Search

In terms of the interplay between manufacturing cost and quality during the molding of fiber reinforced thermosetting composites, this study proposes a multi‐scale collaborative optimization strategy aimed at shortening molding time and reducing the diminishing defects generated during the process. Macro‐scale thermochemical and micro‐scale thermomechanical calculations are conducted on the composite molding to analyze the origins of temperature gradients and residual stresses. Then, the curing cycle, temperature gradient in the macro‐scale model, and residual stresses in the micro‐scale model are optimized by combining finite element analysis with the Non‐dominated Sorting Genetic Algorithm II. Several curing processes within the Pareto front were selected, and their impact on compressive performance was assessed through experimental analysis. Compared to the standard curing process (SCP), the optimal curing process exhibited a 41.4% reduction in maximum temperature gradient (∆Tmax), a 25.2% decrease in effective residual stress σ¯, and a 20.0% increase in compressive strength. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Highlights: Investigated of interactions among components in composites molding.Optimization of composite molding process with Non‐dominated Sorting Genetic Algorithm II and finite element.Examined the impact of composite molding process on compressive performance. [ABSTRACT FROM AUTHOR]

This paper presents an innovative Fuel Cell Combined Heat and Power (FC–CHP) system designed to enhance energy efficiency in hospital settings. The system primarily utilizes solar energy, captured through photovoltaic (PV) panels, for electricity generation. Excess electricity is directed to an electrolyzer for water electrolysis, producing hydrogen which is stored in high-pressure tanks. This hydrogen serves a dual purpose: it fuels a boiler for heating and hot water needs and powers a fuel cell for additional electricity when solar production is low. The system also features an intelligent energy management system that dynamically allocates electrical energy between immediate consumption, hydrogen production, and storage, while also managing hydrogen release for energy production. This study focuses on optimization using genetic algorithms to optimize key components, including the peak power of photovoltaic panels, the nominal power of the electrolyzer, fuel cell, and storage tank sizes. The objective function minimizes the sum of investment, and electricity costs from the grid, considering a penalty coefficient. This approach ensures optimal use of renewable energy sources, contributing to energy efficiency and sustainability in healthcare facilities. • Investigation of innovative FC Micro combined heat and power unit for energy efficiency in a hospital settings. • Intelligent energy management strategy is proposed basing on the energy need, hydrogen production, release, and storage. • Genetic algorithms is applied for optimal use and storage of energy, increasing energy efficiency and sustainability. [ABSTRACT FROM AUTHOR]

The maritime sector is striving to lessen its dependence on fossil fuels, motivated by both economic factors related to fuel efficiency and the policies established by the International Maritime Organization (IMO). Consequently, there has been a growing focus on developing and deploying all-electric ships (AES) as a more sustainable alternative to conventional fossil fuel-powered vessels. AES presents several advantages over conventional ships, including zero emissions, enhanced energy efficiency, and greater sustainability. However, the implementation of AES also brings new challenges, such as the necessity to manage a hybrid microgrid and optimize voyage scheduling. Hybrid fuel cell (FC)-battery energy storage (BES) technology can significantly contribute to addressing these requirements. To tackle these challenges, this article introduces a comprehensive framework for the optimal planning of hybrid microgrids and voyage scheduling in AES. The framework incorporates multi-objective optimization techniques based on genetic algorithms to achieve a balance between cost and voyage time. The proposed framework is applied to a case study of a ship operating on a fixed route between two ports. The simulation results indicated that the total cost per voyage, associated with the objective function of cost minimization and voyage time reduction, has decreased by 12% and increased by 40%, respectively, when compared to the regular ship speed profile. Additionally, the travel time has been reduced by half an hour and 2 h, respectively, in comparison to the regular ship speed profile. To verify the results, the optimization problem for cost and voyage scheduling is also solved by Mayfly Algorithm (MA) separately. Thus, it can be illustrated the effectiveness of the framework in enhancing energy efficiency and fuel economy while preserving the ship's performance. • Fuel cell-powered ship's energy management and voyage scheduling co-optimization. • All-electric ship (AES) optimal planning for economic and sustainable operation. • Considering ship speed profile management in optimal AES voyage scheduling. • Incorporating the dimensional constraints into the AES optimal design strategy. • Considering generation reserve capacity to supply unanticipated shipboard demands. [ABSTRACT FROM AUTHOR]

Roll force prediction plays a significant role in rolling schedule and optimization. For a specific steel grade, the roll force can be determined by the aid of several factors: rolling speed, initial thickness, ratio of thickness reduction, the starting temperature of the strip, and the friction coefficient in the contact region. Roll force prediction mathematical models are sometimes rare and inaccurate. This paper presents a new approach to predict roll separating force using semi-supervised support vector regression (SSSVR). The parameters affecting the sensitivity of the SSSVR were optimized using the genetic algorithm to maximize the r-squared accuracy score. The intelligent system is evaluated using two quality metrics: the root mean square error (RMSE) and the mean absolute error calculated between the measured force from the industrial rolling field and the predicted force using the proposed system from one side, and the measured force and the calculated force from another side. Obtained results show the improvement while using the intelligent predictive system. The reduction in RMSE was achieved by the proposed system by 66.9% and 32.1% for oval and round shape passes, respectively in comparison to the conventional calculation method. [ABSTRACT FROM AUTHOR]

