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As enterprises increasingly rely on cloud-based Human Resource Management Systems (HRMS) deployed on the Hadoop platform, the optimization of weighting algorithms becomes imperative to enhance system efficiency. This paper addresses the complex challenge of load balancing in the cloud environment by proposing Effective Load Balancing Strategy (ELBS) a hybrid optimization approach that integrates both Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). The optimization objective involves the allocation of N jobs submitted by cloud users to M processing units, each characterized by a Processing Unit Vector (PUV). The PUV encapsulates critical parameters such as Million Instructions Per Second (MIPS), execution cost α, and delay cost L. Concurrently, each job submitted by a cloud user is represented by a Job Unit Vector (JUV), considering service type, number of instructions (NIC), job arrival time (AT), and worst-case completion time (wc). The proposed hybrid GA-GWO aims to minimize a cost function ζ, incorporating weighted factors of execution cost and delay cost. The challenge lies in determining optimal weights, a task addressed by assigning user preferences or importance as weights. The hybrid algorithm iteratively evolves populations of processing units, applying genetic operators, such as crossover and mutation, along with the exploration capabilities of GWO, to efficiently explore the solution space. This research contributes a comprehensive algorithmic solution to the optimization of weighting algorithms in enterprise HRMS on the cloud and Hadoop platform. The adaptability of the hybrid ELBS to dynamic cloud environments and its efficacy in handling complex optimization problems position it as a promising tool for achieving load balancing in HRMS applications. The proposed approach provides a foundation for further empirical validation and implementation in practical enterprise settings. [ABSTRACT FROM AUTHOR]

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover's algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), where n is the number of qubits in the individuals' quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals' register and fixing the remaining ones as classical values of '0' and '1' with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in O(2(n−k)/2) oracle queries, where k is the number of fixed classical bits in the individuals' register. [ABSTRACT FROM AUTHOR]

The final product of additive manufacturing (AM) or 3D printing critically depends on the surface quality. An experimental study on the 3D printed intake manifold flange using acrylonitrile butadiene styrene (ABS) material was executed by varying the four process parameters. A fused deposition modeling (FDM) based 3D printer was used to fabricate the flanges. The association between the parameters and the surface roughness of printed ABS flanges was investigated. A feed forward neural network (FFNN) model trained on particle swarm optimization (PSO) optimized with a genetic algorithm (GA) was used to estimate the surface roughness. A Box-Behnken design (BBD) with printing parameters at three levels was used, and 25 parts were fabricated. The suggested model demonstrated a coefficient of determination (R2) of 0.9865 on test values, mean of root-mean-square-error (RMSE) of 0.1231 after 500 times training for generalization. And also mean of overfitting factor is 0.7110. This means that the suggested system could generalize. Comparing the results from the suggested model and ANN, the suggested hybrid model outperformed ANN in predicting the surface roughness values with no overfitting. This suggests that GA optimized PSO based FFNN may be a more suitable method for estimating product quality in terms of surface roughness. [ABSTRACT FROM AUTHOR]

Purpose: Accurate assessment of soil moisture content (SMC) is crucial for applications in climate science, hydrology, ecology, and agriculture. However, conventional SMC characterization and measurement are expensive, time-consuming, and have negative effects on soil. Recently, the application of multispectral technology provides a new idea for SMC accurate detection. The objective of this study was to develop and compare regression and machine learning algorithms to estimate SMC from multispectral images. Materials and methods: A multispectral sensor was used to collect spectral images of 125 soil samples from five distinct soil textures in Shanxi province at varying degrees of soil moisture, ranging from arid to fully saturated. A set of seven spectral parameters was derived from images, and predictive relationships were developed against laboratory-measured SMC. A linear regression (LR) model and a backpropagation neural network model based on genetic algorithm optimization (GA-BP) were compared in this study to predict SMC. Results and discussion: The results showed that (1) the spectral reflectance and SMC exhibit a clear negative correlation, and the lower the SMC, the larger the spectral reflectance is. (2) The GA-BP neural network model exhibits higher prediction accuracy and performance (R2 = 0.978 ~ 0.990, RMSE = 0.366 ~ 0.799%, MAE = 0.360 ~ 0.890%). (3) The GA-BP model exhibits the excellent inversion precision for the fine sand soil (R2 = 0.990, RMSE = 0.518%, MAE = 0.360%). Conclusions: This study introduces an effective methodology for accurate estimation of SMC using multispectral remote sensing technology. It further underscores the significant effectiveness of employing backpropagation neural networks and genetic algorithms in SMC prediction, providing a rapid, precise, non-intrusive, and practical approach towards precision agriculture. [ABSTRACT FROM AUTHOR]

