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

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]

• 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]

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications. [ABSTRACT FROM AUTHOR]

This article introduces and analyzes an integrated energy system that may generate electricity and potable water at a low cost, speeding up the desired clean transition process. The proposed system uses flame-assisted fuel cells to improve upon the inefficiencies of traditional power generation setups by increasing fuel utilization and reducing waste heat. The product's energy cost and overall efficiency are improved by using fuel gas condensation combined with a multi-effect desalination process. The engineering equation solver software examines the feasibility of the proposed system using energy, exergy, exergoeconomic, environmental, and sustainability analyses. The parametric analysis also compares the effects of changing key design variables and performance metrics. Then, the genetic algorithm integrated with an artificial neural network model is applied to minimize the power cost while enhancing the exergy efficiency. According to the results, the proposed efficient hybrid system has an affordable energy cost of 0.123 $/kWh and an environmental impact of 524 kg/MWh. The parametric study demonstrates that increasing the current density improves potable water production while reducing the efficiency and increasing the unfavorable power costs. Also, it can be shown that the sustainability indices improve significantly when a lower fuel utilization factor and equivalence ratio are chosen. The results further reveal that the optimization achieves a higher exergy efficiency of 6.2% and a reduced power cost of 0.03 $/kWh than the design condition. The scatter dispersal of key variables indicates that the fuel utilization factor lacks effectiveness, and keeping the current density and fuel temperature close to their upper limits is techno-economically favorable. Due to the significant temperature increase, mixing of the fuel and air, and chemical interactions between the reactants, the irreversibility analysis shows that the mixer and the flame-assisted fuel cell have the greatest destruction rate under the optimal conditions. • A new hybrid system is introduced based on the flame-assisted solid oxide fuel cell. • The proposed system's feasibility is assessed from all aspects. • A genetic algorithm optimizer integrated with machine learning is applied. • Optimization achieves higher efficiency of 6.2% and a lower cost of 0.03 $/kWh. • Choosing a lower fuel utilization factor improves sustainability indices. [ABSTRACT FROM AUTHOR]

• Design and train 55-layer CNN model on the CIFAR-100 dataset. • Build two-stream network to extract spatial and temporal features from the images. • Feature optimization is performed using a Genetic algorithm. Human Gait Recognition (HGR) is referred to as a biometric tactic that is broadly used for the recognition of an individual by using the pattern of walking. There are some key factors such as angle variation, clothing variation, foot shadows, and carrying conditions that affect the human gait. In this work, a new approach is proposed for the HGR that contains five major steps. In the first step, the video data is converted into image frames. In the second step, RGB to GRAY conversion is carried out. After that, a two-stream network is designed by using a 55-layer CNN model called CNN-55 trained on CIFAR-100. The CNN-55 is designed from scratch and trained on the CIFAR-100 dataset by selecting hyperparameters. This pre-trained CNN-55 is used to build a two-stream network. In Stream-1 the optical flow frames are obtained by Horn and Schunk algorithm. These frames are fed into a CNN-55 to extract temporal features. In Stream-2 the GRAY frames are fed to the CNN-55 model for extraction of spatial features. After that, both vectors are serially fused. In the fourth step, the fused feature vector is fed into the Genetic Algorithm for optimization. Finally, the feature vector is fed into the One-Versus-All SVM classifier for recognition. The system is tested on all CASIA-B angles such as 000, 180, 360, 540, 720, 900, 1080, 1260, 1440, 1620, and 1800 which provides accuracy of 97.10%, 96.80%, 94.60%, 98.0%, 98.30%, 96.80%, 97.60, 96.90%, 99.60%, 96.80%, and 97.60%, respectively. The proposed method produces better outcomes compared to recent techniques. [ABSTRACT FROM AUTHOR]

Cerebral palsy (CP) is a clinical diagnosis and was long categorized as an acquired disorder, but more and more genetic etiologies are being identified. This review aims to identify the clinical characteristics that are associated with genetic CP to aid clinicians in selecting candidates for genetic testing. The PubMed database was systematically searched to identify genes associated with CP. The clinical characteristics accompanying these genetic forms of CP were compared with published data of large CP populations resulting in the identification of potential indicators of genetic CP. Of 1930 articles retrieved, 134 were included. In these, 55 CP genes (described in two or more cases, n = 272) and 79 candidate genes (described in only one case) were reported. The most frequently CP-associated genes were PLP1 (21 cases), ARG1 (17 cases), and CTNNB1 (13 cases). Dyskinesia and the absence of spasticity were identified as strong potential indicators of genetic CP. Presence of intellectual disability, no preterm birth, and no unilateral distribution of symptoms were classified as moderate genetic indicators. Genetic causes of CP are increasingly identified. The clinical characteristics associated with genetic CP can aid clinicians regarding to which individual with CP to offer genetic testing. The identified potential genetic indicators need to be validated in large CP cohorts but can provide the first step toward a diagnostic algorithm for genetic CP. [ABSTRACT FROM AUTHOR]

In this article, we investigate the interaction of multiple steps in hybrid rocket fuel grains. Previous studies have shown the potential of single steps to increase the regression rate. In this study, we evaluate if the single step results can be translated to a multi-step approach. Of special interest are the interactions between the different step configurations. Four grain profiles are assembled by using fuel grain segments with different inner diameters, therefore forming a multi-step approximation of the profiles that can easily be manufactured and scaled up. The multi-step grains enhance the regression rate because of increased mixing and convective heat transfer induced by the recirculation zones of the steps. The experimentally obtained regression rate profiles are similar to the single step experimental data. This signifies that they are reproducible and therefore predictable. Most importantly, multiple steps do not interfere negatively with each other, which proves that steps can be used to approximate different fuel grain profiles. We show experimentally that the regression rate can be increased up to 81% by accumulating the regression rate enhancing potential of single steps. Moreover, we developed a genetic algorithm to estimate the spatially resolved Marxman parameters with only one single burn. • Results from single steps translate to multiple steps. • Regression rate profiles reproducible for multi-steps. • Multi-step grains increase regression rate by up to 81%. • Spatial resolution of Marxman law possible with genetic algorithm. • Complex fuel profiles can be approximated with steps. [ABSTRACT FROM AUTHOR]

To solve the position control problem of the induction motor with parameter perturbations, load disturbances, and modeling errors, a predefined-time position tracking optimization control method with prescribed performance is proposed. In practice, the rotor flux linkage of the induction motor can't be measured, and a predefined-time sliding mode observer (PTSMO) is applied to accurately estimate it. Additionally, predefined-time disturbance observers (PTDOs) are employed to identify the uncertainties in the motor system. The position and flux linkage controllers are then designed by integrating the predefined-time control approach with the prescribed performance function method, realizing accurate tracking control of the induction motor within a predefined time. Next, the adaptive genetic algorithm (AGA) and the improved particle swarm optimization (IPSO) technique are combined to optimize the designed controllers, enhancing the convergence rate and steady-state accuracy of the induction motor. Finally, comparative analyses through simulations and dSPACE simulated experiments validate the efficacy of the proposed control method, highlighting its applicability in practical motor systems. ● The predefined-time sliding mode observer is crafted to observe the rotor flux linkage, and the predefined-time disturbance observers are designed to estimate the system's uncertainties. This not only addresses the challenges of unmeasurable rotor flux linkage in practice, and the observation errors converge within a predefined time. ● This paper combines the predefined-time control and the prescribed performance method, ensuring both the transient and steady-state performances of the system, while also ensuring the motor states converge within a predefined time. ● This paper combines the AGA and the IPSO to generate the AGA_IPSO hybrid algorithm to optimize the primary controller parameters, which not only simplifies the tuning process of system parameters, but also enhances the system's convergence speed and steady-state accuracy.. [ABSTRACT FROM AUTHOR]

