Search

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm's tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system's logistics during critical situations. [ABSTRACT FROM AUTHOR]

This paper uses Long Short-Term Memory (LSTM) networks to predict the stock prices of the Yuanta Taiwan Top 50 ETF (ETF50). To improve the accuracy of the model's predictions, a calibration procedure called "Short-Term Bias Compensation" (STBC) is proposed to adjust the predicted stock prices. In STBC, the daily prediction error is calculated to estimate the short-term bias (STB) in prediction. Then, the predicted price of its next day will be corrected if this STB has exceeded a certain threshold. In this paper, we apply Genetic Algorithms (GAs) to optimize the parameters used in STBC for providing more confidence in its estimation. Based on these predicted stock prices, we propose a Genetic Fuzzy System (GFS) to determine the trading strategy, with trading points for buying and selling stocks. In GFS, various technical indicators are used to establish the fuzzy rules of the trading strategy, and GAs are used to evolve the best parameters for these fuzzy rules. Our experiments cover over 17 years of data (from 2003 to 2020) for ETF50 to consider black swan events such as the 2020 COVID-19 pandemic, the 2018 US–China trade war, and the 2011 US debt crisis. The first 90% of the data is used as training data, and the last 10% is used as testing data. We use 12 technical indicators of these data as the input of LSTM. The predicted values of LSTM are corrected using STBC and compared to the uncorrected prices. We use Mean Square Error (MSE) to evaluate the prediction accuracy. The results show that STBC can nearly reduce 90% of the prediction error (where MSE drops from 11.5758 to 1.2687). By using GFS with STBC to determine trading points, we achieve a return rate of 32.0%. [ABSTRACT FROM AUTHOR]

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH hammer drilling, which significantly affects the performance and longevity of the near-bit measurement while drilling (MWD). To enhance the environmental adaptability of the near-bit MWD in pneumatic DTH operations, a design method for a vibration-damping system based on the parameter optimization of a non-dominated sorting genetic algorithm II (NSGA-II) is proposed in this study. First, the whole structure of the near-bit MWD is designed, including the MWD sub-shell, sensors, measurement circuits, batteries, and connecting structures (the circuit unit). Secondly, this study analyzes the vibration characteristics of the pneumatic DTH hammer near the bit. According to the damping structure, the vibration response model for the circuit unit and the damping model are established. Thirdly, NSGA-II is employed to optimize the parameters of the damping model in terms of the low-frequency, high-intensity vibration characteristics near the bit in pneumatic DTH operations, thereby devising a damping scheme tailored to the unique conditions of DTH hammer drilling. Finally, vibration experiments were conducted to verify the effectiveness of the vibration-damping device. The experimental results indicate that within the vibration frequency range of 5–20 Hz and vibration level of 10–40 g, the peak attenuation rate of the circuit unit is more than 86.446%, and the improvement rate of the vibration stability of the system is more than 75.214%; the anti-vibration performance of the near-bit MWD system in DTH hammer drilling is improved remarkably. This study provides strong technical support for the stability of MWD equipment under such special working conditions. It has broad engineering application prospects. [ABSTRACT FROM AUTHOR]

Geological anomalies within the working face likely induce geological disasters, such as water, gas, and coal mine roof fall, directly impacting the rational planning and safe mining of underground resources. Constrained by the conditions of underground closed spaces, effective reconstruction under incomplete and highly sparse projection is the central challenge when evaluating geo-environmental conditions. This work proposes a new hybrid intelligent optimization model (MPGA-SIRT) that integrates a multiple-population genetic algorithm (MPGA) with the simultaneous iterative reconstruction technique (SIRT) to finely reconstruct the geo-environmental conditions within a working face based on electromagnetic wave tomography theory. MPGA-SIRT can provide a more precise initial inversion model for the conventional linear reconstruction technique of SIRT, incorporating a local search property by leveraging the robust global search capacity of MPGA. The advantages of MPGA-SIRT have been demonstrated through numerical modeling, theoretical testing, and engineering practices on the 8208 working face in the Datong mining area, Shanxi Province. In comparison to individual SIRT inversion models, MPGA-SIRT reconstruction yields more accurate and stable performance, as demonstrated by the evolution curve of the objective function and the corresponding convergence tomography results. Consequently, the geomagnetic wave absorption coefficient within the area of reconstruction can be precisely ascertained through the use of our proposed technique. This innovation represents a groundbreaking strategy for assessing geological anomaly zones within a working face. The introduced method stands as a valuable theoretical instrument for confronting the complexities associated with geo-environmental reconstruction in underground engineering. [ABSTRACT FROM AUTHOR]

The miniaturization of antennas is crucial as it improves the integration of wireless communication system. In order to achieve miniaturization of antennas, a performance-constrained multi-objective optimization method (PCMOM) considering the size and return loss is proposed. In the PCMOM method, an optimization strategy based on a multi-port network model is introduced, enabling the formation of antennas with various structures. Furthermore, we integrate the constraint of return loss performance into the non-dominated sorting genetic algorithm II (NSGA-II), eliminating solutions that do not meet performance requirements. Three pixel antennas are designed using the PCMOM method and two of them are fabricated. Experimental results demonstrate that the proposed PCMOM method can effectively address the complex trade-off issues in antenna miniaturization design. [ABSTRACT FROM AUTHOR]

AbstractThe distribution of discrete piezoelectric patches critically influences the vibration control performance of beams. However, few studies have comprehensively addressed this issue for piezoelectric beams under different boundary conditions, especially when both traditional and optimal control strategies are considered. This paper investigates the optimal placement of piezoelectric actuator/sensor pairs for beams with cantilever, simply supported, and clamped boundary conditions, using a genetic algorithm (GA) based on the principle of minimum retained energy. The performance of active vibration control laws, including classical methods such as direct proportional feedback, constant gain negative velocity feedback, and model-based linear quadratic regulator (LQR) control, is thoroughly investigated. [ABSTRACT FROM AUTHOR]

