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Liquefaction is one of the natural hazards that occurs due to earthquakes and has a significant impact on the loss of human lives and various civil infrastructures. In this study, metaheuristic ANN with optimization techniques (i.e., ANN-GWO, ANN-GTO, ANN-GAO, ANN-HHO, ANN-SSA, and ANN-SMA), machine learning techniques are used to predict the probability of liquefaction ( P L ) from the SPT-based dataset. A dataset of 834 case histories, including seven geotechnical and seismic parameters, was used for training and testing different metaheuristic algorithms. The performance of the proposed machine learning algorithm used at every stage of analysis includes statistical parameters evaluation, score analysis, actual vs. predicted curve, error matrix, Taylor diagram, OBJ criteria, DDR criteria, and AIC criteria. The ANN-GTO model has been found to be the best model for the prediction of the probability of liquefaction potential of soil. However, all proposed models can successfully predict the liquefaction potential of soil with reasonably good accuracy. The proposed models can be used as a key tool in the prediction of the liquefaction susceptibility of any soil deposit. [ABSTRACT FROM AUTHOR]

This paper fuses ideas from reinforcement learning (RL), Learning from Demonstration (LfD), and Ensemble Learning into a single paradigm. Knowledge from a mixture of control algorithms (experts) are used to constrain the action space of the agent, enabling faster RL refining of a control policy, by avoiding unnecessary explorative actions. Domain‐specific knowledge of each expert is exploited. However, the resulting policy is robust against errors of individual experts, since it is refined by a RL reward function without copying any particular demonstration. Our method has the potential to supplement existing RLfD methods when multiple algorithmic approaches are available to function as experts, specifically in tasks involving continuous action spaces. We illustrate our method in the context of a visual servoing (VS) task, in which a 7‐DoF robot arm is controlled to maintain a desired pose relative to a target object. We explore four methods for bounding the actions of the RL agent during training. These methods include using a hypercube and convex hull with modified loss functions, ignoring actions outside the convex hull, and projecting actions onto the convex hull. We compare the training progress of each method using expert demonstrators, employing one expert demonstrator with the DAgger algorithm, and without using any demonstrators. Our experiments show that using the convex hull with a modified loss function not only accelerates learning but also provides the most optimal solution compared with other approaches. Furthermore, we demonstrate faster VS error convergence while maintaining higher manipulability of the arm, compared with classical image‐based VS, position‐based VS, and hybrid‐decoupled VS. [ABSTRACT FROM AUTHOR]

For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SG's Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers. [ABSTRACT FROM AUTHOR]

Our study introduces an enhanced version of the Importance–Performance Analysis (IPA) method, a powerful tool that can be applied across various domains. This method plays a crucial role in our research, aiding in making well-informed decisions about smart tourism destination attributes. We achieved this by evaluating how 911 consumers from four different generations (Baby Boomers, Generation X, Millennials, and Generation Z) rated these attributes based on their most recent tourist destination visit. Unlike traditional methods that often rely on subjective opinions or complex statistical models, the Improved IPA (IIPA) method offers a clear approach to decision-making. It enables decision-makers to focus on the most crucial attributes that drive consumer interest, thereby optimizing resource allocation and marketing efforts. Specifically, to remain competitive, decision-makers for smart tourist destinations should focus on queuing-time forecast and applications, websites, and content accessible for travelers with disabilities for Baby Boomers; e-complaint handling for Generation X; smart emergency response system for Millennials; and tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard systems, and accessible data about physical design features of accommodation, restaurants, and tourist attractions for Generation Z. Theoretically, this study advances the research on managerial decision-making by demonstrating the effectiveness of the IIPA as a clear and straightforward method for making optimal decisions about product or service attributes. In practice, the study provides decision-makers with valuable insights into the importance of different categories of smart attributes in shaping the overall holiday experience at a tourist destination for Baby Boomers, Generation X, Millennials, and Generation Z tourism consumers. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation. [ABSTRACT FROM AUTHOR]

Abstract The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.

