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A robust static model, which incorporates emerging steelmaking scenarios in terms of solid charge mix with the given hot metal in basic oxygen furnace process, is developed. It employs mass and enthalpy balances to comprehend nonequilibrium conditions, considering four key empirical parameters: iron loss, post‐combustion ratio, heat loss, and undissolved lime content in slag, which are fine‐tuned using plant data through a multivariate approach, ensuring the reliability. The model is validated in a basic oxygen furnace (BOF) shop using data from over 4000 heats, achieving a strike rate of ≈77% for input lime prediction within ±1 ton and ≈80% for input oxygen prediction within ±600 Nm3. Model implementation in BOF shop provides valuable guidance to the operators, resulting in the reduction of average oxygen and lime consumption by 139 Nm3 and 652 kg heat−1, respectively. The model also enables the determination of the maximum scrap utilization of ≈16% for 0.8% silicon and ≈14% for 0.6% silicon in hot metal, respectively. The model aids in calculating the maximum tap temperature for varying hot metal silicon and iron ore addition. Overall, the model optimizes primary steelmaking, enhancing efficiency, reducing resource consumption, and offering insights into alternative iron sources like direct reduced iron. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick's Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick's law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm's performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO's effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA. [ABSTRACT FROM AUTHOR]

The seawater desalination and wastewater treatment by the interfacial solar steam generation (ISSG) technique is considered as a green, economical, sustainable, low‐energy‐consumption, and potential fresh water production strategy to solve the water shortage and energy crisis. Recently, efficient biomass‐based ISSGs (BISSGs) have been widely reported due to its efficient photothermal conversion efficiency and good hydrophilic performance. The BISSGs with efficient solar absorption and water transmission performance by design and optimization their various forms and structures and related photothermal properties is also significantly great for its scale application. This review highlights recent advancements in 3D BISSG systems, with a focus on the design and optimization of photothermal conversion materials and substrate materials. Then, the review also discusses the potential of biomass materials in BISSG applications, aiming to provide a theoretical basis for developing cost‐effective, efficient, and sustainable water purification technologies. Finally, the challenges and development prospects of the BISSG system in basic research and practical application will provide theoretical guidance for the further development of this technology. [ABSTRACT FROM AUTHOR]

Aerodynamic stealth design is a key technology for aircraft to realize efficient combat in high-risk environments，and it has been a hot technology researched by aviation powers for a long time. This paper combines the group's many years of experience in aerodynamic stealth design and the demand for aerodynamic stealth design in recent years，summarizes and analyzes the development history and current research status of the aerodynamic stealth design of overall aerodynamic layout，the aerodynamic stealth design of wing and the aerodynamic stealth design of air intake and exhaust system. According to the current research status，three key technologies for comprehensive and optimal aerodynamic stealth design are sorted out：efficient parameterization technology for complex shape， aerodynamic stealth refinement design technology，and integrated aerodynamic stealth design technology for internal and external flow. Finally，combined with the design requirements of aerodynamic stealth for future aircraft， four key research directions are expected：aerodynamic electromagnetic infrared integrated stealth design，aerodynamic stealth design considering coating，aerodynamic/stealth structure integrated design，and active flow control stealth integrated design.

The exploration of Stochastic Gradient Descent (SGD) and its variants within the context of multinomial logistic models on large datasets represents a rich area of research. The existing literature underscores the strengths and weaknesses of various optimization techniques, paving the way for further investigations into their performance across diverse data environments. This article seeks to contribute to this ongoing discourse by systematically assessing the performance of these optimizers through simulations and real-world applications. By conducting simulations, the research will generate data-driven insights that can guide practitioners in selecting the most effective optimization methods for their specific applications. We explore how different SGD variants, including, Stochastic Gradient Descent (SGD), Stochastic Gradient Descent Momentum (SGDM), Adaptive Momentum (Adam), and Decaying Momentum Stochastic Gradient Descent Momentum (DemonSGDM), affect the convergence speed, accuracy, and ROC-AUC value as a generalization performance of the model on large simulated datasets. We compare their performance based on maximum likelihood methods for parameter estimations. The simulation framework allows us to control data characteristics and model parameters, allowing a systematic evaluation of the behavior of each SGD variant. Our findings can provide valuable insights into selecting the optimal SGD variant for modeling multinomial logistic models on large datasets through simulation. The simulation results show that both SGDM and DemonSGDM are more efficient and stable than SGD and Adam in terms of the number of epochs. The findings reveal that all optimizers can converge, but their effectiveness differs across datasets and complexities. SGDM and DemonSGDM perform well in simulations, while Adam has a slight advantage in challenging real-world scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] In recent years, research on organic inorganic hybrid halide perovskite solar cells (PSCs) has surged, achieving a power conversion efficiency over 26%. Despite this progress, significant challenges hinder their commercial viability, and limited stability in typical environmental conditions, and the lack of scalable manufacturing technology for perovskite film production. This review explores various deposition techniques used in large-scale fabrication of perovskite thin-films, including electrospray inkjet, gravure printing, blade coating, slot die, spray coating, inkjet printing, and chemical vapor deposition etc. These techniques are adaptable to both sheet-to-sheet (S2S) and roll-to-roll (R2R) applications, facilitating high-volume production of large-area thin films. The work clarifies the key parameters influencing perovskite film morphology in each deposition approach and concludes with insights into promising engineering advancements for future iterations aimed at enhancing perovskite solar cell technology. [ABSTRACT FROM AUTHOR]