In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats and scalability across diverse network environments. Our approach is validated using two distinct datasets: BoT-IoT, reflecting a range of IoT-specific attacks, and UNSW-NB15, offering a broader context of network intrusion scenarios using GA based hybrid DT-SVM. This selection facilitates a comprehensive evaluation of the algorithm's effectiveness across varying attack vectors. Performance metrics including accuracy, recall, and false positive rates are meticulously chosen to demonstrate the algorithm's capability to accurately identify and adapt to both known and novel threats, thereby substantiating the algorithm's potential as a scalable and adaptable security solution. This study aims to advance the development of intrusion detection systems that are not only reactive but also preemptively adaptive to emerging cyber threats." During the feature selection step, a GA is used to discover and preserve the most relevant characteristics from the dataset by using evolutionary principles. Through the use of this technology based on genetic algorithms, the subset of features is optimised, enabling the subsequent classification model to focus on the most relevant components of network data. In order to accomplish this, DT-SVM classification and GA-driven feature selection are integrated in an effort to strike a balance between efficiency and accuracy. The system has been purposefully designed to efficiently handle data streams in real-time, ensuring that intrusions are promptly and precisely detected. The empirical results corroborate the study's assertion that the IDS outperforms traditional methodologies. [ABSTRACT FROM AUTHOR]

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses. Author summary: Many drug treatments or environmental factors can trigger cardiac arrhythmias, which are dangerous and often unpredictable. Human cardiomyocytes derived from donor stem cells have proven to be a promising model for studying these events, but variability in donor genetic background and cell maturation methods, as well as overall immaturity of stem cell-derived cardiomyocytes relative to the adult heart, have hindered reproducibility and reliability of these studies. Mathematical models of these cells can aid in understanding the underlying electrophysiological contributors to this variability, but determining these models' parameters for multiple cell preparations is challenging. In this study, we tackle these limitations by developing a computational method to simultaneously estimate multiple model parameters using data from imaging-based experiments, which can be easily scaled to rapidly characterize multiple cell lines. This method can generate many personalized models of individual cell preparations, improving drug response predictions and revealing specific differences in electrophysiological properties that contribute to variability in cardiac maturity and arrhythmia susceptibility. [ABSTRACT FROM AUTHOR]

At present, the availability of nonrenewable sources and their usage for electric vehicle technology is reducing gradually because of their disadvantages are more environmental pollution, direct effect on human health, less reliability, and taking more time to start functioning. So, in this article, the proton exchange membrane fuel cell (PEMFC) is considered for the automotive application because of its advantages quick startup, more power density, more safety to handle, high efficiency, and capability of operating at very low operational temperature conditions. However, the drawback of PEMFC is very difficult to identify the accurate MPP position of the fuel system. Here, the improved variable step genetic algorithm is added with the adaptive neuro-fuzzy inference system for tracking the operational point of the proposed system with high efficiency. These hybrid MPPT controller features are easy to understand, more accurate, have a better dynamic response, and have low design complexity. The evaluated proposed MPPT controller operational efficiency, and settling time of the converter voltage at different fuel stack temperature conditions are 98.7402%, and 0.01607 s respectively. Finally, the boost converter is used in this work to enhance the voltage supply capability of the entire system. The proposed system is investigated by applying the MATLAB tool. [ABSTRACT FROM AUTHOR]

Artificial selection is a promising way to improve microbial community functions, but previous experiments have only shown moderate success. Here, we experimentally evaluate a new method that was inspired by genetic algorithms to artificially select small bacterial communities of known species composition based on their degradation of an industrial pollutant. Starting from 29 randomly generated four-species communities, we repeatedly grew communities for four days, selected the 10 best-degrading communities, and rearranged them into 29 new communities composed of four species of equal ratios whose species compositions resembled those of the most successful communities from the previous round. The best community after 18 such rounds of selection degraded the pollutant better than the best community in the first round. It featured member species that degrade well, species that degrade badly alone but improve community degradation, and free-rider species that did not contribute to community degradation. Most species in the evolved communities did not differ significantly from their ancestors in their phenotype, suggesting that genetic evolution plays a small role at this time scale. These experiments show that artificial selection on microbial communities can work in principle, and inform on how to improve future experiments. Artificial selection is a promising way to improve microbial community functions. Here, Arias-Sánchez et al. evaluate a method inspired by genetic algorithms to select small bacterial communities of known species composition based on their degradation of an industrial pollutant. [ABSTRACT FROM AUTHOR]

In the last decade, with the development of the electric vehicle industry and their acceptance in human societies, the participation plan of electric vehicles in supplying the load of the network has been taken into consideration. One of the requirements of this plan is the optimal location of the stations for these vehicles in the network so that they play an effective role in the operation of the network. In this regard, along with the construction of charging and discharging stations for electric vehicles, the construction of renewable sources in the network can play a complementary role for these stations. In this paper, the effect of using renewable resources as a supplement for smart charging stations and the placement of these stations to achieve technical and economic goals have been investigated. In order to manage the demand on the side of consumers and even out the load curve, the time of use mechanism as one of the demand response programs has been considered in this study. In this research, the improved nondominant sorting genetic algorithm is proposed to solve the problem, and the results of the proposed method are also compared with the conventional genetic and particle swarm optimization algorithms. All the simulations have been done in the MATLAB software and on the IEEE 33‐bus network. Based on the obtained results, after the implementation of the proposed plan in the distribution network, the objective functions of the loss, voltage drop, and the total cost have been reduced by 13.6%, 58.7%, and 54.4%, respectively, compared to the base conditions of the network. [ABSTRACT FROM AUTHOR]

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers. [ABSTRACT FROM AUTHOR]