The atmosphere is one of the game elements that can significantly influence player's emotions. However, creating an immersive atmosphere that effectively influences player emotions poses several challenges, necessitating the utilization of various elements, such as audio-visual coordination and gameplay design. This paper introduces a general framework for procedurally generating dungeons with joyful and horror atmospheres in games, providing an abstract perspective to address these challenges. The proposed framework introduces a categorization system for game elements based on their role within the game. Leveraging this categorization, the Comprehensive Arrangement of Game Elements (CAGE) pattern is introduced, which facilitates the appropriate placement of elements within the dungeon environment. Subsequently, the General Framework for Generating Dungeons with Atmosphere (GFGDA) is employed to procedurally create the dungeon using the Feasible–Infeasible Two-Population (FI-2Pop) algorithm. To enhance gameplay experience, similar elements in the dungeon environment that impact gameplay are grouped and their coordination is evaluated by creating a graph based on the CAGE pattern. The transition and coordination of audio-visual elements along the path between these impactful elements are assessed in order to generate an immersive atmosphere within the dungeon. To ensure diversity, examining the variety of dungeons generated over 100 runs demonstrates that our method consistently produces distinct results in each iteration. Moreover, two comparative studies were conducted, one with 51 volunteers and another with 10 volunteers. In the first study, the Game Experience Questionnaire (GEQ) was utilized to assess the emotional impact of dungeons generated by our method. These were compared to dungeons created using a uniform random approach, alongside relevant research. The results suggest that our method significantly influences player emotions across the four components of the GEQ—sensory and imaginary immersion, flow, negative effects, and challenge—when compared to dungeons generated by the uniform random approach and another researched method. In another study, the emotional impact of two dungeons, one generated with joyful elements and the other with eerie elements, was evaluated using the GEQ. The findings indicate significant differences between the two components of the GEQ—tension and positive effects—when players interacted with the level containing joyful elements compared to the one with eerie elements. [ABSTRACT FROM AUTHOR]

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructures, exploiting vulnerabilities within existing security protocols and disrupting the seamless availability of online services. The intricate interconnections of nodes within computer networks contribute to the dynamic structure of this environment, complicating efforts to establish a secure and productive user experience. Effectively mitigating DDoS attacks in this complex networked setting remains a challenge. While current strategies primarily rely on anomaly detection and signature-based techniques, utilizing statistical analysis and predefined patterns to identify and thwart attacks, none have consistently demonstrated efficacy or reliability. Consequently, there is a compelling need for advancements in security mechanisms to address DDoS threats more effectively. This research introduces an innovative and highly efficient approach that incorporates various classification algorithms, including Random Forest, Decision Tree, Gradient Boosting, Linear SVM, Logistics, K-nearest neighbors (KNN), and AdaBoost, for DDoS attack detection. The performance of these machine learning classifiers is evaluated using key metrics such as accuracy, recall, F1-score, and precision. Remarkably, experimental results reveal outstanding accuracy rates, with Random Forest achieving the highest accuracy in detecting attacks. Additionally, a genetic algorithm is employed to select optimal features from the dataset, further enhancing the performance of the classifiers. This results in a notable 25% increase in accuracy, surpassing AdaBoost and Logistics, with K-nearest neighbors emerging as the top performer in terms of accuracy. [ABSTRACT FROM AUTHOR]

AbstractScale in multiscale geographically weighted regression (MGWR) directly impacts the accuracy of coefficient estimates and shapes the comprehensive evaluation of the intensity of spatially non-stationary relationships. Presently, MGWR primarily utilizes back-fitting for sequentially optimizing multiple scales (MGWR-BF). However, the set of individual optima obtained through sequential optimization may not necessarily represent the global optimum. To address this issue, this paper proposes a multi-scale cooperative optimization within MGWR (MGWR-GA) model. Specifically, MGWR-GA employs a genetic algorithm to simultaneously input potential scale combinations, each comprising P scales. Subsequently, it introduces a dedicated overall estimation algorithm designed for these P scales, ultimately determining the optimal scale combinations based on the AICc. Simulation experiments have shown that, at least for global stationarity, the scales obtained by MGWR-GA approximate the true values across twelve different test environments. Additionally, the coefficient estimation bias of MGWR-GA is lower than that of MGWR-BF, especially in low signal-to-noise ratio settings. Empirical experiments further confirm the effectiveness of MGWR-GA in identifying both globally stationary and locally non-stationary scales. Furthermore, MGWR-GA outperforms MGWR-BF in terms of goodness-of-fit, adjusted goodness-of-fit, AICc and spatial autocorrelation of residuals. These findings indicate that MGWR-GA can serve as a valuable tool for modeling spatially non-stationary relationships. [ABSTRACT FROM AUTHOR]

Preconception and prenatal genetic counseling is a well-established means of risk assessment in many parts of the world, and in recent years, an emerging concept in India. Likelihood of an offspring having autosomal recessive disorder increases based on the degree of consanguinity. Hence, genetic testing of the couple for the identification of carrier status for disease-causing variants is crucial. The purpose of this study is to understand the frequency of genetic abnormalities in consanguineous marriages by using a comprehensive genetic testing algorithm where in karyotyping, FISH, exome sequencing and microarray are used sequentially to determine the genetic etiology based on the clinical presentation and to evaluate the need and benefits of preconceptional and prenatal genetic counseling. This retrospective study includes 66 couples having consanguinity referred for genetic counseling and testing. Of the 66 couples, 58 underwent comprehensive genetic testing which included Karyotyping, Fluorescence in Situ Hybridization (FISH), Microarray and Exome sequencing based on their clinical presentation. The analyses revealed a genetic abnormality in approximately 31% and chromosomal polymorphic variations & variants of uncertain significance in 17% of the couples. Counseling in these couples helped in identifying the carrier status and enabled them to take an informed decision in subsequent pregnancies. These findings reiterate the acute need for preconception and prenatal genetic counseling services in India. [ABSTRACT FROM AUTHOR]

