Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band ...(feature) reduction strategies through Feature Extraction (FE) and/or Feature Selection (FS) methods for finding the intrinsic bands' information are typically applied. Principal Component Analysis (PCA) is a frequently employed unsupervised linear FE method whereas cumulative-variance accumulation is used for selecting top features from PCA data. However, PCA can fail to extract intrinsic structure of HSI due to global variance consideration and domination by visible and near infrared bands while cumulative-variance accumulation has no capability to exploit non-linear relationships among the transformed features produced by PCA-based FE methods. Consequently, we propose an information theoretic normalized Mutual Information (nMI)-based minimum Redundancy Maximum Relevance (mRMR) non-linear measure to select the intrinsic features from the transformed space of our previously proposed Segmented-Folded-PCA (Seg-Fol-PCA) and Spectrally Segmented-Folded-PCA (SSeg-Fol-PCA) FE methods. We extensively analyse the effectiveness of the proposed unsupervised FE and supervised FS combinations Seg-Fol-PCA-mRMR and SSeg-Fol-PCA-mRMR with that of PCA-based existing linear and non-linear state-of-the-art methods. In addition, cumulative variance-based top features pick-up strategy is considered with all FE methods and Renyi quadratic entropy-based FS is used with Kernel Entropy Component Analysis (Ker-ECA). The experimental results illustrate that SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR obtain highest classification result e.g. 95.39% and 95.03% respectively for agricultural Indian Pines HSI, and 96.58% and 95.30% respectively for urban Washington DC Mall HSI while the classification accuracies using all original features of the HSIs are 70.28% and 91.90% respectively. Moreover, the proposed SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR outperform all investigated combinations of FE and FS using the real HSI datasets.
The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates ...which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R
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value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.
Communicated by Ramaswamy H. Sarma
The remote sensing hyperspectral images (HSIs) usually comprise many important information of the land covers capturing through a set of hundreds of narrow and contiguous spectral wavelength bands. ...Appropriate classification performance can only offer the required knowledge from these immense bands of HSI since the classification result is not reasonable using all the original features (bands) of the HSI. Although it is not easy to calculate the intrinsic features from the bands, band (dimensionality) reduction techniques through feature extraction and feature selection are usually applied to increase the classification result and to fix the curse of dimensionality problem. Though the Principal Component Analysis (PCA) has been commonly adopted for the feature reduction of HSI, it can often fail to extract the local useful characteristics of the HSI for effective classification as it considers the global statistics of the HSI. Consequently, Segmented-PCA (SPCA), Spectrally-Segmented-PCA (SSPCA), Folded-PCA (FPCA) and Superpixelwise PCA (SuperPCA) have been introduced for better feature extraction of HSI in diverse ways. In this paper, feature extraction through SPCA & FPCA and SSPCA & FPCA, termed as Segmented-FPCA (SFPCA) and Spectrally-Segmented-FPCA (SSFPCA) respectively, has further been improved through applying FPCA on the highly correlated or spectrally separated bands' segments of the HSI rather than not applying the FPCA on the entire dataset directly. The proposed methods are compared and analysed for a real mixed agricultural and an urban HSI classification using per-pixel SVM classifier. The experimental result shows that the classification performance using SSFPCA and SFPCA outperforms that of using conventional PCA, SPCA, SSPCA, FPCA, SuperPCA and using the entire original dataset without employing any feature reduction. Moreover, the proposed feature extraction methods provide the least memory and computation cost complexity.
Deep neural networks have outstanding performance as data-driven solutions for hyperspectral image classification for ground object detection. Among these, convolutional models outperform other ...strategies for their back-propagated local-filtering mechanisms. However, convolutional models lack the support of intuitive details learning and squeezed weighted distribution. These solutions have constraints of exploring only within convolution space. To alleviate these issues, in this paper, a multi-domain kernel network has been proposed leveraging a dynamic attention mechanism in order to harvest spectral-spatial domain information from multiple receptive regions. The Dynamic Kernel Network uses various kernel strategies to gather maximum spectral-spatial features for high-performance classification. Experimental results on the real hyperspectral images explicate the validity of the proposed dynamic kernel network. The proposed framework outperforms the base methods and attained 97.225%, 99.385% & 99.922% overall accuracy (OA) on Indian Pines (IP), University of Pavia (UP) & Salinas Valley (SV) datasets, respectively. Also, the ablative analysis on spatial windows, spectral bands, computation time and training samples proves the robustness of the spectral-spatial kernel learning strategy.