Atomistic simulations are crucial for predicting material properties and understanding phase stability, essential for materials selection and development. However, the high computational cost of density functional theory calculations challenges the design of materials with complex structures and composition. This study introduces new data acquisition strategies using Bayesian-Gaussian optimization that efficiently integrate the geometry of the convex hull to optimize the yield of batch experiments. We developed uncertainty-based acquisition functions to prioritize the computation tasks of configurations of multi-component alloys, enhancing our ability to identify the ground-state line. Our methods were validated across diverse materials systems including Co-Ni alloys, Zr-O compounds, Ni-Al-Cr ternary alloys, and a planar defect system in intermetallic (Ni1−x, Cox)3Al. Compared to traditional genetic algorithms, our strategies reduce training parameters and user interaction, cutting the number of experiments needed to accurately determine the ground-state line by over 30%. These approaches can be expanded to multi-component systems and integrated with cost functions to further optimize experimental designs. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to address the optimal coordination of directional overcurrent relays (DOCRs) in modern power distribution networks. By utilizing the different optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), pattern search (PS), grey wolf optimization (GWO), the study aims to tackle the inherent complexity and nonlinearity of the relay coordination problem effectively. GWO stands out due to its ability to handle highly nonlinear optimization problems by leveraging the social behavior and hunting mechanisms of grey wolves and its ability to quickly converge to near-optimal solutions make it a popular choice. This unique feature enables the algorithm to explore the solution space more efficiently by repositioning solutions around each other, thereby facilitating better exploitation of the solution space. The effectiveness of the proposed GWO algorithm is evaluated using fault data generated from various test systems ranging from small-scale 8-bus networks to large 15-bus systems. The results demonstrate several key advantages, reduced operating time, robust coordination, and reduced coordination interval. Compared to other optimization algorithms, the GWO algorithm achieves a reduced coordination interval between primary and backup relay pairs. This optimization contributes to faster and more precise fault detection and isolation within the network in comparison to other techniques. Overall, the findings highlight the superior performance and robustness of the GWO algorithm in addressing the optimal coordination challenges of DOCRs in modern power distribution networks thereby enhancing the efficiency and reliability of protection systems in complex electrical grids. [ABSTRACT FROM AUTHOR]

The complexity of mechatronic systems has increased with the significant advancements of technology in their components which makes their design more challenging. This is due to the need for incorporating expertise from different domains as well as the increased number and complexity of components integrated into the product. To alleviate the burden of designing such products, many industries and researchers are attracted to the concept of modularization which is to identify a subset of system components that can form a module. To achieve this, a novel product-related dependency management approach is proposed in this paper with the support of an augmented design structure matrix. This approach makes it possible to model positive and negative dependencies and to compute the combination potency between components to form modules. This approach is then integrated into a modified non- dominated sorting genetic algorithm III to concurrently optimize the design and identify the modules. The methodology is exemplified through the case study of a layout design of an automatic greenhouse. By applying the proposed methodology to the case study, it was possible to generate concepts that decreased the number of modules from 9 down to 4 while ensuring the optimization of the design performance. [ABSTRACT FROM AUTHOR]

Agricultural spatial division and suggestions for the optimization of the partition space were obtained by constructing a recognition system of the dominant agricultural space. The prerequisite was to master natural economic development in agriculture. It was vital to maintain national food security and promote healthy and sustainable agriculture. The suitability evaluation of agricultural production and the dominance evaluation of agricultural development were incorporated to recognize the dominant agricultural space in Cangzhou, Hebei, China in 2020. Besides, priority scenarios were set, e.g., economic development, lowcarbon protection, and coordinated development of a low-carbon economy. The NSGA-II genetic algorithm model was used to optimize the quantitative structure of cultivated lands in the agricultural space of Cangzhou in three scenarios in 2030. The research results are as follows: (1) Cangzhou had the largest number of general suitable areas for agricultural production in 2020, accounting for 27.04%; suitable areas were the least, accounting for 10.99%. The proportion of current cultivated lands in unsuitable agricultural production areas still stood at 11.26%; (2) The dominance of agricultural development in 2020 in Cangzhou was mainly at Tier III, accounting for 33.60% with the general dominance of agricultural development; (3) The total area of the dominant agricultural space in Cangzhou was 238208.75 hm2, accounting for 16.72% of the national territorial area of Cangzhou. It included 35 villages and towns beyond ecological red lines, mainly distributed in the western part of Cangzhou; (4) The agricultural space of Cangzhou in 2030, optimized by the multi-objective NSGA-II genetic algorithm model, exhibited decreased cultivated lands across three scenarios. The total amount of cultivated lands was the largest under the priority scenario of economic development, and that was the smallest under the priority scenario of coordinated development of a low-carbon economy. Meanwhile, agricultural economic benefits and carbon emission density were reduced under three scenarios. The benefits and density were moderate under the coordinated development of low carbon and economy. The work provides a reference for further formulating and improving the policies of the agricultural space in various regions. [ABSTRACT FROM AUTHOR]

This paper introduces the multi-objective optimization process of the micromixer with Cantor fractal baffle. The combination of fractal principle and multi-objective optimization is a main feature of this article. The three-dimensional Navier–Stokes equation is used to numerically analyze the fluid flow and mixing. The proxy modeling and Pareto genetic algorithm are used to optimize the shape of the Cantor fractal micromixer. We choose three parameters related to the geometry of the Cantor fractal baffle as design variables, and choose the mixing index, pressure drop and mixing sensitivity at the outlet of the micromixer as three objective functions. For the parameter study of the design space, the Latin hypercube sampling (LHS) method is used to select design points in the design space. We use response surface function (RSA) as a proxy modeling to approximate the objective function. A multi-objective genetic algorithm is used to find the Pareto optimal solution. K-means clustering is used to classify the optimal solution set, and then select representative design variables from it. The representative optimal design is analyzed by using numerical analysis method. The optimization results show that the Cantor fractal baffle is beneficial to promote faster mixing of the two fluids. At the same time, the suitable goal can be weighed in the Pareto optimal solution set. The mixing index and mixing sensitivity are increased by 13.55 and 3.91 %, respectively, compared with the reference design of the micromixer. And we have also proved that this multi-objective optimization method is applicable to any Reynolds numbers (Res). [ABSTRACT FROM AUTHOR]