Conventional supply chains face challenges in reducing overall logistics costs and optimizing the consumption of logistics resources. To address these challenges, we propose a two-phase path planning method for handling periodic logistics tasks. The novelty of our approach lies in harnessing the collective power of vehicles with periodic commuting paths to minimize logistics costs, offering a practical and convenient solution for achieving smart supply chains. In the first phase, we develop an optimization model to determine the optimal delivery paths for periodic logistics tasks, with the objective of minimizing the overall cost. Leveraging the A-star algorithm, we search for suitable sequence sets of commuting vehicles within the commuting path network for each logistics task. In the second phase, we select vehicle sequences from the available sets for each task, forming a combination that can effectively accomplish all logistics tasks. To obtain the optimal solution, we employ an adaptive genetic algorithm to refine and optimize this combination. Experimental results demonstrate that our approach effectively solves the problem of periodic logistics delivery planning in smart supply chains. It achieves cost savings of 15.6% and 13.8% in two cases compared to traditional methods, showcasing its practicality and efficiency in reducing logistics costs. [ABSTRACT FROM AUTHOR]

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders, including Dementia, Parkinson's disease, various Vision disorders, Multiple sclerosis, and transsynaptic degenerative changes represent a significant challenge in aging populations. This editorial synthesizes and discusses recent advancements in understanding the genetic and environmental factors contributing to these diseases. Central to these advancements is the role of neuroinflammation and oxidative stress, which exacerbate neuronal damage and accelerate disease progression. Emerging research underscores the significance of mitochondrial dysfunction and protein aggregation in neurodegenerative pathology, highlighting shared mechanisms across various disorders. Innovative therapeutic strategies, including gene therapy, CRISPR-Cas technology, and the use of naturally occurring antioxidant molecules, are being investigated to target and manage these conditions. Additionally, lifestyle interventions such as exercise and healthy diet have shown promise in enhancing brain plasticity and reducing neuroinflammation. Advances in neuroimaging and biomarker discovery are necessary to improve early diagnosis, while clinical and preclinical studies are essential for the translation of these novel treatments. This edition aims to bridge the gap between molecular mechanisms and therapeutic applications, offering insights into potential interventions to mitigate the impact of neurodegenerative diseases. By establishing a deeper understanding of these complex processes, we aim to move closer to effective prevention and treatment strategies, ultimately improving the quality of life for those affected by neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Heavy metals such as Lead(II), Nickel(II), Manganese(II), Cadmium(II), Chromium(VI), and others are leached from coal ash in thermal power plants, contaminating ash pond water and subsequently contaminating groundwater. Phycoremediation using microalgae or cyanobacteria is an emerging technology to remove such heavy metals from ash pond water. The present study aims at development of an accurate data-driven Genetic Programing (GP) approach for modelling of the phycoremediation process for abatement of the above-mentioned heavy metals using a consortium comprising of a cyanobacterium Synechococcus sp. and green algae Chlorella sp. The developed model was used to find a relation between the average percentage removal of metals with all input parameters such as the initial metal concentrations, pH, and days. To maximise metal removal, Genetic Algorithm (GA) optimisation technique was applied to determine optimal values of input parameters. These optimum input parameters are difficult to get through experimentation using the trial and error method. The established modelling and optimisation technique is generic and can be applied to any other experimental study. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective design optimization of axial slot casing treatments in a 1.5 stage transonic compressor. To perform the optimization, an in-house optimization design platform is constructed using genetic algorithm and Kriging surrogate model. The optimization objectives are the peak efficiency at the design rotating speed and the stall margin improvements at both design and off-design rotating speeds. Instead of massive unsteady simulations that are required to accurately predict the stall margin, a stall margin improvement indicator is introduced based on the time-averaged axial momentum budget analysis at the rotor tip region. With this indicator, only one steady simulation is needed to evaluate the stall margin enhancement ability of a new casing treatment design at a given rotating speed. The axial slots are parameterized with a total of eight design parameters. The meridional profiles are described with two B-spline curves to define parameters such as the size, axial position, and shape of the slots. Circumferential parameters include the open area ratio and inclination angle. When the optimization finally converges, different optimal variants are selected from the database and their effects are investigated with both experiments and numerical simulations. The detailed flow fields are analyzed in depth with results from unsteady simulations. The application of the optimal casing treatments exerts a suction and re-injection effect, pushes the interface between tip leakage flow and incoming main flow downstream and reduced flow blockage in the blade tip region. Consequently, the stability of the compressor is significantly improved. [ABSTRACT FROM AUTHOR]

