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Grid connected photovoltaic (GCPV) rooftop systems have been considered as fast development and promising renewable energy sources due to low maintenance cost, secure investment, noise-free and do not require additional space for installation. Various factors considered for the installation of GCPV are mathematical models of PV module, sizing methods based on techno-economic objectives, PV panels and configurations, selection of the final optimum configuration and environmental criteria. Selection of appropriate controller and its optimal design for power electronic based converters also plays a crucial role in the performance of GCPV. Therefore in this article, a two-fold hysteresis current controller (TFHCC) based on an Improved Arithmetic Optimization Algorithm (IAOA) is introduced and investigated for the first time, for a GCPV system to minimize the switching loss and total harmonic distortion (THD). A novel multi-objective function considering switching frequency and current error is proposed by assigning appropriate weights to obtain the optimal values of duty cycle and hysteresis bands using IAOA, AOA, Forensic Based Investigation (FBI), Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. TFHCC utilizes zero level of the inverter by properly switching it on for a half cycle only and either on or off for the other cycle. Comparative performance analysis of the optimal TFHCC obtained with different algorithms is presented and it is proved that IAOA based TFHCC exhibits substantial reductions in variation and magnitude of the average switching frequency by 2.82 kHz and THD by 0.65%. Initially, the study is carried out with MATLAB Simulink environment and then experimentally validated with real time simulator based on OPAL-RT 4510.

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Wireless Sensor Networks (WSNs) are revolutionizing the globe with their sensing technologies. This initiative aims to design and deploy a cattle health monitoring system (CHMS) for the agriculture sector for the well-being of livestock and the eradication of hunger. The production and consumption of all dairy and meat products can only be conducted responsibly if the health of the source animal is protected. In this regard, WSNs, a component of the Internet-of-things (IoT) system, can be utilized for monitoring cow health due to their adaptability and portability, which enables them to be applied to expansive domains such as cattle healthcare. The integration of IoT and artificial intelligence enables the prediction of livestock illnesses. The primary purpose of this proposed system is to forecast cattle diseases utilizing real-time data from non-invasive body-area sensors and Artificial Neural Networks (ANN) and to display the expected results to authorized personnel via a web application. Popular authentication schemes are used in this system as it is susceptible to hacking and requires robust network security to protect the confidentiality, integrity, and availability of its resources. The presented results validates the accuracy of the proposed novel system for cow diseases prediction that is approximately 98 percent. The proposed CHMS can assist concerned farmers in remotely monitoring the health of their livestock from a variety of locations and in taking appropriate and timely measures to protect animal health. Technological automation will lower prices and labor inputs while enhancing farm output.

Lung cancer is the most common and second leading cause of cancer with lowest survival rate due to lack of efficient diagnostic tools. Currently, researchers are devising artificial intelligence based tools to improve the diagnostic capabilities. The machine learning (ML) requires hand-crafted features to train the algorithms. To extract most relevant features is still a challenging task in the field image processing. We first extracted the texture gray level co-occurrence matrix features. We fed these features to traditional ML algorithms such as k-nearest neighbor (KNN) and support vector machine (SVM). The SVM yielded an accuracy of 83.0%, whereas KNN produced an accuracy of 97.0%. We then optimized and employed the ensemble extreme boosting (XGBoost) algorithm, which improved the detection performance with precision, recall, and accuracy of 100%. We also optimized and employed the deep learning ResNet101 to distinguish the small cell cancer from non-small cell lung cancer and obtained the 100% performance with these evaluation performance measures. The results revealed that proposed approach is more robust than traditional ML algorithms. Based on these results, the proposed methodology can be very helpful in the early detection and treatment of lung cancer for better diagnosis system.

In this study, experiments were performed to measure the inception and breakdown voltages of SF6/CO2 mixed gases with varying mixing ratios at two different pressures (1 and 2 bar). Using the preliminary data obtained from the experiments, a 3D particle model was used to investigate positive streamer initiation in SF6/CO2 with field ionization. Field ionization using Zener’s model was assumed to be similar to the electron detachment in air. The experimental results showed that at $P=1$ bar, the discharge inception voltage increased for CO2 concentrations lower than 50% and decreased for CO2 concentrations higher than 50%. The inception voltage of 80%CO2/20%SF6 was close to the inception voltage of pure SF6. A similar trend of the inception and breakdown voltages was observed at $P=2$ bar. The simulation results show the evolution of the thin filaments of the streamer into three distinct stages. The streamer morphology was similar irrespective of the type and concentration of the mixed gas. Other parameters such as the electric field, electron density, and streamer length increased with increasing CO2 concentration in SF6 at both pressures. Zener’s field ionization could be considered a primary ionization mechanism for SF6/CO2 in the case of very high non-uniform electric fields; however, finding the experimental validation of Zener’s field ionization for electronegative gases is still a challenging task.

