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This paper investigates a lab-on-disk biosensor with the aim of improving its performance by optimizing the particle swarm algorithm through the application of Box-Behnken Design (BBD). The study concluded that the optimal conditions for the Particle Swarm Optimization (PSO) parameters - social learning factor (c $_{1} =0.5$ ), cognitive acceleration factor (c $_{2} =2$ ), inertia weight (w =0.65), and swarm size (Ps =176) - resulted in a significant improvement in prediction accuracy, as evidenced by an R-squared value of 99.9% and a low RMSE of 0.05. The results demonstrate the exceptional effectiveness of Box-Behnken Design (BBD) in optimizing PSO parameters for Artificial Neural Networks (ANNs), resulting in improved performance of the lab-on-disk biosensor. These optimized conditions not only improve response time, but also hold potential for broader applications in microfluidic sensing technologies.

Ranking circular Pythagorean fuzzy sets using distance-based techniques involves calculating the distance between a circular Pythagorean fuzzy set and a reference point that represents either maximum (positive ideal solution) or minimum (negative ideal solution) values. Theoretical design is a major link in the procedure of complex product design, and it is most valuable and dominant to choose the appropriate design scheme, however, there are various kinds and inaccuracies of the evaluation information, and there is a problem of mutual influence among the evaluation criteria, which leads to unreliable decision-making of the optimal solution. In order to evaluate these problems, we concentrate on designing the model of power average/geometric operators based on Schweizer-Sklar operational laws based on the technique of circular Pythagorean fuzzy values. For this, first, we compute the model of Schweizer-Sklar operational laws for circular Pythagorean fuzzy valuable, and then we derive the model of circular Pythagorean fuzzy Schweizer-Sklar power averaging operator, circular Pythagorean fuzzy Schweizer-Sklar power weighted averaging operator, circular Pythagorean fuzzy Schweizer-Sklar power geometric operator, and circular Pythagorean fuzzy Schweizer-Sklar power weighted geometric operator for both t-norm and t-conorm. For the above operators, we also simplify the model of idempotency, monotonicity, and boundedness. Further, we construct the technique of Evaluation Based on the Distance from the Average Solution method based on initiated operators. Additionally, we develop three different procedures for evaluating the problem of transportation for linear programming with the help of a multi-attribute decision-making problem based on the Evaluation Based on Distance from Average Solution method, based on averaging operators, and based on geometric operators. In a sensitive analysis, we compare the proposed techniques with various extant

Static eccentricity fault (SEF) primarily occurs due to external mechanical shocks and incorrect manufacturing processes. In the case of SEF condition, vibration and noise in the electric motor are generated due to unbalanced magnetic forces. Additionally, SEF conditions lead to an increase in the iron loss of motor due to the unbalanced air gap flux density. As iron loss increases, the temperature of the main part of the motor increases, which has a negative effect. Therefore, this paper presents a robust design optimization model where the characteristics of a six-phase interior permanent magnet synchronous motor (IPMSM) do not change drastically even when SEF occurs. First, magnetic flux density, iron loss, and efficiency according to the rotor shape are analyzed using the finite element method (FEM). Second, under identical stator structure conditions, characteristics derived from the finite element method (FEM) are verified through experiments analyzing different rotor shapes. Finally, robust design optimization is performed to ensure that the motor characteristics do not change drastically even when SEF occurs.

The issue of power quality (PQ) has become increasingly critical in modern power systems due to the growing integration of sensitive electronic devices, electric vehicles, and renewable energy sources. Among the various PQ concerns, harmonics have emerged as a significant contributor to the overheating of power system components. This overheating phenomenon can have detrimental effects, including reduced equipment lifespan, increased maintenance costs, and unexpected failures. This comprehensive review paper provides an in-depth examination of the impact of harmonics on power system components, with a focus on the overheating problems they induce. The paper delves into an analysis of PQ indices and standards, underscoring the necessity for efficient mitigation techniques to address harmonic-related overheating and other PQ concerns. The review explores the key PQ devices employed to mitigate harmonics, such as passive power filters, active power filters, and advanced devices like D-STATCOM, unified PQ conditioners, and dynamic voltage restorers. Furthermore, it investigates the various control algorithms and optimization techniques utilized in the design of these power filters, emphasizing their pivotal role in addressing harmonics and preventing overheating. It also discusses the applicability of artificial intelligence techniques in mitigating power harmonics and improving PQ in modern power grid studies.

Optimal management of the electrical power system (EPS) in low Earth orbit satellites requires the design of dedicated algorithms. In this work, a power flow supervisor is proposed by defining decision strategies based on a dynamic EPS model that distinguishes between different operating modes. This approach ensures the primary constraint of load service is met while optimizing battery charging/discharging, and efficiently tracking the maximum power point of photovoltaic panels. The transitions among different modes are governed by exogenous inputs, load power demands, and battery state of charge, providing flexibility and adaptability to varying conditions. In each mode the supervisor determines the duty cycle of the DC-DC buck converter used as the interface between the photovoltaic panels and the battery on the unregulated bus and the current to be absorbed by a bleeder circuit connected in parallel to the battery. The overall design of the EPS supervisor was obtained as the solution of a two-level optimization procedure. Indeed, fine-tuning of the parameters are obtained as the solution of a mathematical problem and then an optimization approach was used to determine the optimal sizing of the battery and photovoltaic panels. The simulation results demonstrated the effectiveness of the optimization strategy.

Multi-target regression has gained popularity owing to its ability to predict multiple outcomes simultaneously, with improved performance over single-target methods. Unlike traditional single-response prediction, multi-target regression faces the additional complexity of managing interdependencies among target variables, which in turn accounts for the relationship between all targets and input-output. In this study, we devised a linear convex multi-target regression (LC-MTR) that simultaneously models and learns the linear convex feature of the output structure that establishes predictor-response and, inter-target correlations. The LC-MTR is designed to capture both the correlations and input-output dependencies among the targets. A novel multi-target optimization technique for large and sparse matrices leverages singular value decomposition (SVD) with the Schur complement, which reduces the computational time associated with estimating weighted regression coefficients in multi-target settings. This optimization technique significantly enhances the training and test performance by ensuring that the learned models are better at capturing complex interlying target patterns. Empirical evaluations demonstrate that LC-MTR outperforms with existing regression methodologies on various real time datasets.

Effective real-time energy management strategies are crucial for optimising hybrid power plants, particularly when challenged with integrating Renewable Energy Sources (RESs) and managing their intermittent nature. This paper presents a comprehensive energy management framework holding real-time optimisation for HPP. The practical implications of this research are significant, as it provides a roadmap for seamlessly integrating RESs with Battery Energy Storage Systems (BESSs) in Hybrid Power Plants (HPPs) to minimise cost while meeting daily household energy demands. Furthermore, it demonstrates how diesel generators (DGs) can be incorporated into the HPP’s energy management system while minimising carbon emissions. An Energy Dispatch Engine (EDE) is introduced to control HPPs that combine PV, BESS, DG and Pumped Hydro Storage (PHS). Two optimisation approaches are used, namely, Mixed-Integer Linear Programming (MILP) and Stochastic Dual Dynamic Programming (SDDP). The system leverages load and RES power data while considering State-of-Charge (SoC) constraints to manage battery health proactively. Optimising discharge and charge profiles of the BESS, with the overarching goal of minimising the total cost of satisfying daily load demand, is an objective. Various tariff schemes were explored to assess the presented EDE. Our testing demonstrates that the SDDP approach consistently results in lower total costs than MILP. The total cost for the MILP method, where the system with PHS incurs higher costs (219.8 ${\$}$ /24h) than the total cost for the SDDP method, where the system with PHS system (180 ${\$}$ /24h). The cost of CO2 emissions was found to be lower in the case of SDDP, amounting to 8.3 ${\$}$ /24h for a total emission of 160 kg. In contrast, the MILP approach resulted in a higher CO2 cost of 10.2 ${\$}$ /24h for a total emission of 200 kg. This suggests that SDDP is more cost-effective in terms of reducing CO2 emissions.