• For the first time 100% accuracy for epileptic seizure detection using electroencephalogram signals is demonstrated. • Both balanced and unbalanced datasets are studied. • Wavelet extraction from the original EEG signal enhances the feature extraction and selection steps. • Automatic segmentation with algorithmic intelligence is used. • As opposed to Genetic algorithms fast GA is used to achieve fast analysis. The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients. [ABSTRACT FROM AUTHOR]

The production of green hydrogen from wastewater presents a significant opportunity to address environmental challenges associated with wastewater treatment and fulfill the increasing demand for renewable and sustainable energy sources. This study aimed to determine the optimal operating parameters for maximizing hydrogen production through wastewater electrolysis. To design the experimental runs, a Box-Behnken design (BBD) with an L 17 array was implemented, while Response Surface Methodology (RSM) in conjunction with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was employed to develop predictive models and optimize the operating parameters. The operating parameters considered were the catalyst amount (g), electrode voltage (V), and electrolysis time (min), and the response variable was the oxyhydrogen (HHO) gas generation rate (L/min). The comparative analysis shows that the optimized parameters obtained through RSM surpassed those obtained through GA and PSO, with a catalyst amount of 99.42 g, an electrode voltage of 22.9 V, and an electrolysis time of 23.17 min. Consequently, the HHO generation rate reached a maximum value of 12.42 L/min. Furthermore, the experimental validation indicated a close agreement between the model's predicted results and the actual experimental outcomes. This study shows the effectiveness of combining RSM, GA, and PSO for accurate prediction and optimization of operating parameters in wastewater electrolysis for green hydrogen production. The identified optimal conditions contribute to enhanced efficiency and increased hydrogen yield, thereby promoting the advancement of sustainable energy systems. • HHO gas generation through a cost-effective device: a 34-plate HHO generator. • Application of RSM, GA, and PSO for prediction and optimization of parameters. • RSM demonstrated the most accurate predictions for key performance indicators. • Optimized conditions yield HHO gas generation of 12.42 L/min. • HHO gas from wastewater addresses treatment challenges and renewable energy demand. [ABSTRACT FROM AUTHOR]

This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. [Display omitted] • 4E analysis and optimization performed on a bio-waste driven polygeneration system. • Plant optimized using artificial neural network and multi-objective genetic algorithm. • The feasibility of integrating a membrane for hydrogen separation investigated. • The optimum exergy efficiency and total cost rate obtained 38.26 % and 58.17 M$/year. • Hydrogen and electricity cost of 1.49 $/kg and 0.0145 $/kWh achieved for optimal case. [ABSTRACT FROM AUTHOR]

In order to solve the deficiencies of traditional board-level assembly CubeSat, such as low modularity, complex assembly process and poor scalability, a sandwich module assembly structure and method with no drive components is proposed, which can achieve quick connection and flexible assembly between modules. First, the proposed CubeSat standard module can be compatible with the standard interface of traditional CubeSat. The 4-in-1 interface of Mechanical-Electrical-Thermal-Data does not occupy the internal space of the standard module, and the electrical and thermal interfaces meet the requirements of CubeSat in-orbit work. Secondly, in order to improve the reliability of the connection between the modules, a plug-in connection scheme of the mechanical interface was designed. Finally, a genetic algorithm was used to optimize the interface size, so that it has better plug-in performance. The simulation results show that the performance of the male interface of titanium alloy material is the best, and the error between the finite element analysis and genetic algorithm results is less than 4.8 %, which is consistent with the actual tensile test results of the optimized prototype, and meets the mechanical requirements of the launch process and space orbit operation for CubeSat. The test results can guide production and provide a reference for subsequent engineering applications of CubeSat modular assembly. • In this paper, a standardized modular assembly method of CubeSat sandwich structure is designed. The assembly structure does not include driving parts, is simple in form, can be quickly connected, is flexible in assembly mode, and does not occupy the internal space of the standard module. • A standard 4-in-1 connection device was designed in this paper, including four interfaces: mechanical, electrical, thermal and data, which can achieve "plug and play". • This paper uses genetic algorithm to optimize the structure of the mechanical connection male joint, and obtains the optimal configuration of the male joint. This ensures the reliability, firmness and stability of mechanical connections, and meets the mechanical requirements of CubeSats during launch and in orbit. [ABSTRACT FROM AUTHOR]

In the context of Mars exploration, the Mars multi-rotor aircraft plays a significant role, with its blades being the key components for achieving flight. Due to the thin Martian atmosphere, conventional Earth blades struggle to generate the required thrust for comprehensive aerial missions on the Red Planet. To solve these challenging conditions, there arises a compelling need to refine the rotor's design, optimizing it to provide sufficient thrust while minimizing power consumption. In this research, a novel method is introduced, combining genetic algorithms and computational fluid dynamics simulations. This approach focuses on enhancing the three-dimensional structure of the rotor blade. The results of this research are verified in experiments conducted within the Martain Atmosphere Simulator, where the blade after optimization demonstrated the ability to achieve the same level of thrust while significantly reducing power consumption. Consequently, this study represents a promising avenue for improving the efficiency of Mars multi-rotor aircraft. • The three-dimensional structure of the blade employed in the Martian atmosphere is optimized. • The three-dimensional structure of the blade is optimized by genetic algorithm. • Optimization results of the blade are verified in the Martain Atmosphere Simulator. • The blade is optimized with figure of merit as the optimization objective. [ABSTRACT FROM AUTHOR]

[Display omitted] • Developing GA-LSTM model to predict gas production of large-scale anaerobic digesters. • Considering long HRT of anaerobic digesters in modeling procedure. • Defining three scenarios based on WWTP parameters to assess the model's performance. • Achieving high prediction accuracies (R2 > 0.84) by the proposed model for all scenarios. An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD 5 , COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models. [ABSTRACT FROM AUTHOR]

The travelling salesman problem (TSP) and its variants have been studied extensively due to its wide range of real-world applications, yet there are challenges in providing efficient algorithms to deal with some of its variants. The multiple travelling salesman problem (MTSP), is the generalization of TSP, which aims to determine m − routes for ' m ' salesmen to cover a set of n − cities exactly once where each route starts and ends at a depot such that the total distance is least. In this, the number of cities in each route of the optimal solution may be distributed disproportionately. This paper presents, a bi-objective MTSP (BMTSP) with the load balancing constraint, where the first objective is to minimize the total travel distance and the second objective minimizes the total time. A metaheuristic based genetic algorithm with tournament selection (GATS) is designed by integrating with mixed strategies, such as flip, swap and scramble in mutation operation to obtain efficient Pareto solution for BMTSP. The computational experiments are carried out on different data sets, which are derived from the TSPLIB. The performance of GATS is compared with different genetic approaches and simulation results show that the proposed GATS obtained improved solutions on some of the benchmark instances. [ABSTRACT FROM AUTHOR]

