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Heterogeneous multicore system could provide high flexibility for applications through integrating different types of cores. As the number of cores increases, more and more transistors are integrated which could lead to the rise of chip temperature, thus have a negative impact on the system performance. At present, the typical method to tackle this problem is dynamic voltage and frequency scaling (DVFS) which reduces the voltage and frequency to achieve cooling. However, DVFS will make the heterogeneous multicore system unable to take full advantage of its performance. This paper proposes a dynamic mapping method called TspDM which maximizes the system performance target under the premise of satisfying the temperature safety power. TspDM exploits the runtime performance counters of running threads to dynamically adjust thread mapping. Specifically, a lightweight Artificial Neural Network (ANN) model is proposed to obtain the thread-to-core type mapping when a remapping process is activated. Subsequently, TspDM determines the specific core locations of threads according to the number of threads mapped to a certain core type and the thermal safety power computation. The experimental results show that the proposed method is 21% better than that of PCMig while ensuring the thermal safety of heterogeneous platform. In addition, TspDM outperforms recent performance constrained and power constrained literature in terms of system throughput. [ABSTRACT FROM AUTHOR]

Modern high-performance processor systems universally employ hardware prefetch engines to address the "memory wall" issue. Nonetheless, prefetchers are typically activated with the default configuration at system startup, and this fixed configuration does not always achieve the intended performance in the face of varied programs and may even degrade performance. As a result, it is crucial to investigate the prefetch configuration tuning method that adapts to different program characteristics in order to take full advantage of hardware prefetching. In this study, a hardware prefetching tuning method based on program phase behavior is proposed to determine the prefetch configuration that maximizes the overall predicted performance of the program through low-overhead online profiling. In the profiling process, the branch instruction vector sampled by the hardware performance counter is used to dynamically classify the program phase behavior, and the performance profiling is performed for each type of phase. Simultaneously, the recurring program phases are no longer profiled to reduce overhead. Following the profiling, the prefetch configuration with the best predicted performance is derived by combining the performance data from each phase and its running time proportion. The results of the tests on prefetch-sensitive programs in SPEC2006, NPB, and PARSEC demonstrate that the prefetch configuration obtained using the suggested method has a geometric average performance improvement of 7.34% over the default configuration and achieves 99.34% of the optimal configuration. Furthermore, the profiling run adds only 2.24% extra overhead as compared to the default configuration. [ABSTRACT FROM AUTHOR]

Researching and improving the low-temperature cold-start performance of PEMFC plays an important role in the promotion and application of PEMFC. Among them, auxiliary preheating is one of the key technologies to achieve fast cold start of PEMFC. In order to reveal the influence law of auxiliary preheating parameters on the cold start of PEMFC, the auxiliary preheating control strategy is proposed. In this paper, a mathematical model for PEMFC performance analysis is first established. On this basis, the influence law of preheating exhaust gas temperature and flow rate on PEMFC temperature rise process is studied. Based on the Screening optimization algorithm, the input and output parameters of the preheating process were optimized, and the optimal auxiliary preheating control parameters were determined under different starting temperatures. Finally, a start-up auxiliary preheating control strategy for different low-temperature environments is designed and performance verification is carried out. The results show that the auxiliary preheating control strategy proposed in this paper reduces the start-up time by 43% at -10 compared without the auxiliary preheating control. The auxiliary strategy designed in this paper is able to achieve normal startup of the PEMFC at -20 and -30. However, the PEMFC startup fails in both without the auxiliary modes. [ABSTRACT FROM AUTHOR]

In this research, SCAPS-1D simulation software (Version: 3.3.10) was employed to enhance the efficiency of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells. By fine-tuning essential parameters like the work function of the conductive glass, the back contact point, defect density, and the thickness of the light absorption layer, we effectively simulated the optimal performance of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells under identical conditions. The effects of different X-site elements on the overall performance of the device were also explored. The theoretical photoelectric conversion efficiency of the device gradually increases with the successive substitution of halogen elements (Cl, Br, I), reaching 6.09%, 17.02%, and 26.74%, respectively. This trend is primarily attributed to the increasing size of the halogen atoms, which leads to better light absorption and charge transport properties, with iodine (I) yielding the highest theoretical conversion efficiency. These findings suggest that optimizing the halogen element in CsSnX3 can significantly enhance device performance, providing valuable theoretical guidance for the development of high-efficiency all-inorganic perovskite solar cells. [ABSTRACT FROM AUTHOR]

We explore the impact of pumping beams with different transverse intensity profiles on the performance of the spin-exchange relaxation-free (SERF) atomic magnetometers (AMs). We conduct experiments comparing the traditional Gaussian optically-pumped AM with that utilizing the flat-top optically-pumped (FTOP) method. Our findings reveal that the FTOP-based approach outperforms the conventional method, exhibiting a larger response, a narrower magnetic resonance linewidth, and a superior low-frequency noise performance. Specifically, the use of FTOP method leads to a 16% enhancement in average sensitivity within 1 Hz–30 Hz frequency range. Our research emphasizes the significance of achieving transverse polarization uniformity in AMs, providing insights for future optimization efforts and sensitivity improvements in miniaturized magnetometers. [ABSTRACT FROM AUTHOR]