Machine learning techniques are potent tools to predict structural failures and forecast future failure patterns. They can provide insights that are not immediately recognizable to human operators, and hence, enhance the maintenance decision-making process, leading to better safety and longevity of structures. This research employed a genetic algorithm based on an identification technique to estimate the location and depth of cracks in circular arches. The arches were made of homogeneous material and had a rectangular cross-section. The study adopted two different procedures for detecting cracks in the circular arches. The first method used natural frequency contours represented in a three-dimensional plot to identify the location and depth of cracks by intersecting a set of natural frequency contours. The second procedure utilized the regression method and an optimization technique to minimize the cost function objective function and determine the crack parameters. The influences of crack location and depth on the vibrational behavior of cracked arches were presented and discussed. Similarly, the results indicate a high level of consistency between the two techniques in predicting both crack size and location. [ABSTRACT FROM AUTHOR]

Phosphorus (P) use in agriculture has witnessed a global increase, leading to significant environmental problems. Nevertheless, the understanding of P kinetics in saline soils amended with nano‐water treatment residuals (nWTR) remains limited. This study aimed to (1) Investigate the impact of different nWTR addition rates (0%, 0.10%, 0.20%, and 0.50%) on the adsorption‐desorption kinetics of P applied to five soils with different salinity levels (1.47–58.50 dS m−1) using batch adsorption experiments. (2) Using different optimization models via Fit Quadratic Model and principal component analysis to predict the optimal utilization of nWTR. The X‐ray diffraction and Fourier transform infrared patterns proposed that the main mechanisms controlling the process are ligand exchange and precipitation. The results revealed that the adsorption level of P in amended soils was rapid, then decreased gradually until reaching equilibrium after 24 h/25°C. The kinetics data were well described by a pseudo‐second‐order model, suggesting a chemisorption‐dependent adsorption process. Increasing soil salinity and nWTR addition led to decline the phosphorus desorption. The application of 0.5% nWTR decreased P‐desorption from 33.95% to 16.22% in the non‐saline soil and from 18.43% to 10.63% in the highly saline soil. principal component analysis distinguished a positive association between P‐adsorbed and nWTR. The optimization models predicted that applying 0.5% nWTR for 965 min maximizes the P‐adsorption rate, reaching 1041 mg Kg−1 in highly saline‐soils. Therefore, nWTR can serve as a cost‐effective and efficient absorbent for mitigating P mobility and reducing its transport in saline soils. [ABSTRACT FROM AUTHOR]

Mechanical properties play a key role in many applications of optical products. PC and PMMA plastic materials are well known for secondary optics production thanks to their high refractive index. It has been discovered that the majority of plastic materials rarely possess good tensile and impact properties simultaneously. Recently, PMMA and PC have been taken into account to improve tensile yield and impact strength. This paper aims to analyze the effects of PC contents and molding conditions on the mechanical and optical behaviors of injection molded PC/PMMA blends. Experimental tensile yield strength was verified by previous models. The results show that tensile strength and toughness increase with PC composition of PC/PMMA blends, especially when PC content reaches 80wt.%. The tensile strength largely enhances before moving to the breakup point. Furthermore, optical behavior characterized by the illumination intensity of the blends greatly depends on the screw revolution of injection molding process. [ABSTRACT FROM AUTHOR]

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV). [ABSTRACT FROM AUTHOR]

This study presents the frequency control of hybrid deregulated power system. The power system is supplied with appropriate system non-linearity's for practicality. A resilient model predictive control based two degree of freedom proportional integral derivative controller is designed. The Covid-19 based optimization algorithm is applied for optimization purpose. The impact of solar and wind on system dynamics are also examined. Further, the capacitive energy storage is also incorporated to check its influence. The distribution companies' participation matrix changes with market fluctuations, so the matrix is varied to check its impact. Lastly, sensitivity assessment is performed to analyze the strength of proposed controller optimized gains achieved under nominal conditions. [ABSTRACT FROM AUTHOR]

