The COVID-19 virus induces infection in both the upper respiratory tract and the lungs. Chest X-ray are widely used to diagnose various lung diseases. Considering chest X-ray and CT images, we explore deep-learning-based models namely: AlexNet, VGG16, VGG19, Resnet50, and Resnet101v2 to classify images representing COVID-19 infection and normal health situation. We analyze and present the impact of transfer learning, normalization, resizing, augmentation, and shuffling on the performance of these models. We explored the vision transformer (ViT) model to classify the CXR images. The ViT model incorporates multi-headed attention to disclose more global information in constrast to CNN models at lower layers. This mechanism leads to quantitatively diverse features. The ViT model renders consolidated intermediate representations considering the training data. For experimental analysis, we use two standard datasets and exploit performance metrics: accuracy, precision, recall, and F1-score. The ViT model, driven by self-attention mechanism and longrange context learning, outperforms other models.

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

This study shows density functional theory (DFT) investigations that 3d transition metals (TM) doping in silicene can greatly alter the geometric, spintronic, and optoelectronic properties of the pristine silicene (p-Si) layer. Significant Bader charge transfer from 3d TM atoms to surrounding Si atoms ensures the tight bonding between dopant and substrate; hence, all the 3d transition metal-doped silicene (3d TM-Si) systems are said geometrically strong and stable. Sc- and Ti-doped systems show the lowest formation energies of -84.72 and -84.21 eV, respectively, while Zn-Si bears the highest (-70.89 eV). 3d TMs from V to Co doping induces magnetic moment (MM) in the silicene layer which mainly comes from d-orbitals of 3d TM atoms and partly from p-orbitals of Si atoms, meanwhile Mn-Si has MM as high as 3.0 mu B. Among 3d TM-Si systems studied, Cr-Si and Mn-Si systems became half metals, Ti-Si became indirect semiconductor, whereas rest others convert into metals. Sc and V doping is found to be p-type doping as the Fermi level shifts into the valence band. Moreover, multiple and broader peaks in the absorption coefficient plot indicate the significant photoabsorption of 3d TM-Si systems. The present study of electronic, magnetic, and optical properties of 3d TM-Si systems extend a helpful proposal for further experimental work to fabricate silicene-based single-spin electron source and other nano-electronic devices.

Accurate prediction models enable the efficacious utilization and integration of solar energy into the power system. Therefore, this study aims to develop novel hybrid prediction models by employing correlation analysis (CA), decomposition techniques, sample entropy (SE), and spatio-temporal attention (STA) based sequence2sequence (S2S) algorithm for accurate prediction of global horizontal irradiance (GHI). The decomposition techniques include variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and maximum overlap discrete wavelet transform (MODWT). The VMD-STA-525 hybrid model surmounts the associated hybrid and standalone prediction models by revealing the highest prediction efficiency and the lowest error. Compared to the SARIMAX, SVR, ANN, XGB, GRU, LSTM, and S2S models, the VMD-STA-S2S model reduced RMSE during testing by 80.927 W/m(2), 75.426 W/m(2), 73.487 W/m(2), 62.394 W/m(2), 57.811 W/m(2), 52.007 W/m(2), and 41.836 W/m(2), respectively. Similarly, the reductions in RMSE by VMD-STA-S2S model compared to SA-S2S, TA-S2S, STA-S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-525, and VMD-SE-STA-S2S models are 24.054 W/m 2 , 20.951 W/m(2), 12.702 W/m(2), 15.396 W/m(2), 9.921 W/m(2), 6.103 W/m(2) and 0.484 W/m(2), respectively, during testing. Furthermore, considering NSE during testing, the VMD-STA-S2S model is 8.66%, 7.72%, 7.41%, 5.71%, 5.07%, 4.31%, 3.11%, 1.43%, 1.19%, 0.64%, 0.81%, 0.47%, 0.35% and 0.07%, more efficient than the SARIMAX, SVR, ANN, XGB, GRU, LSTM, S2S, SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models, respectively. The superior performance of VMD-STA-S2S over its counterparts corroborates the integration of the VMD technique and STA-based S2S algorithm for GHI prediction. The multivariate meteorological data of this study is decomposed by VMD into subcomponents more effectively than the ICEEMDAN and MODWT techniques. VMD decomposed subcomponents are further fed to the STA-S2S to efficiently extract and learn the spatial and temporal features, resulting in the enhanced and superior prediction outcomes of the VMD-STA-S2S model compared to all the counterpart models. Besides GHI prediction, the proposed model is also appropriate for other time-series data, including renewable energy, electrical load, and environment monitoring. (C) 2022 Elsevier Ltd. All rights reserved.

