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Demand for perishable food is sensitive to product prices and is affected by the prices of similar or alternative products. While brand loyalty influences the demand for products, determining a reasonable price requires a precise pricing strategy. In this paper, a pricing model for perishable food is presented in which the brand value of the product and the price of other manufacturers as competitors are considered. To this end, this study first predicts the price of competitors using a combination of optimized Neural Networks and presents an optimized model using a Genetic Algorithm. This algorithm combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a Genetic Algorithm (GA). The proposed model is then used to merge with a game-theory model for the pricing of perishable foods. In this game-theory model, pricing approaches are developed based on identified prices of competitors. In the coordination contract game-theory model, Multi Retailer- one Supplier and Price-sensitive demand of Perishable product are developed with and without quantity discount contract. Obtained results indicate that independent procurement provides retailers with higher profit, while lower profit will be presented when coordination is not considered. Also, with coordination, the ordering cycle increases, and the ordering frequency decrease. [ABSTRACT FROM AUTHOR]

Features within the dataset carry a significant role; however, resource utilization, prediction-time, and model weight are increased by utilizing high-dimensional data in intrusion-detection paradigm. This paper aims to design a novel lightweight intrusion detection system in two phases utilizing a swarm intelligence-based technique. In 1st-phase, essential features are selected using particle swarm optimization algorithm by considering imbalanced dataset. Ant colony optimization algorithm is utilized in 2nd-phase for extracting information-rich and uncorrelated features. Additionally, genetic algorithm is employed for fine-tuning each detection model. Proposed model's performance is evaluated on different base and ensemble classifiers, and it is observed that xgboost achieves best accuracy with 90.38%, 92.63%, and 97.87% on NSL-KDD, UNSW-NB15, and CSE-CIC-IDS2018 datasets, respectively. The proposed model also outperforms other traditional dimensionality reduction and state-of-the-art approaches with statistical validation. This paper also analyses objective function of each metaheuristic algorithm used in this paper, applying convergence graphs, box, and swarm plots. [ABSTRACT FROM AUTHOR]

Nickel-based superalloy such as Inconel 718 has worldwide applications in the manufacturing of aircraft components and defence industries due to superior properties at elevated temperatures. The importance of this material in high-temperature applications due to its excellent thermo-physical properties is subject to extensive area of interest. The machining of nickel-based superalloy is a challenging task due to generation of high heat in grinding zone which impels the study of improvement of surface quality in the present study. The main aim of the present study is to find the optimum process parameters corresponding to minimum Ra value using different optimization techniques so that the production cost of components and time consumption can be minimized. In the present experimental study, investigation has been carried out on Inconel 718 through a CNC surface grinding machine. Due to the complexity involved in tough-to-machine material, the study focuses on the improvement of surface roughness using down grinding process by the optimization of three influential parameters such as wheel speed, depth of cut and table speed. Response surface methodology based central composite rotatable design is used in this study to illustrate the surface roughness value (Ra) which is greatly influenced by wheel speed followed by depth of cut and table speed. For the optimization of machining parameters, RSM coupled with genetic algorithm (GA) and particle swarm optimization (PSO) is used to reduce the time consumption in the selection of machining parameters and desirous output response in grinding. The Ra value corresponding to GA is found to be 0.2735 µm while 0.2586 µm using PSO technique. The best optimal process parameters corresponding to minimum Ra value using PSO technique are depth of cut = 5 µm, wheel speed = 628 m/min, and table speed = 3588 mm/min. Comparatively, PSO provided better results in terms of minimum surface roughness than GA. The validation of experimental results is done with a statistical model that has shown a fine level of corroboration among them. [ABSTRACT FROM AUTHOR]

Rubber particles and straw powder were used to prepare recycled rubber straw concrete, and the freeze–thaw test was conducted on the recycled rubber straw concrete using the quick-freezing method. The frost resistance of the recycled rubber straw concrete was evaluated by determining the relative dynamic modulus of elasticity, the rate of mass loss, and the flexural strength of the recycled rubber straw concrete in the process of freezing and thawing. SEM was used to observe the microstructure of the recycled rubber straw concrete after the freezing and thawing process. SEM observed the microstructure of recycled rubber straw concrete after freezing and thawing. The effect and mechanism of rubber admixture and straw admixture on the frost resistance of concrete were investigated by microanalysis. Based on the experimental data, the particle swarm algorithm and genetic algorithm were used to optimize the BP neural network to establish the prediction model of recycled rubber straw powder, and the results show that the PSO-BP neural network prediction model established in this paper has good accuracy and stability. It has a good prediction effect on the flexural strength and the number of freeze–thaw cycles of recycled rubber straw concrete under different mixing ratios. [ABSTRACT FROM AUTHOR]

This research focuses on experimentally investigating and optimizing heat transfer between two parallel disks, a prevalent system extensively utilized in engineering devices and various machinery. Analyzing heat transfer in this configuration is of paramount interest to researchers and engineers. An experimental setup was built to explore heat transfer between two parallel disks. Local Nusselt number and average Nusselt number were calculated to analyze heat transfer characteristics. The study delved into the effects of vital parameters such as the gap ratio, Reynolds number, and heat flux on heat transfer between two parallel disks. The analysis revealed that the Nusselt number increases with an increase in the gap between two disks up to a certain level, beyond which an inverse effect is observed. Moreover, the Nusselt number demonstrates a positive correlation with the Reynolds number. An in-depth analysis of local and average Nusselt numbers indicated that heat flux initially has a positive effect, followed by an adverse effect after reaching a certain level. To ascertain the optimum solution, three different techniques were employed: Response Surface Method (RSM), Cuckoo Search Algorithm (CS), and Genetic Algorithm (GA). The predicted optimum values for the gap ratio, Reynolds number, and heat flux using GA, CS, and RSM were as follows: gap ratio (17.36, 17.36, and 17.36), Reynolds number (100, 100, and 98.2), and heat flux (689.36, 694.5, and 682.449), respectively. Correspondingly, the resulting average Nusselt numbers were projected to be 48.59, 48.36, and 48.5271. To validate the obtained results, experiments were conducted and compared with the predicted values. The comparison among these techniques indicated that all results fell within an acceptable margin of error. Specifically, RSM exhibited an error of 1.917%, CS showed an error of 0.962%, and GA displayed an error of 0.8931%. [ABSTRACT FROM AUTHOR]

Optical beam steering is essential for maintaining line-of-sight (LOS) connections between communicating terminals, ensuring reliable Free Space Optical (FSO) communication. However, achieving a LOS connection is challenging due to disturbances such as vibrations from communication terminal and/or the host of the terminal, which can misalign the optical beam. The accuracy of the optical beam position sensor, the controller for the beam actuation unit, and the actuation units impact the beam steering system's performance. While extensive research has emphasized the importance of fast-steering mirrors (FSMs) as actuators and quadrant detectors (QDs) as optical beam position sensors in beam steering systems, most of these studies have focused on analyzing FSMs and QDs in isolation. This paper proposes a method to design and evaluate the impact of these components concurrently. Specifically, it suggests a tuning procedure for the controller in the FSM closed feedback loop and models the QD sensor using experimental data. The QD model is then integrated into the FSM control loop. To validate the viability of this integrated approach, its results are compared to those obtained with an ideal sensor. Integrating the QD model into the FSM control loop slightly increases overshoot but a faster and more responsive control is attainable. [ABSTRACT FROM AUTHOR]

The status of production equipment in industries is prone to change due to various factors. Thus, a proper anomaly detection procedure for equipment is indispensable. Supervised learning is commonly used in equipment state monitoring. However, these approaches need much manpower and time to obtain sufficient labeled data. In contrast to the technique, unsupervised learning-based Autoencoder, which learns from unlabeled datasets to discover patterns without human supervision, has recently demonstrated its promising performance in anomaly detection. Based on this advantage, this paper proposes the tool performance estimation (TPE) scheme to clearly detect abnormalities of any machinery equipment in industries. Through the collaborative interaction among four well-designed modules: AE (Autoencoder), PO (parameter optimization), RE (reconstruction error), and DI (deviation indicator), TPE defines three systematic and generic troubleshooting scenarios: (i) equipment, (ii) parameters, and (iii) others, by analyzing the severity of RE and DI. TPE is proven to immediately remind users to troubleshoot when abnormalities occur during production. In example 1, TPE has the best accuracy among benchmark methods. In example 2, TPE saves 33.9 % of productivity and a 34-day earlier warning to prevent production from shutting down. [ABSTRACT FROM AUTHOR]

Silicon wafers find applications in manufacturing large-scale micro-components widely available in consumer-based electronic devices. The milling-electrochemical spark micromachining (Milling-ECSMM) process is popular nowadays as an advanced micromanufacturing process for creating microchannels and several other geometric features. This paper presents a parametric effect of voltage, tool rotation, electrolyte concentration, and duty cycle on MRR, Ra, and TWR, using a tungsten carbide (WC) two-fluted flat-end milling cutter. The experimental model was developed using response surface methodology (RSM), and parametric multi-objective optimization was carried out using desirability function analysis (DFA) and genetic algorithm (GA). SEM and optical images were taken to identify the surface quality of the machined microchannel. The optimal combination of parameters obtained from GA as 90 V, 13 rpm, 64 g/l, and 40% DC gives better convergence and multi-objective response for MRR, TWR, and Ra as 0.409 mg/min, 0.0428 mg/min, and 0.572 µm, respectively. [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]

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for modeling and optimizing the trajectory and behavior of small solid rocket missiles. The proposed framework integrates a six-degree-of-freedom (6DoF) simulation environment experimentally tuned for accuracy, with a combination of genetic algorithms (GAs) and machine learning (ML) to enhance the performance of the missile path. In the initial phase, a GA is employed to optimize the missile's trajectory for efficient target acquisition, defining key launch parameters such as the ramp angle and lateral maneuver force to minimize positional errors and to ensure effective target engagement. Following trajectory optimization, the derived data are used to train an ML model that predicts setup parameters, significantly reducing computational costs and time. This close integration enables real-time adjustments for acquiring moving targets, thereby improving accuracy and minimizing maneuvering costs. This study also explores the application of fluidic thrust vectoring for small rockets, providing an innovative solution to enhance maneuverability and control, especially at low speeds. The proposed framework was validated using experimental launch data from the Icarus Team. The methodology offers a robust and cost-effective solution for precision targeting and improved maneuverability in aerospace and defense contexts. [ABSTRACT FROM AUTHOR]

This work introduces a novel computational approach based on Principal Component Analysis (PCA) for dimensionality reduction of the solution space in optimisation problems with known linear interdependencies among solution variables. By creating synthetic datasets with deliberately engineered properties and applying PCA, the solution space's remapping significantly reduces its dimensionality, leading to faster computation and more robust convergence in optimisation processes. We demonstrate this method by integrating it with a Genetic Algorithm (GA) for solving the optimal director distribution in liquid crystal (LC) devices, specifically addressing 2D and complex 3D spatial light modulator (SLM) structures such as twisted nematic liquid crystals (TN-LC) and parallel-aligned liquid crystal on silicon (PA-LCoS), respectively. The phase profiles obtained from the director vector distributions for horizontal and vertical high-frequency binary phase gratings closely match the theoretical values derived from minimising the traditional elastic Frank–Oseen functional via Euler–Lagrange equations. Beyond this specific application, our method offers a general framework for reducing computational complexity in optimisation problems by directly reducing the dimensionality of the solution space. This approach is applicable across various optimisation scenarios with well-known linear interdependencies among solution variables, enabling significant reductions in computational costs and improvements in robustness and convergence. [ABSTRACT FROM AUTHOR]

The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce security threats in IoT devices has become a topic of great importance. One of the most serious threats comes from botnets, which commonly attack IoT devices by interrupting the networks required for the devices to run. There are a number of methods that can be used to improve security by identifying unknown patterns in IoT networks, including deep learning and machine learning approaches. In this study, an algorithm named the genetic algorithm with hybrid deep learning-based anomaly detection (GA-HDLAD) is developed, with the aim of improving security by identifying botnets within the IoT environment. The GA-HDLAD technique addresses the problem of high dimensionality by using a genetic algorithm during feature selection. Hybrid deep learning is used to detect botnets; the approach is a combination of recurrent neural networks (RNNs), feature extraction techniques (FETs), and attention concepts. Botnet attacks commonly involve complex patterns that the hybrid deep learning (HDL) method can detect. Moreover, the use of FETs in the model ensures that features can be effectively extracted from spatial data, while temporal dependencies are captured by RNNs. Simulated annealing (SA) is utilized to select the hyperparameters necessary for the HDL approach. In this study, the GA-HDLAD system is experimentally assessed using a benchmark botnet dataset, and the findings reveal that the system provides superior results in comparison to existing detection methods. [ABSTRACT FROM AUTHOR]

This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the same input data. The results show that when the number of possible Hamiltonian cycles in a graph is increased, the memetic algorithm finds better solutions. The execution time of both algorithms is comparable. Also, the number of solutions that mutated during the execution of the genetic algorithm exceeds 50% of the total number of all solutions generated by the crossover operator. In the memetic algorithm, the number of solutions that mutate does not exceed 10% of the total number of all solutions generated by the crossover operator, summed with those of the local search method. [ABSTRACT FROM AUTHOR]

As long as humanity exists, it will continue to produce waste. For a livable society, these wastes need to be collected and disposed of at regular intervals. The collection and disposal of these wastes involve many processes and can be quite costly. This study aims to optimize the journey of solid waste from generation to a disposal facility with minimum time and cost, and the solution of the Capacity-constrained Vehicle Routing Problem (CVRP) is used as an optimization technique in this study.A dynamic perspective was added to the capacitated CVRP by incorporating a decision mechanism that determines whether containers should be collected or not based on their predetermined fill levels. The Internet of Things methodology was utilized, and fill sensors were added to the containers. Genetic algorithms, one of the metaheuristic methods, were used in the algorithm's solution, which was implemented through a Python program. After testing the algorithm on a trial network, a specific location was selected. The chosen region is Gerzele Neighborhood in Denizli Province, and the TURKSTAT data pertaining to the neighborhood were used in the calculations. The coordinate data for the neighborhood and the locations of the containers were mapped, and transportation data was generated by integrating them into the Visum program. The collection process was carried out based on the fill levels of the containers using the sensors, and several scenarios were tested. It was determined that cost, distance traveled, and collection times for waste logistics could be reduced by 20-40% based on the obtained results. [ABSTRACT FROM AUTHOR]

In this study, a realistic model of the respiratory tract obtained from CT medical images was used to solve the flow field and particle motion using the Eulerian–Lagrangian approach to obtain the maximum particle deposition in the bronchial tree for the main purpose of optimizing the performance of drug delivery devices. The effects of different parameters, including particle diameter, particle shape factor, and air velocity, on the airflow field and particle deposition pattern in different zones of the lung were investigated. In addition, a genetic algorithm was employed to obtain the maximum particle deposition in the bronchial tree and the effect of the aforementioned parameters on particle deposition. Reverse flow, vortex formation, and laryngeal jet all affect the airflow structure and particle deposition pattern. The mouth–throat region had the highest deposition fraction at various flow rates. A change in the deposition pattern with an increased deposition fraction in the throat was observed owing to the increased diameter and shape factor of the particles, resulting from the higher inertia and drag force, respectively. The particle deposition analysis showed that three parameters, shape factor, diameter, and velocity, are directly related to particle deposition, and the diameter is the most effective parameter for particle deposition, with an effect of 60% compared to the shape factor and velocity. Finally, the prediction of the genetic algorithm reported a maximum particle deposition in the bronchial tree of 17%, whereas, based on the numerical results, the maximum particle deposition was reported to be 16%. Therefore, there is a 1% difference between the prediction of the genetic algorithm and the numerical results, which indicates the high accuracy of the prediction of the genetic algorithm. [ABSTRACT FROM AUTHOR]

In the realm of analog integrated circuit (IC) design, due to the intricate nonlinear relationships between circuit performance metrics and design variables, as well as the complex interdependencies and trade‐offs between various performance criteria, it is difficult to determine the appropriate geometric width and length of the circuit components to meet all performance specifications. This difficulty makes the task of designing analog IC challenging. To address the challenge of analog IC design, this paper introduces a machine learning methodology—specifically employing the genetic algorithm (GA)—to automate the selection of circuit components' geometrical parameters, aiding the work of analog circuit designers in determining the appropriate dimensions for transistors within the charge‐pump circuit. The GA, implemented in Python and integrated with a script program written in the OCEAN language, synergistically collaborates in the GA‐OCEAN framework. The result, which ensures compliance with all specifications with remarkably low error margins, ranging as low as 0.03% and as high as 1.24%, highlights the proposed GA‐OCEAN framework's remarkable capacity to determine optimal dimensions for components inside the charge‐pump circuit. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A significant advancement in wireless communication has occurred over the past couple of decades. Nowadays, people rely more on services offered by the Internet of Things, cloud computing, and big data analytics-based applications. Higher data rates, faster transmission/reception times, more coverage, and higher throughputs are all necessary for these emerging applications. 5G technology supports all these features. Antennas, one of the most crucial components of modern wireless gadgets, must be manufactured specifically to meet the market's growing demand for fast and intelligent goods. This study reviews various 5G antenna types in detail, categorizing them into two categories: conventional design approaches and machine learning-assisted optimization approaches, followed by a comparative study on various 5G antennas reported in publications. Machine learning (ML) is receiving a lot of emphasis because of its ability to identify optimal outcomes in several areas, and it is expected to be a key component of our future technology. ML is demonstrating an evident future in antenna design optimization by predicting antenna behavior and expediting optimization with accuracy and efficiency. The analysis of performance metrics used to evaluate 5G antenna performance is another focus of the assessment. Open research problems are also investigated, allowing researchers to fill up current research gaps. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV's flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions. [ABSTRACT FROM AUTHOR]

The influences of biological, chemical, and flow processes on soil structure through microbially induced carbonate precipitation (MICP) are not yet fully understood. In this study, we use a multi-level thresholding segmentation algorithm, genetic algorithm (GA) enhanced Kapur entropy (KE) (GAE-KE), to accomplish quantitative characterization of sandy soil structure altered by MICP cementation. A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation (SR) micro-CT with a resolution of 6.5 μm. After validation, tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores. The spatial distributions of porosity, pore structure parameters, and flow characteristics were calculated for quantitative characterization. The results offer pore-scale insights into the MICP treatment effect, and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

In order to regulate work in process (WIP) to the desired value in the job shop production control system, capacity adjustment as an effective and efficient measure, which is typically achieved by flexible staffs and working time. In this paper, instead of traditional labour-oriented approaches, we consider a machinery-based capacity adjustment via reconfigurable machine tools (RMTs) to compensate for unpredictable events. To this end, we employ model predictive control (MPC) in combination with genetic algorithm (GA) to explicitly consider complex reconfiguration strategies and address the related integer assignment optimisation problems. To further reduce energy consumption and avoid frequent and unnecessary reconfigurations while keeping a certain level of performance, we adopt an event-triggered MPC scheme with the proposed 'Double-layer event-triggering conditions'. Through extensively illustrated simulations, we demonstrate the effectiveness and plug-and-play availability of the proposed method for a six-workstation four-product job shop system and compare it to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the Internet of Vehicles (IoV), the combination of IoV and edge computing, known as vehicle edge computing (VEC), has received considerable attention from both academia and industry. However, task offloading in diverse intersection scenarios remains suffering from inefficiency of resource allocation and low quality of service for task execution due to the imbalance of traffic flow and the rigid requirement of latency. To address these issues, we develop a task offloading strategy by a fuzzy decision-making algorithm to handle uncertainty and imprecision. This task offloading strategy comprises two components: (1) A VEC resource pool with available vehicles at each intersection is constructed when taking the rotating direction of the recognition region. Then, we introduce a fuzzy decision-making algorithm to select a set of high-quality service vehicles from this VEC resource pool as an auxiliary edge server (AS). (2) We employ an edge service provider (ESP) to manage the computational resources of a main edge server (MS) and an AS deployed at a traffic intersection. The negotiation between the ESP and the task vehicles is modeled as a Stackelberg game. We prove the existence of the unique perfect Nash equilibrium, and a genetic algorithm is applied to find the optimum. Finally, we conduct simulation experiments with datasets collected in real-world scenarios. The results demonstrate that our scheme decreases task execution time by 9.73% compared to the cloud server scheme and reduces energy consumption by 13.78% compared to the state-of-the-art reinforcement learning (RL) strategy. [ABSTRACT FROM AUTHOR]

ABSTRACT Vertical axis wind turbine (VAWT) has a rotating axis perpendicular to the wind direction. This type of wind turbine that is suitable for urban environments has low wind direction dependency and noise. In this research, a novel surrogated approach for optimizing a VAWT is proposed, used, tested, and verified, which is not reported in literature. The proposed method consisted of 3D computational fluid dynamics (CFD) analysis of wind flow through the wind turbine with FLUENT software by solving the unsteady turbulent equations. However, 3D CFD analysis was time and cost consuming to obtain the output result (power coefficient) from input values (airfoil chord length, pitch angle, and tip speed ratio as turbine design variables). Thus, artificial neural network (ANN) was applied to obtain weight functions to correlate FLUENT software inputs and outputs after learning process. Finally, genetic algorithm was used for maximizing the turbine power coefficient considering three defined design variables. The optimum value of power coefficient was improved to 0.244, and the optimum values of design variables for blade chord length, blade pitch angle, and blade tip speed ratio were 0.218, −0.453, and 1.24, respectively. This novel surrogated method reduced the computational time and cost of VAWT optimizing considerably.

In this study, an Artificial Neural Network-Genetic Algorithm (ANN-GA) approach was successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids (UFAs) in the microalgae P. kessleri UCM 001. The optimized model recommended specific cultivation conditions, including glucose at 29 g/L, NaNO3 at 2.4 g/L, K2HPO4 at 0.4 g/L, red LED light, an intensity of 1000 lx, and an 8:16-h light-dark cycle. Through ANN-GA optimisation, a remarkable 66.79% increase in UFAs production in P. kessleri UCM 001 was achieved, compared to previous studies. This underscores the potential of this technology for enhancing valuable lipid production. Sequential variations in the application of physicochemical factors during microalgae culture under mixotrophic conditions, as optimized by ANN-GA, induced alterations in UFAs production and composition in P. kessleri UCM 001. This suggests the feasibility of tailoring the lipid profile of microalgae to obtain specific lipids for diverse industrial applications. The microalgae were isolated from a high-mountain lake in Colombia, highlighting their adaptation to extreme conditions. This underscores their potential for sustainable lipid and biomaterial production. This study demonstrates the effectiveness of using ANN-GA technology to optimise UFAs production in microalgae, offering a promising avenue for obtaining valuable lipids. The microalgae's unique origin in a high-mountain environment in Colombia emphasises the importance of exploring and harnessing microbial resources in distinctive geographical regions for biotechnological applications.

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system's adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system's adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system's net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels. [ABSTRACT FROM AUTHOR]

The inefficient assignment of nurse shifts poses a significant problem for medical centres due to stringent constraints and preferences, such as staffing requirements and hospital needs. In this study, we address these challenges by applying an improved hybrid algorithm that combines the Round Robin (RR) and Genetic Algorithms (GA) to develop an intelligent system for managing nurse schedules in hospitals in Peru. The development of the system consists of seven phases: dataset definition, variable definition, constraint definition, applying RR algorithm, applying GA, evaluation of algorithms, and intelligent system architecture. Nurse schedule records from a medical centre in Peru were used as the dataset. A total of 53 individuals, including head nurses and nurses, participated in the proposed system. The use of the proposed system resulted in a 99.71% reduction in "execution time" and a 30.08% improvement in "fairness in shift distribution" for nurses compared to previous methods. The key findings demonstrate significant improvements in both efficiency and fairness, highlighting the potential for future applications in health centres. The unique contributions of this research lie in the enhanced hybrid algorithm and its successful implementation in a real-world medical centre in Peru. [ABSTRACT FROM AUTHOR]

Low inertia poses a significant challenge to the widespread integration of photovoltaic (PV) systems into the grid, particularly affecting frequency stability. This paper addresses this challenge by proposing an advanced hybrid inertia (AHI) approach, characterized by the integration of real and virtual inertia, combined with the application of intelligent control techniques, to enhance the primary frequency response of a PV system. Internal inertia is provided by a synchronous generator (SG) acting as a compensator, while virtual inertia is generated through a real-time deloading strategy in the PV plant. The AHI is implemented on an isolated grid, utilizing various intelligent control techniques to improve frequency stability. The intelligent techniques employed include genetic algorithms (GA) and fuzzy logic controllers (FLC), which do not require mathematical modeling. This allows them to overcome the nonlinear dynamics of the PV plant and the uncertainties associated with climatic changes. These techniques are applied to the nonlinear components on the generator side, including the governor loop, DC-DC converter loop, and power reserve injection management loop. A frequency fluctuation scenario, induced by generating power perturbations in the proposed system, demonstrates and validates the improvement in frequency stability achieved by the HI and intelligent techniques. All tests are conducted using MATLAB Simulink. Various simulation results show a significant improvement in frequency stability through the incorporation of HI and intelligent control techniques. [ABSTRACT FROM AUTHOR]

Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation systems that can be used as a sentiment word scoring system. In its first step, the proposed solution uses two mathematical approaches to find two initial populations for a heuristic method. The heuristic solution solves a pseudo-linear NLP scoring scheme in a polarity detection method and determines the final scores. The proposed solution was validated using three scenarios on the SemEval-2013 competition, the ESWC dataset, and the Taboada dataset. The simulation results revealed that the proposed solution is comparable to the best state-of-the-art methods in polarity detection. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and effective treatment of thyroid conditions, such as hypothyroidism and hyperthyroidism, are crucial due to their wide-ranging symptoms and consequences. However, conventional back-propagation neural networks have limitations, including slow convergence and susceptibility to local minima. To deal with these problems, the paper suggests an integrated strategy that includes back-propagation (BP) and genetic algorithms (GA). The suggested method uses evolutionary algorithms to investigate various weight combinations and back-propagation to modify weights in response to the discrepancy between expected and actual results. The study evaluated the method using three steps of experimentation, including network building, local search and optimization, and evaluation, on a real-world dataset of thyroid illnesses. The obtained results are very satisfying and promising, indicating that the MLP-GA/BP model is robust enough to detect and categorize thyroid disorders effectively and efficiently. This makes it a reliable diagnostic tool for medical practitioners, allowing them to effectively diagnose and treat patients with thyroid diseases. [ABSTRACT FROM AUTHOR]

A sustainable city is a smart city with a minimal impact on the environment, by incorporating technologies to reduce pollution. Traffic congestion which is a major concern contributes to global warming and climate change. Traffic forecasting projects future traffic patterns, using historical and current data to enhance traffic flow management. We propose a whole novel approach for predicting traffic congestion rate based on air quality data. We developed a new ensemble voting model based on Long Short Term Memory (LSTM) and Polynomial Regression (PolReg) models that use a new voting thresholded algorithm instead of the existing voting ones. The hyperparameters were optimized with the Genetic Agorithm, to overcome the non-stationarity of time series. A comparative study with the literature confirmed that our framework outperforms existing researches by keeping an absolute effectiveness according to learning curves, with Mean Absolute Error of 0.04, R-Squared of 0.93, and Root Mean Square Error (RMSE) of 0.05. [ABSTRACT FROM AUTHOR]

Summary: The existing single‐phase grounding (SPG) fault section location methods typically suffer from difficulty in feature selection, limited feeder terminal units (FTUs) configuration, and excessive dependence on communication, which weaken their generalization and robustness. To overcome these challenges, an SPG fault section location approach based on feature subset optimization is proposed. First, the relation between the position of FTU and its three‐phase current variation is analyzed, and its fault features are extracted to construct the candidate feature sets as feature subset optimization objects. Then, genetic algorithm and support vector machine (SVM) are combined to select the optimal feature subset with small dimensions and recognition error, which avoids the empirical errors of artificial feature selection. To reduce the cumulative errors, the SVM hyperparameters are simultaneously optimized. Finally, the SVM model is trained based on the optimal feature subset and hyperparameters. In the absence of zero‐sequence current measurement, three‐phase currents measured by FTU are locally processed to locate the fault section by the trained SVM. The experimental results verified the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives. [ABSTRACT FROM AUTHOR]

In this study, an Artificial Neural Network-Genetic Algorithm(ANN-GA) approachwas successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids (UFAs) in the microalgae P. kessleri UCM 001. The optimized model recommended specific cultivation conditions, including glucose at 29 g/L, NaNO3 at 2.4 g/L, K2HPO4 at 0.4 g/L, red LED light, an intensity of 1000 lx, and an 8:16-h light-dark cycle. Through ANN-GA optimisation, a remarkable 66.79% increase in UFAs production in P. kessleri UCM 001 was achieved, compared to previous studies. This underscores the potential of this technology for enhancing valuable lipid production. Sequential variations in the application of physicochemical factors during microalgae culture under mixotrophic conditions, as optimized by ANN-GA, induced alterations in UFAs production and composition in P. kessleri UCM 001. This suggests the feasibility of tailoring the lipid profile of microalgae to obtain specific lipids for diverse industrial applications. Themicroalgaewere isolated froma high-mountain lake in Colombia, highlighting their adaptation to extreme conditions. This underscores their potential for sustainable lipid and biomaterial production. This study demonstrates the effectiveness of using ANN-GA technology to optimise UFAs production in microalgae, offering a promising avenue for obtaining valuable lipids. Themicroalgae's unique origin in a high-mountain environment in Colombia emphasises the importance of exploring and harnessing microbial resources in distinctive geographical regions for biotechnological applications. [ABSTRACT FROM AUTHOR]

Vertical axis wind turbine (VAWT) has a rotating axis perpendicular to the wind direction. This type of wind turbine that is suitable for urban environments has low wind direction dependency and noise. In this research, a novel surrogated approach for optimizing a VAWT is proposed, used, tested, and verified, which is not reported in literature. The proposed method consisted of 3D computational fluid dynamics (CFD) analysis of wind flow through the wind turbine with FLUENT software by solving the unsteady turbulent equations. However, 3D CFD analysis was time and cost consuming to obtain the output result (power coefficient) from input values (airfoil chord length, pitch angle, and tip speed ratio as turbine design variables). Thus, artificial neural network (ANN) was applied to obtain weight functions to correlate FLUENT software inputs and outputs after learning process. Finally, genetic algorithm was used for maximizing the turbine power coefficient considering three defined design variables. The optimum value of power coefficient was improved to 0.244, and the optimum values of design variables for blade chord length, blade pitch angle, and blade tip speed ratio were 0.218, −0.453, and 1.24, respectively. This novel surrogated method reduced the computational time and cost of VAWT optimizing considerably. [ABSTRACT FROM AUTHOR]

This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application. [ABSTRACT FROM AUTHOR]

Beam–slab structures account for 50–65% of a building's total dead load and contribute to 20% of the overall cost and CO2 emissions. Despite their importance, conventional beam–slab structural optimization methods often lack search efficiency and accuracy, making them less effective for practical engineering applications. Such limitations arise from the optimization problem involving a complex solution space, particularly when considering components' arrangement, dimensions, and load transfer paths simultaneously. To address the research gap, this study proposes a novel two-stage genetic algorithm, optimizing beam–slab layout in the first stage and component topological relationships and dimensions in the second stage. Numerical experiments on the prototype case indicate that the algorithm can generate results that meet engineering accuracy requirements within 100 iterations, outperforming comparable algorithms in both efficiency and accuracy. Additionally, this heuristic approach stands out for its independence from prior dataset training and its minimal parameter adjustment requirement, making it highly accessible to engineers without programming expertise. Statistical analysis of the algorithm's optimization process and case studies demonstrate its robustness and adaptability to various beam–slab structural optimization problems, revealing its significant potential for practical engineering scenarios. [ABSTRACT FROM AUTHOR]

The article introduces a newer hybrid method (HAIS-GA) of optimizing and choosing the ideal machining parameters for cryogenic processing. It depends on two late methodologies genetic algorithm (GA) and artificial immune system (AIS), which are connected to numerous troublesome combinatorial streamlining issues with specific qualities and shortcomings. These developmental calculations are proposed to find the best arrangement of process factors for the clashing prerequisites in multi objective capacities. Hybrid model optimization also comes with challenges, such as selecting the right combination of techniques, tuning parameters, potential increases in complexity, and the need for expertise in multiple optimization methods. The key reason for this hybrid approach (HAIS-GA) is the improvement in the results that is achieved due to the characteristics of GA and AIS. Three test functions are employed to compare the outcomes in terms of these functions' ability to achieve the lowest value. Cryogenic processing is used to validate the optimised values that were obtained. The attained results showcase that HAIS-GA approach, in conclusion exhibits a more favourable minimal objective function within a reasonable duration. Due to the nature of Unrestricting to local optima, and it being self-adaptive HAIS-GA provides better result compared to GA and AIS. Based on the least value of the objective function and time for each method. [ABSTRACT FROM AUTHOR]

In the pursuit of improved productivity and efficiency, the search for alternative fuels that reduce dependence on fossil fuels is of utmost importance. This research study aims to investigate the influence of process parameters, namely blend percentage, fuel injection pressure, exhaust gas recirculation (EGR) rate, and engine load, on various engine performance parameters such as torque, brake thermal efficiency (BTE), brake mean effective pressure (BMEP), hydrocarbon emissions (HC), and mechanical efficiency. The experiments are conducted following the Taguchi orthogonal array (L25) design of experiments. Additionally, grey relational analysis, a multi-objective optimization technique, is applied to identify the optimal levels of the process variables that optimize all the response parameters. The optimal values obtained through grey relational analysis are determined as 0% blend, 600 bar fuel injection pressure, 12% EGR rate, and 12 kg engine load. Moreover, the Genetic Algorithm-based Multi-Objective Genetic Algorithm (MOGA) is employed for multi-objective optimization of the engine input variables, yielding optimal levels of 19.45% blend, 594.35 bar fuel injection pressure, 14.12% EGR rate, and 12 kg engine load. Furthermore, multivariable regression models are developed to predict the response variables within the experimental domain. These models are validated through a confirmation test. The findings of this study provide insights into the optimization of process variables for enhanced engine performance, with the developed models serving as valuable tools for future predictions and optimization. [ABSTRACT FROM AUTHOR]

In this article, a special type of fault isolation problem is reported where temporal information is required for the estimation of parameters from susceptible fault sub-space. Any physical system always obeys certain mathematical constraints under the normal operating condition, and that is incorporated in a behavioral model, which is essentially flowing and force or moment balance. In a fault detection and identification (FDI) model, only concurrent states are known. In FDI, the constraint relationships need to be derived only in terms of known variables, i.e., the measurements and nominal parameters, which are termed analytical redundancy relations (ARRs). The numerical evaluation of ARRs is residuals, and those oscillate within a definite bound of error when computed using test data. In this work, a novel function is formulated using the ARRs at different time instants and that function is minimized to estimate the suspected parameter values belonging to the non-isolable sub-space. Single-fault hypothesis is considered, and a genetic algorithm (GA) is used for the optimization. This FDI analysis may be beneficial for power hydraulic-driven heavy machinery such as hydraulic excavators, dumpers, and front-end loaders, which are mostly used in mines and construction sites. [ABSTRACT FROM AUTHOR]

Soil–structure interaction (SSI) is a well-established phenomenon with significant implications for the dynamic response of structures subjected to earthquake loads. This study aimed to find the optimum stiffness of a lead rubber bearing (LRB) isolator within a multi-degree-of-freedom (MDOF) structure using a genetic algorithm (GA) while accounting for SSI effects through analytical formulation and numerical simulation. The simplified linear cone model was utilized to model the SSI effect, with two degrees of freedom of the soil: sway and rocking. Analytically, the main contribution is the presentation of an equivalent frequency formula for both the structure and the isolation layer, allowing the presentation of these later with respect to dimensionless parameters. Numerically, the displacement of the base level was targeted as the response to be reduced through the GA optimization, which is a further contribution of this work. To achieve this, 28 earthquake records with two components each were used during the optimization procedure. These records were regarded as near-field excitations, and 14 of them had pulse-like characteristics while the other 14 did not. The obtained results showed a local optimum of isolator stiffness for each earthquake and soil type. Owing to the stochastic nature of earthquakes, a weighted averaging method was used to obtain the global optimum isolator stiffness for each soil type, which is a novelty in this work. Further investigation of the local and general optima in the time and frequency domains showed a significant reduction in response in terms of base-level displacement, root-mean square (RMS) of top-floor acceleration, and strain energy of the entire structure. These findings may represent added values for designers working on base isolation (BI) devices in various soil conditions. [ABSTRACT FROM AUTHOR]

Robots assist emergency responders by collecting critical information remotely. Deploying multiple cooperative unmanned ground vehicles (UGVs) for a response can reduce the response time, improve situational awareness, and minimize costs. Reliable communication is critical for multiple UGVs for environmental response because multiple robots need to share information for cooperative navigation and data collection. In this work, we investigate a control policy for optimal communication among multiple UGVs and base stations (BSs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to update the control policy for the maximum signal-to-interference ratio. The UGVs communicate with both the fixed BSs and a mobile BS. The proposed control policy can navigate the UGVs and mobile BS to optimize communication and signal strength. Finally, a genetic algorithm (GA) is proposed to optimize the hyperparameters of the MADDPG-based training. Simulation results demonstrate the computational efficiency and robustness of the GA-based MADDPG algorithm for the control of multiple UGVs. [ABSTRACT FROM AUTHOR]

Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn. [ABSTRACT FROM AUTHOR]

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Studying the order delivery strategy considering urban traffic restrictions to reduce the average delivery time of orders, improve the consistency of delivery time and customer satisfaction is of great significance. A steering-restricted distribution network was constructed by setting up steerable nodes. And with the goal of minimizing the average delivery time, a multi-vehicle real-time pickup and delivery path optimization model was established considering the dynamics of the orders based on the turn restriction network. And two delayed distribution strategies, IGNORE and wait & return (W&R), were designed to solve the problem. In the study analysis, the number and distance of steerable nodes were adjusted to simulate two kinds of road networks: dense and sparse. Through numerical simulation and genetic algorithm solution, the stability of the model and the applicability of the strategy were verified, the average delivery time of IGNORE and W&R strategies under these two networks, different order quantities, the number of delivery personnel and lengths of rolling time domains were obtained, and the reasons for the fluctuation of the average delivery time were analyzed. The results show that the IGNORE strategy is applicable to the case of small network with few orders, and the average delivery time of orders decreases as the rolling time domain time shortens. The W&R strategy is suitable for larger distribution networks, and the more the number of steerable nodes in the network, the average delivery time of orders decreases. The research conclusion has certain reference significance for the real-time delivery platform to optimize the delivery strategy and improve customer satisfaction. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) emerges as a cutting-edge technique that effectively alleviates the computational burden on mobile devices through task offloading. In the realm of Mobile Edge Computing (MEC) augmented by 5G technology, the process of delegating computing tasks from edge devices to edge servers within the edge network can significantly diminish latency. Overcoming the challenge of designing a well-balanced task offloading strategy in a resource-limited multi-user with MEC environment to address users' requirements remains a noteworthy concern. This paper introduces Greedy method based Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and Mobile Edge Servers' (MES) computing resources. The method is designed to handle scenarios where certain computing tasks can be partially offloaded to the MES. By considering the limitations imposed by wireless transmission resources and Edge servers' processing capacity, GA is employed to optimize the task completion time for users. The offloading strategy, which utilizes the combination of Greedy with GA, is evaluated and compared against both the genetic algorithm (GA) and the Greedy method through a series of simulation experiments demonstrates its effectiveness in reducing Energy Consumption, Delay time, and ensuring fairness in Resource cost for users' task completion times. [ABSTRACT FROM AUTHOR]

FDM is the world's most widely used and certified additive manufacturing technology because of its simplicity of use and production flexibility compared to other additive manufacturing techniques. Several input process variables impact the surface quality of manufactured components. The present study investigated the effect of print speed, acceleration, and jerk on the surface roughness of FDM-fabricated parts. The surface roughness of PLA-fabricated items is minimized by optimizing all three input parameters. To investigate the effect of input variables on surface roughness, a total of 20 test specimens were developed and fabricated using a face-centered central composite design technique. The surface roughness has been measured on the lateral sides of components in two directions (along the x-axes and y-axes). Further, the hybrid genetic algorithms-artificial neural networks (GA-ANN) heuristic optimization tool, in which GA is integrated with ANN, is employed to find the best feasible combination of input variables. It is observed that the surface roughness obtained for the x-axis direction is 0.0551512 μm at (speed: 20 mm/s; acceleration: 1137 mm/s2; jerk: 29.36 mm/s) and for the y-axis direction is 11.8919 μm at (speed: 98.60 mm/s; acceleration: 988.35 mm/s2; jerk: 16.508 mm/s). The optimized values are validated experimentally. [ABSTRACT FROM AUTHOR]

Shafting alignment plays an important role in the marine propulsion system, which affects the safety and stability of ship operation. Air spring vibration isolation systems (ASVISs) for marine shafting can not only reduce mechanical noise but also help control alignment state by actively adjusting air spring pressures. Alignment prediction is the first and a key step in the alignment control of ASVISs. However, in large-scale ASVISs, due to factors such as strong interference and raft deformation, alignment prediction faces problems such as alignment measurement sensors failure and difficulty in establishing a mathematical model. To address this problem, a data model for predicting alignment state is developed based on a back propagation (BP) neural network, fully taking advantage of its self-learning and self-adaption abilities. The proposed model exploits the collected data in the ASVIS instead of the alignment measurement data to calculate the alignment state, providing another alignment prediction approach. Then, in order to solve the local optimum issue of BP neural network, we introduce the genetic algorithm (GA) to optimize the weights and thresholds of the BP neural network, and an improved GA-BP model is designed. The GA-BP model can leverage the advantages of the global search capability of GA as well as the BP neural network's fast convergence in local search. Finally, we conduct experiments on a real ASVIS and evaluate the prediction models using different criteria. The experimental results show that the proposed prediction model with the GA-BP neural network can accurately predict the alignment state, with a mean-square error (MSE) of 0.0114 . And compared to the BP neural network, the GA-BP neural network reduces the MSE by approximately 74 % . [ABSTRACT FROM AUTHOR]