The global energy landscape is in the midst of a transformative shift, compelled by the urgent need to reduce our reliance on fossil fuels and embrace eco-friendly alternatives. Organic photovoltaics (OPVs) have emerged as a promising alternative, offering the distinct advantage of performing well in low-light conditions, including indoor environments. Extensive research and development efforts are dedicated to realizing the full potential of OPVs as adaptable, cost-effective, and environmentally friendly solar energy solutions. This paper conducts a thorough examination of the intricate characterization of organic solar cells, with a specific emphasis on crucial parameters like power conversion efficiency, opencircuit voltage, and fill factor. The study utilizes a single diode model to simulate these cells' behavior, employing a meticulous process for parameter extraction. This method leverages Origin software and Python programming, incorporating open-source packages to ensure robust validation. This systematic and rigorous approach significantly enhances our comprehension of OPVs and plays a substantial role in optimizing their performance. In essence, this research represents a significant step forward in advancing sustainable energy technologies, laying a foundation for a greener and more environmentally conscious future. [ABSTRACT FROM AUTHOR]

The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins.

This survey paper provides a comprehensive overview of integrating Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS) in wireless communication systems. IRS is known as reconfigurable metasurfaces, have emerged as a transformative technology to enhance wireless communication performance by manipulating the propagation environment. This work delves into the fundamental concepts of MIMO and IRS technologies, exploring their benefits and applications. It subsequently investigates the synergies of resource allocation and energy efficiency that emerge when these technologies are combined, elucidating the IRS improved in MIMO systems through signal manipulation and beamforming. Through an in-depth analysis of various techniques and cutting-edge algorithms in resource allocation and energy efficiency can explore the key research areas such as optimization techniques, beamforming strategies and practical implementation consideration. Furthermore, it provides open research directions, individually addressing topics such as limitations of resource allocation and energy efficiency in the MIMO IRS system. This paper offers insights into MIMO-enabled IRS systems challenges and future trends. Through presenting a consolidated view of the current state-of-the-art, this survey underscores their potential to revolutionize wireless communication paradigms, ushering in an era of enhanced connectivity, spectral efficiency and improved coverage.

Open resonant cavity antenna (ORCA) and its recent advances promise attractive features and possible applications, although the designs reported so far are solely based on the classical electromagnetic (EM) theory and general perception of EM circuits. This work explores machine learning (ML)-assisted antenna design techniques aiming to improve and optimize its major radiation parameters over the maximum achievable operating bandwidth. A state-of-the-art method, e.g., parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA) has been exercised upon a reference ORCA geometry revealing a fascinating outcome. This modifies the shape of the cavity which was not predicted by EM-based analysis as well as promising significant improvement in its radiation properties. The PSADEA-generated design has been experimentally verified indicating 3dB-11dB improvement in sidelobe level along with high broadside gain maintained above 17 dBi over the 18.5% impedance bandwidth of the ORCA. The new design has been theoretically interpreted by the theory of geometrical optics (GO). This investigation demonstrates the potential and possibilities of employing artificial intelligence (AI)-based techniques in antenna design where multiple parameters need to be adjusted simultaneously for the best possible performances.

In this millennium, non-wood plants have emerged as a substitute for fibrous substantial attributing to growth in population, industrialization and education. Natural fiber composites are crucial for creating high-performance engineering resources because of their easy accessibility, capacity to be recycled and environmental friendliness. Products from the palm are important and cost-effective for both civic and pastoral locations. The palm fruit cluster, leaf trunks and leaves are utilized domestically and parts are thrown away as waste. This palm waste product is used as a raw substantial for reinforcement in polymer composites as well as a possible source for natural fibers. An overview of the advancements made in the field of Arecaceae (palm) family-based fiber-reinforced composites is presented including information on their current market, matrix combinations, physical qualities, environmental implications, manufacturing methods, chemical processing and acoustic features. In order to optimize the utilization of this bio-based substance and ensure its bright future, a number of important challenges are explored along with ideas for further investigation. The discussion of the impact of palm fibers on final products and the literature analysis highlight additive manufacturing and machine learning-based optimization methods for natural fiber composites (NFC).