Due to the numerous environmental issues associated with fossil fuel power plants, using solar energy to generate electricity is a viable alternative. The organic Rankine cycle (ORC) is a thermodynamic process used to convert low- and medium-temperature heat sources into electricity, often utilizing organic fluids as the working medium. Entransy is a relatively new concept that many readers may not be familiar with. Moreover, entransy loss ( G ̇ loss ) is derived from the entransy concept, which quantifies the inefficiency in transferring thermal energy through a system. In this study, ( G ̇ loss ) is used for the first time when designing an ORC cycle coupled with parabolic trough collectors. The entransy loss relations were driven with assumption that the heat capacity is a function of temperature. A genetic algorithm is a search heuristic inspired by natural selection. It is used to find optimal or near-optimal solutions to complex problems by evolving a population of candidate solutions. Two scenarios utilized the genetic algorithm in MATLAB to optimize the system (scenario 1: maximizing the output power and scenario 2: maximizing the G ̇ loss ). In addition, the optimization parameters included turbine inlet temperature ( T tur ), boiler pressure ( P boil ), condenser pressure ( P cond ), and the temperature of the collector fluid at the boiler outlet ( T h f , out ). This optimization was performed for the temperature of the collector fluid at the boiler inlet ( T h f , i n ) in the range 310–400 °C at 10 °C intervals with four working fluids (i.e., toluene, cyclohexane, MM, and water). The land area and the beam solar radiation were considered to be 100 hectares and 800 W/m2, respectively. The results indicated that according to scenario one, at temperatures of 310–320 °C, the maximum power was obtained for the case of toluene fluid with values 59.8 and 63.5 MW. For the collector fluid temperature from 330 to 400 °C, water had the most optimal power with values ranging from 66.2 to 88.2 MW. Furthermore, toluene exhibited superiority to two other organic fluids in the 330–400 °C temperature range after water, with net power values ranged between 65.7 and 76.3 MW. The results indicated that the maximum entransy loss does not correspond to the maximum output power because the application preconditions of the entransy loss concept are not all satisfied. Across all working fluids and T h f , i n , scenario 2 resulted in lower optimal output power, cycle efficiency, and system efficiency compared to scenario 1. [ABSTRACT FROM AUTHOR]

To improve the cutting performance when cutting hard alloys and achieve reasonable optimization of micro-texture parameters, this paper proposes a sinusoidal micro-textured tool, with four parameters set as micro-textured spacing, period, width, and amplitude. Establish threedimensional models of non-micro-textured and micro-textured milling tools and simulate the milling process using finite element simulation. Study the effects of milling force and milling temperature on tool performance. Prepare micro-textured milling tools for orthogonal experiments, analyse the effects of milling force and the surface roughness of a titanium alloy workpiece, and study the impact of different micro-textured parameters on milling tool performance. Obtain the parameters that have the greatest impact on milling tool performance, and then use genetic algorithm to optimize three sets of parameter combinations. Use the optimized parameters to prepare milling tools for comparative experiments, and then determine the optimal parameter combination. The research results indicate that micro-textured tools can effectively improve the milling performance of the tool, and the spacing between sinusoidal micro-textures has the greatest effect on improving the milling performance of the tool. When the period of sinusoidal micro-texture is 2.87 and the amplitude is 25.13 μm, width is 79.89 μm, and spacing is 134.54 μm, the milling performance of the tool is optimal. [ABSTRACT FROM AUTHOR]

To explore the best mixing working fluid of the two-stage evaporation series organic Rankine cycle system under specific working conditions, three groups of mixing working fluids with different characteristics of R21/R113, R1234ze/R141b and R227ea/R245fa were selected. According to thermodynamic performance, economic index and environmental index, the non-dominated sorting genetic algorithm was used for the multi-objective optimization. By solving Pareto boundary, three decision methods were used to select the optimal solution for each working fluid group. The optimal decision method was selected according to the offset. The results show that when the heat source temperature is 150 C with the cold source temperature of 15 C, the exergy efficiency of the optimal solution of the mixing working fluid of R1234ze/R141b is relatively high with relatively low levelized cost of energy (LCOE) and equivalent carbon emission (ECE), and the heat source temperature is also suitable, which illuminates that R1234ze/R141b is relatively suitable mixing working fluid in this working condition. [ABSTRACT FROM AUTHOR]

The relative sensitivity analysis method is important in the field of vehicle lightweighting. Combined with optimization algorithms, experiment of design (DOE), etc., it can efficiently explore the impact of unit mass of components on performance and search for components with lightweight space. However, this method does not take into account the size level of each component and the order of magnitude differences in sensitivity under different operating conditions. Therefore, this paper proposed a sensitivity hierarchical comparative analysis method, on the basis of which the thicknesses of 10 groups of components were screened out as design variables by considering the lightweighting effect, cab performance, and passive safety. Through the optimal Latin hypercube method, 70 groups of sample points were extracted to carry out the experimental design, the Kriging surrogate model was established and the NSGA-II genetic algorithm was used to obtain the Pareto optimal solution set, and ultimately a weight reduction of 13.13 kg was realized under the premise that the entire performance of the cab improved. [ABSTRACT FROM AUTHOR]

In recent years, there have been considerable improvements in how we keep track of mental health, especially with devices you can wear, which give us a better chance of spotting and dealing with problems like stress before they become serious. This research paper presents an innovative approach. Experimental validation uses a comprehensive dataset of 15 subjects working as multinational company employees. Heart Rate Variability(HRV) was obtained from wearable sensors using Apple Watch during working hours. We have calculated time, frequency and non-linear domains as well and added personalized features like a person's age, height, weight, etc. Recurrent Neural Network(RNN)and Long Short-Term Memory (LSTM)models are applied and get an accuracy of 87% and 90%, respectively. To enhance stress detection accuracy by optimizing hyperparameters using a genetic algorithm (GA) explicitly targeting the configuration of LSTM models. Key hyperparameters, including the number of units in the LSTM layer and the number of training epochs, are optimized to maximize stress detection accuracy. Model Through 5 generations of evolution, the GA identifies optimal hyperparameter settings of 45 units in the LSTM layer 49 epochs, significantly improving stress detection accuracy compared to baseline configurations. It gives 92 % accuracy with optimized hyperparameters. Analyzing recorded data, we observe that the time per training step decreases gradually, indicating efficient convergence during optimization. Simultaneously, stress detection accuracy steadily improves over epochs, showcasing the model's effectiveness in learning patterns from physiological data. So, This study provides insights into the practical application of genetic algorithms for hyperparameter optimization in healthcare contexts, contributing to advancements in personalized monitoring and intervention strategies for mental well-being. [ABSTRACT FROM AUTHOR]