Reverse engineering is a burning issue in Integrated Circuit (IC) design and manufacturing. In the semiconductor industry, it results in a revenue loss of billions of dollars every year. In this ...work, an area efficient, high-performance IC camouflaging technique is proposed at the physical design level to combat the integrated circuit's reverse engineering. An attacker may not identify various logic gates in the layout due to similar image output. In addition, a dummy or true contact-based technique is implemented for optimum outcomes. A library of gates is proposed that contains the various camouflaged primitive gates developed by a combination of using the metal routing technique along with the dummy contact technique. This work shows the superiority of the proposed technique's performance matrix with those of existing works regarding resource burden, area, and delay. The proposed library is expected to make open source to help ASIC designers secure IC design and save colossal revenue loss.
Antimony (Sb) chalcogenides such as antimony selenide (Sb2Se3) and antimony sulfide (Sb2S3) have distinct properties to be used as absorber semiconductors for harnessing solar energy including high ...absorption coefficient, tunable bandgap, low toxicity, phase stability. The potentiality of Sb2Se3 and Sb2S3 as absorber material in Al/FTO/Sb2Se3(or Sb2S3)/Au heterojunction solar cells (HJSCs) with 2D tungsten disulfide (WS2) electron transport layer (ETL) layer has been investigated numerically using SCAPS-1D solar simulator. A systematic investigation of the impact of physical properties of each active material of Sb2Se3, Sb2S3, and WS2 on photovoltaic parameters including layer thickness, carrier doping concentration, bulk defect density, interface defect density, carrier generation, and recombination. This study emphasizes the exploration of causes of low performance of actual devices and demonstrates the individual variation in the open-circuit voltage (VOC), short-circuit current density (JSC), fill factor (FF), power conversion efficiency (PCE) and quantum efficiency (QE). Thereby, highly potential heterostructures of Al/FTO/WS2/absorber (Sb2Se3 or Sb2S3)/Au proposed, in which, the PCE over 28.20 and 26.60% obtained with VOC of 850 and 1230 mV, Jsc of 38.0 and 24.0 mA/cm2, and FF of 86.0 and 89.0% for Sb2Se3 and Sb2S3 absorber, respectively. These detailed findings revealed that the Sb-chalcogenide heterostructure with potential WS2 ETL can be used to realize the fabrication of feasible thin film solar cells and thus the design of high-efficiency high-current (HEHC) and high-efficiency high-voltage (HEHV) solar panels.
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•Antimony chalcogenide (Sb2Se3 and Sb2Se3)-based TFSCs with WS2 electron transport layer were studied by SCAPS-1D simulator.•Systematic investigation on the impacts of thickness, doping, bulk, and interface defect densities on the PV performance.•PCE of 28.20% (26.60%) was found in a 1280 nm thick n+/n/p junction Sb2Se3 (Sb2S3) solar cell under adjusted condition.•The simulation was verified with the Shockley–Queisser (SQ) limit including experimental as well as simulation works.
Heterojunction solar cell; Sb2Se3; Sb2S3; WS2 electron transport layer; SCAPS-1D.
The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy is ...not often satisfactory in a cost-effective way using the entire original HSI for practical applications. To enhance the classification result of HSIs the band reduction strategies are applied which can be divided into feature extraction and feature selection methods. PCA (Principal Component Analysis), a linear unsupervised statistical transformation, is frequently adopted for the extraction of features from HSIs. In this paper, PCA and SPCA (Segmented-PCA), SSPCA (Spectrally Segmented-PCA), FPCA (Folded-PCA) and MNF (Minimum Noise Fraction) as linear variants of PCA together with KPCA (Kernel-PCA) and KECA (kernel Entropy Component Analysis) as nonlinear variants of PCA have been investigated. The top transformed features were picked out using accumulation of variance for all other feature extraction methods except for MNF and KECA. MNF uses SNR (Signal-to-Noise Ratio) values and KECA employs Renyi quadratic entropy measurement for this purpose. The studied approaches are equated and analyzed for Indian Pine agricultural and urban Washington DC Mall HSI classification using SVM (Support Vector Machine) classifier. The experiment illustrates that the costly effective and improved classification performance of the feature extraction approaches over the performance using the entire original dataset. MNF offers the highest classification accuracy and FPCA offers the least space and time complexity with satisfactory classification result.
