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Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Utilization of prediction and optimization techniques in machining operations assists with the decision-making required for machining parameter selection, directly impacting process outcomes including the surface roughness of the workpiece. Often these methods exclude the consideration of tool wear and critical information contained within sensor data collected during the cutting process. This study enhances the application of a hybrid physics-based and machine learning predictive framework for Ra that incorporates tool wear information via a focused investigation of computer numerical control (CNC) turning AISI 316, followed by a comparative analysis of metaheuristic optimizers to determine the optimal machining parameters. The experimental results include an analysis on the influence of the machining parameters, flank wear, and total cutting distance of the tool on the surface roughness. The proposed prediction model was improved for AISI 316 from a previous study conducted by the authors. The model achieved a root-mean-square error (RMSE) of 0.108 μm and testing results indicated that 87% of predictions fell within limits set by the ASME B46.1-2019 standard. Genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) are compared using the modified prediction model as the objective function. Despite GA producing the lowest minimum Ra for constrained and unconstrained testing cases, SA generated the highest accuracy during validation testing, achieving an error of 4.54% for a constrained scenario. The outcomes of this study strengthen the hybrid prediction framework proposed by the authors and reinforce the value the optimization process provides to machining operators in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Considering the extensive applications of turning and facing operations in mechanical engineering manufacturing, the energy consumption of machining equipment has emerged as a significant concern in the manufacturing industries, such as aerospace and automotive. This article focuses on establishing a predictive model and optimizing energy consumption for facing and turning operations on two types of steel, namely, AISI 1038 and AISI 4142, using two different cutting tools. The experiments were planned using Taguchi's L16 orthogonal array, and the coefficients of the predictive model were determined through a linear regression approach. Additionally, a genetic algorithm (GA) with two distinct selection techniques was employed to optimize the three main variables: cutting depth, cutting speed, and feed rate, all aimed at reducing energy consumption. The results of this study show that the selection method used in GA significantly affects convergence toward the optimal solution, while the choice of cutting tool has a considerable impact on energy usage. Moreover, the variation in the effect of cutting parameters on machining energy consumption is analyzed to determine which parameters contribute most to energy savings. The results underscore the importance for manufacturers of using advanced predictive and optimization tools, offering them a competitive edge by achieving economic, environmental, and performance-related benefits. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective prediction and optimization integration method combining response surface method (RSM) and non-dominated sorting genetic algorithm-II (NSGA-II) was proposed to improve the overhead low-power pulsed laser-MAG (metal active gas) hybrid welding quality. A four-factor with a five-level experiment matrix considering the weld current(I), weld speed(V), laser power(P), and assembly clearance(C) was established based on te central composite design method. The relationship between weld parameters and back weld width (BW) and weld reinforcement (WR) was approximated by RSM. The significance and adequacies were validated by the analysis of variance and validation experiments, and the average error of validation experiments between predict and actual value is less than 5%, indicating that the model exhibits high predictive accuracy. The individual effect of a single parameter and the interaction of multiple parameters on BW and WR were studied, and the weld current and assembly clearance are the most significant parameters influencing BW and WR, respectively. NSGA-II was used for multi-objective optimization taking the constructed RSM models as objective functions and generating the Pareto-optimal front composed of optimal solutions. Finally, the verification experiments show that the optimal solutions of different fronts can obtain the desired welding morphology without any defect. [ABSTRACT FROM AUTHOR]

• Analyzed key factors in real-world satellite constellation tracking of time-sensitive moving targets. • Introduced PDA-GA, employing dual adaptive and parallel mechanisms to enhance genetic algorithm efficiency. • Demonstrated the effectiveness of our approach through rigorous experiments in simulated scenarios. The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm's sensitivity to parameters, while the parallel mechanism increases the evolutionary process's computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation. [ABSTRACT FROM AUTHOR]

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs. [ABSTRACT FROM AUTHOR]

ABSTRACT The time domain is the most widely chosen alternative in mechanical fatigue tests to completely represent random events. However, the main disadvantages of this approach are the long time required to complete the tests and the high cost of the complex equipment. On the other hand, fatigue test in the frequency domain is a potential alternative to overcome these disadvantages. That is, it requires less sophisticated equipment, because it uses only simple actuators instead of servo actuators. Moreover, the duration of the fatigue test is reduced, because the total length of the event is replaced by constant repetitions and constant damage. The objective of this work is to propose and develop a methodology that uses the results of structural fatigue as a starting point, and through an optimization problem solved by the genetic algorithm, it determines which loads/displacements caused them in the frequency domain. The purpose is to assist future developments in fatigue tests, supplanting tests in the time domain without losing the correlation in results. [ABSTRACT FROM AUTHOR]

