic cells. We have discovered that the cancerous A549 cells show stronger sensitivity to the nanomaterials than the noncancerous MDCK-cells. The pro- and antioxidant activity of all nanomaterials were studied according to the free-radical oxidation reactions (pH 7.4 and pH 8.5) in the following chemiluminescent model systems: 1) Chemical, with Fenton`s reagent (H2O2- FeSO4) - for the generation of hydroxyl radicals (.OH) 2) Chemical, with oxidant hydrogen peroxide (H2O2) 3) Chemical (NAD.H-PhMS), for the generation of superoxide radicals (O2.-). All tested nanomaterials presented definitive antioxidant activity in both tested media at neutral and alkaline pH. The only exception was RGO Ag nanoparticles, sized 30nm, that exhibited less than 10% prooxidant activity in the Fenton`s system, at pH 8.5. Those results support the idea to use such nanomaterials in body implants.
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The security of healthcare and telemedicine systems is a critical issue that must be significantly investigated. Several smart telemedicine applications are expected to be adopted in the medical sector in the incoming years. Healthcare smart products that are connected through Internet to be accessible anytime and anywhere are expected to deal with critical and confidential information such as personal medical images. Therefore, medical image encryption is an important task in telemedicine and healthcare applications. This paper presents an efficient cryptosystem for medical image security based on exploiting the advantages of the de-oxyribo nucleic acid (DNA) rules and chaos maps. In the proposed medical image cryptosystem, logistic chaos map, piecewise linear chaotic map (PWLCM), and DNA encoding are employed. The PWLCM is employed to generate a secret key image. Then, the DNA rules are utilized for encoding the secret key image and the input plain image by rows that are encoded with the logistic chaos map. After that, the proposed logistic map is employed to obtain an intermediate image as another secret key image to set DNA functions row-by-row on the coded original image. Moreover, the intermediate image is decoded in the following stage. Finally, the previous actions are iterated through image columns once again to obtain the best ciphered image. The experimental results reveal that the suggested cryptosystem has a high security with an acceptable processing time. In addition, it can resist various kinds of attacks, such as known-plaintext and chosen-plaintext attacks.
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This paper investigates the networked control system (NCS) with faults and disturbances which affect both actuator and sensor. A fast high-order sliding mode (FHOSM) controller is proposed to compensate for actuator faults and sensor disturbances, which is constructed based on the estimated information of the NCS. Accordingly, an adaptive observer with multi-stage is designed to estimate the states and sensor disturbances and protect the NCS from actuator faults. Furthermore, a new sliding function is assembled to realize the finite-time convergence of system states. The stability of the system with the suggested procedure is illustrated by the stability analysis underneath the designed control law. Finally, the simulation results are implemented and confirm the effectiveness of the proposed method in defending the system against both issues and inhibiting system failures.
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This work was to verify the effect of chitosan (2 and 4 g/L) and its nano-form (0.2 and 0.4 g/L) against blue rot disease on apples and their effect on the expression of six defense-related genes as well as fruit quality parameters Acetic acid was the best effective treatment where it completely inhibited the growth of tested P. expansum. Nano-chitosan was more effective at reducing the growth of P. expansum than their natural forms. Chitosan at 2 g/L was the least effective at reducing P. expansum growth. The highest percentage of decay incidence was recorded for water check treatment. Chitosan nanoform performed better as compared to its raw material for both artificial and natural infections. The highest efficacy was obtained for nanochitosan at 0.4 g/L. In vivo test, the most effective treatment was combined treatment between acetic acid vapor followed by chitosan solutions, which reduced the disease and rotted part tissues by more than 87.0%. The highest firmness value was recorded for nano-chitosan at 0.2 g/mL. Additionally, control fruit show the lowest firmness values in all cases. In most cases, chitosan in raw material at 2 g/L gave the highest TSS values. The studied genes were chitinase, peroxidase, b-1, 3-glu, XET, PR8, and PAL1. In general, both chitosan NPs and bulk material at both concentrations upregulated the six defense-related genes’ expression with different patterns higher than the other treatments. The most pronounced increase of the studied genes’ accumulation was observed in leaves of apple trees treated in the field with chitosan NPs compared to the treatment with chitosan bulk and acetic acid after 48 h from the treatments. The mRNA quantity of chitinase, peroxidase, b-1,3- glu, XET, PR8, and PAL1 genes were much higher in apple tissues treated with chitosan NPs at 0.4 mg/mL (8.3, 5.4, 7.8, 27.5, 6.8, 7.9-fold increase, respectively) than other treatments. The expression of XET defense-related gene recorded the highest mRNA quantity in response to chitosan NPs at both concentrations 0.2 and 0.4 mg/mL, as well as chitosan bulk at 4 mg/mL (18.6, 27.5, 12.1-fold increase, respectively). All studied genes recorded the lowest mRNA quantity in response to acetic acid, which confirmed the chitosan efficiency at SAR induction against the investigated pathogen. Noteworthy, there was a great difference between the levels of expression in response to chitosan NPs and chitosan bulk materials at 0.4 and 4 mg/mL, respectively.
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High entropy alloys (HEA) and nitride (HEN) are currently very attractive to the automotive, aerospace, metalworking and materials forming manufacturing industry, among others, for exhibiting higher mechanical properties, wear resistance, and thermal stability than binary and ternary alloys. In this work, the effect of nitrogen content and bias voltage on the microstructure, chemical composition, mechanical and tribological properties as well as on the thermal stability of the high entropy (TiTaZrNb)Nx coatings deposited by direct current reactive magnetron sputtering was studied. H13 hot-work steel samples were used as substrates. The chemical composition and microstructure of the coatings were analyzed by scanning and transmission electron microscopy SEM, TEM, atom force microscopy AFM and X-ray diffraction. The mechanical and tribological properties were determined by nanoindentation and using a ball on disk tribometer, while the thermal stability was evaluated by thermogravimetry and differential scanning calorimetry TGA and DSC. All the deposited coatings exhibited a columnar structure of cubic phase fcc. The grain size and surface roughness decreased with increasing nitrogen content and bias voltage, while hardness and wear resistance increased reaching a hardness of 32 GPa. All coatings showed thermal stability up to 800°C and began to oxidize above this temperature until complete oxidation at approximately 1000°C.
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A composite material prepared by polymerization of β-cyclodextrin (β-CD) on the surface of natural hydroxyapatite using citric acid as cross linker, was employed as electrode material for the detection of Pb(II). Hydroxyapatite was obtained from bovine bones, following a three-step procedure including pre-calcination, chemical treatment with (NH4)2HPO4, and calcination. The structure and morphology of the pristine hydroxyapatite (NHAPP0.5) and its functionalized counterpart (NHAPp0.5-CA-β-CD) were examined using XRD, FTIR, and SEM. Upon deposition as thin film on a glassy carbon electrode (GCE), the ion exchange ability of NHAPp0.5-CA-β-CD was exploited to elaborate a sensitive sensor for the detection of lead. The electroanalytical procedure was based on the chemical accumulation of Pb(II) ions under open-circuit conditions, followed by the detection of the preconcentrated species using differential pulse anodic stripping voltammetry. The reproducibility of the proposed method, based on a series of 8 measurements in a solution containing 2 μM Pb(II) gave a coefficient of variation of 1.27%. Significant parameters that can affect the stripping response of Pb(II) were optimized, leading to a linear calibration curve for lead in the concentration range of 2×10−8 mol L−1 – 20 × 10−8 mol L−1 (R2 = 0.998). The detection limit (3S/M) and the sensitivity of the proposed sensor were 5.06×10−10 mol L−1 and 100.80 μA.μM−1, respectively. The interfering effect of several ions expected to affect the determination of lead was evaluated, and the proposed sensor was successfully applied in the determination of Pb(II) ions in spring water, well water, river water and tap water samples.
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The fast growth of nanotechnology opens up new opportunities in biomedicine, notably tumor therapy. The cellular absorption of nanosystems is not only a key assumption for realizing a wide range of biomedical applications, but it is also an important factor in determining the eventual therapeutic impact. It is necessary to provide a comprehensive and detailed overview of recent research breakthroughs on the increase of nanosystem cellular absorption for cancer therapy. Based on how they enter the body, nanosystems may be classified into three groups. The first portion tries to stimulate the extracellular microenvironment in order to promote nanosystem accumulation and penetration into tumor cells. The second segment requires active targeting to enhance extracellular to intracellular cellular incorporation. The third component seeks to enhance therapeutic intracellular retention by subcellular localization. The essential components of delivery can be employed to construct multifunctional nanosystems for improving tumor therapy. Finally, the key challenges and opportunities of the growing research frontier are thoroughly examined. Herein, we present novel ideas, intriguing methodologies, and prospective approaches for building enhanced anticancer nanosystems for therapeutic use.
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In this paper, a study on the microhardness and flow strength (tensile) of a shell of an African Giant snail (Achatina Fulica) was studied as a function of indentation load. This study aimed to investigate the resistance of the shell to indention and external pulling force to determine its suitability for biofillers to be used as polymer reinforcement. In this regard, the influence of loading direction on the hardness of the nacreous (inner layer) and prismatic (outer layer) structure of the shell material was analyzed to determine the hardness variance on both layers. The results revealed that microhardness measured on the shell was dependent on the load on the nacreous and prismatic structures. Indentation loading between 50-500 kN induced tensile strengths that ranged between 675-1050 and 390 -810 MPa on the prismatic and nacreous layers, respectively. Also, the morphology of the shell surface exhibited an interlocking structure with a large surface for binding to the organic matrix. The observed reinforcement of the shell explained the hardness property of the shell. The improved hardness of the shell suggests that it can be beneficiated into filler that may be used to improve the mechanical properties of polymeric composite materials.
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Traditional human–computer interaction devices such as the mouse and keyboard have now become ineffective for the recent virtual environment applications. Gestures provide more natural and user friendly substitute to such external devices for interaction between computer and human. Hand gesture recognition systems are built so as to make the interaction between human and computer easier. This is done by making them learn the gestures being provided to them so that the next time a person gesticulates, the gesture class is being identified by the machine. The primary objective of any gesture recognition system is to build a model that has the ability to recognize human gestures and use them to control an application. In this thesis, hand gesture recognition system has been proposed which works under different scenarios such as presence or absence of variation in gesticulation speed and pattern. The first system is developed in the absence of any variation in gesticulation speed or pattern. It has been observed that some of the gestures consist of unwanted movements during gesticulation. This is termed as ‘self co-articulation’. The self co-articulated strokes were detected using speed information during pre-processing stage of the system. The major contribution in this system is the introduction of four new features such as position of the hand, self co-articulated features, ratio feature and first half trajectory features. Moreover, a hybrid classifier is designed which performs better than the individual classifiers. The second system is developed to handle the variations in the gesticulation pattern while making a gesture by different users. The gesticulation pattern of a gesture may vary from person to person. In many of the earlier researches, the users were allowed to gesticulate according to the reference template pattern provided to them. This restricted the naturalness of the system. To make the system independent of the gesticulation pattern, two new features such as left sector trajectory features and right sector trajectory features were added along with the features developed in the first system. Thus, the combination of these features provides satisfactory results under varying gesticulation pattern. In the third system, the users were allowed to gesticulate at natural speed with the provided gesture pattern. Different users gesticulate at different speed for the same gesture. Variation in speed and time of gesticulation of a particular gesture results in variation in length of the extracted trajectory. In the proposed system, the variations in the gesticulation speed were addressed using two level normalization processes. In the first level, the trajectory points were aligned based on dynamic time warping (DTW) technique and 10 best gestures were selected based on minimum distortion. The final gesture is selected from the pre-selected gestures using Euclidean distance in the second level. In order to make a hand gesture recognition system more natural and user friendly, the fourth system was developed where the users were allowed to gesticulate with bare hands. But, it has been observed that in any vision based approach, hand detection and hand tracking is a challenging task due to difficulties like variations in its appearance, complex and dynamic background, occlusion problem and illumination changes. Thus, a hand gesture recognition system is developed using bare hand which is independent of the above problems occurring during gesticulation. A combination of three frame differencing for colored frames, three frame differencing of grayscale frames and skin filtering were used to detect the hand from the background. For tracking the hand, Kanade-Lucas-Tomasi (KLT) algorithm has been modified and some additional features like compact criterion, orientation information and velocity information were added to the existing KLT algorithm. It has been observed that this modified KLT performed better than the KLT and CamShift algorithms. A hand gesture recognition system was developed which could identify meaningful gestures within the continuous stream of hand motions. A continuous sequence may comprise of three types of strokes such as gesture strokes, self co-articulated strokes and movement epenthesis. Each meaningful gestures needs to be identified which is done in gesture spotting phase. Gesture spotting is essential for a recognition system to work continuously without need of human intervention. By spotting gestures in a continuous video stream, the unintentional movements arising between the gestures (movement epenthesis) or within the gestures (self co-articulation) can automatically be removed. Two basic problems in continuous gesture recognition is addressed in this system. Firstly, identifying self co-articulation and movement epenthesis from the normal gesture strokes in a continuous gesture sequence and secondly, differentiating movement epenthesis from the self co-articulation for gesture spotting. The combination of speed and pause information were used to obtain the movement epenthesis and self co-articulated strokes separately. Thus, the meaningful gestures are separated from the continuous sequence. The proposed system for unintentional movements detection and subsequent recognition of individual gestures in a continuous stream of gestures promises to perform well on different types of gesture sequences having different spatiotemporal characteristics and motion behaviour.
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Governments, enterprises, civil organizations, academics, are all engaged to promote normative guidelines aiming to regulate the development and application of AI in different fields. Although there have been more than 160 guidelines proposed globally, it is still uncertain whether or not they are sufficient to meet the governance challenges of AI. Given the absence of a holistic theoretical framework to analyze the potential risk AI, it is difficult to see what is overestimated and what is missing in the extant guidelines. Based on the classic theoretical model in the field of risk management, the research developed a four-dimension structure as a benchmark to analyze the risk of AI and its corresponding governance measures. The structure is consisted of four pairs of risks including specific-general, legal-ethical, individual-collective and generational-transgenerational. Using the framework, a comparative study of the extant guidelines is conducted by coding the 123 guidelines with 1016 articles. We show that the extant guidelines are indeed eccentric while collective risk and generational risk are largely underestimated by stakeholders. Based on the analysis, three gaps and conflicts are proposed for future improvements.
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Objectives and scope : Silver (AgNPs) and Cupric oxide nanoparticles (CuONPs) were phytofabricated utilizing leaf extract of Simarouba glauca (SG) and aerial extract of Celastrus paniculatus (CP) to evaluate anticancer effect and to verify the apoptosis, cell cycle analysis. Methods used: Free radical scavenging assays like DPPH, ABTS and NO; MTT assay, flow cytometry and caspase-3; EAC model with biochemical and haematological parameters. Results and discussions: Characterization was validated using FTIR, SEM-EDX, TEM, XRD and UV-Vis analysis. The green synthesized AgNPs and CuONPs showed potent antioxidant potential with IC50 value of about 34.01+0.64 µg/mL correlated to ascorbic acid. The anticancer activities on cancerous cell lines like MCF-7 and HT-29 cell lines revealed that AgNPs and CuONPs synthesized using S. glauca and C. paniculatus indicated IC50 values ranging from 70.85+0.67 to 240.6+0.57 µg/mL. They could not effectively prohibit the growth of immortalized normal human breast epithelial cell lines (MCF-10A). To be more precise for anticancerous effect, molecular mechanism was examined in MCF-7 cell line treated with CuO-CP NPs by cell cycle analysis that depicted 75.28 % of cell arrest in Sub G0/G1 phase and 71.29 % of cells were gated in late apoptotic phase of Annexin V and propidium iodide (PI) compared to control cells. The synthesized nanoparticles also demonstrated less hemolysis efficiency and are evidenced by SEM images. We have also evaluated the in vivo antitumor efficacy of CuO-CP NPs treated against Ehrlich ascites carcinoma (EAC) bearing C57 mice for the first time and examined by variations in growth parameters, biochemical assays (like lactoperoxidase, reduced glutathione and myeloperoxidase), hematological profile, and histopathological analysis in comparison with control. Conclusion: The green synthesized nanoparticles exhibited effective control of cancer cells for both in vitro and in vivo laboratory conditions and thus can be evaluated in preclinical cancer models.