Wireless communication technologies are playing a very important role in the modern world. The use of multiple antennas with orthogonal frequency division multiple access (MIMO-OFDM) has enhanced spectral efficiency and coverage area. The transmission channel in MIMO-OFDM systems changes with time, creating Inter Symbol Interference (ISI) and Channel Impulse Response (CIR). Modern MIMO-OFDM systems have a high Peak-to-Average Power Ratio (PAPR). Recently, numerous strategies have been developed to minimize PAPR by increasing phase factors. Due to nonlinear effects, high PAPR causes system deterioration. In this paper, we propose hybrid optimal MMSE-MLSE equalizers to enhance PAPR performance in MIMO-OFDM system. First, we introduce hybrid channel equalizer called MMSE and MLSE at the receiver end to improve the performance and to achieve a maximum diversity order. After that, we develop an Adaptive Fruit fly based Deep Neural Network (AF-DNN) by Adaptive Fruit fly Optimization (FOA) to optimally select the equalizer by using the design metrics. Finally, the performance of proposed PAPR reduction using hybrid optimal MMSE-MLSE equalizer is evaluated and their simulation results are compared with the existing state-of-art PAPR reduction techniques in terms of data rate loss, PAPR, BLER, BER and SER. [ABSTRACT FROM AUTHOR]

This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models' deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification. [ABSTRACT FROM AUTHOR]

This paper provides a concise overview of research and techniques aimed at enhancing the performance of wind power systems, with a focus on mitigating power quality concerns. Although wind energy has become a more substantial component of the world's energy mix, there are still concerns because wind resources are naturally variable, especially when it comes to electricity quality. The paper highlights the potential of wind energy in reducing greenhouse gas emissions and meeting rising electricity demand. In order to improve overall effectiveness in wind power generation, the study emphasizes optimization methodologies and emphasizes the significance of control systems for wind turbines in maximizing power output, adjusting to changing wind conditions, and addressing power quality issues. A grid connected system incorporating the wind power system network along with D STATCOM is modelled with proportional-integral (PI) controller and detailed comparative total harmonic distortion (THD) analysis has been done for various faults. The STATCOM is tuned with genetic algorithms (GA), ant colony optimization (ACO), and harmony search (HS) based PI controller as a control device to achieve the desired power quality. Leveraging ACO for mitigating THD in wind systems under fault conditions holds promise for substantial enhancements in power quality. This approach has the potential to safeguard equipment, improve system reliability, and contribute to overall performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) refers to the vast network of interconnected devices that collect, exchange, and analyze data, enabling automation and improved efficiency in various domains. However, the rapid growth of IoT also raises significant security concerns. With the unlimited number of devices connected to the internet, the IoT ecosystem becomes a prime target for cyberattacks, threatening the privacy, integrity, and availability of data, decentralization as well as the safety of individuals and critical infrastructure. The challenges mentioned above are inherent attributes of a novel and emerging technology, blockchain. The need to address challenges in the realm of IoT has led to the combination of it with Blockchain technology, prompting scholars to explore the most effective ways to leverage blockchain's capabilities in this context. The ubiquitous implementation of the IoT requires a uniform and resilient framework that can ensure security, compliance with data retention regulations, and efficacy in terms of commercial viability. The Lightweight Secure Blockchain (LSB) is among the various blockchain variations and arrangements created to address these requirements. This research focuses on improving security and performance by implementing various measures, including encryption of transaction delivery, enhancement of transaction flow, verification of blocks, optimization of hash rate, and improvement of hash quality. For the improved effectiveness of the research conducted in the field of IoT, an empirical evaluation was carried out using data collected from the Vainu Bappu Observatory (Indian Institute of Astro Physics, Kavalur) and the Temperature sensor. The study showcases achievements by implementing and comparing outcomes with the standard LSB. The utilization of the hybrid Edward Curve El Gamal approach in transaction encryption has been found to improve the security of transmitted transactions. [ABSTRACT FROM AUTHOR]

The ram pressure is lower than the total pressure of the cooling fan in stationary or low-speed vehicles, and the heat is typically managed by the speed of the cooling fan or opening the enclosure. However, the long-term noise exposure to the working space can be a problem due to stationary or low speed operation. In this paper, the opening search technique for the enclosure using the adjoint method has been proposed to solve the dilemma between heat management and noise control. [ABSTRACT FROM AUTHOR]