The adsorptions of toxic gas molecules (CO2, CO, H2S, HF and NO) on pristine and Ti atom doped hexagonal boron nitride (hBN) monolayer are investigated by density functional theory. Weak physisorption of gas molecules on pristine hBN results in micro seconds recovery time, limiting the gas sensing ability of pristine hBN. However Ti atom doping significantly enhances the adsorption ability. Ti atom best fits to be doped at B vacancy in hBN with lowest formation energy (-3.241 eV). Structural analysis reveals that structures of gas molecules change after being chemisorbed to Ti doped hBN monolayer. Partial density of states analysis illustrates strong hybridization among Ti-3d, gas-2p and BN-2p orbitals, moreover Bader charge transfer indicates that gas molecules act as charge acceptors. Ti doped hBN monolayer undergoes transition from semiconductor to narrow band semiconductor with adsorption of CO2, H2S and NO, while with CO and HF adsorption it transforms into metal. The change of conductance of Ti doped hBN monolayer in response to adsorption of gas molecules reveals its high sensitivity, however it is not selective to HF and NO gases. The recovery times of gas molecules desorption from monolayer are too long at ambient condition however it can significantly be shortened by annealing at elevated temperature with UV exposure. Since recovery time for NO removal from monolayer is still very long at 500 K with UV exposure, Ti doped hBN monolayer is more suitable as a scavenger of NO gas rather than as a gas sensor. It is thus predicted that Ti doped hBN monolayer can be a work-function type CO2, CO, H2S and HF sensor and NO gas scavenger.

Concerns about fuel exhaustion, electrical energy shortages, and global warming are growing due to the global energy crisis. Renewable energy-based distributed generators can assist in meeting rising energy demands. Micro-energy grids have become a research hotspot as a crucial interface for connecting the power produced by renewable energy resources-based distributed generators to the power system. The integration of micro-energy grid technology at the load level has been the focus of recent studies. Direct Current Micro-energy-grids have been one of the major research fields in recent years due to the inherent advantages of DC systems over AC systems, such as compatibility with renewable energy sources, storage devices, less losses, and modern loads. Nevertheless, control and stability of the grid are the paramount constituents for the reliable operation of power systems, whether at generation or load level. This research article focuses on the power flow between DC feeders of an autonomous DC micro-energy grid. To achieve this objective, a mathematical model and classical control strategy for power flow between two DC feeders are proposed using a conventional dual active bridge converter. The control objective is to minimize the DC element in the High-Frequency Transformer. Firstly, the non-linear-switched converter model and generalized average model for converter control are presented. Then, these mathematical models are used to get a small-signal linear model so a classical control strategy can be implemented. The control method enables output voltage regulation while abstaining from the high-frequency transformer's winding saturation. The stability analysis endorses the validity of the proposed control scheme. Also, the system response to load changes and varying control parameters is consistent. The simulation results validate the proposal's performance for changing converter and control parameters.

In this paper, a novel method based on LMP with the title of system cost index is presented for the optimum placement of distributed generation (DG) sources in the electricity market based on optimum power flow. Along with optimum placement, the optimum size of these sources is also calculated. Desirable locations are determined for optimum DG order based on local margin price (LMP). The LMP index is defined in the Lagrange coefficient of active power flow in each bus. Another index used to find desirable locations for DG placement is the customer payment (CP) index, which can be calculated for each bus by multiplying LMP by busload. In the presented method, in addition to considering two fundamental problems, i.e., system cost and line congestion, in the electricity market, the determination of the optimum size of DG is a criterion that is introduced as the system cost index (SCI). The optimum buses are selected based on considering the SCI criterion; two methods of bus ranking based on LMP and CP are used. In the LMP ranking, the bus with the highest LMP and the bus with the highest consumed power are chosen in the CP method. The proposed method is implemented on the modified IEEE 9-bus system. The simulation results suggest that the proposed method satisfies the engineering aspect of operation and the economic aspect of the process in the market. The optimum placement of DGs in the market environment leads to a decrease in system cost and management of line congestion.

