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This work presents the study of effect of morphology of carbonyl iron, carbonyl iron-based, and e-waste-based mixtures on microwave absorption properties. Firstly, the flake shaped carbonyl iron particles and its mixture are prepared using conventional and efficient ball milling and blending method, respectively. It provides the absorption in lower frequency region. Further, the covered frequency range starts to shifts slowly towards higher frequency ranges due to the morphological changes. On the other hand, effect of complex morphology of e-waste-based mixtures are also studied. These heterogeneous mixtures are developed using the cutting mill and blending methods that can provide the absorption in higher frequency with very narrow bandwidth. In this manner, effect of morphology on single layer microwave absorption performance of developed heterogenous material is proposed. It is also explored that the developed samples cover a wider bandwidth by utilizing the multilayering technique. A three-layer absorber is designed using genetic algorithm and the results are also validated. The structural and morphological study of the developed heterogeneous materials has been done using X-ray diffraction (XRD) and Field Emission Scanning Electron Microscope (FESEM) analysis. Also, rigorous study has been done on magnetic properties and Cole-Cole plots to understand the magnetic and dielectric losses respectively occurring inside the materials. The results showed wide bandwidth absorption of 11.62 GHz (≤−10 dB bandwidth) in the lower frequency region ranges from 0.5 to 12.12 GHz (i.e., L, C, and X-band). The total thickness achieved is 1.85 mm which is very compact (<2 mm). Thus, a thin and wideband microwave absorber at lower frequency range using heterogeneous morphology of developed materials is also presented. The proposed microwave absorbing materials can be potentially utilized for the shielding applications. [Display omitted] • Study explores the impact of morphology on microwave absorption in heterogeneous mixtures. • Milled Carbonyl iron and its mixture exhibit absorption in lower frequency regions. • E-waste-based mixtures with complex morphology exhibit absorption in higher frequency regions. • Multilayering techniques extend bandwidth, with a three-layer absorber designed using genetic algorithms. • Wideband absorption (11.62 GHz) achieved in the lower frequency range (0.5–12.12 GHz) at a compact (<2 mm) thickness. [ABSTRACT FROM AUTHOR]

Catalytic methane decomposition (CMD) stands out as a promising alternative for hydrogen production, distinguished by its ability to generate hydrogen with minimal byproducts. While some studies have explored the use of biochar as an alternative catalyst in the CMD process, the role of biochar surface functional groups and inherent properties have not been well understood. This study aimed to explore the influence of various woody biochar types, acidic sites, physicochemical properties, and operating temperatures on their catalytic activity in catalytic methane decomposition (CMD) process. Biochar derived from three biomass sources (pine, poplar, and bark) were examined at 600, 800, and 1000 °C reaction temperature. The findings revealed that heat-treated (HT) poplar biochar exhibited the lowest specific surface area (215.1 m2g⁻1) and the highest concentration of acidic sites. All samples exhibited improved performance at 1000 °C compared to other temperatures. The CH 4 conversion order was observed as HT poplar > HT red pine > HT bark, with the initial conversion of approximately 50%, 35%, and 25%, respectively. Additionally, employing genetic algorithms, kinetics models for the CMD reaction were developed. The study concluded a consistent second order reaction and an activation energy in the range of 300–330 kJ/mol for all three biochar samples. [Display omitted] • The role of acidic functional groups on biochar activity during CMD was revealed. • Poplar demonstrated the highest acidity and best catalytic performance at 1000 °C. • The O:C and H:C ratios suggested potential for carbon sequestration potential. • Biocarbon-catalyzed CMD exhibited an activation energy between 300 and 330 kJ/mol. [ABSTRACT FROM AUTHOR]

This paper articulates the fund allocation and prioritization challenge for existing building facilities' Maintenance and Repair (M&R) interventions. Despite the existing conspicuous research in funding allocation, there is a dearth of comprehensive insight into which facilities necessitate maintenance, when to intervene, and the strategies to adopt. Integrating Building Information Modeling (BIM) and Facility Management data for fund allocation decisions remains challenging, and existing models lack a thorough portrayal of as-is information for M&R interventions and funding allocation. To this end, this study proposes an integrated BIM-based fund allocation and prioritization framework to bridge these gaps. The framework incorporates as-is BIM semantic knowledge and utilizes a genetic algorithm optimization scheme to determine interventions based on condition state, future deterioration rate, and relative weighted importance. The research objectives include constructing an as-is BIM model, formalizing as-is knowledge using BIM standards, allocating funding efficiently, and determining optimum timing and type of interventions. The developed framework is enacted in a real-case scenario for validation and verification, and findings unveiled the proposed paradigm capabilities as a decision support system for fund allocation and prioritization, enabling facility managers to constitute informed decisions based on building facilities' as-is information, deterioration rates, and relative importance weight. [ABSTRACT FROM AUTHOR]