A “partial ordering” is a way to heuristically order a set of examples (partial orderings are a set where, for certain pairs of elements, one precedes the other). While these orderings may only be approximate, they can be useful for guiding a search towards better regions of the data. To illustrate the value of that technique, this paper presents iSNEAK, an incremental human-in-the-loop AI problem solver. iSNEAK uses partial orderings and feedback from humans to prune the space of options. Further, in experiments with a dozen software models of increasing size and complexity (with up to 10,000 variables), iSNEAK only asked a handful of questions to return human-acceptable solutions that outperformed the prior state-of-the-art. We propose the use of partial orderings and tools like iSNEAK to solve the information overload problem where human experts grow fatigued and make mistakes when they are asked too many questions. iSNEAK mitigates the information overload problem since it allows humans to explore complex problem spaces in far less time, with far less effort.

Selective harmonic elimination techniques have developed a crucial feature in the domain of power electronics and in innumerable drives control schemes. The main intention of this review paper is to make the readers delve into the numerous control methods employed in SHE techniques, formulations and its applications, challenges faced in grid and the allowable standards of harmonics provided by various standards like IEEE and IEC. By exploring the mathematical formulations, historical development of SHE and comparison of numerous optimization algorithms as well as SHE applications in numerous sectors, this review attempts to impart a complete understanding of the importance of SHE in electrical engineering. Furthermore, this work will also analyze the existing challenges met by SHE methods and it emphasizes the potential future target for exploration and advancement in this field.

Doors are part of the building infrastructure that mobile robots have to pass through to reach zones on the other side. If robots were to clear these obstacles, they would require human assistance, advanced end-effectors, and complex control systems, making it challenging for robots. Therefore, a robot deployed in an environment should be capable of minimizing the passing through doors as well as path distance to improve overall efficiency. This paper proposes a novel Door-Density-Aware (DDA) path planning method. A vision-based door-detecting framework based on YOLOv8 has been developed to tag the door locations in a robot’s navigation map. The proposed DDA path planner uses a door-tagged map to plan an efficient path considering the cost of moving through doors and the path distance. Genetic Algorithm (GA) and Gray Wolf Optimization (GWO) have been considered for solving this optimization problem. According to the experimental results, the proposed method can effectively detect and tag doors in the navigation map and plan efficient paths. In summary, the proposed DDA path planner with GA outperformed other approaches, achieving cost reductions of 66%, 34%, 49%, and 60% compared to random selection, DDA with GWO, GA minimizing only distance, and GWO minimizing only distance, respectively.

Identifying Ordinary Differential Equations (ODEs) from measurement data requires both fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data. The Sparse Identification of Nonlinear Dynamics (SINDy) method does so in two steps: a derivative estimation and smoothing step and a sparse regression step on a library of ODE terms. Previously, the derivative step in SINDy and its python package, pysindy, used finite difference, L1 total variation minimization, or local filters like Savitzky-Golay. We have incorporated Kalman smoothing, along with hyperparameter optimization, into the existing pysindy architecture, allowing for rapid adoption of the method. Kalman smoothing is a classical framework for assimilating the measurement data with known noise statistics. As a first SINDy step, it denoises the data by applying a prior belief that the system is an instance of integrated Brownian motion. We conduct numerical experiments on eight dynamical systems show Kalman smoothing to be the best SINDy differentiation/smoothing option in the presence of noise on four of those systems, and tied for three of them. It has particular advantage at preserving problem structure in simulation. The addition of hyperparameter optimization further makes it the most amenable method for generic data. In doing so, it is the first SINDy method for noisy data that requires only a single hyperparameter, and it gives viable results in half of the systems we test.

In the face of escalating climate challenges, environmental sustainability has greatly become an urgent and non-negotiable priority, necessitating revolutionary advancements in energy management to reduce carbon footprints and drive profound improvements in overall sustainability. This paper presents an advanced optimization framework, PST-CESS, for managing power-sharing among multiple tenants within the centralized energy storage system (ESS). Our thorough evaluation demonstrates that the centralized ESS facilitated by PST-CESS substantially exceeds the performance of individualized ESS systems in pivotal areas such as peak load reduction, variability mitigation, and financial profitability. Specifically, the centralized ESS model achieves up to a 44.05% reduction in annual peak load for certain tenants and reduces electricity consumption variability by up to 57.67%. From a financial perspective, the centralized ESS model delivers remarkable advantages, reaching a break-even point in just 2.48 years, compared to the 5.08 years required for individualized ESS systems, even when accounting for battery capacity loss costs, also known as battery degradation costs. These results highlight the centralized ESS approach as a more economically advantageous and efficient solution, providing superior financial returns and optimized energy management for multi-tenant buildings. The strategic benefits and compelling evidence presented in this study strongly support the widespread adoption of centralized ESS models to maximize both economic and environmental benefits, establishing a new standard for sustainable energy management.

Complex optimization problems, especially those encountered in real-life scenarios, pose significant challenges due to their multifaceted nature and the involvement of numerous variables. In such contexts, the application of intelligent optimization algorithms emerges as a valuable tool for effectively tackling these intricate problems. Firefly Algorithm (FA) is a popular meta-heuristic algorithm for continuous domain and lacks application in discrete domain. While there are a few applications but hardly on combinatorial optimization problems. Combinatorial optimization problem, which consist of selecting an optimal object from a finite number of objects is a challenging domain. In this study, a novel discrete version of stepping ahead FA together with its hybridization with another algorithm are proposed to solve the Multidimensional Knapsack Problem (MKP). The proposed algorithms are called discrete stepping ahead Firefly Algorithm (FA-Step) and hybridization of discrete stepping ahead Firefly Algorithm with Covariance Matrix Adaptation Evolution Strategy (FA-CMAES). The proposed algorithms make full use of the problem-solving expertise while also incorporating diversity to improve exploitation with stepping ahead mechanism and preference operator. The proposed algorithms are tested on 38 well-known knapsack instances and compared with some novel works from the literature. The proposed methods allow researchers to utilize discretization techniques in other state-of-the-art techniques to solve discrete domain problems with ease.

This paper addresses the critical issue of efficient power management and hydrogen consumption in DC microgrids powered by fuel cells. A novel energy management strategy involving a State Machine control (SMC) combined with a Fractional Order Proportional-Integral-Derivative (FO-PID) controller, optimized using the Grey Wolf Optimization (GWO) Algorithm (GWO-SMC-FPID), has been proposed. The key advantage of this approach lies in its enhanced controller stability and efficiency compared to traditional methods. The efficacy of the proposed system has been demonstrated through several performance metrics: hydrogen consumption ( $Fu\_H_{2}Com$ ) is reduced to 250 g, overall system efficiency ( $\eta $ ) is improved to 83.53%, battery state of charge ( $SoC_{batt}$ ) and supercapacitor state of charge ( $SoC_{sup}$ ) are effectively managed. Additionally, a novel methodology for assessing the stress on each energy source using instantaneous power analysis has been introduced, which is critical for extending the lifecycle of the system components. Comprehensive simulations validate the robustness and reliability of the proposed methodology, showcasing significant improvements over classical state machine and traditional PID controllers in terms of hydrogen consumption, efficiency, and stress management. This work significantly advances the field of energy management in DC microgrids by introducing an innovative, optimized strategy for improving system performance and resource utilization.