The present study analyzes and optimizes machining characteristics, including feed rate (fz), depth of cut (ap), cutting speed (Vc), cutter-coated material (Mtc) and cutting-edge radius (rt), impacting on surface roughness (Ra), material removal rate (MRR) and tool wear (VB) of 42CrMo4 steel during dry end-milling. A total of 108 experimental runs were conducted, focusing on Ra, VB and MRR as response parameters. The nano TiAlN PVD-coated tool yielded better results for Ra and VB than did the TiCN/Al2O3 MT-CVD-coated tool. Then, Ra, VB and MRR optimization was carried out simultaneously using a Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and Machine Learning (ML) models. Pareto solutions were found to offer a range of values for the three performance objectives: Ra (0.315–0.556 µm), VB (12.33–32.48 µm) and MRR (0.44–3.58 cm3/min). A quantitative performance score (Ps) ranking index was calculated to rank Pareto solutions for practical case studies. Validation experiments were subsequently performed to affirm that the optimal solution fell within a reasonable error range, with MAPE of 9.58% for Ra, 9.25% for VB and 13.39% for MRR. The validation results underscore the versatility of this approach, suggesting its applicability to a wide array of machining optimization challenges. Highlights: 108 experiments were conducted considering cutting speed (Vc), axial depth of cut (ap), feed per tooth (fz), cutting-edge radius (rt) and cutting-coated material (Mtc); The effect of cutting characteristics on surface roughness (Ra), flank wear (VB) and material removal rate (MRR) of 42CrMo4 was revealed; Machine learning models were developed to predict Ra, VB and MRR accurately; The multiobjective NSGA-III technique was employed to optimize Ra, VB and MRR simultaneously; These Pareto solutions underwent meticulous evaluation using a quantitative ranking index—the performance score (PS)—which thoughtfully considered user preferences and reliability; Experiments were performed to validate the optimum solution within an acceptance range of errors; [ABSTRACT FROM AUTHOR]

Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed‐species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non‐additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom‐up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top‐down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment. [ABSTRACT FROM AUTHOR]

The genetic algorithm with aggressive mutations GAAM, is a specialised algorithm for feature selection. This algorithm is dedicated to the selection of a small number of features and allows the user to specify the maximum number of features desired. A major obstacle to the use of this algorithm is its high computational cost, which increases significantly with the number of dimensions to be retained. To solve this problem, we introduce a surrogate model based on machine learning, which reduces the number of evaluations of the fitness function by an average of 48% on the datasets tested, using the standard parameters specified in the original paper. Additionally, we experimentally demonstrate that eliminating the crossover step in the original algorithm does not result in any visible changes in the algorithm's results. We also demonstrate that the original algorithm uses an artificially complex mutation method that could be replaced by a simpler method without loss of efficiency. The sum of the improvements resulted in an average reduction of 53% in the number of evaluations of the fitness functions. Finally, we have shown that these outcomes apply to parameters beyond those utilized in the initial article, while still achieving a comparable decrease in the count of evaluation function calls. Tests were conducted on 9 datasets of varying dimensions, using two different classifiers. [ABSTRACT FROM AUTHOR]

In gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators. [ABSTRACT FROM AUTHOR]

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. [ABSTRACT FROM AUTHOR]

Uncertainty in robot dynamic systems is caused by model errors in the dynamic parameters, and accurate identification of the dynamic parameters is essential to improve the control accuracy of the robot. In this paper, a hybrid optimization strategy for modular robot manipulator dynamic model parameter identification is proposed to accurately identify the dynamic parameters of the robot manipulator. Firstly, the robot dynamics model with Coulomb viscous friction is established. Secondly, the cosine adaptive learning and reversal strategies are introduced to improve the genetic algorithm, and the improved genetic optimization algorithm is applied to optimize the excitation trajectories, and all the robot arm joints are commanded to follow the optimized excitation trajectories. In addition, considering that the Coulomb viscous friction model is not sufficient to accurately express the friction terms, a two-step identification method is proposed by analyzing the sensitivity of the parameters of the Stribeck friction model, combining the significantly identified friction coefficients with the quadratically optimized coefficients of the adaptive inverse genetic algorithm, which solves the problem of lower accuracy caused by the inaccuracy of the friction parameter identification. Then, the dynamic parameters are calculated using the least squares method to determine the system dynamics model information. Finally, the parameter identification and load identification are verified using a 6-degree-of-freedom modular robot manipulator, and the proposed hybrid optimization strategy effectively solves the defect of the low accuracy of the robot manipulator dynamics model compared to the dynamics model moment with Coulomb viscous friction, which in turn improves the control accuracy. Meanwhile, the load identification accuracy can reach 97% depending on the identified dynamics information. [ABSTRACT FROM AUTHOR]