The seismic signals collected underground in coal mines often exhibit complex waveforms accompanied by significant noise interference, leading to a reduction in the accuracy of first arrival time picking of seismic signals and thereby impacting the inversion and interpretation of seismic data. In response to the low signal-to-noise ratio seismic signals collected in complex interference environments, a method for seismic noise suppression and first arrival extraction based on the Variational Mode Decomposition (VMD) and the Genetic Algorithm-optimized Support Vector Machine (GA-SVM) is proposed to enhance the quality of seismic signals under complex noise conditions in coal mines. The approach employs the Variational Mode Decomposition for adaptive decomposition of the noisy seismic signals, yielding several Variational Mode Components (IMF). The feature extraction is applied to the IMFs obtained from VMD decomposition, utilizing the extracted signal features as the basis for discerning signal validity. Genetic Algorithm is utilized to optimize the Support Vector Machine model, obtaining the optimal penalty factor (c) and kernel function parameter (g). The optimized SVM model is then employed for the validity discrimination of the IMF components, reconstructing them into high signal-to-noise ratio signals. By applying the noise suppression algorithm to artificially noised seismic signals, the common types of noise encountered in coal mines are effectively suppressed, validating the feasibility of the algorithm. Noise suppression processing is applied to the seismic records obtained from mine roadways, successfully mitigating interference noise in the data and significantly improving the signal-to-noise ratio of the seismic records, thereby enhancing the accuracy of first arrival time picking. The results indicate that the seismic noise suppression and first arrival extraction method based on the VMD and GA-SVM can effectively separate and extract valid signals from noisy seismic records, improving first arrival time picking accuracy. This approach demonstrates a significant potential for its application in complex interference conditions in mines, which is of significance for the seismic exploration in the mining environments with complex interference conditions.

Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and segmenting the tumour region in brain pictures. The processes of image processing that are included in the proposed idea include preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation. In order to convert the pixels connected to the spatial domain into a multi-resolution domain, the Gabor transform is first applied to the brain test image. The Gabor converted brain image is then used to extract the parameters of the multi-level features. After that, the Genetic Algorithm (GA) is used to optimize the extracted features, and Neuro Fuzzy System (NFS) is used to classify the optimistic prominent section. Finally, the tumour region in brain images is found and segmented using the normalized segmentation algorithm. The effective detection and classification of brain tumours by the characteristics of sensitivity, specificity, and accuracy are described by the suggested GA-based NFS classification approach. The trial findings are displayed with an average of 99.37% sensitivity, 98.9% specificity, 99.21% accuracy, 97.8% PPV, 91.8% NPV, 96.8% FPR, and 90.4% FNR. [ABSTRACT FROM AUTHOR]

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning. [ABSTRACT FROM AUTHOR]

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover’s algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), where n is the number of qubits in the individuals’ quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals’ register and fixing the remaining ones as classical values of ‘0’ and ‘1’ with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in O(2(n−k)/2) oracle queries, where k is the number of fixed classical bits in the individuals’ register.

Education's evolution in the context of energy systems is essential for addressing sustainable energy challenges and developing a workforce equipped for future innovations, emphasizing both formal curricula and informal lifelong learning through successful energy case studies. As the global energy sector transforms to reduce carbon emissions and reliance on fossil fuels, innovations in renewable technologies like solar thermal are pivotal for promoting energy security and economic stability, supported by an educational foundation that fosters awareness and technical skills for sustainable development. Exposure to successful renewable energy systems, such as solar-powered refrigeration, offers an informal educational experience that enhances understanding and supports global educational goals, initiating with the innovative design and optimization of these systems using artificial intelligence (neural networks). Based on this formulation, the current article developed two sustainable energy systems by comparing the refrigeration cycles with two different operating fluids and various arrangements, and multi-objective optimization with an evolutionary genetic algorithm is performed for the proposed systems. The studied systems are refrigeration cycles using an ejector and without an ejector with two working fluids of lithium bromide and lithium chloride. The present work's main aim is to examine the working fluid and refrigeration system arrangements. Energy and economic modeling were performed for the proposed systems, and then parametric analysis and two-objective optimization were extracted. Parameters such as generator temperature, condenser temperature, absorber temperature, and evaporator temperature, which significantly impact the proposed system's performance, have been selected as decision parameters, and parametric analysis has been extracted for them. In addition to the mentioned parameters, diffusion mixing efficiency, nozzle efficiency, and heat exchanger have also been studied in the ejector asset system. To find the best values of decision variables, multi-objective optimization for both arrangements is conducted, and results are presented. The results have indicated that the refrigeration system using lithium chloride working fluid without an ejector achieves a coefficient of performance of 0.766 and a cost of 0.922 $/h at the optimal point, while the system with an ejector yields a higher coefficient of performance (1.047) and a slightly lower cost rate (0.991 $/h). The outcomes of this work can play a critical role for higher education institutions in advancing innovative solutions to pressing energy challenges. Lifelong learning, at the heart of educational innovation, can benefit from the integration of sustainable energy systems as a core component of informal education through the optimization of ejector refrigeration systems.