Abstract IFOS3D is a three‐dimensional elastic full‐waveform inversion (EFWI) tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. However, due to the significant computational costs associated with 3D EFWI, leveraging the computing power of a supercomputer for implementation is a logical choice. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer, which is a powerful system globally. These optimizations encompass a process‐level communication overlapping strategy, thread‐level data partitioning and layout approaches, a remote memory access optimized master‐slave communication scheme, and a thread‐level data reuse and overlapping strategy. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59× and an overall program speedup of about 14×. Our findings demonstrate the ability of our proposed optimization strategies to overcome the computational challenges associated with 3D EFWI, providing a promising framework for future advancements in the field of subsurface imaging.

Abstract In recent years, penetration testing (pen‐testing) has emerged as a crucial process for evaluating the security level of network infrastructures by simulating real‐world cyber‐attacks. Automating pen‐testing through reinforcement learning (RL) facilitates more frequent assessments, minimizes human effort, and enhances scalability. However, real‐world pen‐testing tasks often involve incomplete knowledge of the target network system. Effectively managing the intrinsic uncertainties via partially observable Markov decision processes (POMDPs) constitutes a persistent challenge within the realm of pen‐testing. Furthermore, RL agents are compelled to formulate intricate strategies to contend with the challenges posed by partially observable environments, thereby engendering augmented computational and temporal expenditures. To address these issues, this study introduces EPPTA (efficient POMDP‐driven penetration testing agent), an agent built on an asynchronous RL framework, designed for conducting pen‐testing tasks within partially observable environments. We incorporate an implicit belief module in EPPTA, grounded on the belief update formula of the traditional POMDP model, which represents the agent's probabilistic estimation of the current environment state. Furthermore, by integrating the algorithm with the high‐performance RL framework, sample factory, EPPTA significantly reduces convergence time compared to existing pen‐testing methods, resulting in an approximately 20‐fold acceleration. Empirical results across various pen‐testing scenarios validate EPPTA's superior task reward performance and enhanced scalability, providing substantial support for efficient and advanced evaluation of network infrastructure security.

Computational superalloy development is a powerful alternative to the conventional experimental approach. Based on thermodynamic databases and the CALPHAD method, it is possible to estimate the properties of a large number of potential alloys and select the most promising ones. However, the accuracy of the databases and complementary property models can be unsatisfying. The accuracy of two mass density and γ/γ′$\gamma / \left(\gamma\right)^{&aposx;}$ lattice parameter models and the TTNI8 and TCNI10 databases is analyzed in detail on the experimental basis of computationally optimized single‐crystalline superalloys. Various properties are measured and compared to the results of the property models and databases. Neither of the databases is superior to the other and especially the γ′$\left(\gamma\right)^{&aposx;}$ solvus temperature is not accurately described in both. The new mass density model, a linear regression based on the molar mass, is more reliable for low‐density alloys. Both lattice parameter model versions slightly overestimate the room‐temperature γ$$ \gamma $$ lattice parameter. The γ′$\left(\gamma\right)^{&aposx;}$ lattice parameter, however, is more accurately calculated using the new model version. The results of this study can be readily used to improve a multicriteria alloy optimization tool for computational superalloy design. [ABSTRACT FROM AUTHOR]

