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SARS-CoV-2, a severe acute respiratory syndrome that is related to COVID-19, is a novel type of influenza virus that has infected the entire international community. It has created severe health and safety concerns all over the globe. Identifying the outbreak in the initial phase may aid successful recovery. The rapid and exact identification of COVID-19 limits the risk of spreading this fatal disease. Patients with COVID-19 have distinctive radiographic characteristics on chest X-rays and CT scans. CXR images can be used for people with COVID-19 to diagnose their disease early. This research was focused on the deep feature extraction, accurate detection, and prediction of COVID-19 from X-ray images. The proposed concatenated CNN model is based on deep learning models (Xception and ResNet101) for CXR images. For the extraction of features, CNN models (Xception and ResNet101) were utilized, and then these features were combined using a concatenated model technique. In the proposed scheme, the particle swarm optimization method is applied to the concatenated features that provide optimal features from an overall feature vector. The selection of these optimal features helps to decrease the classification period. To evaluate the performance of the proposed approach, experiments were conducted with CXR images. Datasets of CXR images were collected from three different sources. The results demonstrated the efficiency of the proposed scheme for detecting COVID-19 with average accuracies of 99.77%, 99.72%, and 99.73% for datasets 1, 2 and 3, respectively. Moreover, the proposed model also achieved average COVID-19 sensitivities of 96.6%, 97.18%, and 98.88% for datasets 1, 2, and 3, respectively. The maximum overall accuracy of all classes—normal, pneumonia, and COVID-19—was about 98.02%.

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The proposed framework achieves an accuracy of 97%. Our model’s experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification.

The biocidal action mechanism of single element noble metal anisotropic nanoparticles has remained a perplexing challenge. Herein, we investigated the photogenerated anisotropic AgNP ROS production kinetics and each ROS species' direct impact on Gram-negative and Gram-positive bacteria. Three shapes (Triangular, Cubes, Rods) of AgNP with excellent morphology were fabricated via plasmon mediated synthesis. The results demonstrated a distinct bactericidal capacity of each NP shape where Ag-Tri outperformed Ag-Cub and Ag-Rod by displaying complete bacterial mutilation at a very low dose of 18 μg mL−1 for the shortest exposure time of 180 min. In contrast, Ag-Cub needed 66.6% higher NP concentration, while Ag-Rod was unable to achieve complete bacterial mutilation. In contrast to O2˙−, (Ag-Tri 69 ± 3.2, Ag-Cub 72 ± 2.9, Ag-Rod 68.5 ± 3.7 μM), the amount of ˙OH production was considerably lower (Ag-Tri 11 ± 1.6, Ag-Cub 10.4 ± 1.9, Ag-Rod 11.3 ± 2.2 μM), while 1O2 remained undetected for all NP shapes. Moreover, antimicrobial activity of selective ROS species revealed O2˙− as a dominant species among ROS. However, O2˙− was not found as a decisive factor in microbial mutilation. SEM images affirmed the significance of the specific geometrical shape and its resultant attachment to bacterial surface to be of paramount significance. The sharp-tip morphology with high-atom density active {111} facets played a pivotal role in physically deteriorating bacterial cells. Ag-Tri morphology in synchronization with ROS species assisted its wedging into the bacterial cell, translating into superior and multifaceted antibacterial performance.

Metaheuristic algorithms are widely used to solve NP-complete problems in several domains. Distributed predicates detection is a fundamental distributed systems problem that has many useful applications. The problem of distributed predicates detection, in general, is known to be an NP-complete problem. In this paper, we developed a detection algorithm inspired by the particle swarm optimization algorithm, one of the well-known metaheuristic algorithms applied to solve problems in several domains. The proposed detection algorithm deal with distributed predicates under the possibly modality. We compared the performance of the proposed distributed predicates detection algorithm with several other detection algorithms. The experimental results reveal the effectiveness of the suggested distributed predicates detection algorithm.

The exponential increase in new coronavirus disease 2019 (COVID-19) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model's architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.

