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Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used. [ABSTRACT FROM AUTHOR]

The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach's effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations. [ABSTRACT FROM AUTHOR]

This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images' reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors. [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]

In this work, a bionic structure was redesigned for a specific type of crossbeam and the corresponding modification strategies were formulated. Three modification schemes were put forward and the corresponding parameterized analysis models established. Sensitivity analysis results guided the variable parameter selection and the total design of an orthogonal test. Data obtained were utilized to establish a neural network model that reflects the dynamic nonlinear mapping relation. Then, a collaborative optimization design method was proposed that was based on the neural network and the genetic algorithm for secondary design of the rib thickness. Through optimizing the neural network model by NSGA-II, Pareto optimal results were obtained. The results showed that the mass of the newly designed crossbeam had been reduced by 3.74%, while the first three orders of natural frequency had been increased by 6.67%, 3.47% and 14.20%, respectively. The bionic modification structure and the collaborative optimization design method were proved to be valuable for redesign in the remanufacturing field. [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]

To solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention. [ABSTRACT FROM AUTHOR]

By designing stator teeth using grain-oriented sheets (GO) and other parts still with non-grain-oriented sheets (NGO), the electromagnetic performance of the interior permanent magnet synchronous machine (GO-IPMSM) can be improved greatly. As the stator core of the designed GO-IPMSM is a hybrid core that is composed of two different silicon sheets, both the electromagnetic and mechanical performances are affected by the stator joint shape between GO and NGO sheets. Meanwhile, with the adoption of GO, the torque ripple of GO-IPMSM is increased though the average torque is increased as well. For reducing the torque ripple, optimizing the rotor barrier shape is an effective way. In this paper, the piecewise linear interpolation method is employed for establishing the stator joint shape between GO and NGO sheets, and the polynomial method is proposed to establish the rotor barrier shape. Through the analysis, it can be seen that the stator joint shape plays a strong role in affecting the mechanical performance of the GO-IPMSM, while the effect on the electromagnetic performance is weak. The genetic algorithm is used to optimize the rotor barrier shape for achieving high average torque and low torque ripple. Lastly, a quick and accurate efficiency map calculation method based on the Kriging model is proposed for GO-IPMSM with less finite element method (FEM) samples are required. [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]

Aggregate blending is a complex process especially when more sizes of aggregates need to be blended. For rough usage or to offer an initial answer, the conventional approaches are adequate. However, to obtain a cost-effective blend that meets specification requirements, more aggregate sizes must be accommodated. Several methods were investigated by various authors to deal with this complex process. Most of the methods are confined to a blending of three or four sizes of aggregates only. In this study, an attempt has been made to assess various computational techniques for aggregate blending to optimize the aggregate gradation area. Aggregates grading requirements specified for Dense Bituminous Macadam Grade-2 in MORT&H 500-10 was chosen as a reference. For this grading specifications, various methods like trial-and-error, mid-point gradation, least-square optimization, Generalised Reduced Gradient (GRG) non-linear, GRG multi start, simplex linear programming, regression analysis, IRC SP:89-2010, quadratic programming, support vector machines-SMoreg, path variate analysis, interval method, R programming were employed, and optimal proportions for aggregate blending were obtained. Curve fitting along with integration was applied to determine the aggregate gradation area under the combined gradation curve and maximum density line. The existence of a gradation curve between upper and lower limits and close to the mid-point line is considered as the first check and the minimum aggregate gradation area is considered as a second check while evaluating different optimal proportions arrived. Least aggregate gradation area which implies the minimum percentage of air voids was obtained for simplex LP (UL and LL) and evolutionary algorithm (Genetic algorithm) methods, i.e., 6.885. But these two methods yielded a filler of zero percentage which is practically not possible, hence simplex LP (MID) satisfies all the criterion and is adopted as the best method for aggregate blending. [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]