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students, enterprises and industries. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method.

Cryogenic energy storage (CES) is a viable method for grid-scale electrical energy storage. Considering the high energy density and mature application of liquefied natural gas (LNG), we proposed an LNG cryogenic energy storage (LNGES) system. A steady-state process model of the LNGES system was established using Aspen HYSYS. The effects of the natural gas composition and key operating parameters such as the charging pressure, discharging pressure, throttling temperature, and liquid storage pressure on the system performance were investigated. A multi-parameter genetic algorithm model built using the MATLAB software was used to optimize the LNGES system to optimize the round-trip efficiency (RTE). Then, an exergy analysis of the optimal configuration was conducted. The results suggested that the LNGES system could achieve optimal RTE and exergy efficiency values of 60.14% and 71.64%, respectively. Exergy destruction mainly occurred during the compression, throttling, expansion, and heat exchange. The proposed LNGES system could be a promising candidate for the large-scale application of CES technology in power grids and gas networks.

Abstract To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4 +–N loading rate, etc., and the energy consumption could be reduced by 13–27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants.

To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4+–N loading rate, etc., and the energy consumption could be reduced by 13–27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants. [ABSTRACT FROM AUTHOR]

Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize behavior exploration and minimize the test suite size while covering more server-side vulnerable paths. Three diversity metrics (i.e. phenotypic coupling, intent coupling and competitiveness) are proposed to measure the underlying relationship between test cases from user behavior, code logic and test execution history. Moreover, a 3-dimensional weight graph is designed to model association among metrics, which provides fine-grained guidance for the genetic algorithm to generate diverse test cases from the client-side behavior model. Our empirical evaluation on five web applications shows that DTester can efficiently and robustly generate better test suites than the state-of-the-art TCG method. The maximum improvement is 8 5 % , 6 0 % , 7 1 6 % and 8 8 % in efficiency, test suite size, diversity and robustness. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have been used extensively for the optimal design of water distribution networks (WDNs). There is evidence in the literature that search space reduction is highly effective. However, practical methods that do not introduce extra computational requirements are lacking. A dynamic search space reduction methodology is proposed to search the entire solution space without eliminating any part of the search space beforehand. The proposed methodology works on the information explored during the execution of the algorithm. Further, a self-adaptive penalty is suggested which is based on both flow and pressure deficits instead of only pressure deficit and is obtained using pressure dependent analysis. In this study, the methodology is demonstrated using a Genetic Algorithm (GA). The effectiveness of the methodology is demonstrated on the Ramnagar Network of Nagpur City, India and two benchmark problems from the literature. The proposed methodology resulted in a substantial reduction in the computational efforts and provided nine improved solutions as compared to the best solution available in the literature for one of the networks. The techniques proposed are generic and can be incorporated in other EAs. [ABSTRACT FROM AUTHOR]

This study focuses on the Wartsila 9L34DF engine and proposes an integrated system for low-temperature carbon capture using the coupling of cold and hot energy recovery with membrane separation in LNG-powered ships. By utilizing a series dual-pressure organic Rankine cycle (SDPORC) system to recover waste heat from the engine exhaust gases and generate electricity, the system provides power support for the low-temperature carbon capture compression process without consuming additional ship power. To validate the accuracy and reliability of the mathematical model, the simulation results are compared with the literature's data. Once the model's accuracy is ensured, the operational parameters of the integrated system are analyzed. Subsequently, working fluid optimization and genetic algorithm sensitive parameter optimization are conducted. Finally, under the optimal operating conditions, the thermodynamic performance and economic evaluation of the integrated system are assessed. The results demonstrate that the net power output of the integrated system is 100.95 kW, with an exergy efficiency of 45.19%. The unit carbon capture cost (UCC) is 14.24 $/ton, and for each unit of consumed LNG, 1.97 kg of liquid CO2 with a concentration of 99.5% can be captured. This integrated system significantly improves the energy utilization efficiency of ships and reduces CO2 emissions. [ABSTRACT FROM AUTHOR]