The directional propulsion of liquid droplets at the nanoscale is quite an interesting topic of research in the fields of micro/nano-fluidics, water filtration, precision medicine, and cooling of electronics. In this study, the unidirectional spontaneous transport of a water nanodroplet on a solid surface with a multi-gradient surface (MGS) inspired by natural species is modeled and analyzed using molecular dynamics (MD) simulations. There are three different MGSs considered in this study containing different wedge angles of the hydrophilic region of the solid surface. The MGSs contain two regions: a hydrophilic wedge-shaped region with a constant surface energy parameter equal to 50 meV and a hydrophobic region with a tuned surface energy parameter. The energy parameter of the hydrophobic region is set equal to 1, 5, 10, 20, 30, and 40 meV in order to alter the intensity of the wettability gradient of the two surfaces and its effect on the propulsion of the water nanodroplet is analyzed. Furthermore, three different sizes of water droplets containing 6000, 8000, and 10 000 water molecules are also used in this study and their effect on the transport behavior of the water nanodroplet is also measured. Moreover, two different designs on a solid surface with a continuous wettability gradient are modelled and the impact of solid surface geometry on the transport of the water droplet is calculated by means of mean square displacement (MSD) and average velocity data. In addition, the wedge-shaped surface is found to be more superior to the parallel-shaped surface for the spontaneous propulsion of the water droplet.

In this study, a surfactant stabilized water-in-oil emulsion has been successfully separated by using only NaCl particles as a filter. This novel strategy is suitable for continuous filtration of a large quantity of water-in-oil emulsion with a volume of up to 1500 mL. Moreover, a filtration flux of up to 40 000 L m−2 h−1 is reported, which is around ten times higher than the conventional filtration methods.

Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

Transportation and position control of objects on the surface of liquids is an important part of automation. To drive an object on the surface of a liquid, many methods have been proposed. However, these methods mainly focus on the driving of the object itself, and it is still difficult to precisely control its position. In our study, we propose a new method that uses vapor released from a suspended drop to achieve precise position control and transport of different types of objects at the air-liquid interface. These objects can be a plastic plate, a liquid marble, or an oil drop. The mechanism for controlling objects is that vapor released from a suspended drop causes a surface tension gradient around the object. When the vapor dissolves on the surface of a liquid, the surface tension of the liquid increases. Due to the surface tension gradient, the object moves from the surrounding area to the area below the suspended drop and follows the motion of the suspended drop with the trajectory of a letter. To show that the position of the objects can be precisely controlled by our method, we control the object on the center of a circle, and the maximum offset distance from the center of the circle is less than 3 mm. In addition, we also use vapor released from a suspended drop to transport an oil drop close to an object. After the drop adhered with the object, the object is driven by the oil drop. Compared with other methods that drive the motion of objects by reducing the surface tension of a liquid, our method is easy and the position of objects can be precisely controlled.

Although artificial superhydrophobic materials have extensive and significant applications in antifouling, self-cleaning, anti-icing, fluid transport, oil/water separation, and so forth, the poor robustness of these surfaces has always been a bottleneck for their development in practical industrial applications. Here, we report a facile, economical, efficient, and versatile strategy to prepare environmentally friendly, mechanically robust, and transparent superhydrophobic surfaces by combining adhesive and hydrophobic paint, which is applicable for both hard and soft substrates. The coated substrates exhibit excellent superhydrophobic property and ultralow adhesion with water (contact angle ≈ 160° and sliding angle

As a decision support tool, life cycle assessment (LCA) is prone to multiple uncertainties associated with the data, model structures, and options offered to practitioners. Therefore, to make the results reliable, consideration of these uncertainties is imperative. Among the various classifications, parameter, scenario, and model uncertainty are widely reported and well-acknowledged uncertainty types in LCA. There are several techniques available to deal with these uncertainties; however, each strategy has its own pros and cons. Furthermore, just a few of the methods have been included in LCA software, which restricts their potential for wider application in LCA research. This paper offers a comprehensive framework that concurrently considers parameter, scenario, and model uncertainty. Moreover, practitioners may select multiple alternatives depending on their needs and available resources. Based on the availability of time, resources, and technical expertise three levels—basic, intermediate, and advanced—are suggested for uncertainty treatment. A qualitative method, including local sensitivity analysis, is part of the basic approach. Monte Carlo sampling and local sensitivity analysis, both of which are accessible in LCA software, are suggested at the intermediate level. Advanced sampling methods (such as Latin hypercube or Quasi-Monte Carlo sampling) with global sensitivity analysis are proposed for the advanced level.