Natural disasters, which are the major causes of large-scale power outages, have become far more common in recent decades. Therefore, this study evaluates the importance and relevance of optimal Tie-line construction in improving the service restoration performance of power distribution systems following high-impact, low-probability occurrences. In doing so, an improved genetic algorithm (GA) is developed to find the optimal locations for the construction of new Tie-lines in distribution systems. In particular, the restoration process of distribution systems, including fault isolation and system reconfiguration, is planned to deploy Tie-lines in the most appropriate locations to improve distribution system resilience and reliability according to considerations related to the costs of constructing new Tie-lines. To validate the effectiveness of the proposed model, it is tested and verified on the RBTS standard network and the results of the proposed approach have been compared with other optimization algorithms such as particle swarm optimization, gravitational search (GS) and shuffled frog leaping. Examining the simulation results shows the efficiency of the proposed performance compared to other evolutionary methods. For instance, the value of the total cost function resulting from the proposed GA in the first and third scenarios has decreased by about $1589 and $946, compared to the GS method, respectively. [ABSTRACT FROM AUTHOR]

Floating production storage and offloading systems (FPSOs) are gaining prominence in the offshore industry. FPSOs are generally designed to weathervane, necessitating surge vibration control for station keeping. Since the natural period of surge vibration is far higher than the dominant period of the input wave load, instead of supplemental damping, a high inertial device, such as the tuned mass damper inerter (TMDI), would be effective. This concept has hitherto not been investigated for the FPSO. A frequency domain formulation of the FPSO-TMDI system under wave force, considering interaction of the liquid cargo is presented. Utilizing Froude-Krylov theory, suitably modified to account for diffraction effects, wave force on the FPSO is obtained using field data from North Sea. TMDI parameters are optimized using Genetic Algorithm under different tank-fill conditions. The results demonstrate that the TMDI holds potential in providing an effective and robust performance in controlling the surge motion of FPSOs. [ABSTRACT FROM AUTHOR]

The inverse design of nanophotonic devices has been widely concerned by researchers, and on-demand design is the difficulty of inverse design. In inverse design, researchers usually define a target spectrum based on the performance indicators and experiences and then inverse design the structural parameters from the target spectrum. Due to the uncertainty of inverse design and "one-to-many" problem, it is not usually possible to guarantee that the target spectrum is sure to correspond to a real nanostructure. In order to solve these problems, an inverse design method combining generative model and genetic algorithm is proposed in this paper. Before the inverse design, the real spectrum is compressed into a latent space by the generation model, and then, the target spectrum is decoded from the latent space according to the performance index. Finally, the hybrid optimization algorithm combining genetic algorithm and forward prediction network is used to optimize the generated spectrum. The design method follows the process from performance indicators to target spectrum to structural parameter, and we successfully realized the inverse design of multilayer nanofilms on demand by using this method in the experimental part. The inverse design method proposed in this paper provides a possible solution for the inverse design of nanophotonic devices on demand. [ABSTRACT FROM AUTHOR]

Herein, the structural evolution, electronic and magnetic properties of CrSin−/0 for n = 19–25 have been investigated at density functional theory (DFT) level. The global minimum structures of these clusters have been fully searched through a self-developed genetic algorithm code combined with DFT calculations. Both of anionic and neutral CrSin for n = 19–25 share the same configurations. All these clusters prefer to adopt endohedral structures (Cr@Si14 for sizes n = 19–21, and Cr@Si13 for larger ones) as the structural motif with the remaining Si atoms attached on the surface. Among all these clusters, CrSi22 and CrSi23 are the most prominent through the analysis of HOMO − LUMO gaps, average binding energies, and the second order of energy differences. All of these medium-sized cluster anions possess the same total magnetic moment of 1 μB, but with very different contributions from that of small sizes (n ≤ 18), except for size 22. [ABSTRACT FROM AUTHOR]

The recent emergence of modular space-based solar power concepts has brought the topic to the forefront of alternatives for power generation. Additionally, advancements in wireless power transmission, space robotics, and reusable launch vehicles have improved the viability of space-based solar power concepts. Modular concepts have led to several benefits compared to classical space-based solar power designs in terms of improved manufacturability and assembly, but they also have introduced new challenges with respect to orbital and attitude design. In particular, the coupling of sunlight collection and power beaming on opposing sides of modular configurations, such as for flat-plane sandwich configurations, requires a compromise between power collection and beaming within the orbital and attitude kinematics. This study develops a simultaneous power-beaming geometric efficiency optimization approach that integrates orbital dynamics and attitude kinematics of space-based solar power satellite arrays. The efficacy of the developed approach is demonstrated through three case studies, each reflecting typical constraints on the relative geometry of the satellite array with respect to the Sun and the ground; thereby, enabling a comprehensive exploration of various orbits. Near-optimal solutions of orbits and attitudes that maximize the efficiency of power beaming to the ground are generated, broadening the spectrum of orbits previously investigated in the literature. The approach and findings presented lay the groundwork for evaluating power-beaming systems and conducting analyses that take into account different types of perturbations. • Concurrent orbit and attitude optimization for space-based solar power arrays. • Optimizations assess power beaming for general, Molniya, and Sun-synchronous orbits. • Evaluates multiple configurations of modular sandwich configurations with mirrors. • Analyzes effects of ground locations and time of year on power-beaming efficiency. • Investigates power-beaming efficiency from inclined highly elliptical orbits. [ABSTRACT FROM AUTHOR]