One possible approach to improve the fast charging performance of lithium‐ion batteries (LIBs) is to create diffusion channels in the electrode coating. Laser ablation is an established method for creating such structures and improving the performance of conventional LIBs. However, this method has not yet been used in industrial battery production due to different reasons. The drawbacks of this method are thermal stress, loss of active material, and discontinuous process flow. Herein, a novel concept is presented that enables the structuring of electrodes by selectively introducing a secondary fluid into the wet coating of the electrode. This displaces the active material prior to drying, resulting in no loss of active material and maintaining mechanical integrity. This innovative process is being tested for graphite anodes on a lab scale. Initial results show that the process can create pores that are comparable to laser‐structured electrodes. There is no visible damage to the electrode and no decrease in mechanical strength due to the structuring is observed. By varying the process parameters, different pore geometries can be generated with continuous process control, which are visually measured in this work. There is also a noticeable increase in performance when fast‐charging these electrode anodes. [ABSTRACT FROM AUTHOR]

To satisfy the requirements of five-axis processing quality, this article improves and optimizes the machine tool structure design to produce improved dynamic characteristics. This study focuses on the investigation of five-axis machine tools' static and dynamic stiffness as well as structural integrity. We also include performance optimization and experimental verification. We use the finite element approach as a structural analysis tool to evaluate and compare the individual parts of the machine created in this study, primarily the saddle, slide table, column, spindle head, and worktable. We discuss the precision of the machine tool model and relative space distortion at each location. To meet the requirements of the actual machine, we optimize the structure of the five-axis machine tool based on the parameters and boundary conditions of each component. The machine's weight was 15% less than in the original design model, the material it was subjected to was not strained, and the area of the structure where the force was considerably deformed was strengthened. We evaluate the AM machine's impact resistance to compare the vibrational deformation observed in real time with the analytical findings. During modal analysis, all the order of frequencies were determined to be 97.5, 110.4, 115.6, and 129.6 Hz. The modal test yielded the following orders of frequencies: 104, 118, 125, and 133 Hz. Based on the analytical results, the top three order error percentages are +6.6%, +6.8%, +8.1%, and +2.6%. In ME'scope, the findings of the modal test were compared with the modal assurance criteria (MAC) analysis. According to the static stiffness analysis's findings, the main shaft and screw have quite substantial major deformations, with a maximum deformation of 33.2 µm. Force flow explore provides the relative deformation amount of 26.98 µm from the rotating base (C) to the tool base, when a force of 1000 N is applied in the X-axis direction, which is more than other relative deformation amounts. We also performed cutting transient analysis, cutting spectrum analysis, steady-state thermal analysis, and analysis of different locations of the machine tool. All of these improvements may effectively increase the stiffness of the machine structure as well improve the machine's dynamic characteristics and increases its machining accuracy. The topology optimization method checks how the saddle affects the machine's stability and accuracy. This research will boost smart manufacturing in the machine tool sector, leading to notable advantages and technical innovations. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of an efficient multiparametric recommender engine for low-power applications with incremental post-processor optimizations. The need for this work arises from the increasing demand for energy-efficient recommender systems that can provide accurate recommendations with low energy consumption and delay levels. To address this need, we propose a novel approach that combines the use of Fourier, Cosine, LSTM, GRU, Wavelet, and Gabor features for the representation of collected network datasets& samples. These features are processed using low-power recurrent neural networks (LP RNN) to reduce energy consumption and delay levels. To further improve the accuracy and efficiency of the recommender engine, we apply an augmented post-evaluation Q-learning process. Our proposed method is compared with existing models, and we demonstrate its superior performance in terms of accuracy, precision, recall, energy consumption, and delay levels. Experimental results on multiple datasets and samples show that our proposed approach achieves an accuracy of 98.9%, precision of 97.5%, and recall of 98.3% while consuming less energy and incurring lower delay compared to existing models. Overall, our proposed multiparametric recommender engine provides an efficient and effective solution for low-power applications that require accurate recommendations for data patterns. [ABSTRACT FROM AUTHOR]

Search/optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search/optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements: answer set programming or MaxSAT or min-one, to name a few. These paradigms vary significantly in their languages and in the ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called weight systems that eliminates syntactic distinctions between paradigms and allows us to see essential similarities and differences between optimization statements provided by paradigms. This unifying outlook has significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation. It also supplies researchers with a convenient tool for proving the formal properties of distinct frameworks; bridging these frameworks; and facilitating the development of translational solvers. [ABSTRACT FROM AUTHOR]

