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This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350 × 18 ). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946 % ), Mutual Information (i.e., 98.902 % ), Independent Component Analysis (i.e., 99.62 % ), and Principal Component Analysis (i.e., 98.884 % ) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89 % . The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI). [ABSTRACT FROM AUTHOR]

In the process of heat treatment of 8620 low alloy steel, the heating rate is a critical parameter that must be carefully controlled to achieve the desired combination of microstructural features, mechanical properties, and corrosion resistance while simultaneously ensuring process efficiency and cost-effectiveness. This study investigates, for the first time, the microstructural evolution and electrochemical properties of 8620 steel under identical quenching and tempering heat treatment routes with slow-rate (SR) and fast-rate (FR) heating rates. Microstructural analysis revealed martensitic phases for SR, while FR exhibited a dual-phase microstructure containing ferrite. Upon tempering, for both samples, the martensite transformed into tempered martensite, with tempered (Temp) FR exhibiting around 50% smaller ferrite grains. Mechanical testing indicated that SR had 17% higher hardness than FR, although hardness decreased after tempering by 22% (SR) and 17% (FR). All electrochemical tests indicated that the as-quenched SR exhibited significantly superior corrosion resistance than FR. For instance, the polarization resistance of SR was 440 Ω higher than that of the FR samples. Tempering resulted in a considerable decrease in corrosion resistance for Temp SR, whereas Temp FR improved. Electrochemical characterization revealed Temp FR displayed close-to-ideal capacitive behavior and low double-layer capacitance, indicating enhanced overall corrosion resistance. [ABSTRACT FROM AUTHOR]

In this study, mechanical and electrochemical performance of borided additively manufactured (AM) and wrought 8620 low alloy steel were investigated and compared to their bare counterparts. The microstructure of borided 8620 exhibited the presence of FeB and Fe2B phases with a saw tooth morphology. Both AM and wrought samples with boride layers showed a similar performance in hardness, wear, potentiodynamic polarization (PD), electrochemical impedance spectroscopy (EIS), and linear polarization resistance (LPR) experiments. However, borided steels exhibited about an 8-fold increase in Vickers hardness and about a 6-fold enhancement in wear resistance compared to bare ones. Electrochemical experiments of borided specimens (both AM and wrought) in 0.1 M Na2S2O3 + 1 M NH4Cl solution revealed a 3–6-fold lower corrosion current density, about a 6-fold higher charge transfer resistance, and about a 6-fold lower double-layer capacitance, demonstrating an improved corrosion resistance compared to their bare counterparts. Post-corrosion surface analysis revealed the presence of thick sulfide and oxide layers on the bare steels, whereas dispersed corrosion particles were observed on the borided samples. The enhanced wear and electrochemical performance of the borided steels were attributed to the hard FeB/Fe2B layers and the reduced amount of adsorbed sulfur on their surface. [ABSTRACT FROM AUTHOR]

In this study, corrosion resistance of TiZrN-coated additively manufactured (3D-printed) 8620 steel has been separately studied in a 60–40 wt% NaNO3 + KNO3 molten salt and in an aqueous nitrate salt environment. Microstructural characterization of the as-built bare 3D-printed 8620 sample revealed martensitic microstructures. High-temperature corrosion analysis in molten nitrate salt revealed improved film stability and corrosion resistance of the coating. The coated 3D-printed sample showed no scale of corrosion products, whereas the bare 3D-printed sample exhibited a layer of corrosion products of 2.62 ± 0.24 µm thickness. Potentiodynamic polarization and electrochemical impedance spectroscopy tests in aqueous nitrate salts at room temperature exhibited an ~8-fold decrease in corrosion current density and a ~7-fold increase in charge transfer resistance, indicating enhanced corrosion resistance of the coated 8620. The coated wrought 8620 showed comparable corrosion resistance to that of the coated 3D-printed sample. However, in aqueous solution, the bare 3D-printed sample exhibited localized corrosion, whereas the bare wrought revealed uniform corrosion on the surface. [ABSTRACT FROM AUTHOR]