In 5G cellular communication systems, achieving low latency is crucial to support the ‘tactile’ Internet with response times of less than one millisecond. Traditional initial synchronization methods face challenges due to high delays. This manuscript presents a novel approach using an EXtrinsic Information Transfer (EXIT) chart-aided method to investigate the concurrence of $m$ -sequences using Iterative Soft Sequence Estimation (ISSE) in a communication channel. The ISSE technique leverages the concept of an Automatic Gain Controller (AGC), which gradually increases the gain as the number of chips in the $m$ -sequence generator grows, both at the transmitter and the receiver. Our ISSE method stands out by achieving sequence synchronization at the receiver with as few as $F$ successive chips for a sequence of ( $2^{F}-1$ ) chips. We base our work on the EXIT chart, eliminating the need for interleavers, which introduce transmission delays. To address the delay issue, we exploit the inherent interrelationship of the $m$ -sequence generator’s concern chips, which have a duration of ( $2^{F}-1$ ), as influenced by the Linear Feedback Shift Register (LFSR) in our ISSE model. We observe that low-order Primitive Polynomials (PPs) exhibit lower Erroneous Loading Probability ( $P_{e}$ ) than higher-order PPs at a specific Signal-to-Noise Ratio (SNR). PPs with identical order but fewer connection taps outperform those with more connection taps. The EXIT chart analysis reveals that lower-order PPs exhibit a larger opening tunnel between the outer and inner curves, resulting in higher achievable rates in our proposed system. Moreover, PPs with lower order achieve higher Mutual Information (MI) more efficiently with the assistance of our ISSE system compared to higher-order PPs.

Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.

Over the years, there has been a rise in the number of fabricated and fake news stories that utilize both textual and visual information formats. This coincides with the increased likelihood that users will acquire their news from websites and social media platforms. While there has been various research into the detection of fake news in text using machine learning techniques, less attention has been paid to the problem of multimedia data fabrication. In this paper, we propose a Stylometric, and Semantic similarity oriented for Multimodal Fake News Detection (SSM). There are five distinct modules that make up our methodology: Firstly, we used a Hyperbolic Hierarchical Attention Network (Hype-HAN) for extracting stylometric textual features. Secondly, we generated the news content summary and computed the similarity between Headline and summary. Thirdly, semantic similarity is computed between visual and textual features. Fourthly, images are analyzed for forgery. Lastly, the extracted features are fused for final classification. We have tested SSM framework on three standard fake news datasets. The results indicated that our suggested model has outperformed the base line and state-of-the-art methods and is more likely to detect fake news in complex environments.

Recently, research papers have shown a strong interest in modeling count data. The over-dispersion or under-dispersion are frequently seen in the count data. The count data modeling with a broad range of dispersion has been successfully accomplished using the Conway-Maxwell-Poisson regression (COMP) model. The multicollinearity issue is known to have a detrimental impact on the maximum likelihood estimator's variance. To solve this issue, biased estimators as a ridge estimator have repeatedly shown to be an appealing way to minimize the effects of multicollinearity. In this paper, we suggested the jackknife ridge estimator (JCOMPRE) and the modified version of the jackknife ridge estimator (MJCOMPRE) for the COMP model. The proposed estimators (JCOMPRE and MJCOMPRE) are reducing the effects of the multicollinearity and the biasedness of the ridge estimator at the same time. According to the findings of the Monte Carlo simulation study and real-life applications, the proposed estimators have a minimum bias and a minimum mean squared error. This means that the proposed estimators are more efficient than the maximal likelihood estimator and the ridge estimator.

The Conway–Maxwell–Poisson (COMP) model is defined as a flexible count regression model used for over- and under-dispersion cases. In regression analysis, when the explanatory variables are highly correlated, this means that there is a multicollinearity problem in the model. This problem increases the standard error of maximum likelihood estimates. To manage the multicollinearity effects in the COMP model, we proposed a new modified Liu estimator based on two shrinkage parameters (k, d). To assess the performance of the proposed estimator, the mean squared error (MSE) criterion is used. The theoretical comparison of the proposed estimator with the ridge, Liu, and modified one-parameter Liu estimators is made. The Monte Carlo simulation and real data application are employed to examine the efficiency of the proposed estimator and to compare it with the ridge, Liu, and modified one-parameter Liu estimators. The results showed the superiority of the proposed estimator as it has the smallest MSE value.

Dusty fluids are utilized to minimize heat in systems like gas-freezing systems and nuclear-powered reactors, among other places. The present study aims to investigate the effect of Newtonian heating on dusty fluid flow. Between two parallel plates, the two-phase MHD fluctuating flow of the dusty fluid is considered. The dust particles inside the fluid are thought to be spherical and uniformly distributed. The generation and absorption of heat were also taken into consideration. The motion of the fluid is due to the motion of the right plate with free stream velocity Ut. Partial differential equations are utilized to represent the flow regime. The Poincare-Light Hill Technique is used to find exact solutions. The impact of parameters on the temperature and velocity profiles are shown graphically. Skin friction and rate of heat transfer, two essential fluid parameters for engineers, are also evaluated in tabular form. The velocity of the fluid is shown to decrease with increasing magnetic field. The Newtonian heating phenomena has an effect on the plate’s heating.