The performance of a deep neural network is affected by its configuration as well as its training process. Determining the configuration of a DNN and training its parameters are challenging tasks due to high-dimensional problems. Therefore, there is a need for methods that can optimize the configuration and parameters of a DNN. Most of the existing DNN optimization research concerns the optimization of DNN parameters, and there are only a few studies discussing the optimization of DNN configuration. In this paper, enhanced harmony search is proposed to optimize the configuration of a fully connected neural network. The proposed harmony search enhancement is conducted by introducing various types of harmony memory consideration rate and various types of harmony memory selection. Four types of harmony memory consideration rate are proposed in this research: constant rate, linear increase rate, linear decrease rate, and sigmoid rate. Two types of harmony memory selection are proposed in this research: rank-based selection and random selection. The combination of types of harmony memory consideration rate and types of selection generates eight harmony search scenarios. The performance of the proposed method is compared to random search and genetic algorithm using 12 datasets of classification problems. The experiment results show that the proposed harmony search outperforms random search in 8 out of 12 problems and approximately has the same performance in 4 problems. Harmony search also outperforms genetic algorithm in five problems, approximately has the same performance in six problems, and has worse performance in one problem. In addition, combining various types of harmony memory consideration rate and rank-based selection increases the performance of the ordinary harmony search. The combination of harmony memory consideration with linear increase rate and rank-based selection performs the best among all combinations. It is better than the ordinary harmony search in seven problems, approximately equal in three problems, and worse in two problems. The results show that the proposed method has some advantages in solving classification problems using a DNN. First, the configuration of the DNN is represented as an optimization problem so that it can be used to find a specific FCNN configuration that is suitable for a specific problem. Second, the approach is a global optimization approach as it tunes the DNN hyperparameter (configuration) as well as the DNN parameter (connection weight). Therefore, it is able to find the best combination of DNN configuration as well as its connection weight. However, there is a need to develop a strategy to balance the hyperparameter tuning and the parameter tuning. Inappropriate balance could lead to a high computational cost. Future research can be directed to balance the hyperparameter and parameter tuning during the solution search. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm intelligence, artificial intelligence, risk management, financial monitoring, etc. With the development of CI, abundant publications have arisen related to many research directions and hotspots. Based on the technical support from bibliometrics and the corresponding approach as well as the content analysis, this study conducts a science mapping analysis and a coherent knowledge picture of the research field in CI. The research contributes to clear future development trends and provides more ideas for scholars in this field. First, this paper focuses on the fundamental characteristics of CI publications, including annual numbers, term co-occurrence, and hot research directions, as the preliminary exploration of this field. Then, according to the widely used core database, i.e., Web of Science (WoS), and the technologies of software, VOS viewer, and CiteSpace, the productive institutions, authors, and journals are explored. Next, the corresponding internal characteristics of the CI research are analyzed, including the citation features of countries/regions, institutions, journals and authors. Furthermore, to analyze the development trend of research hotspots, the keywords of all CI publications are studied: (a) classifying them into three phases in chronological order aimed; (b) implementing the burst detection algorithm to intuitively reflect the scientific research in the field of CI. Finally, this paper provides a relatively throughout perspective for the CI articles and reviews and discloses the future development trend, which will help the scholars interested in this area conduct deep research. We conclude this bibliometric overview with the limitations and recommendations for future research in the field of CI. [ABSTRACT FROM AUTHOR]

Several real-life problems are comprised of finite single-server acyclic queueing networks. The performance optimization of these queueing networks has been the subject of several studies. The present study extends the minimization of the total buffer area and overall service rates in the network simultaneously with the maximization of throughput. It is well known that these three objectives are conflicting. This fact leads to a multi-objective approach that the literature in the area has already addressed. Nevertheless, this study aims to demonstrate that there are algorithms with low computational costs that can produce solutions more efficiently than those obtained previously. Furthermore, the provided solutions can enhance throughput by solving a stochastic knapsack problem. The greedy procedure utilizes a technique of redistributing buffers between the queues, ensuring that the overall capacity is less than or equal to the previous overall capacity; thus, one objective is improved (the throughput) without compromising the other objective (total buffer allocation). Several computational experiments attest to the quality of this proposition. In addition, we provide a comparison with previously proposed solutions. [ABSTRACT FROM AUTHOR]

Research on multi-objective discrete optimization of Waste Electrical and Electronic Equipment (WEEE) remanufacturing remains under-studied in the literature. Remanufacturing laptops to extend their useful life is viewed as the best End-Of-Life (EOL) alternative considering environmental and social factors. This paper develops a model to decide the best EOL option, namely reuse, conditional repair, and disposal of quality-graded laptop parts, with economic and environmental objectives. The first objective is to maximize the profit of remanufactured laptops over a multi-period planning horizon. The second objective is to minimize the emissions associated with remanufacturing. A Multi-Objective Discrete Particle Swarm Optimization (MODPSO) algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are embedded as a decision support tool in Microsoft Excel with a user interface to yield Pareto optimal solutions, and the results are compared. The Taguchi approach is applied to find the optimum value of the control parameters of the proposed algorithms. The approach is tested with inputs from an authorized remanufacturer in Bangalore, India. The performance of the algorithms is further investigated using randomly generated test problems. The MODPSO algorithm provided better solutions for all problem instances based on the convergence and diversity metrics. The inclusion of conditional repair options and parts with low and medium-quality grades in remanufacturing leads to higher profit, albeit with more emissions. A variation in the quality grade assigned to the conditional repair option for the parts needed for higher profit margin laptops is observed. A sensitivity analysis is conducted to observe the impact of supply, demand, repair cost, shortage cost, and emissions on the two extreme Pareto solutions. The decision-making tool offers a continuum of trade-offs to help a remanufacturer choose the EOL options, depending on economic and environmental performance preferences. [ABSTRACT FROM AUTHOR]

Background: In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time. Purpose: Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations. Methods: We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB‐BNCT. Results: The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR). Conclusion: The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges. [ABSTRACT FROM AUTHOR]

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery. [ABSTRACT FROM AUTHOR]

In education testing, the items that examinees receive may be selected for a variety of reasons, resulting in missing data for items that were not selected. Item selection is internal when based on prior performance on the test, such as in adaptive testing designs or for branching items. Item selection is external when based on an auxiliary variable collected independently to performance on the test, such as education level in a targeting testing design or geographical location in a nonequivalent anchor test equating design. This paper describes the implications of this distinction for Item Response Theory (IRT) estimation, drawing upon missing-data theory (e.g., Mislevy & Sheehan, 1989, https://doi.org/10.1007/BF02296402; Rubin, 1976, https://doi.org/10.1093/biomet/63.3.581), and selection theory (Meredith, 1993, https://doi.org/10.1007/BF02294825). Through mathematical analyses and simulations, we demonstrate that this internal versus external item selection framework provides a general guide in applying missing-data and selection theory to choose a valid analysis model for datasets with missing data. [ABSTRACT FROM AUTHOR]