The lower hook mechanism is one of the key mechanisms in netting machine. The weft reel diameter of the new generation netting machine has doubled to the original one, resulting in a large route change of lower hook, thus the lower hook mechanism needs to be redesigned. By comparing the fitting results under different methods, the hook tip trajectory with smaller acceleration is determined. By using the closed vector method, the inverse and forward kinematic models of the mechanism is established and solved. By using NSGA-II genetic algorithm, the transmission performance parameters of the mechanism are effectively optimized. The kinematic simulation of the optimized mechanism is completed in Adams to verify the correctness of kinematic models and the effectiveness of optimization results. The comparison result of the simulated and initial fitted trajectory shows that the lower hook mechanism designed in this paper could meet the technical requirements of the position and posture of the lower hook. [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]

Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution's surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG. [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]

An optimization model that may be applied to analyze building retrofit strategies is presented in this research. The aim of this research paper is to identify the optimal thermal envelope configuration that will assure the minimum energy requirement for heating in the case of a residential building, while also considering price restrictions obtained through a specific market survey. To achieve this, several values for the following parameters are considered: thermal insulation materials' conductivities and thicknesses, windows' overall heat transfer coefficients and total solar energy transmittance and doors' thermal proprieties. Additionally, this paper presents a method used to find the best option from among the available heat pumps that could cover most of the energy requirements for heating and domestic hot water systems, also considering the products' prices. The proposed method is based on a Non-dominated Sorting Genetic Algorithm II (NSGA-II) model developed in the Pymoo (Multi-Objective Optimization in Python) library. The result shows that the energy requirement for heating can be reduced by up to approximately 75% compared to that obtained in the case of a non-insulated building by using suitable insulation materials and doors and windows with superior thermal proprieties chosen by the NSGA-II. [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]

In this article, the practical issues connected with guided wave measurement are studied: (1) the influence of gluing of PZT plate actuators (NAC2013) on generated elastic wave propagation, (2) the repeatability of PZT transducers attachment, and (3) the assessment of the possibility of comparing the results of Laser Doppler Vibrometry (LDV) measurement performed on different 2D samples. The consideration of these questions is crucial in the context of the assessment of the possibility of the application of the guided wave phenomenon to structural health-monitoring systems, e.g., in civil engineering. In the examination, laboratory tests on the web of steel I-section specimens were conducted. The size and shape of the specimens were developed in such a way that they were similar to the elements typically used in civil engineering structures. It was proved that the highest amplitude of the generated wave was obtained when the exciters were glued using wax. The repeatability and durability of this connection type were the weakest. Due to this reason, it was not suitable for practical use outside the laboratory. The permanent glue application gave a stable connection between the exciter and the specimen, but the generated signal had the lowest amplitude. In the paper, the new procedure dedicated to objective analysis and comparison of the elastic waves propagating on the surface of different specimens was proposed. In this procedure, the genetic algorithms help with the determination of a new coordinate system, in which the assessment of the quality of wave propagation in different directions is possible. [ABSTRACT FROM AUTHOR]

A modified 3D re-entrant honeycomb is designed and fabricated utilizing Laser Cladding Deposition (LCD) technology, the mechanical properties of which are systematically investigated by experimental and finite element (FE) methods. Firstly, the influences of honeycomb angle on localized deformation and the response of force are studied by an experiment. Experimental results reveal that the honeycomb angles have a significant effect on deformation and force. Secondly, a series of numerical studies are conducted to analyze stress characteristics and energy absorption under different angles (α) and velocities (v). It is evident that two variables play an important role in stress and energy. Thirdly, response surface methodology (RSM) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented with high precision to solve multi-objective optimization. Finally, the final compromise solution is determined based on the fitness function, with an angle of 49.23° and an impact velocity of 16.40 m/s. Through simulation verification, the errors of energy absorption (EA) and peak crush stress (PCS) are 9.26% and 0.4%, respectively. The findings of this study offer valuable design guidance for selecting the optimal design parameters under the same mass conditions to effectively enhance the performance of the honeycomb. [ABSTRACT FROM AUTHOR]