We propose a three-stage stochastic programming model to inform risk-averse investment in power system resilience to winter storms. The first stage pertains to long-term investment in generator winterization and mobile battery energy storage system (MBESS) resources, the second stage to MBESS deployment prior to an imminent storm, and the third stage to operational response. Serving as a forecast update, an imminent winter storm’s severity is assumed to be known at the time the deployment decisions are made. We incorporate conditional value-at-risk (CVaR) as the risk measure in the objective function to target loss, represented in our model by unserved energy, experienced during high-impact, low-frequency events. We apply the model to a Texas-focused case study based on the ACTIVS 2000-bus synthetic grid with winter storm scenarios generated using historical Winter Storm Uri data. Results demonstrate how the optimal investments are affected by parameters like cost and risk aversion, and also how effectively using CVaR as a risk measure mitigates the outcomes in the tail of the loss distribution over the winter storm impact uncertainty.

Mean curvature is fundamentally important for various research fields, including differential geometry in mathematics, cell membrane modeling in biophysics and image processing in computer vision. However, its optimization is a challenging task. Conventionally, its optimization can be carried out by Partial Differential Equations (mean curvature flows) or solved by Augmented Lagrangian Methods. These approaches are numerically slow because they have to satisfy several numerical stability constraints. To tackle this issue, in this paper, we propose to use deep neural networks for the mean curvature optimization. Our method has several advantages. First, the proposed network has only 0.046M parameters, leading to a compact model. Second, the proposed network can be trained on low resolution images but applied on any resolution images. Third, it is flexible in the loss function design and thus can be used as a generic optimization method. To numerically valid the effectiveness and efficiency of the proposed method, several experiments are conducted. The results confirm that the proposed method achieves lower energy than traditional methods and thus establishes a new the-state-of-the-art for mean curvature optimization. The proposed method can be applied in a large range applications, where mean curvature optimization is involved.

This paper presents the design and implementation of a compact, wearable, printed monopole antenna for Long-Range (LoRa) location tracking applications, covering the 915 MHz and 923 MHz LoRa bands. Fabricated on a Rogers Duroid RO3003TM substrate, the antenna integrates meandered monopoles to enable miniaturization, alongside a matching stub and a partial ground plane for enhanced impedance matching and performance efficiency. It features a bidirectional radiation pattern in the E-plane and an omnidirectional pattern in the H-plane at both frequencies, achieving a peak gain of 2 dBi and a radiation efficiency of 98% across its operational bands. With a total dimension of $50 \times 50~\text {mm}^{2}$ ( $0.153~\lambda _{0} \times 0.153~\lambda _{0}$ ), where $\lambda _{0}$ represents the free-space wavelength at 915 MHz), it represents the most compact LoRa wearable antenna known to the authors. The design, simulation, and analysis were carried out using CST Microwave Studio (MWS) software. Bending investigations showed excellent efficiency with minimal impact on bandwidth and gain, while Specific Absorption Rate (SAR) analysis indicated compliance with safety limits set by the Federal Communications Commission (FCC) and International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards. SAR values at 915 MHz were measured at 0.94 W/kg for 1 gram of tissue and 0.85 W/kg for 10 grams, affirming the antenna’s suitability for wearable applications. Correspondingly, at 923 MHz, SAR values stood at 0.92 W/kg for 1 gram of tissue and 0.82 W/kg for 10 grams of tissue. Furthermore, real-world experimental validation employing the proposed antenna within a wearable LoRa tracking system revealed superior performance in LoRa localization. The average improvement in Received Signal Strength Indicator (RSSI) of 3 dBm, compared to a conventional antenna, was observed within a range of 1.5 km. This comprehensive assessment solidifies the proposed antenna’s potential as a promising and dependable solution for efficient and reliable LoRa-based location tracking across various scenarios.

This paper presents a modified bonobo optimizer (MBO) that integrates the Gaussian local mutation, restart strategy, and random contraction strategy into the original bonobo optimizer (BO). BO, inspired by the unique reproductive schemes and fission-fusion social behaviors of bonobos, has previously demonstrated promising results in solving a range of optimization problems. With the new modifications, MBO seeks to improve exploration and exploitation abilities, achieving enhanced convergence speed and solution quality. The Gaussian local mutation aids in fine-tuning solutions by introducing localized variations, the restart strategy provides a mechanism to escape potential local optima, while the random contraction strategy ensures better global search capabilities. The enhanced MBO’s performance is critically assessed on the 10 and 100-dimensional CEC 2017 and 10 and 20-dimensional CEC 2022 benchmark suites, along with seven engineering optimization problems, including cantilever beam design, industrial refrigeration system design, welded beam design, speed reducer design, pressure vessel design, multi-product batch plant design, and three-bar truss design. The MBO algorithm exhibits significant improvements in optimization performance, evidenced by highly significant p-values (as low as 1.25E-11) in the Wilcoxon’s Signed Rank Test. Preliminary results indicate that the MBO exhibits a marked improvement in both solution accuracy and robustness over its predecessor and other state-of-the-art optimization algorithms such as original bonobo optimizer, sand cat swarm optimization, Chernobyl disaster optimizer, driving training-based optimization, Harris hawk optimizer, Archimedes optimization algorithm, smell agent optimizer, grasshopper optimization algorithm, particle swarm optimization, hybrid sine cosine algorithm with differential evolution, modified capuchin search algorithm, liver cancer algorithm, and modified chameleon swarm algorithm. The algorithm’s robust performance can be attributed to its accelerated convergence rate, stability across diverse functions, good exploration-exploitation behavior, and adaptability to high-dimensional and complex solution spaces. The systematic enhancement of proposed algorithm’s convergence capabilities positions it as a reliable and efficient tool for addressing challenging engineering optimization problems.

This paper presents a comparative study of various grid-forming control strategies under different series compensation levels to suggest the optimal control scheme for compensated lines. Series compensation is a technique used to insert a fixed capacitor in transmission line to maximize its power-transfer capacity and enhance the stability of the power system, however it may result in sub-synchronous oscillations issues. Virtual oscillator control and matching control schemes are recently proposed grid forming control strategies to ensure the power-sharing and synchronization capability of inverters under parallel operation. The mentioned schemes are designed for grid-forming control of inverter-based resources, with keeping traditionally used droop control as a benchmark for comparison. The IEEE first-benchmarked test model is modified by inserting series capacitance and energizing it with 1000MW grid forming inverter-based source in MATLAB/ Simulink. Distinct grid forming schemes, namely droop, matching, and dispatchable virtual oscillator control are implemented and critically analyzed for sub-synchronous oscillations under the same specifications as the test model. The simulation response of subsynchronous oscillations are investigated in time domain, frequency domain, and by calculating performance indices to verify the results in every aspect. The overall analysis justifies the damped, low transient overshoot and fast settling of sub-synchronous oscillations with the virtual oscillator control scheme at various compensation levels.