Estimating the number of latent factors underlying a set of dichotomous items is a major challenge in social and behavioral research. Mokken scale analysis (MSA) and exploratory graph analysis (EGA) are approaches for partitioning measures consisting of dichotomous items. In this study we perform simulation-based comparisons of two EGA methods (EGA with graphical least absolute shrinkage and selector operator; EGAtmfg with triangulated maximally filtered graph algorithm), two MSA methods (AISP: automated item selection procedure; GA: genetic algorithm), and two widely used factor analytic techniques (parallel analysis with principal component analysis (PApc) and parallel analysis with principal axis factoring (PApaf)) for partitioning dichotomous items. Performance of the six methods differed significantly according to the data structure. AISP and PApc had highest accuracy and lowest bias for unidimensional structures. Moreover, AISP demonstrated the lowest rate of misclassification of items. Regarding multidimensional structures, EGA with GLASSO estimation and PApaf yielded highest accuracy and lowest bias, followed by EGAtmfg. In addition, both EGA techniques exhibited the lowest rate of misclassification of items to factors. In summary, EGA and EGAtmfg showed comparable performance to the highly accurate traditional method, parallel analysis. These findings offer guidance on selecting methods for dimensionality analysis with dichotomous indicators to optimize accuracy in factor identification. [ABSTRACT FROM AUTHOR]

Sander lucioperca is one of the species whose fishing has developed considerably worldwide during the last decades. However, the lack of scientific research and the ignorance of the bio-ecological of this species of important economic interest constitutes a great problem for the management of stocks in the Moroccan fresh waters. For this purpose, this study was devoted to the analysis of the population structure, morphometric relationships (length- length and length -weight) as well as von Bertalanffy's growth parameters (VBGP) using length and weight data of 1019 specimens caught at Al Massira Dam Lake between January and December 2019. VBGP parameters based on frequency-length (LFQ) data were estimated by the ELEFAN _GA method which is based on a genetic algorithm using the "TropFishR" package. These parameters are given as follows: L∞ = 88.87, K = 0.46 and t_anchor = 0.51. The growth performance index (φ') was 3.56, with a maximum age value of 7.62. In addition, the results showed a rapid growth of S. lucioperca and a major allometry demonstrating that this species has a significant weight growth. This study provides new knowledge, necessary for the assessment of stocks and populations, which could contribute to the implementation of new management strategies for the populations of S. lucioperca exploited at the level of the Al Massira Dam Lake in Morocco. [ABSTRACT FROM AUTHOR]

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model's simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function's limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing. [ABSTRACT FROM AUTHOR]

Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use of Interactive Genetic Algorithms (IGAs) for ceramic design is a new approach, but an IGA requires a significant amount of user evaluation, which can result in user fatigue. To overcome this problem, this paper introduces the LightGBM algorithm and the CatBoost algorithm to improve the IGA because they have excellent predictive capabilities that can assist users in evaluations. The algorithms are also applied to a vase design platform for validation. First, bicubic Bézier surfaces are used for modeling, and the genetic encoding of the vase is designed with appropriate evolutionary operators selected. Second, user data from the online platform are collected to train and optimize the LightGBM and CatBoost algorithms. Finally, LightGBM and CatBoost are combined with an IGA and applied to the vase design platform to verify their effectiveness. Comparing the improved algorithm to traditional IGAs, KD trees, Random Forest, and XGBoost, it is found that IGAs improve with LightGBM, and CatBoost performs better overall, requiring fewer evaluations and less time. Its R2 is higher than other proxy models, achieving 0.816 and 0.839, respectively. The improved method proposed in this paper can effectively alleviate user fatigue and enhance the user experience in product design participation. [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]

In single crystal diamond tool grinding, cutting-edge quality is affected by various parameters, with tool vibration playing a crucial role. Due to environmental factors and the complexity of the process, vibration signals are often noisy and non-stationary. This study proposes a denoising method that optimizes the wavelet threshold function using an Improved Genetic Algorithm (IGA). The method introduces a configurable value α in the wavelet threshold function, which is optimized with IGA to improve denoising. MATLAB R2018b simulations show that this approach achieves better denoising, a higher signal-to-noise ratio, and lower mean square error. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) is a powerful tool for modeling the phase and structural transformations and the evolution of defects and their influence on the metallic material properties. The accuracy of MD modeling directly depends on the quality of interatomic potentials. Modern machine-learning potentials are typically trained on random atomic configurations. This approach has significantly improved the quality of new potentials over traditional EAM potentials. In this work, exact solutions to the equations of atomic motion are offered to train the machine learning potentials. [ABSTRACT FROM AUTHOR]

Nowadays, online examination (exam in short) platforms are becoming more popular, demanding strong security measures for digital learning environments. This includes addressing key challenges such as head pose detection and estimation, which are integral for applications like automatic face recognition, advanced surveillance systems, intuitive human–computer interfaces, and enhancing driving safety measures. The proposed work holds significant potential in enhancing the security and reliability of online exam platforms. It achieves this by accurately classifying students' attentiveness based on distinct head poses, a novel approach that leverages advanced techniques like federated learning and deep learning models. The proposed work aims to classify students' attentiveness with the help of different head poses. In this work, we considered five head poses: front face, down face, right face, up face, and left face. A federated learning (FL) framework with a pre-trained deep learning model (ResNet50) was used to accomplish the classification task. To classify students' activity (behavior) in an online exam environment using the FL framework's local client device, we considered the ResNet50 model. However, identifying the best hyperparameters in the local client ResNet50 model is challenging. Hence, in this study, we proposed two hybrid bio-inspired optimized methods, namely, Particle Swarm Optimization with Genetic Algorithm (PSOGA) and Particle Swarm Optimization with Elitist Genetic Algorithm (PSOEGA), to fine-tune the hyperparameters of the ResNet50 model. The bio-inspired optimized methods employed in the ResNet50 model will train and classify the students' behavior in an online exam environment. The FL framework trains the client model locally and sends the updated weights to the server model. The proposed hybrid bio-inspired algorithms outperform the GA and PSO when independently used. The proposed PSOGA not only outperforms the proposed PSOEGA but also outperforms the benchmark algorithms considered for performance evaluation by giving an accuracy of 95.97%. [ABSTRACT FROM AUTHOR]

This research focuses on integrating seakeeping indices into the trajectory planning of a mother ship in order to minimize risks during UAV (unmanned aerial vehicle) takeoff and landing in challenging sea conditions. By considering vessel dynamics and environmental factors, the proposed trajectory planning algorithm computes optimal paths that prioritize the stability and safety of the ship, mitigating the impact of adverse weather on UAV operations. Specifically, the new adaptive weather routing model presented is based on a genetic algorithm. The model uses the previously evaluated response amplitude operators (RAOs) for the reference ship at different velocities and encounter angles, along with weather forecast data provided by the global wave model (GWAM). Preliminary evaluations confirm the effectiveness of the presented model in significantly improving the reliability of autonomous UAV operations from a mother ship across all encountered sea state conditions, particularly when compared with a graph-based solution. The current results clearly demonstrate that it is possible to achieve appreciable improvements in ship seakeeping performance, thereby making UAV-related operations safer. [ABSTRACT FROM AUTHOR]