How to quickly and accurately identify the kinematic parameter errors is an important prerequisite for robot accuracy compensation. The laser tracker is used to measure and identify the kinematic parameters of the robot. The influence of laser tracker layout in robot pose measurement is analyzed, and the evaluation function of laser tracker layout is constructed to determine the optimal layout position. Then, the mapping relationship between the position and orientation deviation of the robot end and the deviation of each kinematic parameter is established, and the kinematic parameter identification model integrating the position and orientation information is constructed. The identification model is compared to the identification model based on position information to clarify the intrinsic reason of high accuracy. Next, aiming at the problem that the genetic algorithm is easy to prematurely converge to the local optimal solution, a hybrid genetic algorithm is constructed by introducing the simplex method to determine the optimal calibration pose set of the robot. On this basis, the results of robot kinematics parameter identification based on position measurement data, pose measurement data, and optimal calibration pose set are compared and analyzed through experiments, which verifies the effectiveness of the robot kinematics parameter identification model based on pose measurement data and optimization strategy. [ABSTRACT FROM AUTHOR]

As global energy demands surge and the environmental implications of fossil fuel dependence become more pronounced, there is an urgent need to transition toward more sustainable and eco-friendly energy alternatives. This underscores the dire need for sustainable, secure, and environmentally friendly energy solutions. To this end, efficient energy management strategies combined with the optimal design of hybrid renewable energy systems are paramount for judiciously harnessing renewable resources. In such systems, wind turbines, photovoltaic panels, diesel generators, and battery storage must be meticulously sized to ensure cost-efficiency, environmental sensitivity, and resilience against unpredictable load variations. Addressing these design challenges, our study emphasized the significance of strategic efficiency, prudential component selection, and system dependability. We designed an off-grid hybrid renewable energy system, incorporating photovoltaic panels, wind turbines, battery storage, and diesel generators, to meet the annual energy requirements of a university campus. After recording data for a full year, which included metrics on solar radiation, wind speed, ambient temperature, and campus load, we developed a model founded on comprehensive energy management strategies. This model aims to identify optimal design parameters, reduce annual costs, achieve sustainable energy benchmarks, and ensure a harmonious power exchange between system components. For optimization, we used an array of algorithms, notably the genetic algorithms, particle swarm optimization, gravity search algorithms, and hybrid algorithms, such as the hybrid genetic algorithm-particle swarm optimization and the hybrid gravity search algorithm-particle swarm optimization, supplemented by the HOMERPro software. Our findings revealed that the integration of photovoltaic panels with battery storage led to an annual system cost of $671,474.98, a levelized cost of energy of $0.1800, a total net present cost of $10,898,221.74, and a renewable energy fraction of 100%. It became evident that the hybrid genetic algorithm combined with particle swarm optimization, when aligned with astute energy management strategies, was more effective in determining optimal design parameters than other methodologies. Through this research, we offer profound insights into the dynamics of hybrid renewable energy systems, serving as a guide for pragmatic design and tangible implementation. [ABSTRACT FROM AUTHOR]

Compared with the traditional AC grid, the flexible DC grid has the advantages of low wire loss and large transmission capacity, but it is difficult to extract fault signals and diagnose various faults. Therefore, a fault detection method based on complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) multiscale entropy (MSE) and genetic algorithm optimization support vector machine (GA-SVM) is proposed. Firstly, CEEMDAN is used to decompose the extracted fault line mode voltage signal into several intrinsic mode function (IMF). The IMF containing more fault information is selected to reconstruct the denoising signal. The MSE of the reconstructed signal is calculated and input into the GA-SVM classifier as the fault feature, and the fault line mode voltage signals of different fault types under different operating conditions are classified and recognized. A large number of simulation results prove that the proposed method has strong anti-interference ability and high reliability, and has high classification accuracy in the case of small sample data. Compared with Linear-SVM, PSO-SVM, KNN and Fine Tree intelligent algorithms, the proposed method shows a significantly improved accuracy, 93.8888% on average. [ABSTRACT FROM AUTHOR]

The classical methods for optimizing wireless power transfer (WPT) systems using mathematical equations or finite element methods can be time-consuming and may only sometimes yield optimal designs. In order to overcome this challenge, this paper introduces a novel approach integrating machine learning techniques with meta-heuristic methods to design and optimize a miniaturized, high-efficiency WPT receiving coil for biomedical applications. The objective is to achieve dimensions below 20 mm, a depth of 30 mm within the tissue, and a frequency of 13.56 MHz. Our approach leverages a neural network (NN) model to predict efficiency based on geometric coil parameters, eliminating the need for complex equations. The NN was trained on a dataset generated via finite element method simulations. We employ two meta-heuristic algorithms, the genetic algorithm and the coyote optimization method, to find optimal parameters that maximize efficiency. Our NN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, the proposed WPT coil design approach enhances transfer efficiency by up to 76%, significantly reducing computation time compared to classical methods. Finally, we validate our results using finite element simulation with Ansys Maxwell 3D. [ABSTRACT FROM AUTHOR]

The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR. [ABSTRACT FROM AUTHOR]

The paper partially covered Active Constrained Layer Damping (ACLD) cantilever beams' dynamic modeling, active vibration control, and parameter optimization techniques as the main topic of this research. The dynamic model of the viscoelastic sandwich beam is created by merging the finite element approach with the Golla Hughes McTavish (GHM) model. The governing equation is constructed based on Hamilton's principle. After the joint reduction of physical space and state space, the model is modified to comply with the demands of active control. The control parameters are optimized based on the Kalman filter and genetic algorithm. The effect of various ACLD coverage architectures and excitation signals on the system's vibration is investigated. According to the research, the genetic algorithm's optimization iteration can quickly find the best solution while achieving accurate model tracking, increasing the effectiveness and precision of active control. The Kalman filter can effectively suppress the impact of vibration and noise exposure to random excitation on the system. [ABSTRACT FROM AUTHOR]

Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest R P 2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the R P 2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance. [ABSTRACT FROM AUTHOR]

The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution. [ABSTRACT FROM AUTHOR]

The current work proposes the incorporation of an artificial neural network to solve the Gross–Pitaevskii equation (GPE) efficiently, using a few realistic external potentials. With the assistance of neural networks, a model is formed that is capable of solving this equation. The adaptation of the parameters for the constructed model is performed using some evolutionary techniques, such as genetic algorithms and particle swarm optimization. The proposed model is used to solve the GPE for the linear case ( γ = 0 ) and the nonlinear case ( γ ≠ 0 ), where γ is the nonlinearity parameter in GPE. The results are close to the reported results regarding the behavior and the amplitudes of the wavefunctions. [ABSTRACT FROM AUTHOR]

This paper aims to study the effect of eliminating some elements from the original planar array (OPA) to generate a useful flat‐top coverage pattern (FTCP) using a genetic algorithm. The study involves dividing the fully planar array (i.e., OPA) into two regions with an unequal number of elements. The first region includes the elements located in the core (center) of the array, and the remaining elements are the second region, and their location is within the boundary (side) of the array. Only the elements of the first region are included in the optimization process, while the elements of the second region are eliminated in the form of a specific geometric composition without affecting the FTCP generation. Comparing the OPA that N×M elements with the first region (active subarray) elements, it is noted that the number of elements has been reduced from N×M to (N − 2x) × (M − 2y) to reduce the complexity of the system as much as possible without affecting the performance in general. Different boundary subarray shapes are presented in this paper with different numbers of elements. The obtained results showed the effectiveness of the proposed method in generating FTCP close to its counterpart in the OPA. [ABSTRACT FROM AUTHOR]

In this research, researcher has implemented supervised machine learning, namely k-nearest neighbor (k-NN) which is optimized using genetic algorithms, and the internet of things (IoT) on the nutrient film technique (NFT) hydroponic system. The aim of this research is to improve the accuracy of classification of nutrient and light conditions in NFT system, and evaluating the harvest of hydroponic farming. The dataset was obtained by observing and recording nutritional and light conditions using sensors for 35 days during the growing period of lettuce in the NFT system, thus obtaining 1,680 data. Then, a training dataset is created based on that dataset. The system architecture is divided into 3 parts, namely the sensor system, data processing, and actuator system. The conclusion of this research is the IoT can be used to monitor the nutritional and light conditions of NFT system in real time and automatic control actions can be carried out using actuators controlled by the Raspberry Pi, the impact of applying the k-NN algorithm and the genetic algorithms is the accuracy of classifying nutritional and light conditions is 92%, the lettuce in a NFT system controlled by the system grow better than the lettuce in a NFT system controlled manually. [ABSTRACT FROM AUTHOR]

The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn's index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM. [ABSTRACT FROM AUTHOR]

The presented work outlines a new genetic crossover operator, which can be used to solve problems by the Grammatical Evolution technique. This new operator intensively applies the one-point crossover procedure to randomly selected chromosomes with the aim of drastically reducing their fitness value. The new operator is applied to chromosomes selected randomly from the genetic population. This new operator was applied to two techniques from the recent literature that exploit Grammatical Evolution: artificial neural network construction and rule construction. In both case studies, an extensive set of classification problems and data-fitting problems were incorporated to estimate the effectiveness of the proposed genetic operator. The proposed operator significantly reduced both the classification error on the classification datasets and the feature learning error on the fitting datasets compared to other machine learning techniques and also to the original models before applying the new operator. [ABSTRACT FROM AUTHOR]

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs' potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations' safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2. [ABSTRACT FROM AUTHOR]

In the production of hybrid composites made of thermoplast and metal sheets, certain limitations in the linear forming process can be overcome through a free kinematic forming process. This process involves distributing the thermoplast press mass using a tumbling or cyclic rolling movement of the forming tool. Due to this special kinematics, only a small part of the tool is ever in engagement with the workpiece, so the component is formed incrementally. Therefore, the final geometry does not have to be created in one stroke but can be carried out step by step in several partial strokes. In order to determine the optimal trajectory of the forming tool, the kinematic interplay between the workpiece, forming tool, and toolpath should be numerically captured in a mathematical model. The specific toolpath depends on the component being formed, as well as the actual geometry of the forming tool. In this research, the free kinematic forming process is modeled and the optimal toolpath is determined using a genetic algorithm. The optimal toolpath enables a uniform distribution of the thermoplast to a specified thickness and collisions between the upper and lower tool are avoided. This enables key insights into the free kinematic forming process and opens up further avenues of research for this innovative and future‐oriented manufacturing concept. [ABSTRACT FROM AUTHOR]

CFRP is characterized by its anisotropic properties. Therefore, further structural weight reduction might be achieved by introducing the fiber direction as a design variable. For this study, steering fiber laminates defined by a Bezier curve are applied to a skin panel of CFRP stiffened panels. Then, simultaneous optimization of the fiber paths and stringer dimensions is performed to minimize the panel weight. We propose a new surrogate model that combines two neural networks and Latin hypercube sampling. Stiffness, manufacturability, and buckling load are introduced as constraints. Also, optimization is performed using the surrogate model and a genetic algorithm (GA). Furthermore, the stringer dimensions and fiber directions are optimized for straight fiber laminates. Comparison of steering fiber laminates and straight fiber laminates demonstrated that the introduction of steering layup into skin plates can reduce the stringer weight by up to 25.6%. An effective method for designing the geometric dimensions and layup path is provided. [ABSTRACT FROM AUTHOR]