In this letter, attempt has been made to successfully design a pipelined VLSI architecture for the computation of genetic algorithm (GA). The concept of stochastic computing is uniquely exploited in the proposed pipelined GA architecture to attain significant area and power efficiency with reasonably high speed of operation. The prototype 8-bit fixed point GA architecture is realised using VHDL on Xilinx Vivado 2020.3 and implemented on Zynq Ultrascale+ MPSoC (XCZU7EV-2FFVC1156) to train an arbitrary 4:3:2 fully connected neural network in real-time. The performance of the prototype GA architecture in case of real-time training of the neural network outshines the software and other existing GA architectures. The proposed GA-trained 4:3:2 network exhibits 6X reduction in training time and 720X increase in power efficiency, only at the cost of 0.06 % reduction in accuracy with respect to other existing works and software in case of the image classification of MNIST data-set. [ABSTRACT FROM AUTHOR]

Isothermal compression tests for ERNiCrMo-3 alloy specimens were conducted under temperatures of 990–1170 °C with strain rates of 0.01–10 s−1. Hot deformation behavior of ERNiCrMo-3 was analyzed based on the obtained flow stress curves. Three constitutive models were developed to anticipate the flow stress: the Arrhenius model compensated by strain, the Arrhenius model optimized by genetic algorithm (GA), and the artificial neuron network (ANN) model. The correlation coefficient (R) and average absolute relative error (AARE) were used to assess the predictive ability for these three models. The R values of the strain-compensated, GA optimized, and ANN models were 91.04, 93.54, and 99.01%, respectively, while the AARE values were 12.30, 8.19, and 2.92%, respectively. Furthermore, the absolute relative error of the ANN model was the most concentrated and mainly around 0. Therefore, the ANN model provides the maximum predictability and accuracy for flow stress. The ANN model was implemented via subroutine USRMTR in DEFORM 2D to simulate the hot compression process of the ERNiCrMo-3 alloy specimens. The numerical results were in good agreement with the experimental results, indicating a high potential for applicability to actual production processes. Finally, hot working characteristic of ERNiCrMo-3 alloy was analyzed by processing map and microstructural investigation. According to the result, the suitable thermal processing domain for ERNiCrMo-3 alloy should be at a temperature range of 1130–1170 °C and a strain rate range of 0.03–0.36 s−1. [ABSTRACT FROM AUTHOR]

Background: Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective: The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods: A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results: The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion: Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy. [ABSTRACT FROM AUTHOR]

A strategy to obtain the DNA base-pair sequence is proposed from the information of the time series data of the size fluctuations (breathing) of local denaturation zones (bubbles). In one of the previous publication of the author,1 it was shown how the DNA stability parameters were evaluated from an equilibrium distribution profile of bubble opening for a known sequence of heteropolymeric DNA. This paper attempts to know whether the reverse could be done, i.e., by knowing the stability parameters and the distribution profile for an unknown DNA sequence, whether the proper base-pair sequence can be evaluated or not. To get the DNA sequence, a non-trivial search process has been used. The problem has been cast as one of optimization problem and stochastic optimizer Genetic Algorithm has been used to optimize the DNA sequence in order to reach the correct or objective equilibrium distribution profile. We have tested the strategy for three DNA sequences, L33B9, L42B18 and the promoter sequence of the T7 bacteriophage. The DNA base pair sequence could be obtained from the information of the time series data of the size fluctuations (breathing) of local denaturation zones (bubbles). The problem has been cast as one of optimization problem and stochastic optimizer Genetic Algorithm. [ABSTRACT FROM AUTHOR]

There is a growing need for the lightweight acquisition, tracking, and pointing (APT) system during satellite launches due to the escalating demand in space missions. The APT system may work under multiple loading cases during different launch steps. Hence, this study introduces an innovative amalgamation of genetic operation and bi-directional evolutionary structural optimization (BESO) to fulfill the multi-objective requirements through the attainment of Pareto optimal fronts. A typical instance in two dimensions illustrates the effectiveness of the innovative multi-objective approach by contrasting the outcomes acquired from a solitary fulfillment requirement under two distinct burdens. Furthermore, the novel multi-objective method is utilized to remove inefficient material from the APT system by 20.12%. To ensure the safety of the lightweight design, the simulation and experiment of random vibration are both investigated according to the fundamental natural frequency of the launcher. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

This paper proposes a method to reduce torque ripple in axial gap motors by multi objective optimization of permanent magnets (PMs) shape using genetic algorithm (GA). Torque ripple is a problem because it causes vibration and noise. Conventionally, torque ripple has been reduced by quantitatively designing the PMs in the shape of multiplicative wave. However, it is difficult to optimize the objective function only by quantitative evaluation through sensitivity analysis. Therefore, in this study, the functions constituting the PMs interface shape are expressed as a Fourier-based series. The PMs is optimized by optimizing combination of their coefficients with GA. As a result, the proposed model is almost equal to the average torque of the basic model and the torque ripple is significantly reduced. Furthermore, fillets are applied and the effect on each characteristic is verified. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for optimizing the design of CMUT sensors using genetic algorithms. Existing CMUT sensors face frequency response and sensitivity limitations, necessitating optimization to enhance their sensing performance. Traditional optimization methods are often intricate and time-consuming and may fail to yield the optimal solution. Genetic algorithms, which simulate the biological evolution process, offer advantages in global optimization and efficiency, making them widely utilized in the optimization design of Microelectromechanical Systems (MEMS) devices. Based on the theoretical framework and finite element model of CMUT sensors, we propose a CMUT array element optimization design method based on genetic algorithms. The optimization and validation results demonstrate that we have successfully designed a broadband CMUT array element consisting of four microelements with a 1–2 MHz frequency range. Compared with a randomly arranged array element, the optimized array shows a 63.9% increase in bandwidth and a 7.5% increase in average sensitivity within the passband. Moreover, the sensitivity variance within the passband is reduced by 50.2%. Our proposed method effectively optimizes the design of high sensitivity CMUT sensors with the desired bandwidth, thereby offering significant reference value for the optimization design of CMUT sensors. [ABSTRACT FROM AUTHOR]