Materials with a disordered atomic structure, often termed glassy materials, are the focus of extensive research due to the possibility of achieving remarkable mechanical, electrochemical, and magnetic properties compared to crystalline materials. The glassy materials are observed to have an improved elastic modulus combined with a higher strength and hardness. Moreover, better corrosion resistance in different mediums is also observed for glassy solids, which is difficult to attain using conventional crystalline materials. As a result, the potential applications of metallic-glass systems are continually increasing. Amorphous materials are usually divided into two categories based upon their size. Materials with a thickness and diameter larger than the millimeter (mm) scale are termed as bulk metallic glass (BMG). However, the brittle nature of the bulk-sized samples restricts the size of metallic-glass systems to the micron (µm) or nanometer (nm) range. Metallic glasses with a specimen size in the scale of either µm or nm are defined as thin-film metallic glass (TFMG). In this review, BMGs and TFMGs are termed as metallic glass or MG. A large number of multi-component MGs and their compositional libraries reported by different research groups are summarized in this review. The formation of a multicomponent metallic glass depends on the constituent elements and the fabrication methods. To date, different unique fabrication routes have been adopted to fabricate BMG and TFMGs systems. An overview of the formation principles and fabrication methods as well as advantages and limitations of conventional MG fabrication techniques is also presented. Furthermore, an in-depth analysis of MG inherent properties, such as glass forming ability, and structural, mechanical, thermal, magnetic, and electrochemical properties, and a survey of their potential applications are also described. [ABSTRACT FROM AUTHOR]

As a country, Pakistan is mostly dependent on fossil fuels for fulfilling its energy demand, which is expensive, as well as being environmentally unfriendly. It is high time that the country decides to shift from fossil fuels to renewable energy resources like geothermal, wind, solar, etc., to cater for global warming issues. Pakistan has a lot of potential geothermal sites, as the location of Pakistan lies on several fault lines and hot springs, thus making it very easy to extract the temperature from deep inside the earth and harness it for Geothermal Energy. Also, a sound knowledge of ground temperature is essential to use geothermal energy, which is obtained by drilling boreholes and putting in sensors. However it becomes a very expensive and labor intensive procedure. Therefore, to avoid the huge cost for drilling boreholes, particularly for ground temperature analysis, a numerical approach has been considered for determining ground temperature. Furthermore, correlation charts between air and ground temperatures have been developed, as there were no proper studies on the ground temperature of Pakistan. Then, with the help of a boreholes drilled in the National University of Sciences and Technology, Islamabad, Pakistan, the actual ground and numerically calculated temperatures have been compared. The results show a temperature error margin in the range between 0.27% for higher depths of about 5.6 m and 7.3% near the surface of about 2.7 m. Thus, it is shown that the proposed method is easy to implement and better than large scale testing methods for the depths at which geothermal energy is extracted. [ABSTRACT FROM AUTHOR]

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios. [ABSTRACT FROM AUTHOR]

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types. [ABSTRACT FROM AUTHOR]

Seismic design of structures taking into account the soil-structure interaction (SSI) methods is considered to be more efficient, cost effective, and safer then fixed-base designs, in most cases. Finite element methods that use direct equations to solve SSI problems are very popular, but the prices of the software are very high, and the analysis time is very long. Even though some low-cost and efficient software are available, the structures are mostly analyzed for the superstructure only, without using the geotechnical properties of the ground and its interaction effects. The reason is that a limited number of researchers have the knowledge of both geotechnical and structural engineering to model accurately the coupled soil-structure system. However, a cost-effective, less time-consuming and easy-to-implement technique is to analyze the structure along with ground properties using machine learning methods. The database techniques using machine learning are robust and provide reliable results. Thus, in this study, machine learning techniques, such as artificial neural networks and support vector machines are used to investigate the effect of soil-structure interactions on the seismic response of structures for different earthquake scenarios. Four frame structures are investigated by varying the soil and seismic properties. In addition, varying sample sizes and different optimization algorithms are used to obtain the best machine learning framework. The input parameters contain both soil and seismic properties, while the outputs consist of three engineering demand parameters. The network is trained using three and five-story buildings and tested on a three-story building with mass irregularity and a four-story building. Furthermore, the proposed method is compared with the dynamic responses obtained using fixed-base and ASCE 7-16 SSI methods. The proposed machine learning method showed better results compared with fixed-base and ASCE 7-16 methods with the nonlinear time history analysis results as a reference. [ABSTRACT FROM AUTHOR]

The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid's energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load. [ABSTRACT FROM AUTHOR]

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s. [ABSTRACT FROM AUTHOR]