Extended periods of driving, lasting up to 8 hours, can have a severe impact on a driver's health due to the presence of low-frequency vibrations caused by uneven road surfaces. This paper presents a novel biodynamic system modelling method for investigating how the human body responds to low-frequency vibrations. The lumped biodynamic parameter model predicts time-domain head acceleration and frequency-domain transmissibility ratio. This paper's design innovation involves a three-dimensional printed cushion composed of honeycomb cells and a seating suspension system featuring six double-diamond isolators. Using the Design of Experiments, Response Surface Method, and Genetic Algorithm methods, the structural parameters of the seating suspension system and cushion in the model are optimized for the minimum peak transmissibility ratio. The uniqueness of this paper's approach lies in its sensitivity analysis and optimization of the seat cushion and seating suspension system design parameters within a quarter-vehicle suspension system environment, which sets it apart from other literature. Furthermore, this paper explores the connection between the negative Poisson's ratio and quasi-zero stiffness as a means of reducing vibration transmission. [ABSTRACT FROM AUTHOR]

In order to achieve more scientific design of the reduction gear and reduce material waste, pre-stressed modal analysis method was combined with multi-objective optimization algorithm to optimize the structure of the reduction gear basic body. The model was simplified and parameterized and the maximum stress and equivalent stiffness under different parameter size combinations were obtained through finite element analysis. Separately, genetic clustering method, neural network method, and Kriging method were used to construct the response surface function. Through error verification and comparison, it was found that the Kriging method was more suitable for the gear model. In the design of variable extremum search, multi-objective genetic algorithm and sequential quadratic programming were compared and analyzed. The results show that the mass of the gear can be reduced by 39.9 %, while the maximum stress remains unchanged, the equivalent stiffness is not reduced, and a good optimization design effect is achieved. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning-based techniques are now widely used to identify and diagnose various diseases. However, getting a sufficient data for these machine learning models is difficult, and usually collected data is unbalanced i.e. less number of instances with disease class and a large number of classes without the disease. These imbalanced data cause poor performance of the classifier in the detection of minority or disease classes. To address this class imbalance problem for medical data we have applied 17 different class imbalance handling techniques on four publically available datasets with Random forest as a base classifier. The comprehensive review covering the time frame of 1990 to 2023 is also done on "Class imbalance handling techniques" to gather insights using VOSviewer software. Performances of different class imbalance handling techniques are statistically evaluated and impact of different disease datasets on the prediction performance is also statistically assessed. Two novel techniques Genetic Algorithm-Cost sensitive-Deep neural network(GA-CS-DNN) and Class imbalance handling technique-Genetic Algorithm-Deep neural Network(CIH-GA-DNN)are proposed for handling class imbalance problems. Performances of proposed techniques are compared with other state of art class imbalance handling techniques and obtained results showed that OnesidedSelection outperformed all other techniques. A statistical test further demonstrated that OnesidedSelection performs differently than SMOTENN. Significant statistical differences in illness prediction can be seen between the kidney and diabetes, prostate and kidney, and kidney and heart datasets when compared pair-wise. [ABSTRACT FROM AUTHOR]

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems including several models and nonlinearity features. Additionally, it offers a comprehensive solution for optimizing parameters in various field-road segmentation models. [ABSTRACT FROM AUTHOR]

This study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence‐dependent setup times (DRCFJS‐SDSTs). We introduce a pioneering tri‐objective mixed‐integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi‐skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence‐dependent setup time‐related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP‐hard nature of the proposed DRCFJS‐SDST, we employ the epsilon constraint method to derive exact optimal solutions for small‐scale problems. For larger instances, we develop a modified variant of the multi‐objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi‐objective particle swarm optimization (MOPSO) and non‐dominated sorted genetic algorithm (NSGA‐II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA‐II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies. [ABSTRACT FROM AUTHOR]

Phononic crystals are periodically artificial composite structures that have garnered widespread attention for their bandgap properties and potential applications in wave propagation control, offering novel solutions for engineering vibration and noise reduction. The design of these materials is a current research focus. This study takes two-dimensional layered phonon crystal structure as an example, presents an innovative method for designing two-dimensional layered phononic crystal structures, utilizing LightGBM and an improved genetic algorithm. Initially, the LightGBM algorithm was employed for bandgap prediction of the phononic crystals, with the structural arrangement vectors subjected to categorical feature processing, and hyperparameter tuning conducted through the simulated annealing algorithm, achieving a prediction accuracy with an overall error of no more than 2% within 1/3134 of the traditional finite element method computation time. Subsequently, a design method integrating a genetic algorithm based on an elite preservation strategy was introduced. Using the environmental vibration issue caused by subway operation as an example, a fitness function optimized for bandgap width was established, resulting in a population with structural bandgaps covering the 30 ~40 Hz frequency range. Finally, finite element analysis of one structure verified its bandgap characteristics align closely with expectations. This research provides a new solution for the efficient optimization design of multiple phononic crystal structure schemes. [ABSTRACT FROM AUTHOR]