In this paper, a 3D conformal meta-lens designed for manipulating electromagnetic beams via height-to-phase control is proposed. The structure consists of a 40 × 20 array of tunable unit cells fabricated using 3D printing, enabling full 360° phase compensation. A novel automatic synthesizing method (ASM) with an integrated optimization process based on genetic algorithm (GA) is adopted here to create the meta-lens. Simulation using CST Microwave Studio and MATLAB reveals the antenna's beam deflection capability by adjusting phase compensations for each unit cell. Various beam scanning techniques are demonstrated, including single-beam, dual-beam generation, and orbital angular momentum (OAM) beam deflection at different angles of 0°, 10°, 15°, 25°, 30°, and 45°. A 3D-printed prototype of the dual-beam feature has been fabricated and measured for validation purposes, with good agreement between both simulation and measurement results, with small discrepancies due to 3D printing's low resolution and fabrication errors. This meta-lens shows promise for low-cost, high-gain beam deflection in mm-wave wireless communication systems, especially for sensing applications, with potential for wider 2D beam scanning and independent beam deflection enhancements. [ABSTRACT FROM AUTHOR]

The bandgap properties of elastic metamaterials can be efficiently utilized to tailor the propagation characteristics of elastic and acoustic waves, which have promising applications in noise and vibration reduction and isolation. In this paper, an elastic metamaterial with a multilayered honeycomb structure (EMHS) is proposed to enlarge the bandgaps in the low-frequency range and its bandgap properties are analyzed. To meet the requirement of the lightweight design, an optimization model for maximizing the total relative bandgap width with a mass constraint is established. A novel optimization approach combining the Kriging surrogate model with the genetic algorithm (GA) is proposed to reduce the huge computational cost of the corresponding optimization problem. In the Kriging-GA approach, a high-precision Kriging-based surrogate model with addition of supplementary points is constructed to predict the bandgap objective function value, and the GA is employed to search for the optimal parameters. The performance of the proposed Kriging-GA approach is investigated by numerical examples, and the results are compared with those obtained by the commonly used FEM-GA method. The results show that the proposed Kriging-GA approach is highly efficient for the design optimization of the EMHS and can remarkably reduce the computational cost of the considered optimization problem, which has promising prospects in a wide range of engineering applications. [ABSTRACT FROM AUTHOR]

Primary healthcare plays a pivotal role in enhancing health conditions. In Singapore, such services are predominantly manifested through the implementation of the Community Health Assistance Scheme (CHAS). CHAS is an initiative aimed at providing fundamental preventive and therapeutic services, especially for those seniors and low‐income adults with chronic diseases. In spite of considerable efforts in policy and research in this domain, there is a dearth of studies focusing on the spatial optimization of these primary healthcare services. In this study, an innovative multi‐objective medical service facility siting model has been developed based on coarse‐grained parallel genetic algorithm to address the intricate challenges associated with the optimization of locations for CHAS clinics. The proposed optimization model aims to simultaneously maximize accessibility, minimize inequity, and minimize the number of clinics. The successful application of this model in the siting of CHAS clinics in Singapore demonstrates its effectiveness in enhancing residents' access to healthcare services. Apart from its novel academic contributions to the field of spatial optimization of primary healthcare facilities in general, we have also discussed the inherent limitations and identified certain aspects as the future directions of this research. [ABSTRACT FROM AUTHOR]

Vibration-driven robots are innovative mechanical systems that exhibit a wide range of capabilities and applications due to their simple propellers. The applications of these robots span from clearing debris after an incident, traversing pipelines for inspection and maintenance, or medical purposes. In this paper, optimization and motion control of an energy-efficient two-module vibration-driven robot are investigated. The robot contains two blocks connected by a spring as its main body, and an unbalanced rotating mass to drive the robot rectilinearly. At first, the governing dynamic equations of the robot are derived and solved using MATLAB/Simulink. Then, the dynamic model of the robot is verified by comparing the obtained results with the simulation results from MSC Adams. Subsequently, a parametric study is conducted to investigate the effect of various physical parameters of the robot on its average velocity and consumed energy. Afterward, optimization variables are determined and a proper objective function is considered. By performing an optimization process using a genetic algorithm (NSGA-II), optimal parameters of the robot are obtained. Moreover, for motion control of the optimized robot, two control schemes, PID and Fractional Order PID, are designed. To evaluate and compare the performance of the proposed controllers, parameters of both controllers are optimized using a genetic algorithm in which both tracking error and control input are minimized, simultaneously. Finally, tracking error and control effort of the mentioned controllers for motion control of the robot are compared and discussed. [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]