Inorganic CdTe and FeSi2-based solar cells have recently drawn a lot of attention because they offer superior thermal stability and good optoelectronic properties compared to conventional solar ...cells. In this work, a unique alternative technique is presented by using FeSi2 as a secondary absorber layer and In2S3 as the window layer for improving photovoltaic performance parameters. Simulating on SCAPS-1D, the proposed double-absorber (Cu/FTO/In2S3/CdTe/FeSi2/Ni) structure is thoroughly examined and analyzed. The window layer thickness, absorber layer thickness, acceptor density (NA), donor density (ND), defect density (Nt), series resistance (RS), and shunt resistance (Rsh) were simulated in detail for optimization of the above configuration to improve the PV performance. According to this study, 0.5 µm is the optimized thickness for both the CdTe and FeSi2 absorber layers in order to maximize the efficiency (η). Here, the value of the optimum window layer thickness is 50 nm. For using CdTe as a single absorber, η is achieved by 13.26%. However, for using CdTe and FeSi2 as a dual absorber, η is enhanced and the obtaining value is 27.35%. The other parameters are also improved and the resultant value for the fill factor is 83.68%, the open-circuit voltage (Voc) is 0.6566 V, and the short circuit current density (Jsc) is 49.78 mA/cm2. Furthermore, the proposed model performs well at 300 K operating temperature. The addition of the FeSi2 layer to the cell structure has resulted in a significant quantum efficiency enhancement because of the rise in solar spectrum absorption at longer wavelengths (λ). The findings of this work offer a promising approach for producing high-performance and reasonably priced CdTe-based solar cells.
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•Recent advances in solar water heating (SWH) systems have been systematically reviewed.•The design criteria of the major components of the SWH system were discussed elaborately.•The ...potential applications based on thermal performance were studied thoroughly.•Nanofluids selection criteria for a specific SWH system were reviewed extensively.
The solar water-heating (SWH) system is one of the most convenient applications of solar energy, which is considered an available, economical, and environmentally friendly energy source to fulfill the energy demands of the world. In this review, existing SWH systems and design aspects of major components e.g., solar thermal collector, storage tank, heat exchanger, heat transferring fluid, absorber plate, etc. were extensively studied. Recent research to further improve SWH systems and potential practical applications are critically reviewed. Moreover, a relatively new concept in SWH systems, which is using nanofluids in solar collectors as heat transfer fluid has been studied in terms of design criteria for the development of SWH systems. Stationary flat plate collector (FPC) and single-axis tracking compound parabolic collector (CPC) exhibit thermal efficiencies of 45–60 % (operating range: 25–100 °C) and 30–50 % (operating range: 60–300 °C), respectively. The use of thermal stratification structures e.g., diffusers, baffles, membranes, fabrics, etc. is an effective tool to reduce heat losses from the storage tank as well as to harvest the highest energy from the solar collector. Coating of nanomaterials e.g., nickel, copper, etc. was found to reduce the backside heat loss in SWJ systems which eventually increases the thermal performance of the system. Nanofluids consisting of multiwall carbon nanotubes (MWCNTs) and Al2O3 increased the effectiveness of FPC by 28.3 and 35 %, respectively. Moreover, using CuO nanofluids, the collector efficiency of a typical evacuated tube collector (ETC) was increased by up to 12.4 %. Several potential future recommendations for improving the performance of the SWH system were stated.
The recent outbreak of viral zoonotic disease-monkeypox-caused by the monkeypox virus, has infected many people worldwide. This study aims to explore the knowledge, attitudes, and practices (KAP) ...concerning monkeypox among university students in Bangladesh. Data were collected using purposive snowball sampling from 887 university students through an online survey using Google Form. The participants were mostly in their twenties (M = 22.33 SD 2.01 years), and they spent, on average, 2.59 SD 1.91 hours/day on electronic and social media. The participants generally showed moderate knowledge (39.5%), low attitude (25.1%), and moderate practice (48.6%) toward monkeypox, with 47.6% having had a moderate KAP score. Findings further showed that personal attributes of university students, i.e., age, sex, year of schooling, residence, living status, geographical distribution, e.g., division, were statistically and significantly associated with knowledge, attitudes, and practices regarding monkeypox and overall KAP score. It is also apparent that health status, susceptibility to monkeypox, and exposure to social media were the most common factors significantly associated with knowledge, attitudes, and practices regarding monkeypox and overall KAP score. The current study's findings underscore the need for developing appropriate information, education, and communication (IEC) materials and their dissemination, which could play an important role in reducing the risk of monkeypox and similar other infectious diseases, particularly among students in Bangladesh.
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