With the field of traffic signal control progressing towards more adaptive, and computationally efficient models, the integration of sparse travel time data and advanced optimization techniques enhances the intersection performance. This paper presents a novel optimization framework for traffic control using sparse travel time data, with a field penetration rate as low as 10%. The paper focuses on developing mathematical models that can handle the variability in arrival rates both within and across signal cycles. By utilizing delay polygons, we accurately model and minimize the total intersection delay caused by non-uniform vehicle arrivals and signals, leading to optimal signal timing solutions. The model effectively prioritizes queue dissipation for highly saturated phases while minimizing overall intersection delay. The algorithm accommodates variations in phase-wise delays across cycles, indirectly reflecting changes in traffic demand. Additionally, the sample-based design demonstrates performance comparable to volume-based dynamic design in terms of average delay, average speed, and total travel time across cycles. With the innovative use of sample re-identification data obtained through various sensor technologies, the proposed algorithm is capable of delivering optimal control of time varying traffic demand with minimal data input.

Due to the substantial interdependencies between water and energy infrastructures, the water-energy nexus (WEN) is regarded as an appealing focal point for potential malicious intruders, particularly in the context of smart cities. Driven by the increasing vulnerability of these critical infrastructures to sophisticated cyber-attacks and the lack of comprehensive protection strategies, this study aims to address these challenges. This paper introduces a pioneering deep learning-driven framework for detecting, locating, and mitigating stealthy false data injection attacks (FDIAs) targeting electrical and water networks within the WEN. The proposed framework, which leverages two lightweight deep learning models, is designed to accurately detect and locate cyber-attacks in real-time while effectively mitigating their impact, thereby enhancing the operational resilience and security of the WEN. These models undergo training and testing using a wide range of challenging cyber-attack scenarios, including Pulse, Random, Ramp, and Scaling attacks, to ascertain their efficacy and resilience. The detection model accurately identifies and locates the cyber-attacks, achieving a remarkable accuracy rate of 99.73% for identifying attacks aimed at the electrical network and 96.66% for detecting attacks directed at the water network. After detecting the cyber-attack, the mitigation model reconstructs manipulated measurements to ensure the continued operation of the WEN and prevent the occurrence of disruptions or failures. Remarkably, the model attains an outstanding recovery success rate of 98.98% for electrical measurements and 87.64% for water measurements. Additionally, it effectively reduces the operational cost increase (OCI) due to cyber-attacks from 2.19% to 0.0048% for the electrical network and from 5.17% to 0.34% for the water network. The dataset employed in this study is characterized by its imbalanced nature, with cyber-attack instances being infrequent compared to normal instances. Consequently, the adoption of balanced affinity loss, designed to tackle the dataset’s inherent imbalance, achieves superior performance, with an accuracy of 99.73% and an area under the curve (AUC) of 99.86%. The proposed framework offers a robust and efficient solution for safeguarding the WEN against cyber-attacks, making it a valuable tool for enhancing the resilience and security of smart city infrastructures.

Control of the wastewater treatment processes presents significant challenges due to the fluctuating nature of inflow and wastewater composition, alongside the system’s non-linear dynamics. Traditional control methods struggle to adapt to these variations, leading to an economically suboptimal operation of the process and a violation of norms imposed on the quality of wastewater discharged to the catchment area. This study proposes using a reinforcement learning approach to control a wastewater treatment plant. Specifically, the twin-delayed deep deterministic policy gradient algorithm is employed and comprehensively validated. This algorithm enhances control efficiency, optimizing costs while improving the plant’s wastewater treatment capabilities. The proposed control approach is implemented and evaluated using Benchmark Simulation Model No. 1. The evaluation is based on four representative weather scenarios and examines sixteen different metrics. In the proposed case study, an agent controls oxygen transfer coefficients in three oxygen tanks of a wastewater treatment plant. Two distinct reward functions are employed to guide the reinforcement learning agent. The first one focuses on maintaining aerobic conditions and minimizing total nitrogen. The second function retains these objectives and also aims to minimize the aeration energy. The experimental results demonstrate that the proposed control strategy significantly enhances aeration energy efficiency and reduces the overall cost index, indicating a more cost-effective operation. However, future work is required to improve further the performance of reinforcement learning that optimizes the wastewater treatment process.

The numerical derivation of high-order Extended Stability Runge-Kutta (ERSK) integration schemes presents significant challenges due to the high-dimensional, non-convex, non-linear optimization problems with hard equality constraints. Traditional optimization techniques often struggle to converge on valid solutions efficiently. This paper proposes a hybrid approach that integrates Genetic Algorithm (GA) techniques with an interior-point optimizer to address these challenges. GA-generated values are used as starting points for the interior-point method, which refines candidate solutions to meet the stringent order conditions. Experimental results demonstrate a significant improvement in efficiency when GA-generated values are used. The average number of iterations required for convergence is reduced from 477 (with random initialization) to 143 when GA values are used. The median number of iterations drops from 280 to 105. The standard deviation is also reduced from 361 to 112, indicating more consistent performance. These reductions highlight the efficiency of the GA-plus-polish approach, which outperforms traditional random initialization in terms of convergence time and consistency. Despite some remaining variance, this hybrid method significantly reduces the computational cost of deriving stable Butcher tableaux, particularly in high-dimensional, non-convex optimization problems.

This paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We define the system model and estimate the channel using linear minimum mean square error (LMMSE) estimation. To analyze system performance, we derive closed-form expressions for the signal-to-interference-plus-noise ratio (SINR), and harvested energy. These expressions allow us to evaluate the impact of low-resolution ADCs/DACs on both the achievable rate and energy harvesting. Based on these expressions, we formulate an optimization problem fair AE distribution. To solve this non-convex problem, we propose an alternating optimization (AO) algorithm that optimizes the downlink/uplink (DL/UL) powers and the power splitting (PS) ratio. Monte Carlo simulations confirm that our closed-form analysis accurately represents the system performance and that the optimization algorithm distributes AE more fairly compared to the equal power and power splitting case.

Compiler is a tool that converts the high-level language into assembly code after enabling relevant optimizations. The automatic selection of suitable optimizations from an ample optimization space is a non-trivial task mainly accomplished through hardware profiling and application-level features. These features are then passed through an intelligent algorithm to predict the desired optimizations. However, collecting these features requires executing the application beforehand, which involves high overheads. With the evolution of Natural Language Processing (NLP), the performance of an application can be solely predicted at compile time via source code analysis. There has been substantial work in source code analysis using NLP, but most of it is focused on offloading the computation to suitable devices or detecting code vulnerabilities. Therefore, it has yet to be used to identify the best optimization sequence for an application. Similarly, most works have focused on finding the best machine learning or deep learning algorithms, hence ignoring the other important phases of the NLP pipeline. This paper pioneers the use of NLP to predict the best set of optimizations for a given application at compile time. Furthermore, this paper uniquely studies the impact of four vectorization and seven regression techniques in predicting the application performance. For most applications, we show that tfidf vectorization and huber regression result in the best outcomes. On average, the proposed technique predicts the optimal optimization sequence with a performance drop of 18%, achieving a minimum drop of merely 0.5% compared to the actual best combination.