Multifunctional metasurfaces have demonstrated extensive potential in various fields. During the design of metasurfaces, optimization of components for polarization, amplitude distribution, phase distribution, and other factors is necessary. This process typically demands expert involvement and is time‐consuming. In this paper, a metasurface inverse design method is introduced that combines a high‐precision ultra‐wideband spectrum forward prediction utilizing a neural network and a genetic algorithm. A neural network is constructed and trained to accurately predict the amplitude and phase of a 16 × 16 discrete grid structure with high degrees of freedom in the frequency range of 0.5–2 THz. Leveraging the neural network's ultra‐fast spectrum prediction capabilities (producing approximately 1000 spectra per second), the average optimization time for a single component is reduced to 1.5 min. Finally, the effectiveness of this inverse design method is validated through the design and simulation of multifunctional reflective deflection metasurfaces with two sets of 3‐bit frequency multiplexing and polarization multiplexing. The proposed metasurface inverse design method offers a new approach for the rapid design of components in complex application scenarios and holds significant reference value for metasurface designers. [ABSTRACT FROM AUTHOR]

The cubic stiffness force model (CSFM) and Bouc-Wen model (BWM) are introduced and compared innovatively. The unknown coefficients of the nonlinear models are identified by the genetic algorithm combined with experiments. By fitting the identified nonlinear coefficients under different excitation amplitudes, the nonlinear vibration responses of the system are predicted. The results show that the accuracy of the BWM is higher than that of the CSFM, especially in the non-resonant region. However, the optimization time of the BWM is longer than that of the CSFM. [ABSTRACT FROM AUTHOR]

Recent studies demonstrated that high-frequency earthquakes have the most effect on the economic losses during seismic events. However, code-based seismic design criteria mainly aim to provide the occupants' life safety. In this regard, code-compliant structures may result in significant economic losses during their life span. These damages contribute to the higher repair costs and lengthy functional disruptions of these structures. Improving the seismic behaviour of the structures by control devices could be one of the most efficient solutions. On this subject, applying fluid viscous dampers (FVDs) is an economical and reliable method. Optimizing the properties of these devices contributes to more efficient seismic behaviour of the structures. In this study, the key design parameters of viscous dampers, including damping coefficient and velocity exponent, are considered as optimization variables to achieve minimum life cycle cost (LCC) using the genetic algorithm (GA). To investigate the efficiency of the proposed approach, four-, eight- and twelve-story structures in high-seismic regions are investigated to consider different assumptions. The Endurance Time method (ETM) is adopted to conduct nonlinear analysis due to its high computational cost-efficiency and reliability. The results show that the nonlinear viscous dampers would decrease the total seismic loss of the structures by up to 40% in comparison with linear dampers. The optimal amount of velocity exponent is also obtained as a function of variation with the height and seismic region of the structures. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an extended version of Deeper, a search‐based simulation‐integrated test solution that generates failure‐revealing test scenarios for testing a deep neural network‐based lane‐keeping system. In the newly proposed version, we utilize a new set of bio‐inspired search algorithms, genetic algorithm (GA), (μ+λ) and (μ,λ) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain‐specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber‐physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure‐revealing test scenarios for testing an ML‐driven lane‐keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints. [ABSTRACT FROM AUTHOR]

In order to ensure flight safety, the requirement of foundation settlement of high-plateau airport is stricter than that of airport in plain area. In order to monitor the abnormal state of runway foundation in the process of use of a high-plateau airport and prevent and resolve the major risk of foundation settlement to flight safety. It takes a high-plateau airport in the southwest mountain area as an example, selecting two representative A and B sections for analysis. It takes the first 60 days' monitoring data as training samples, which shows nonlinear characteristics. The Long and Short Term Memory neural network (LSTM) prediction model and BP neural network model are constructed to predict the trend of foundation settlement after construction. In the process of building the LSTM model, the minimum root-mean-square error of test samples was selected as the fitness function, and the parameters of the LSTM model were modified by Genetic Algorithm (GA). And then the modified LSTM prediction model based on the early settlement of the foundation was constructed. The results shows that the modified LSTM model and BP model constructed in this paper are generally consistent with the field measured values in the prediction of airport foundation settlement of high-plateau, but the modified LSTM model is more sensitive to the abrupt change of data and has a more stable trend than the BP model. The predicted values of the modified LSTM model are all greater than those of the BP model, and the predicted values of the modified LSTM model are closer to the monitored values in the field than the predicted values of the BP model, and the relative error between the predicted values and the monitored values is less than 3%. The research can provide a reliable theoretical reference for the design, construction, operation management and later maintenance of high-plateau airport. [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]