In the article, the problem of multi-criteria optimization of quality control mechanisms is analyzed. The presented method assumes the use of the NPGA genetic algorithm to simultaneously manage costs and the level of detecting non-conformities. The main assumption of the presented approach is to treat individual quality control procedures as vectors, whose elements are probability generating functions of defect detection. Each of these procedures generates certain operational costs and covers specific types of defects within its scope. The task of the presented algorithm is to indicate which procedure and to what extent should operate to ensure an appropriate level of non-conformity detection while minimizing costs. The article presents the theoretical foundations of the developed algorithm and the results of its implementation. The software has been developed in C++ with a particular focus on performance aspects. Its essence lies in the implementation of data structures introduced in the theoretical part, as well as methods for their rapid processing. Thanks to this approach, the entire program is scalable and can be used to solve multidimensional optimization problems. The presented approach may also find application in other areas of enterprise management. This will be possible primarily in cases where the effectiveness of procedures or devices is primarily evaluated based on probability. Therefore, the presented methods can provide effective optimization of other areas related to enterprise management. [ABSTRACT FROM AUTHOR]

The vibration of machine tool spindle is very important for machining. Firstly, in order to explore the relationship between spindle vibration and process parameters, the spindle vibration acceleration when polishing aero-engine blade was measured, and the quadratic polynomial models between the spindle vibration acceleration in X, Y and Z direction and the process parameters were established. Secondly, a quadratic polynomial model for the influence of process parameters on polished surface roughness was also determined. Thirdly, through the Analysis of Variance (ANOVA) and main effect analysis, it can be seen that the spindle speed has the greatest impact on the vibration. Finally, a multi-objective optimization model was established with the optimization objective of minimizing spindle vibration acceleration and polished surface roughness, and the optimal process parameters were solved using genetic algorithm. The optimal process parameters were verified and the results show that the polished surface roughness with the optimal process parameters are all less than 0.4 μm, and the deviation rates between the theoretical optimization results of spindle vibration acceleration and the experimental results are all less than 10%, indicating that the optimization results are good. [ABSTRACT FROM AUTHOR]

Feature selection is crucial for minimizing redundancy in information and addressing the limitations of traditional classification methods when dealing with large datasets and numerous features in many machine learning applications. To improve the classification, this article introduced two hybrid methods utilizing a genetic algorithm and a gray wolf algorithm with structured dispersion norms for feature selection. These techniques involved the utilization of a genetic algorithm and a gray wolf algorithm for feature selection. The features selected by these algorithms were used in the classification process by employing a two‐layer perceptron as a classifier. The novel sparse norm is employed to assess and compute classification errors in these methodologies. To assess the effectiveness of the suggested techniques, they were compared with the existing feature selection methods using various publicly accessible datasets. The results of the experiments consistently demonstrate that the proposed methods outperform other approaches. [ABSTRACT FROM AUTHOR]

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges. [ABSTRACT FROM AUTHOR]

Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 106 kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system. [ABSTRACT FROM AUTHOR]

As wireless communication systems increasingly require compact and efficient antennas, conventional antenna design methods are proving difficult to meet the rigorous demands of modern applications. For this purpose, this study introduces a methodology which uses pixelated Inverted-F Antenna (IFA) inspired designs optimised through genetic algorithms to enhance performance in constrained spatial environments. As pixels the antenna features the exemplary use of Einstein Hat-shaped tiles, enabling the antenna to efficiently utilize space. A with the proposed method optimised antenna is compared to traditional IFA designs and shows improved properties like enhanced antenna gain and efficiency as well as smaller reflection coefficient offering a promising solution for future compact antenna systems in the Internet of Things and beyond. Finally, a prototype was manufactured and the scattering parameters and antenna gain were measured within an anechoic chamber. [ABSTRACT FROM AUTHOR]

In the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and complex instrumentation processes. Additionally, many approaches fail to account for model uncertainties, leading to significant discrepancies between the actual structure and its numerical reference model, thus compromising the accuracy of damage identification. This study introduces an innovative computational method aimed at minimizing data requirements, reducing instrumentation costs, and functioning with fewer vibration modes. By utilizing information from a single vibration sensor and at least three vibration modes, the method avoids the need for higher-mode excitation, which typically demands specialized equipment. The approach also incorporates model uncertainties related to geometry and mass distribution, improving the accuracy of damage detection. The computational method was validated on a steel frame structure under various damage conditions, categorized as single or multiple damage. The results indicate up to 100% accuracy in locating damage and up to 80% accuracy in estimating its severity. These findings demonstrate the method's potential for detecting structural damage with limited data and at a significantly lower cost compared to conventional techniques. [ABSTRACT FROM AUTHOR]