In this paper, the optimizations of multi-hop cooperative molecular communication (CMC) system in cylindrical anomalous-diffusive channel in three-dimensional enviroment are investigated. First, we derive the performance of bit error probability (BEP) of CMC system under decode-and-forward relay strategy. Then for achieving minimum average BEP, the optimization variables are detection thresholds at cooperative nodes and destination node, and the corresponding optimization problem is formulated. Furthermore, we use conjugate gradient (CG) algorithm to solve this optimization problem to search optimal detection thresholds. The numerical results show the optimal detection thresholds can be obtained by CG algorithm, which has good convergence behaviors with fewer iterations to achieve minimized average BEP compared with gradient decent algorithm and Bisection method which are used in molecular communication. [ABSTRACT FROM AUTHOR]

Herein, it is shown that machine learning (ML) methods can be used to predict the parameter that limits the solar‐cell performance most significantly, solely based on the current density–voltage (J–V) curve under illumination. The data (11'150 J–V curves) to train the model is based on device simulation, where 20 different physical parameters related to charge transport and recombination are varied individually. This approach allows to cover a wide range of effects that could occur when varying fabrication conditions or during degradation of a device. Using ML, the simulated J–V curves are classified for the changed parameter with accuracies above 80%, where Random Forests perform best. It turns out that the key parameters, short‐circuit current density, open‐circuit voltage, maximum power conversion efficiency, and fill factor are sufficient for accurate predictions. To show the practical relevance, the ML algorithms are then applied to reported devices, and the results are discussed from a physics perspective. It is demonstrated that if some specified conditions are met, satisfying results can be reached. The proposed workflow can be used to better understand a device's behavior, e.g., during degradation, or as a guideline to improve its performance without costly and time‐consuming lab‐based trial‐and‐error methods. [ABSTRACT FROM AUTHOR]

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs. [ABSTRACT FROM AUTHOR]

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

Evidently, paper-based microfluidic devices, including fuel cells, have been proven to power low-power integrated miniaturized devices. However, the harvested energy depends on various design parameters, positioning and other ancillary factors. Herein, design of experiment is used to make a boisterous study of the data used in paper-based microfluidic fuel cell and to make various optimizations and studies of the raw data used in the microbial fuel cell paper. The paper-based microfluidic fuel cell was analysed for two different positioning, horizontal and vertical, and the maximum power outputs were noted. A statistical technique based on full factorial design was used to study the performance of paper-based microfluidic fuel cell. In the microbial fuel cell, a rigorous study was conducted pertaining to the electrode separation, channel variation and absorbent pad stability. In both these cases, the analysis of mean, analysis of variance, signal-to-noise ratio and desirability study were performed. For the paper-based microfluidic fuel cell, the best desirability values for the horizontal and vertical arrangements were measured to be 0.8842 and 0.92768, respectively. For the microbial fuel cell, in the case of 2 mm electrode separation, the present analysis of variance model came out to be significant. Inclusively, this work provides a pathway to realize optimum paper-based microfluidic fuel cell, and such study can be extrapolated to develop other microfluidic devices. [ABSTRACT FROM AUTHOR]

Continuum robots stand out due to their high dexterity, which allows them to effectively navigate in confined spaces and dynamic environments. At present, motor‐controlled tendons represent the most prominent actuation method used in continuum robots which require large drive units, increasing the overall system size and weight. A potential alternative technology to overcome those limitations is represented by shape memory alloy (SMA) wire actuators, which are characterized by extremely high energy density and flexibility, leading to a reduction of the size, weight, and design complexity of continuum robots. The complex thermomechanical behavior of SMA wires, however, makes the design of SMA‐based applications a challenging task, and systematic approaches to design SMA‐driven continuum robots are poorly understood. To overcome this issue, this article presents a novel systematic methodology for designing SMA‐driven continuum robots capable of motion in a three‐dimensional environment. First, the kinematic relationship between SMA wires and continuum robot deformation as well as the required actuator force in quasi‐static conditions, is mathematically described based on the assumption of a constant curvature deformation. Subsequently, the model is validated by in‐plane experiments for different design parameters. Based on the results, a fully integrated, antagonistic SMA continuum robot is built and validated. [ABSTRACT FROM AUTHOR]