• Fusion of advanced deep-learning models CNN, LSTM, DNN and RNN with GA optimizes GWPZ mapping. • Overlaying GWPZ with drought maps unveils links between drought levels and groundwater potential. • CMIP6 climate models project changing GWPZ under different socioeconomic scenarios. • Workshops and local collaboration empower communities for sustainable water management. This study presents a novel approach to groundwater resource assessment and drought vulnerability, achieved through integrating cutting-edge deep-learning algorithms into a comprehensive framework. In regions susceptible to drought, ensuring groundwater availability is of paramount importance. Addressing this critical need, our research employs an ensemble of advanced algorithms, including long short-term memory, convolutional neural network, deep neural network, and recurrent neural network. These algorithms are further enhanced through optimization using a genetic algorithm to map groundwater potential zones (GWPZ). Leveraging model validation based on the area under the receiver operating characteristic curve (AUCROC), the long short-term memory-genetic algorithm model emerges as the superior algorithm, boasting the highest values of AUCROC: 0.995 for training and 0.996 for testing). Utilizing this optimized model, this study developed the GWPZ map, subsequently overlaying it with established drought maps developed by the Bangladesh Agricultural Research Council across distinct periods—Pre-Kharif, Kharif, and Rabi. The derived baseline GWPZ spatial distribution revealed five categories—very low (34.99%), low (27.67%), moderate (13.26%), high (11.71%), and very high (12.37%)—and their intersection with drought-prone regions, indicative of probabilities of drought occurrence ranging from very severe to low (15.14% to 24.69%). Moreover, employing the best-predicted model, this study projected future GWPZ for 2050 and 2100 using the coupled model intercomparison project 6 general circulation model data under the ambit of three distinct shared socioeconomic pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our findings suggested a contraction in the groundwater potential area by the end of the twenty-first century (2100). This pioneering integration sheds light on the intricate relationship between groundwater availability and drought susceptibility, furnishing invaluable insights for formulating targeted water resource management strategies. By amalgamating advanced computational techniques with geospatial analyses, this research contributes to a more comprehensive grasp of water resource dynamics within the context of escalating climate challenges. Consequently, it offers a foundation for informed decision-making and implementing sustainable water management practices in regions grappling with the dual challenges of groundwater scarcity and recurrent droughts. [ABSTRACT FROM AUTHOR]

The amount of space debris is rising rapidly, and the number of satellites launched by humans is also growing. This poses a serious threat to the security of many important space assets including the International Space Station and the China Space Station. Due to the frequent occurrence of hazardous space conjunction (potential collisions) events, we aim to use a constellation composed of a few optical satellites to observe these space dangerous conjunction events, thereby improving the observation accuracy and timeliness of key conjunction events. We consider a variety of factors to establish an optical nanosatellite constellation design model, develop constellation observation efficiency evaluation criteria, construct a constellation configuration evaluation function, and design the performance parameters of space-based optical telescopes. The optimal orbit parameters and constellation configuration are obtained by using the genetic algorithm for optimization calculation. It is determined that a constellation of 6 satellites can track and observe more than 96% of potentially dangerous conjunction objects 24–48 h before a dangerous conjunction occurs. According to the observation data, high-precision orbit information of space objects can be obtained, which can reduce the collision false alarm rate by 99%. As a result, operators are notified to take collision avoidance measures only when the space object is truly in danger. This approach greatly reduces the number of avoidance operations caused by false early warnings, lowers the operating cost of space assets, and ensures the safety of space assets. [ABSTRACT FROM AUTHOR]

Regasification of liquefied natural gas (LNG) releases significant cooling potential, but improper usage can easily lead to wastage of resources and environmental pollution. The study developed an integrated air separation system and Organic Rankine Cycle (ORC) power generation system to effectively harness the inherent cold energy within LNG. Two distinct optimization algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Particle Swarm Optimization (PSO), were employed to determine the optimal operational parameters. The results showed the superior performance of PSO over NSGA-II, as demonstrated by the exergy efficiency, net output power, and levelized cost of energy at 78.12 %, 2.29*1011 kW, and 0.0665 $/kWh, respectively. Additionally, an assessment of various factors influencing the system's performance was conducted. The performance of both conventional and proposed systems was studied under varying regasification rates. The findings indicated that incorporating the cold recovery and storage unit enhanced the efficient utilization of LNG cold energy, resulting in an increased valley-to-peak ratio of the total power output from 0.03 in the conventional system to 0.5. The proposed system exhibits alignment with the energy consumption demands of users, and this study is anticipated to furnish a theoretical foundation for harnessing the cooling capacity of liquid hydrogen. [Display omitted] • The novel utilization of LNG cold energy by combining air separation with ORC. • PSO is superior than NSGA-II for optimization: 14.4 % decrement in LCOE. • PSO: 17.9 % and 7.0 % increment in the exergy efficiency and net output power. • Both LNG steady state and fluctuating in regasification rates were investigated. • The valley-to-peak ratio of the system's power output was increased to 0.5. [ABSTRACT FROM AUTHOR]

Water electrolysis, powered by renewable energy, is a leading method for producing high-purity hydrogen and oxygen. Among electrolyzer systems, alkaline and polymer exchange membrane (PEM) electrolyzers are prevalent, with PEM being preferred for large-scale energy storage due to its superior energy efficiency. Therefore, the current development of PEM technology is mainly focused on costs reduction, improvement of performance and durability. However, the degradation at high current densities has not received thorough exploration thus far. In this research, machine learning algorithms were applied to experimental data from commercial PEM water electrolyzers (PEMWE) to examine the impact of various factors including operating temperature, current density, the transport/catalyst layer interface, and catalyst loading. The study focused on how temperature affects three anode catalysts (Ir-black, IrO 2 and I r 0.7 R u 0.3 O x), the role of pore diameter in the transport layer, and different anode catalyst loadings on cell potential performance. Additionally, the influence of temperature and current density on the degradation of PEMWEs, particularly on the anode side, was analyzed using machine learning techniques. In this study, the machine learning techniques employed were support vector regression (SVR) and artificial neural network (ANN). The findings indicated that SVR, when its hyper-parameters are fine-tuned using a genetic algorithm (GA), can achieve prediction accuracy comparable to an ANN with a fully connected architecture consisting of two hidden layers, each with 30 nodes. The algorithms' predictions indicated that PEMWE can be operated at conditions that enhance the performance and life time, subsequently leading to cost reduction in hydrogen ensuring widespread adoption of PEM electrolyzers for hydrogen production. • Machine learning algorithms were used for experimental data of commercial PEMWE. • Effects of transport-catalyst layer interface and catalyst loading investigated. • Temperature and current density on PEMWE degradation have been investigated. • Support vector regression (SVR) and artificial neural network (ANN) were studied. • SVR adjusting by genetic algorithm (GA) give high accuracy prediction as ANN. [ABSTRACT FROM AUTHOR]