This research developed a pressure-based thrust vectoring angle estimation method for fluidic thrust vectoring nozzles. This method can accurately estimate the real-time in-flight thrust vectoring angle using only wall pressure information on the inner surface of the nozzle. We proposed an algorithm to calculate the thrust vectoring angle from the wall pressure inside the nozzle. Non-dominated sorting genetic algorithm II was applied to find the optimal sensor arrays and reduce the wall pressure sensor quantity. Synchronous force and wall pressure measurement experiments were carried out to verify the accuracy and real-time response of the pressure-based thrust vectoring angle estimation method. The results showed that accurate estimation of the thrust vectoring angle can be achieved with a minimum of three pressure sensors. The pressure-based thrust vectoring angle estimation method proposed in this study has a good prospect for engineering applications; it is capable of accurate real-time in-flight monitoring of the thrust vectoring angle. This method is important and indispensable for the closed-loop feedback control and aircraft attitude control of fluidic thrust vectoring control technology. [ABSTRACT FROM AUTHOR]

Generally, side slotting and directional techniques can improve the performance of a conventional Vivaldi antenna (CVA), but the optimal structure and distribution of slots and directors may be irregular or even complex, requiring significant manual effort, thus limiting the design possibilities. In this paper, a genetic algorithm (GA) is introduced to assist in designing and optimizing a new type of balanced side slotted Vivaldi antenna with director (SSVAD). The methods of artificial intelligence make the process of searching for the optimal structure of such a multi-objective and multi-dimensional problem simpler and more diverse. The GA-generated SSVAD antenna consists of a CVA and 34 slots with varying lengths, as well as 5 metal strips. It has a compact size of 38.2×49×0.8 mm3 (or 0.32λL×0.41λL×0.007λL, where λL is the lowest operating frequency of 2.48 GHz). The measured results show that the antenna has a peak gain >0 dBi over 2.48-10.88 GHz and >5 dBi over 4.6-10.88 GHz with S11<-10 dB standard, and exhibits directional characteristics at most of the operating frequencies. Since the measured results are basically consistent with the simulation ones, the effectiveness of the designed scheme has been proven. [ABSTRACT FROM AUTHOR]

In this novel bench-top experimental and statistical investigation, the effects of influencing parameters on cellulose extraction have been studied and optimized. Chemically isolated cellulose was used at the optimum condition for the purpose of cellulose nanofiber preparation by deep eutectic solvent (DES) application. For this purpose, the Taguchi method was applied for experimental design. Six parameters which have more signal-to-noise ratio were selected as influencing parameters. These parameters consist of solvent ratio for dewaxing, depectinization pH and time, delignification pH and time, and pH of hemicellulose removal. The effect of these parameters on the cellulose extraction was modeled and optimized by the application of artificial neural network (ANN), coupled with genetic algorithm (GA). The ANN model, by establishing of feedforward backpropagation network with topologies 6, 13, and 1, was applied to model the relation between the extraction condition and cellulose extraction. The results confirmed that the best neural network structure has the ability of cellulose extraction yield prediction with an acceptable level of accuracy. The optimization results show the maximum cellulose extraction would be achieved at the following conditions: pectin extraction time of 1.62 h, delignification time of 2.19 h, the solvent ratio for dewaxing of 0.89 (V/V%), depectinization pH of 2.06, delignification pH of 5.48, and hemicellulose removal pH of 11.36. Under these conditions, the experimental extraction condition yield was 91.7% for cellulose extraction, which matched with the predictive extraction yield of 92.3%. Chemically extracted cellulose at the above condition was applied to prepare cellulose nanofibers. For this purpose, choline chloride–lactic acid–based DES pretreatment was used. The structure of the extracted cellulose and prepared cellulose nanofibers by the application of FTIR, XRD, Fe-SEM, and TEM methods was characterized. The XRD diffractograms alongside with FTIR analyses show that in the extraction process by impurity elimination, the crystallinity index increases. Also, as the characterization results show, by the DES application, glyosidic bonds were cleaved, enhancing the crystallinity and nanofibrilization. [ABSTRACT FROM AUTHOR]

Component parameters directly affect the dynamic characteristics of suspension systems in small rescue craft. To study and improve the vibration reduction performance of a new suspension system, sensitivity analysis and genetic algorithm (GA) optimization were performed for a three-degree-of-freedom (3-DOF) vibration reduction suspension system. The system performance was analyzed using AMESim multi-condition simulations, and the sensitivity of the system to parameters that affect its dynamic characteristics was analyzed. Furthermore, the parameters were optimized using the GA. The simulation results indicated that the hydraulic cylinder inner diameter, the piston rod diameter, the accumulator volume, the accumulator pre-charge pressure, and the damper valve aperture size all influenced the working performance of a small salvage vessel. The optimization results showed that the stability of the ship was improved by 60% and that the main hull acceleration root mean square value decreased by 2.24% as a result of the optimization. The stability and riding comfort of the small salvage ship were improved, and there was an evident stability optimization effect. The comprehensive performance of the salvage ship was significantly improved. [ABSTRACT FROM AUTHOR]