This study aims to optimize the process parameters of wire arc additive manufacturing for ER70S6 steel and to build a machine‐learning (ML) model to predict the properties of deposited specimens. Process parameters such as current, voltage, and travel speed are optimized considering other process parameters constant (gas flow rate, contact tip to the work distance, and preheat). The optimization is made using the response surface method and validated the properties by experimentation, including tensile testing and metallography. A support vector regression (SVR) ML model is implemented to predict the material's properties to substantiate the outcomes' values in every possible combination for the given parameters. In the study's findings, a significant enhancement is revealed in specimen quality, marked by reduced irregularities and porosity, and a remarkable increase in ultimate tensile strength up to 40%, validated through the SVR model. In this study, a valuable path that can be extended to predict properties of other material systems is sketched. [ABSTRACT FROM AUTHOR]

[Display omitted] Sodium-ion batteries (SIBs) have recently reemerged as a promising technology in the fields of large-scale energy storage systems and low-speed electric vehicles, owing to the abundance and even distribution of sodium resources. Moreover, the similarity in working principles between SIBs and lithium-ion batteries (LIBs) further accelerates their development. However, the development of SIBs still faces challenges, such as the limited availability of electrode materials that demonstrate both satisfactory cycling stability and high-rate performance. Typically, common electrodes utilize specific binders to integrate the active materials with conductive additives. Unfortunately, frequently used binders are often dielectric and mechanically unstable, leading to a decrease in specific capacity and poor cycling stability. In addition, strongly electronegative groups within binders can irreversibly capture Na+ ions, resulting in an increase in irreversible capacity. Therefore, the use of binder-free, free-standing electrodes is crucial for the development of high-performance SIBs due to their enhanced electronic conductivity and reversible electrochemical reactions. This review provides a comprehensive overview of the recent advancements in free-standing electrodes for SIBs and flexible SIBs. It examines the challenges specific to free-standing electrodes and flexible SIBs and proposes effective strategies to overcome these obstacles. By addressing these challenges, this review aims to stimulate significant progress in the development of flexible energy storage devices, fostering their extensive utilization across diverse applications. [ABSTRACT FROM AUTHOR]

A microgrid is a low‐voltage distribution network designed to provide power for small‐scale and isolated communities consisting of distributed generation and energy storage systems. In order to achieve reliable power and proper energy utilization, energy storage systems plays a vital role in microgrids by storing energy during off peak hours and discharges energy during peak hours. One of the major issues in the isolated microgrids with intermittent nature of distributed generations is the balance of energy demand. This can be achieved by appending renewable energy sources with suitable battery energy storage systems (BESS), to provide the reserve support in meeting the load demand. Battery degradation effect plays a major role in analyzing the performance of BESS lifetime. Battery degradation effect relates the capacity reduction of energy of BESS that is to be delivered to meet the load demand. Therefore, microgrid systems with BESS considering degradation effect should be optimized in such a way as to obtain minimum operating cost while ensuring minimum electricity cost to customers. Simulations of hourly battery discharges rates and simulation of actual discharge rates with obtained simulated rates need to be calculated to determine the degradation effect. The battery degradation cost and lifetime can be calculated correspondingly to minimize the objectives. Correspondingly, herein, an optimization algorithm for microgrid operating cost and customer electricity cost minimization for 24 h time horizon while considering BESS degradation effect by determining kWh and MWh throughput is presented. Particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), Jaya optimization (JAYA) technique, and linear programming interior point algorithm (LP‐IP) are applied to determine the optimal operating cost and electricity cost by simulating BESS degradation parameters. Results obtained using PSO, APSO, JAYA are compared with LP‐IP solver‐based algorithm for BESS lifetime, BESS degradation cost, microgrid operating cost, and customer electricity cost for 24 h. [ABSTRACT FROM AUTHOR]

С ростом популярности модульной парадигмы программирования количество косвенных переходов в продуцируемом коде значительно возросло. Аппаратные способы уменьшить задержки, связанные с такими переходами, требуют сложной логики непосредственно на кристалле, которую зачастую невыгодно реализовывать из-за дополнительного энергопотребления процессора. Существующие в компиляторе GCC-подходы преобразования косвенных переходов позволяют существенно увеличить производительность программ, однако без сбора статистики и перекомпиляции кода все еще остается большое количество непреобразованных переходов, которые приводят к уменьшению производительности программ. В этой статьи предлагается два метода повышения производительности, связанных с оптимизацией косвенных переходов. Статический метод позволяет расширить существующие оптимизационные возможности компилятора. Динамический метод является новым подходом подстановки целевых адресов переходов во время выполнения программы. Наше исследование показывает, что эти подходы способны увеличить производительность отдельных участков программ, содержащих косвенные переходы, до 2,3 раза. Применение статического метода позволило улучшить производительность отдельных тестов из пакета CPUBench на 15 % при увеличении затраченного на компиляцию времени не более чем на 1 %.