The research on particle size classification in lunar microgravity has gradually attracted the attention of scholars. Based on the discrete element method (DEM), this work proposes a double-layer auxiliary screening vibrating screen in the form of intermediate feeding, and takes the screening efficiency and screening time as the evaluation indicators. Firstly, the Helmholtz-Maxwell coil is used to simulate the force of particles under lunar microgravity, which verifies the reliability of the numerical simulation of particles under lunar microgravity based on the discrete element method. Then, the influence of the parameters of each screen on the comprehensive performance of screening was analyzed by a single-factor experiment. Finally, based on the BP (Back Propagation) neural network optimized by genetic algorithm, the evaluation model of screen parameters and screen performance is established, and the multi-objective genetic algorithm is used to optimize the screen parameters combination scheme with excellent performance. An efficient comprehensive model of screening is provided to improve the screening performance and the design of the screen machine under lunar microgravity. [ABSTRACT FROM AUTHOR]

This paper presents the development of a web-based course timetabling system based on an enhanced genetic algorithm. The enhanced method utilizes a heuristic mutation which concentrates on mutating the infeasible genes to improve the algorithms' exploration and exploitation capability. The method was implemented using a free and open-source application and can be accessed online. Based on the actual datasets from Caraga State University, the enhanced method optimized the use of classroom resources by using a smaller number of rooms. The generated timetable is more efficient as it satisfies not just hard constraints, which are conflicting schedules, but also soft constraints. [ABSTRACT FROM AUTHOR]

The increased internet usage after the pandemic led the UN Forum to improve cybersecurity measures, with zero-knowledge proofs (ZKP) being a viable solution for securing confidential information. ZKP protocols can be demonstrated through the binary puzzle, an NP-complete logic puzzle with four specific constraints. The key contribution of this paper is its successful implementation of the genetic algorithm as a new method to solve the binary puzzle. The optimized fitness function determined the solution at an average of 1.33-2.33 generations for populations ranging from 100 to 500. Its quadratic property calculated the solution faster than the ordinary linear fitness function. [ABSTRACT FROM AUTHOR]

Product distribution for companies in the industrial sector is an important issue as it is necessary to increase customer satisfaction and reduce costs. In this study, Nondominated-Sorting Genetic Algorithm II is used to solve Capacitated Vehicle Routing Problem in a case study dataset from the biggest fertilizer company in Indonesia. NSGA-II algorithm reduced the need for vehicles in product distribution with an advantage of 44.7%, while GWO algorithm yielded better results with an increase of time of 16.9%. [ABSTRACT FROM AUTHOR]

The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample. • The correlation between creep-fatigue life (CFL) of metal hydride reactor (MHR) and design parameters is established. • Numerical models combining BPNN with ASME Code are available for assessment of CFL of MHR. • The sensitivity analysis is applied to measure the effect of design parameters on CFL of MHR. • Optimal design scheme of MHR is performed through an integrated design optimization method. [ABSTRACT FROM AUTHOR]

The extractive distillation is a commonly used method for the separation of azeotrope in the chemical industry. In this article, we investigate the safety issue of the extractive distillation process for targeted audience process engineers from the perspective of process safety. We conduct inherently safer design and multi-objective optimization of extractive distillation processes for the separation of methyl acetate/methanol via computer-aided molecular design, thermal hazard analysis, and multi-objective genetic algorithm. Firstly, the entrainers with good safety and separation performances are pre-screened by computer-aided molecular design (CAMD) based on flash point and entrainer selectivity. Then the entrainers with top-ranking separation performance are manually screened based on vapor-liquid equilibria. Next, the Pareto front solution is obtained by optimizing both safety and economic objective functions using a multi-objective genetic algorithm. Finally, the thermal hazard of the entrainer is also investigated via DSC (Differential scanning calorimetry) test. Finally, the optimization result and thermal hazard investigation demonstrate that 1,3-propanediol is inherently safer than dimethyl sulfoxide (DMSO) as a common entrainer in terms of not only a fire hazard but also a thermal hazard for the separation of methyl acetate/methanol mixture. The optimal TAC using 1,3-propanediol as an entrainer is 3.72% lower than that of dimethyl sulfoxide, and the corresponding GISI is 57.73% lower than that of dimethyl sulfoxide. As for DMSO, thermal decomposition can occur near its normal boiling point (189 ℃) and 1,3-propanediol, can be thermally stable over 330 ℃. [ABSTRACT FROM AUTHOR]

The mesh reflector suffers inevitably from complicated environmental interferences during long-term on-orbit operation and consequently the reflector may deviate significantly from its initial state. It is thus crucial to identify the deformed reflector on-orbit in order to evaluate timely the mesh reflector accuracy. However, the existing reflector identification methods are difficult to achieve this object as the most of them need to install additional equipment on antenna or satellite. Aiming at realizing on-orbit mesh reflector identification, a novel identification approach based on tie cable tensions sensing (TCTS) is proposed in the paper. The TCTS method leverages real-time measurements of tie cable tensions through embedded force sensors to calculate the remaining cable tensions required to address reflector deformations. For this purpose, an optimization problem is established based on tension balance equation and then solved using a proposed iteration solution algorithm incorporating the genetic algorithm. The actual configuration of mesh reflector is then determined by all cable tensions according to the force density method and the reflector can be identified ultimately. The simulated studies on reflector identification of a mesh reflector with 8 m aperture diameter are conducted under temperature loads and cable stiffness degradations respectively. The results indicate that the TCTS method possesses high reflector identification accuracy and good anti-noise capability under different environmental interferences. Furthermore, a reflector identification method based on partial tie cable tensions sensing (PTCTS) is developed in order to reducing system complexity and cost. The sensitivity analyses of tie cable tensions are performed in order to provide a reference for location placement of force sensors in the PTCTS method. The simulated results indicate that the optimized location placement scheme can enhance significantly the reflector identification accuracy of PTCTS method. Besides, the PTCTS method exhibits equal anti-noise capability with the TCTS method. Finally, the experimental studies are conducted on reflector identification of a mesh reflector test model with 1.5 m diameter and 0.5 m height under cable stiffness degradations. Through comparing with the reflector identification results obtained by the photogrammetry method under different cable stiffness degradation degrees, the accuracy and effectiveness of the TCTS/PTCTS methods are demonstrated experimentally. • The mesh reflector is identified based on tie cable tension sensing (TCTS). • Partial tie cable tensions sensing (PTCTS) can reduce system complexity. • The TCTS/PTCTS method provides a promising way to identify mesh reflector in orbit. • The TCTS/PTCTS approach is demonstrated numerically and experimentally. [ABSTRACT FROM AUTHOR]