Plant salt tolerance is controlled by various physiological processes such as water and ion homeostasis, photosynthesis, and cellular redox balance, which are in turn controlled by gene expression. In the present study, plants of six canola cultivars (DGL, Dunkled, Faisal Canola, Cyclone, Legend, and Oscar) were evaluated for salt tolerance by subjecting them to 0 or 200 mM NaCl stress. Based on growth, cultivars DGL, Dunkled, and Faisal Canola were ranked as salt tolerant, while cultivars Cyclone, Legend, and Oscar were ranked as salt-sensitive ones. Differential salt tolerance in these canola cultivars was found to be associated with a relatively lower accumulation of Na+ and greater accumulation of K+ in the leaves, lower oxidative damage (MDA), and better antioxidative defense system (Superoxide dismutase, SOD; peroxidase, POD, and catalase, CAT). Cultivar Oscar was the poorest to discriminate Na+ and K+ uptake and accumulation in leaves and had poor antioxidant potential to scavenge ROS. Salt stress did not affect the structural stability of photosystem-II (PSII) till three weeks, thereafter it caused a significant decrease. Salt stress increased the performance index (PIABS) by increasing the density of active reaction centers in Oscar. Salt stress decreased the antenna size thereby lowering the absorption and trapping energy flux, and maintaining the electron transport with an increase in heat dissipation. This may represent a potential mechanism to cope with salt stress. Transcriptome analysis of salt-sensitive cultivar Oscar further revealed that salt stress down-regulated DEGs related to hormonal signal transduction pathways, photosynthesis, and transcription factors, while DEGs related to the biosynthesis of amino acid and ion transport were up-regulated. In conclusion, salt tolerance in canola cultivars was associated with ion exclusion and maintenance of photosynthesis. Salt stress sensitivity in cultivar Oscar was mainly associated with poor control of ion homeostasis which caused oxidative stress and reduced photosynthetic efficiency. [ABSTRACT FROM AUTHOR]

Transient stability improvement of power systems in the event of short-circuit faults has always been an important issue in power systems analysis and studies. Resistive-type superconducting fault current limiters (RSFCL), owing to their capability in restricting fault currents, have been often taken into account as an efficient method to improve the transient stability of a power system. Regarding technical constraints as well as economic concerns, optimal allocation and sizing of RSFCLs in a power system play a crucial role in their efficient utilization. This paper aims to continue the authors' previous work and enhance the transient stability of power systems by proposing an optimization approach for optimal sizing and the allocation of various candidate numbers of RSFCLs, as the most employed type of SFCL and the most efficient one in transient stability improvement. To solve the optimization problem, a PSO-based algorithm is solved in MATLAB through an objective function and related constraints. The efficacy of the proposed algorithm is evaluated by numerical studies on the IEEE 39-Bus New England system in various scenarios through the assessment of critical fault clearing time (CCT) as well as the generators rotor angle deviations as two crucial criteria for the transient stability of power systems. Simulating the optimization results in DIgSILENT Power Factory indicates an evident enhancement of the power system transient stability via employing optimized RSFCLs resulted from the proposed optimization algorithm. Moreover, the level of transient stability enhancement highly depends on the number of optimized RSFCLs employed in the power system. The results of this paper present a helpful guideline for power system planners to select an appropriate stability scheme based on RSFCLs besides other related technical and economic issues. [ABSTRACT FROM AUTHOR]

For economical and sustainable benefits, conventional retaining walls are being replaced by geosynthetic reinforced soil (GRS). However, for safety and quality assurance purposes, prior tests of pullout capacities of these materials need to be performed. Conventionally, these tests are conducted in a laboratory with heavy instruments. These tests are time-consuming, require hard labor, are prone to error, and are expensive as a special pullout machine is required to perform the tests and acquire the data by using a lot of sensors and data loggers. This paper proposes a data-driven machine learning architecture (MLA) to predict the pullout capacity of GRS in a diverse environment. The results from MLA are compared with actual laboratory pullout capacity tests. Various input variables are considered for training and testing the neural network. These input parameters include the soil physical conditions based on water content and external loading applied. The soil used is a locally available weathered granite soil. The input data included normal stress, soil saturation, displacement, and soil unit weight whereas the output data contains information about the pullout strength. The data used was obtained from an actual pullout capacity test performed in the laboratory. The laboratory test is performed according to American Society for Testing and Materials (ASTM) standard D 6706-01 with little modification. This research shows that by using machine learning, the same pullout resistance of a geosynthetic reinforced soil can be achieved as in laboratory testing, thus saving a lot of time, effort, and money. Feedforward backpropagation neural networks with a different number of neurons, algorithms, and hidden layers have been examined. The comparison of the Bayesian regularization learning algorithm with two hidden layers and 12 neurons each showed the minimum mean square error (MSE) of 3.02 × 10−5 for both training and testing. The maximum coefficient of regression (R) for the testing set is 0.999 and the training set is 0.999 for the prediction interval of 99%. [ABSTRACT FROM AUTHOR]