This study addresses the pressing issue of student dropout in higher education institutions and explores the potential of artificial intelligence (AI) to mitigate this challenge. Student dropout is a complex phenomenon influenced by diverse factors, including internal and external, student characteristics and skills. To enhance retention strategies, it is crucial to identify the nuanced reasons behind dropout decisions, which often go unnoticed by university staff. Therefore, this study investigates the integration of metaheuristic optimization techniques with Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) machine learning (ML) models for student dropout identification. By leveraging these well-known ML techniques, the goal is to enhance the accuracy and reliability of dropout predictions in terms of standard classification metrics. Further, by harnessing the exploration and exploitation capabilities of metaheuristics, the study aims to fine-tune both models, thereby increasing their accuracy and robustness in identifying at-risk students. Additionally, to address limitations of existing metaheuristics, a modified version of recently proposed Sinh Cosh Optimizer (SCHO) was developed, that manages to generate well-performing XGBoost and AdaBoost models for students dropout prediction. The study demonstrates that both tuned models can effectively identify at-risk students, providing valuable insights for targeted educational support initiatives. Three experimental evaluations, two with binary and one with multi-class student dropout classification, are conducted on real-world datasets along with rigid comparative analysis and statistical validation with other cutting-edge metaheuristics. According to experimental outcomes, proposed methodology outscores significantly other approaches in terms of performance. Finally, a comprehensive analysis of influential factors was performed using SHapley Additive exPlanations (SHAP) and Shapley Additive Global importancE (SAGE) explainable AI techniques on the best generated models to identify the factors that most significantly influence dropout decisions. This work contributes to advancing AI applications in higher education, providing insights for policymakers and institutions to design targeted interventions for student retention, ultimately enhancing the overall success and effectiveness of higher education systems.

The Internet of Things (IoT) ecosystem presents substantial challenges in terms of privacy and security, rendering it an attractive target for malicious actors. In this context, the literature review highlights the ongoing difficulty in addressing privacy and security through a unified mechanism owing to the heterogeneous nature of IoT devices, their dynamic behavior, and the continual advancement of intelligent hacking tools. Hence, encoding techniques have been considered from the perspective of privacy in Artificial Intelligence (AI) models. To overcome these challenges, this study introduces an integrated single mechanism for privacy and security in the IoT. In order to safeguard sensitive data during AI model training, a novel privacy mechanism called Replacement Encoding (RE) is proposed. This mechanism ensures the camouflage of sensitive information while preserving the integrity and utility of trained models. Additionally, this approach provides automated preprocessing, enhancing the performance of AI models. Message packet features were derived, extracted, and analyzed from the CICIoT2023 dataset (PCAP files) using Wireshark. The proposed replacement encoding scheme is integrated with AI classifiers to detect attacks, achieving an accuracy of 88.94% and 86.61% for the Random Forest (RF) and Deep Neural Network (DNN) models, respectively, utilizing 100 features. These results are compared to accuracies of 90.16% and 94.81% for the same models with up to 15 features using genetic algorithm-based correlation features. Finally, the proposed RF mechanism demonstrates its utility across multiple domains, including privacy preservation, automated data preprocessing, and protection of sensitive user data in Generative Pre-Training Transformer (GPT) applications as well as AI models.

The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

Wind Driven Optimization (WDO) Algorithm is a novel metaheuristic algorithm inspired by the continuous flow of air resulting from differences in air pressure until the air reaches a state of pressure balance. Owing to its simple structure, few parameters, intuitive nature, and straightforward programming, WDO has garnered increasing attention from scholars since its inception. WDO’s novelty lies in its utilization of aerodynamic principles to orchestrate the search process, WDO draws on the dynamics of wind and atmospheric pressure differences to propel the search for optimal solutions. However, WDO has limitations such as sensitivity to algorithm parameters and premature convergence. Consequently, various WDO variants have been proposed to overcome the limitations of the original WDO. To identify potential avenues for further research and to develop WDO for future investigation. This article systematically reviews WDO and its variants from multiple perspectives. Initially, the principle of WDO is outlined. Subsequently, the impact of modifications to the WDO on its overall effectiveness is investigated. Furthermore, the distinctive characteristics of WDO variants and their practical applications are analyzed. Moreover, the conclusions of the review are summarized, and future research directions for WDO variants and their applications are described.

This study aims to improve the low-voltage ride-through (LVRT) capabilities of wind power plants (WPPs) during various types of grid faults, such as line-to-ground (L-G), double line-to-ground (LL-G), line-to-line (L-L), and three-phase-to-ground (LLL-G) faults. The paper proposes an enhanced cascaded Fractional-Order Proportional-Integral and Proportional-Integral (FOPI-PI)-controlled static synchronous compensator (STATCOM). The Dandelion Optimizer (DO), a novel optimization approach, is employed to fine-tune the STATCOM being studied. The DO optimizer was selected for its exceptional performance and robustness. The efficacy of the DO algorithm was assessed in comparison to three well-established methods such as the Water Cycle Approach (WCA), the Particle Swarm Optimizer (PSO), and a novel hybrid technique that combines WCA and PSO. The analysis focuses on two test systems: the 9MW wind power plant connected to an infinite bus system over a 30 km transmission line, and the IEEE 39 bus system. The results are displayed using MATLAB (R2018b) using time-domain simulation. The suggested system monitors multiple LVRT metrics, including WPP active and reactive power, voltage, and speed as well as DC link capacitor voltage. Upon conducting a comparative analysis, it was found that both the proposed cascaded (FOPI-PI) and conventional PI controller-based DO outperform other methods, such as the PI controller-based WCA algorithm, PSO optimizer, and even a combination WCA/PSO algorithm. The findings indicate that the FOPI-PI controller demonstrates superior performance compared to the PI controller. When the 9MW system has a fault, the recommended FOPI-PI controller restricts the voltage drop of the WPP to specific percentages. Specifically, the voltage drop is limited to 3% for L-G faults, 5% for L-L faults, 6% for LL-G faults, and 10% for LLL-G faults. Finally, the performance of wind power plants is much improved by adopting the proposed FOPI-PI controller optimized by DO in comparison to all other controllers in various test scenarios.

Plants play a crucial role in human history by providing essential resources such as food. However, they are susceptible to various diseases like leaf blight, grey spots, and rust, posing significant challenges for farmers and ranchers due to the potential damage and financial losses incurred. The current method of diagnosing plant diseases relies heavily on manual inspection by trained professionals, which becomes increasingly complex and resource-intensive, particularly on large farms. To address this issue, a novel approach is proposed, integrating clustering methods and neural networks to enhance the efficiency of identifying and studying plant disease samples. This method aims to expedite the diagnostic process and accurately quantify disease-induced damage. It introduces a formula for assessing damage, calculating the diseased leaf area as a fraction of the total leaf area. With an impressive accuracy rate of 96–97%, the system successfully identifies diseases such as Alternaria, Anthracnose, Bacterial Blight, and Cercospora Leaf Spot. This advancement signifies a notable improvement over previous methods, highlighting the evolution of the system. By harnessing the capabilities of clustering techniques and neural networks, this innovative approach seeks to revolutionize the detection and monitoring of plant diseases in agriculture, offering a more accessible and precise solution.