In the literature, the reliability analysis of one-shot devices is found under accelerated life testing in the presence of various stress factors. The application of one-shot devices can be extended to the bio-medical field, where we often evidence that inflicted with a certain disease, survival time would be under different stress factors like environmental stress, co-morbidity, the severity of disease etc. This work is concerned with a one-shot device data analysis and applies it to SEER gallbladder cancer data. The two-parameter logistic-exponential distribution is applied as a lifetime distribution. For robust parameter estimation, weighted minimum density power divergence estimators (WMDPDE) is obtained along with the conventional maximum likelihood estimators (MLE). The asymptotic behaviour of the WMDPDE and the robust test statistic based on the density power divergence measure are also studied. The performances of estimators are evaluated through extensive simulation experiments. Later those developments are applied to SEER gallbladder cancer data. Citing the importance of knowing exactly when to inspect the one-shot devices put to the test, a search for optimum inspection times is performed. This optimization is designed to minimize a defined cost function which strikes a trade-off between the precision of the estimation and experimental cost. The search is accomplished through the population-based heuristic optimization method genetic algorithm. • A robust inference based on weighted density power divergence. • Elaborated study of the asymptotic property. • Testing of hypothesis based on a robust statistic. • Search for optimum inspection times using genetic algorithm. • Application to SEER gallbladder cancer data. [ABSTRACT FROM AUTHOR]

Grain boundary (GB) segregation of Al in ZnO plays an important role in lowering its thermal conductivity for thermoelectric applications. However, the effect of Al concentration on the GB complexions and their transition is not well understood. Herein, a genetic algorithm assisted multiscale modelling framework was used to study the role of GB concentration on the GB segregation of Al on five special twin GBs of ZnO. A critical concentration of 5–6 atoms/nm2 was determined for the complexion transition from single layer to multilayer. Calculated segregation energies were used in a phenomenological model to link GB concentration with the nominal concentration of dopants. The model was used to calculate the nominal solubility of Al in ZnO as a function of grain size, which was validated with the experimental data from the literature. The proposed framework provides a path for establishing GB-structure – property correlation and thereby, predictive dopant engineering of ceramics. [ABSTRACT FROM AUTHOR]

Fischer Tropsch synthesis (FTS) is a promising catalytic solution to achieve ultra-clean fuel in which catalyst has a significant role. Even though several kinetic models have been rendered for it, representing a new one based on a meticulous mechanism is noteworthy, particularly when employing a novel catalyst. Hence, in this work, molecular dynamic (MD) simulation is utilized to identify the appropriate pathway from the viewpoint of minimum energy of the elementary reactions. In this regard, we consider a majority of plausible FTS elementary reactions over Ru–CO@C(Z-d)@void@CeO 2 (cobalt carbon-MOF based plus ruthenium and ceria with porous structure) catalyst, and by employing MD computations forward and reverse activation energy barrier values for each reaction are evaluated. Then, based on the minimum energy pathway (MEP), the appropriate mechanism is derived, which is inferred from the H-assisted CO dissociation pathway for the beginning step. Subsequently, a kinetic model is developed based on this mechanism, and the model parameters are correlated and optimized employing the genetic and Levenberg-Marquardt algorithms, respectively. In addition, we use various statistical methods to assess the significance of the model. For instance, the Mean Absolute Percentage Deviation (MAPD) of the correlated values from the experimental value is 4.46%. Additionally, the relative residual percentage (RR%) plots illustrate that 99% of the correlated data have less than a 10% deviation. [Display omitted] • Rendering a comprehensive kinetic model based on an evaluated mechanism for Fischer Tropsch Synthesis. • Presenting a mechanism by evaluating the minimum energy pathway using Molecular dynamic computations. • Evaluating the H-assisted CO dissociation pathway meticulously. • Assessing the significance of the model employing statistical methods. [ABSTRACT FROM AUTHOR]

Hydrogen purification from steam methane reforming (SMR) by pressure swing adsorption (PSA) technology is a common method to obtain high-purity hydrogen. The breakthrough time can well reflect the adsorption dynamics and help PSA cycle design. In order to avoid time-consuming and labor-intensive breakthrough curve experiments, it is very necessary to develop a fast and accurate surrogate model. In this study, a genetic algorithm (GA) and artificial neural network (ANN) are combined to predict and optimize breakthrough times of adsorbates in activated carbon/zeolite layered beds. Using the Latin hypercube sampling strategy, training data sets of GA-optimized ANN (GA-ANN) obtains from the physical model of adsorption, heat and mass transfer model. The genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. The ANN topology has 4 input variables (superficial velocity, activated carbon height, adsorption pressure and feed temperature) and 3 output variables (breakthrough times of CH 4 , CO and CO 2). The number of neurons in the hidden layer for the GA-ANN model was optimized as 7 to predict and optimize the maximum breakthrough time of SMR with a four-component (H 2 /CH 4 /CO/CO 2) system. The sensitivity analysis displays that relative importance to the breakthrough time is in the order of superficial velocity (40.88%) > adsorption pressure (24.55%) > activated carbon height (21.04%) > feed temperature (13.53%). To obtain high-purity hydrogen, the GA-ANN model combined with multi-objective GA optimization is used to maximize the breakthrough times of CH 4 and CO. The GA-ANN surrogate model proposed in this paper can not only accurately and quickly achieve the purpose of predicting the system breakthrough time with a high correlation coefficient (R = 0.9937) but also obtains the optimal operating conditions of PSA hydrogen purification. • A rigorous heat and mass transfer model of multi-component adsorption is established. • Artificial neural network (ANN) is well-trained by Latin hypercube sampling method. • Genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. • Superficial velocity has the highest relative importance value on breakthrough time. • GA-optimized ANN model and GA combined for maximal breakthrough time of impurities. [ABSTRACT FROM AUTHOR]

Accurate isotherm models contribute to predicting the amount of adsorbate adsorbed in the adsorbent within a certain range of pressure and temperature. Experimental adsorption isotherms of five components (CO 2 , CO, N 2 , CH 4 and H 2) on pelletized zeolite 13X from publication data were correlated with common-used fundamental isotherm models, including Langmuir, Sips, dual-site Langmuir (DSL), and temperature-dependent (TD) isotherm models, including TD Langmuir, TD Sips and TD DSL. Overall, the average relative errors (AREs) of TD models are higher than those of common-used fundamental isotherm models, but TD models have better applicability. In addition, three surrogate artificial neural networks (ANNs) optimized by genetic algorithm (GA) of single- and multi-component gas adsorption isotherm models are proposed. Three GA-ANN models set the adsorption amount as the output and take one, two or three potential inputs, such as adsorption pressure, temperature and adsorbate components. Three GA-ANN models generally have lower AREs and better applicability than fundamental isotherm models and TD isotherm models. The proposed GA-ANN3 model, which has three inputs, makes a new attempt at adsorption isotherms and can fit the multi-component gas adsorption at different temperatures with the change of adsorption pressure. The adsorber dynamics for multi-component adsorption (CO 2 : CO: N 2 : H 2 = 19.9: 0.1: 44.6: 35.4 mol%) in zeolite 13X bed are compared by the extended TD Langmuir model and extended TD DSL model. Since the AREs of the TD DSL model are smaller than that of the TD Langmuir, the breakthrough curves of multi-component gas simulated by the TD DSL model are better than that of TD Langmuir. These results can contribute to relevant researchers choosing suitable adsorption isotherm models for numerical simulations of adsorption processes. • Fitted dual-site Langmuir model performs better in breakthrough curve simulation. • Artificial neural network (ANN) enables construction of adsorption isotherm models. • Genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. • Three GA-ANN models have lower average relative errors and better applicability. • GA-ANN3 attempts to express adsorption isotherms for multi-component mixtures. [ABSTRACT FROM AUTHOR]