Steel products are coated with Aluminum (Al) and Zinc (Zn) alloys to improve their corrosion properties. Bulk steel products are coated in batches; however, steel sheets are coated by a continuous hot-dip galvanizing process. Steel sheets are guided into and out of the molten Al-Zn-Si (AZ) bath with the help of stainless-steel rolls, known as guiding, and sink rolls. These rolls are subjected to excessive surface corrosion with molten AZ bath and, hence, are replaced frequently. The surface deterioration of the immersed rolls has been a long-standing issue in the galvanizing industry. In this study, 316L stainless-steel (SS) rods are immersed in the AZ alloy at 600 °C. The immersion time varied from 1 day to 7 days under the static melt conditions in the iron (Fe)-saturated AZ bath. Microstructural analysis of the immersed SS samples revealed two distinct intermetallic compound (IMC) layers forming between the SS substrate and AZ alloy. The IMC layer 1 (AL-1) formed between the SS substrate and IMC layer 2 (AL-2), growing in thickness from 68 µm to 120 µm within 5 days of immersion. The AL-2, which formed between AL-1 and AZ alloy after 24 h of immersion, then grew in thickness up to 150 µm with an uneven trend. The AL-1 is composed of Fe2Al5 and that of AL-2 is composed of FeAl3 that were predicted by the FactSage thermodynamic analysis. Crack development between AL-1 and AL-2 layers, and disintegration of AL-2 into the AZ bath, are key findings of this study. A drastic hardness increase was observed because the IMC layers produce a hard and brittle sink roll surface. [ABSTRACT FROM AUTHOR]

In the field of orthopedics, magnesium (Mg) and magnesium-based composites as biodegradable materials have attracted fundamental research. However, the medical applications of magnesium implants have been restricted owing to their poor corrosion resistance, especially in the physiological environment. To improve the corrosion resistance of Mg/HA/TiO2 nanocomposites, monolayer MgO and double-layer Si/MgO coatings were fabricated layer-by-layer on the surface of a nanocomposite using a powder metallurgy route. Then, coating thickness, surface morphology, and chemical composition were determined, and the corrosion behavior of the uncoated and coated samples was evaluated. Field-emission scanning electron microscopy (FE-SEM) micrographs show that an inner MgO layer with a porous microstructure and thickness of around 34 µm is generated on the Mg/HA/TiO2 nanocomposite substrate, and that the outer Si layer thickness is obtained at around 23 µm for the double-layered coated sample. Electrochemical corrosion tests and immersion corrosion tests were carried out on the uncoated and coated samples and the Si/MgO-coated nanocomposite showed significantly improved corrosion resistance compared with uncoated Mg/HA/TiO2 in simulated body fluid (SBF). Corrosion products comprising Mg(OH)2, HA, Ca3(PO4)2, and amorphous CaP components were precipitated on the immersed samples. Improved cytocompatibility was observed with coating as the cell viability ranged from 73% in uncoated to 88% for Si/MgO-coated Mg/HA/TiO2 nanocomposite after nine days of incubation. [ABSTRACT FROM AUTHOR]

The performance of a typical solar energy-based system can be improved by accurately modeling the current versus voltage characteristics of the involved solar cells. However, estimating the exact value of parameters related to solar cells is quite challenging. The optimization function, considering the current–voltage characteristics of solar cells, requires the solution of sophisticated non-linear and multi-modal optimization methods. So far, various optimization approaches have been reported. This paper proposes the application of a new hybrid algorithm, i.e., Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), which is a combination of two algorithms, i.e., Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) method. The hybrid PSOGSA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. Moreover, the developed hybrid algorithm is benchmarked using ten standard test functions to verify its efficiency. In this manuscript, monocrystalline and polycrystalline solar cells are considered. The parameter optimization results are obtained using PSOGSA and further compared with those obtained using other algorithms presented in the literature, such as PSO, GSA, MVO, HBO, PO and SCA. The complete error analysis is carried out for the modified single-diode model (MSDM), the modified double-diode model (MDDM), and the modified three-diode model (MTDM) of photovoltaic (PV) cells to prove the superiority of the PSOGSA. Moreover, statistical results are carried out based on Friedman's ranking and Wilcoxon's rank sum test. The comparison results show that the proposed PSOGSA is better than other algorithms in estimating the unknown PV model parameters. [ABSTRACT FROM AUTHOR]