A two-stage planning form of multi-energy supply optimization such as power, cooling, and heating is presented in this paper as a micro energy grid (MEG) To cover the effect of uncertainty in renewable energy sources (RES), the scheduling cycle is considered in this paper. Next, the results of the day-ahead prediction are considered as random variables for the upper-layer model. To realize the random variables at the lower layer, the revised model of energy storage and the demand response (DR) planning model are considered. Finally, the modified version of the artificial bee colony (ABC) algorithm is utilized to find the optimal solution. The improved ABC algorithm is a shape-memory method based on the collective intelligence and behavior of bees in a colony for finding the best nutrition source. In the improved ABC algorithm, with information exchange between the bees, based on Newton's law of universal gravitation, the full potential of this algorithm is used to find the optimal solution given the constraints applied to the system. The proposed method is applied to a real system and the results show that the two-stage optimization algorithm and the proposed intelligent algorithm obtained the simultaneous optimization of different energy forms. The obtained numerical analysis results in test cases prove the following points: (1) The optimal synergistic supply of multiple energy forms has been provided based on the two-stage optimization algorithm and solution approach. (2) The surplus energy can be converted to natural gas by the power-to-gas converter (P2G) based on power cascade conversion in a multi-directional mode. (3) To get some revenue, the MEG is flexible enough to cooperate with the upper-grade energy network. (4) The DR-based price can smooth the load shape and increase the MEG operation revenue using some supplementary features. Also, P2G can sequentially develop the flexible multidirectional energy conversion in energy - gas - energy - cooling as a cascade. When the evaluated P2G energy rises by 450 kW, the total GST output raises by 1244 kWh. For more economic benefits, MEG can be connected to the upstream energy grid. Load management also increases the net revenue of the system.

Due to the sheer global energy crisis, concerns about fuel exhaustion, electricity shortages, and global warming are becoming increasingly severe. Solar and wind energy, which are clean and renewable, provide solutions to these problems through distributed generators. Microgrids, as an essential interface to connect the power produced by renewable energy resources-based distributed generators to the power system, have become a research hotspot. Modern research in the field of microgrids has focused on the integration of microgrid technology at the load level. Due to the complexity of protection and control of multiple interconnected distributed generators, the traditional power grids are now outmoded. Microgrids are feasible alternatives to the conventional grid since they provide an integrating platform for micro-resources-based distributed generators, storage equipment, loads, and voltage source converters at the user end, all within a compact footprint. A microgrid can be architected to function either in grid-connected or standalone mode, depending upon the generation, integration potential to the main grid, and consumers’ requirements. The amalgamation of distributed energy resources-based microgrids to the conventional power system is giving rise to a new power framework. Nevertheless, the grids’ control, protection, operational stability, and reliability are major concerns. There has yet to be an effective real-time implementation and commercialization of micro-grids. This review article summarizes various concerns associated with microgrids’ technical and economic aspects and challenges, power flow controllers, microgrids’ role in smart grid development, main flaws, and future perspectives.

Hydrogen adsorption on titanium (Ti) atom-doped single vacancy silicene (SV-SL) is investigated through first principles density functional theory (DFT) study. Strong hybridization of d-orbitals of Ti atom to p-orbitals of Si atoms results in a tight bond to the silicene sheet with energy of -6.48 eV and keeps away from metal clustering. Maximum 8 H-2 molecules firmly bind to Ti atom-doped SV-SL sheet with an adsorption energy ranging from -0.481 to -0.201 eV per H-2 molecule and hydrogen storage capacity (HSC) of 6.3 wt%. Double-side H-2 adsorptions on hollow sites of Ti atom-doped SV-SL sheet are verified by structural and electronic properties. The partial density of states (PDOS) analysis shows the kubas interaction mainly caused the molecular H-2 adsorption. Further, the absence of spin-up and spin-down channels in electronic band structures of nH(2) molecule adsorption to Ti atom-doped SV-SL systems indicates its nonmagnetic nature. Conclusively, this study reveals that the Ti atom-doped SV-SL can be a promising candidate for hydrogen storage applications.

The term "biological" is often used as synonym of term "natural." Hence, natural composites are referred to as bionanocomposites. All natural metabolites of living organisms are known to be nanocomposites. These materials are associated with the two separate phases minimally, in which one phase is of nanometer scale and the second phase includes polymers of biomolecules. Internal and external stimuli initiate the assembling of provided raw materials. This article describes the dynamic structures and composition of natural materials. Like nucleic acids, proteins, lipids, and carbohydrates exhibit a variety of structures and compositions. For example double-stranded DNA templates conjugated with fluorescent molecules are used as biosensors to detect specific targets in tumor cells. DNA template methods allow us to use DNA templates in the formation of bionanocomposites. Similarly, proteins are also used as biopolymers in the formation of bionanocomposites. Different approaches are used to formulate bionanocomposites with desirable physical and functional characteristics. By conjugation of different biopolymers with nanoparticles, their functionality can be amalgamated and the consequent bionanocomposites exhibit traits of both biopolymer and nanoparticle. High diversity in structures and characteristics diversified the application of bionanocomposites. Here, we have discussed the procedures to synthesize bionanocomposites and their applications across various fields of science. Moreover, recent advancements in the field of medicine for the detection of various diseases have also been discussed in the presented chapter.