The increasing global demand for energy-efficient cooling systems, combined with the need to reduce greenhouse gas emissions, has led to growing interest in using low-GWP (global warming potential) refrigerants. This study conducts a multi-objective optimization of a small-scale organic Rankine cycle–vapor compression cycle (ORC-VCC) system, utilizing refrigerants R1233zd, R1244yd, and R1336mzz, both individually and in combination within ORC and VCC systems. The optimization was performed for nine distinct cases, with the goals of maximizing the coefficient of performance (COP), maximizing cooling power, and minimizing the pressure ratio in the compressor to enhance efficiency, cooling capacity, and mechanical reliability. The optimization employed the Non-dominated Sorting Genetic Algorithm III (NSGA-III), a robust multi-objective optimization technique that is well-suited for exploring complex, non-linear solution spaces. This approach effectively navigated trade-offs between competing objectives and identified optimal system configurations. Using this multi-objective approach, the system achieved a COP of 0.57, a pressure ratio around 3, and a cooling capacity exceeding 33 kW under the specified boundary conditions, leading to improved mechanical reliability, system simplicity, and longevity. Additionally, the system was optimized for operation with a cooling water temperature of 25 °C, reflecting realistic conditions for contemporary cooling applications. [ABSTRACT FROM AUTHOR]

A two-dimensional (2D) piston electro-hydraulic pump has been proposed further to enhance the power density of the electro-hydraulic pump. The 2D piston pump, characterized by high power density and a slender shape, is embedded within the stator of the motor in a co-rotor configuration where the piston and the motor's rotor are in tandem. The intimate design of the hydraulic pump and the motor results in a coupling between the two, with intricate relationships and influences existing between the geometric parameters of the piston pump and the dimensions of the motor's rotor. Based on the operational requirements and structure of the 2D piston pump, a permanent magnet synchronous motor (PMSM) designed for use with a 2D piston electro-hydraulic pump is developed. This study examines the impact of the motor's stator iron core geometric parameters on both the electromagnetic and mechanical properties of a PMSM and completes the necessary performance validations. The optimization objectives of the motor are determined through an analysis of the influence of the key parameters of the rotor and stator on torque, torque ripple, and motor loss. A surrogate optimization model is constructed using a metamodel of optimal prognosis (MOP) to optimize the torque, torque ripple, and motor loss. Evolutionary genetic algorithms are utilized to achieve the multi-objective optimization design. A finite element simulation is used to compare the electromagnetic performance of the initial motor and optimal motor. Based on the optimal motor parameters, a 2.5 kW motor prototype is manufactured, and the experimental results validate the feasibility and effectiveness of the motor design and optimization. [ABSTRACT FROM AUTHOR]

This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications. [ABSTRACT FROM AUTHOR]

Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development. [ABSTRACT FROM AUTHOR]

A physical region can have multiple parts, each part is monitored with the help of a Special DDN (SDDN). In the existing methods, namely, LEACH, the Fuzzy method has a larger path between the initiator DDN to destination DDN. Non-healthy DDNs can occur in the Group-based Detection Data Network (GDDN) when the battery level of the DDN reaches below the threshold. The possibility of more Non-healthy DDNs can be of multiple reasons (i) when the link path is of larger length (ii) Same DDN is used multiple times as an SDDN and (iii) repeated communication between base station to DDNs causes the DDN to lose more battery. If a mechanism is created to recover the DDNs or recharge them, then the number of Non-healthy DDNs can be reduced and DDN performance can be improved a lot. The Proposed Genetic (PGENETIC) method will find the SDDN in a battery-aware manner and also at path will be of minimum length along with regular interval trigger to identify DDNs which are non-healthy and replace or recharge them. PGENETIC is compared with LEACH, Fuzzy method, and Proposed CHEF (PCHEF) and proved that PGENETIC exhibits better performance. [ABSTRACT FROM AUTHOR]

The internet and technology have developed so fast that the entire world is experiencing the fourth industrial revolution. However, organizations' traditional digital capabilities are not enough to respond to the growing market needs. With the use of Internet technologies, virtualization can be used dynamically in the operational management of supply chains. As a result, supply chains can be controlled, planned, and optimized remotely and through the internet based on virtual objects instead of direct observation. This research seeks to optimize the supply chain by considering the dimensions of supply chain virtualization in the form of two objective functions, profit and processing rate. The presented model has been optimized under conditions of demand uncertainty and in a triangular Neutrosophic environment using the Non-Dominated Sorting Genetic Algorithm II. Finally, by solving a numerical example, the sensitivity analysis of the algorithm performance and its application in the model have been investigated. The results of this research showed that the profit of the virtual supply chain increases compared to the traditional supply chain due to tracking defective parts and identifying returnable products. [ABSTRACT FROM AUTHOR]

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress exerted on LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is generally presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from LBS is increased by 14.18% as compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. Thus the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste distinctly later. [ABSTRACT FROM AUTHOR]