In the past few decades, cutting-edge information and communication technology has been used in several ways to keep an eye on the marine environment. Underwater wireless sensor networks (UWSNs) can measure the amount of water and soil conditions, such as soil salinity, moisture, and movements, to predict landslides. UWSNs are made up of many wireless underwater sensor nodes (WSNs) that are spread out across the thalassic environment. These networks have several uses, including data collection, navigation, resource analysis, surveillance, disaster prediction, etc. Nowadays, energy efficiency becomes a complex issue to handle in the design of the UWSN due to the limited battery capacity and the challenges associated with changing or charging the integrated batteries. According to previous research, clustering and routing have already been effective methods of improving energy efficiency in the UWSN, as unreplaceable batteries and long-distance communication delays are particularly vulnerable. As a result, one of the UWSN’s critical issues is determining how to extend the network’s lifespan while balancing its energy consumption and shortening transmission distances. In UWSN clustering, the most important considerations are acquiring a suitable count of clusters, constituting the clusters, and picking the most satisfactory cluster head (CH) for each cluster. Based on several factors, such as residuary energy, total energy consumption, and other considerations, our proposed approach picks CHs and arranges them into clusters. Also, the proposed SS-GSO method constructs a fitness function by including various sources of information, like total energy, residual energy, and luciferin value. Several simulation runs were executed to test how much better the SS-GSO approach worked. The comparison results showed that while evaluating clustering time, our proposed SS-GSO technique performs 22.91%, 50.03%, 42.42%, 58.06% better, in case of Total energy consumption 27.02%,14%,33.76%,41.97% more energy efficient, in Cluster lifetime 9.2%,19.88%,35.91%,40.54% less and in Packet delivery rate 8.29%,14.05%,17.67%,23.97% better as compared with other heuristic techniques, such as ACO, GWO, MFO and LEACH.

Groundwater is a vital component of life; without its identification, it is impossible to live. Therefore, identifying groundwater potential zones (GWPZs) is critical. For this purpose, the study area of the Kohat District was selected to identify GWPZs using the multi-influencing factors (MIF) approach. The Kohat area has a semi-arid to sub-humid subtropical climate and is classified as a sub-tropical, triple-season, semi-arid, sub-mountainous area. The geology, land use, soil, rainfall, lineament density, and drainage density are important parameters of ground water potential identification. The GWPZs were classified into five types: very poor, poor, good, high, and very high. We determined that 37.72% of the study area had high GWPZs, which were predominately in or near the northwest region of the study area, whereas 4.62% of the study area, in the southeast region, had very poor GWPZs. The water table data from the study varied due to different parameters used to identify the GWPZs. Our MIF results revealed that a large area of the Kohat District has good water potential. Still, due to topographic elevation changes, the groundwater potential has been limited in hilly areas. Our final results were compared with water level field data collected from different sources across the Kohat District.

In medical field, accurate decisions are very important as they risk human lives. decision support system (DSS) plays important role in making accurate decisions and used for classification/prediction. In gene expression analysis, genes are not only inflated by the external environmental conditions but also the expression values of certain genes are affected (like cancer, obesity etc). in this study, various traditional (Support Vector Machine, Decision Trees, and Linear Discriminant Analysis, naïve Bayes, logistic regression, and multilayer perceptron) and proposed methods (combination of traditional with ensemble and probabilistic classifiers) are used in order to perform the classification and prediction analysis. In this study we used the publicly available datasets comprised of Lymphoid, Leukemia and Colon Cancer. The classification performance on Colon dataset with traditional methods was obtained with accuracy (56%) and proposed probabilistic ensemble methods with accuracy (88%). For dataset, Leukemia, the accuracy was obtained using traditional methods (78%) and proposed methods (92%). Similarly, on Lymphoid dataset, the traditional methods yielded accuracy (75%) and proposed methods (87%). The results revealed that proposed methods yielded the improved detection performance. The proposed methods can be used as a better predictor for early diagnosis and improved diagnosis to improve the healthcare systems.

The study of fluid dynamics due to the stretching surface is one of the most eminent topics due to its potential industrial applications viz. drawing wire and plastic films, metal and polymer extrusion, fiber and glass production. In the present article, the author is going to study the effects of hybrid nanofluids flow on an inclined plate including CuO (Copper Oxide), and Cu (Copper). The Casson fluid with a couple-stress term has been used in the flow analysis. The surface of the plate is considered slippery. The convection has been taken nonlinear with thermal radiation. The governing equation of the flow of hybrid nanofluids with energy equation has been transformed into highly nonlinear ODEs through similarity transformation. The proposed model has been solved through a numerical RK-4 method. Significant variables of the physical process such as solar radiation, nonlinear convection parameters, heat transfer rates, and their effect on the solar power plant have been noticed.