Bagasse-based electricity is gaining attention as an affordable, reliable, sustainable, and promising renewable energy source in Pakistan. Therefore, the focus of this analysis was to identify the environmental burdens associated with bagasse-based electricity production via high-pressure cogeneration. The scope of this study was defined as cradle to gate, involving cane production, cane transportation to the mill, the production of bagasse, and then the burning of bagasse in the cogeneration power plant to produce electricity. The overall results revealed that most of the impacts were contributed by the agricultural phase. For some impact categories&mdash, such as global warming, fine particulate matter formation, terrestrial acidification, and fossil resource scarcity&mdash, the bagasse-based electricity performed better than the grid mix electricity. However, the grid mix electricity performed better than the bagasse-based electricity in terms of eutrophication, land use, and water consumption. When considering the final damage, the bagasse-based electricity proved to be the favorable option in terms of human health and resource availability, however, ecosystem quality was poor in bagasse-based electricity. In terms of environmental prices, the bagasse-based electricity was found to be a promising option compared to the grid mix electricity.

In this work, the directional motion and underlying mechanism of a circular gold nanoparticle (AuNP) on nanocone (NC) surface were studied with the aid of molecular dynamics simulation. Three specific sized AuNP(s) having particular number of atoms and three nanocones having distinct apex angle were utilized in this work. The simulation was run in a temperature range of 300–1300 K to observe the effect of temperature on the directional motion of AuNP located outside of NC surface. The results revealed that the AuNP tends to move toward the larger-diameter direction of the NC at higher temperature range, whereas it stayed intact on the area around the tip of NC at lower temperature range. It was observed that the average velocity of AuNP located outside the NC surface with large apex angle was comparatively lower than that on the NC surface having small apex angle. Furthermore, it was inspected that the interaction energy of the AuNP is reduced heavily as the temperature is increased, and it decayed more i...

Voice-operated smart houses often face security problems when there is pathology in the voice of an individual. If an error occurs during the process of authentication, then the system should detect whether this error is due to pathology or someone is trying to deceive the system. In the case of successful pathology detection, the system offers a second option of the pin code. Pathology detection is one of the major components in this research work. Voice pathology is the disorder of the vocal fold, which is normally diagnosed in people working in different areas, such as education, courtroom, etc. Many researchers have proposed different voice pathology detection systems (VPDS) where they used a common feature extraction technique, i.e., Mel Frequency cepstral coefficient (MFCC), or compared it with their proposed technique and produced results that are different in almost every case. In this paper, we propose a system that is based on MFCC features fused with pitch and it achieves 99.97% accuracy of pathology detection for the MEEI data set. This voice pathology detection accuracy is better as compared to most of the VPDS available for MEEI.

In this study, a superhydrophobic and superoleophilic stainless mesh coated with polytetrafluoroethylene/silicon dioxide (PTFE/SiO2) was fabricated through electrostatic self-assembly method followed by sintering treatment. The PTFE was utilized to construct low-surface-energy surface and the SiO2 nanoparticles were added to enhance its surface roughness. The as-prepared stainless mesh exhibited desirable superhydrophobicity and superoleophilicity with a water contact angle of 152° and oil contact angle of 0°. The coated stainless mesh could separate a variety of oil/water mixtures with high efficiency and it also exhibited good recyclability. Moreover, the corrosion-resistance of stainless mesh was greatly improved by coating it with PTFE. The thermogravimetric analysis (TGA) measurements showed that the coated mesh could withstand high temperature of up to 430°C, indicating excellent thermal-resistance. It is believed that this ultra-robust stainless mesh would have significant potential applications in industry.