This paper focuses on the difficulties in high-quality and high-efficiency micro-milling nickel-based superalloy micro thin-walled parts. The second-generation Non-dominated Sorting Genetic Algorithm (NSGA-II) is improved. A central composite experiment is designed, and a surface roughness prediction model is developed for micro-milling thin-walled parts. A prediction model for surface residual stress on thin-walled parts is developed using an L9(34) orthogonal simulation experiment. Using the NSGA-II algorithm, the four cutting parameters (spindle speed, feed per tooth, axial cutting depth, and radial cutting depth) are optimized to achieve low surface roughness and high material removal rate, while stable cutting and surface compressive residual stress are considered constraints. Finally, the high-quality and high-efficiency micro-milling of the Inconel 718 cross-shaped thin-walled parts is realized. [ABSTRACT FROM AUTHOR]

To realize atmospheric monitoring missions for carbon neutrality, this paper proposes an low earth orbit (LEO)-LEO occultation orbit design and optimization method for the user-specified point target revisit problem. First, by using the linear J2 -perturbed model, a fast numerical calculation method for occultation events was proposed. Then, the revisit conditions of occultation events for a specified point target were expressed as equality and inequality constraints. For different numbers of transmitting and receiving satellites, by analyzing the numbers of the revisit constraints and free variables, the maximum numbers of user-specified revisit point targets were obtained. Finally, the maximum total observation duration for all specified point targets was optimized by the genetic algorithm. The numerical results show that the proposed method is accurate for the user-specified point target revisit, and the maximum revisit error is 0.2° in 10 days. [ABSTRACT FROM AUTHOR]

This study aims to develop a decision-making approach to help instructors be more effective in selecting appropriate individuals as teammates in the same group to engage in learning together. The social network analysis (SNA) technique is employed to extract the implicit social relationships between each student and any of the other students in the class. The search mechanism based on a genetic algorithm (GA) is developed to find several feasible solutions of organized members for groups under consideration of the acquired relationship measures by SNA and an optimum teammate selection can be effectively achieved. Besides, a simulation based on the SIR model is applied to find the team network topology is of a better knowledge transfer performance than the original one (i.e., without splitting students into groups for learning). An application showcases the developed deci-sion-making approach is of good efficiency and is a promising way to help enhance the capability of an instructor in classroom management while teaching. [ABSTRACT FROM AUTHOR]

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO's applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains. [ABSTRACT FROM AUTHOR]

The risk of liquid agitation in pump-driven tanks within integrated tanks has significantly escalated due to the growing demands for tank integration in new- energy vehicles. In order to solve the problem of liquid sloshing in integrated tanks, this paper presents the design of a baffle structure aimed at reducing waves in integrated water tanks. The numerical simulation method of combining the level-set function with the volume of fluid (CLSVOF) has been employed, significantly enhancing the accuracy of numerical calculations related to a two- phase flow field inside an integrated tank. A comparison was made by analyzing different factors, notably baffle length (L), baffle depth (H), and baffle angle (θ), to investigate their influences in suppressing liquid agitation within the integrated water tank. Numerical computations were conducted utilizing design points acquired by the Latin hypercube sampling technique. The Kriging approximation modelling method was employed to hold down computing time. The Pareto solution was obtained by means of the non-dominated sorting genetic algorithm II, while the optimal solution set was evaluated and ranked using the multi-criteria decision-making algorithm (MCDM). The results show that increasing the baffle depth within a certain range can effectively suppress the wave height in the tank. When the baffle depth is increased to a certain value, the effect on wave-height suppression in the water tank is limited. When the baffle length and angle of the baffle exceed a certain value, it will also have the effect of suppressing the wave height in the tank. After comparing various factors of the baffle, it was ultimately found that the wave suppression effect is maximal when the length of the baffle is 13 millimeters, the depth of the baffle is 49 millimeters, and the angle of the baffle is -20 degrees. The main contribution of this study is the proposed wave-suppressing baffle structure, which provides new insights for the future structural design of integrated water tanks. [ABSTRACT FROM AUTHOR]

At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm. [ABSTRACT FROM AUTHOR]

The intelligent expressway exemplifies a prominent application of intelligent transportation systems. Roadside units (RSUs), strategically deployed alongside roadways, serve as pivotal infrastructure in facilitating interactions within intelligent expressways. A well‐planned RSU deployment strategy is crucial for enhancing service quality, it necessitates balancing performance improvements with significant financial costs due to the limited transmission range and high deployment expenses of RSUs. To tackle these challenges, an adaptive approach for RSU deployment is proposed, which takes into account economic feasibility, service requirements, and dynamic traffic demands. A traffic adaptability‐based RSU deployment (TARD) model, which integrates factors such as deployment cost, the effectiveness of information coverage, road network topology, and traffic flow characteristics have been devised. The TARD aims to minimize deployment expenses while maximizing the benefits of information coverage and alignment with road traffic demands. The Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) is employed to solve this optimization model. To validate its efficacy, simulations are conducted on the G2 expressway in Shandong Province, China, demonstrating the superior performance of the TARD compared to three other deployment strategies. Ablation experiments further underscore the critical role of tunnel deployments and comprehensive coverage along long sections in bolstering network connectivity and elevating service quality. [ABSTRACT FROM AUTHOR]