The editorial in Frontiers in Energy Research discusses the importance of data-driven approaches in improving the efficiency, reliability, and sustainability of smart grid systems (SGSs). Machine learning techniques, such as neural networks and deep learning, are highlighted for their role in accurate forecasting of electricity demand, renewable energy generation, and system loads. The editorial explores various research topics, including forecasting techniques, optimization in power systems, data quality, and research trends in energy systems, showcasing the potential of machine learning to enhance smart grid operations and support global energy sustainability. [Extracted from the article]

To satisfy the requirements of five-axis processing quality, this article improves and optimizes the machine tool structure design to produce improved dynamic characteristics. This study focuses on the investigation of five-axis machine tools’ static and dynamic stiffness as well as structural integrity. We also include performance optimization and experimental verification. We use the finite element approach as a structural analysis tool to evaluate and compare the individual parts of the machine created in this study, primarily the saddle, slide table, column, spindle head, and worktable. We discuss the precision of the machine tool model and relative space distortion at each location. To meet the requirements of the actual machine, we optimize the structure of the five-axis machine tool based on the parameters and boundary conditions of each component. The machine’s weight was 15% less than in the original design model, the material it was subjected to was not strained, and the area of the structure where the force was considerably deformed was strengthened. We evaluate the AM machine’s impact resistance to compare the vibrational deformation observed in real time with the analytical findings. During modal analysis, all the order of frequencies were determined to be 97.5, 110.4, 115.6, and 129.6 Hz. The modal test yielded the following orders of frequencies: 104, 118, 125, and 133 Hz. Based on the analytical results, the top three order error percentages are +6.6%, +6.8%, +8.1%, and +2.6%. In ME’scope, the findings of the modal test were compared with the modal assurance criteria (MAC) analysis. According to the static stiffness analysis’s findings, the main shaft and screw have quite substantial major deformations, with a maximum deformation of 33.2 µm. Force flow explore provides the relative deformation amount of 26.98 µm from the rotating base (C) to the tool base, when a force of 1000 N is applied in the X-axis direction, which is more than other relative deformation amounts. We also performed cutting transient analysis, cutting spectrum analysis, steady-state thermal analysis, and analysis of different locations of the machine tool. All of these improvements may effectively increase the stiffness of the machine structure as well improve the machine’s dynamic characteristics and increases its machining accuracy. The topology optimization method checks how the saddle affects the machine’s stability and accuracy. This research will boost smart manufacturing in the machine tool sector, leading to notable advantages and technical innovations.

Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review of the state-of-the art modeling and optimization methods for the power and propulsion systems of ships. Modeling is a tool to investigate the performance of actual systems by running simulations in the virtual world. There are two main approaches in modeling: physics-based and data-driven, which are both covered in detail in this survey paper. The output from the simulations might not be optimal in terms of certain performance criteria such as energy consumption, fuel cost etc. Hence, it is vital to optimize the systems considering the efficient interaction between the components, to yield the optimal performance for the integrated vessel’s powertrain. In this paper, the optimization case studies, for the ship energy systems, will be divided in terms of a) optimal design (topology and sizing), b) optimal control and energy management strategies, c) combined optimal design and control. Tables that summarize the literature review outcomes will also be presented at the end of each section. The main outcome is that limited literature is available for optimizations of ship powertrains using data-driven models, especially surrogate models. Surrogate-assisted optimizations for integrated ship energy systems can yield optimal solutions at fast computational speeds, with sufficient accuracy, even for complex, nested, multi-level, multi-objective optimizations.

Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development.