In this article, a response of the complex-phase high-strength steel SZBS800 was modelled by considering the strain-rate influence. The material's response was first measured with a series of standard tensile tests at lower strain rates. Higher strain rates were achieved using the unconventional test of shooting the ball into flat specimens. A viscoplastic formulation of the Cowper–Symonds material model was applied to consider the strain-rate effects. The parameters SIGY, p, and C of the material model were estimated using a step-wise procedure. First, rough estimates of the three parameters were obtained from the tensile tests using the grid search method. Then, the parameters p and C were fine-tuned using the reverse engineering approach. With the help of explicit dynamic simulations and all the experimental data, a multi-criteria cost function was defined and applied to obtain a smooth response function for the parameters p and C. Its optimum was determined by a real-valued genetic algorithm. The optimal values of the estimated parameters model the material response well, although a domain of optimum candidates spans two orders of magnitude for the parameter p and a few orders of magnitude for the parameter C. [ABSTRACT FROM AUTHOR]

AbstractThe drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control. [ABSTRACT FROM AUTHOR]

Recent advances in optimal diabetes control have made it possible for diabetic patients to live longer, healthier, and happier lives. In this research, particle swarm optimization and genetic algorithm are applied in order to control the non-linear fractional order chaotic system of glucose–insulin optimally. A fractional system of differential equations discussed the chaotic behavior of the growth of the blood glucose system. Particle swarm optimization and genetic algorithm were used to solve the presented optimal control problem. The results showed that when the controller is applied from the beginning, the results of the genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method is also very successful and the results are very close to the genetic algorithm method. [ABSTRACT FROM AUTHOR]

• A new constellation lifecycle cost optimization problem was introduced. • Useful insights to the satellite burn-in problem can guide satellite manufacturers. • Long testing durations are undesirable; testing can be traded off with number of satellites produced. • Combining burn-in, warranty and availability analysis opens new paths for constellation research. Constellation lifecycle costs are an important metric for constellation designers. Recurring costs of mega constellations in particular are large relative to the non-recurring costs, yet little attention has been given to the reduction of recurring costs. Moreover, few studies exist on the impacts of pre-launch activities such as satellite testing on subsequent operational reliability. We thus study the lifecycle cost impacts of environmental stress screening during acceptance testing to minimize constellation lifecycle costs. Parallels are drawn with the field of electronics testing, and costs arising from burn-in, replacement and availability are considered in our formulation. A single-objective cost minimization problem and a multi-objective cost and unavailability minimization problem are presented to capture different mission applications that require different objectives. The effects of system homogeneity and the use of sparing strategies are investigated. We propose genetic algorithms to solve the optimization problems and give some insight into how burn-in should be carried out for minimizing total costs. The optimization is also applied to a case study of a proposed mission scenario and the constellation lifecycle costs of an optimized burn-in plan are compared to those of a typical "reliability target" burn-in plan. The optimization resulted in lifecycle cost savings of around 19% while significantly reducing the burn-in duration, showing much promise in the burn-in cost optimization approach. [ABSTRACT FROM AUTHOR]

In recent years, a great interest has focused on the use of bicomponent filaments in several high-performance textile articles such as swimwear, sportswear and even high-quality denim. To dye fabrics containing these filaments, it is necessary to establish appropriate dye recipes allowing to obtain desired shades. In this article, we developed a genetic algorithm to optimize the color matching step of these bicomponent filaments, especially (PET/PTT) filaments. Three disperse dyes with different molecular weights were used for dyeing. The objective is to reproduce the reference color by choosing the appropriate disperse dyes among the available dyestuffs and their corresponding quantities to use on the mixture. For modeling, two sets of parameters (lied to the color formulation problem and the genetic algorithm), the objective function as well as the different stages of the algorithm were defined and described. In addition, different techniques of selection and mutation were applied and evaluated. The optimization criterion is to reduce the CMC color difference between the desired reference colors and the colors proposed by the algorithm. The developed algorithm showed good performances with very small color differences. The results indicate that the roulette wheel selection technique outperforms both rank and uniform selection methods. Moreover, employing a simple mutation strategy yields favorable outcomes with CMC color differences all lower than 1. [ABSTRACT FROM AUTHOR]

Background: In recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results. Results: A biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function. Conclusion: The results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance. [ABSTRACT FROM AUTHOR]

AbstractThe propulsion system of mine boring machines often experiences severe vibration during mining. To suppress the vibration of the main beam system of roadheader and prevent the excessive fatigue of propulsion system, a magnetic nonlinear energy sink (MNES) is designed to suppress vibration. In this paper, firstly, the structure and design principle of MNES are introduced, and the dynamic equations of the main beam-MNES system are established. On this basis, the genetic algorithm is applied to optimize the vibration suppression of MNES, and the optimal parameters are determined. In results, the accumulative energy dissipation rate of optimal MNES in transient vibration can reach 90.2% and the vibration reduction effect of the optimal MNES in steady-state vibration is about 82.2%. [ABSTRACT FROM AUTHOR]

In this paper, we explored the different performances of the evolution of polarization control parameters under algorithm optimization in one‐dimensional, two‐dimensional, and three‐dimensional parameter space. We found that the genetic algorithm (GA) operation has a typical converging trend as the epoch increasing of groups in ultra‐fast pulse generation. The corresponding deviations were calculated to be 10.8%, 11.2%, and 5.0%, respectively, which is different from the traversal algorithm operation. The typical noise‐like pulse state was effectively automatically searched at different length cavities, with center wavelengths of 1973, 1988.7, and 2002.7 nm, repetition rate of 9.4, 10.4, and 5.4 MHz, and typical spike duration of ~224 fs. Our research shows the effectiveness of the GA in searching and controlling the ultrashort pulse output of a 2‐µm thulium‐doped fiber laser. [ABSTRACT FROM AUTHOR]

In order to analyze the optimization problem of bus route departure time, two intersecting bus routes with transfers were taken as the research object. Taking into account passenger congestion, a bus timetable optimization model was constructed with the objective function of minimizing the total waiting time, transfer waiting time, and non transfer passenger waiting time. Two city bus routes in Hohhot City, namely Line 4 “JINYU Global Center to Affiliated Hospital of Medical University” section and Line 78 “Hohhot Bus to Saihan District Education Bureau” section, were studied, and the optimization model was solved using genetic algorithm. The results show that the proposed city bus timetable optimization scheme can shorten the transit waiting time and total waiting time by 18.1% and 10.7% respectively, thereby validating the proposed optimization model’s effectiveness in improving the operating efficiency of intersecting routes. [ABSTRACT FROM AUTHOR]

In order to improve the stability of power grid operation, it is necessary to build a stable and reliable power grid dispatching system, and an active-active dispatching system considering the characteristics of parallel systems was proposed. By building two data centers, active and standby. According to the application business volume, adjustments were made between the primary and backup adjustments, achieving full utilization of resources. In the event of a failure, another functioning center will take on all the business. The parallel system was regarded as the virtual operation system of the power grid, and the active-active dispatching system of the power grid was obtained after combining the real and virtual systems, with functions such as management and control module, experiment and evaluation module, and learning and training module. By initializing the data, calculating the power distribution results, establishing a two-layer task scheduling model, and using genetic algorithms to solve the model, the optimization scheduling of the dual active scheduling system can be achieved. The test results show that the method proposed has the advantages of short time, high efficiency, large total amount of grid information dispatching, strong dispatching ability and high security of dispatching information to reach the optimal distribution value. It shows that the designed active-active dispatching system is feasible and beneficial to the optimal dispatching of regional power grid. [ABSTRACT FROM AUTHOR]

Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA). The transfer function parameters were fine-tuned using the GA, with clinical ECG records serving as reference signals. The proposed model, which is based on polynomials and delays, approximates a real ECG with a root-mean-square error of 4.7% and an R 2 value of 0.72 . The model achieves the periodic nature of an ECG signal by using a single periodic impulse input. Its simplicity makes it possible to adjust waveform parameters with a predetermined understanding of their effects, which can be used to generate both arrhythmic patterns and healthy signals. This is a notable advantage over other models that are burdened by a large number of differential equations and many parameters. [ABSTRACT FROM AUTHOR]

Pulmonary nodules are abnormal tissue masses in the lungs, typically less than 3.0 cm in diameter, commonly detected during imaging of the chest and lungs. While most pulmonary nodules are not cancerous, surgical resection may be required if growth is detected between scans. This resection is typically performed without the benefit of intraoperative imaging, making it difficult for surgeons to confidently provide appropriate margins. To enhance the efficacy of wedge resection, researchers have developed a modified ultrasound imaging approach that utilizes both multiple scattering (MS) and single scattering (SS) to enhance the accuracy of margin delineation. Clinical deployment of this novel ultrasound technology requires a highly maneuverable ultrasound probe, ideally one that could be deployed and actuated with minimal invasiveness. This study details the design optimization and tradeoff analysis of an actuated continuum probe for pulmonary nodule localization and resection. This device, deployed through intercostal ports, would enable the intraoperative imaging and precise mapping of nodules for improved margin delineation and patient outcomes. To achieve this objective, multiple objective genetic algorithms (MOGAs) and a design of experiments (DOE) study are used to explore the design space and quantify key dimensional relationships and their effects on probe actuation. [ABSTRACT FROM AUTHOR]

Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used. [ABSTRACT FROM AUTHOR]