Presently, scientists across the world are carrying out theoretical and experimental examinations for describing the importance of nanofluids in the heat transfer phenomena. Such fluids can be obtained by suspending nanoparticles in the base fluid. Experimentally, it has proved that the thermal characteristics of nanofluids are much better and more appealing than those of traditional fluids. The current study investigates the heat transfer for the flow of blood that comprises micropolar gold nanoparticles. The influence of chemically reactive activation energy, thermophoresis, thermal radiations, and Brownian motion exists between the walls of the channel. A microorganism creation also affects the concentration of nanoparticles inside the channel. Suitable transformation has been used to change the mathematical model to its dimensionless form and then solve by using the homotopy analysis method. In this investigation, it has been revealed that the linear velocity behavior is two-folded over the range 0≤η≤1. The flow is declining in the range 0≤η≤0.5, whereas it is augmenting upon the range 0.5≤η≤1. Thermal characteristics are supported by augmentation in volumetric fraction, thermophoresis, radiation, and Brownian motion parameters while opposed by the Prandtl number. The concentration of the fluid increases with augmentation in activation energy parameters and decays with an increase in thermophoresis, Brownian motion, chemical reaction parameters, and the Schmidt number. The density of microorganisms weakens by growth in Peclet and bioconvection Lewis numbers.

Nanofluids are a new generation of fluids which help in improving the efficiency of thermal systems by improving heat transport rate and extensive applications of this class extensively fall in biomedical engineering, the electronics industry, applied thermal and mechanical engineering, etc. The core concern of this study is to examine the interaction of Al2O3-Fe3O4 hybrid nanoparticles of lamina shaped with blood over a 3D surface by impinging novel impacts of non-linear thermal radiations, stretching, velocity slippage, and magnetic field. This leads to a mathematical flow model in terms of highly non-linear differential equations via nanofluid-effective characteristics and similarity rules. To know the actual behavior of (Al2O3-Fe3O4)/blood inside the concerned region, mathematical investigation is performed via numerical technique and the results are obtained for different parameter ranges. The imposed magnetic field of high strength is a better tool to control the motion of (Al2O3-Fe3O4)/blood inside the boundary layer, whereas, stretching of the surface is in direct proportion of the fluid movement. Furthermore, thermal radiations (Rd) and γ1 are observed to be beneficial for thermal enhancement for both (Al2O3-Fe3O4)/blood and (Al2O3)/blood.

Due to their accelerated rate of heat transfer, nanofluids are of immense interest. This work analyzes an innovative concept of hybrid nanoemulsion with an optimized design under the chemical radiative flow and its thermophysical properties. We are able to achieve great aspects of the flow with the assistance of the sheet’s permeable texture and inclined surface. Furthermore, the effects of thermal conductivity mix convection, chemical reaction, and thermal radiations on velocity, temperature, and concentration fields are also investigated. After converting the fundamental equations to ordinary differential equations with the use of similarity transportation, the problem is then solved analytically with the HAM technique. To investigate key attributes and parameters, a hybrid nanofluid with Ag and Al2O3 nanoparticles as well as Al2O3 for conventional nanofluids with the base solvent water is taken. To illustrate the effects of chemical radiative and mix convection on the thin-film flow, numerous graphs, charts, and tables are shown. Calculations and reviews are performed for reduced friction coefficient, heat, and mass transportation. According to this study, hybrid nanofluids have a higher heat-transfer rate than nanofluids when exposed to thermal radiation and at the appropriate surface angle of inclination. Due to ϕAl2O3, ϕAg,S, Rd, the temperature increases, but velocity has the opposite effect. This investigation’s innovative findings will promote the study of condensed nanostructures and nanomaterials.

In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem. To reduce this problem's effects, the Tobit ridge and the Tobit Liu estimators are proposed. Therefore, this study proposes a new kind of the Tobit estimation called the Tobit new ridge-type (TNRT) estimator. Also, the TNRT estimator was theoretically compared with the Tobit maximum likelihood, the Tobit ridge, and the Tobit Liu estimators via the mean squared error criterion. Moreover, we performed a Monte Carlo simulation to study the performance of the TNRT estimator compared with the previously defined estimators. Also, we used the Mroz dataset to confirm the theoretical and the simulation study results.

Recently, controlling a wind energy conversion system (WECS) under fluctuating wind speed and enhancing the quality of power delivered to the grid has been a demanding challenge for many researchers. This paper provides a comprehensive review of synchronous generator-based WECSs. This paper will investigate the growth of wind energy in Egypt and throughout the world, as well as the technological and financial significance of wind energy. The block diagram of a typical grid-connected WECS, power control techniques, characteristic power curve-based maximum power point tracking (MPPT), and MPPT techniques are also presented in this study. Moreover, this study compares different power converter topologies for grid-connected and independent WECSs that use a permanent magnet synchronous generator (PMSG).