This research explores an extended method of fracture mechanics to determine the parameters of the Johnson Cook and GISSMO models. The primary objective of the optimization process and iterative finite element method (FEM) was to identify optimised modelling parameters suitable for specimens with different shapes to predict the failure behaviour of dual-phase steels (DP), specifically DP600 and DP800 steels. Numerous experimental tests were conducted on these DP steels, which mainly consist of ferrite and martensite phases. The specimens underwent deformation at three different tensile velocities. To determine the flow curves, a Simplified Johnson-Cook model (MAT_SJC_098) was employed, while the Johnson-Cook model (MAT_JC_015) was used to identify failure, and a combined JC-GISSMO model was used to determine damage. The numerical simulation results were then compared with the experimental results. In conclusion, all modelling methods used in this research yielded the desired results. [ABSTRACT FROM AUTHOR]

Increased use of renewable energy sources is expected to contribute to long‐term sustainability and robust economic growth. A better global economy and lower greenhouse gas emissions are possible due to intensive research into more effective energy production, storage, and utilization technologies. The exciting solution for advancing the development of green hydrogen production and the clean transportation sector is the conversion of electrical energy generated from wind parks into green hydrogen. Therefore, herein, a technoeconomic optimization of hydrogen production for refueling fuel cell vehicles by comparing two different renewable energy sources is presented. Results obtained in this article demonstrate that Riyadh possesses sufficient wind speed and sunlight, which can be used to produce renewable hydrogen. It is evident from the evaluated and optimized results that the selected wind‐based hybrid energy system has the lowest net present cost, levelized cost of energy, and levelized cost of hydrogen of $247,654.00, $/kWh 0.1720, and $/kg 4.23, respectively, as compared to the other system counterpart, making the selected optimized hybrid energy system the preferred choice to fulfill the electricity and hydrogen production demand of the refueling fuel cell vehicles. It is expected that the stakeholders through this study can encourage the production of hydrogen in Saudi Arabia. [ABSTRACT FROM AUTHOR]

In this paper, we first optimized the structures of Cl benzaldehydes using Gaussian 09 software with the B3LYP/631-G' (d,p) basis set. The title compound's polarizability and hyperpolarizabilities values have been computed, along with an examination of its nonlinear optical characteristics. The title molecule's total initial static hyperpolarizability as determined by DFT studies may be a topic for future NLO content that is appealing. [ABSTRACT FROM AUTHOR]

Computations on video cards and specialized accelerators are widely used to solve many important practical tasks. Developers working with OpenCL, SYCL, CM, and ISPC rely heavily on the quality of optimizations in graphics compilers. For the Intel GPU, the compiler has two parts: a scalar part that works in the SIMT model, and a vector part that targets SIMD languages. It is the vector part of the compiler that contributes the most when it comes to critical tasks such as training neural networks, solving systems of equations, rendering images, and so on. Unfortunately, until recently, the Intel graphics compiler architecture lacked the ability to properly decompose into vector registers, which led to particular performance problems in programs written in ISPC, such as Embree and OSPRay. To solve this problem, we propose a structure partitioning algorithm for the vector optimizer of the Intel graphics compiler. A detailed description of the algorithm and performance measurements are given, showing an increase of up to 80% on some tasks.

Control flow optimizations are of particular importance when optimizing programs for graphics accelerators and video cards. In addition to scalar control flow optimizations, which are widely known and well represented in modern compilers, there is also a current issue of vector control flow optimizations. On the one hand, vector control flow is natural for high-level languages such as ISPC and CM, where vector control constructs are part of the semantics of ordinary programs. On the other hand, vector primitives, including those for vector control flow, are present in modern graphics accelerators, such as Intel XE. Support in the hardware can significantly improve the performance of programs. In this case, the main problem is the lack of vector control structures in a stable intermediate representation. This paper proposes an intermediate scalar representation for vector control structures through explicit predicates and an algorithm for restoring the vector control flow from this representation in a graphical optimizer.

Drug candidates with poor solubility have been recognized as the cause of many drug development failures, owing to the fact that low solubility is unfavorable for physicochemical, pharmacokinetic (PK) and pharmacodynamic (PD) properties. Given the imperative role of solubility during drug development, we herein summarize various strategies for solubility optimizations from a medicinal chemistry perspective, including introduction of polar group, salt formation, structural simplification, disruption of molecular planarity and symmetry, optimizations on the solvent exposed region as well as prodrug design. In addition, methods for solubility assessment and prediction are reviewed. Besides, we have deeply discussed the strategies for solubility improvement. This paper is expected to be beneficial for the development of drug-like molecules with good solubility. [Display omitted] • Solubility is an important physicochemical property for drug development. • We summarize various strategies for solubility optimizations. • Methods for solubility assessment are reviewed. [ABSTRACT FROM AUTHOR]