In Sweden, steel bridges are the most common solution regarding prefabricated short-span (15-35 m) pedestrian bridges. The most common bridge type for this application is a carbon steel truss bridge with an orthotropic deck consisting of a 10-12 mm deck plate and trapezoidal stiffeners. For these bridges, the deck is the main driver regarding economic and environmental costs, originating from, e.g., high material consumption and maintenance activities. This paper aims to compare the economic aspects between a conventional steel deck and an extruded aluminum deck from an investment cost perspective. The aluminum deck is homogenous, i.e., containing no mechanical connections. To perform this case study, an optimization routine is developed and executed for the aluminum alternative. This optimization study targets the minimum weight and is constrained by the Eurocode design code. A sub-model approach is adopted for the numerical analysis, based on shell and solid elements. The procedure to go from a cross-section defined by a set of parameters to reach the optimized bridge deck is presented. The studies that are reported in this paper show that the developed optimization routine gives satisfactory results. Furthermore, they indicate that homogenous extruded aluminum decks can be a viable deck option for pedestrian bridges. [ABSTRACT FROM AUTHOR]

Focusing on the gasoline compression ignition (GCI) combustion mode, the operating parameters, fuel composition, and piston bowl geometry were collaboratively optimized at high loads to improve engine performance. A meliorative genetic algorithm coupled with a three-dimensional computational fluid dynamics (CFD) program was adopted as the optimization tool. The optimal GCI combustion strategy is summarized based on the optimization results, and the key parameters affecting engine performance at high loads are further analyzed. The results show that the employment of the open-type piston bowl, high-reactivity fuel, and late start of injection (SOI) is desirable for GCI engines at high loads. Under this strategy, fuel burns in multiple stages, which can reduce the heat release rate and advance the combustion phase. The width of the piston bowl is the geometric parameter with the most notable impact on GCI combustion. Due to the weaker turbulent kinetic energy and the smaller surface area, the larger piston bowl width is conducive to diminishing the heat transfer losses of the engine and promoting the oxidation of unburned hydrocarbon (HC) emissions. Relative to the optimal case, the strategy with the open-type piston bowl, high-reactivity fuel, and early SOI can reduce fuel consumption but results in a higher in-cylinder pressure rise rate and increased ringing intensity (RI). On the contrary, the re-entrant type piston bowl, low-reactivity fuel, and late SOI can significantly reduce the pressure rise rate and RI while deteriorating thermal efficiency. [ABSTRACT FROM AUTHOR]

H7N9 avian influenza is a novel virus with high morbidity and mortality that threatens human health and life. Therefore, it is necessary to diagnose H7N9 avian influenza in a timely and rapid manner to prevent further transmission of the virus and greatly reduce the infection and mortality rates. This paper proposes an H7N9 avian influenza diagnostic model that is based on a multilayer belief rule‐based (BRB) inference methodology by considering five typical characteristics of influenza: epidemiology, clinical manifestations, complications, characteristics of imaging tests and positive pathogen test results. Specifically, the severity of H7N9 avian influenza is gradually identified by a multilayer BRB model, and then the diagnostic model is optimized by a genetic algorithm (GA) to improve the diagnostic accuracy. Finally, the feasibility of the model is verified by fivefold cross‐validation with a real clinical dataset. The performance of the proposed diagnostic model is compared with those of the BP neural network (BPNN) model and support vector machine (SVM) model, and the results show that the multilayer BRB model can achieve rapid and satisfactory diagnostic results for H7N9 avian influenza. The experiment shows that the accuracy of the BRB model for H7N9 avian influenza hierarchical diagnosis provided in this paper is 0.903, which is higher than 0.818 of the BP neural network (BPNN) modules and 0.844 of the support vector machine (SVM) models. Especially when diagnosing the suspected and confirmed degree of H7N9 disease, it is more realized satisfactory diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The cooling capacity of a radiator under complex environmental conditions is improved by controlling the detector temperature within a range of the thermal control index. An optimal design method for the radiation cooler based on the response surface method (RSM) and genetic algorithm is proposed, and the area and location of the radiation cooler of the space ultraviolet imager detector are optimized. First, the physical model and thermal simulation model of the space ultraviolet imager are established. Then, the detector temperature is used as the objective function, and the length, height, and inclination angle of the radiator in space are used as the design variables. The mathematical model of the objective function is established by the RSM, and the rationality of the mathematical model is verified by analysis of variance (ANOVA). Finally, the Pareto optimal frontier is obtained using the genetic algorithm. The optimization results confirm that the optimal design parameters of the radiator are XW = 165.58 mm, XH = 160.29 mm, θ1 = 7.28°, θ2 = 5.87°. Compared with before optimization, the area of the radiator is reduced by 18.18%, and the weight is reduced by 48.78 g. Based on verifying the optimization results, the optimized radiation cooler can control the detector within the target temperature of −20 to −10 °C. Therefore, the optimal design of the radiation cooler meets the requirement of lightweight while improving the cooling capacity of the radiator.