Aiming at addressing the security and efficiency challenges during image transmission, an efficient image cryptosystem utilizing difference matrix and genetic algorithm is proposed in this paper. A difference matrix is a typical combinatorial structure that exhibits properties of discretization and approximate uniformity. It can serve as a pseudo-random sequence, offering various scrambling techniques while occupying a small storage space. The genetic algorithm generates multiple ciphertext images with strong randomness through local crossover and mutation operations, then obtains high-quality ciphertext images through multiple iterations using the optimal preservation strategy. The whole encryption process is divided into three stages: first, the difference matrix is generated; second, it is utilized for initial encryption to ensure that the resulting ciphertext image has relatively good initial randomness; finally, multiple rounds of local genetic operations are used to optimize the output. The proposed cryptosystem is demonstrated to be effective and robust through simulation experiments and statistical analyses, highlighting its superiority over other existing algorithms. [ABSTRACT FROM AUTHOR]

Group buying is attracting a lot of attention, but studies on group buying of bundled products are still in their infancy. In this study, a group purchasing structure is proposed in which each purchaser needs a portion of the items in a bundle and has his or her preferences. However, when the buyers of a bundle are scattered across different locations, the shipper must travel long distances between buyers, which increases shipping costs. Therefore, besides bundle prices, shipping costs are an inseparable expenditure for group purchasing. It is assumed that shipping costs increase on certain days, e.g. around Christmas. A tailored mathematical formulation is formulated to maximize the total group saving. Considering the complexity of the model, a genetic algorithm (GA) is developed as a solution algorithm, and compared with the CPLEX solver. The results confirm the performance of the GA, which provides up to 24% better solutions than CPLEX. [ABSTRACT FROM AUTHOR]

Purpose: Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach: In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as "input variables" were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings: The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value: By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines' input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines. [ABSTRACT FROM AUTHOR]

Compressive strength, a crucial mechanical property of cement mortars, is typically measured destructively. However, there is a need to evaluate the strength of unique cement-based samples at various ages without causing damage. In this paper, a predictive framework using a genetic algorithm (GA) is proposed for estimating the compressive strength of ordinary cement-based mortars based on their dynamic elastic modulus, measured non-destructively using the impulse excitation technique. By combining the Popovics model (PM) and the Lydon–Balendran model (LBM), the static elastic modulus of samples was calculated using constant coefficients, representing an equivalent compressive strength. A GA was then employed to determine optimal values for these coefficients. The results showed that the LBM-based strength was dominant in the middle range of the dynamic Young's modulus while the PM-based strength was dominant for higher and lower values of the dynamic Young's modulus. The model was found to have a small root mean square error (3.1%). The findings suggest that this non-destructive model has potential for predicting the mechanical properties of cement mortars. It allows efficient evaluation of compressive strength without destructive testing, offering advantages for reliable assessments of cement-based materials. [ABSTRACT FROM AUTHOR]

The problem of computing first edge server deployment is a fundamental problem in computing first network. In the actual scenario, the computing first edge server is close to computing power resources and provides access services for them to join the computing first network. However, the structure of computing resources is often determined by the actual demand, and changes with the change of demand. Under the constraint of computing first edge server resources, how to reasonably deploy computing first edge servers to ensure the effective construction of computing networks has become a hot topic of concern for all sectors. Firstly, this paper analyzed the deployment problem of computing first edge servers and transformed it into a multi-objective optimization problem with constraints. It proposed an improved genetic algorithm to address this issue. The advantages of this algorithm were as follows. It found non repetitive feasible solutions as the initial population provided more room for selection operations. When selecting, it adopted an individual balanced selection strategy to ensure the diversity and decentralization of the iterative population. When crossing and mutating, it adopted different types of random two point crossing and rotating random single point mutation strategies, thereby ensuring the diversity and diversity of the newborn population. The experiments is verified by resources deviation rate, load error rate, convergence rate. And expectation solution error rate shows that the algorithm is very effective and reasonable. [ABSTRACT FROM AUTHOR]

To address the issue of resource wastage and increasing latencies in the placement process of edge servers, this paper analyzed and modeled the user density and average access time of edge server placement schemes as multi-objective optimization problems. It designed an edge server placement scheme based on user density and average access time, and proposed a multi-objective sea-horse genetic algorithm (MOSGA) to solve this problem. The MOSGA algorithm firstly used the idea of multi-objective optimization algorithm to improve the SHO algorithm, so that the SHO algorithm could be applied to multi-objective optimization problems. It used the genetic algorithm to improve the propagation operation of the SHO algorithm, so that the MOSGA could better jump out of the local optimal solution and accelerate the solution of the problem. The proposed algorithm was verified on the data set of Shanghai Telecom, and the simulation experiment results show that MOSGA is obviously better than RA, K-means, NSGA and LMM, which not only effectively solves the problem of server resource waste, but also greatly reduces the time of terminal equipment to access the server. [ABSTRACT FROM AUTHOR]

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development. [ABSTRACT FROM AUTHOR]

Nafion, a versatile polymer used in electrochemistry and membrane technologies, exhibits complex behaviors in saline environments. This study explores Nafion membrane's IR spectra during soaking and subsequent drying processes in salt solutions at various concentrations. Utilizing the principles of Fick's second law, diffusion coefficients for these processes are derived via exponential approximation. By harnessing machine learning (ML) techniques, including the optimization of neural network hyperparameters via a genetic algorithm (GA) and leveraging various regressors, we effectively pinpointed the optimal model for predicting diffusion coefficients. Notably, for the prediction of soaking coefficients, our model is composed of layers with 64, 64, 32, and 16 neurons, employing ReLU, ELU, sigmoid, and ELU activation functions, respectively. Conversely, for drying coefficients, our model features two hidden layers with 16 and 12 neurons, utilizing sigmoid and ELU activation functions, respectively. [ABSTRACT FROM AUTHOR]