This study introduces an asymmetric V-shaped interior permanent magnet synchronous motor (AVIPMSM) by modifying the conventional flux barrier of a V-shaped interior permanent magnet synchronous motor (VIPMSM) including optimization-based finite element analysis (FEA). Nowadays usage of VIPMSM is increasing where higher torque density with high efficiency by decreasing the torque ripple factor is required. To improve these properties, flux barriers are modified in VIPMSM by applying optimization methods. For each pole of VIPMSM, modification has been depleted based on symmetric and asymmetric barriers. It introduces the magnetic field shifting effect in the motor. After deciding the flux barrier in the rotor, an optimized method is applied for achieving the proper dimensions of each barrier to estimate the maximum torque and other performances of the motor. Here, a 24/4 pole VIPMSM is considered for design and analysis. ANSYS Maxwell platform is chosen for performance analysis of motor. In addition to using the inbuilt minimum function optimization techniques of ANSYS, genetic algorithm (GA)/particle swarm optimization (PSO) in MATLAB is applied in the proposal to optimize the flux barriers of the motor. To validate the effectiveness of the proposed method, FEA is adopted by taking into account optimized parameters. The paper also investigates the effect of flux barriers on loss factors to improve the motor efficiency. A comparative study with the benchmark VIPMSM is presented to validate the designed AVIPMSM using optimization and its results are reported. Eventually, the FEA result proves that PSO optimized model is more appropriate for the case understudies. [ABSTRACT FROM AUTHOR]

This paper presents a novel computer‐aided computational framework to determine the optimum shape parameters in a welding heat source model using a coupled supervised Gaussian process regression (GPR) and genetic algorithm (GA) approach in estimating the welding residual stresses. The experimental X‐ray diffraction (XRD) approach validates the optimization‐improved thermal–mechanical simulation method. The validation of the proposed approach includes the welded plates with simple geometry to welded pipe‐to‐plate and the circular hollow section joints with complex topologies. The optimization‐derived shape parameters in a welding heat source model lead to close estimates of the temperature history and the welding residual stresses for these types of welded joints considered in this study. [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]

In today's global business competition environment, the significant changes in the market environment make it very difficult for supply chain enterprises to gain a place in the fierce market only by their strength. This article will do the following research work to effectively play the role of supply chain management. First, the concepts, and characteristics, related applications of supply chain information sharing (IS) and genetic algorithm back propagation (GABP) neural networks, are expounded. Then, the feasibility of applying BP neural network (BPNN) to evaluate supply chain IS is analysed. Next, the assessment indicator system for IS is constructed from four aspects: IS infrastructure, IS member characteristics, IS content, and the quality of supply chain enterprises. Finally, the principle and algorithm of BPNN are analysed, the network model is constructed, the BPNN process based on GA is designed, and every step in the process is explained in detail. The sample input is provided for the network model by obtaining and sorting data. The supply chain IS assessment model based on the GABP neural network is constructed. The results show that the assessment model of supply chain IS based on the GABP neural network has high accuracy, and the highest network accuracy reaches 95.01%, which proves that the assessment model based on BPNN has certain advantages for the supply chain IS of the financial industry. [ABSTRACT FROM AUTHOR]