With global communities being susceptible to adverse impacts of greenhouse gases, hydrogen has substantially drawn the attention of scientists to investigate it as a promising fuel. Hence, the present investigation aims to conduct an exhaustive thermodynamic analysis of an innovative biomass-assisted system driven by the Brayton cycle through multi-objective optimization to detect the different optimal scenarios for generating and storing hydrogen. This system contains a Brayton cycle as a prime mover, steam gasification of biomass and three-stage compressors to generate and store hydrogen, and multi-effect distillation to produce freshwater. The ultimate goal of this study is to use the generated steam through the produced freshwater as a steam agent of the gasifier to conclude a water-efficient scenario in producing hydrogen. To discern the impact of the chosen design parameters on the performance of the system, an exhaustive parametric study has been carried out. Afterward, multi-objective optimization via the non-dominated sorting genetic algorithm has been undertaken to find the optimal values by regarding the financial constraints. Tri-objective optimization results demonstrated that optimum values of total cost rate, exergy efficiency, and hydrogen production rate are 1.43 $/s, 47.56%. , and 0.12 kg/s. Besides, by regarding the consumption of water as an additional objective function, the results demonstrate that by reducing consumed water up to 47.5%, the generated hydrogen only decreases by 8%. • An innovative multi-generation system based on biomass is proposed. • The most influential design parameters are detected through the parametric study. • A water-saving scenario is discussed to generate efficient hydrogen. • NSGA-II is utilized to detect the optimal values of the proposed system. • The hydrogen production rate is 0.12 kg/s in the optimal design. [ABSTRACT FROM AUTHOR]

This article presents an efficient energy system to generate power via renewable sources. Biomass is the running mover of a cycle that operates on a gasification process to generate syngas. The hot syngas is then used to create hydrogen via a green and sustainable method of hydrogen production called the vanadium chlorine cycle (VCLC). The generated hydrogen is mixed with the syngas before entering the PEM fuel cell. The proposed system's energy, exergy and economic impacts are examined. Also, the effect of different variables is studied on the system performance indexes. Finally, multi-objective optimization is implemented to ensure the highest efficiency and lowest cost. Results indicate that the moister content of the biomass, the PEMFC temperature, and current density play a crucial role in the system performance. Rising the PEMFC temperature and the moister content works in favor of the system performance indices, unlike the current density of the fuel cell. The results of the multi-objective optimization represent that under optimal conditions, when moisture content, PEM current density and temperature are chosen equal to 0.314, 0.71 A/cm2 and 360.78 K respectively, system has the exergy efficiency of 32.10% and total cost rate of 52.95 $/h. • A multi-generation system consisting a Fuel cell, ORC, and VCLC is developed. • The system is analyzed from energy, exergy, economic, and environmental views. • The effect of various parameters was investigated. • Multi-objective optimization is performed using a genetic algorithm. • The results indicate that the system is effective in achieving 4E goals. [ABSTRACT FROM AUTHOR]

We propose a new method, called a deep-genetic algorithm (deep-GA), to accelerate the performance of the so-called deep-BSDE method, which is a deep learning algorithm to solve high dimensional partial differential equations through their corresponding backward stochastic differential equations (BSDEs). Recognizing the sensitivity of the solver to the initial guess selection, we embed a genetic algorithm (GA) into the solver to optimize the selection. We aim to achieve faster convergence for the nonlinear PDEs on a broader interval than deep-BSDE. Our proposed method is applied to two nonlinear parabolic PDEs, i.e., the Black-Scholes (BS) equation with default risk and the Hamilton-Jacobi-Bellman (HJB) equation. We compare the results of our method with those of the deep-BSDE and show that our method provides comparable accuracy with significantly improved computational efficiency. • We introduce a deep-GA method. • The method provides a better accuracy and efficiency than the deep-BSDE solver. • Efficiency and accuracy is obtained by embedding a genetic algorithm (GA). • We solve the Black-Scholes with default risk and Hamilton-Jacobi-Bellman equations. [ABSTRACT FROM AUTHOR]

The optimization of the membrane electrode assembly (MEA) is a crucial aspect in improving the performance of high-temperature proton exchange membrane fuel cells (HT-PEMFCs). Through experimental validation, a three-dimensional HT-PEMFC computational fluid dynamics (CFD) model was established to simulate the transport of charge, mass, and momentum across various porous layers. A deep neural network (DNN) was designed to quantify the influence of porous layer structures on cells performance and determined the optimal solution of the MEA parameters for different Pt loadings. The training data of DNN were generated using 11 porous layer structural parameters selected via a Monte Carlo method, and their corresponding current densities predicted by the validated HT-PEMFC CFD model. The DNN model showed an average relative error of only 0.76% in predicting current density when compared to CFD simulations. Using a bi-objective genetic algorithm (GA) in conjunction with the DNN model, the optimized cell power densities at a voltage of 0.4 V and Pt loadings of 0.3 mg/cm2 and 0.5 mg/cm2 are 18% and 9% higher than the maximum values reported in the literature, respectively. The proposed approach combining DNN model and GA can serve as a useful guide for the design of HT-PEMFC in practical applications. • HT-PEMFC CFD model is validated by published experimental data. • Data-driven DNN model achieves 0.76% mean relative error for predictions. • DNN model couples with genetic algorithm to optimize porous layer structures. • Cell performance can improve 18% and 9% at Pt loadings of 0.3 and 0.5 mg/cm2. [ABSTRACT FROM AUTHOR]

The optimisation of green hydrogen production systems is challenging. Moreover, an accurate simulation of the system is required for effective optimisation. This study presents a novel method for optimising utility-scale hybrid photovoltaic–wind systems for hydrogen production using accurate simulation models. The optimisation objective is to minimise the levelised cost of hydrogen (LCOH) using genetic algorithms. Different types of systems (such as islanded systems, grid-connected systems with or without the possibility of purchasing electricity from the grid, and grid-connected systems considering power curtailment), are evaluated and optimised. Each combination of components and control strategy is simulated during the system lifetime (20 yrs) in time steps of 5 min, considering the degradation of renewable generators during the system lifetime and different real-time pricing curves and renewable resource curves for each year of the system lifetime. Accurate models are used in the simulations, including electrolyser efficiency dependent on the input power and cold-start extra ageing. An application example located in Zaragoza (Spain) is shown, obtaining LCOH from 4.74 to 16.06 €/kg, depending on the type of project and electrolyser. • Utility-scale PV-wind-hydrogen optimisation of components and control. • Minimisation of the levelised cost of hydrogen (LCOH). • Four different types of systems, isolated or grid-connected. • Advanced models including electrolyser variable efficiency. • Each year different electricity price and irradiation; components degradation. [ABSTRACT FROM AUTHOR]