Fog computing (FC), extending cloud services to the network edge, has emerged as a key paradigm for low-latency applications like the Internet of Things (IoT). However, efficient resource management, task scheduling, and load balancing pose challenges in fog environments. This review surveys recent research efforts aimed at addressing these challenges and optimizing FC performance. We conducted a systematic analysis of relevant research papers on FC published in reputable academic databases. The review focused on studies published between 2019 and 2024 and emphasized artificial intelligence based studies exploring resource management, task scheduling, and load balancing techniques within the FC domain. The review identifies a diverse range of techniques applied to optimize FC performance. These include machine learning (ML) and deep learning (DL) for resource allocation, heuristic algorithms for task scheduling, and nature-inspired meta-heuristics for load balancing. The review evaluates the strengths and limitations of these approaches, highlighting their impact on metrics like latency, energy consumption, and Quality of Service (QoS). This review demonstrates the significant progress made in optimizing FC through innovative techniques. ML and meta-heuristics have emerged as promising approaches for resource management, task scheduling, and load balancing, respectively. However, challenges persist in areas like real-world implementation complexities and ensuring service quality across geographically distributed fog networks. Future research directions are identified, emphasizing the need for further exploration of these challenges and the integration of emerging technologies like deep reinforcement learning for enhanced FC performance.

The effective solution of system of nonlinear equations (SNEs) is crucial for the creation of engineering and scientific models. SNEs can be represented and analyzed as an optimization problem. The objective of this research is to introduce a new optimization technique, called Chaotic Noise-Based Particle Swarm Optimization Algorithm (CN-BPSOA), to effectively address the SNEs. CN-BPSOA is a particle swarm optimization algorithm (PSOA) that utilizes chaotic noise to enhance its performance. The configuration of CN-BPSOA will involve the utilization of a novel definition, namely chaotic noise, to address the limitations associated with optimization methods. These limitations include the insufficient variety of solutions, the imbalance between exploiting current solutions and exploring new ones, and the sluggish convergence towards the optimal solution. The objective of chaotic noise is to minimize the occurrence of duplicated solutions and iterations in order to accelerate the rate of convergence. The chaotic logistic map is employed due to its widespread adoption by researchers and its demonstrated efficacy in enhancing solution quality and optimizing performance. CN-BPSOA is evaluated utilizing numerous renowned SNEs. The performance of the proposed method is compared with five other algorithms that also solve the same benchmark issues and with the results of the original PSOA to emphasize the significance of the modifications made in CN-BPSOA.CN-BPSOA’s effectiveness in addressing SNEs was demonstrated by the promising findings that obtained, where the best solution obtained by CN-BPSOA is less than that obtained by all algorithms by improvement percentage (percentage drop (PD%)) more than or equal to 93.30% in all benchmark’s problems. Ultimately, while comparing the outcomes of CN-BPSOA with those of prior investigations, the application of statistical analysis through Friedman and Wilcoxon’s tests conclusively revealed its better performance and efficacy in resolving this kind of problem.

A method for optimizing multiple responses in solenoids utilized in a magnetic nozzle assembly for an electric plasma thruster is presented. The primary goal is to minimize power usage while maintaining a sufficiently high magnetic flux density at the coil axis. This is especially critical during testing in a vacuum chamber, where heat dissipation is a challenge, especially for small components and intricate assemblies. This paper presents a method for quickly calculating the magnetic field along the solenoid axis that provides accurate estimates, independent of coil size. Additionally, we present general and concrete results for the employed magnetic nozzle design to illustrate the applied optimization method. The paper details a methodology for optimizing multiple parameters of a solenoid, including its radius, length, current, number of turns, and wire diameter. The aim is to minimize, maximize, or meet specific targets for magnetic flux density, power, or mass. Additional studies could utilize a suitable metric to optimize the homogeneity of the magnetic field of multiple coils. The coils designed using the algorithm have a radius of 17.5 mm, a length of 21 mm, consist of 300 turns with a wire diameter of 1 mm, and operate at a direct current of about 5 A. The potential maximum magnetic field is 40 mT with a power consumption of around 30 W.

Due to its improved spectral efficiency, non-orthogonal multiple access (NOMA) is regarded as a promising multiple access technology for beyond fifth-generation (5G) networks. In this paper, we examine the performance of a cell-edge user and suggest a beamforming scheme in a cooperative two-user multiple-input multiple-output (MIMO)-NOMA system with Rayleigh fading channels. In the envisaged scenario, a cell-center user with better channel gain harvests energy and assists a cell-edge user with poorer channel conditions by using a simultaneous wireless information and power transfer mechanism. We first obtain the outage probability expressions in closed form for the cell-edge user for the Kronecker structured channel model in the covariance shaping and indefinite quadratic form. Next, the beamformers at the transmitter and receiver are introduced to reduce outage probability, with transmit beamformers maximizing the signal-to-leakage-plus-noise ratio and receive beamformers minimizing cross-covariance across all users. Furthermore, beamformers are adopted in the two-user network to adjust the power ratio and power allocation coefficients for better performance of the cell-edge user. Moreover, our scheme is also compared with a transmit antenna selection baseline scheme. Simulation results demonstrate that our approach enhances the performance of the two-user cooperative MIMO-NOMA system’s performance, validating the theoretical analysis.

Asthma exacerbations pose a significant global health concern, necessitating effective predictive models to anticipate and manage these events. This systematic literature review examined the optimization techniques employed in asthma exacerbation prediction models, spanning machine learning algorithms and computational optimization methods. The objective was to synthesize existing evidence, identify trends, and delineate future research directions in predictive modeling for asthma exacerbations to enhance predictive accuracy and clinical utility. A comprehensive search strategy was devised, yielding 27 eligible articles for analysis. The result revealed various optimization techniques, including feature selection, model optimization, and environmental factor integration. The result also revealed that machine learning algorithms’ effectiveness in predicting asthma exacerbations varied depending on various factors (such as dataset quality and model complexity), with various optimization techniques (such as feature selection and ensemble learning) used for improving predictive accuracy. Integrating environmental and spatial factors enhanced prediction models, enabling tailored interventions. In addition, personalized asthma management strategies informed by predictive models led to better control and reduced healthcare utilization. The review also highlighted the implications for personalized asthma management, as well as methodological limitations, and proposed future research directions to improve model reliability and advance personalized healthcare understanding, thereby contributing to the United Nations’ Sustainable Development Goals related to health, innovation, and sustainability. Thus, progress made in asthma exacerbation prediction and the identification of challenges and areas for improvement were covered, providing valuable insights for researchers, clinicians, and policymakers aiming to enhance asthma care through predictive modeling.

Because of their attractive features, switched reluctance motors (SRMs) are strong candidates with real chances on the market for several industrial applications including vehicle propulsion. However, the SRMs suffer from torque ripple and associated acoustic noise. A proper control algorithm can handle torque ripples to an accepted limit. However, the internal faults in stator windings of SRMs deteriorate not only the performance of control algorithm but also the output power, efficiency, and noise (torque ripple). They cause fast degradation of motor lifetime. Therefore, this paper introduces a novel method to counteract the internal faults in winding of SRMs. A new simulation method for SRMs including the capability of interturn faults is introduced. The proposed novel interturn fault tolerant control (FTC) is achieved based on average torque control (ATC) algorithm. The switching angles are optimized smartly providing a less complicated control algorithm to fit properly with the industrial platforms. The simplicity of overall control algorithm is still functional. The performance of proposed FTC is evaluated compared to healthy motor conditions as well as faulty conditions. The results show the superior performance of the proposed control.