Twist error exists when continuously generating grinding helical gears with lead modification, which leads to problems such as meshing impact and low load bearing. To improve the meshing performance of helical gears with twist error, a multi-objective optimization design method is proposed. Firstly, the mathematical equation of helical gear tooth surface with twist error is established. With helix angle and the modified amount as design variables, the maximum contact stress and amplitude of loaded transmission error as objective functions, the loaded tooth contact analysis (LTCA) was carried out by finite element method (FEM) and 25 sets of data were obtained. Then the function model was constructed by the BP neural network, and a fast elitist nondominated sorting genetic algorithm (NSGA-II) is used to solve the model. The results show that the maximum contact stress and amplitude of loaded transmission error after optimization are reduced by 29.6 % and 92.2 % respectively. [ABSTRACT FROM AUTHOR]

Due to the increased cost and reliability, only few life failure data can be gathered. Therefore, it is significant to improve the parameter estimation accuracy of Weibull distribution with small samples. In this study, a Weibull parameter estimation method based on hybrid gray genetic algorithm with modified maximum likelihood method (HGLM) is proposed. In the HGLM, the solution model for the location parameter is established by using gray genetic algorithm. On the basis of the location parameter, the shape and scale parameters are acquired with improved maximum likelihood method. Then, a full Monte-Carlo sampling simulation study is conducted to compare the parameter estimation accuracy and fitting of the HGLM with three existing methods. Results show the HGLM can accurately and stably estimate the Weibull parameters with small samples. Finally, two cases are analyzed to demonstrate the application of the HGLM method and the feasibility of sample expansion is also discussed. [ABSTRACT FROM AUTHOR]

The logistics demands of industries represented by e-commerce have experienced explosive growth in recent years. Vehicle path-planning plays a crucial role in optimization systems for logistics and distribution. A path-planning scheme suitable for an actual scenario is the key to reducing costs and improving service efficiency in logistics industries. In complex application scenarios, however, it is difficult for conventional heuristic algorithms to ensure the quality of solutions for vehicle routing problems. This study proposes a joint approach based on the genetic algorithm and graph convolutional network for solving the capacitated vehicle routing problem with multiple distribution centers. First, we use the heuristic method to modularize the complex environment and encode each module based on the constraint conditions. Next, the graph convolutional network is adopted for feature embedding for the graph representation of the vehicle routing problem, and multiple decoders are used to increase the diversity of the solution space. Meanwhile, the REINFORCE algorithm with a baseline is employed to train the model, ensuring quick returns of high-quality solutions. Moreover, the fitness function is calculated based on the solution to each module, and the genetic algorithm is employed to seek the optimal solution on a global scale. Finally, the effectiveness of the proposed framework is validated through experiments at different scales and comparisons with other algorithms. The experimental results show that, compared to the single decoder GCN-based solving method, the method proposed in this paper improves the solving success rate to 100% across 15 generated instances. The average path length obtained is only 11% of the optimal solution produced by the GCN-based multi-decoder method. [ABSTRACT FROM AUTHOR]

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim's robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity. [ABSTRACT FROM AUTHOR]

The development of transport infrastructure is crucial for economic growth, social connectivity, and sustainable development. Many countries have historically underinvested in transport infrastructure, necessitating more efficient strategic planning in the implementation of transport infrastructure investment projects. This article addresses the selection and scheduling of transport infrastructure projects, specifically within the context of utilizing pre-allocated funds within a multi-annual budget investment program. The current decision-making process relies heavily on expert judgment and lacks quantitative decision support methods. We propose a genetic algorithm as a decision-support tool, framing the problem as an NP-hard 0–1 multiple knapsack problem. The proposed genetic algorithm (GA) is unique for its matrix-encoded chromosomes, specially designed genetic operators, and a customized repair operator to address the large number of invalid chromosomes generated during the GA computation. In computational experiments, the proposed GA is compared to an exact solution and proves to be efficient in terms of quality of obtained solutions and computational time, with an average computational time of 108 s and the quality of obtained solutions typically ranging between 85% and 95% of the optimal solution. These results highlight the potential of the proposed GA to enhance strategic decision-making in transport infrastructure development. [ABSTRACT FROM AUTHOR]

The accurate prediction of gas emissions has important guiding significance for the prevention and control of gas disasters in order to further improve the prediction accuracy of gas emissions in the mining face. According to the absolute gas emission monitoring data of the 1417 working face in a coal mine in Shaanxi Province, a GA-VMD-SSA-LSTM gas emission prediction model (GVSL) based on genetic algorithm (GA)-optimized variational mode decomposition (VMD) and sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) is proposed. Firstly, a VMD evaluation standard for evaluating the amount of decomposition loss is proposed. Under this standard, the GA is used to find the optimal parameters of the VMD. Then, the SSA is used to optimize the key parameters of the LSTM to establish a GVSL prediction model. The model predicts each component and finally superimposes the prediction results for each component to obtain the final gas emission result. The results show that the accuracy of the evaluation indexes of the GVSL model and VMD-LSTM model, as well as the SSA-LSTM model and Gaussian process regression (GPR) model, are compared and analyzed horizontally and vertically under three scenarios with prediction sets of 121,94 and 57 groups. The GVSL model has the best prediction effect, and its fitting degree R2 values are 0.95, 0.96, and 0.99, which confirms the effectiveness of the proposed GVSL model for the time series prediction of gas emission in the mining face. [ABSTRACT FROM AUTHOR]