Customized bus services typically focus on single‐trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi‐objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi‐trip requests and time‐dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP‐hard problem, a non‐dominated sorting genetic algorithm II‐based method is proposed to handle different sizes of instances. Finally, the instances with multi‐trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search‐based multi‐objective algorithm approach. [ABSTRACT FROM AUTHOR]

This study presents a case focused on sustainable farming practices, specifically the cultivation of tilapia (Mozambican and aureus species) in ponds with geothermal water. This research aims to optimize the hydrochemical regime of experimental ponds to enhance the growth metrics and external characteristics of tilapia breeders. The dataset encompasses the hydrochemical parameters and the fish feeding base from experimental geothermal ponds where tilapia were cultivated. Genetic algorithms (GA) were employed for hyperparameter optimization (HPO) of deep neural networks (DNN) to enhance the prediction of fish productivity in each pond under varying conditions, achieving an R 2 score of 0.94. This GA-driven HPO process is a robust method for optimizing aquaculture practices by accurately predicting how different pond conditions and feed bases influence the productivity of tilapia. By accurately determining these factors, the model promotes sustainable practices, improving breeding outcomes and maximizing productivity in tilapia aquaculture. This approach can also be applied to other aquaculture systems, enhancing efficiency and sustainability across various species. [ABSTRACT FROM AUTHOR]

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance. [ABSTRACT FROM AUTHOR]

In this study, a combination of Plackett–Burman and Box–Behnken designs is applied to discover the relationships between the components of rubber compounds and technical specifications. Optimization of rubber compound formulation is realized by support vector regression integrated genetic algorithm to minimize compound cost. Twelve components potentially affecting the technical specifications of rubber compound, which are natural rubber, carbon black, white filler, stearic acid, zinc oxide, antiozonant, antioxidant, process oil, curing retarder, curing agent, and accelerator, are screened through Plackett–Burman design to decide the significant variables. Afterwards, four significant parameters, including carbon black, process oil, curing agent, and accelerator are analyzed using Box–Behnken design to minimize the number of experiments while obtaining the correlation between formulation and specifications. Lastly, a support vector regression integrated genetic algorithm is implemented to predict optimum compound formulation at minimum cost. Highlights: Optimization of rubber compound to reduce the mixture and curing cost.Combination of Plackett–Burman and Box–Behnken designs.Integration of support vector regression to genetic algorithm.Correlations between the amounts of components and technical specifications. [ABSTRACT FROM AUTHOR]

To ensure that ecotourism development remains sustainable, the best place for such activities should be chosen based on the ecological potential. This study attempts to identify suitable ecotourism sites by developing a quantitative geographic model using multi-criteria evaluation (MCE), optimized by a non-dominated sorting genetic algorithm (NSGA-II). Three criteria (physical, biological, and socio-economic features), 13 sub-criteria, and 33 indices were first collected from primary and secondary data sources. Then, MCE method was applied to find ecotourism suitable areas, in which two methods of fuzzy overlay and weighted linear combination (WLC) were used to overlay criteria maps. Finally, NSGA-II was used to optimize ecotourism zoning through defining three objectives, including minimizing the distance from the sub-criteria of natural attractions, vegetation, and historical-cultural sites. Results show the WLC method is better than the fuzzy method at combining different layers to determine suitable zones for ecotourism, through which more than 50% of the study area, about 28,000 hectares, was classified as suitable for ecotourism. Matching 85% of suitable areas obtained by NSGA-II with high and very high suitable classes obtained by WLC shows that combining the MCE method with NSGA-II provided a more suitable hybrid method for ecotourism site suitability evaluation. This study creates a valuable tool for those responsible for planning and carrying out ecotourism initiatives, allowing them to further assess and conduct ecotourism projects. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is a well-known discrete optimization problem that has an impact on theoretical and practical applications. In this paper, a freight distribution model that includes a charging system located at the depot, making it feasible for real world-implementation, is proposed. Two different solution methods are proposed and compared: a genetic algorithm (GA) and a population-based simulated annealing (PBSA) with the number of moves increasing during the iterations. Among the variety of algorithm used to solve the VRP, population-based search methods are the most useful, due to the ability to update the memory at each iteration. To demonstrate the practical aspects of the proposed solution a case study is solved using travel time on a real network to evaluate the potentiality for a real-world application. [ABSTRACT FROM AUTHOR]