Opting for the right supplier is critical for a firm’s prosperity due to the impact of decisions made on uncertainty in symmetrical decision factors like pricing, manufacturing, service efficiency, and client satisfaction. Deciding on an appropriate supplier can be problematic because there are many considerations to consider, and they occasionally contradict each other. The supplier selection problem (SuSP) has been discussed in the literature in various situations while considering different considerations. Under the symmetry of uncertainty, operational risk analysis is nevertheless inadequate. This study is accomplished in two phases. In the first phase, we delve into the notions, basic properties, and theorems associated with hypersoft mapping (HypSM). These basic features are indispensable, as they endow the theoretical base essential for the subsequent phase. In the second phase, we leverage the aggregations of HypSM to develop a robust decision-assisted mechanism. This mechanism, which incorporates the robust algorithm and SuSP analysis, is constructed directly upon the insights and aggregations explored in the first phase, revealing the practical use of HypSM in decision-making. Concerning soft mapping (SOM), HypSM is more adaptable since its approximate function takes into account multiple argument domains to help decision-makers handle convoluted structural challenges involving uncertainty. Additionally, the proposed decision system considers various operational risks, like material quality-based risks, service quality-based risks, supplier’s market profile-based risks, and deep-rooted cooperation-based risks, for the approximation and evaluation of appropriate suppliers. This approach is expected to enhance decision-making by comprehensively evaluating supplier risks.

EXplainable Artificial Intelligence (XAI) aims to reveal the reasons behind predictions from non-transparent classifiers. Explanations of automated decisions are important in critical domains such as finance, legal, and health. As a result, researchers and practitioners in recent years have actively worked on developing techniques that explain decisions from machine learning algorithms. For instance, an explanation technique called counterfactual explanation has recently been gaining traction in XAI. The interest in counterfactual explanations stems from the ability of the explanations to reveal what could have been different to achieve a desired outcome, as opposed to only highlighting important features. For instance, if a customer’s loan application is denied by the bank, a counterfactual will indicate the changes required for the customer to qualify for the loan in the future. For a counterfactual to be considered effective, several counterfactual properties must hold. This paper proposes a novel optimization formulation designed to generate counterfactual explanations that possess multiple properties concurrently. The efficacy of the proposed method is assessed on a publicly available credit dataset. The results showed a trade-off between validity and sparsity, which are both parts of a suite of counterfactual properties. Furthermore, the results showed that our proposed approach compromises validity to some degree but strikes a good balance between validity and sparsity.

Load Frequency Control (LFC) plays a crucial role in controlling power exchanges within multi-area networks, ensuring grid stability in modern power systems. This study proposes a fractional calculus-based TFOID (Tilt fractional order integral and derivative) control for frequency regulation loops. The effectiveness of the proposed controller against existing control techniques such as Proportional Integral Derivative with Filter (PIDF) and Fractional-Order Proportional-Integral–Derivative (FOPID) has been assessed. The imperialist competitive algorithm (ICA) has been utilized to find the optimal parameters of the controller. Five different case studies investigate LFC strategies in various scenarios within modern power systems. Firstly, it explores LFC under the high penetration of hybrid RESs in multi-area networks, examining the challenges and solutions associated with integrating a substantial amount of RESs. Secondly, the study delves into LFC in the context of power deregulation, navigating the complexities of competitive power markets where consumers have the freedom to choose their electricity suppliers. Thirdly, it investigates LFC models coupled with DC links, exploring the integration of direct current links to enhance frequency control strategies. Fourthly, the case study addresses LFC models tailored for single-area grids to the specific power requirements and challenges of regional power systems. Lastly, it examines LFC models in multi-micro grid scenarios, acknowledging the dynamic landscape of interconnected microgrids and their implications for frequency control. Statically data in terms of settling time, overshoot, undershoot, and fitness function validated the superiority of the proposed hybrid controller. The proposed controller reduces undershoot, overshoot, and settling time by approximately 90.91%, 66.78%, 49.40% and 78.89%, 50.49%,36.83% compared to the PIDF and FOPID controller in multi-load perturbation. Other simulated results in terms of $\Delta F_{1}$ , $\Delta F_{2}$ , and $\Delta P_{tie}$ also validated the excellence of the considered controller.

Detecting drones is a complex challenge, primarily due to their small feature size for extraction and variable lighting conditions. It is crucial to effectively capture and model features for drone detection. To accurately detect drones, we propose feature capture and modeling modules. The feature capture module has a minimal number of FLOPs and parameters. It consists of both local and global attention branches, which capture contextual information and global dependencies across the entire feature set. Complementing this, our feature modeling module innovatively calculates attention maps without any additional parameters. This module augments the capability of the feature capture mechanism to represent complex patterns more effectively. Finally, to ensure rapid deployment, we convert the proposed models to machine codes by introducing a compiler, accelerating drone detection. The compiler unifies inter- and intra-operator scheduling with task abstraction. It optimizes the codes for hardware. In compiling time, the effective schedule is performed. The compilation ensures that drone detection is real-time and accuracy remains unchanged. Through rigorous testing, our methods have demonstrated superiority over most current ones in several metrics, including accuracy, parameter quantities, FLOPs, average FPS, visual effects, and latency. Our method yields at least 23.5% and 12.47% higher $AR_{M}$ than existing methods on DUT-Anti-UAV and Online Drone datasets. Our inference speed is at least 6.49% higher than other methods on NVIDIA RTX 2080 Ti GPU.

Cloud computing has grown significantly in recent years and is now widely used by both individuals and businesses. Cloud service providers need to be prepared for failures to ensure that they can respond to them appropriately by minimizing downtime and achieving business continuity. This can be accomplished through fault tolerance mechanisms which aim to predict, prevent, and recover from failures. One of the common causes of task failures in the cloud is the deterioration of physical machines. If deterioration is detected in the physical machine, the virtual machines (VMs) being hosted must be moved to healthy physical machines. This paper proposes an approach to VM allocation in the event of host deterioration or failure. Namely, an integer linear programming optimization model is proposed for the allocation of the failing VMs while minimizing VM migration time. In addition, hybrid genetic and simulated annealing (HGA and HSA) algorithms are proposed for providing efficient solutions for the optimization model. The HGA is hybridized by a procedure that penalizes infeasible solutions to reduce their chances of being selected in the offspring populations and a hill-climbing procedure that modifies feasible solutions to produce better (or more fit) ones. The HSA starts from a feasible solution and also uses elitism as the HGA in its search to ensure that good candidate solutions are preserved. To validate the quality of the obtained solutions, we compare our approach to the commercial optimization solver CPLEX. In comparison to the CPLEX solver, the results show that for small size data center problems, the proposed HGA and HSA achieve near-optimal solutions with 70.80% and 81.43% increase in speed with an average solution quality trade-off by 20.57% and 78.52%, respectively. Notably, for medium, large and very large size problems the HGA and HSA obtained solutions where the CPLEX solver could not provide solutions within acceptable time. For medium size problems HGA attains solutions with better quality (75.82%) yet with higher execution time (57.27%). For large and very large problems HGA significantly outperforms HSA in terms of solution quality (better by 84.88% and 86.46%) yet with significantly higher execution time (74.22% and 76.55 %), respectively. Thus, the designer may choose which algorithm to use based on whether the focus is on quality or execution time.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) networks incorporate a cell-free architecture with distributed antennas in a geographical area, and aim to deliver high data rates and support large numbers of users. It is crucial that such networks operate in an energy-efficient manner within the available spectrum. Thus, we focus on maximizing the energy efficiency (EE) of a CF-mMIMO network, which coexists with a collocated primary network in underlay mode. The EE maximization is a non-convex problem and to compute a power allocation policy efficiently, we propose a weighted minimum-mean-square-error (WMMSE) based Dinkelbach’s algorithm. Besides this, we also provide a simplified algorithm for maximum-ratio precoding in which we approximate the non-convex EE objective function with a lower bound, transforming the non-convex EE problem into a convex problem. Subsequently, we propose a policy for downlink power allocation that maximizes the EE of the secondary CF-mMIMO network while adhering to power constraints at each access point and interference constraints at each primary user. We also compare with some heuristic power allocation policies. The results demonstrate that the proposed WMMSE based power allocation scheme outperforms the heuristic power allocation schemes to a significant degree.