The flow field dominates the process of mass transport and distribution inside the proton exchange membrane water electrolysis cell (PEMEC). Initially, a three-dimensional and non-isothermal PEMEC channel model was estimated to investigate the impact of channel height and width. Based on these results, a complete PEMEC model with a parallel flow-field pattern was developed, and the channel heights and widths of the model were optimized using a genetic algorithm (GA). Compared to the original model, the optimized model decreased the pressure drop by 34.97% at a lower voltage. In addition, because of the optimized flow-field pattern, the optimized model improves the current density on the proton exchange membrane as well as the gas holdup situation in a partial position in the cathode gas diffusion layer. • PEMEC channel height influences the gas fraction, voltage, and pressure drop. • The channel width has a stronger influence on voltage. • GA was used to optimize PEMEC channel structure with two objectives. • The optimized model was characterized by a decrease in pressure drop of 34.97%. [ABSTRACT FROM AUTHOR]

According to the operation parameters and the design experience parameters, a liquid hydrogen centrifugal pump was developed and tested to meet an application requirement of small flow and high head in cryogenic systems or cryogenic storage tanks. The test results showed that 6 m of hydraulic loss in the pump at the rated condition and the inlet pressure of the cryogenic fluid decreased and fluctuated, which resulted in the pump being prone to cavitation. To improve cavitation resistance and hydraulic efficiency of liquid hydrogen centrifugal pumps, the model was optimized based on the genetic algorithm of MATLAB applying 7 parameters of impeller taken as optimization variables, each sub-objective function was given corresponding weights, and the optimal solution combination at the minimum of the objective function was finally obtained, and an inducer was matched. The results showed that by reducing the angle of the inlet, increasing the diameter of the impeller, and adding an inducer, the cavitation performance of the pump can be improved and the hydraulic loss can be reduced by appropriately reducing the impeller outlet diameter, to improve the head of the pump. A comparison of the performance of the preposition inducer optimized pump with the preliminary pump shows that, at the rated condition point, the head was increased by 4.6 m resulting in an efficiency of 43%. A numerical calculation considered the thermodynamic effect of non-isothermal cryogenic cavitation on the Reynolds-averaged Navier-Stokes combined with the k - ε turbulence model was applied for getting the pump hydrodynamic performances. It was proved that the multi-objective optimization method and preposition inducers were effective, and the calculating results were by experimental data well. Feasibility of the model had been validated to a certain extent. It can bring a high level of pump optimization, integration, and quality to new and existing designs. • A LH 2 centrifugal pump is optimized by the genetic algorithm of MATLAB. • LN 2 centrifugal pump performances are carried out on the cryogenic test platform. • Multi-objective optimization and inducers can improve the LH 2 centrifugal pump. • Numerical model is feasible and prediction performance of LH 2 centrifugal pumps. • Efficiency and anti-cavitation improvement are confirmed by experiments. [ABSTRACT FROM AUTHOR]

Cryogenic supercritical hydrogen storage technology is a high-density physical hydrogen storage method, which is a highly promising hydrogen storage approach. However, existing research has mainly focused on the development of storage devices for cryogenic supercritical hydrogen, with limited research on the cooling process of supercritical hydrogen. Therefore, a novel cooling process for cryogenic supercritical hydrogen production based on helium expansion cycle with liquid nitrogen (LN 2) pre-cooling is proposed in this paper. In this process, after being compressed and cooled in multiple stages, the feed hydrogen is pre-cooled by LN 2. Then, cryogenic supercritical hydrogen (10 MPa, −235.15 °C, 63.36 g/L) is obtained by helium multi-stage expansion refrigeration. The process is simulated by Aspen HYSYS and optimized by genetic algorithm (GA) to obtain lower specific energy consumption (SEC). Thermodynamic analyses are conducted to evaluate the process. And compared with the conventional hydrogen liquefaction process, the results show that the proposed process is reasonable and superior. The SEC 0 , figure of merit (FOM), and the total exergy loss of the system are 5.432 kWh/kg H2 , 43.88%, and 289.69 kW respectively. This study indicates that the proposed process has significant implications for the hydrogen industry and provides valuable information for cryogenic supercritical hydrogen storage systems. • A novel high-density cryogenic supercritical hydrogen storage system is proposed. • Twelve key variables in the process are optimized to reduce the energy consumption. • The process has higher exergy efficiency than conventional LH 2 plants. • The volume storage density of hydrogen can reach 63.36 kg/m3 in the system. [ABSTRACT FROM AUTHOR]

To handle the impact of solar radiation fluctuations on the solar reactor, a new proportional-integral (PI) hybrid control strategy based on back propagation neural network (BPNN) is designed in this study to achieve stable operation. Firstly, the BPNN model optimized by genetic algorithm is trained using reactor model simulation data. Then, the BPNN model is combined with PI feedback control to obtain a BPNN-based PI feedforward-feedback hybrid control strategy for regulating the inlet flow rate. Finally, the effectiveness of the control strategy during transient operation under different disturbances is tested. The results show that during actual solar radiation disturbance, the integral square errors (ISE) of outlet hydrogen fraction and methane conversion for BPNN-based PI hybrid control are 7.03 × 10−6 and 1.05 × 10−4, respectively. Compared to traditional PI feedback control, the new hybrid control strategy represents a reduction of 94% and 91% in ISE, indicating that the strategy can significantly improve production stability. • BPNN based PI hybrid control strategy is designed for solar methane reformer. • Different control strategies are examined with various solar fluctuation conditions. • Stabilities of outlet hydrogen fraction and methane conversion are clearly improved. • The proposed BPNN based PI hybrid control strategy reduces ISE by 90%. • Dynamic tracking performance of proposed control strategy is revealed. [ABSTRACT FROM AUTHOR]

Arginine deiminase (ADI) has been widely employed for the biological production of nutraceutical citrulline. However, sub-optimal enzyme properties, operational instability, and difficulty in reuse impede its industrial usage as well as increase the production cost. The present study describes a simple, cost-effective carrier-free immobilization technology that can circumvent these limitations. ADI was precipitated and cross-linked with glutaraldehyde to obtain novel cross-linked arginine deiminase aggregates (ADI-CLEA). A neural network-based multiobjective genetic algorithm (MOGA-NN) was employed to optimize the immobilization of ADI. MOGA-NN optimization yielded ADI-CLEA with 0.30 IU/mL activity and 61 % recovery. Structural characterization using TEM, SEM, FTIR, DLS/ZETA revealed that the developed ADI-CLEA were monodisperse, ultra-porous, and highly stable. ADI-CLEA showed an improved pH /temperature profile and resistivity against denaturants than free enzyme. The efficiency of ADI-CLEA in the industrial production of nutraceutical citrulline was also studied. ADI-CLEA produced 215.4 g/L citrulline in first cycle of usage. Reusability and storage stability analysis further showed that ADI-CLEA could be reused for more than seven consecutive cycles and stable for up to 50 days. In conclusion, this novel formulation increases the stability/robustness of ADI, eases the product separation process and could be reused for economically viable bioproduction of nutraceutical citrulline in industries. [Display omitted] • Novel cross-linked enzyme aggregates of arginine deiminase (ADI-CLEA) were reported. • Immobilization conditions were optimized by network-based multiobjective genetic algorithm. • ADI-CLEA exhibited enhanced catalytic efficiency, stability and robustness. • Improved citrulline yield (215 g/L) was obtained by ADI-CLEA. • ADI-CLEA could be recycled for seven cycles without losing its activity. [ABSTRACT FROM AUTHOR]