This paper presents an improved topology for a DC–DC converter suitable for PV applications. The proposed converter has the ability to be energized from multiple DC sources. Hence, it can be energized from two, three or a higher number of sources according to the number of modules adopted in its design. The proposed converter can supply a single load with DC power at a voltage lower or higher than the summation of all excitation DC voltages with a non-reversed voltage polarity at its output. Moreover, it provides a more reliable operation compared to other DC–DC converters due to its ability for operation with partial failures in its exciting sources. In this paper, the theoretical discussion of the proposed converter is presented considering its construction and its principle of operation. The performance of the proposed converter is theoretically evaluated using simulation based on power simulation (PSIM) software at different conditions. The performance of the converter is theoretically evaluated using PSIM considering photovoltaic (PV) sources as input sources for the proposed converter to show its validity for renewable energy applications. For more evaluation, experimental work is carried out by building a prototype and testing it at different operating conditions.

The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate this problem. Additionally, the PML estimator is sensitive to multicollinearity. Therefore, several biased Poisson estimators have been provided to cope with this problem, such as the Poisson ridge estimator, Poisson Liu estimator, Poisson Kibria–Lukman estimator, and Poisson modified Kibria–Lukman estimator. Despite different Poisson biased regression estimators being proposed, there has been no analysis of the robust version of these estimators to deal with the two above-mentioned problems simultaneously, except for the robust Poisson ridge regression estimator, which we have extended by proposing three new robust Poisson one-parameter regression estimators, namely, the robust Poisson Liu (RPL), the robust Poisson Kibria–Lukman (RPKL), and the robust Poisson modified Kibria–Lukman (RPMKL). Theoretical comparisons and Monte Carlo simulations were conducted to show the proposed performance compared with the other estimators. The simulation results indicated that the proposed RPL, RPKL, and RPMKL estimators outperformed the other estimators in different scenarios, in cases where both problems existed. Finally, we analyzed two real datasets to confirm the results.

Zinc (Zn) ion supercapacitors (ZISCs) have attracted considerable attention as a viable energy storage technology because they are cost-effective, safe, and environmentally friendly. However, cathode materials with suitable properties are rare and need to be explored. In this regard, metal carbides (MXenes) are a good choice for capacitive energy storage, but they exhibit low capacitance. The energy storage performance of MXenes can be bossed using functionalization with heteroatom doping, e.g., nitrogen (N), to simultaneously modify ZISCs’ fundamental characteristics and electrochemical properties. Herein, we present an in-situ N-functionalization of Ti3C2Tx-MXene via a hydrothermal reaction with urea (denoted as N-Ti3C2Tx-MXene). N-functionalization into Ti3C2Tx-MXene raised Ti3C2Tx-MXene’s interlayer spacing and boosted the Zn-ion storage in 1 M ZnSO4 electrolyte. The N-Ti3C2Tx-MXene electrode delivered an excellent specific capacitance of 582.96 F/g at 1 A/g and retained an outstanding cycle stability of 94.62% after 5000 cycles at 10 A/g, which is 1.8 times higher than pristine Ti3C2Tx-MXene at identical conditions. Moreover, the N-Ti3C2Tx-MXene//Zn device demonstrated a maximum capacitance of 153.55 F/g at 1 A/g, retained 92% of its initial value after 5000 cycles, and its Coulombic efficiency was ~100%. This strategy considerably reduced Ti3C2Tx-MXene nanosheet restacking and aggregation and enhanced electrochemical performance. Further, this research elucidated N-Ti3C2Tx-MXene’s charge–storage process and offered a fresh approach to the rational design of novel electrode materials for ZISCs.

Increasing global environmental problems and awareness towards the utilization of eco-friendly resources enhanced the progress of research towards the development of next-generation biodegradable and environmentally friendly material. The development of natural-based composite material has led to various advantages such as a reduction in greenhouse gases and carbon footprints. In spite of the various advantages obtained from green materials, there are also a few disadvantages, such as poor interfacial compatibility between the polymer matrix and natural reinforcements and the high hydrophilicity of composites due to the reinforcement of hydrophilic natural fibers. This review focuses on various moisture-absorbing and sound-absorbing natural fiber polymer composites along with the synopsis of preparation methods of natural fiber polymer composites. It was stated in various studies that natural fibers are durable with a long life but their moisture absorption behavior depends on various factors. Such natural fibers possess different moisture absorption behavior rates and different moisture absorption behavior. The conversion of hydrophilic fibers into hydrophobic is deemed very important in improving the mechanical, thermal, and physical properties of the natural-fiber-reinforced polymer composites. One more physical property that requires the involvement of natural fibers in place of synthetic fibers is the sound absorption behavior. Various researchers have made experiments using natural-fiber-reinforced polymer composites as sound-absorbing materials. It was found from various studies that composites with higher thickness, porosity, and density behaved as better sound-absorbing materials.

The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlike existing technologies that provide a reliable comparison of different AI-based classification algorithms. This paper also predicts a reliable bandwidth for the BCI process and provides exact placements of EEG electrodes to verify different arm moments. We have applied different classification algorithms, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree, to four different users. The accuracy of all prescribed classifiers has been calculated by considering the first user as a reference. The presented results validate the novel deployment, and the comparison shows that the accuracy for Random Forest remained optimal at around 76%, Gradient Boosting is around 74%, while the lowest is 64% for Decision Tree. It has been observed that people have different activation bandwidths while the dominant frequency varies from person-to-person that causes fluctuations in the EEG dataset.