Non-classical crystallization, in which charged nanoparticles (NPs) are the building blocks of film growth, has been extensively studied in chemical vapor deposition (CVD). Here, the deposition behavior of silicon oxide films by the two-step growth process, where NPs are generated in the gas phase at high temperature and deposited as films at low temperature, was studied in the CVD process. Although we supplied SiH4, H2, and N2, the deposited film turned out to be silicon oxide, which is attributed to relatively poor vacuum. Also, silicon oxide NPs were captured on transmission electron microscopy (TEM) carbon membranes of a copper grid for 10 s under various conditions. When the quartz tube with a conical nozzle was used, the size of nanoparticles increased drastically with increasing processing time (or delay time) and porous films with a rough surface were deposited. When the quartz tube without a nozzle was used, however, the size did not increase much with increasing processing time and dense films with a smooth surface were deposited. These results suggest that the size of nanoparticles is an important parameter for the deposition of dense films for two-step growth at low temperatures. [ABSTRACT FROM AUTHOR]

Advances in nanotechnology have opened new doors to overcome the problems related to contaminated water by introducing photocatalytic nanomaterials. These materials can effectively degrade toxic contaminants, such as dyes and other organic pollutants, into harmless by-products such as carbon dioxide and water. Consequently, these photocatalytic nanomaterials have the potential to provide low-cost and environment-friendly alternatives to conventional water and wastewater treatment techniques. In this study, a nanocomposite of zinc oxide and graphene oxide was developed and evaluated for photocatalysis. This nanocomposite was characterized by XRD, FTIR, FESEM, Diffuse Reflectance Spectroscopy (DRS), TEM and UV-Vis spectrophotometer. The photocatalytic behavior of the nanocomposite was studied through the degradation of methyl orange under ultraviolet light. It is reported that the weight ratios of zinc oxide and graphene oxide do not considerably affect the photocatalytic performance, which gives this process more compositional flexibility. Moreover, hydrogen peroxide was used as an electron scavenger to increase the time-efficiency of the process. The photodegradation rate can be significantly improved (up to 24 times) with the addition of hydrogen peroxide, which increases the number of trapped electrons and generates more oxidizing species, consequently increasing the reaction rate. [ABSTRACT FROM AUTHOR]

Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software. [ABSTRACT FROM AUTHOR]

Plant salt tolerance is controlled by various physiological processes such as water and ion homeostasis, photosynthesis, and cellular redox balance, which are in turn controlled by gene expression. In the present study, plants of six canola cultivars (DGL, Dunkled, Faisal Canola, Cyclone, Legend, and Oscar) were evaluated for salt tolerance by subjecting them to 0 or 200 mM NaCl stress. Based on growth, cultivars DGL, Dunkled, and Faisal Canola were ranked as salt tolerant, while cultivars Cyclone, Legend, and Oscar were ranked as salt-sensitive ones. Differential salt tolerance in these canola cultivars was found to be associated with a relatively lower accumulation of Na + and greater accumulation of K + in the leaves, lower oxidative damage (MDA), and better antioxidative defense system (Superoxide dismutase, SOD; peroxidase, POD, and catalase, CAT). Cultivar Oscar was the poorest to discriminate Na + and K + uptake and accumulation in leaves and had poor antioxidant potential to scavenge ROS. Salt stress did not affect the structural stability of photosystem-II (PSII) till three weeks, thereafter it caused a significant decrease. Salt stress increased the performance index (PI ABS ) by increasing the density of active reaction centers in Oscar. Salt stress decreased the antenna size thereby lowering the absorption and trapping energy flux, and maintaining the electron transport with an increase in heat dissipation. This may represent a potential mechanism to cope with salt stress. Transcriptome analysis of salt-sensitive cultivar Oscar further revealed that salt stress down-regulated DEGs related to hormonal signal transduction pathways, photosynthesis, and transcription factors, while DEGs related to the biosynthesis of amino acid and ion transport were up-regulated. In conclusion, salt tolerance in canola cultivars was associated with ion exclusion and maintenance of photosynthesis. Salt stress sensitivity in cultivar Oscar was mainly associated with poor control of ion homeostasis which caused oxidative stress and reduced photosynthetic efficiency.