To improve the cell productivity of Corynebacterium glutamicum , its initial specific growth rate was improved by medium improvement using deep neural network (DNN)-assisted design with Bayesian optimization (BO) and a genetic algorithm (GA). To obtain training data for the DNN, experimental design with an orthogonal array was set up using a chemically defined basal medium (GC XII). Based on the cultivation results for the training data, specific growth rates were observed between 0.04 and 0.3/h. The resulting DNN model estimated the test data with high accuracy (R2 test ≥ 0.98). According to the validation cultivation, specific growth rates in the optimal media components estimated by DNN-BO and DNN-GA increased from 0.242 to 0.355/h. Using the optimal media (UCB_3), the specific growth rate, along with other parameters, was evaluated in batch culture. The specific growth rate reached 0.371/h from 3 to 12 h, and the dry cell weight was 28.0 g/L at 22.5 h. From the cultivation, the cell yields against glucose, ammonium ion, phosphate ion, sulfate ion, potassium ion, and magnesium ion were calculated. The cell yield calculation was used to estimate the required amounts of each component, and magnesium was found to limit the cell growth. However, in the follow-up fed-batch cultivation, glucose and magnesium addition was required to achieve the high initial specific growth rate, while appropriate feeding of glucose and magnesium during cultivation resulted in maintaining the high specific growth rate, and obtaining a cell yield of 80 g/L ini. • A novel growth medium for Corynebacterium glutamicum was developed using deep neural networks and optimization algorithms. • During the validation cultivation, high specific growth rates were observed in the proposed media. • The limiting factors for high biomass yield were identified in the proposed medium. • To optimize productivity and yield, a fed-batch culture was designed using the proposed medium. [ABSTRACT FROM AUTHOR]

Ensuring running stability and minimising wheel tread wear are critical factors for enhancing the dynamic performance of high-speed trains. As an efficient method for improving vehicle performances, previous studies on suspension parameter optimisation have not fully considered these two factors. It is essential to pay attention to stability at extreme wheel-rail contact states, and the wheel wear evolution needs to be revealed instead of investigating the wear performance only through an index like wear number. This research aims to investigate the stability/wear Pareto optimisation of bogie suspension parameters for a high-speed passenger train. Stability-related indices are defined as the lateral ride comfort index at low equivalent conicity and the bogie frame acceleration at high conicity. To represent the long-term wheel wear performance, a contact spreading index is proposed and combined with the wear number index. The key suspension parameters are optimised through the genetic algorithm NSGA-II. The obtained Pareto front clearly reveals the relationship between the objective functions. To meet different performance requirements, four distinct matching rules of the suspension parameters are summarised by employing clustering analysis. Finally, the applicability of the parameter matching rules is examined by analysing stability at different equivalent conicities and wheel wear evolution. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users' preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor's preferences has proved to be very effective in the portfolio's composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation. [ABSTRACT FROM AUTHOR]

This study presents a design optimization of a circumferential feedback channel on the shroud casing between rotor trailing and leading edges to maximize the aerodynamic performance of a single-stage transonic axial compressor, NASA Stage 37, using three-dimensional Reynolds-averaged Navier–Stokes analysis (RANS) with the k-ε turbulence model. The location of the injection and bleeding ports, angles of injection, and bleeding were the most influential factors on the aerodynamic performance and were selected as the design variables. A hybrid genetic algorithm and particle swarm optimization algorithm coupled with three different surrogate models were utilized to maximize the peak adiabatic efficiency and stall margin of the transonic axial compressor. The optimization results for the circumferential feedback channel indicated that an increase in stall margin was achievable with a slightly reduced peak efficiency in comparison with that of the smooth casing. [ABSTRACT FROM AUTHOR]

The management and operation of water distribution networks (WDNs) by establishing district metered areas (DMAs), especially in existing and old WDNs, has many advantages, including water leakage control, ease of operation, and network management in emergencies. Using DMAs in existing WDNs designed and developed traditionally is a challenging issue because of the need to cover several criteria. In the present study, a method for creating DMAs is suggested to minimize the cost as well as to reduce the average pressure in high-pressure WDNs. The proposed method uses the Girvan–Newman clustering and nondominated sorting genetic algorithm (NSGAII) optimization algorithms. This method was implemented on Modena's and Wolf–Cordera Ranch's WDNs. The proposed method can generate several solutions based on performance indicators, including cost, hydraulics, and quality. The results show that by applying the proposed method in the Modena and Wolf–Cordera Ranch WDNs, the average pressure of the networks was reduced by 17% and 31%, respectively, in comparison with the primary WDNs in the base demand and peak demand conditions. This method can be used by water utilities for partitioning WDNs, especially when the pressure is high. [ABSTRACT FROM AUTHOR]