This research work aims at providing power quality improvement for the nonlinear load to improve the system performance indices by eliminating maximum total harmonic distortion (THD) and reducing neutral wire current. The idea is to integrate a shunt hybrid active power filter (SHAPF) with the system using machine learning control techniques. The system proposed has been evaluated under an artificial neural network (ANN), gated recurrent unit, and long short-term memory for the optimization of the SHAPF. The method is based on the detection of harmonic presence in the power system by testing and comparison of traditional pq0 theory and deep learning neural networks. The results obtained through the proposed methodology meet all the suggested international standards of THD. The results also satisfy the current removal from the neutral wire and deal efficiently with minor DC voltage variations occurring in the voltage-regulating current. The proposed algorithms have been evaluated on the performance indices of accuracy and computational complexities, which show effective results in terms of 99% accuracy and computational complexities. deep learning-based findings are compared based on their root-mean-square error (RMSE) and loss function. The proposed system can be applied for domestic and industrial load conditions in a four-wire three-phase power distribution system for harmonic mitigation.

Sound event detection (SED) plays an important role in understanding the sounds in different environments. Recent studies on standardized datasets have shown the growing interest of the scientific community in the SED problem, however, these did not pay sufficient attention to the detection of artificial and natural sound. In order to tackle this issue, the present article uses different features in combination for detection of machine-generated and natural sounds. In this article, we trained and compared a Stacked Convolutional Recurrent Neural Network (S-CRNN), a Convolutional Recurrent Neural Network (CRNN), and an Artificial Neural Network Classifier (ANN) using the DCASE 2017 Task-3 dataset. Relative spectral–perceptual linear prediction (RASTA-PLP) and Mel-frequency cepstrum (MFCC) features are used as input to the proposed multi-model. The performance of monaural and binaural approaches provided to the classifier as an input is compared. In our proposed S-CRNN model, we classified the sound events in the dataset into two sub-classes. When compared with the baseline model, our obtained results show that the PLP-based ANN classifier improves the individual error rate (ER) for each sound event, e.g., the error rate (ER) is improved to 0.23 for heavy vehicle events and 0.32 for people walking, and minor gains are shown in other events as compared to the baseline. Our proposed CRNN performs well when compare to the baseline and to our proposed ANN model. Moreover, in cross-validation trials, the results in the evaluation stage demonstrate a significant improvement compared to the best performance of DCASE 2017 Task-3, reducing the ER to 0.11 and increasing the F1-score by 10% in the evaluation dataset. Erosion and dilation were used during post-processing.

This paper presents a systematical and progressive appraisal of the technology since the inception of AC-AC conversion, which is seen to be an indispensable and vital for the advancement of the Solid State Transformer (SST) in a distribution system. Special attention is given to the concepts/topologies and architecture of the SST, the DC-DC conversion devices in the isolation facet, the inversion gadget in the bridge arrangement and their integration in the development of an economically viable and efficient SST design. For the purpose of this article, a number of research papers, research proposals and research dissertations/studies have been accessed that mostly cover work related to this device and brief discussions about several aspects. The various sections of this article are correspondingly devoted to the review of SST design and its various configurations, the significant stages of the DC-DC converter from a transformer-specific perspective and lastly the high-frequency inverter. In critical appraisal, the SST in a three-stage perspective, the dual half-bridge converter and high-frequency half-bridge inverter are the most viable and promising means, offering tremendous advantages over other configurations with emphasis on economy, high flexibility and control performance; thereby weighed as the most workable, practical and realistic solutions. The foremost potential application of this expedient includes a vital component of the power grid.

Unmanned aerial vehicles (UAVs), particularly quadrotor, have seen steady growth in use over the last several decades. The quadrotor is an under-actuated nonlinear system with few actuators in comparison to the degree of freedom (DOF); hence, stabilizing its attitude and positions is a significant challenge. Furthermore, the inclusion of nonlinear dynamic factors and uncertainties makes controlling its maneuverability more challenging. The purpose of this research is to design, implement, and evaluate the effectiveness of linear and nonlinear control methods for controlling an X3D quadrotor’s intended translation position and rotation angles while hovering. The dynamics of the X3D quadrotor model were implemented in Simulink. Two linear controllers, linear quadratic regulator (LQR) and proportional integral derivate (PID), and two nonlinear controllers, fuzzy controller (FC) and model reference adaptive PID Controller (MRAPC) employing the MIT rule, were devised and implemented for the response analysis. In the MATLAB Simulink Environment, the transient performance of nonlinear and linear controllers for an X3D quadrotor is examined in terms of settling time, rising time, peak time, delay time, and overshoot. Simulation results suggest that the LQR control approach is better because of its robustness and comparatively superior performance characteristics to other controllers, particularly nonlinear controllers, listed at the same operating point, as overshoot is 0.0% and other factors are minimal for the x3D quadrotor. In addition, the LQR controller is intuitive and simple to implement. In this research, all control approaches were verified to provide adequate feedback for quadrotor stability.