COVID-19 caused a global public disaster as well as an economic crisis, and other challenges. The fifth-generation network, or 5G, connects practically every machine, person, and thing on the planet. We can analyse the public's opinions and sentiments connected to COVID-19 from 5G user-generated content on social media, which will eventually aid in promoting health intervention strategies and designing successful projects based on public perceptions. The BERT language model is first used to preprocess data that has been obtained from Sina Weibo. Following that, the features of the preprocessed data are chosen using a class-wise information technique. Finally, a capsule network (CapsNet) approach is used to identify the 5G user perception and experience optimization. Dynamic routing algorithm is used for optimizing the capsule network. By comparing the suggested framework's performance with certain existing approaches, its effectiveness is evaluated. Simulation results show that the proposed method is more accurate than previous approaches at identifying 5G user experiences. [ABSTRACT FROM AUTHOR]

This study investigates the complex dynamics of a viscoelastic fluid subjected to magneto-hydrodynamics over a stretching sheet, incorporating the Cattaneo–Christov heat flux model. This model is especially advantageous for explaining heat transfer in materials possessing significant thermal conductivity, where the conventional Fourier’s law might not be precise. The investigation revolves around evaluating how the thermal relaxation time affects the boundary layer and how both thermal radiation and viscous dissipation influence the thermal field. The significance of this research lies in its contribution to understanding the intricate behavior of such fluids in the presence of magnetic fields and non-Fourier heat conduction. The primary objective is to analyze the impact of viscoelasticity, magnetohydrodynamics, and Cattaneo–Christov heat flux on the flow and heat transfer characteristics over the stretching sheet. The research methodology involves the application of mathematical models and numerical techniques, particularly the use of the shifted Chebyshev polynomials of the sixth-order approximation and spectral collocation technique. The major conclusion of the study underscores the significant influence of viscoelasticity, magnetic field, and Cattaneo–Christov heat flux on the transport properties of the fluid, providing valuable insights for applications in various engineering and industrial contexts. Certain notable results arising from the current issue indicate that heat transfer is more pronounced for the viscoelastic factor and magnetic parameter, whereas the thermal relaxation parameter exhibits the opposite trend. In addition, the inclusion of the Cattaneo–Christov term enhances our comprehension of thermal behavior.

Our study introduces an enhanced version of the Importance–Performance Analysis (IPA) method, a powerful tool that can be applied across various domains. This method plays a crucial role in our research, aiding in making well-informed decisions about smart tourism destination attributes. We achieved this by evaluating how 911 consumers from four different generations (Baby Boomers, Generation X, Millennials, and Generation Z) rated these attributes based on their most recent tourist destination visit. Unlike traditional methods that often rely on subjective opinions or complex statistical models, the Improved IPA (IIPA) method offers a clear approach to decision-making. It enables decision-makers to focus on the most crucial attributes that drive consumer interest, thereby optimizing resource allocation and marketing efforts. Specifically, to remain competitive, decision-makers for smart tourist destinations should focus on queuing-time forecast and applications, websites, and content accessible for travelers with disabilities for Baby Boomers; e-complaint handling for Generation X; smart emergency response system for Millennials; and tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard systems, and accessible data about physical design features of accommodation, restaurants, and tourist attractions for Generation Z. Theoretically, this study advances the research on managerial decision-making by demonstrating the effectiveness of the IIPA as a clear and straightforward method for making optimal decisions about product or service attributes. In practice, the study provides decision-makers with valuable insights into the importance of different categories of smart attributes in shaping the overall holiday experience at a tourist destination for Baby Boomers, Generation X, Millennials, and Generation Z tourism consumers.

This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization problem consists of an objective area profile subject to certain constraints and an objective function that quantitatively calculates the design level. Designers must formulate suitable optimization problems to achieve an optimal design. However, because a method to alleviate the influence of the sliver section is not yet available, the optimization problem is typically solved based on experience, which is time- and effort-intensive. Consequently, a more practical and objective grain design method must be developed. In this study, an optimal design method using machine learning was developed to increase the convenience and success rate. A support vector machine was used to train a classification model that predicts a class. The classification model was used to alleviate the influence of the sliver zone and correct the search problem to ensure that an optimal solution existed in the region satisfying the requirements. The proposed method was validated through star grain optimal design using the genetic algorithm. The optimization was performed considering the area profiles, and the effectiveness of the proposed method was demonstrated by the enhanced accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8–15% and throughput by 5–10%. [ABSTRACT FROM AUTHOR]