This article presents a methodology to determine the control objectives of conduction angle control in generating mode of operation in a switched reluctance machine. First, the performance in motoring mode of control is compared with generating mode for different operating points. Then, the key optimization objectives are established to improve a switched reluctance machine's performance in generating mode. A multiobjective optimizer is used to select the conduction angles. The proposed generating-specific objectives are maximizing source current per torque and minimizing torque ripple. These objectives are then compared with the motoring-specific objectives, such as maximizing average torque and minimizing torque ripple for a wide speed range. Finally, the proposed generating objectives have been validated experimentally using a three-phase 12/8 switched reluctance machine.

Overvoltage transient is the major danger facing large offshore wind farms, as it may lead to a complete failure of the system components. A 150 MW offshore wind farm was modeled using the ATP-EMTP package to analyze the effect of the temporary overvoltage resulting from de-energization conditions, and the ferro resonance phenomenon resulting from the asymmetric opening of circuit breaker pole(s). The results showed that the best scenario to shut down such a system from the grid with minimum values of temporary overvoltage is to disconnect it, feeder, by a feeder. The stuck of one or more poles during the opening operation of the circuit breaker is a dangerous phenomenon, as it induces a high overvoltage value, which lasts for a long time causing more electric stress on the sensitive components of the system. The use of the Pre-Insertion Resistor PIR was recommended as a mitigation technique for the temporary overvoltage in large offshore wind farms, as it proves a significant reduction in the overvoltage values reaches to 79.44 % compared to surge arrester and shunt reactor which reduce the over voltage by 68.33%, 66.29% respectively. The genetic algorithm was used to find the optimum value of the PIR and shunt reactor that leads to the minimum TOV. The optimum value of PIR which was recorded is equal to 57.352 Ohm, which leads to more reduction in the overvoltage value by 9.67%. Also, the optimum values of the resistor and inductor of the shunt reactor were found to be 11.238 Ohms and 37.246 mH which leads to more reduction in the overvoltage value by 21.97% compared to the reduction without optimization.

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Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors' previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller. [ABSTRACT FROM AUTHOR]

This paper proposes an improved recompression combined cycle configuration to explore the potential of using CO 2 -propane in a direct-heating solar power tower (SPT) plant. The effects of critical parameters on overall performance were detailly analyzed. The exergy distribution of CO 2 and CO 2 -propane cycles was compared respectively after being optimized by a genetic algorithm. The results show that the exergy loss of regenerators was reduced, and the entire exergy efficiency of the SPT plant was improved by configuring two high-temperature regenerators. The cycle performance of propane, serving as the second additive, becomes more significant when the cooling temperature is increased. The overall thermal efficiency of the CO 2 -propane-configured SPT plant is 2.08% higher than using CO 2 alone at a condensation temperature of 50 °C. Meanwhile, the exergy efficiency of the SPT plant with CO 2 -propane cycle decreases less (1.67%) than CO 2 (2.02%) when the condensation temperature rises from 42 °C to 50 °C. However, the temperature difference of the receiver would decrease when using CO 2 -propane. This study provides a valuable reference for the application of CO 2 -propane mixture in the dry-cooling Brayton cycle of the direct-heated SPT plant. [ABSTRACT FROM AUTHOR]

Fine-blanking is a molding process based on the common blanking process, which obtains hydrostatic stress through blank holder reverse jacking, in order to increase material plasticity. It requires special equipment, namely a fine-blanking press, to complete the fine-blanking process. In this paper, the problem of the speed of the slide block fluctuation found in the actual use of a 12,000 kN hydraulic fine-blanking press after multi-stage pressure source optimization is studied. Firstly, the mathematical model of the motion of the slide block in the blanking stage of the hydraulic fine blanking press is established, and the accurate mathematical model in the blanking stage of the hydraulic fine-blanking press is obtained through the least square method system identification experiment. Aiming at the complex working situation of the fine-blanking press, a phased PID control strategy is creatively proposed. The optimal PID control parameters are obtained by a genetic algorithm, and established a fuzzy PID controller for the blanking stage to accurately control the movement speed of the slide block. The results show that the new control strategy is very effective in improving the movement accuracy of the slide block, effectively improving the machining accuracy and reducing the impact vibration of the hydraulic system. [ABSTRACT FROM AUTHOR]