It is a goal that manufacturing companies strive towards on a regular basis, and it involves enhancing the efficiency and productivity of maintenance operations. It is especially vital to avoid unforeseen breakdowns, which may result in costly charges and production losses if they do not occur in advance. While the execution of an acceptable management plan affects maintenance productivity, it also affects the adoption of proper procedures and tools to help in the assessment processes in this field. This difficulty, among other things, affects a company's capacity to achieve high performance with the equipment it employs, as well as the judgement process and the design of the firm's maintenance plan. In order to achieve this goal, the aim of this paper is to exemplify how intelligent systems can be used to enhance judgement techniques in the implementation of the lean maintenance perspective, allowing for an advancement in the functional capabilities of the industry's technological infrastructure. The reseachers employed artificial intelligence technologies to look for connections between specific operations carried out as part of the deployment of lean maintenance and the findings achieved. The raw set notion, which was used in this situation, was used to determine whether or not the lean maintenance method was being used in this study. The crisis management process carries with it some of the most complex data technology concerns ever encountered. It necessitates, among other items, active information gathering and information transfer efforts, that are used for a range of functions, such as decreasing uncertainty, attempting to measure and manage consequences, and attempting to manage resources in a way that goes beyond what is generally possible to deal with daily problems. It also needs the employment of artificial intelligence technology, among other things, to increase crisis awareness. [ABSTRACT FROM AUTHOR]

To mitigate the low frequency problem in a transmission system in an event of a power station failure or during low renewable generation production, UK National Grid (NG) Electricity System Operator has balancing mechanism in place with generators to provide temporary extra power, or with large energy users to reduce load demand or so call fast reserve services. This paper presents an alternative method to aggregately control the existing distribution network primary on load transformer tap changers as a voltage-led customer load active service. The main benefits of the proposed method are (i) to unlock the distribution network load demand flexibility without causing any negative impact on customers, and (ii) to provide the lowest cost of fast reserve service from a distribution network to transmission network. In this paper an optimal control strategy based on genetic algorithm is proposed and developed to achieve an optimized voltage-led customer load active service with the aim of finding the optimal dispatch of on load transformer tap changers by minimizing each transformer tap switching operation as well as network losses. Two practical 102 buses and 222 buses UK distribution networks have been modelled and used to demonstrate and compare the effectiveness of the proposed control methods under different operating conditions. The performances of the proposed method are also compared with both the rule-based and the branch-and-bound methods. The results show that the proposed optimal control strategy based on the genetic algorithm is more effective by achieving more accuracy and a faster solution for a large distribution network than other two methods. These are important findings as the fast reserve service by transmission network requires the accuracy of the load demand reduction delivery within 2 minutes.

Abstract Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer‐by‐layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi‐parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML‐based approach allowing to quickly identify optimized growth conditions for HKUST‐I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. In addition, an analysis of the results allows to gain further insights into the factors governing the growth of MOF thin films.

In this paper, we first optimized the structures of Cl benzaldehydes using Gaussian 09 software with the B3LYP/631-G’ (d,p) basis set. The title compound’s polarizability and hyperpolarizabilities values have been computed, along with an examination of its nonlinear optical characteristics. The title molecule’s total initial static hyperpolarizability as determined by DFT studies may be a topic for future NLO content that is appealing.

Stirling engines are efficient heat engines distinguished by their capacity to employ alternate fuel sources as a source of heat, such as solar energy, making them an important study issue. The goal of this research is to discover new ways and strategies for improving Stirling engine performance for future development. This article presents reviews a series of research studies on Stirling engine technology, focusing on the main factors and characteristics influencing engine performance, such as Stirling engine types and mechanical configuration, operating parameters, geometric parameters, and working fluid properties. It demonstrated its capacity to work in a varied range of temperatures, and many different types of fuels which can be used as a heat source for operation. Regarding Stirling engine types, the Gamma engine is the most efficient due to its capacity to produce high efficiency and power when compared to Alpha and Beta engines. Stirling engines require working fluids that have high thermal conductivity, high heat capacity, and low viscosity, helium and air are common working fluids used practically. Moreover, phase angle is an important geometric parameter affecting Stirling engine performance. The optimal value is theoretically around 90°. The findings obtained in this article describe the Stirling engine's potential. In conclusion, the mechanical and physical characteristics of the engine and the properties of the working fluids are the most important factors in the performance of Stirling engines. [ABSTRACT FROM AUTHOR]

Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs. [ABSTRACT FROM AUTHOR]