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points' operation was presented as the probability of the occurrence of an emergency situation hindering a point's operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software. [ABSTRACT FROM AUTHOR]

In this study, a neural network and a multi-objective genetic algorithm were used to optimize the geometric parameters of segmented thermoelectric generators (TEGs) with trapezoidal legs, including the cold end width of thermoelectric (TE) legs (Wc), the ratios of cold-segmented length to the total lengths of the n- and p-legs (Sn,c and Sp,c), and the width ratios of the TE legs between the hot end and the cold end of the n- and p-legs (Kn and Kp). First, a neural network with high prediction accuracy was trained based on 5000 sets of parameters and the corresponding output power values of the TEGs obtained from finite element simulations. Then, based on the trained neural network, the multi-objective genetic algorithm was applied to optimize the geometric parameters of the segmented TEGs with the objectives of maximizing the output power (P) and minimizing the semiconductor volume (V). The optimal geometric parameters for different semiconductor volumes were obtained, and their variations were analyzed. The results indicated that the optimal Sn,c, Sp,c, Kn, and Kp remained almost unchanged when V increased from 52.8 to 216.2 mm3 for different semiconductor volumes. This work provides practical guidance for the design of segmented TEGs with trapezoidal legs. [ABSTRACT FROM AUTHOR]

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity. [ABSTRACT FROM AUTHOR]

The purpose of this article is to establish a prediction model of joint movements and realize the prediction of joint movemenst, and the research results are of reference value for the development of the rehabilitation equipment. This will be carried out by analyzing the impact of surface electromyography (sEMG) on ankle movements and using the Hill model as a framework for calculating ankle joint torque. The table and scheme used in the experiments were based on physiological parameters obtained through the model. Data analysis was performed on ankle joint angle signal, movement signal, and sEMG data from nine subjects during dorsiflexion/flexion, varus, and internal/external rotation. The Hill model was employed to determine 16 physiological parameters which were optimized using a genetic algorithm. Three experiments were carried out to identify the optimal model to calculate torque and root mean square error. The optimized model precisely calculated torque and had a root mean square error of under 1.4 in comparison to the measured torque. Ankle movement models predict torque patterns with accuracy, thereby providing a solid theoretical basis for ankle rehabilitation control. The optimized model provides a theoretical foundation for precise ankle torque forecasts, thereby improving the efficacy of rehabilitation robots for the ankle. [ABSTRACT FROM AUTHOR]

The timely and precise prediction of cardiovascular disease (CVD) risk is essential for effective prevention and intervention. This study proposes a novel framework that integrates the two-phase Taguchi method (TPTM), the hyperparameter artificial neural network (HANN), and a genetic algorithm (GA) called TPTM-HANN-GA. This framework efficiently optimizes hyperparameters for an artificial neural network (ANN) model during the training stage, significantly enhancing prediction accuracy for cardiovascular disease (CVD) risk. The proposed TPTM-HANN-GA framework requires far fewer experiments than a traditional grid search, making it highly suitable for application in resource-constrained, low-power computers, and edge artificial intelligence (edge AI) devices. Furthermore, the proposed TPTM-HANN-GA framework successfully identified the optimal configurations for the ANN model's hyperparameters, resulting in a hidden layer of 4 nodes, a tanh activation function, an SGD optimizer, a learning rate of 0.23425849, a momentum rate of 0.75462782, and seven hidden nodes. This optimized ANN model achieves 74.25% accuracy in predicting the risk of cardiovascular disease, which exceeds the existing state-of-the-art GA-ANN and TSTO-ANN models. The proposed TPTM-HANN-GA framework enables personalized CVD prediction to be efficiently conducted on low-power computers and edge-AI devices, achieving the goal of point-of-care testing (POCT) and empowering individuals to manage their heart health effectively. [ABSTRACT FROM AUTHOR]

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high‐quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43–1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky–Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full‐band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS‐SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC‐(CARS‐SPA)‐GA‐SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS‐SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA‐SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification. [ABSTRACT FROM AUTHOR]

In this paper the actual dynamic behavior of the civic Clock tower of Rotella, a little village in central Italy heavily damaged by the recent 2016 seismic sequence, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal properties obtained through Ambient Vibration Tests. The goal is to update the uncertain parameters of two behavioral material models applied to the Finite Element Model (elastic moduli, mass densities, constraints, and boundary conditions) to minimize the discrepancy between experimental and numerical dynamic features. A sensitivity analysis was performed with the definition of a metamodel to reduce the computational strain and try to define the necessary parameters to use for the calibration process. Due to the high nonlinear dependency of the objective function of this optimization problem on the parameters, and the likely possibility to get trapped in local minima, a machine learning approach was meant. A fully automated Finite Element Model updating procedure based on genetic algorithms and global optimization is used, leading to tower uncertain parameters identification. The results allowed to create a reference numerical replica of the structure in its actual health state and to assess its dynamic performances allowing better control over their future evolution. [ABSTRACT FROM AUTHOR]

In this paper, the spoke structure of a non-pneumatic tire is optimized. The finite element model of the spoke structure is created and structural dynamic analysis is made using ABAQUS. The optimization goal is to simultaneously reduce three characteristics of the spoke structure, the mass, vertical displacement and acceleration. Simulations are performed according to the center composite design table, and the objective function is estimated using response surface analysis (RSA), random forest regression (RFR), gradient boosting regression (GBR) and artificial neural network (ANN). The genetic algorithm is used for minimization, and the optimization results of each objective function are analyzed through verification simulation. As a result of the optimization, the error between the predicted value and the actual value is 18.6% for RSA 3.1% for RFR, 1.7% for GBR, and 15.0% for the ANN. The ANN proposes the optimum design variables which derive output values smaller than the current minimum value because the ANN properly represents the nonlinear characteristics of the spoke model generated in this study.Highlights Simulation based optimization of the spoke structure of a non-pneumatic tireVarious regression models are used and their accuracy is comparedA two-step genetic algorithm is used for optimizationSimulation based optimization of the spoke structure of a non-pneumatic tireVarious regression models are used and their accuracy is comparedA two-step genetic algorithm is used for optimization [ABSTRACT FROM AUTHOR]

To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space. A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them. Our model exhibited excellent performance with the following metrics: R 2 = 0.980 , MSE = 6.74 e − 04 , and the Pearson correlation coefficient of 0.990 (p value <.0001) between predicted and actual values. We identified 20 key genes, the majority of which had been proven to be associated with radiation. However, we uniquely established them as space radiation sensitive genes for the first time. Through further analysis of the SSNs, we discovered that the different tissues exhibited distinct mechanisms in response to space stressors. The topology structures of SSNs effectively predicted radiation doses under spaceflight conditions, and the SSNs revealed the gene regulatory patterns within the organisms under space stressors. [ABSTRACT FROM AUTHOR]

With the increasing integration of renewable energy sources into distribution and transmission networks, the efficiency of cascade H-bridge multilevel inverters (MLIs) in power control applications has become increasingly significant for sustainable electricity generation. Traditionally, obtaining optimal switching angles of MLIs to minimize total harmonic distortion (THD) requires solving the selective harmonic elimination equations. To this end, this research aims to use two recently developed intelligent optimization algorithms, dandelion optimizer and gold rush optimizer, to solve this problem. To evaluate the effectiveness of the proposed algorithms, an eleven-level cascaded H-bridge MLI (CHB-MLI) was considered in the study. Simulation results for different modulation indices were obtained, and the accuracy and solution quality were compared with genetic algorithm and particle swarm optimization algorithms. MATLAB/Simulink-based models were used to verify numerical computations, ensuring the reliability of the findings. This research contributes to the field by providing insights into obtaining optimal switching angles and minimizing THD in MLIs by applying intelligent optimization algorithms. [ABSTRACT FROM AUTHOR]

The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that relies on constructing a pairwise comparison matrix (PCM) based on decision-makers' (DMs) judgments regarding alternatives and criteria. The objective is to obtain weights and ultimately rank and select alternatives. However, in some cases, DMs may not provide judgments due to a lack of experience, knowledge, or reluctance to express opinions on the subject, resulting in missing pairs in the PCM. Existing techniques for filling these missing pairs have inherent limitations. This paper demonstrates the application of the pymissingAHP algorithm, implemented in Python and available at https://bit.ly/3UFdqSZ, to address missing pairs using mono and multiobjective approaches. The pymissingAHP algorithm employs a Genetic Algorithm (GA) featuring a specialized encoding to address this issue. Our approach can handle mono or multiple objective scenarios, which involve minimizing the consistency index and maintaining the ranking of weights as defined by experts. Additionally, the algorithm accommodates fuzzy numbers within the AHP framework (FAHP). PCMs containing numerous missing values may yield multiple solutions and not accurately reflect experts' opinions. Although our approach can solve entirely depleted PCMs, obtaining as many comparisons as possible is recommended to ensure a faithful representation of expert opinions for the decision-makers. The pymissingAHP algorithm provides a significant advantage: the capacity to seek solutions that address single or multiple objectives, utilizing continuous or discrete values, and additionally, the ability to solve FAHP problems. [ABSTRACT FROM AUTHOR]

Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data. If a list of penalties for a class of linear models is given, we introduce a predictive stability criterion based on data perturbation to select a shrinkage method from the list. Simulation studies show that our predictive method identifies shrinkage methods that usually agree with existing literature and help explain heuristically when a given shrinkage method can be expected to perform well. If the preference is to construct a penalty customized for a given problem, then we propose a technique based on genetic algorithms, again using a predictive criterion. We find that, in general, a custom penalty never performs worse than any commonly used penalties and there are cases the custom penalty reduces to a recognizable penalty. Since penalty selection is mathematically equivalent to prior selection, our method also constructs priors. Our methodology allows us to observe that the oracle property typically holds for penalties that satisfy basic regularity conditions and therefore is not restrictive enough to play a direct role in penalty selection. In addition, our methodology, can be immediately applied to real data problems, and permits us to take model mis-specification into account. [ABSTRACT FROM AUTHOR]