Electric vehicles (EVs) are essential to the modernization of transportation systems. However, optimizing EV charging to align with grid stability and renewable energy availability remains a challenge. To address this challenge, this study introduces a machine learning-based framework to optimize EV charging by considering driver satisfaction—a novel approach quantifying this multidimensional construct through socio-demographic attributes, State of Charge (SoC), proximity to charging stations, and variable charging fees. Driver satisfaction is defined as the extent to which the EV charging experience aligns with drivers’ expectations, integrating these key factors to influence decision-making and overall happiness with the charging service. Trained on a dataset from Hungarian EV users, the developed model predicts outcomes with high accuracy (87.9%), leading to an optimization algorithm that maximizes driver satisfaction while minimizing grid power purchase costs. Our results from a simulated smart grid demonstrate the model’s effectiveness, achieving an average charging satisfaction score of 98.5% compared to 69.54% from a traditional method. Additionally, the proposed method maintained the SoC of the EV fleet at a stable average around 50%, optimizing energy use and grid stability. By dynamically assigning EVs to charging stations and leveraging photovoltaic sources, our solution not only boosts driver satisfaction but also aids in the sustainable growth of smart grids. This research marks a significant step forward in the smart management of EV charging by introducing a driver-centric optimization model, filling a critical gap in current literature and offering insights into its application in enhancing urban mobility solutions.

In the contemporary world, where the escalating demand for energy and the imperative for sustainable sources, notably solar energy, have taken precedence, the investigation into solar radiation (SR) has become indispensable. Characterized by its intermittency and volatility, SR may experience considerable fluctuations, exerting a significant influence on energy supply security. Consequently, the precise prediction of SR has become imperative, particularly in the context of the potential proliferation of photovoltaic panels and the need for optimized energy management. Several works in the existing literature review the state of the art in SR prediction, focusing on trends identified using machine learning (ML) or deep learning (DL) techniques. However, there is a gap in the literature regarding the integration of optimization algorithms with ML and DL techniques for SR prediction. This systematic review addresses this gap by studying prediction models for SR that leverage metaheuristic optimization algorithms alongside artificial intelligence (AI) techniques, aiming primarily for maximum prediction accuracy. Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have featured in 29% and 12.1% of the analyzed articles, respectively, while intelligent approaches like Convolutional Neural Networks (CNN), Extreme Learning Machine (ELM), and Multilayer Perceptron (MLP) have emerged as the predominant choices, collectively accounting for 43.9% of the studies. Analysis has encompassed studies examining SR across hourly, daily, and monthly intervals, with daily intervals representing 48.7% of the focus. Noteworthy variables including temperature, humidity, wind speed, and atmospheric pressure have surfaced, capturing proportions of 90%, 68.2%, 56%, and 41.4%, respectively, within the reviewed literature.

Users have different sensitivities to different attributes for the same data set. Disregarding this can result in inadequate data confidentiality or reduced data availability. To address this, this paper proposes a multi-level personalized local differential privacy mechanism optimization method. In high-dimensional heterogeneous data scenario, this paper first adopts the optimal privacy budget allocation scheme to allocate the privacy budget of different attributes, and then categorizes the privacy levels into high, medium, and low. Users can freely select the privacy level for each attribute or choose the same level for all attributes. For data analysts, reorganizing data with different privacy levels to achieve histogram estimation is a challenging task. The paper introduces a histogram optimization estimation method based on two evaluation criteria. It proposes a combinatorial optimization method, OC, which minimizes mean square error, and a combinatorial optimization method, OP, based on perturbation theory, which minimizes maximum error. The paper comprehensively studies the balance between data availability and privacy protection based on these two rules.

This paper proposes the Shrike Optimization Algorithm (SHOA) as a swarm intelligence optimization algorithm. Many creatures, who live in groups and survive for the next generation, randomly search for food; they follow the best one in the swarm, a phenomenon known as swarm intelligence. While swarm-based algorithms mimic the behaviors of creatures, they struggle to find optimal solutions in multi-modal problem competitions. The swarming behaviors of shrike birds in nature serve as the main inspiration for the proposed algorithm. The shrike birds migrate from their territory in order to survive. However, the SHOA replicates the survival strategies of shrike birds to facilitate their living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modeling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019 and 12 from CEC-2022’s most recent test functions, for a total of 41 competitive mathematical test functions and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon ranking sum and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have gained attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art techniques for 3D reconstruction, including the generation of novel, unseen views. An overview of recent developments in the Gaussian Splatting method is provided, covering input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this domain and the numerous opportunities for enhancing 3D reconstruction methods, a comprehensive examination of algorithms appears essential. Consequently, this study offers a thorough overview of the latest advancements in Gaussian Splatting.

The usage of cloud computing service platforms are exponentially growing to provide on-demand services for end-users for using advanced technologies. These platform services are achieved through resource virtualization to maximize the resource usage and minimize energy requirements. Energy consumption is a key factor for designing efficient and manageable cloud data centers. Optimal techniques are used for placing virtual machines in physical machines to reduce the energy consumption ratio of physical hosts. This paper proposes a novel efficient virtual machines placement algorithm for a cloud computing environment. This method exploits a modified artificial bee colony optimization algorithm for identifying under-utilized physical machines based on energy consumption and resource allocation charts. An adaptive threshold method is then proposed to select suitable threshold levels for energy consumption to identify under-utilized physical host machines. A comparative analysis with state of art methods is carried out by using the CloudSim 3.0 simulator. Simulation results show the superiority of our method, able to achieve the highest accuracy values of 97.2% for accuracy and of 97.9% for precision rate, thus confirming the efficacy of our approach for virtual machine placement in cloud environments.

This paper presents a wide-dynamic-range, DC-coupled, time-based neural-recording integrated circuit (IC), which is resilient against stimulation artifacts, for bidirectional neural interfaces. The proposed neural-recording IC based on delta-sigma modulation consists of an input Gm cell, current-controlled oscillator (CCO)-based integrator, phase quantizer, and tri-level current-steering DACs. The feedback current-steering DACs embedded in the current sources of the input Gm cell enable the recording IC to achieve a wide enough dynamic range to directly digitize the neural signals on top of stimulation artifacts while maintaining a moderately high input impedance. Moreover, the free-running frequency of the CCO-based integrator is set to be the optimum frequency of 0.49 times the sampling rate, thereby achieving high loop gain while utilizing inherent clocked averaging (CLA). Designed and post-layout simulated in a 65-nm process, the neural-recording IC achieves an SNDR of 76.3 dB over a signal bandwidth of 10 kHz while consuming low power of $5.04~\mu $ W with a sufficiently wide linear input range of 200 mVPP.