Near-zero refractive index metamaterials (NZIM) have drawn a lot of attention recently for the development of high directivity antennas. However, their application has been constrained by the tedious and time-consuming manual design process. In this work, we offer an automated optimization design technique for NZIM that combines a modeling process based on pixelated metamaterials with an optimization process based on heuristic algorithms. The interactive CST-Python simulation is utilized for accomplishing the above automated design. In detail, the elite-preserving genetic algorithm (EGA) is specifically employed because of its improved capacity in locating the superior unit, and the fitness function proposed guarantees the performance of bandwidth and optimization process based on the principles of proportionate integration and penalty-like functions, respectively. Utilizing the suggested technique, we designed a NZIM unit that operates between 8.0 and 8.52 GHz. In order to validate the performance of the NZIM, a rectangular microstrip patch antenna (MPA) resonating at 8.2 GHz was employed as the radiation source for studies. According to the simulation results, the developed NZIM has a clearly beam focusing effect which causes the MPA's spherical waves to converge into quasi-plane waves. Furthermore, discussing the NZIM’s application in high directivity antennas designing, the prototypes of the NZIM and MPA were fabricated and tested. The results revealed that the MPA’s gain enhancement achieves 5.3 dB on average and 6.05 dB on maximum. Due to the automated optimization and design, this work is promised to advance NZIM's further applicability in high directivity antennas.

This study focuses on the Wartsila 9L34DF engine and proposes an integrated system for low-temperature carbon capture using the coupling of cold and hot energy recovery with membrane separation in LNG-powered ships. By utilizing a series dual-pressure organic Rankine cycle (SDPORC) system to recover waste heat from the engine exhaust gases and generate electricity, the system provides power support for the low-temperature carbon capture compression process without consuming additional ship power. To validate the accuracy and reliability of the mathematical model, the simulation results are compared with the literature’s data. Once the model’s accuracy is ensured, the operational parameters of the integrated system are analyzed. Subsequently, working fluid optimization and genetic algorithm sensitive parameter optimization are conducted. Finally, under the optimal operating conditions, the thermodynamic performance and economic evaluation of the integrated system are assessed. The results demonstrate that the net power output of the integrated system is 100.95 kW, with an exergy efficiency of 45.19%. The unit carbon capture cost (UCC) is 14.24 $/ton, and for each unit of consumed LNG, 1.97 kg of liquid CO2 with a concentration of 99.5% can be captured. This integrated system significantly improves the energy utilization efficiency of ships and reduces CO2 emissions.

This research developed a pressure-based thrust vectoring angle estimation method for fluidic thrust vectoring nozzles. This method can accurately estimate the real-time in-flight thrust vectoring angle using only wall pressure information on the inner surface of the nozzle. We proposed an algorithm to calculate the thrust vectoring angle from the wall pressure inside the nozzle. Non-dominated sorting genetic algorithm II was applied to find the optimal sensor arrays and reduce the wall pressure sensor quantity. Synchronous force and wall pressure measurement experiments were carried out to verify the accuracy and real-time response of the pressure-based thrust vectoring angle estimation method. The results showed that accurate estimation of the thrust vectoring angle can be achieved with a minimum of three pressure sensors. The pressure-based thrust vectoring angle estimation method proposed in this study has a good prospect for engineering applications; it is capable of accurate real-time in-flight monitoring of the thrust vectoring angle. This method is important and indispensable for the closed-loop feedback control and aircraft attitude control of fluidic thrust vectoring control technology.

In Sweden, steel bridges are the most common solution regarding prefabricated short-span (15–35 m) pedestrian bridges. The most common bridge type for this application is a carbon steel truss bridge with an orthotropic deck consisting of a 10–12 mm deck plate and trapezoidal stiffeners. For these bridges, the deck is the main driver regarding economic and environmental costs, originating from, e.g., high material consumption and maintenance activities. This paper aims to compare the economic aspects between a conventional steel deck and an extruded aluminum deck from an investment cost perspective. The aluminum deck is homogenous, i.e., containing no mechanical connections. To perform this case study, an optimization routine is developed and executed for the aluminum alternative. This optimization study targets the minimum weight and is constrained by the Eurocode design code. A sub-model approach is adopted for the numerical analysis, based on shell and solid elements. The procedure to go from a cross-section defined by a set of parameters to reach the optimized bridge deck is presented. The studies that are reported in this paper show that the developed optimization routine gives satisfactory results. Furthermore, they indicate that homogenous extruded aluminum decks can be a viable deck option for pedestrian bridges.