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In today’s scenario, non-Linear self-sustaining oscillations otherwise called as limit cycles is one of the most important entity that limits the performance of most of the physical systems in the world. It is a formidable task to suppress the limit cycles for 2x2 systems with memory type nonlinearity in particular. Backlash is one of the nonlinearities commonly occurring in physical systems that limit the performance of speed and position control in robotics, automation industry and other occasions. The feasibility of suppression of such nonlinear self-oscillations has been explored by using pole placement technique. The novelty of the work lies with the investigation in case of the memory type non-linearity like backlash especially which is an inherent Characteristic of a Governor used for usual load frequency control of an inter-connected power system and elsewhere. Suppression of Limit Cycle using pole placement is adopted either arbitrary or optimal selection using Riccati Equation through State Feed Back. The analysis is based on harmonic linearization / harmonic balance method using graphical method which has been substantiated by digital simulation / use of SIMULINK Tool Box and the same have been illustrated through example. The Poles / Eigen values are determined for Limit Cycling Systems with Memory type nonlinearities whose describing functions (harmonic linearization) are complex functions of X and ω. Hence, it is felt necessary to develop a graphical technique using harmonic balance method. The poles of such systems are shifted or placed suitably by state feedback so that the systems do not exhibit limit cycles. The optimal selection of feedback gain matrix K for suppression of Limit cycle has not been addressed elsewhere. There is ample scope of extension of the present work for prediction of limit cycles and it’s suppression in 3 X 3 or higher dimensional memory type nonlinear systems. Example 1 - Consider a system as shown in Fig.1 where N1 and N2 are two nonlinear elements with backlash type input-output characteristics. G1, G2 are the transfer functions of the linear elements. Backlash nonlinearities contribute additional phase angle to the loop phase angles of G1 (j ω) and G2 (j ω) of the subsystems (s1) and (s2). The graphical method developed in the present work has been illustrated through the Fig. 2 for the example 1. The results are shown partially in
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The implantation of transition metal ions into insulators is an effective way to create nanostructured composite materials on both sides of the insulator-to-metal transition (IMT) with different mechanisms of electron transfer, both diamagnetic and magnetically ordered. The report provides a systematization and generalization of the results of studying the electrical, magnetic and galvanomagnetic properties of metal-polymer composite materials obtained by implanting magnetic (Fe+, Co+), and non-magnetic (Cu+, Ag+) metal ions with an energy of 40 keV in the dose range D = 1 ×1016–1.5×1017 cm-2 into thin polyimide (PI) and polyethyleneterephthalate (PET) films. The temperature dependences of resistance and magnetization, as well as magnetization hysteresis loops and magnetoresistive effect in the temperature range of 300-4.5 K and magnetic field up to 5 T have been studied. It has been established that the implantation of magnetic ions leads to the IMT, while the IMT is not observed at Cu+ and Ag+ ions implantation. The metal-polymer composite PET-Fe at low temperature shows metallic conductivity, while PI-Co - weak localization processes. In the PI-Co composites the size of the formed Co clusters was determined, which varies in the range of 4-11 nm depending on the dose. On the isulating side of the IMT in the PI-Cu and PET-Ag composites the magnetoresistance was not measured, while in PET-Fe and PI-Co, regardless of the side of the IMT and in the weak localization mode, it has a negative sign. The effects of the catalytic action of magnetic ions on the processes of graphitization in carbonized polymer layer and the π-electrons blocking are discussed.
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This paper discusses about the power source for e-vehicle obtained from solar panel. A buck boost converter is used to get a stable voltage / current output from the solar panel. The energy will be stored in battery. Then electric-vehicles utilizes the energy from the battery and charges to same level via solar panel. The e-vehicle driven by the DC geared motor via motor driver. Voltage sensor is used to find the charge of the battery. Current sensor is used to compare the charge from the converter to battery. The temperature sensing is done in order to find the health of the vehicle and monitor it. To detect obstacles and pot holes to avoid accidents ultrasonic sensors are used. Microcontroller receives all the values as input, and then ARM microcontroller monitors the values through IOT and display on the LCD. Thus, the E-vehicle’s energy and health are monitored through IOT.
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Spherocobaltite, CoCO3, or cobalt carbonate is a mineral of calcite group. It finds applications in high-performance Li-ion batteries, as an animal feed trace element, and also used as a precursors to synthesize cobalt oxide. In this study, spherocobaltite nano-particles are synthesized by hydrothermal method using cobalt sulphate solution and ammonium carbonate solution in the ratio of 1:3. The mixture was then transferred into a 100 ml Teflon-lined autoclave and heated at 140 °C for 4 h. The final sample is recovered by centrifugation redispersion cycle. The hexagonal crystal structure is confirmed by powder XRD, and the average crystallite size is determined by Scherrer’s formula and Williamson-Hall method, which is found to be within 14 nm. The presences of Carbon, Oxygen and Cobalt were confirmed by EDAX analysis. The TEM image suggests spherical morphology of the nano-particles. The FT-IR spectrum confirms the presence of functional groups such as O-H and C-O. The TGA curve suggests that the material starts dehydrating first and then decomposes in to cobalt oxide at 390o C temperature. The impedance and dielectric studies are carried out within 20 Hz to 2 MHz range at room temperature. The complex impedance and modulus plots are drawn and exhibiting the grain effect only. The VSM data taken at room temperature and low temperatures from 20 K to 300 K suggests a paramagnetic nature of the sample.
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While computational fluid dynamics (CFD) can solve a wide variety of fluid flow problems, accurate CFD simulations require significant computational resources and time. We propose a general method for super-resolution of low-fidelity flow simulations using deep learning. The approach is based on a conditional generative adversarial network (GAN) with inexpensive, low-fidelity solutions as inputs and high-fidelity simulations as outputs. The details, including the flexible structure, unique loss functions, and handling strategies, are thoroughly discussed, and the methodology is demonstrated using numerical simulations of incompressible flows. The distinction between low- and high-fidelity solutions is made in terms of discretization and physical modeling errors. Numerical experiments demonstrate that the approach is capable of accurately forecasting high-fidelity simulations.
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Background: Citral is the main ingredient of the lemongrass plant with anti-inflammatory properties. Aim: In this study, the effects of citral on reducing inflammation in experimental diabetes in rats were investigated. Methods: Forty rats were randomly divided into four groups. There were two control groups (healthy controls (H) and citral alone-treated control (HC)) and two diabetic groups (diabetes (D) and diabetes+citral treatment (DC)). After diabetes confirmation on day 7, treatment with citral (300 mg/kg) was started for 2 weeks by gavage in the DC and HC groups. Results: On days 0, 7, and 21 of the study, inflammatory elements of blood serum, IL-6, TNF-α, haptoglobin, and α2-macroglobulin were compared between the four groups. Also, on day 21 of the study, the expression level of IL-6 and TNF-α in the liver tissue was analyzed by quantitative real-time PCR. On day 21 of the study, following treatment with citral for 14 days, there was a significant difference in the DC group’s inflammatory factors compared to the D group (P < 0.005). However, no significant difference was observed in DC and the two control groups’ inflammatory factors. Regarding gene expression, the levels of IL-6 and TNF-α in the liver were significantly downregulated in the DC group compared to those in the D group (P < 0.05). Conclusion: According to the results of this study, citral can be used as a suitable ingredient to reduce the inflammatory complications of diabetes.
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Current dentistry requires the constant search for materials with potential antimicrobial properties that allows the eradication of bacterial biofilms in the oral cavity and enhance the durability of dental treatments. This is of great relevance in the prevention of oral infections due to the increasing bio-resistance of the microorganisms involved, mainly produced by the abuse of antimicrobial drugs. That is why the objective of this research was the development of nanostructured three-dimensional materials (gels), formed in one-step with silver and copper suspensions (NPs) and surfactant polymers as polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP). The compounds were prepared by mixing different molar ratios of PVA/NP/PVP (1:1:6, 1:3:6; 1:5:6) in ethylene glycol. Subsequently, the mixture was slowly stirred for 4 h by a magnetic stirrer and subjected to microwave-assisted heating for 20 min at maximum power. The gels were poured into a mold and thawed at room temperature for 24 h. In the process of preparation, the concentration of AgNPs and CuNPs into the gel was 10,787 ppm and 6,354 ppm respectively. The compounds were characterized by UV-vis, analyzing the surface plasmon signal of the AgNPs and CuNPs. FTIR-ATR to verify stability and quantification. In addition, with microscopic studies in SEM, TEM and AFM. The antibacterial and cytotoxic activity of nanocoposites was determined using E. faecalis and Fibroblast cell line (L-929). It was evidenced that crosslinking with PVA influences the antibacterial activity of the particles, since the concentration in which cell death is generated is decreased, which translates into inhibition with lower concentrations of nanoparticles as they are not in the presence of PVA. These results are expected due to the coordination of the particles by the polymeric chains of PVA, which in turn represents an advantage in terms of the toxic properties of the nanoparticles, which decrease with increasing crosslinking.
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A new hybrid gradient simulated annealing algorithm is introduced. The algorithm is designed to find the global minimizer of a nonlinear function of many variables. The function is assumed to be smooth. The algorithm uses the gradient method together with a line search to ensure convergence from a remote starting point. It is hybridized with a simulated annealing algorithm to ensure convergence to the global minimizer. The performance of the algorithm is demonstrated through extensive numerical experiments on some well-known test problems. Comparisons of the performance of the suggested algorithm and other meta-heuristics methods were reported. It validates the effectiveness of our approach and shows that the suggested algorithm is promising and merits to be implemented in practice.
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Background: Male factor is the major contributor in roughly half of infertility cases. Genetic factors account for 10–15% of male infertility. Microdeletions of azoospermia factors (AZF) on the Yq region are the second most frequent spermatogenesis disorder among infertile men after Klinefelter syndrome. We attempted in this study for the first time to evaluate the frequencies of all AZF sub-regions microdeletions and to analyze reproductive hormonal profiles in idiopathic cases of azoospermic and oligozoospermic men from Sudan. Methods: A group of 51 medically fit infertile men were subjected to semen analysis. Four couples have participated in this study as a control group. Semen analysis was performed according to WHO criteria at Elsir Abu-Elhassan Fertility Centre where samples have been collected. We detected 12 STSs markers of Y chromosome AZF microdeletions using a multiplex polymerase chain reaction. Analysis of reproductive hormone levels including Follicle Stimulating, Luteinizing, and Prolactin hormones was performed using ELISA. Comparisons between outcome groups were performed using Student’s t-test Chi-square test or Fisher’s exact test. Results: AZF microdeletion was identified in 16 out of 25 Azoospermic and 14 out of 26 of the Oligozoospermic. Microdeletion in the AZFa region was the most frequent among the 30 patients followed by AZFc, AZFd and AZFb. Among the Oligozoospermic participants, the most frequent deletions detected were in the AZFa region and was significantly associated with Oligozoospermic phenotype, Fisher’s Exact Test (2-sided) p=0.009. Among the Azoospermic patients, the deletion of the AZFc region was the most frequent and was significantly associated with Azoospermia phenotype Fisher’s Exact Test p=0.026. There was a significant difference in Y chromosome microdeletion frequency between the two groups. The hormonal analysis showed that the mean levels of PRL, LH, and FSH in Azoospermic patients were slightly higher than those in oligozoospermic. A weak negative correlation between prolactin higher level and Azoospermic patients was detected. (AZFa r=0.665 and 0.602, p=0.000 and 0.0004, AZFb r=0.636 and 0.409, p=0.000 and 0.025, and AZFd r=0.398 and 0.442, p=0.029 and 0.015). The correlation was positive for AZFa and negative for AZFb and AZFd.
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Glucose biofuel cells (BFCs) are devoted to harvest energy from body fluids. In particular, BFCs produce energy through the biocatalytic oxidation of glucose. This reaction is catalyzed for specific enzymes, such as Glucose Oxidase, that usually is immobilized on conductive supports through different techniques to perform the enzymatic transformation. BFCs are devoted to be employed as energy source for wearable devices mainly applied in medicine, sport and wellness. Therefore, the development of novel materials for enzymatic electrodes with conductive, biocompatible and flexible properties at the same time, able to withstand dairy routines of the wearer is of interest. Chitosan, as natural polymer, has been widely used in the development of biodevices since it is a promising material for enzyme immobilization due to its biocompatibility, hydrophilicity, no toxicity and film forming ability, together with stable enzyme immobilization. In this work, our findings regarding the use of chitosan as immobilization matrix or self-standing support is presented, using it together with conductive materials for the development of conductive, flexible and biocompatible membranes as supports for bioelectrodes development. The membranes based on the use of non-conductive materials (chitosan), is in a comparable range of conductivity with respect to the state of the art. However, the main advantages that are a key improvement compared with the state of the art are the features of biocompatibility, improved mechanical properties and capability of stable incorporation of enzymes. It is worth to highlight that this membrane include all this properties while prevents enzyme denaturation and bioelectrocatalytic activity thanks to chitosan presence. Moreover, it provides good power densities when using as BFCs, ranging between 15 and 150 μW/cm2 in the presence of glucose. Thus, it could be easily tailored to be integrated in real miniaturized systems, showing their great potential to be integrated in the new trend of wearable technologies.
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Cell-surface receptors (e.g., EGFR and integ-rin) and their interactions play determining roles in signal transduction and cytoskeletal activation, which affect cell attachment/detachment, invasion, motility, metastasis (intra-cellular), and cell−cell signaling. For instance, the interactions between the EGFR and integrin (α6β4) may cause increased mechanical force and shear stress via enhanced cytoskeleton activation. Here, we design a DNA nanodevice (DNA-ND) that can simultaneously target the EGFR and integrin receptors on the caveolae. The piconewton (pN) forces in response to the EGFR−integrin coactivation can be sensed upon the unfolding of the DNA hairpin structure on the side arm of the device via changes of the fluorescence and plasmonic signals. We find that simultaneous activation of EGFR−integrin receptors causes enhanced signal transduction, contractions of the cells, and initiation of the biochemical pathways, thus resulting in a change of the cell division and endocytosis/exocytosis processes that affect the cell proliferation/apoptosis. The DNA-ND further enables us to visualize the cointernalization and degradation of the receptors by lysosomes, providing a novel approach toward bioimaging and mechano-pharmacology.
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The aim of the present study is to improve the adsorption of tetracycline (TC) onto biochar of microalgae modified by nanocomposite of MnMoO4 (MM40BC60). The synthesized nanocomposite was characterized by Scanning electron microscopy (SEM), BrunauerEmmett-Teller (BET) surface area, Fourier transforms infrared (FTIR) spectroscopy to investigate the morphology, surface area, pores and the functional groups of MM40BC60, respectively. The effect of various parameters including initial pH, TC concentration, and temperature on the adsorption performance of TC to the adsorbent was evaluated with considering kinetics, isotherms, and thermodynamics models. The adsorption of TC on MM40BC60 shows good agreement with the pseudo-second-order kinetic and the Langmuir isotherm models. Results of the thermodynamic study showed that the adsorption process was a spontaneous and endothermic reaction in nature.
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High quality, low cost, defect free, naked eye viewed honeycomb structured (NIVHCS) liquid single crystals (LSC) of GO (graphene oxide) is still now rare and a big challenge to synthesize, which has been successfully done using waste pencil leads as raw material. The prepared GO has been characterized by different physico-chemical, microscopic, spectroscopic and electrochemical methods. And it was used for the preparation of rGO (reduced GO) using an aqueous leaf extract of bryophyllum pinnatum as reducing agent. Moreover, the rGO was applied as nanocomposite with copper oxide (Cu2O) for the electrochemical conversion of CO2 into useful products.
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The two-dimensional (2D) surface oxide films of liquid metals are among the main natural 2D semiconductor structures with atomically-thick dimensional characteristics. These fundamental layers of semiconductor films are formed in contact with low-concentration of oxygen in reactive atmosphere. Owning to the negligible vapor pressure, tunable surface properties, soft and dynamic interfaces, outstanding catalytic activities, and more importantly the formation of natural 2D films on their surface, the gallium based liquid metal alloys are promising platforms for development of novel 2D materials. The incorporation of trace impurities and introduction of doping elements into 2D semiconductor films are still challenging technological process for electronic applications. By taking the advantages of plasma, the selective incorporation of doping elements into surface oxide films of liquid alloys are facilitated. In the present research, the cold plasma environments of various gases (H2S, NH3 & CH4) were employed to incorporate reactive elements into the surface oxide films of liquid metal (LM) alloy. As the representative of room temperature LM, galinstan alloy (Ga 61 wt. %, Sn 25 wt. %, In 13 wt. %) was employed as the platform for the growth of 2D gallium oxide films. The combined effects of plasma electric field, presence of ionic charged particles and more importantly the catalytic activities of galinstan/ Ga2O3 substrate facilitated the incorporation of reactive elements of decomposed gases into the 2D surface oxide Ga2O3 film. Various material characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), Raman, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy and transition electron microscopy (TEM) were employed to investigate and confirm the level of elemental incorporation into 2D Ga2O3 films. The conductive atomic force microscope (c-AFM) was successfully employed to measure the resistive switching characteristics and electrical properties of functionalized 2D surface oxide films.
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Nanotechnology is nanosciences have a vast subject to study and green chemistry principles is one of the main subjects. Green approach is one step process which is eco-friendly, cost effective, non-toxic and sustainable. The synthesis can be per-formed by bio-reduction process using the extracts of medicinal plants, microorgan-isms (bacteria, fungi etc.). In this research, the ability of the leaf extract of Justicia Adhatoda is observed as unique reducing agents for bioconversion of copper ions to copper oxide nanoparticles (CuONPs). Among various metal nanoparticles, CuONPs have chosen because of their efficient antibacterial activity and nontoxicity towards human. The formation of nanoparticles is confirmed by the color change of the solution from light blue to brown in color because of the trouble of surface plasmon resonance (SPR). The optical study showed SPR peak at 240 nm. FT-IR showed the reduction of CuNPs was due to the biomolecules present in the leaf extract which acted as reducing in addition to capping agents. FESEM has been applied to recognize the size, shape and morphology of nanoparticles. The synthesized CuONPs were tested for antibacterial activity to both gram positive and gram negative bacterial strains which are applicable for the fabrication of antibacterial textile cotton and alkali oxygen plasma treated pure cotton coated by CuONPs is used to application gram-negative (E. coli) bacteria found a promising result.
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In materials research, catalyst plays critical role by decreasing chemical reaction activation energy, improving manufacture operating conditions, and increasing production volumes. Transition metal-catalyzed olefin polymerization particularly dominates the Chemistry literature and polyolefin education, science, engineering, and industry over 70 years. Saudi Arabia is listed as the 4th largest polyolefin producer in the world. This talk, therefore, highlights polyolefin catalyst research at the Interdisciplinary Research Center for Refining & Advanced Chemicals (IRCRAC), Research Institute, King Fahd University of Petroleum & Minerals, Saudi Arabia. We particularly address catalyst performance and kinetic evaluation, novelty in supported metallocene catalysts, the apparently absurd residual catalyst structure and solid-state electronic environment, illustration of supported metallocene catalyst active site distribution thru model and experiment, preparation of spheroidal MgCl2 support, and catalytic synthesis of energy-saving drag reducing UHMW polymers using local petrochemical feedstock. Each area has been assessed from the product development perspective. Our PO catalyst research aligns with Saudi Arab’s Vision 2030 and National Strategic Plan (NSP). We also present a circular research concept which shows how product-driven research with a commercial driving force can significantly advance fundamental PO catalyst chemistry to valuable applications for Saudi Arabia. Finally, we focus on establishing spinoffs using local raw materials. In this context, we highlight the role, to be played by researchers, R&D management, and potential investors, to develop the appropriate innovation diffusion culture. We especially underscore the six relevant diffusion tools. The critical need is to understand why an innovation will fail to be marketed. We particularly stress the importance of conducive sociology (environment), psychology (mental state), and mindset (preparedness to make and accept changes) that precede innovation and technology and make research promote economy.
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Big data and data security are among the most widely discussed topics across the globe. Every organization adopts one or the other user identification and authentication methods to secure the data and avoid unauthorized access. Big data systems like Apache Hadoop heavily relied on the Kerberos Protocol to authenticate the user. Many improvements have already been proposed by various experts in dealing with the inherent security weakness of Kerberized systems. Password Guessing Attacks, Replay Attacks, Time Synchronization and Single Point of Failure problems are the few challenges of Kerberos to mention. Since Kerberos relies on a Trusted Third Party Server called Key Distribution Center (KDC), single point of Failure is considered as most critical among these. The Key Distribution Center required being online always and any failure can put the entire system to be down. For Big data systems, which demand a fast, reliable, and real-time data analytics and user access, this is not favourable. Kerberos protocol uses symmetric key cryptography to provide mutual authentication and authorization for client-server applications. The Key Distribution Center is logically divided into two parts – the Authentication Server and the Ticket Granting Server. These servers issue the ticket and session keys for service access. All user information and credentials are stored in a local database at the KDC. Thus, the KDC is always the target of attackers and the entire system fails once this is compromised. This novel user authentication mechanism is an amalgam of modern technologies like Blockchain technology, Digital Signatures and Threshold ElGamal Cryptosystem to deal with the problem of single point of failure in Kerberized Hadoop Clusters.
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In this study, Pure and Zr doped ZnO nanoparticles were effectively synthesized through easy Co-precipitation method. The synthesized Zr-ZnO NPs were characterized by XRD, FESEM, UV-Vis, FTIR and PL. X-ray diffraction analysis proved the creation of the hexagonal wurtzite structure. The average crystallite size was found to be 25- 30 nm. The FESEM and TEM analysis verified that the sphere-shaped morphology for both ZnO and Zr - ZnO NPs. UV – Visible Spectroscopy confirmed that an increase in the optical bandgap involving the concentration of dopant Zr increases. The bandgap values were found to be 3.57-3.54 eV. FTIR spectra showed that the existence of the characteristic stretching and bending vibrational band of Zn – O bonding at 400- 600 cm-1 and shifts in vibrational bands were noticed for Zr - ZnO NPs. PL spectra of Zr - ZnO NPs at various concentrations show a strong UV and Green emission band. SAED pattern proves the crystalline nature of synthesized samples. EDAX spectra confirm the existence of Zr, O, and Zn and verify that Ti4+ ions are present in the ZnO lattices. ZnO nanoparticles are widely used as electron transport layer (ETL) in organic solar cell. (PEDOT: PSS) hole extraction layer. Power conversion efficiency (PCE) of fabricated organic solar cell is 8.10% Fill.
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Rhodium (Rh) nanoparticles were embedded in the mesopores of TUD-1 siliceous material and denoted as Rh-TUD-1. Five samples of Rh-TUD-1 were prepared with different loadings of Rh that ranged from 0.1 to 2 wt% using the sol-gel technique. The prepared samples were characterized by means of several chemical and physical techniques. The obtained characterization results show the formation of highly distributed Rh0 nanoparticles with an average size ranging from 3 to 5 nm throughout the three-dimensional silica matrix of TUD-1. The catalytic activity of the prepared catalysts was evaluated in the solvent-free hydrogenation of cyclohexene to cyclohexane at room temperature using 1atm of hydrogen gas. The obtained catalytic results confirm the high activity of Rh-TUD-1, in which aturn over frequency(TOF)rangingfrom4.94to0.54s−1 was obtained. Moreover, the change in reaction temperature during the reaction was monitored, and it showed an obvious increase in the reaction temperature as an indication of the spontaneous and exothermic nature of the reactions. Other optimization parameters, such as the substrate/catalyst ratio, and performing the reaction under non-ambient conditions (temperature = 60◦C and hydrogen pressure = 5 atm) were also investigated. Rh-TUD-1 exhibited a high stability in a consecutive reaction of five runs under either ambient or non-ambient conditions.
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The advantages of nanoparticles as bioanalytical markers are determined by increased total surface of their suspensions that accelerate formation of target products and increase their amount [1]. Currently gold nanoparticles are widely applied as a colorimetric marker in immunochromatographic analysis (ICA) due to their simple synthesis, conjugation with proteins and optical detection. However, variation of their size and shape provides additional facilities for more efficient and sensitive assays [2]. The presented study was focused on analytical application of gold nanoflowers (GNFs), which are flower-like nanoparticles with a developed surface in the form of wavy or sharp protrusions (tips) [3]. The integration of the GNFs with ICA of fatty acid-binding protein (FABP), an important biomarker of acute myocardial infarction, was considered GNFs were synthesized by growing gold nanospheres with diameter 10 nm (nuclei) with HAuCl4 with the use of sodium citrate and hydroquinone as reducing agents. The obtained product had an average diameter of 118±4 nm (Fig. 1A). The GNF-antibody conjugate was synthesized by physical adsorption of the specific monoclonal antibodies against FABP on the surface of the GNFs.It was shown that ~75% of the added anti-FABP antibodies were adsorbed on the GNFs surface, which is higher compared to the commonly used gold nanospheres (65% at the same conditions). The observed effects logically follow from the developed surface of the GNFs, which is inhomogeneous in curvature and due to this generate different sites of sorption. This conjugate was used as detector agent in the ICA for FABP detection, where the formed nanopareticle-labeled immune complexes are detected as colored lines at a test zone of a membrane strip (Fig. 1B). For a correct comparative evaluation of GNFs and gold nanospheres as markers, the conditions for the both cases were optimized.
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As a method of information filtering, the Recommender System (RS) has gained considerable popularity because of its efficiency and provision of the most superior numbers of useful items. A recommender system is a proposed solution to the information overload problem in social media and algorithms. Collaborative Filtering (CF) is a practical approach to the recommendation; however, it is characterized by cold start and data sparsity, the most severe barriers against providing accurate recommendations. Rating matrices are finely represented by Nonnegative Matrix Factorization (NMF) models, fundamental models in CF-based RSs. However, most NMF methods do not provide reasonable accuracy due to the dispersion of the rating matrix. As a result of the sparsity of data and problems concerning the cold start, information on the trust network among users is further utilized to elevate RS performance. Therefore, this study suggests a novel trust-based matrix factorization technique referred to as CFMT, which uses the social network data in the recommendation process by modelling users' roles as trustees and trusters, given the trust network's structural information. The proposed method seeks to lower the sparsity of the data and the cold start problem by integrating information sources including ratings and trust statements into the recommendation model, an attempt by which significant superiority over state-of-the-art approaches is demonstrated an empirical examination of real-world datasets.
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The fabrication of hierarchical layered TiO2/ graphene/chitin composite membranes from the liquid crystal (LC) self-assembly of graphene oxide (GO) nanosheets, chitin nanospindles, and peroxotitanate is presented. The multilayer co-assembly evolves the lamellar arrangement to mimic a fascinating nacre’s structure in the solidified graphene/ chitin composites. The core of our routine is the self-assembly of both GO LCs and chitin LCs into a flexible nacre-mimicking membrane structured by graphene-wrapped chitin layers. The intrinsic electron mobility of graphene nanosheets and mechanical toughness of chitin nanocrystals endow these reinforced membranes with functions in catalyst supports and electronics. The nacre-mimicking composite homogeneously incorporates with TiO2 nanoparticles by simultaneous LC coassembly of GO, chitin and peroxotitanate to afford layered TiO2/-graphene/chitin composites that can function as a photocatalytic membrane for the mineralization of organic compounds. The LC integration creates hierarchical assemblies to increase the permeability of the TiO2/- graphene/chitin nanohybrid membranes, offering its potential use for developing photocatalysis fo treatment antibiotics in water. The sustainable materials as promising precursors for further investigation in energy storage and conversion and gas sensing.
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Research is devoted to the synthesis of chitosanstabilized mono- and bimetallic nanoparticles (NPs) of metals such as Ag, Cu, Co, Ag/Cu, Ag/Co. It was found, that in the absence of chitosan, occur agglomeration and oxidation of Cu and Co NPs, with formation oxides - CuO and Co3O4. Silver NPs were obtained in “in situ” in following conditions: Na3C6H5O7/Ag+=0.736–4.416 and CS/Ag+=20 mol. It was revealed that there is a correlation between synthesis conditions-size (shape) of the NPs. It is determined, that Na3C6H5O7 performs a dual function, both a reducing agent and a stabilizer of the particles. X-ray diffraction analysis established signals corresponding to Ag NPs at 2Θ = 38.40, 43, 64.0, 78 and 82°. The size and distribution of the silver NPs were studied by microscopic methods, and the spherical particles were 15–300 nm, and the needle-like particles were 1–8 nm. The size and shape of chitosan stabilized bimetallic NPs can be controlled by varying pH, the concentration of the reducing agent and the molar ratios of metal ions. Established that an increase in the concentration of the reducing agent, as well as metal ions, promote the formation of fibrillar nanoparticles. On the based DLS-studies of chitosan and bimetallic NPs discovered that, 73-76% of the NPs had a size in the range of sizes from 25 to 250 nm. The results demonstrated, that for the ratio Cu2+/Ag+=2:3 mol, an increase in the concentration of Cu2+ led to the formation of fibrillar particles from d=180÷260 nm and l=25 micron. In summary, found the optimal conditions for obtaining stabilized mono-, bimetallic NPs and "a protective effect" of chitosan. Computer modeling has proved that stabilization occurs due to "chemisorption". The results indicate that the synthesized samples have bactericidal and fungicidal properties.
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The inadequate public water supply by the Government to the general public has created several ways of sourcing water to sustain daily demand in Nigeria. Inadequate access to quality water for consumption craved the idea of the designing of new trend silver nitrate impregnated locally made Point-Of-Use (POU) ceramic filters to enhance water purification efficiency for household use. This study utilized silver nitrate-molded ceramic filters prepared with Kaolin from Owode, silt soil, sodium silicate, sawdust, and distilled water in three varying proportions to ascertain pollution removal efficiencies. Heating was carried out by firing the filters at 900oC and further preheating at 400oC after dipping in silver nitrate solution. Silver nanoparticle and dissociated particle discharge from filter pot painted with 0.03 mg/g casein-covered nAg or AgNO3 were estimated as an element of pH (5-9), ionic strength (1-50mM), and cation species (Na+, Ca2+, Mg2+). Silver delivery was constrained by disintegration as Ag+ and resulting cation exchange measures, paying little heed to silver structure applied. Water analysis for both heavy metals (Pb and Cd) and microbial load (E. coli) evaluated, corroborate the maximum removal efficiency. It was observed that kaolin-sawdust with the Silver nitrate filters showed a constant and effective removal of both heavy metals and disinfection of microbial load. The minimum flow rates observed were 4.97mL/min for batch filter used for Iju River water sample one (AF1) and 4.98 mL/min for batch filter used for Iju River water sample two (AF2) having porosity 49.05% and 50.00%, respectively, while the higher flow rate was 5 mL/min for batch filter used for borehole water sample one (BF1) and batch filter used for well water sample two (CF2) with porosity of 50.00%.
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As a relatively new model, the artificial bee colony algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modifying the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on CEC2014, and CEC2015 benchmark suites, real-life problems. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching–Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.
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Thermophysical properties such as latent heat, viscosity and melting temperature could be changed for different physical properties of dispersed nanoparticle such as size, shape, and concentration. In this study, Nanocomposites-Enhanced Phase Change Materials NePCM are formed by dispersing Aluminium (Al) and Copper (Cu) nanoparticles into paraffin wax in various mass fractions (0.1, 0.3, 0.6, 1, 2.5 and 5%). The impact on the thermophysical properties of paraffin wax by the nanoparticles is also investigated. Heat conduction and differential scanning calorimeter experiments are used to investigate the effects of different nanoparticle concentrations on the melting point, solidification point, and latent capacity of nanocomposites. Experimental results show that the dispersion of nanoparticles of Al and Cu can decrease the melting temperature and increase the solidification temperature of PCM. This dispersion could also be limited due to increase in dynamic viscosity of the NePCM. Furthermore, Al and Cu nanocomposites with mass fractions of 2% and 1%, respectively, show better enhancements in the thermal storage characteristics of the paraffin compared to the next higher mass fraction.
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Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.
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Microsupercapacitors are gaining increasing interest for energy storage in miniaturized electronics devices. However, the production of porous electrode material with standard microfabrication techniques is a big problem. Here, we report on the oblique angle deposition of highly porous and nanostructured columnar titanium nitride (TiN) films on silicon substrate using magnetron sputtering for high-performance microsupercapacitors. The intercolumnar porosity of the sputtered TiN films can be systematically controlled as a function of the oblique angle α achieved by tilting the substrate. The denser morphologies in TiN films deposited at α = 0° lead to moderate capacitive behavior in 1M Na2SO4 electrolyte solution. While a high areal capacitance of 17.5 mF•cm-2 is obtained for 60° oblique angle due to high intercolumnar porosity in films which increases the specific surface area and thus facilitates easy electrolyte permeation. The electrodes also retain 91.3% of the initial specific capacitance after 5000 charging/discharging cycles. An on-chip interdigitated microsupercapacitor has been subsequently fabricated based on optimized TiN thin film serving as both an efficient electrode and a current collector. The device was electrochemically tested using polyvinyl alcohol (PVA)-Na2SO4 hydrogel electrolyte and delivered energy densities of 0.46 µWh•cm-2 while maintaining a high-power density of 703.12 µW•cm-2. This work gives insight into the use of oblique angle deposition for obtaining highly porous films of other electrode materials for microsupercapacitor applications and at the same time presents major technological advances toward the large-scale production of on-chip power sources.
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In the field of automatic recognition of apparent personality, several studies have been carried out that reach different levels of certainty based on previously labeled video and voice data sets. On the other hand, there are standardized personality tests that allow, based on a model of personality factors, to determine the level of development of each factor in a person. However, there is no platform that allows the researcher to collect new video data sets (including voice) and, likewise, to apply a standardized personality test, and store that information to later evaluate the accuracy of the automatic recognizers applied to the collected data sets. The present work describes the development of a data collection platform (PersonApp) with the objective of analyzing the effectiveness of automatic apparent personality recognizers with respect to the results of a standardized personality test of the same participant and in this way, have elements that allow the improvement of the evaluated models. Likewise, the results of the evaluation of an automatic apparent personality recognition model are presented, in order to test the platform. Regarding the standardized test, the platform collected results from 32 different participants. For each of them, the values corresponding to each of the personality traits were obtained. Within the analyzed sample, it was observed that the traits of agreeableness and openness obtained the highest average value. On the other hand, neuroticism was the trait with the lowest mean value. An experiment was carried out where the participants were asked to record a video (including audio) with a duration of 1 minute. 84 videos corresponding to 20 participants were obtained. These videos were used to test an automatic apparent personality recognizer based on a convolutional neural network.
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The unwanted deposition of material on the surface is one of the vast majority issues that are occurred in industrial heat exchangers, which reduces their performance and thus constitute the biggest challenges in heat transfer. Despite their large economic losses and environmental damage, prediction and prevention of fouling remain unresolved issues in process engineering. To surmount these issues, artificial neural network (ANN) with back propagation method was used to predict the fouling resistance from some easily measurable variables of the phosphoric acid concentration loop. Indeed, fouling resistance is predicted according to the inlet and outlet temperature, density and flow rate of the phosphoric acid, the steam temperature and time. The accuracy analysis justified the existence of the highest interrelation between these independent variables and fouling resistance. The ANN model was developed and validated using the collection of the operating data of the concentration loop in cross-flow heat exchanger. The optimal number of hidden neurons was determined by maximizing a series of statistical accuracy measurements. The best topology was found with a network consisting of one hidden layer with 6 neurons using tangent sigmoid transfer function for the hidden and output layers. The reliable quality indices with overall AARD=0.048%, MSE=1.81 10-11, RMSE=4.25 10-6 and r2All=0.995 reflect the accurately performance of the developed model to predict fouling resistance. Consequently, the developed model is used to predict fouling resistance of cross flow heat exchanger and can be applied to plan a cleaning schedule and control operation of the phosphoric acid concentration plant.
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There is a global research interest in metal nanoparticles (MNPs) due to their diverse applications, rapidly increasing use, and increased presence in the aquatic environment. Currently, most MNPs in the environment are at levels unlikely to cause overt toxicity. Sub-lethal effects that MNPs may induce, notable immunotoxicity, could however have significant health implications. Thus, deciphering the immunological interactions of MNPs with aquatic organisms constitutes a much-needed area of research. In this article, we critically assess the evidence for immunotoxic effects of MNPs in bivalves and fish, as key wildlife sentinels with widely differing ecological niches that are used as models in ecotoxicology. The first part of this review details the properties, fate, and fundamental physicochemical behavior of MNPs in the aquatic ecosystem. We then consider the toxicokinetics of MNP uptake, accumulation, and deposition in fish and bivalves. The main body of the review then focuses on immune reactions in response to MNPs exposure in bivalves and fish illustrating their immunotoxic potential. Finally, we identify major knowledge gaps in our current understanding of the implications of MNPs exposure for immunological functions and the associated health consequences for bivalves and fish, as well as the general lessons learned on the immunotoxic properties of the emerging class of nanoparticulate contaminants in fish and bivalves.
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Our work exhibits a green method of formation for gold nanoparticles (AuNPs) from its precursor salt, tetra-chloroaurate through the reducing and capping action of Ziziphus mauritiana leaves (ZmL) extract with the assistance of heat in aqueous medium. The formation of so called ZmL-AuNPs was confirmed via color change of solution mixture to ruby red which was further confirmed by surface plasmon resonance (SPR) band at 521 nm using ultraviolet-visible (UV– Vis) spectroscopy. Further characterization of ZmL-AuNPs includes Fourier transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), X-ray diffraction (XRD) technique, and zeta-potential analysis (ZPA) respectively. The synthesized ZmL-AuNPs were probed and recognized to perform as a highly sensitive and selective colorimetric sensor for the detection of Cr3+ in the presence of other expected interfering cations including Cr6+. Importantly, the developed ZmL-AuNPs based colorimetric sensor functioned linearly in the range of 16– 283 nM of Cr3+, based on aggregation induced decrease in absorption along with red shift in the resulting spectra exhibiting R2 value of 0.9977. The limit of detection and limit of quantification for Cr3+ were estimated as 0.48 nM and 1.6 nM respectively. The developed colorimetric sensor was effectively used for detecting Cr3+ in real water samples.
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The present work relates to a development and fabrication of epoxy blended polybenzoxazine (PBZ) resinl and its subsequent characterization by advanced surface analytical techniques to find their suitability as advanced composite materials. DOPO based BZ matrix was synthesized from appropriate chemical reactants. The varying weight percentages of GPTMS functionalized Al-MCM-41 were incorporated into the DOPOBZ matrix to fabricate PBZ nano-composites. The synthesized monomer was confirmed by 1H NMR and FT-IR. The BZ exhibits good compatibility with F-Al-MCM-41 filler and makes it possible to form cross-linking network of the cured products. The thermal stability and mechanical properties of the composites was also increased, due to the presence of alumina present in the nano-composites that produces an additional heat capacity to the polymer composites with a higher char yield as well. The value of dielectric constant of PBZ was found to be increased with the increasing weight percentages of F-Al-MCM-41. The important factors that influence the dielectric properties of the polymer nano-composites are highly aromatic structure and the lon- conjugated delocalization of the polymer matrix that possess the bulk polarizability. Data obtained from dielectric and DMA studies infer that 5 wt% F-AL-MCM-41reinforced nano-composites possesses the maximum value of dielectric constant (7.5) and storage modulus than those of the neat PBZ matrix.
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In this study, the idea of recycling the concrete wastes and reuse of them for reproduction of green concrete has been presented. Thus, we have tried to study mechanical parameters using recycled aggregate concrete. For this purpose, three mix designs including natural, recycled and recycled fiber concrete were tested. Moreover, at the end of the paper, estimation of compressive strength using ANN methods, has been presented. Based on the results, the recycled concrete and recycled fiber concrete with the proposed mix design, has a high compressive strength and due to relatively high porosity of the recycled aggregate concrete, its density has decreased by 2.48% and its water absorption increased by 54% compared to the natural concrete. Two artificial intelligence method of ANN and SVM benefit from a quite equal coefficient of consistency and the results of 124 test specimens with the results obtained from SVM are in a better agreement. Finally, two artificial intelligence methods were compared with the MLR using K-fold cross validation, indicating superior performance of the artificial intelligence. In order to determine effectiveness of each one of the input parameters on the compressive strength, a sensitivity analysis using the Milne method with adjusted weights stemmed from the optimized neural network was conducted whose results indicated great impact of the content of natural gravel and low effect of water on target function of the neural network (NN). Hence, it is concluded that for attaining an efficient mix design, more care should be taken regarding selection of the coarse NAs and fine RAs.
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In this study, the free and forced vibrations of piezoelectric carbon nanotubes with surface effects were placed in a magnetic field situated on a viscoelastic foundation with nonlinear damping and stiffness elements under the influence of external harmonic force were investigated. This structure would be formed when a piezoelectric layer material was attached to the outer surface of a carbon nanotube with a relatively uniform thickness. The nonlocal theory was used to illustrate the effects of the nanoscale in the theoretical model. The equations of motion of the system were also extracted using the dynamic equilibrium conditions of the element. The Galerkin method was used to reduce the order of the obtained dynamical equations. Considering the boundary conditions of the problem, which are both simple support (SS) or clamped (CC), the nonlinear time differential equations of the system and its coefficients were obtained. With the elimination of external forces and nonlinear terms, the eigenvalue problem of the system was solved to find the frequency of free vibration. Then, using the multiple time scales method, an analytical closed-form solution aimed at amplitude-frequency response curves for forced vibration of a nonlinear system is extracted. Moreover, the effect of different parameters such as nonlocal parameter, CNT surface effect, voltage magnitude and externally applied magnetic field, nonlinear viscoelastic foundation stiffness, and damping coefficients on eigenvalues and dynamic response results curves would be discovered. Finally, the obtained results were validated with the expected ones.
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Allicin is the main component in garlic extract, which gives garlic its characteristic taste and odor. In this study, allicin was extracted from the garlic and used for the preparation of the allicin-mediated silver nanoparticles. Allicin exhibited a broader surface plasmon resonance (SPR) peak at 240 nm, while high-performance liquid chromatography yielded one prominent peak corresponding to allicin. The allicin-mediated silver nanoparticles and chemically synthesized silver nanoparticles were characterized by UV-Visible spectroscopy, particle size analyzer (PSA), zeta potential, Fourier transform infrared spectroscopy (FTIR), and Transmission electron microscopy (TEM) analyses. The allicin-silver nanoparticles demonstrated good radical scavenging activity (in vitro) and antioxidant potential in albino mice (in vivo). Reduced glutathione and catalase were elevated (p<0.05), and superoxide dismutase (SOD) was depleted (p<0.05) in some groups. The histopathological analysis and all other findings revealed the safer biological nature of allicin-mediated silver nanoparticles than the chemically synthesized silver nanoparticles. It is concluded that allicin-mediated silver nanoparticles are less toxic and safer for biomedical applications.
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Allicin is the main component in garlic extract, which gives garlic its characteristic taste and odor. In this study, allicin was extracted from the garlic and used for the preparation of the allicin-mediated silver nanoparticles. Allicin exhibited a broader surface plasmon resonance (SPR) peak at 240 nm, while high-performance liquid chromatography yielded one prominent peak corresponding to allicin. The allicin-mediated silver nanoparticles and chemically synthesized silver nanoparticles were characterized by UV-Visible spectroscopy, particle size analyzer (PSA), zeta potential, Fourier transform infrared spectroscopy (FTIR), and Transmission electron microscopy (TEM) analyses. The allicin-silver nanoparticles demonstrated good radical scavenging activity (in vitro) and antioxidant potential in albino mice (in vivo). Reduced glutathione and catalase were elevated (p<0.05), and superoxide dismutase (SOD) was depleted (p<0.05) in some groups. The histopathological analysis and all other findings revealed the safer biological nature of allicin-mediated silver nanoparticles than the chemically synthesized silver nanoparticles. It is concluded that allicin-mediated silver nanoparticles are less toxic and safer for biomedical applications.
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A new model of coherent conditional previsions, based on Hausdorff measures is proposed in a metric space and its nonlinear extensions to the class of all random variables, named coherent upper and lower conditional previsions, are investigated. The conditional expectation is defined by the Hausdorff measure of order s, or s-dimensional Hausdorff measure, if the conditioning event has positive and finite Hausdorff measure in its Hausdorff dimensions. Otherwise it is defined by a 0-1-valued finitely additive, but not countably additive probability. In this way the conditional expectation and its extensions depend on the complexity of the piece of information represented by a set, which can be also a fractal set. It is proven that the coherent upper conditional previsions satisfy the monotone convergence theorem and the disintegration property if all the conditioning events are measurable sets. The model can be applied to study fractal antenna in particular to investigate stochastic independence between random variables given the fractal set that represented the antenna. The model can also be to represent the conscious and unconscious activities of the human brain in AI. By adopting this specific Bayesian approach to human behaviour and reasoning mathematical representation of fundamental functions of the human brain - usually considered as bias and detrimental to an account of normative rationality - are provided without incurring in the usual inconsistencies. In particular it is proven that the model solves classical problems connected to probability bias of human brain such as the solution to Linda’s conjunction fallacy and the bias of selective attention describes in the so-called invisible gorilla experiment, often taken as a typical example of the limitations of human perception.
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In this study, active nanofilms were developed using basil seed mucilage (BSM) containing zinc oxide nanoparticles (ZnONPs), according to casting method. Different amount of ZnO-NPs at the range of 0% (control), 0.1, 0.25, and 0.5% were incorporated into BSM film, then the physical, permeability, mechanical, thermal, and antimicrobial properties, as well as color index of fabricated films were examined. The results showed that moisture content, water absorption, water solubility, water vapor permeability (WVP), melting temperature (Tm), glass transition temperature (Tg) decreased with increasing the amount of ZnO, while the melting enthalpy of films increased (P < 0.05). The addition of ZnO-NPs up to 0.25% resulted in significant increase in the ultimate tensile strength, light (L*) and white indexes. Additionally, the basil seed mucilage film did not show antibacterial performance, while added of ZnO-NPs to the film, caused an increased trend in the antibacterial activity of films. The fabricated nanofilms prevented the growth of Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, and Salmonella typhimurium (P<0.05). All in all, the developed nanobiocomposite film using BSM containing 0.25% ZnO-NPs could be used as biodegradable and antibacterial film for food active packing to increase the shelf life of food.
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Despite numerous advantages, the challenges for wireless sensor communication always remains open due to which a continue effort is being applied to tackle the unavoidable conditions regarding wireless network coverage. Somehow, the uncouth deployment of the sensor nodes is making the tribulation queue longer day by day which eventually has great impact over sensor coverage range. In order to address the issues related with network coverage and uncouth energy wastage, a sensor node redeployment based shrewd mechanism (NRSM) has been proposed where new intended positions for sensor node are rummaged out in the coverage area. The proposed algorithm operates in two phases; in first phase it locates the intended node positions through Dissimilitude Enhancement Scheme (DES) and moves the node to new position. While second phase is called a Depuration, when the moving distance between initial and intended node position is shrewdly reduced. Further, different variations factors of NRSM such as loudness, pulse emission rate, maximum frequency, and sensing radius has been explored and related optimized parameters are identified. The performance metric has been meticulously analyzed through simulation rounds in Matlab and compared with state of art algorithms like Fruit Fly Optimization Algorithm (FOA), Jenga-inspired optimization algorithm (JOA) and Bacterial Foraging Algorithm (BFA) in terms of mean coverage range, computation time, standard deviation and network energy diminution. The performance metrics vouches the effectiveness of the proposed algorithm as compared to the FOA, JOA and BFA.
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Significant advances in development of strategies and approaches on novel materials design and processing have been made. Here we particularly highlight the advantages of combination of multi-functionalities to achieve synergetic effect on energy materials performance towards applications. These include combination of carbon coating with band engineering for alteration of electronic properties; universal general approach for morphology control; combination of physical confinement with catalytic effect to control polysulphide loss in metal sulphur battery; Multiple strain engineering for increase of reactive sites in catalysts; Additive & subtractive engineering for controlled growth of nanomaterials with designed size, shape and composition; Multiple dimension manipulation to achieve optimized electronic and ionic properties; Hybridization at materials, structure & device level to achieve high reactivity in energy storage materials. Among these the interface/surface science and engineering is the most critical element for energy materials design and processing at both fundamental and applied level. Most of the research is limited within the block of research inputs to research outputs while there is a huge gap between research outputs and commercial benefits which need to be addressed. Scaling-up remains as a great challenge to facilitate industry transformation processes from laboratory to real world applications. The design and construction of battery pack driven minning vehicles through development of advanced battery management system by UOW team sets unique example for transfering lab success to industry applications.
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Pickings the von Kármán geometric nonlinearity into tally, the current manuscript presents a nonlinear approach of nanorod, which is based on the nonlocal elasticity and axial beam theories. Imperfect nanorod is subjected to the axial compression or various fields in terms of thermal and transvers magnetic loads. Clamped-clamped and clamped-free nanorods are considered under axial compression in view of thermal and magnetic loads. The governing equations of the nanorod are derived by means of the Hamilton's Principle. The coupled nonlinear dimensionless differential equations are solved employing He's variational method. To evaluate the accuracy of the results, the results of this method are compared with the values obtained from the finite element method. Numerical results are provided to explore the influences of the low and high temperatures, nonlocal parameter, magnetic force of transvers field, and amplitude of vibrations for nanorods.
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Development of technology have shown the importance of intelligent magnetostrictive actuators in fields such as robotics, medicine, security, nanotechnology and many more. The models made present in the literature have exhibited undesirable behaviors such as chaos and multistability, etc. Which is a problem because under these conditions the actuators are less stable. It is shown that if we increase the order of the nonlinearity, the more reliable the information obtained about the system. The main objective of this work is to propose on the one hand an improved model of a magnetostrictive actuator more stable than the existing models and on the other hand to develop a protocol for the implementation of the model for the design of a surgical assistance robot. The Venkataraman cubic nonlinear magnetostrictive actuator model served as the basis for this study. Firstly, a quintic model is proposed which is richer than the cubic model. A study of the whole-order dynamics is made analytically by the multiple time scales method and numerically by plotting the phase portraits, the time series, the bifurcation diagrams, the maximal Lyapunov exponent and the diagrams of stability. Secondly, a feedback control law for chaos control and the linear augmentation method for controlling the multistability of the magnetostrictive actuator in a desired periodic state are designed, eliminating all possible chaotic behavior. The resulting magnetostrictive actuator is more stable than existing magnetostrictive actuators. Thirdly, a protocol is proposed for the design of a surgical robot for high precision cuts.
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In this paper, a rose-flower variety classification scheme, using color and shape features is presented. The first three statistical moments of the R, G, and B planes of the image were calculated to describe the color, while Fourier coefficients are used to describe the shape. For shape description, signatures (wave-forms) of the boundary contour of the binary images were extracted. Fourier coefficients that are used to describe the shape were estimated using the signatures generated. Depending on the Fourier coefficients, a representation of sums of angles formed along the boundaries of the flowers was defined. Using these sums and the color features as input to an artificial neural network (ANN), the flowers were classified into their respective target classes. The eighteen flower varieties considered in this study were classified with an accuracy of 95.6%, 98.9%, and 100% using their shape, color, and combination of both shape and color features, respectively. Comparing these results, it was found that the combination of the two features is an efficient criterion for rose flower variety discrimination and classification.
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In the area of water purification, nanomaterials offer the potential for the efficient eliminating very wide of variety of pollutants and biological contaminants. Oil is one of the most important hydrocarbon product in the modern world. Huge of cubic meters of oily wastewaters are produced daily, it can be discharged to the environment at various stages of production, transportation, refining and use. The utilization of iron oxide nanomaterials or magnetic nanoparticles has received great attention due to their unique properties, such as biocompatibility, small size, high surface area to volume ratio besides magnetite can be regenerated. An electrocoagulation treatment process was developed for treatment of petroleum refinery effluent (wastewater), instead of the conventional methods, which can consume higher amounts of chemicals and produce larger amounts of sludge. Electrocoagulation process was integrated with magnetite nanoparticles for the treatment of oily wastewater, where these processes have been used individually in previous studies. Such combination and enhancement will reduce the requirements for time, power, and cost to reach the allowable limits of oil content. Experiments were conducted in a bench scales electrocoagulation reactor where voltage was applied across a perforated plate of aluminum as anode, and iron mesh as cathode. Commercial grade of magnetite (Fe3O4) with an average nanoparticle size of 50 nm was used. The effect of some factors on the efficiency of the process such as pH of the solution (5-9), current density (5-25 mA/cm2), time (10-30 min), and magnetite dosage (0.27-1.6 g/L) were studied. The results verified that the current density required to obtain 90% oil removal efficiency for the 275-ppm initial oil concentration decreased from 25 to 15-mA/cm2 after the addition of 0.93-mg/L magnetite to the electrocoagulation process and time decreased from 30 min to 10 min, which is an indication of the enhancement of nanoparticles in the electrocoagulation process. In addition, the minimum oil content reached by electrocoagulation + magnetite process was 6.4 ppm, while the electrocoagulation process gave 19.4 ppm final oil content at the best operating conditions. The treated oily wastewater by the electrocoagulation + magnetite process found to be feasible for reusing in other processes or reinjection in the oil fields.
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Green logistics is focused on producing and distributing goods in a sustainable way, considering environmental, ecological, and social effects. This area is receiving an increasing and close attention from governments, academic and business organizations. Their importance is motivated by the fact that current production and distribution logistics strategies are not sustainable in the long term. This research presents an innovative study of a sustainable green logistic which consists in a multistage multi-objective fixed charge multi-item solid transport problem in intuitionistic fuzzy environment by considering recycling centers at the final stage which aims to reuse products and materials. In the proposed model, the parameters are assumed to be intuitionistic fuzzy numbers which are defined by membership and non membership functions. We use an expected value model to obtain a deterministic model and propose a solution methodology based on fuzzy goal programming approach to find Pareto-optimal solutions. Further, we incorporate an application example connected with a real-life industrial problem to display the feasibility and potentiality of the proposed model. Conclusions about the findings and future study directions are also offered.
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Skin-like sensors have huge potential in human-machine interaction, human health monitoring, and robotics. They can be worn flexibly to assess human vital signs or be extended toward enabling a machine to interact with its environment. However, many challenges hinder its further development, including stretchability, distribution measurement capability for multi parameters and resilience to various complex conditions, which are still a challenging and interesting subject. Herein, we proposed a skin-like optical sensor (SSOF sensor) with excellent stretchability of up to 100%, distributed monitoring capability and multi environment applicability. A unique hybrid coding approach based on the wavelength and the light intensity of two Fiber Bragg gratings (FBGs) was proposed and applied to the signal processing of SSOF sensor, achieving the resistance for environmental interference and the capability of distributed measurement. Meanwhile, the SSOF sensor shows outstanding durability (>10,000 tests), waterproofness, resistance for large temperature changes (0~55 ℃), and anti-impact. The SSOF sensor can instantly replicate the physiological activities of the human body in the form of digital signals, and convert them into virtual instructions and project them to the human machine interface. These properties have been successfully used in the comprehensive assessment for multi-parameters induced by human breathing, muscle activity, and body movement. Furthermore, a SSOF sensor based human-machine interaction system was created to monitor human physiological signals, track and reproduce human full-body movement. The proposed SSOF sensor puts forward a novel design idea of skin-like optical fiber sensors, and emerges huge potential in healthcare monitoring, virtual reality (VR), digital twin and intelligent human-machine interaction.
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Today, every technical effort is due to the digital processing of the image. The X-rays are used in the imaging. Among the most well-known cases is the use of the beams in medical diagnosis and medical imaging. Computerized axial tomography (CAT) due to its detection and 3D capabilities has revolutionized medicine. Therefore it has been available since the 1970s by applying x-rays in medical imaging. Any CAT image is a cut that is perpendicular to the patient's body. Fig.1 shows a sample of a cat image cut from the human head. Similar techniques are used in the industrial processes but x-rays with higher energy are used. Fig.2 is an x-ray image of an electronic circuit board. These images show hundreds of industrial uses of the beams used in the testing of electronic boards to find possible defects, such as unassembled components or unclipped paths. If possible industrial cat scans are useful in recognizing components by the X-ray. In this paper the image processing method is implemented in the spatial domain. The spatial domain is the page containing pixels of an image. Each pixel of an image with spatial coordinates x and y with a function f (x, y) has a certain numeric value that can be in the range of 0 to 255, this number represents the brightness or the gray level of the image. Each of these numbers represents a specific gray color with different concentrations. The main part of the noise is generated in digital images when shooting or transmitting. The light level and ambient temperature contribute to the noise level of the image obtained by the cameras and the images are contaminated during the transmission due to interference in the channel. Thus the presence of noise in digital images is common and often occurs, so different methods have been developed to eliminate this unwanted disorder. Edge detection is the boundary between the two regions separated by the distinctive gray level properties. Many of the classical methods for edge detection are based on the derivation of the original image pixels. Classical edge detection operators such as Roberts, Sobel, and Prewitt detect the edges by calculating partial derivatives in a neighborhood. Edge detection based on the derivatives is sensitive to noise and to reduce the noise effect, first the image is smoothed and then the edge is detected. However this action reduces the contrast of the edges and it is difficult to locate some of the poor edges. In this regard, research has been carried out for edge detection by the information theory. A study by Singh in this regard was carried out by Shannon entropy. This method results in the continuity in the resultant edges but only extracts the strong edges which is a disadvantage. This method is limited to the overall threshold of the mean brightness of the image and with these thresholds the image is made binary and then in this partitioned image the boundaries between the widths of these partitions are detected. Therefore some of the edges of the image will be lost due to the binary decision making process. Tsallis attempted to improve this problem. In Tsallis method Tsallis entropy has been used to obtain all changes and edges of the image used by both Shannon and Tsallis but this high insistence on identifying all the changes creates thick edges and high noise. To solve this problem, Kiani et al has investigated the region around the threshold of different regions of the image and then using the Shannon algorithm, the entropy has detected the edges of the image. This method has been compared with standard Canny, Sobel, Roberts, Log, edge detection Ant Colony Optimization algorithms and Tsallis edge detection method which is more efficient than the mentioned methods. But its most important disadvantage is sensitivity to noisy digital images and it is extremely inefficient. To reveal the weakness of this method an example of the effect of noise is examined on a simple dummy image. In this case, the histogram is calculated and, as shown in Fig. 3, its histogram consists of two impulses. The mean of the brightness intensity of this image determines the range between the two impulses, thus it is not a good sample for the initial threshold. In the Kiani method for choosing the initial threshold, the mean brightness intensity in each region of the image is used. Therefore, edge detection in this method is very much affected by noise effects.In another researchaims to implement the Canny edge detection method, combining with Otsu thresholding to detect the edges. Otsu thresholding is used to gain threshold value for Canny Method. In result, some edges are well detected, but some others are missed. The goal is detecting edge by using morphological operations. Unwanted edges (noise pixels) are eliminated by edge thinning and, ultimately, connecting the edges. A new combination of the mean and median filters leads to the creation of a new smoothing filter, which removes most of the image noises. The result is optimized edge detection. An image processing algorithm is created based on contour detection according to Mamdani. In addition, fuzzy rules are applied to detect blood veins in fundus images of the retina. Changing the contrast and application of a mean filter leads to a method that can carry out edge detection of retina's network of blood vessels properly. As a state of art, methods are based on gradient operator, but the accuracy level is poor. Therefore, computing based methods are proposed which are more accurate, still, these methods fail to detect some of the true edges. In this paper, the two-level approach is adopted for edge detection, firstly image edges are enhanced using guided image filtering and secondly on these enhanced images enhanced ant colony optimization method is applied for edge detection. Fuzzy sets have prepared a framework to combine human knowledge as an efficient unsupervised machine-learning tool to solve problems. For instance, presents a public transfer learning plan using a rule-based fuzzy logic system to conduct edge detection in digital images. A specific language value is selected in the input fuzzy set for optimal achievement in the fuzzy inference process. The second order scope of difference separates edge pixels from non-edge pixels. Therefore, the method of brings out edge pixels from noise pixels by strengthening and presents optimal edge detection by decreasing impact noise. In unsupervised learning, as its name suggests, no external supervisor has the right to dictate the process. Unlike the supervised learning methods such as the artificial neural networks, MLP, RBF and SVM where the external supervisors must provide a series of solved examples, based on training data. When the data is not tagged, it can be solved by the unsupervised mode. Different methods exist for unsupervised learning. All of these methods can be defined based on similarity. A similarity measurement parameter is a distance. As the distance is closer to zero, these two are more similar and distance more between means that they are more different. The purpose of our research is to find a suitable method for detecting the edges of noisy digital images by eliminating the noise effects. The image will be partitioned into equal partitions and the initial threshold of that image partition will be calculated. By applying all these thresholds into the self-organized map (SOM) neural network input optimized for learning and training based optimization algorithm (TLBO), threshold clustering will be performed. The partitioned image will be edge detected by entropy method. Choosing the threshold for image segmentation is of great importance. The mean of the brightness of digital noise images is not a good representative of the initial threshold. Noise causes the mean intensity of the brightness to take distance from the main range of the intensity of the image so the resulting edge detected image will be severely noisy and truncated. By determining the highest frequency of brightness intensity instead of the mean brightness, the above-mentioned weaknesses will be eliminated. This method outperforms many current methods, such as Tsallis entropy, Singh and Kiani and even Canny Edge Detection which demonstrates the effectiveness of the proposed method.
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Type 2 diabetes, the most prevalent form of diabetes mellitus, is a complex autoin-flammatory metabolic disease affecting more than 450 million individuals worldwide. Studies have shown that both genetic and environmental factors are involved in the pathogenesis of type 2 diabetes. Genetic studies have shown that type 2 diabetes is caused by hundreds of genetic variant. Interestingly, however, the genetic variants identified so far, can only explain 15% of type 2 diabetes heritability; a concept called "missing heritability". Epigenetic effects could account for the missing heritability of type 2 diabetes. The aim of this study was to investigate changes in epigenetic markers such as circulatory miRNAs and DNA methylation which are associated with high blood glucose levels in the plasma and blood cells of type 2 diabetic individuals. We focused on the expression and methylation status of several key inflammatory genes including IL-1β and IL-6 using qPCR and bisulfite sequencing, respectively. Healthy, pre-diabetic and type 2 diabetic individuals were enrolled and categorized based on their fasting plasma glucose and glycated hemoglobin levels. We found that in T2D patients with high blood glucose, IL-1β gene expression was increased by 2.69-fold whereas IL-6 gene expression was decreased by 3.45 fold. DNA methylation analysis revealed that both CpG sites in IL-1β gene are affected by hyperglycaemia and display decreased methylation while only one CpG site in IL1R1 gene is affected by hyperglycaemia. Next, we examined the plasma levels of two type 2 diabetes specific miRNAs, miR-30d-5p and miR-126-3p. We found that the plasma levels of miR-30d and miR-126 increase by 3.1 and 11.16 times, respectively, in individuals with intermediate hyperglycemia compared to non-diabetic controls. We propose that epigenetic changes such as those investigated in this study could help understand the mechanism of metabolic memory and serve as markers of metabolic memory diagnosis.
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Internet of things (IoT) in health care is gaining popularity in the field of research to improve quality in smart health care systems and applications. However, security and privacy of Smart Health (S-Health) data are the challenging issues due to Sybil attacks. Sybil attack is one of the most common attacks where a malicious node uses morphed identities to gener- ate Sybil nodes. Sybil nodes can acquire an authorized node identity and misbehaves by affecting its routing information, incurs interruption on communication line and storage. One of the IoT based smart health methodology is Privacy-Aware Smart Health (PASH), in which policy hiding is used to protect the privacy of users. The major issues in PASH is expensive to implement in S-Health applications, also it does not deal with attribute revocation and node traceability. To addresses these issues, a novel SybilWatch Enhanced Privacy-Aware Smart Health (E-PASH) approach is proposed in this paper. This approach has three major phases such as initialization phase, secure communication and Sybil node detection. A Lightweight Encryption Algorithm (LEA) is used to transmit SHRs (Smart Health Record) in encrypted form using prime order group- ing. A novel BlueTits Detection (BTD) algorithm is used in detection phase where cluster head gathers the recent activities of the suspicious user, and based on the gathered parameters (Master key and Last One-Time Authentication), the cluster head declares it as a Sybil node. As soon as Sybil node is detected, revised revocation list is shared with active users. The proposed approach is less expensive compared to the existing approach, it also supports attribute revocation and node trace- ability which are the major setbacks is PASH. Simulation results and comparison analysis shows that proposed Sybil Watch is efficient and cost effective compared to the existing approach, the proposed approach yields high detection rate of 99.7% and also false positive rate is reduced to 1% in the smart health systems, which is better compared to the existing approaches.
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In the recent years, the volume of text documents in the form of digital way has grown up extremely in size. As significance, there is a need to be competent to automatically bring together and classify the documents based on their content. The main goal of text classification is to partition the unstructured set of documents into their respective categories based on its content. The main aim of this research work is to automatically classify the documents which are stored in the personal computer into their relevant categories. This work has two significant phases. In the first phase, the important features are selected for classification and the second phase is the classification of text documents. For selecting the optimal features, this research work proposes a new algorithm, optimization technique for feature selection (OTFS) algorithm. To estimate the proficiency of proposed feature selection algorithm, the OTFS algorithm was compared with the existing approaches artificial bee colony, firefly algorithm, ant colony optimization and particle swarm optimization. In the second phase, this research work proposed machine learning-based automatic text classification (MLearn-ATC) algorithm for text classification. In classification, the MLearn-ATC algorithm was compared with widely used classification techniques probabilistic neural network, support vector machine, K-nearest neighbor and Naïve Bayes. From this, the output of first phase is used as the input for classification phase. The decisive results establish that the proposed algorithms achieve the better accuracy for optimizing the features and classifying the text documents based on their content.
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By applying density functional theory (DFT) and ab-initio molecular dynamics (AIMD) simulations, we predict the ultrahigh hydrogen storage capacity of K and Ca decorated single-layer biphenylene sheet (BPS). It was found that 2*2*1 supercell of biphenylene sheet can adsorb eight K, or eight Ca atoms and each K or Ca atom can adsorb 5 H2, leading to 11.90 % or 11.63 % of hydrogen uptake, respectively, which is significantly higher than the DOE-US demands of 6.5 %. The average adsorption energy of H2 for K and Ca decorated BPS is -0.24 eV and -0.33 eV, respectively, lie in the suitable range for reversible H2 storage. Hydrogen molecules get polarized in the vicinity of ionized metal atoms hence get attached to the metal atoms through electrostatic and van der Waals interactions. We have estimated the desorption temperatures of H2 and found that the adsorbed H2 can be utilized for reversible use. We have found that a sufficient energy barrier of 2.52 eV exists for the movement of Ca atoms, calculated using the climbing image nudged elastic band (CI-NEB) method. This energy barrier can prevent the clustering issue of Ca atoms. The solidity of K and Ca decorated BPS structures were investigated using AIMD simulations.
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Forward osmosis (FO) has received significant attention recently. FO has high potentials for integration with other water treatment technologies. However, concentration polarization (CP) remains a significant challenge in FO membrane applications (see Figure.1). In particular, internal CP (ICP) reduces the permeability of FO membranes by nearly 80%. The development of FO processes and their applications can greatly benefit from strategies that detect and control CP in standalone and integrated FO systems. This requires consideration of FO membrane structures, materials, configurations, operating conditions, and modification and cleaning strategies. This review provides a state-of-the-art analysis of recent literature on CP detection and control with a specific focus on ICP as a major issue in FO processes. This helps to understand current CP mitigation strategies and their challenges and prospects. The first section reviews the structures of different FO membranes and related CP mechanisms. Research on CP and the impacts of various parameters on its magnitude are then discussed, followed by a review of CP phenomena in hybrid FO processes and applied ICP control strategies. Finally, recommendations for future research in CP detection and control are made. This review serves as a valuable reference for future research on FO processes and may contribute to developing more ICP-resistant FO membranes.
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This presentation gives a detailed review on the ultrathin catalyst layer (UTCL) design strategy for high performance proton exchange membrane fuel cell (PEMFC). Specifically, the motivation towards the further reduced Pt loadings by applying the UTCL electrode design is firstly introduced from both the historical and mechanism deductions. Then, the recent developments on the UTCL designs belonging to different classifications are summarized with their respective merits. In particular, the critical issues remained on these ultra-thin, low Pt-loaded electrodes are proposed with alternative solutions. Finally, the whole presentation is concluded with the perspectives on the possible future directions settling the remaining challenges.
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This research presents bending responses of hybrid laminated nanocomposite reinforced axisymmetric circular/ annular plates (HLNRACP/ HLNRAAP) within the framework of non-polynomial under mechanical loading and various type of initially stresses via the three-dimensional elasticity theory. The current structure is on the Pasternak type of elastic foundation and torsional interaction. The state-space approach and differential quadrature method (SS-DQM) are studied to present the bending characteristics of the current structure by considering various boundary conditions. To predict the material proper- ties of the bulk, the role of mixture and Halpin–Tsai equations are studied. For modeling the circular plate, a singular point is studied. Finally, a parametric study investigates the impacts of various types of distribution of laminated layers, stacking sequence on the stress/strain information of the HLNRACP/ HLNRAAP. Results reveal that the system’s static stability and bending behavior improve due to increasing the value of Winkler and Pasternak factors, and the stress distribution becomes more uniform.
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To meet the food demand in parallel with the global population growth and to achieve the goal of eradicating hunger, traditional plant production methods need to be renewed. Nano products (NP) offer important opportunities to meet the need for healthy food in a sustainable way. It is expected that the successes achieved in various production and processing processes with NP will be carried over to large food production sectors such as viticulture. In this literature review, developments in nanoscience and nanotechnology were examined from articles published for NP viticultural applications. The nono-technological approaches to the development of new grapevine genotypes, in vitro and in vivo propagation, NP seed priming, increasing growth and productivity in the production process, coping with biotic and abiotic stresses, and improving the biochemical content of grapes were evaluated. In addition, the mechanisms of action in various NP-plant interactions, the risks that may occur in terms of producers, the environment and consumers, products and risk creation methods were examined. Moreover, the use of the latest innovations in nanoscience and technology in the viticulture sector for a better future will be possible by developing practical usage recipes at the NP level and evaluating them together with risk analysis.
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To explore the influence of cyclic impact and axial static pressure on the damage of chemically corroded sandstone, a series of cyclic impact tests were conducted on white sandstone by using the Split Hopkinson Pressure Bar. Besides, the longitudinal sections and fractures of samples were observed with the scanning electron microscope for the purpose of investigating the damage characteristics and structural changes of sandstone subjected to the coupling of force and chemistry. The results show: (1) When pH of the solution is 7, the number of cyclic impacts and stress peaks of specimens increases, and the specimens respond with a significantly high resistant strength. (2) The stress wave transmission coefficient of sandstone decreases gradually with the increase of the number of cyclic impacts, while the reflection coefficient shows a tendency of ‘‘decreasing firstly and then increasing’’. (3) Cylindrical specimens with a certain axial static pressure present an ‘‘X’’ shaped conjugate failure under cyclic impact. When axial static pressure is too large or there is excessive impact, the ‘‘X’’ shaped conjugate undergoes shear to a state of broken cones. (4) The vertical section and fracture surface damage degree of white sandstone soaked in the sodium sulfate solution is more serious than that in the sodium sulfate solution.
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Air pollution exposure is among the most prevalent reasons for environmentally induced oxidative stress and inflammation, both of which are involved in the development and progression of central nervous system (CNS) diseases. Ultrafine particles (UFPs) plays an important role in global air pollution and the diesel exhaust particles (DEPs) are the most important component in this regard. There are more than 40 toxic air pollutants in diesel exhaust (DE), which is one of the main constituents of an environmental pollutant and including particulate matter (PM) especially UFPs. Thus, in this study, adult female and male NMRI mice were exposed to DEPs (350–400 μg/m3) for 14 weeks (6 h per day and 5 days per week). After 14 weeks of exposure, expression of pro-inflammatory cytokines (IL-1α, IL-1β, IL-6, TNF-α), nNOS, HO1, NR2A, and NR2B and malondialdehyde (MDA) level were analyzed in various brain regions such as the hippocampus (HI) and olfactory bulb (OB). Exposure to DEPs also caused neuroinflammation and oxidative stress in female and male mice. The effects observed in females were less pronounced than in male mice. The male mice emerged to be more susceptible significantly than the female mice to induced neuroinflammation following DEPs exposure. Also, our findings indicate that long term exposure to DEPs results in altered expression of hippocampal NMDA receptor subunits, and suggests that gender can play important role in the modulating susceptibility to neurotoxicity induced by DEPs exposure.
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Polymeric nanocomposites of polystyrene nanofibers and multiwall carbon nanotubes and natural pigment were synthesized using electrospinning technique the polystyrene concentration was 12 wt.% and multiwall carbon nanotube was added by (0, 100, 140, 200 ppm), and natural pigment added by two drops (0.063g) to the prepared solutions and many tests were carried out to the prepared solutions and the final samples. The solution tests included viscosity test by using cone-plate viscometer and surface tension test using du-nouy ring method. The nano textiles tests included the Fourier transform infrared spectroscope (FTIR) test, Field emission scanning electron microscopy (FESEM) test, contact angle test, and ultraviolet test to extract the energy bandgap using (tauc plot) method. The tests results showed that the viscosity increased by increasing the multiwall carbon nanotube and natural pigment and surface tension slightly increased at a high ratio of 200 ppm of multiwall carbon nanotube and natural pigment and physical type of reaction between the components were confirmed through FTIR, and the addition of multiwall carbon nanotube and natural pigment makes the fibers smoother and fewer beads formation and increase the multiwall carbon nanotube addition made the samples more hydrophobic and the charts of tauc plot show that increasing the MWCNT with natural pigment addition will increase the electrical sensitivity of the prepared samples in which the energy band gap dropped from 1.18 ev to 0.2 ev for the sample of Polystyrene/200 ppm MWCNT/natural pigment this is regarded as an indication of using it as a typical electrical sensor.
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Nanobots are robots with a size of less than 200nm. A nanobot is more difficult to create than a microbot because of the multiple constraints and multifarious elements of the latter. (Scale in micrometers). Nanobots are made with a lot of care. Molecular nanotechnology and mechano-synthetic materials interaction. These are (Nano-electro Mechanical Systems) robots. Devices that are configured to perform one of two tasks one or more high-efficiency jobs with minimum work output and energy consumption. The size and range of programming options Nanobots' characteristics allow them to be used in a wide range of biomedical, pure medical, and pharmaceutical applications. There are some Nanobots can be manufactured in a variety of methods, including by hand Bio Hybrid nanobots are completely synthetic, biodegradable, or a combination of both. This research gives an outline of a few of the issues building strategies that might be utilized to construct nanobots for biomedical applications and current research Nanobots in Biomedical Applications rends and Future Scope.
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The represented work is driven by the well to improve vehicle crashworthiness. Herby, a crash-pitch controller integrated with Magneto-Rheological (MR) dampers is suggested. Hence, sets of MR dampers are implemented within both the vehicle suspension system and front-end structure. The methodology of the work is built on modeling the vehicle's dynamic behavior. a 3 degrees of freedom (DOF) half-car mathematical model is developed to represent the longitudinal, bounce, and pitch motions. Then, the model is used to study the vehicle’s dynamic response in a full-frontal collision against a fixed barrier. Moreover, the integration of the suspension MR dampers with the vehicle’s front end structure MR dampers is suggested via a fuzzy logic controller. Four cases were simulated and compared according to the results. The mentioned cases can be expressed as: 1. Free-rolling in which a conventional suspension and front-end structure are assumed. 2. The vehicle’s conventional suspension dampers are replaced with MR dampers 3. MR dampers are implemented within the vehicle’s front-end structure. While the suspension system remains conventional. 4. A Fuzzy logic controller is used to integrate MR dampers implemented within both the front-end structure and suspension system. The simulation results showed noticeable improvements as below: • The front-end structure deformation is reduced. • Angle had been reduced as well as its settling time. • pitch acceleration had been reduced which benefits human body exposure.
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Folding is an effective technique to alter the optoelectronic properties of two-dimensional (2D) materials such as interlayer coupling, bandgap, etc. Optical techniques such as PL, Raman were used in the past to probe the folds localization. Here, we show that optical second harmonic generation (SHG), which is sensitive to the crystalline symmetry of 2D materials, is a powerful probe to monitor the fold localization in TMDCs. Two dimensional 2H Transition Metal Dichalcogenides (TMDC) are particularly well-suited for the study because their SHG investigation has already been done, in additional, they can be easily folded due to their high flexibility. Our study includes the fabrication of clean folds on ultra-thin layers of TMDCs, optical characterization of the folds using SHG imaging and theoretical calculations to prove our findings. We find that SHG from the folds is a coherent superposition of the SHG from the individual layers of the fold, with a very small phase difference depending on the folding angle. The SHG response is theoretically calculated as a function of the folding angle. Our results establish SHG as an effective tool to monitor folds localization in 2D TMDCs.
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The further development of composite manufacturing methods is characterized by the progress of their mechanical properties, which are widely used in many applications as automotive, aerospace, and marine industries. The automated composite production techniques are as follows: automatic tape layering, automatic fiber placement, and filament winding methods used in many industries. Photo polymerized composites and their additive manufacturing methods are promising with new advances in technology. This method for printing continuous fiber reinforced plastic composite parts by a six axis industrial robotic arm is based on fused deposition modeling technology. The objective of this work is to obtain a better understanding of the mechanical properties of robotic three dimensional printed photopolymer resin continuous fiberglass–reinforced composites (CFGRCs) as a function of different printing speeds (10, 20 and 30 mm/s), fiber densities (45, 55 and 65%), and fiber orientations (0, 0/90 and ±45°). This work infers that mechanical properties are significantly affected by the fiber density and fiber orientation of CFGRC. With this method, approximately 300 MPa tensile strength can be obtained and structurally preferred instead of ferrous materials in many areas.
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The purpose of the study: To determine the most optimal ratio of thread and honeycomb structure to ensure the primary and secondary stability of the screw. The method of conducting the study is to calculate the maximum values of mechanical stresses and displacements for several types of screws under similar conditions, where the values of stress concentrators can indicate cyclic reliability (the lower the values, the higher the cyclic reliability), and displacements can indicate resistance to shock loads. For this, three versions of an osseointegrable screw 100 mm long and 12 mm in diameter were developed, where the thread was made HB4 according to GOST R 50582-93, and the cellular structure consisted of a dodecahedron graph with an edge length 3.3 mm and 0.6 mm in diameter. Screw analysis was performed under two conditions: primary stability and osseointegration. Each condition had two cases of study: axial normal load on the implant and axial shear load. The load on the implant Fa was from 2000N to 4000N in steps of 500N. Under conditions of primary stability between the bone and the implant had a frictional contact with the coefficient of friction 0.4. Under osseointegration conditions: The honeycomb structure was "Bounded" with bone and the threads had a friction coefficient of 0.7. The prosthesis was made of an isotropic Ti6Al4V alloy (fig.1). For the calculation, a solid finite element model was developed. Parts were decomposed into simple bodies: cylindrical, conical, and threads. Bonded linear contacts were used to connect simple bodies. The finite element mesh was modeled with octagonal hexahedral elements and 2 mm 4-gonal prismatic elements. The threaded surface was modeled with 0.75 mm hexahedral elements. The result of the study: When designing bone intramedullary screws with a cellular structure and bone thread, it is necessary to evaluate the area of the cellular structure from the priority, since the thread performs its function even with a large pitch.
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Saline pollution of wastewater is one of the environmental concerns which needs to be addressed. Saline waste represents around 5% of the total generated influents worldwide. Different industries which generate saline wastewater include the fish, food processing, textile, and petroleum industries. To treat this saline wastewater many physico-chemical treatments are utilized which are expensive and have high energy requirements. The application of halophilic bacteria to treat saline wastewater is an effective and sustainable strategy. In a microbial fuel cell (MFC), chemical energy hidden in the waste can be converted into electrical energy with the bio-electrocatalytic activity of microorganisms. In the MFC system when the conductivity/ salinity is low there is a limitation in the ion transport and ohmic resistance is increased due to increased internal resistance of the system. So, increasing the salinity can increase the power performance of the system. In this study, a Microbial Fuel Cell (MFC) capable of treating saline wastewater at anode and nitrate contaminated water at the cathode was developed (Figure-1). Sodium chloride (NaCl) concentrations of 20 g/L and 40 g/L were tested at the anode. Nitrate was used as an electron acceptor at the biocathode. The halophilic bacteria were isolated from the Arabian Sea, Mumbai India. Results indicated successful removal of nitrate (89%) and COD (83%) with a concomitant power output of 162.09 mW/m2 at 40 g/L NaCl concentration. An increase in power density from 96.77 mW/m2 to 162.09 mW/m2 (1.7 folds) was observed when NaCl concentration was increased. EIS (Electrochemical impedance spectroscopy) analysis revealed that charge transfer resistance at 40 g/L salinity was lower than 20 g/L. Cyclic voltammetry analysis also revealed high electrochemical activity in 40 g/L NaCl concentration. This is the first study of power production by halophilic bacteria in MFC isolated from the Mumbai Sea water.
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In order to obtain a symmetrical and stable circuit structure and improve the yield in the wet etching process, it is essential to control the uniformity of the etchant velocity and pressure on the surface of the FPCB. In our work, a numerical method implemented with Euler multiphase flow model is proposed to simulate the interaction of etchant from the multi-nozzle array with three different nozzle rotation angles. The full model of the multi-nozzle array in the etching process of FPCB is established to study the interaction of etchant in a 3 × 8 spraying array, the inner channel structure of the nozzle, 24nozzles, the relative position between the nozzles, the rotation angle of the nozzle an the spraying domain. The results of the simulations demonstrate that the optimal rotation angle is 1.3°with the smallest distribution of the low speed flow zone on the surface of the FPCB, reaching about 60mm along the X axis direction. Then we conducted experiments to validate the simulations with pressure sensors. When the rotation angle is 1.3°, the standard deviation of the pressure is the lowest, reduced by up to 67.6% compared to 0°. The cross-section of the FPCB circuit is close to an isosceles trapezoid, which is a relatively stable structure and benefits from the uniform etchant flow field. The experimental results are shown in the Figure 1 and Table 1.
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There is an increasing demand in joining dissimilar material, and riveting is not always the best solution. Though conventional welding provides a good solution, certain materials like aluminum are difficult weld. Friction stir welding developed by The Welding Institute offers a greater potential in welding dissimilar materials e.g. copper/ aluminium, aluminium/aluminium, with greater advantages in aerospace industry. In the space exploration and rocket propulsion, attention is paid to the payload capacity of the vehicle so that to reduce the weight of structural parts. Due to their lightweight and refractory properties at high temperatures, Aluminium alloys are used as main materials for the external tank on launch vehicles which are subjected to extreme temperatures at lift off and re-entry into earth atmosphere. The welding aluminium alloys is a challenging task due the rapid oxidation of aluminium. However, NASA was the first to use welded aluminum-lithium alloy Al 2195 at cryogenic temperatures for their space vehicle external tank. New development in the technology of welding aluminium alloys allowed reducing the tank weighed 34.500 metric tons to 29.900 metric tons by the sixth mission. The AA 2050 aluminium lithium alloy was introduced and provide an increase of the payload by 3400 Kg and the majority of parts were friction stir welded. The idea proposed here is making leapfrog on current technology to harvest the advantaged of graphene nano-powder, and fusing it into aluminium-lithium alloys pushing to news boundaries the thermo-mechanical performance a newly engineered nano-composite Aluminium-lithium-graphene. The new super alloy and the technology to be developed here has direct application to fuel tanks in space propulsion vehicles and in other aerospace and automotive industry.
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System with self-healing mechanism has been successfully applied in many practical engineering fields. When we discuss the reliability performance of system subject to shocks, cumulative shocks have the greatest impact on most systems fields such as high-speed railway systems or civil structural components. In the present research, we deal with two different types of cumulative shock models in discrete time with self-healing effect, and consider the damage evolution effect of the system. We study a system with self-healing mechanism from a reliability point of view under these two shock models. The self-healing system will fail when the cumulative damage effect exceeds the given threshold. In the first model, there are shock events at instant of time , while in the second model at some time point there was no shock occurring event. Along with the work in this research, the system reliability formulas and the means and variances of their lifetimes are given under two different types shock models proposed, then simulation methods are adopted to analyze the reliability of the system for the second cumulative model. Finally, numerical examples and future researches are discussed.
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In recent years, studies on chaotic neural networks have been increased to construct a robust and flexible intelligent network resembling the human brain. To increase the chaotic performance and to reduce the time- complexity of conventional chaotic neural networks, this paper presents an innovative chaotic architecture called cascade chaotic neural network (CCNN). Cascade chaotic system is inspired by cascade structures in electronic circuits. Cascade structure is based on a combination of two or more one-dimensional chaotic maps. This combination provides a new chaotic map that has more complicated behavior than its grain maps. The fusion of this structure into the network neurons makes the CCNN more capable of confronting nonlinear problems. In the proposed model, cascade chaotic activation function (CCAF) is introduced and applied. Using the CCAF with inherent chaotic features such as increasing variability, ergodicity, maximum entropy, and free saturation zones can be promising to solve or reduce learning problems in conventional AFs without increasing complexity. The complexity does not increase because no parameter is added to the system in use. The required chaos for neural network is generated by the Li oscillator, and then when using the neural network, parameters are considered as constants. Chaotic behavior of the CCNN is investigated through bifurcation diagram. Also, modelling capability of the proposed model is verified through popular benchmark problems. Simulation and analysis demonstrate that in comparison with outstanding chaotic models, the CCNN provides more accurate and robust results in various conditions.
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Escherichia coli O157 (E. coli O157) is responsible for outbreaks of high morbidity in food-borne infections. The development of sensitive, reliable, and selective detection systems is of great importance in food safety. In this work, it was designed and validated two high fundamental frequency (HFF) piezoelectric genosensor (100 and 150 MHz) for the rfbE gene detection, which encodes O-antigen in E. coli O157. HFF resonators offer improved sensitivity, small sample volumes, and the possibility of integration into lab-on-a-chip devices, but their sensing capabilities have not yet been fully explored. This HFF-QCM genosensor uses the method of physisorption based on the union between the streptavidin protein and the biotin molecule to immobilize the genetic bioreceptor on the surface and detect its hybridization with the target sequence. Parameters such as molecular coating, specificity, and variability have been tested to enhance its performance. Although, the genosensors evaluated can determine the target, the 100 MHz device has a higher response to the analyte than the 150 MHz platform. This is the first step in the development of an HFF-QCM genosensor that could be used as a trial test of E. coli O157 in large batch samples.
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Background: Green synthesis as a new method of synthesis of nanoparticles with a simple, biocompatible, safe, and economical approach can be an alternative to chemical and physical processes. Fungi can convert some toxic ions into less harmful forms, including nanoparticles. Nanoparticles with a size of 1 to 100 nanometers have unique quantum properties. Today, the problems of drug resistance have been seen in different species of fungi. Selenium nanoparticles (SeNPs) are substances that have been reported to have antifungal properties. The present study aimed to investigate the antifungal effect of biosynthesized SeNPs using Aspergillus fumigatus. Material and Methods: For this purpose, SeNPs were biosynthesized with a specific concentration using A. fumigatus. The presence of nanoparticles was confirmed by various methods, including UV-Vis, FT-IR, FE-SEM, EDX, XRD, DLS, and Zeta potential. Then, susceptibility determination based on the Minimum Inhibitory Concentration (MIC) test was performed on standard fungal strains treated with SeNPs. Results: After confirming the results of nanoparticle biosynthesis, the MICs for itraconazole and amphotericin B against the standard fungal strains were 8 and 4 μg/mL. In comparison, MIC values for SeNPs-treated samples were reduced to 1 μg/mL and below. Conclusion: Due to the increasing resistance of opportunistic fungi to target antifungal drugs, the use of biosafety SeNPs even at low concentrations can have favorable inhibitory effects on the growth of fungal pathogens.
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This work aims to investigate the interaction of diphenylcarbazide (DPC) with tetradecyltrimethyaammonium bromide (TTAB) over concentrations ranging from below and above the critical micelle concentration (CMC) of the surfactant and subsequent complexation of DPC with Zn(II) of the surfactant by visible and NMR spectrophotometry. For this purpose, we determined the CMC of the surfactant in pure water and in the presence of DPC conductometrically. Lower specific conductance values of the surfactant in the presence of DPC indicate the interaction between TTAB and DPC both below and above the CMC. The CMC of the surfactant was found to increase with increasing the concentration of DPC, indicating that hydrophobic interaction between the alkyl chains of the surfactant becomes unfavourable in the presence of DPC. The interaction of Zn(II) with DPC is strongly influenced by the concentration of the surfactant as well as the solubiliZation site of DPC in the micelle. The absorbance of the Zn(II)-DPC complex increases initially, attains maximum near the CMC and then decreases with further increase in the concentration of the surfactant. Visible spectra together with NMR data of the Zn(II)- DPC complex in surfactant systems under different conditions provided information about the possible location of the DPC in the micelles.
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Biosynthesis of nanoparticles can replace the available chemical and physical methods by offering new procedures as green syntheses that have proved to be simple, biocompatible, safe, and cost-effective. Recently, antifungal resistance has been reported against different species of Aspergillus and Candida opportunistic fungi. Selenium nanoparticles (Se-NPs) were biosynthesized using standard strains of Aspergillus flavus and Candida albicans. The presence of nanoparticles was confirmed by UV-Vis, FT-IR, FESEM, EDX, XRD, and Zeta potential. Common fungal strains were cultured in Sabouraud dextrose agar medium to perform the sensitivity test based on the minimum inhibitory concentration (MIC) method in duplicate. The utilization of Se-NPs at concentrations of 1, 0.5, and 0.25 μg/ ml or in some strains even more minor than 0.125 μg/ ml resulted in zero growth of fungal agents. However, antifungal drugs inhibited their growth at concentrations of 2, 4, 8, 16, and 64 μg/ ml itraconazole (ITC). Also, MIC breakpoints for amphotericin B (AMB) and anidulafungin (AFG) were 2 μg/ ml for defining resistance in some isolates. Based on the obtained results, biological NPs produced by Aspergillus and Candida at different concentrations exhibited favorable inhibitory effects on the growth of fungal strains.
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Solar energy's significant intermittency remains an issue that must be addressed. Thermal storage has been shown to be an effective method, with phase change material (PCM) being particularly promising. In this study, SEM/EDS microanalysis was used to examine the efficacy of infiltration into porous materials. Infiltrated samples were classified using thermal conductivity, infiltration efficacy, and power storage density. Infiltration, power, and the possibility of using stainless to ease the design of a single-chamber infiltration device are expected to reduce the overall expenses of composited preparation compared to other common approaches. an NANO-composite with a 70% porosity was employed for PCM infiltration. It is 90% effective to infiltrate PCM into nominal 100-200 µm foam pores using the study's infiltration device, vacuum, and pressurization. Based on PCM density at ambient temperature, infiltration used just 8.5% of the original PCM available in the setup. The thermal conductivity was found to be 1.8 W/mK at 23°C and 101 W/mK when infiltrated. Thermal conductivity increased by 56.1% at 150°C, from 1.7 W/mK for pure PCM to 78.2 W/mK for the infiltrated sample. Thermal conductivity increased by 46% at 300°C, whereas the infiltrated sample is 62.9 W/mK. The energy density loss estimated to be approximately 30% and more than 92% of pores were effectively infiltrated. They lost less than 18% of their weight after the infiltration cycles. For a large-scale, practical, and simple energy storage solution, infiltrating foam PCM encapsulations are required. They may now be employed for heat storage and other high temperature applications.
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The novel coronavirus disease outbreak started in December 2019 in Wuhan, China. On March 11, 2020, WHO declared the COVID-19 outbreak as a pandemic due to an uncontrolled situation. Pakistani government management against COVID-19 was excellent having 204.65 million population, all four provinces, two independent territories, and the federal state took the different initiatives in the pandemic situation c. that's why the situation was under control in Pakistan due to state respond urgently to halt the spread of disease. With rapid response and full support of the government of Pakistan, the situation was under control in all aspects of life, June 17, 2019, each district of Pakistan recorded at least one confirmed case of COVID-19 due to remarkable effort against the pandemic. The state of Pakistan declare urgency and fastest action to control the situation, economic package, ventilator manufacturing, and diagnostic kits were manufactured locally. The DRAP Pakistan permitted to use of different drugs against COVID-19 and purchased vaccines from China. Due to the planning and management of the Pakistani Government, the situation was under control as compared with neighbourhoods countries (China and India), in both countries COVID-19 waves were lethal.
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With the COVID-19 pandemic, point of care and rapid testing have been on the rise. Their continuous use brought new advancements in point of care testing causing the demand to increase in developing countries due to the urgent need for more quick and efficient disease diagnosis for the large population. Current point of care tests require expensive and scarce materials causing difficulty when bringing them into both the developed and developing healthcare regions. However, with the use of an emerging field known as nanotechnology and quantum dots, point of care testing could become the new normal. This review will look at immediate testing and their applications within developing countries alongside using quantum dots to improve the analytical performance and to simplify the detection process. Overall, this paper focuses on the quantum mechanics of quantum dots and their use within biomarkers, their nanofabrication and understanding how they work by diving deep into the quantum confinement effect and Schrödinger's equation. It also looks at graphene quantum dots and their use in POCT, specifically in paper-embedded strips for testing within 5-10 minutes. As a whole, the combination of point of care testing and quantum dots could remodel the entire healthcare system and save millions of lives each year.
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Research involving welding of dissimilar materials has received large investments, starting from industries or research institutions. This type of research is essential for the development of new equipment and/ or manufacturing methods for the aerospace industry. In this study, plasma electrolytic oxidation (PEO) technology was applied to an AA2024-T3 aluminum alloy, to create on this, an oxide coating with improved mechanical and chemical properties. To improve the surface adhesion with thermoplastic composite material, from oxy fuel welding (OFW). An alkaline solution based on sodium silicate was used, and the process had its time and voltage varied, to evaluate in which parameter it would be possible to obtain an ideal Lap Shear value. After the anodization, welding and mechanical testing processes based on ASTM D-1002:2010, it was observed that the hybrid samples presented mean values of 0.5 to 4.3 MPa. These low values can be associated with the Si content in the coating that decreased the shear strength of the hybrid junction, close to the aluminum, being observable the beginning of micro-cracks in the coating.
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Background: In the field of nanotechnology, the metallic nanoparticles are of remarkable interest because of their unique electronic, magnetic, chemical, and mechanical properties. Purpose: In the present work, silver nanoparticles (AgNPs) were synthesized using bio-reduction method. Research Design: Silver nitrate was used as metallic precursor and the extract of Moringa oleifera leaves with different concentrations was used as reducing as well capping agent. The extract exhibited strong potential in rapid reduction of silver ions for the synthesis of silver nanoparticles. The synthesized silver nanoparticles were characterized by UV-visible spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM) techniques. Results: The absorption SPR peaks appeared in the range of 415 to 439 nm. SEM analysis exhibited that particles were spherical in shape with size distribution range from 10 nm to 25 nm. The synthesized silver nanoparticles were pure crystalline in nature as confirmed by the XRD spectra with average crystallite size 7 nm. In vitro antibacterial activity of the prepared silver nanoparticles colloidal samples as well the extract was studied using different concentrations of AgNPs (C1 = 100 μg/ml, C2 = 50 μg/ml, C3 = 25 μg/ml) by well diffusion method against Gram negative Escherichia coli. The antibacterial performance was assessed by measuring the zone of inhibition (ZOI). Conclusions: The results suggested that AgNPs prepared by green approach can be considered as an alternative antibacterial agent.
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Smart Hybrid Nano composite sensors are developed to diagnosing their own state of strain in which Nano-materials used as conducting filler. Nano-material like carbon fibre (CF), multiwall carbon nanotube (MWNCT), and graphene have a unique property that they can change applied strain into electric resistance. Hence, they can be used as conducting filler in the cement matrix. The present study mainly concerns with developing smart hybrid Nano composite cement based sensors and implementation of these sensors for structural health monitoring by embedding sensors into structural components. Two different combinations of sensors are developed by inserting Nano materials (0.25 and 0.5% weight of cement of MWCNT, CF, and Graphene) into cement mortar matrix. The insertion of carbon Nano filler into base material makes them into sensitivity to mechanical modification. This self-sensing property for material is achieved by adding piezoresistive materials into them. These piezo resistivity makes the materials self-sensing by indicating a detectable change in their electrical resistivity with applied stress or strain and make them useful for health monitoring of structures. Many studies shows that nano materials possess greater conductivity properties which can be used as smart materials. The size of specimen sensor was fixed to 80 mm 80 mm 50 mm with three 10 mm diameter tube was the filler is filled with copper electrode. From the electromechanical test it indicates that both combination sensors show good strain sensing with respect to applied load and also it is observed that is increased in flexural strength about 30.08% for 0.5% combination. A flexural and compression test was conducted by embedding Nano composite cement based sensors into structural components. The test results shows the variation in resistance for both compression and flexural loading which indicates into self sensing property of structural element by embedding sensors into them, hence it will be helpful in monitoring the structure. Scanning electron microscope is carried to understand the morphology of sample. A Finite elemental modeling is done to validate this experimental result. A FEA modelling is carried out using ANSYS software, subjected to steady steady static loading and electric analysis were done. Form the experimental is observed that addition carbon fiber induces conductivity property and the resistance decrease for failure load. The resistivity from experimental study observed is 9.2 kilo ohms and 11.2 kilo ohms for embedded carbon fibre sensor into beam and column respectively. The percentage error in electrical analysis of experimental tests compared with analytical modelling, found to be 15 %. A molecular dynamic simulation is carried to understand the atomic level interaction particles to evaluate the mechanical and electrical properties. Based on these results it can be concluded that carbon fiber cement composites have great potential and they can be used for structural health monitoring applications.
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Integration of nanomaterials for wearable health technology: Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring the health of individuals to reduce time-consuming and high medical costs in the current clinical practices. This provides an opportunity for various disease prediagnosis and immediate therapy. In particular, wearable sensors based on polymer nanocomposites have opened up a new concept of personalized health monitoring by measuring physical states (e.g., wearable strain/pressure sensors) and chemical signals (wearable biosensors/microfluidics). In this regard, various conductive nanofillers including carbon-based fillers such as carbon nanotubes (CNTs), and graphene (Gr), and metal-based fillers such as gold nanoparticles (AuNPs), and silver nanowires (AgNWs) have been incorporated into the polymers as functional elements to induce sensing capabilities. They also provide stretchability, mechanical compliance, and durability. Despite the progress made so far, due to the limitation in nanomaterial synthesis, process, and structures, and design and fabrication of reliable devices, successful translation to the commercial market is challenging. Here, we developed various nanomaterials including flexible and stretchable polymers combined with conductive nanofillers such as CNTs, AgNWs, Gr, with unique structures and fabrication techniques that can be used for the fabrication and commercialization of wearable electronic devices such as wearable sensors with improved sensitivity and stretchability, and wearable biosensors/ microfluidics with improved flexibility, sensitivity and selectivity towards human health monitoring, and stretchable conductors as interconnectors in the electronic circuits.
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Carbon spheres (CNSs) and carbon nanotubes (CNTs) are attracting more attention from researchers due to their excellent potential applications as adsorbents, catalyst carriers, and drug delivery. The synthesizing of CNTs and CNSs is a challenging and tedious process. A researcher has to produce different catalyst materials and precursors to synthesize CNTs and CNSs. Hence, an attempt has been made to combined in-situ synthesize of CNSs and CNTs in a single experiment. NiO/CuO/Al2O3 catalyst was used as a substrate. The thermal chemical vapor deposition (CVD) process with acetylene as a precursor gas was used to synthesize the CNSs and CNTs. The influence of process parameters and the growth mechanism of carbon nanomaterials were investigated. The obtained carbon nanomaterials samples were characterized using FESEM, HRTEM with SAED pattern, XRD analysis, Raman spectroscopy, and FTIR analysis. Results indicated that the catalyst morphology has a greater influence on the growth of CNTs and CNSs. The CVD process parameters play a vital role in deciding the structure of carbon nanomaterials. A schematic representation was proposed to understand the formation of carbon nanomaterials on top of the catalyst particles.
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The SARS- CoV-2 pandemic has created a huge impact across the world threatening the immuno-compromised individuals including the cancer patients; due to their weakened immune response it makes them more vulnerable and prone to the virus. The patients as well as oncologists are facing many issues for their treatment sessions as they need to reschedule or postpone their surgery, chemotherapy or radiotherapy. That’s the concerning issue as they are avoiding hospital visits due to the high risk of virus infection. In our study, we aim to adopt a strategy especially concerning the cancer patients with the amalgamation of immunotherapy and nanotherapy to reduce the burden on the healthcare system with the rise of different variants of virus. As well as there is a high demand to predict or analyze the data of cancer patients prone to higher chance of getting exposed to contract a virus infection to reduce the mortality rate. Artificial Intelligence (AI) will step in to access and track the data in a real time basis which would be available to the physicians to understand their patient’s clinical study and their past treatments. With this strategy, it would become much easier for them to modify or replace the treatment to achieve higher efficacy against the virus infection. The combination of immunotherapy and nanotherapy will be targeted for the treatment of the cancer patients diagnosed with SARS-CoV-2 and the AI will act as icing on the cake to monitor, predict and analyze the data of the patients in order to improve the treatment regime for the vulnerable cancer patients.
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Globally, there has been a change in the population pyramid with an accelerated aging process. This increase requires a greater challenge to maintain autonomy and independence. Currently, there are technologies developed with a focus on health. This is given by the development of wearables and their areas of applications. As a general context, this technology is characterized by the research field in energy generation, development of external devices for human control and monitoring, clothing, smart textiles, and electronics. The latter are classified into three areas of application: monitoring and safety; fabrics, perception, and physical activity; and rehabilitation. A literature review is conducted to identify the state-of-the-art in these fields within the last years. The progress in monitoring systems and intelligent textiles is evidenced, being able to highlight remote feedback, materials, and wearability both at a commercial and user level. A discussion is included to address the main challenges and future trends in the application of wearables in elderly people.
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India is undertaking large scale Road infrastructure development in the country, through different National Highways and Rural Road development programs. Considering the magnitude of the projects under these programmes, there is a strain on natural resources like soil and aggregates, which are depleting at a fast rate. Restrictions on quarrying because of environmental considerations has resulted in increased lead distances, especially in urban areas, which in turn significantly increases the total cost of road construction. The effective utilization of locally available waste marginal/alternative materials, not only reduces the total cost of the project, but also protect our environment and results in sustainable road construction. Municipal Solid Wastes (MSW) in the landfills have become a nuisance affecting significantly the health, hygiene, sanitation and aesthetics of surrounding area. If these wastes are not properly disposed off, they can prove perilous and an environmental hazard. It is very important for Engineers and Environmentalists to adopt sustainable waste management programs. As a part of sustainable road construction, CSIR-Central Road Research Institute, New Delhi, India carried out R&D studies to investigate the possibility of utilizing the Municipal Solid Wastes collected from Ghazipur, East Delhi and Ramana landfill, Varanasi, Uttar Pradesh, India. About 200 tons of Municipal Solid Waste from Ghazipur landfill, and 150 tons from Ramana dump site, were collected from different locations on the landfill site, based on its age. These materials were dried/ segregated into different sizes in the existing compost plant/ and were laboratory characterized to investigate for their suitability in road embankment construction. A segregation methodology was proposed in the study which can be adopted in the plant to arrive at a final material for use in road embankment construction. The segregated final MSW is then characterized for its Geotechnical characteristics. Stability and settlement analysis were carried out to arrive at suitable conclusions regarding its feasibility for embankment construction. It was concluded that about 62-75% of segregated Municipal Solid wastes can be effectively used for embankment construction.
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Dynamic target searching is one of the most practical and realistic problems within a multi-agent system. It requires an effective and efficient cooperation strategy to address the challenges of real-world robotic applications. Mostly, centralized cooperation based strategy proves to be less effective and inefficient in terms of search time, flexibility, scalability, and robustness. This paper discusses a distributed cooperation-based strategy that uses swarm intelligence based on a robotic particle swarm optimization (RPSO) algorithm to search dynamic targets to overcome these challenges. It uses evolutionary speed, aggregation degree, and inertia coefficient to enhance exploration and distributed cooperation for scalability and robustness. The simulation considers many practical constraints like limited communication, arbitrary initialization of swarms and targets, and decentralized cooperation among swarm robots. Existing cooperative algorithms were analyzed to compare the proposed algorithm regarding detection rate and the number of iterations needed to complete the search. The simulation experiment results show that the proposed algorithm maintains a detection rate of more than 80% for searching dynamic targets compared to the existing algorithms. It also shows that the proposed algorithm has a faster ability to search targets, higher detection rates, lower intercommunications, and better scalability.
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Demand for light-packaging materials for food and beverage is on the rise globally, especially in developing countries where several depend on packaged food. Furthermore, poly(ethylene terephthalate) (PET) a semi-crystalline thermally stable polyester, is widely used for carbonated soft drink, water and juice bottles, but shows a poor degradability properties after their lifespan. In this investigation, a series of novel random partially degradable poly(carbonate-co-esters) (PTB/ PTBCn) containing 2, 5-thiophenedicarboxylic acid (TDCA), and different amounts of bis(2-hydroxyethoxy)benzene (BHEB) and 1,4-cyclohexanedimethanol (CHDM) sub-units were successfully synthesized via a two-step melt polymerization as a facile and green semi-continuous process. The copolymers were thermally stable with tunable Tg values ranging from 47 to 71°C, while their 5% decomposition temperature (Td, 5%) under N2 varied from 463 to 432°C. Herein, focus was made on the synthesis of eco-friendly polyesters with satisfactory O2- gas barrier properties (5.5 cm3mm/m2 × day × atm) at 25°C suitable for most packaging applications. The mechanical and thermal analysis of PTB and PTBCn polyesters revealed excellent properties comparable to commonly used packaging materials such as poly(vinyl chloride), poly(lactic acid) and PET, whereby the incorporation of cyclohexane (CHDM) and phenyl (BHEB) rings units greatly enhanced the thermal and mechanical properties, transparency, oxygen permeability, and biodegradability of these polyesters.
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