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NOx, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively. [ABSTRACT FROM AUTHOR]

With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency. [ABSTRACT FROM AUTHOR]

A novel and original computer scaling-up system for the counter-rotating twin screw extrusion of polymers was developed. In the system, each scaling parameter (scaling criterion) may be considered as an objective function to be minimized for the single parameters or the functional relationships along the length of the screw. Scaling was based on the process simulation, which was performed using the comprehensive (or global) counter-rotating twin screw extrusion program called TSEM (Twin Screw Extrusion Model). The extrusion process was scaled by applying GASESTWIN (Genetic Algorithms Screw Extrusion Scaling) software developed for this purpose using genetic algorithms. Scaling up the extrusion process was carried out to enhance extrusion process throughput according to the scaling criteria specified by the single quantities of polymer melt temperature at the die exit and relative melting length, and by distributions along the screw length of the extrusion parameters of the polymer melt temperature and polymer plasticating. The global objective function had the lowest value for the selected extrusion parameters, which means the minimal differences between the values of the scaled-up processes and extrusion throughput was significantly increased. The solution to the problem of scaling the counter-rotating process presented in this paper complements the existing solutions for optimizing and scaling basic variants of the extrusion process, i.e., flood-fed and starve (metered)-fed single-screw extrusion, as well as co-rotating and counter-rotating twin-screw extrusion. [ABSTRACT FROM AUTHOR]

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration. [ABSTRACT FROM AUTHOR]

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water–paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σc, tensile strength σt, shear strength τf, elastic modulus E, Poisson's ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water–paste ratio had extremely significant effects on σc, σt, τf, E, ν and c. The iron powder content had a highly significant effect on σc, σt, τf and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σc, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water–paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials. [ABSTRACT FROM AUTHOR]

Although the double-double (DD) laminates proposed by Tsai provide a promising option for achieving better structural performance with lower manufacturing and maintenance costs, the buckling performance of perforated DD laminates still remains clear. In this study, optimal ply angles, rotation angles, and the corresponding maximum buckling loads are determined for DD laminates with various cutouts, which are used for comparisons to evaluate the effects of cutout size and shape on the buckling behaviour of perforated DD laminates. Apart from conventional circular and elliptical cutouts, the use of a combined-shape cutout for DD laminates is also investigated. As a large number of optimisations are required to obtain the maximum buckling loads for different cases in this study, an efficient optimisation method for perforated DD laminates is proposed based on an artificial neural network (ANN) and a genetic algorithm (GA). Unlike conventional quadaxial (QUAD) laminates, the repetition of a four-ply sublaminate in DD laminates makes their layup to be represented by only two ply angles; hence, the application of ANN models for predicting the buckling behaviour of various perforated DD laminates is studied in this paper. The superior performance of the ANN models is demonstrated by comparisons with other machine learning models. Instead of using the time-consuming FEA, the developed ANN model is utilised within a GA to obtain the maximum buckling load of perforated DD laminates. Compared to the circular cutout, the use of elliptical and combined-shape cutouts leads to more noticeable changes in the optimal ply angles as the cutout size increases. Based on the obtained results, the use of the combined-shape cutout is recommended for DD laminates. [ABSTRACT FROM AUTHOR]

Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium Earth orbit (MEO) navigation satellites, this paper designs the LEO navigation constellation and considers the task requirements of different stages of constellation deployment. Firstly, the LEO navigation constellation is designed by a non-dominated sorting genetic algorithm II (NSGA-II). The average position dilution of precision (PDOP) is 1.676, which is an improvement compared to the average PDOP offered by the four traditional GNSS. Secondly, the staged deployment of constellation takes into account the degradation of constellation performance caused by the failure of MEO navigation satellites, and the Monte Carlo method is used to analyze the case of three simultaneous satellite failures. The results show that a single satellite failure within each orbital plane and adjacent satellites with close phase separation has a great impact on the performance of the MEO navigation constellation. On this basis, a staged deployment strategy was adopted in order to balance cost, risk, and performance. The three phases deploy 66, 156, and 288 satellites, respectively; as a make-up constellation under contingencies, a navigation enhancement constellation, and an independent navigation constellation, the deployment of the staged sub-constellations meets the mission requirements. The constellation design and staged deployment method proposed in this paper can provide reference for the future study of LEO navigation constellations. [ABSTRACT FROM AUTHOR]

This study investigates the application of LiDAR point cloud datasets for building segmentation through a combined approach that integrates unsupervised segmentation with evolutionary optimization. The research evaluates the extent of improvement achievable through genetic algorithm (GA) optimization for LiDAR point cloud segmentation. The unsupervised methodology encompasses preprocessing, adaptive thresholding, morphological operations, contour filtering, and terrain ruggedness analysis. A genetic algorithm was employed to fine-tune the parameters for these techniques. Critical tunable parameters, such as the interpolation method for DSM and DTM generation, scale factor for contrast enhancement, adaptive constant and block size for adaptive thresholding, kernel size for morphological operations, squareness threshold to maintain the shape of predicted objects, and terrain ruggedness index (TRI) were systematically optimized. The study presents the top ten chromosomes with optimal parameter values, demonstrating substantial improvements of 29% in the average intersection over union (IoU) score (0.775) on test datasets. These findings offer valuable insights into LiDAR-based building segmentation, highlighting the potential for increased precision and effectiveness in future applications. [ABSTRACT FROM AUTHOR]