In regards to present power system setup, this article presents a frequency stabilization of hybrid power system (HPS) assimilated with distributed generation (DG) resources, conventional thermal, geo‐thermal and electric vehicle (EV). The insertion of appropriate system non‐linearities in addition to the intermittency in renewable based generation sources leads to a more practicality into the developed HPS. For such systems, a vigorous control action with swift responding controllable device are an instantaneous requirement. In this view, a cascaded fractional order proportional‐derivative (foPD)‐multi‐stage fractional order proportional integral derivative (MSfoPID) (cascaded foPD‐MSfoPID) as secondary controller is explored for frequency stabilization of HPS. Further, the established power system is provided with erratic load deviations. The influence of EVs on frequency and tie‐line power fluctuations are examined comprehensively. Moreover, the outcome of unified power flow controller (UPFC) device as flexible AC transmission system (FACT) device for system dynamic control are illustrated. Further, sensitivity examination is exhibited to examine the resiliency of proposed cascaded foPD‐MSfoPID controller parameters for deviances in solar irradiance and number of vehicles. Finally, a proposed secondary controller is tested in case studies on standard IEEE‐39 bus system and four‐area hybrid power system. [ABSTRACT FROM AUTHOR]

Automated recognition or categorization of fruits or crops through image processing technology is a highly sought-after technique in agriculture. This approach has been demonstrated to outperform manual monitoring in humans. It reduces the time and expenses incurred by farmers and ensures that end users receive high-quality products. This study details the process for identifying dry beans. We employed a support vector machine (SVM) model and three optimization techniques: Bayesian optimization, random search optimization, and grid search optimization. This study aimed to determine the optimal optimization technique for identifying dry beans using an SVM model. Various metrics, including the validation accuracy, test accuracy, total validation cost, total test cost, and minimum classification error, were computed and analyzed to compare the performance of each optimization method. In comparison, Bayesian optimization was determined to be the most effective optimization technique for identifying dry beans, with a validation accuracy of 93.06%, test accuracy of 92.29%, and minimum classification error of 0.069384. [ABSTRACT FROM AUTHOR]

The aerodynamic configuration of large swept-back tri-wings is generally adopted for hypersonic vehicles, but the structural stiffness of the ailerons is weak, which may lead to damage due to the flutter behavior. In the initial stage of structural design, studying the flutter characteristics of tri-wing flight vehicles is necessary and can provide the stiffness index of the tri-wing structural design. To assess the flutter characteristics of tri-wing flight vehicles efficiently, a rapid modeling technique of the finite element method was used in this paper. For the structural scheme of large swept-back tri-wing flight vehicles, a structural dynamic model was modeled using the rapid modeling technique, the unsteady aerodynamic was computed using the double-lattice method, and the flutter characteristics were analyzed using the P-K method. Variable parametric studies were conducted to evaluate the effects of the stiffness of the aileron skin, the stiffness of the control mechanism, and the mass distribution of the aileron on the flutter characteristics of large swept-back tri-wing flight vehicles. The results showed that the key flutter coupling modes of such vehicles are symmetric and anti-symmetric combinations of aileron rotation and torsion. Additionally, optimizing the control mechanism stiffness and mass distribution of the aileron could improve the flutter boundary, which can be helpful in the structural design of such vehicles. The flutter optimization technique effectively improved the flutter boundary, significantly enlarged the flight envelope, and accurately provided the stiffness index for the structural design of large swept-back tri-wing flight vehicles. [ABSTRACT FROM AUTHOR]

Research on using energy storage devices in ships is actively underway globally to reduce marine environmental pollution. This is due to the fact that existing internal combustion engines emit CO2 continuously. In this study, we investigated a control parameter optimization technique to reduce overshoot during the CC charging of lithium-ion batteries, which are applicable as energy storage devices for ships. We modeled a synchronous buck converter, a lithium-ion battery, and a PID controller using MATLAB/Simulink. Then, we performed a search for the optimal parameters of the PID controller and conducted simulations by varying the state of charge (SOC) values of the lithium-ion battery to 50, 70, and 80.We compared the simulation results of the proposed control method with those of the existing Ziegler-Nichols PID controller, and confirmed that the proposed method effectively suppressed 0.5 A overshoot during the charging of the lithium-ion battery. [ABSTRACT FROM AUTHOR]