Faulty elements (FEs) in a two-dimensional array (TDA) directly impact the performance and configuration of the coverage pattern due to the long operation of the antenna system. Therefore, the process of dealing with these failed elements, knowing their locations, and reducing their negative impact in practice is the main goal of designing a large TDA. In this article, three types of FE locations (faulty random elements, faulty clustered elements, and faulty subarray elements) are studied. Based on the genetic algorithm (GA), the damaged coverage pattern due to the presence of these failed elements is recalculated. The method relies on re-optimizing the amplitude-only weights of non-FE optimally while neglecting the the defective elements. Therefore, the entire TDA elements do not need to be redesigned again but rather rely on the working elements only. This gives great simplification for recalculating the coverage pattern. To further control the coverage pattern in terms of main beam width, directivity (D), first null to null beam width (FNBW), and sidelobe level (SLL), a fitness function is added to the optimization process under specific constraints. Simulation results for different scenarios are presented to demonstrate the validity and effectiveness of the proposed approach for dealing with FE. [ABSTRACT FROM AUTHOR]

Spatial modelling approaches to aid land‐use decisions which benefit both wildlife and humans are often limited to the comparison of pre‐determined landscape scenarios, which may not reflect the true optimum landscape for any end‐user. Furthermore, the needs of wildlife are often under‐represented when considered alongside human financial interests in these approaches.We develop a method of addressing these gaps using a case‐study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA‐II with a process‐based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we 'evolve' a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives.We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real‐life landscapes promote or compromise objectives for different landscape end‐users.Our investigation suggests that optimisation set‐up (decision‐unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human‐centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape‐level needs when using genetic algorithms to support biodiversity‐inclusive decision‐making in multi‐functional landscapes. [ABSTRACT FROM AUTHOR]

This examination presents an original Broadband Oscillation Concealment Procedure in Power Systems utilizing a Genetic Algorithm (GA). The philosophy's suitability is deliberately assessed through comprehensive examinations, including affiliation investigation, strength appraisal, and near investigations with elective optimization algorithms. Results show that the GA-based approach displays predominant affiliation, appearing at a health worth of 0.05 after 100 ages, beating Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA). Strength examination features the versatility of the proposed procedure, with a standard wellbeing worth of 0.08 ± 0.02 under changing power framework conditions. Similar investigation against related work reveals the procedure's advantage, showing its genuine breaking point with regards to helpful broadband oscillation concealment. The GA-based philosophy changes speedy mixing and computational capacity, with an ordinary execution season of 120 seconds. The examination contributes important pieces of information into power framework strength, offering a good answer for mitigating broadband oscillations in various working situations. [ABSTRACT FROM AUTHOR]

This paper mainly studies the collaborative operation of a cross-border e-commerce supply chain composed of manufacturers, e-commerce platforms and foreign warehouses. Firstly, the decision models of transnational e-commerce enterprises based on decentralized, centralized, and hybrid modes are established. The sensitivity of the contract and each determining variable is analyzed using the simulation method. Through the joint contract of "revenue sharing + volume discount," the production efficiency of the enterprise and the logistics service of the international warehouse can be improved. The genetic algorithm combined with simulated annealing was adopted. Finally, a concrete example is given to verify the feasibility of the proposed method. The results show that the member departments can work together better under centralized decision-making compared to the decentralized management mode. Manufacturers to increase production capacity and improve the level of logistics services in overseas warehouses will help improve the profitability of cross-border e-commerce. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are critical for modern intelligent processing but cause significant latency and energy consumption issues on mobile devices due to their high computational demands. Moreover, different tasks have different accuracy demands for DNN inference. To balance latency and accuracy across various tasks, we introduce PreDNNOff, a method that offloads DNNs at a layer granularity within the Mobile Edge Computing (MEC) environment. PreDNNOff utilizes a binary stochastic programming model and Genetic Algorithms (GAs) to optimize the expected latency for multiple exit points based on the distribution of task inference accuracy and layer latency regression models. Compared to the existing method Edgent, PreDNNOff has achieved a reduction of about 10% in the expected total latency, and due to the consideration of different tasks' varying requirements for accuracy, it has a broader applicability. [ABSTRACT FROM AUTHOR]

The "Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)" presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm's evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm's operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management. [ABSTRACT FROM AUTHOR]