• Aging of FeS nanoparticles seriously affects arsenic removal but slight on copper. • Controllable FeS are the targeted sulfurizing agents in different acidity wastewater. • BP-GA model are developed to precisely predict copper and arsenic removal with R of 0.9923. • Temperature and FeS aging time are necessary to gain high separation accuracy. • The model provides an essential reference in selecting sulfurizing reagent types. Iron sulfide (FeS) is a promising material for separating copper and arsenic from strongly acidic wastewater due to its S2− slow-release effect. However, uncertainties arise because of the constant changes in wastewater composition, affecting the selection of operating parameters and FeS types. In this study, the aging method was first used to prepare various controllable FeS nanoparticles to weaken the arsenic removal ability without affecting the copper removal. Orthogonal experiments were conducted, and the results identified the Cu/As ratio, H 2 SO 4 concentration, and FeS dosage as the three main factors influencing the separation efficiency. The backpropagation artificial neural network (BP-ANN) model was established to determine the relationship between the influencing factors and the separation efficiency. The correlation coefficient (R) of overall model was 0.9923 after optimizing using genetic algorithm (GA). The BP-GA model was also solved using GA under specific constraints, predicting the best solution for the separation process in real-time. The predicted results show that the high temperature and long aging time of FeS were necessary to gain high separation efficiency, and the maximum separation factor can reached 1,400. This study provides a suitable sulfurizing material and a set of methods and models with robust flexibility that can successfully predict the separation efficiency of copper and arsenic from highly acidic environments. [Display omitted] [ABSTRACT FROM AUTHOR]

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method. [ABSTRACT FROM AUTHOR]

Diclofenac (DCF) and Acetaminophen (ACT) are among the most important nonsteroidal anti–inflammatory drugs (NSAIDs) that sought after for their antipyretic, analgesic, and anti‐inflammatory properties. In addition, ACT and DCF show synergistic activity toward pain relief. Therefore, appropriate analytical strategies are required for the simultaneous quantification of these NSAIDs in prescription drugs. This study introduces a method for the direct and simultaneous determination of ACT and DCF using the voltammetric approach in conjunction with chemometric tools such as partial least squares (PLS) and genetic algorithm–multiple linear regressions (GA−MLR). The calibration set was established using differential pulse voltammetry for 25 distinct mixtures of ACT and DCF. The calibration curve exhibited linearity within the concentration ranges of 0.1–1000 μM for ACT and 0.1–100 and 100–1000 for DCF. The effectiveness of chemometric methods was evaluated by analyzing synthetic mixtures of ACT and DCF and applying these methods to real samples containing a commercial pharmaceutical preparation of the specified drugs. Both methods yielded favorable results. [ABSTRACT FROM AUTHOR]

It is crucial to optimize energy consumption in buildings while considering thermal comfort. The first step here involved an EnergyPlus simulation on a trade center building located in Tehran, Bandar Abbas, and Tabriz, Iran. A multi-objective optimization was then performed based on non-dominated sorting genetic algorithm II (NSGA-II) in jEPlus + EA to establish the building in the selected city where would benefit the most from implementing the radiant ceiling cooling system. Efforts were undertaken to choose environmentally-friendly materials. The final solution by Pareto charts resulted in a 52% reduction in energy consumption, a 37.3% decrease in cooling load, and a 17.4% improvement in comfort hours compared to the original design. Annual emission of greenhouse gas reduced as 167.67 tone of CO2 equivalent emission, 25.77 ton of CH4, and 0.2 ton of NO2. The mentioned algorithm was conducted for the first time on a trade center, including a DOAS system and radiant ceiling cooling system. Simultaneously, the environmental-friendly materials were dealt with. The procedure holds significant relevance for the design and optimization of buildings in Iran, especially wherever the climate is hot and humid. This approach offers advantages to the environment by reducing the impact on energy resources and utilizing environmentally-friendly materials. [ABSTRACT FROM AUTHOR]