Optimal coordination of overcurrent relays has become a major challenge in power distribution systems due to the increasing participation of distributed generation (DG) and electric vehicle (EV) charging stations, especially vehicle-to-grid (V2G) technology. This paper proposes a novel approach for optimal coordination of directional overcurrent relays (DOCRs) to achieve the minimum operating time and obtain optimal tuning in terms of time dial setting (TDS) and pick-up current (IP) considering V2G integration. The optimization was conducted using three different Artificial Intelligence (AI) techniques: grey wolf optimization (GWO), cuckoo search (CS), and Hunger Games Search (HGS), and was performed on two systems: IEEE 33-bus system and a local grid located in El-Shorouk City–district#8 (ShC-D8), Cairo, Egypt. The proposed methodology is constructed and validated on ETAP and MATLAB software packages. The simulation results demonstrate that the proposed optimization techniques provide high dynamic performance and accurate coordination of DOCRs. The results show that HGS achieves the minimum operating time of all overcurrent relays as compared with GWO and CS. The effect of V2G integration on the coordination methodology is also considered. The proposed approach provides a significant contribution to the field of power system protection and can help improve the reliability and efficiency of power distribution systems with V2G integration.

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies.

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

The rapid growth and development of AI-based applications introduce a wide range of deep and transfer learning model architectures. Selecting an optimal optimizer is still challenging to improve any classification type's performance efficiency and accuracy. This paper proposes an intelligent optimizer selection technique using a new search algorithm to overcome this difficulty. A dataset used in this work was collected and customized for controlling and monitoring roads, especially when emergency vehicles are approaching. In this regard, several deep and transfer learning models have been compared for accurate detection and classification. Furthermore, DenseNet201 layers are frizzed to choose the perfect optimizer. The main goal is to improve the performance accuracy of emergency car classification by performing the test of various optimization methods, including (Adam, Adamax, Nadam, and RMSprob). The evaluation metrics utilized for the model’s comparison with other deep learning techniques are based on classification accuracy, precision, recall, and F1-Score. Test results show that the proposed selection-based optimizer increased classification accuracy and reached 98.84%.

The main goal of the research is to investigate the effects of temperature and concentration slip on the MHD Casson–Williamson nanofluid flow through a porous sheet, with a focus on viscous dissipation. This research has a novel approach, as it seeks to incorporate various factors that have not been previously studied in this context. Moreover, an inclined magnetic field from the outside is applied to the stretched surface. Besides this, it is considered that the viscosity of nanofluids depends on temperature while all other physical characteristics are taken to be constant. It is hypothesized that the expanding surface that stretched exponentially is what caused the flow motions of the nanofluid. There are several engineering applications for this type of study, which is based on MHD non-Newtonian nanofluid flow across a stretched surface with heat generation and viscous dissipation. They include solar energy storage, energy distribution, chemical reactors, and the manufacturing of polymers. To examine the heat transmission and mass transfer rates, tools like thermophoresis and Brownian motion were implemented. The flow problem is formally represented as a set of nonlinear PDEs that are then, through the use of dimensionless variables, converted into a set of ODEs. To numerically obtain the solution to the problem, the shifted Chebyshev polynomials of the third-kind approximation along with the spectral collocation technique are utilized. This process transforms the existing model into an algebraic equation system that was created as a restricted optimization problem, which is then optimized to obtain the solution and the unknown coefficients. For a variety of pertinent parameter values, the graphic and tabular representations of the concentration, temperature, velocities, rate of heat mass transfer, and shear stresses of nano-fluids at the surface of the sheet are shown. Finally, there is a remarkable amount of agreement between the earlier investigation and the comparison research that was conducted. The findings suggest that higher values of the magnetic parameter, Casson parameter, viscosity parameter, and suction parameter lead to a decrease in both velocity and boundary layer thickness. Furthermore, an increase in the viscosity parameter, Casson parameter, and magnetic parameter results in elevated values for both temperature and concentration.