Cyclohexane and isopropyl alcohol are widely used in the chemical and pharmaceutical industries. There is a strong industrial need for the refinement of cyclohexane and isopropyl alcohol. In this study, the recovery of cyclohexane and isopropyl alcohol from wastewater was carried out sustainably and efficiently by extractive distillation. The suitability of ethylene glycol as entrainer was determined by relative volatility analysis. The process was optimized via multi-objective optimization, and the extractive distillation process obtained optimal parameters and process scheme. Finally, a comprehensive analysis was conducted to evaluate the economic, environmental and exergy efficiency of different process schemes. The results show that the extractive distillation coupled pervaporation technology has a 14.07% lower total annual cost than extractive distillation, and the pervaporation technology reduces gas emissions by 15.65% compared to the extractive distillation. This work explores the recovery of cyclohexane and isopropyl alcohol from wastewater by different process schemes, which has certain guiding significance for the sustainable and efficient separation of ternary azeotrope. [Display omitted] • Multi-objective optimization was used to obtain optimize parameters of process. • A pervaporation model of EG dehydration was established to realize solvent recovery. • The different energy saving intensification schemes were studied. • Detailed performance evaluation and analysis of process was done. [ABSTRACT FROM AUTHOR]

The problem of optimizing variable-specific impulse electric propulsion maneuvers is presented, proposing a novel multi-objective methodology for the computation of near-optimal planetocentric solutions. The methodology relates a parameter expressing the instantaneous maneuvering efficiency, or thrust effectivity, with the specific impulse through an arbitrary parametric function. The parameters of this function are determined by a multi-objective genetic algorithm, which minimizes propellant consumption and maneuver duration. The study includes the analysis of several scenarios, with the results demonstrating that the methodology can be successfully employed in the context of space mobility of small satellites. The findings reveal that variable-specific impulse maneuvers can achieve improvements exceeding 17% in both propellant mass and maneuver duration compared to a constant-specific impulse case. Consequently, the resultant Pareto fronts showcase enhanced trade-offs and more favorable solutions for the optimization problem. The results highlight the significance of this optimization methodology for electric propulsion technologies with throttling capabilities, particularly in systems with near-constant propulsive efficiency. [ABSTRACT FROM AUTHOR]

Microinjection, which is widely employed in modern biology and medicine research, is a crucial method for transferring biomaterial in a single cell. Despite substantial research into microinjection automation technology, how to achieve high precision, which is crucial for quantitative analysis, has gotten scant attention. This paper offers a dual-loop control syringe-based picoliter-scale injection system, with the inner being a pneumatic servo loop with a baroreceptor providing pressure feedback and the outside being a position servo loop with a microscope providing visual feedback. Specifically, significant crawling phenomenon are found throughout the characteristic test trial, rendering the gas-liquid interface uncontrollable. To mathematically characterize this phenomenon, a theoretical model is developed that accounts for static friction, capillary force, capillary cone angle, and fluid viscous force. The genetic algorithm is used to identify difficult-to-measure and calibrate parameters. Based on the established model, an integral sliding-mode controller (ISMC) with saturation function and a PID controller for the inside loop control were established, both of which have a fast convergence rate and do not overshoot. Finally, experimentation is used to validate the designed cell injection system, demonstrating that the suggested system can regulate the smallest droplet in the tip of the needle to within 0.25 pL. • A syringe-based picoliter-scale injection system with injecting accuracy of 0.25 pL is developed. • Severe crawling phenomenon is observed, and a mathematical model is established to describe this phenomenon. • An integral sliding-mode control with saturation function is designed, demonstrating fast convergence rate and non-overshoot. [ABSTRACT FROM AUTHOR]

This study introduces a data-driven surrogating modeling approach for determining thermal parameters in undetermined thermal mathematical models (TMM) used in spacecraft systems. Maintaining appropriate temperature levels for onboard components is crucial for the success and safety of space missions. However, accurately determining thermal parameters in the undetermined TMM presents a challenge. To address this, we propose an approach that combines TMM correlation with test data and data-driven surrogate modeling. Our approach minimizes an objective function that measures the deviation between predicted temperatures from a surrogate model, incorporating thermal parameters, and steady state temperatures obtained from thermal vacuum balance test results. This optimization is performed using a genetic algorithm while imposing constraints to ensure the physical validity of the thermal parameters and correlation criteria related to temperatures. We demonstrate the effectiveness and validity of our approach by applying the obtained thermal parameters to the TMM. The resulting calculated temperatures exhibit excellent agreement with the test results. This methodology offers a user-friendly and reliable way to determine thermal parameters in undetermined TMM within spacecraft systems, ultimately enhancing temperature control and mission success. • Efficient thermal parameter determination in spacecraft TMMs through data-driven surrogate models. • TMM correlations achieved by minimizing temperature deviation with surrogate models and test data. • Reliable TMM correlations achieved by considering realistic constraints, overcoming thermal parameter non-uniqueness. • Reasonable selection of thermal parameters, enhancing reliability and reducing user knowledge requirements. [ABSTRACT FROM AUTHOR]

Sustainable management of water distribution networks (WDNs) is essential to ensure the continuous supply of water. However, the water pipes in WDNs often experience unprecedented failure, which causes disruption in services, flooding, increased maintenance costs, and reduced water quality. Although researchers have developed models to predict the failure of water pipes, the literature lacks fully optimized and robust models. Therefore, this study proposes a new methodology to develop optimized models for predicting the failure probability of water pipes by fusing logistic regression with genetic algorithms. The methodology was applied to the data of the Hong Kong WDN, and experiments were conducted to optimize the hyperparameters and features of logistic regression models. The performance of the proposed methodology is evaluated using five key metrics: accuracy, precision, recall, F1 score, and Area Under the Curve (AUC). The results show significant improvement over conventional approaches, with the best model achieving an F1 score of 0.868 and an AUC of 0.944. These results show that the model can effectively predict the failure probability of water pipes. The relative contribution of each feature to the model's outcome was investigated using the SHapley Additive exPlanations. Additionally, a web application based on the proposed methodology in this study was developed for Hong Kong that other water utility management can benefit from, which can facilitate reliable decision-making for the management of WDNs. [ABSTRACT FROM AUTHOR]

Bundle AI is a marketing tool project motivated to provide solutions for manual product bundling, a popular marketing strategy that boosts sales of low-selling products and encourages mass purchases with promising customer satisfaction. It is a cost-effective solution as it does not depend on large volumes of transactional data, which is suitable for small businesses. The bundling of products was defined as a Multiple Constraints Knapsack Problem where the best solution is determined using Genetic Algorithm. Bundle AI delivers the fit product bundle with the incorporation of the value and reward system. [ABSTRACT FROM AUTHOR]

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved. [ABSTRACT FROM AUTHOR]