This study aims to produce variant composite structures that can precisely maintain equilibrium after changing stable states. A rapid and precise prediction method for the cure‐induced bi‐stable warping was developed, and the inverse design for the required part's structural and material parameters was investigated. Firstly, the mechanism causing the warping was investigated. Then, in order to acquire basic data for a data‐driven model to achieve rapid and accurate warping prediction, a finite element analysis (FEA) was developed, with most of the prediction deviations being under 10%. Additionally, deformation forms of variant parts with various layups and shapes were studied to offer guidance for the different shape requirements. The structural and material parameters were analyzed using the response surface methodology and sensitivity analysis. After removing parameters like all rubbery properties with low influence, basic data from the parameterized FEA was utilized to build a precise proxy model. Finally, for the variant structures requiring reasonable shape forms, the inverse design of the material, layup, and sizes can be achieved using the proxy model and the non‐dominated sorting genetic algorithm II. All optimization results were validated using FEA or experiments. The deformation form in the two steady states of each case meets the specifications. The shape difference between the produced part and the required shape is less than 5 mm, and the design deviation for the maximum deformation is less than 10%. Consequently, the rapid prediction and inverse design methods for the bi‐stable shape of variant composite parts were effective and accurate. Highlights: All the rubbery properties are almost uncorrelated to the bi‐stable warping.The proxy model can rapidly and precisely predict the bi‐stable shape.The inverse design can be achieved for a part requiring proper bi‐stable shapes.The design part can precisely maintain equilibrium with the required shapes. [ABSTRACT FROM AUTHOR]

Objective. In this study, nonlinearly frequency-modulated (NLFM) ultrasound was applied to magneto-acousto-electrical tomography (MAET) to increase the dynamic range of detection. Approach. Generation of NLFM signals using window function method—based on the principle of stationary phase—and piecewise linear frequency modulation method—based on the genetic algorithm—was discussed. The MAET experiment systems using spike, linearly frequency-modulated (LFM), or NLFM pulse stimulation were constructed, and three groups of MAET experiments on saline agar phantom samples were carried out to verify the performance—respectively the sensitivity, the dynamic range, and the longitudinal resolution of detection—of MAET using NLFM ultrasound in comparison to that using LFM ultrasound. Based on the above experiments, a pork sample was imaged by ultrasound imaging method, spike MAET method, LFM MAET method, and NLFM MAET method, to compare the imaging accuracy. Main results. The experiment results showed that, through sacrificing very little main-lobe width of pulse compression or equivalently the longitudinal resolution, the MAET using NLFM ultrasound achieved higher signal-to-interference ratio (and therefore higher detection sensitivity), lower side-lobe levels of pulse compression (and therefore larger dynamic range of detection), and large anti-interference capability, compared to the MAET using LFM ultrasound. Significance. The applicability of the MAET using NLFM ultrasound was proved in circumferences where sensitivity and dynamic range of detection were mostly important and slightly lower longitudinal resolution of detection was acceptable. The study furthered the scheme of using coded ultrasound excitation toward the clinical application of MAET. [ABSTRACT FROM AUTHOR]

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods. [ABSTRACT FROM AUTHOR]

AbstractThis article presents a new kind of higher-order deformation theory, called Parametric Higher-order Deformation Theory (PHDT), for the static analysis of functionally graded plates (FGPs). The novelty of the PHDT is the use of strain shape functions that are calibrated by a set of tuning parameters to approximate 3D results along the plate thickness. The tuning parameters are assumed to vary with side-to-thickness ratios and power-law indexes. In contrast to higher-order shear deformation theories (HSDTs), the PHDT is not mathematically constrained to satisfy the traction-free boundary condition on the bottom plate’s surface. The proposed plate model is based on a 5-unknown HSDT previously presented by one of the authors. The governing equations are derived from the principle of virtual works, and Navier-type closed form solutions have been obtained for simply supported FGPs subjected to bisinuisoidal transverse pressure. A general methodology that uses genetic algorithms to determine the optimal tuning parameters of PHDTs for FGPs with various side-to-thickness ratios and power-law indexes is presented. The accuracy of the PHDT is assessed by comparing the results of numerical examples with a 3D elasticity solution, HSDTs reported in the literature, and the well-known Carrera Unified Formulation. The results show that quasi-3D displacement and stress distribution are obtained using a set of tuning parameters to form adaptable strain shape functions that are optimized for the given structural problem. [ABSTRACT FROM AUTHOR]