The existing nodes of IoT networks are very small in size, deployed for long periods, and have very limited resources, which means that an IoT network must be very energy efficient to survive for a long time. Therefore, finding optimal routing techniques that lead to better data sharing without wasting energy can lead to more energy savings. This is an optimization problem, which means that we need to use optimization algorithms to find the optimal path in an IoT network. Some of the optimization algorithms are called meta-heuristic algorithms, these algorithms are inspired by nature, such as Artificial Neural Networks (ANN), which are Gradient methods to find the most suitable solution for a given problem. Our next algorithm is Particle Swarm Optimization (PSO). If the search combination of both algorithms is used in parallel, the search power will increase and better answers will be found in less time. For this reason, we suggest using a combination of the above algorithms. This idea is a combination of two optimization algorithms, PSO (Particle Swarm Optimization) and ANN (Neural Network) to optimize routing and reduce latency when sending information between IoT nodes in an IoT system. The proposed protocol is focused on optimizing energy consumption and execution time with the help of the GA-PSO algorithm based on routing-based clustering. Finally, to evaluate the proposed protocol, it was simulated using C++ software and compared with the method presented in the reference article based on the enhanced Ant Colony Algorithm, and the results show the efficiency of the proposed method in terms of energy consumption and execution time. The results show that in the presented algorithm, the execution time has been reduced to almost a quarter of the execution time in the algorithm of the reference article. Also, the results showed that our proposed method consumed 20 kJ less energy. [ABSTRACT FROM AUTHOR]

Introduction: This paper proposes a novel all-dielectric design of lens antenna and its performance is optimized using genetic algorithm (GA). The optimization objective are 1-dB and steady gain that are directly optimized. The GA also optimizes the topological design of the lens. Methods: The method consists of two main components: the design of the objective function and the initial population selection. The first lens structure fed into the algorithm and the initial population match. The lens has a diameter of 150 mm and a thickness of 30 mm at its thickest point with working frequency of 6–18 GHz. The 3D printing technology is used for the antenna fabrication that reduces the implantation cost. Results: The experimental results show that the gain and peak aperture efficiency of the proposed antenna are 23.8 dBi and 51.9%, respectively, better than those of the existing designs. Discussion: It advantages are low-cost, easy to fabricate, simple design, high gain, narrow beams, low side lobes. It can be used in future ultra-wideband (UWB) applications. [ABSTRACT FROM AUTHOR]

By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm (GA) based on a Gaussian matrix mutation (GMM) operator, which maintains the diversity of the population while speeding up the algorithm's convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction. Finally, an experimental simulation is performed using data obtained from a commercial supermarket, thereby verifying the effectiveness of the proposed algorithm and comparing it with other algorithms. The results reveal that compared with the classical GA, the average convergence speed of the improved GA can be increased by 50–60% and the consumed algorithm time can be reduced by 48% while maintaining the difference in solution accuracy within 1%. [ABSTRACT FROM AUTHOR]

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes. [ABSTRACT FROM AUTHOR]

Optimizing extraction conditions can help maximize the efficiency and yield of the extraction process while minimizing negative impacts on the environment and human health. For the purpose of the current study, an artificial neural network (ANN) combined with a genetic algorithm (GA) was utilized for that the extraction conditions of Hypericum spectabile were optimized. In this particular investigation, the main objective was to get the highest possible levels of total antioxidant status (TAS) for the extracts that were obtained. In addition to this, conditions of the extract that exhibited the maximum activity have been determined and the biological activity of the extract that was obtained under these conditions was analyzed. TAS values were obtained from extracts obtained using extraction temperatures of 30–60 °C, extraction times of 4–10 h, and extract concentrations of 0.25-2 mg/mL. The best model selected from the established ANN models had a mean absolute percentage error (MAPE) value of 0.643%, a mean squared error (MSE) value of 0.004, and a correlation coefficient (R) value of 0.996, respectively. The genetic algorithm proposed optimal extraction conditions of an extraction temperature of 59.391 °C, an extraction time of 8.841 h, and an extraction concentration of 1.951 mg/mL. It was concluded that the integration of ANN-GA can successfully be used to optimize extraction parameters of Hypericum spectabile. The total antioxidant value of the extract obtained under optimum conditions was determined as 9.306 ± 0.080 mmol/L, total oxidant value as 13.065 ± 0.112 µmol/L, oxidative stress index as 0.140 ± 0.001. Total phenolic content (TPC) was 109.34 ± 1.29 mg/g, total flavonoid content (TFC) was measured as 148.34 ± 1.48 mg/g. Anti-AChE value was determined as 30.68 ± 0.77 µg/mL, anti-BChE value was determined as 41.30 ± 0.48 µg/mL. It was also observed that the extract exhibited strong antiproliferative activities depending on the increase in concentration. As a result of LC-MS/MS analysis of the extract produced under optimum conditions in terms of phenolic content. The presence of fumaric, gallic, protocatechuic, 4-hydroxybenzoic, caffeic, 2-hydoxycinamic acids, quercetin and kaempferol was detected. As a result, it was determined that the H. spectabile extract produced under optimum conditions had significant effects in terms of biological activity. [ABSTRACT FROM AUTHOR]

In the present investigation, process parameters were optimized in order to enhance the reduction of calcium oxide (CaO) from sugarcane molasses using acid centrifugation treatment. To predict the effects of process factors on CaO reduction efficiency, a response surface approach with a central composite design was selected. The polynomial quadratic equation was used to predict CaO removal efficiency, and the analysis of variance (ANOVA) test was utilized to assess the relevance of process factors. The appropriateness of the developed model was determined by regression analysis, which yielded a higher R‐squared value of 0.99334 ± 0.01. At the optimum process parameters of 100°C temperature, 50°Bx, and 3.50 pH, the CaO clarification efficacy of 66.17 wt.% was achieved. The experimental results indicated that for acidic centrifugation treatment, the experimentally observed CaO reduction of 65.94 wt% is in close agreement with the model equation's predicted maximum CaO reduction of 66.17 wt% with a t‐test value of 0.497726. Under such conditions, 0.982 wt.% CaO sugarcane molasses was obtained, which is low when compared to the world average of 1.5% CaO content of sugarcane molasses. Furthermore, the implementation of an artificial neural network (ANN) provided a better prediction model for CaO reduction, with a substantial R‐squared value of 0.99866. However, the genetic algorithm (GA) optimization resulted in an actual CaO reduction of 66.21 wt.% with a t‐test value of 0.497726. [ABSTRACT FROM AUTHOR]

The excessive main girder displacement responses of the large-span cable-stayed bridges during seismic events may precipitate collisions between the main girder and the approach bridge. The viscous dampers (VDs) have been extensively employed in the seismic response control of long-span bridges, yet their effectiveness is limited. To augment the seismic-induced vibration control performance of the VD, inerter element, negative stiffness (NS) element, and spring element have been incorporated into the VD to achieve energy dissipation capacity enhancement. The equations of motion for the simplified cable-stayed bridge-damper systems are established under stochastic seismic excitation, and the design parameters of inerter and NS-based damper are optimized by using the genetic algorithm. The seismic control performance of the VD and five types of dampers are systematically compared, demonstrating that these dampers can substantially enhance the main girder displacement mitigation performance in cable-stayed bridges. Owing to the NS characteristics attributes of the inerter element and NS element and the tuning effect of the spring element, the tuned viscous mass damper (TVMD) with NS achieves a 24.56% higher efficiency in control performance compared to the VD. [ABSTRACT FROM AUTHOR]

Energy harvesting technology, as an emerging energy technology, has contributed outstandingly to the application of online monitoring sensors in power systems. The accurate parameter identification of the harvesters is crucial for their power supply applications. This work aims to investigate the parameter identification of harvesters with complex equivalent circuit models, here taking a current transformer as an example. However, previous studies focused on optimizing algorithms rather than data sources. This paper demonstrates a vector impedance quantum genetic algorithm (QGA) parameter identification method to identify the amplitude and phase information of the impedance responses. By comparing the results of the proposed method with the genetic algorithm and PSO based on impedance responses and QGA based on load resistance responses, it is proved that the proposed vector impedance QGA identification method is optimal in terms of accuracy, speed, and robustness. The root mean square errors of the output voltage and the phase difference with the primary current for the proposed method are 5.884 × 10−5 V and 4.473 × 10−4 ms. Moreover, the practical applicability of this method is validated, demonstrating its effectiveness in real‐life and industrial settings. The proposed identification method in this paper changes the source data so that the sample data are small in volume and extensive in information, which enables faster and more accurate parameter identification. This study provides a new idea for parameter identification researches. [ABSTRACT FROM AUTHOR]

The severe damage observed in the Kathmandu Basin, Nepal, during past earthquakes necessitates a thorough study of the seismic behavior of the basin sediments. As the shear-wave velocity is directly related to the elastic shear modulus of the material, it is essential to determine it to incorporate the behavior of the soil in the design of the structure. Hence, we determined average shear-wave velocity in upper 30 m (Vs30) of soil in Bhaktapur district in the eastern part of the Kathmandu Basin at 73 observation points, employing two methods involving the use of non-invasive microtremor array measurements (MAMs). These MAMs are widely used for determining subsurface soil characteristics by analyzing the ambient vibrations of the ground. The first method involves inversion using a genetic algorithm, and the second is a method for obtaining Vs30 directly from the dispersion curve. We found that Vs30 in the southeastern part of the study area was higher than that in other parts. Conversely, Vs30 in the western region was lower. The calculated Vs30 values were used to classify the sites. The elevated eastern and southeastern areas with high Vs30 were categorized as dense soil or soft rock, whereas the areas with low Vs30 that had suffered significant damage during the 2015 Gorkha earthquake were classified as soft soil sites. [ABSTRACT FROM AUTHOR]