Recent advancements in green building technologies (GBTs) have grown substantially, as an outcome of the environmental, economic and societal benefits. It has the potential to move toward sustainable development, specifically related to climate change. In GBTs, the main objective is to use energy, water and other resources in a balanced way, without using them extensively. This will improve the environmental conditions. Green buildings (GBs) are beneficial when it comes to energy consumption and emissions; low maintenance and operation costs; boosting health and productivity; etc. There is a lack of a critical review of the past or present research work in the area of the Green Building Technology (GBT) sector to identify the future roadmap for sustainable green building technologies. A critical review, with the help of proper research methodology, was identified. The scope of this study is to analyze the existing work on different issues, and find different key issues in green building research, which has minimal use of natural resources, is cost-effective and is designed and constructed for a longer duration, considering future prospects. This paper examines the state of green building construction today and makes recommendations for further study and development which will be necessary for a sustainable future. In order to encourage research, this study also identified a few possible future research directions in sustainable development.

The interaction between cathode and anode materials is critical for developing a high-performance asymmetric supercapacitor (SC). Significant advances have been made for cathode materials, while the anode is comparatively less explored for SC applications. Herein, we proposed a high-performance binder-free anode material composed of two-dimensional ZnFe2O4 nanoflakes supported on carbon cloth (ZFO-NF@CC). The electrochemical performance of ZFO-NF@CC as an anode material for supercapacitor application was examined in a KOH solution via a three-electrode configuration. The ZFO-NF@CC electrode demonstrated a specific capacitance of 509 F g−1 at 1.5 A g−1 and was retained 94.2% after 10,000 GCD cycles. The ZFO-NF@CC electrode showed exceptional charge storage properties by attaining high pseudocapacitive-type storage. Furthermore, an asymmetric SC device was fabricated using ZFO-NF@CC as an anode and activated carbon on CC (AC@CC) as a cathode with a KOH-based aqueous electrolyte (ZFO-NF@CC||AC@CC). The ZFO-NF@CC||AC@CC yielded a high specific capacitance of 122.2 F g−1 at a current density of 2 A g−1, a high energy density of 55.044 Wh kg−1 at a power density of 1801.44 W kg−1, with a remarkable retention rate of 96.5% even after 4000 cycles was attained. Thus, our results showed that the enhanced electrochemical performance of ZFO-NF@CC used as an anode in high-performance SC applications can open new research directions for replacing carbon-based anode materials.

Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction.

The demand for uninterruptible electricity supply is increasing, and the power engineering sector has started researching alternative solutions. Distributed generation (DG) dissemination into the electric grid to cope with the accelerating demand for electricity is taken into consideration. However, its integration with the traditional grid is a key task as sudden changes in load and their fickle nature cause the frequency to deviate from its adjusted range and affect the grid’s reliability. Moreover, the increased use of DG will significantly impact power system frequency response, posing a new challenge to the traditional power system frequency framework. Therefore, maintaining the frequency within the nominal range can improve its reliability. This deviation should be removed within a few seconds to keep the system’s frequency stable so that supply and demand are balanced. In a traditional grid system, the controllers installed at the generation side help to control the system’s frequency. These generators have capital installation costs that are not desirable for system operators. Therefore, this article proposed a comprehensive control framework to enable high penetration of DG while still providing adequate frequency response. This is accomplished by investigating a grasshopper optimization algorithm-based (GOA) fuzzy PD-PI controller (FPD-PI) for analyzing frequency control and optimizing the FPD-PI controller gains to minimize the frequency fluctuations. In this paper, interconnected hybrid power systems (HPS) are considered. In this study, the response of a system is analyzed, and the results validate that the oscillations in frequency are substantially reduced by the proposed controller. Moreover, our model is the best option for controlling frequency instead of conventional controllers, as it is efficient and fast to regulate frequency by switching the preferred loads on or off.

Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source (241Am and 133Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241Am and 133Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry.

Faults in the power system affect the reliability, safety, and stability. Power-distribution systems are familiar with the different faults that can damage the overall performance of the entire system, from which they need to be effectively cleared. Underground power systems are more complex and require extra accuracy in fault detection and location for optimum fault management. Slow processing and the unavailability of a protection zone for relay coordination are concerns in fault detection and location, as these reduce the performance of power-protection systems. In this regard, this article proposes an optimized solution for a fault detection and location framework for underground cables based on a discrete wavelet transform (DWT). The proposed model supports area detection, the identification of faulty sections, and fault location. To overcome the abovementioned facts, we optimize the relay coordination for the overcurrent and timing relays. The proposed protection zone has two sequential stages for the current and time at which it optimizes the current and time settings of the connected relays through Newton–Raphson analysis (NRA). Moreover, the traveling times for the DWT are modeled, which relate to the protection zone provided by the relay coordination, and the faulty line that is identified as the relay protection is not overlapped. The model was tested for 132 kV/11 kV and 16-node networks for underground cables, and the obtained results show that the proposed model can detect and locate the cable’s faults speedily, as it detects the fault in 0.01 s, and at the accurate location. MATLAB/Simulink (DigSILENT Toolbox) is used to establish the underground network for fault location and detection.