Improving spontaneity, shifting control, and efficiency are the main goals of actuators for hydraulic automated transmissions. Friction losses of piston-seals play an essential role in achieving these goals. Therefore, modeling the complex friction behavior of piston seals leads to a better understanding of the determinant factors of energy losses and, consequently, the realization of more efficient transmission actuators. This paper proposes a piston-seal friction model based on the Generalized Maxwell-Slip model. The proposed model introduces an additional hydraulic-pressure dependency that emulates the influence of cylinder-pressure on the displacement variable while accounting for various piston-seal structures. A Genetic Algorithm is also applied to identify and optimize the parameters of the proposed friction model. Simulations with O-Ring, D-Ring, and Bonded Piston seals were developed to show the validity of the proposed model in practical scenarios. The results were also compared with the original Generalized Maxwell Slip friction model to show the superiority of the proposed model in representing the experimental data.

Multi-band microstrip patch antennas are convenient for mm-wave wireless applications due to their low profile, less weight, and planar structure. This paper investigates patch geometry optimization of a single microstrip antenna by employing a binary coded genetic algorithm to attain triple band frequency operation for wireless network application. The algorithm iteratively creates new models of patch surface, evaluates the fitness function of each individual ranking them and generates the next set of offsprings. Finally, the fittest individual antenna model is returned. Genetically engineered antenna was simulated in ANSYS HFSS software and compared with the non-optimized reference antenna with the same dimensions. The optimized antenna operates at three frequency bands centered at 28 GHz, 40 GHz, and 47 GHz whereas the reference antenna operates only at 28 GHz with a directivity of 6.8 dB. Further, the test result exhibits broadside radiation patterns with peak directivities of 7.7 dB, 12.1 dB, and 8.2 dB respectively. The covered impedance bandwidths when S11≤-10 dB are 1.8 %, 5.5 % and 0.85 % respectively.

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s ∙ m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications. • The datasets were generated by a laboratory-scale prototype of the IEC system. • The ANN model for IEC considering spray operation was trained and validated. • Effects of nozzle and operation characteristics parameters were investigated. • The significance of the influential parameters was evaluated. • The optimal parameters for IEC were determined based on genetic algorithms. [ABSTRACT FROM AUTHOR]

H 2 S is a byproduct in anaerobic digesters (ADs), causing significant challenges due to its negative impact on biogas quality and equipment corrosion. As a solution, previous researchers developed complex mechanistic models that are computationally expensive due to solving equations with many variables. These models were only tested within normal H 2 S ranges. This research introduces a novel hybrid fuzzy-decision tree (F-DT) model to predict H 2 S production from primary/secondary sludge ratio (P/S), dry solids (DS), and volatile solid (VS) reduction parameters in the Qom wastewater treatment plant in Iran. Notably, the H 2 S concentrations in this facility consistently surpass the acceptable thresholds reaching levels as high as 6500 ppm, in contrast to the designed accepted limit of 3000 ppm. The process involved collecting and pre-processing data, creating a decision tree classifier for rule generation, developing a fuzzy model suited for complex systems like AD, and optimizing it with a genetic algorithm. The analysis of the final model revealed that the P/S ratio significantly influences elevated H 2 S levels, indicating that maintaining a P/S ratio > 1.2 holds promise as an effective strategy. The model is both simple and interpretable, with decent accuracy in predicting H 2 S compared to previous models. This study not only introduces a useful method for optimizing industrial-scale ADs, but also aims to uncover the reasons behind high H 2 S levels. [Display omitted] • F-DT is a hybrid fuzzy and AI-based model to address high uncertainty of AD. • DS is the least influential factor on H 2 S production. • Low primary to secondary sludge (P/S) is critical for high H 2 S production in AD. • Increasing P/S ratio >1.2 is crucial to control H 2 S and optimal biogas production. [ABSTRACT FROM AUTHOR]

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students , enterprises and industries. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Component parameters directly affect the dynamic characteristics of suspension systems in small rescue craft. To study and improve the vibration reduction performance of a new suspension system, sensitivity analysis and genetic algorithm (GA) optimization were performed for a three-degree-of-freedom (3-DOF) vibration reduction suspension system. The system performance was analyzed using AMESim multi-condition simulations, and the sensitivity of the system to parameters that affect its dynamic characteristics was analyzed. Furthermore, the parameters were optimized using the GA. The simulation results indicated that the hydraulic cylinder inner diameter, the piston rod diameter, the accumulator volume, the accumulator pre-charge pressure, and the damper valve aperture size all influenced the working performance of a small salvage vessel. The optimization results showed that the stability of the ship was improved by 60% and that the main hull acceleration root mean square value decreased by 2.24% as a result of the optimization. The stability and riding comfort of the small salvage ship were improved, and there was an evident stability optimization effect. The comprehensive performance of the salvage ship was significantly improved.

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved.