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The performance of MALDI-TOF MS combined with analysis platform for identification of toxin-producing Clostridiodes difficile is yet to be known. Between August 2018 and September 2020, 61 isolates from stool specimens of patients with C. difficile -associated diarrhea were analyzed using the MALDI Biotyper system. A C. difficile toxin-producer detection model was developed using ClinProTools. The model was validated using 28 known strains that differed from the isolates used to develop the model. The sensitivity and specificity of the Genetic Algorithm (GA) model using isolates grown on Brucella with hemin and vitamin K (BHK) agar plates were 91.7% and 44.4%, respectively. When isolates grown on cycloserine-cefoxitin mannitol agar were analyzed by the model, sensitivity and specificity were 6.3% and 100%, respectively. The GA model using BHK medium showed the highest discriminatory performance in detection of toxin-producing C. difficile. However, a discrepancy in detection of toxin-producing C. difficile was observed in the results generated when the model was being developed and when the model was validated which suggests that incubation conditions may have affected the results. MALDI-TOF analysis using ClinProTools has a potential to be a cost-effective tool for rapid diagnosis and contribute to antimicrobial stewardship by differentiating toxin-producing C. difficile from non-producers. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework's capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy transition is a key driver to combat climate change and achieve zero carbon future. Sustainable and cost-effective hydrogen production will provide valuable addition to the renewable energy mix and help minimize greenhouse gas emissions. This study investigates the performance of in-situ hydrogen production (IHP) process, using a full-field compositional model as a precursor to experimental validation The reservoir model was simulated as one geological unit with a single point uniform porosity value of 0.13 and a five-point connection type between cell to minimize computational cost. Twenty-one hydrogen forming reactions were modelled based on the reservoir fluid composition selected for this study. The thermodynamic and kinetic parameters for the reactions were obtained from published experiments due to the absence of experimental data specific to the reservoir. A total of fifty-four simulation runs were conducted using CMG STARS software for 5478 days and cumulative hydrogen produced for each run was recorded. Results generated were then used to build a proxy model using Box-Behnken design of experiment method and Support Vector Machine with RBF kernel. To ascertain accuracy of the proxy models, analysis of variance (ANOVA) was conducted on the variables. The average absolute percentage error between the proxy model and numerical simulation was calculated to be 10.82%. Optimization of the proxy model was performed using genetic algorithm to maximize cumulative hydrogen produced. Based on this optimized model, the influence of porosity, permeability, well location, injection rate, and injection pressure were studied. Key results from this study reveals that lower permeability and porosity reservoirs supports more hydrogen yield, injection pressure had a negligible effect on hydrogen yield, and increase in oxygen injection rate corelated strongly with hydrogen production until a threshold value beyond which hydrogen yield decreased. The framework developed in the study could be used as tool to assess candidate reservoirs for in-situ hydrogen production. [Display omitted] • Increase in oxygen injection rate leads to higher hydrogen yield until threshold. • Permeability has greater impact than porosity on hydrogen yield in an IHP process. • Lower permeability reservoirs favour more hydrogen production. • Injection pressure shows negligible effect on hydrogen yield during the IHP process. [ABSTRACT FROM AUTHOR]

A robust trajectory planning method for non-cooperative target rendezvous is investigated based on closed-loop uncertainty analysis. The proposed method addresses the issue that uncertainties affect the optimality of objective functions and the reliability of constraints. Considering multiple uncertainties, a stochastic optimal rendezvous model with a robustness performance index and chance constraints is presented with line-of-sight dynamics. The guidance model, angles-only navigation model and control model are derived, and uncertainty propagation equations for closed-loop GN & C system is proposed by using linear covariance analysis. The stochastic optimal rendezvous model is transformed into a robust optimal rendezvous model by using 3- σ of the random variables. The genetic algorithm is used to solve the robust trajectory planning problem, and a series of examples demonstrate that the robust maneuver solution can be significantly better than the solution corresponding to deterministic trajectory optimization problem. [ABSTRACT FROM AUTHOR]

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments. [ABSTRACT FROM AUTHOR]

[Display omitted] • The chemical structures of recovered fibers were similar to those of virgin fibers. • The recovered carbon fibers had more surface oxygen-rich functional groups. • The TSR value of the reCFRPs was mainly affected by the oxidation temperature. • Oxidation had a greater impact on the ISSR value of the reCFRPs than pyrolysis. • Both the TSR and ISSR values of reCFRPs exceeded 90% after the optimization. Carbon fiber-reinforced polymer composites (CFRPs) have gained widespread usage due to their promising physiochemical properties, while this causes large amounts of waste CFRPs worldwide. In this study, carbon fibers were successfully recovered from waste CFRPs through the pyrolysis-oxidation method, and the recovered fibers were reused in remanufacturing the secondary generation CFRPs. Moreover, the individual and interactive effects of pyrolysis-oxidation recovering parameters on the mechanical strength of the resulting remanufactured CFRPs (reCFRPs) were investigated. The recovered carbon fibers displayed surface chemical structures similar to virgin fibers but with high contents of oxygen-containing bonds. The tensile strength retention (TSR) of the reCFRPs was primarily influenced by oxidation temperature. Notably, a higher oxidation temperature, especially exceeding 560 °C, amplified the impact of oxidation duration on the TSR value. Similarly, concerning interlaminar shear strength retention (ISSR), the oxidation stage had a more substantial effect compared to the pyrolysis stage. As the oxidation temperature increased from 500 °C to 600 °C, the ISSR value initially increased and then decreased, irrespective of variations in pyrolysis parameters. Additionally, through integrating the response surface methodology (RSM) analysis and multi-island genetic algorithm (MIGA) global optimization, three recovery strategies, along with the corresponding processing parameters, were proposed to meet diverse requirements. The conclusions could provide valuable insights for optimizing the recovery and reuse of carbon fibers from waste CFRPs. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of satellite formation reconfiguration from the best passive safety parameters while ensuring the minimum fuel cost and desired formation configuration. Based on the relative eccentricity and inclination vectors, a hybrid method is proposed that combines the analytical solution and the genetic algorithm optimization method. It transforms the fuel-optimal conditions into the constraints of optimization model, which theoretically ensures the optimal fuel cost. The proposed hybrid approach is applicable to satellite formation reconfiguration problems with variable relative eccentricity and inclination vectors and passive safety concerns. Compared with the optimal three-impulse strategy and the analytical method in two different reconfiguration scenarios, the proposed method is proven to be more general and has advantages in passive safety performance. [ABSTRACT FROM AUTHOR]

• This algorithm attains attitude inversion with less data, confirming accuracy and noise resilience. The attitude of space debris holds significant implications for active debris removal and collision warnings. This study constructs a comprehensive photometry dataset for the target, utilizing angle transformation relationships and drawing upon the CZ-4C photometric data tested in the laboratory. The photometry in the dataset with noise and translation simulates the telescope observed one. We use the genetic algorithm to search for attitude parameters of the photometry that matches the simulated photometry in the dataset under the constraint of attitude angle rules. The derived and preset values of attitude parameters exhibit a strong alignment, applicable to light curves with an amplitude of about 2.5 magnitudes and noise levels up to 1.0 magnitudes. This suggests that the algorithm's accuracy is validated against the preset values, demonstrating its robust resistance to noise. Furthermore, the algorithm proposed in this study for deriving attitude from photometry requires a small amount of photometry data. This study provides a promising algorithm for future attitude analyses from telescope-observed photometry. [ABSTRACT FROM AUTHOR]

The arrangement of charging infrastructure has become an urgent issue as electric logistics vehicles (ELVs) transition from urban areas to intercity operations. In this study, we established a multi-stage layout model for intercity charging and swapping infrastructure (CSI) based on the travel characteristics of electric logistics vehicles, to minimize the investment cost of operators and the time cost of vehicle users in Hubei Province, China. Furthermore, the effect of "random", "conservative", and "radical" layout strategies for the infrastructure deployment was analyzed based on an improved genetic algorithm. It is found that the random layout strategy leads to a balance between infrastructure operators and users with the lowest total cost, which is 81.2% and 88.8% of the costs based on the radical strategy and the conservative strategy, respectively. The conservative strategy could reduce investment risks for operators with the lowest investment cost, while the radical strategy prioritizes the convenience of electric logistics vehicles, with the lowest user time cost. The case study suggests that choosing conservative strategies can bring benefits when the budget is limited, while radical strategies have more advantages when the budget is abundant. Random strategies bring the best overall social benefits. The configuration of CSI and travel characteristics of ELVs play important roles in determining layout strategies. • Travel characteristics of electric logistics vehicles is explored. • Multi-stage layout model for intercity charging and swapping infrastructure. • To minimize the investment cost of operators and the time cost of vehicle users. • The effect of random, conservative, and radical strategies is analyzed. • Results show that each strategy has its specific application scenarios. [ABSTRACT FROM AUTHOR]

• Interstellar exploration is defined as bi-objective multi-vehicle problem with profits. • Mission return increases approximately linearly with mission duration. • Lower probe numbers enable more efficient routing through shorter transfers. In the past decade, the discovery of exoplanets has sparked new interests in the idea of interstellar travel and exploration. Despite various proposals for probe concepts and relevant technologies, there is a lack of extensive literature on viable exploration strategies for journeys beyond a single star system. Such exploration strategies might not only have implications on technology development strategies for achieving interstellar exploration but could also enrich existing models for galactic exploration feeding into solutions of the Fermi Paradox. This article presents optimal strategies for the exploration of a large number of near-by stars, using a dedicated, novel methodology, which sets it apart from existing literature: For the first time, the mission design problem of interstellar exploration is redefined as a bi-objective multi-vehicle open routing problem with profits. It is tackled by an adapted hybrid multi-objective genetic algorithm, which is further improved and modified according to the problem characteristics (e. g. large search space). The overall mission model assumes probes traveling on straight trajectories, utilizing flybys, and maintaining an average velocity of 10% of the speed of light. Surpassing prior research that typically relies on statistical models or restricted star data, the star models are founded on the second Gaia data release (Gaia DR2), which represents the most extensive star catalogue to the date of this study and is employed for the first time in the context of interstellar exploration. The resulting star model contains a maximum of 10,000 stars within a spherical region around Sol, covering a distance of 110 light years. It is found, that the number of explored stars J 1 scales with mission duration J 2 and probe number m according to J 1 ∼ J 2 m 0.66 , which provides an initial guidance for future interstellar mission design. Furthermore, the routes and selection of stars vary depending on the number of probes used: When conducting missions with a large number of probes, stars in close proximity to the Solar System are given more focus. On the other hand, missions with a small number of probes include more distant stars to facilitate shorter transfers along the route. Based on these findings, the following recommendations for interstellar exploration strategies can be drawn: When energy resources such as fuel reserves are scarce and the exploration mission is not limited to nearby stars, low probe numbers are more efficient. In contrast, high probe numbers enable faster exploration of nearby stars but involve less resource-efficient transfers, making them a suitable option for small, remotely propelled probe concepts. To address crowding effects in high probe number missions, swarm-based probe concepts are recommended based on the scaling law characteristics derived. [ABSTRACT FROM AUTHOR]

• A novel method was proposed for accurate constitutive description and workability characterization of Mg alloy during hot deformation. • Combining the AT model with PSO-ANN can significantly improve its prediction accuracy and enhance the generalization ability when describing flow behavior. • The activation energy-processing map established using the PSO-ANN-AT model can exhibit superior predictive capability for microstructure evolution. Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation. [ABSTRACT FROM AUTHOR]

The manufacturing process is the last opportunity to build an ideal design reliability index into a product. With the advancement of intelligent manufacturing technology, the concept of quality evolves from conformance to fitness for use, which emphasizes that reliability should be built into product with quality control. To effectively implement reliability assurance in the manufacturing process, it is necessary to accurately identify the vital few characteristics that are critical to reliability. Thus, a heuristic key reliability characteristic (KRC) analysis in manufacturing model fusing big quality data is proposed. First, on the basis of the fusion big quality data in manufacturing-by-manufacturing system Reliability-operational process Quality- output product Reliability (RQR) chain, a data driven KRC analysis model is proposed, and a reliability proactive control framework in manufacturing driven by KRC is expounded. Second, considering mass quality and reliability data, an effective KRC identification method based on data mining using multi-objectives genetic algorithm (MOGA) is established. Third, considering manufacturing data and product failure risk, an extended risk priority number (RPN) for KRC ranking is proposed. Finally, an example of an insulating base of subway locomotive is provided to verify the proposed approach. • A novel KRC conceptual model is proposed. • A heuristic KRC identification approach based on MOGA is presented. • A KRC ranking approach based on extended RPN is proposed. • The proposed method is illustrated using an insulating base of subway locomotive example. [ABSTRACT FROM AUTHOR]

For an operational satellite in orbit, potential risks may arise when an unknown target approaches. Therefore, it is essential for the satellite to infer the intentions of the target in space. To achieve this objective, this paper proposes a method of sequential intention inference based on meta-fuzzy decision tree. The proposed method initially defines three situational functions: flyaround, collision, and observation. These functions help organize the inherent characteristics found in the trace and relative attitude of the target. Subsequently, by combining fuzzy decision tree (FDT), spatio-temporal modeling based on Long Short-Term Memory (LSTM), and task migration using meta-learning techniques, we can realize sequential intention inference for the target while ensuring quick adaptation to new tasks. Additionally, our proposed method takes into account the impact of fuzzy elements within FDT. Through quantum genetic algorithm (QGA) optimization, these fuzzy elements can be fine-tuned to enhance intention inference accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a mission concept designed for satellite-to-cell connectivity through a fractionated CubeSat approach, aiming to fulfill the objectives set by the BlueWalker-3 specifications. The envisioned system consists of 41 CubeSats, each adopting a 16U form factor, to provide dedicated coverage across specified sub-regions, as a reasonable solution. The pre-Phase-A design capitalizes on commercially available components with proven flight heritage, ensuring reliability and cost-efficiency. A genetic algorithm (GA)-based method has been introduced to tackle the complex challenge of designing a large antenna's folding pattern, with an analysis on how this design influences the overall system cost. The study conducts an in-depth analysis of system architecture and operational scenarios. Employing Monte-Carlo simulations, it assesses the Life Cycle Cost (LCC) and the system's resilience to failures, comparing it with similar configurations differing in CubeSat size and number. The results demonstrate that a fractionated approach significantly enhances the mission's scalability and robustness, providing substantial operational flexibility. The comparison highlights a cost-effective configuration within fractionated systems, emphasizing the proposed system's potential advantages. Furthermore, the paper addresses the environmental implications of deploying a fractionated spacecraft constellation, especially concerning astronomical studies. It underscores the CubeSats' minimized brightness and their reduced potential to interfere with telescopic observations, positioning the system as a more compatible option with nighttime sky studies. This consideration is particularly pertinent amidst increasing concerns over the impact of satellite constellations on astronomical research and night sky quality. • Introduction of a novel mission concept employing fractionated spacecraft for satellite-to-cell connectivity, showcasing an innovative design utilizing 41 CubeSats. • Comparative analysis demonstrating the proposed system's performance and cost advantages over the BlueWalker-3 mission, with findings supported by Monte-Carlo simulations. • Detailed discussion on the use of commercially available off-the-shelf components, highlighting the system's feasibility and reliability. • Exploration of the fractionated system's scalability and robustness, revealing potential for enhanced mission flexibility and economic efficiency. • Addressing the impact on astronomical observations, presenting a significant reduction in brightness and interference, enhancing compatibility with observational studies. [ABSTRACT FROM AUTHOR]

Miniature scroll compressors (MSCs) are recognized for their superior characteristics—compact design, silent operation and energy-saving performance. One of its main challenges is to maintain dynamic sealing between the fixed and orbiting scrolls during the operation. This paper adopts a passive magnet mechanism (PMM) to balance the axial gas force in MSCs. The PMM with an integral magnet configuration (IMC) is investigated first, and the dimensions of the magnet and fixed ring are found as the paramount factors to determine the magnitude of axial magnetic force. However, the IMC is unable to follow the unsteady characteristics of the target axial gas force, so the scrolls will experience a large period of over-sealing during its operation. To overcome this limitation, an innovative modular magnet configuration (MMC) is proposed and investigated. The height of each magnet segment is treated as individual design variable, and genetic algorithm optimization is carried out to seek for the best force balance performance according to two indices—maximum force difference (MFD) and integral-average force difference (IAFD). With precise design of each magnet segment, the MMC is able to provide unsteady magnetic force and thus better balance performance. Compared with the IMC, the improvements are up to 52 % reduction of MFD and 66 % reduction of IAFD. The derived superior MMC designs reveal a common characteristic that the "N" and "E" magnets have short segments at their outer fringe and the "S" and "W" magnets have short segments at their inner fringe. The axial magnetic force characteristics of the IMC and MMC are validated by prototype experiments. [ABSTRACT FROM AUTHOR]

With the increase of the complexity of space environment and the improvement of satellite maneuverability, the satellite maneuver game has become the focus of space research. In view of the need for pre-observation during debris removal or on-orbit maintenance missions, a scenario of observation and counter-observation similar to the pursuit-evasion game is proposed. In this paper, the impulsive observation and counter-observation game (OCOG) of two satellites is comprehensively investigated using a multi-objective optimization method. A sequential decision impulsive maneuver model is constructed and the impulsive OCOG is transformed into a bilateral optimization problem in terms of multiple constraints such as maneuverability, illumination angle, and fuel consumption, etc. To determine the optimal impulsive maneuvers for both satellites, the ring topology multi-objective particle swarm optimization (RTMOPSO) is designed. This novel algorithm uses adjacent particles to form multiple small populations to make the population converge in multiple directions during the optimization process, avoiding the single convergence of the population. Compared with non-dominated sorting genetic algorithm II (NSGA-II) and self-organized speciation based multi-objective particle swarm optimization (SSMOPSO) in the OCOG scenario, proposed method always has superiority in the game. Additional simulation is conducted and shows that the proposed algorithm is also applied for the case of non-maneuvering satellites. This research provides an effective maneuver strategy for future space game. [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]

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

Machine tool (MT) thermal errors induced by external and internal heat sources (e.g. changing environment, friction and power losses in MT components) are an important element in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors on account of their economic and ecological aspects. Many slightly different approaches are described in the literature. If thermal error models are properly identified based on experiments with sufficiently varying input parameters, they usually work within similar calibration and verification conditions. As the sensory equipment of machines increases, models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into model structures. However, adaptive functionalities require discrete interruptions of the MT's work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. In this research, a novel approach of automatic post-process adaptation suitable for repetitive manufacturing, which takes advantage of transfer functions (TF), is proposed for thermal error reduction. The method respects basic heat transfer mechanisms in the MT structure by combining linear parametric models, requires a minimum of additional gauges, and feedback from direct measurements is obtained only during technological pauses in the production process. Post-process adaptation is solved retrospectively by updating model parameters using the heuristic method of genetic algorithms (GA). First, the approach was tuned on a case study of a heavy-duty milling machine with a horizontal headstock for a configuration with an exchangeable spindle head and a configuration with a change in the position of the headstock in the workspace, both of which were different from the initial model's calibration range. Subsequently, the developed approach was applied to repeated production on a medium-sized milling centre. Another goal of the paper is to emphasize the need for quality input information for modelling efforts and the industrial applicability of scientific results. • An indirect compensation model of machine tool (MT) thermal errors distinguishing between the heating and cooling phases is developed. The model includes the influence of ambient temperature change. • Granular structure of transfer-function-based model enables possibility of modifying the magnitude of the approximation value and of adapting the time constant of the simulation to the approximated phenomenon. • The thermal error compensation strategy based on post-process updating of model parameters is demonstrated on heavy-duty milling machine and applied on medium-size milling centre. • Spatial thermal errors, MT equipment modularity and process liquid presence are considered in post-process modelling effort. • The necessity of quality and correct input signals to the compensation model is emphasized. • Only the measured residual deformations in technological pauses are used to update the parameters of the model for repeated production. [ABSTRACT FROM AUTHOR]

SiC p /Al composites are suitable for satisfying the lightweight requirements of terahertz wave-reflecting components. In this paper, diamond-turned surfaces of 60 % high volume fraction SiC p /Al composites are characterized in terms of surface roughness and terahertz reflectivity. The finished surfaces of SiC p /Al composites are obtained using single-point diamond turning with different process parameters, and surface roughness measurements as well as SEM and EDS analyses are conducted on the surfaces. The theoretical relationship between surface roughness and terahertz reflectivity is analyzed, and terahertz reflectivity testing experiments are conducted and validated. The surface roughness evaluation system and prediction model for SiC p /Al composites with a volume fraction of 60 % are established, and the errors between the experimental results and the predicted results are verified within 10 % by random experiments. Finally, the turning process parameters are optimized using a genetic algorithm, and the optimized turning process parameters are obtained as the spindle speed of 750 r/min, the feeding speed of 1 mm/min and the turning depth of 6 μm. • Spindle speed and feeding speed have a significant impact on surface roughness. • The relationship between roughness and reflectivity was analyzed and experimented. • Roughness prediction model is established, and the model error is verified. • A genetic algorithm is adopted to optimize the turning process parameters. [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]

• 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]

Hydraulic turbine control system is a complex system with strong nonlinearity and multiple variables. Therefore, in order to better control the turbine system, it is necessary to obtain the parameters of key components. Aiming at the limitation of the traditional particle swarm optimization algorithm in global search ability, mutation operator and dynamic inertia weight coefficient are introduced to enhance the search ability of the algorithm. In addition, in order to further improve the global search performance of the algorithm, this paper combines the optimized particle swarm optimization algorithm with genetic algorithm to form a hybrid parameter identification algorithm. The hybrid algorithm not only uses the fast convergence of particle swarm optimization (PSO), but also uses the global search advantage of genetic algorithm (GA) to realize efficient and accurate identification of turbine torque and load parameters. Through MATLAB2021a/Simulink simulation experiments, the application effect of the algorithm in the identification of turbine torque and generator load parameters is verified. The simulation results show that the optimized particle swarm optimization algorithm has significant advantages in the accuracy and robustness of parameter identification, and the identified parameters have a high degree of fitting with the actual measured torque and speed. This study not only provides a new optimization strategy for the parameter identification of turbine regulation system, but also provides an effective intelligent algorithm solution for the parameter identification of nonlinear system. [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]

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]

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]

The molds industry, being a highly competitive sector, is constantly driven by organizations looking to optimize their business processes to gain a competitive edge. To access advanced management tools, there has been a strong push for integrating Industry 4.0 technologies, which in turn has resulted in a significant increase in the amount of manufacturing process data, enabling more accurate and real-time analysis of processes. However, challenges persist due to the unpredictable and ad-hoc nature of this industry's business processes. As a result, effective resource management has become a crucial aspect that organizations are actively addressing. In this study, our objective is to leverage process mining algorithms and complement them with a genetic algorithm to generate efficient scheduling solutions to produce mold parts on factory floors. By analyzing existing data and identifying the workflows through process mining, we seek to gain a more realistic perspective on the mold manufacturing business process. Through this analysis, our aim is to uncover meaningful workflows and sequences for producing mold parts, ensuring that critical activities are appropriately incorporated while minimizing unnecessary repetitions. By identifying these purposeful sequences, we can provide the factory floor with schedules that optimize resource usage and enhance the overall efficiency of mold manufacturing processes. [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]

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]

• 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]

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]

The last-mile delivery challenges and complexity are increasing simultaneously with the e-commerce growth, the e-commerce growth constitutes a threat to urban transport, especially for big cities and developing countries. This paper examines the economic impact of crowd-shipping based on public transport collaborated with the automated parcel locker and the impact of traditional delivery, as two different methods of last-mile delivery under the same circumstances. This paper aims to assess crowd-shipping as an alternative solution for last-mile delivery compared to traditional delivery. Data has been simulated based on Cairo reality, one of the largest cities in the world. The mathematical models were formulated for the problem, where the first model objective function minimizes the costs of vehicle possession and vehicle kilometers traveled, while the second model objective function minimizes the costs of establishing lockers, vehicle possession, and vehicle kilometers traveled. A genetic algorithm has been used due to the complexity of the problem. The results show that the economic impact of the crowd-shipping delivery system is better than the traditional delivery system under the conditions of large-sized and medium-sized demand, while the economic impact of traditional delivery is better than the crowd-shipping delivery system under the conditions of small-sized 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]

[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]

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]

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]

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]

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]

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]

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 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]

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]

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]