Collective Thomson scattering (CTS) is a diagnostic technique that obtains ion temperature and ion composition of plasma by spectral decomposition from scattering spectra. Bayesian estimation and least squares fitting are usually applied in this spectral decomposition process. Nevertheless, these spectral decomposition methods strongly rely on measurements of other diagnostic systems, and the measurement errors of other systems would influence the spectral decomposition results. In this article, an improved genetic algorithm is applied to decompose the scattering spectra of CTS. By analyzing the sensitivity of plasma parameters, the width and slope of the scattering spectrum are found to be strongly associated with ion temperature. Based on this correlation relation, a new fitness function is designed to provide a more precise estimation of ion temperature. Meanwhile, adaptive crossover and mutation operators are introduced to solve the premature convergence problem. This improved genetic algorithm with the new fitness function can obtain a more precise ion temperature from scattering spectra of CTS and does not rely on the measurement of other diagnostic systems, which has an extensive application prospect in data processing of CTS. [ABSTRACT FROM AUTHOR]

In photon-deficient, noncollective Thomson scattering diagnostics, filter polychromators are typically employed in the spectral analysis of Thomson-scattered signals to achieve acceptable signal-to-noise performance. Currently, the most common polychromator filter configuration employs a set of single-passband optical filters that define individual spectral channels. Here, we introduce a new spectral analysis method for Thomson scattering based on spectral filters with multiple passbands, referred to as Thomson scattering spectral multiplexing. Implementing multi-bandpass spectral filters on polychromators increases the achievable range of electron temperature measurement for a given number of filters employed. In addition, Thomson scattering spectral multiplexing reduces systematic measurement uncertainty, with fewer required spectral channels, thereby decreasing light loss from reduced optical element interactions. A multi-bandpass filter set, optimized by a genetic algorithm, has been successfully installed and tested on the Helically Symmetric eXperiment (HSX), demonstrating the benefits of the Thomson scattering spectral multiplexing method. [ABSTRACT FROM AUTHOR]

Honey's chemical and sensory characteristics depend on several factors, including its botanical and geographic origins. The consumers' increasing interest in monofloral honey and honey with a clear indication of geographic origin make these types of honey susceptible to fraud. The aim was to propose an original chemometric approach for honey's botanical and geographic authentication purposes. The volatile fraction of almost 100 Italian honey samples (4 out of which are from Greece) from different regions and botanical origins was characterized using HS-SPME-GC-MS; the obtained data were combined for the first time with a genetic algorithm to provide a model for the simultaneous authentication of the botanical and geographic origins of the honey samples. A total of 212 volatile compounds were tentatively identified; strawberry tree honeys were those with the greatest total content (i.e., 4829.2 ng/g). A greater variability in the VOCs' content was pointed out for botanical than for geographic origin. The genetic algorithm obtained a 100% correct classification for acacia and eucalyptus honeys, while worst results were achieved for honeydew (75%) and wildflower (60%) honeys; concerning geographic authentication, the best results were for Tuscany (92.7%). The original combination of HS-SPME-GC-MS analysis and a genetic algorithm is therefore proposed as a promising tool for honey authentication purposes. [ABSTRACT FROM AUTHOR]

Background: Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. Methods: This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. Results: Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. Conclusions: The study's results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects. [ABSTRACT FROM AUTHOR]

The oblique collision and reflection of a detonation wave can lead to extremely high pressure and considerable dynamic load mixing in the resulting product, which has been a focus of research in detonation and defense applications. In this paper, the relationships among wave velocity, mass velocity, pressure, and the specific volume of detonation products (DPs) under overdriven detonation (ODD) conditions are analyzed. Additionally, the equations of state (EOS) of the ODD are calibrated by using real-coded genetic algorithms combined with experimental Hugoniot strong detonation data, and the effects of different DPs on pressure accuracy are assessed. Accordingly, a dynamic evolution model of detonation wave interactions in a collision zone is established, and theoretical calculations of regular and Mach reflections occurring after the interactions of typical condensed explosive [such as PBX9501 (95% HMX, 2.5% Estane, 2.5% BDNPA/F)] detonation waves are carried out. The results show that the overpressure Hugoniot data and the isentropic expansion line can be better fitted by using the JWL (Jones–Wilkins–Lee) + γ equation than other EOSs, and the deviation of the calculated pressure and the height of the Mach stem from the experimental value is within 5%. Additionally, a formula is derived for the slow-variable function k(ξ) by combining the improved Whitham method and the JWL + γ EOS, and a propagation law is obtained for the detonation wave interaction. [ABSTRACT FROM AUTHOR]

The caret intake could not only provide compressed airflow to the aircraft engine stably but also should have applicable radar stealth performance. Radar stealth means that the echo intensity of the intake should be low when detected by radar, which could be measured by radar cross section (RCS). The airflow distribution of the double S-duct caret intake was numerically optimized to improve aerodynamic performance, with radar stealth performance as constraints. Self-adaptive genetic algorithm and Kriging model were used to improve the optimization efficiency. There could be multiple boundary-layer separations in the S-duct caret intake, resulting in obvious total pressure distortion at the outlet and secondary flow. To suppress these adverse airflow phenomena, the optimization of the double S-duct intake was defined as a problem with two targets and multiple constraints. By defining the probabilities of selection and variation as functions of the iteration number and the fitness, the self-adapting non-dominated sorting genetic algorithm can be applicable for such a problem. Using the Kriging model, which was proven to have satisfactory accuracy in predicting performance parameters, an optimized intake with both excellent aerodynamic and stealth performance was obtained, with a 30% reduction in DC60 value and a 47% reduction in SC60 value compared to the intake before optimization. [ABSTRACT FROM AUTHOR]