In this paper, a proposed electric drive system for a three-phase induction motor is presented. The proposed drive system is suggested for a golf car as one type of electric vehicle. The suggested system consists of three similar single-phase buck–boost converters. Hence, each single-phase buck–boost converter is used as a buck–boost inverter and is used to energize only one phase of the induction motor. The suggested system has the advantage of high reliability, as it can deal with different fault conditions such as battery and motor winding faults. The suggested electric drive system depends on a buck–boost converter which gives variable voltages as well as variable frequencies. Thus, variable speeds of the electric vehicles can be easily achieved. A variable DC voltage (positive or negative) can be achieved at the output of the adopted buck–boost converter, which is considered another advantage of the proposed drive system. This DC voltage can be used to achieve braking of the induction motor used to drive the electric vehicle. Therefore, this advantage can be used instead of ordinary mechanical braking to increase vehicle reliability. To demonstrate our proposed idea, a simulation study is presented. The simulation is carried out using Power Simulation Program (PSIM) software. The simulation study takes into consideration the performance of the adopted buck–boost converter under different conditions to present its advantages. Furthermore, a performance study of the suggested induction motor drive system is carried out under different conditions ranging from healthy to faulty conditions to test system reliability. For more illustration, an experimental prototype of the adopted buck–boost converter is built, and its performance is studied. From all the obtained results, the efficacy of the proposed system is demonstrated.

Respiratory viruses known as coronaviruses infect people and cause death. The multiple crown-like spikes on the virus’s surface give them the name “corona”. The pandemic has resulted in a global health crisis and it is expected that every year we will have to fight against different COVID-19 variants. In this critical situation, the existence of COVID-19 vaccinations provides hope for mankind. Despite severe vaccination campaigns and recommendations from health experts and the government, people have perceptions regarding vaccination risks and share their views and experiences on social media platforms. Social attitudes to these types of vaccinations are influenced by their positive and negative effects. The analysis of such opinions can help to determine social trends and formulate policies to increase vaccination acceptance. This study presents a methodology for sentiment analysis of the global perceptions and perspectives related to COVID-19 vaccinations. The research is performed on five vaccinations that include Sinopharm, Pfizer, Moderna, AstraZeneca, and Sinovac on the Twitter platform extracted using Twitter crawling. To effectively perform this research, tweets datasets are categorized into three groups, i.e., positive, negative and natural. For sentiment classification, different machine learning classifiers are used such as Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). It should be noted that the Decision tree classifier achieves the highest classification performance in all datasets as compared to the other machine learning algorithms. For COVID-19 Vaccine Tweets with Sentiment Annotation (CVSA), the highest accuracy obtained is 93.0%, for the AstraZeneca vaccine dataset 90.94%, for the Pfizer vaccine dataset 91.07%, 88.01% accuracy for the Moderna vaccine dataset, for the Sinovac vaccine dataset 92.8% accuracy, and 93.87% accuracy for the Sinopharm vaccine dataset, respectively. The quantitative comparisons demonstrate that the proposed research achieves better accuracy as compared to state-of-the-art research.

In this paper, the role of the accumulated surface charge on the surfaces of nanoparticles on breakdown strength for liquid and solid dielectrics is presented. The breakdown strengths of a nanofilled liquid dielectric and a solid dielectric are evaluated. The evaluation was conducted considering different nanoparticle material types with different nanofiller loadings. Accordingly, the preparation of transformer oil nanofluid and silicone rubber nanocomposites was performed with different nanofillers of the same average particle size. Breakdown voltage was measured for all the prepared samples, both liquid and solid. The interpretation of the obtained results is presented.

The measurement of a power frequency current including low- and high-order harmonics is of great importance in calibration as well as in testing processes. Therefore, this paper presents the measurement of the power frequency current of light-emitting diode (LED) luminaires. LED luminaires were chosen as their input current includes both low- and high-order harmonics. The measurement process depends on reconstructing an LED luminaire current without using the coil parameters. Hence, the current reconstruction process is designed to be dependent on the measured characteristics of the Rogowski coil itself considering the frequency range at which the measurement process is required. An evaluation of the proposed measurement process was theoretically and experimentally carried out. A theoretical evaluation was carried out using MATALB SIMULINK software. However, the experimental evaluation was performed by building a Rogowski coil to measure the input currents of different LED luminaires having different power ratings of 300 W, 400 W, and 600 W. The currents measured using the Rogowski coil were compared with reference currents measured using a standard measurement technique. The obtained results show the efficacy of the proposed measurement method.