The significance of earth observation data spans diverse fields and domains, driving the need for efficient management. Nevertheless, the exponential increase in data volume brings new challenges that complicate processing and storing data. This article proposes an optimized multi-modular service for earth observation data management in response to these challenges. The suggested approach focuses on choosing the optimal configurations for the storage and processing layers to improve the performance and cost-effectiveness of managing data. By employing the recommended optimized strategies, earth observation data can be managed more effectively, resulting in fast data processing and reduced costs. [ABSTRACT FROM AUTHOR]

Drilling fluid is pivotal for efficient drilling. However, the gelation performance of drilling fluids is influenced by various complex factors, and traditional methods are inefficient and costly. Artificial intelligence and numerical simulation technologies have become transformative tools in various disciplines. This work reviews the application of four artificial intelligence techniques—expert systems, artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms—and three numerical simulation techniques—computational fluid dynamics (CFD) simulations, molecular dynamics (MD) simulations, and Monte Carlo simulations—in drilling fluid design and performance optimization. It analyzes the current issues in these studies, pointing out that challenges in applying these two technologies to drilling fluid gelation performance research include difficulties in obtaining field data and overly idealized model assumptions. From the literature review, it can be estimated that 52.0% of the papers are related to ANNs. Leakage issues are the primary concern for practitioners studying drilling fluid gelation performance, accounting for over 17% of research in this area. Based on this, and in conjunction with the technical requirements of drilling fluids and the development needs of drilling intelligence theory, three development directions are proposed: (1) Emphasize feature engineering and data preprocessing to explore the application potential of interpretable artificial intelligence. (2) Establish channels for open access to data or large-scale oil and gas field databases. (3) Conduct in-depth numerical simulation research focusing on the microscopic details of the spatial network structure of drilling fluids, reducing or even eliminating data dependence. [ABSTRACT FROM AUTHOR]

Traditional potato grading in China relies mostly on manual sorting, which is labor-intensive, time-consuming, costly, and inefficient. To enhance the operational performance of potato-grading devices, this paper focuses on optimizing the slide rail structure, which is the key component of a self-developed first-generation potato-grading device. A five-factor, three-level orthogonal experiment was designed, with the experimental factors being the height of the horizontal slide rail, angle of the first-stage inclined slide, angle of the second-stage inclined rail, chain horizontal movement speed, and conveyor belt speed. The indoor experiments were conducted using grading accuracy and grading efficiency as the experimental indicators. On the basis of the analysis of the orthogonal experiment results, two relatively optimal solutions were obtained, and validation experiments were conducted. The validation results show that when the height of the horizontal slide rail was 185 mm, the angle of the first-stage inclined rail was 4°, the angle of the second-stage inclined rail was 2.5°, the horizontal movement speed of the chain was 700 mm/s, and the movement speed of the conveyor belt was 275.60 mm/s, the performance of the movable rotating plate (MRP)-type grading device for potatoes reached its optimum. At this point, the grading accuracy was 94.88%, and the grading efficiency was 13.9477 t/h. Compared with the first-generation grading device, the optimized grading device achieved an improvement of 3.84% in grading accuracy and 12.94% in grading efficiency. The research methodology provided in this paper serves as a reference for the performance optimization of potato-grading devices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Refrigeration is the property of Journal of Refrigeration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lacrosse players are required to perform multiple sprints and changes of direction over the course of a game. These skills are reliant on the ability to rapidly produce lower-body force to be successful. Currently, little research examining the relationship between specific performance indicators and relative strength within this population exists. The purpose of this study was to investigate relationships between measures of lower-body strength and power to sprinting and change of direction speed (CODS) among male lacrosse players. Archived data for (n= 18) NCAA Division II male lacrosse athletes were used for this analysis. Lower-body strength was assessed using a one-repetition maximum back squat. Power was assessed using a countermovement jump (CMJ), squat jump (SJ), and standing long jump (SLJ). Sprint speed at 10 and 30 yds (i.e., 9.14 and 27.43 m) and CODS (i.e., T-Test (TT) and modified T-Test (MTT) were also assessed. Pearson's correlation was used to determine relationships between lower-body strength and power to sprint speed and CODS. Significant relationships (r = -0.51 - -0.64, p = 0.05) were discovered between all measures of power and CODS, as well as SLJ and sprint speed (r = -0.51; p = 0.05 and -0.67; p =-0.01, respectively). No significant relationships between relative strength and any measures of sprint speed or CODS were discovered. Although relative strength was not directly related to sprint speed or CODS performance, it may indirectly affect these measures based on its relationship to power, and power's relationship to sprint and CODS performance. [ABSTRACT FROM AUTHOR]

Copyright of New Building Materials / Xinxing Jianzhu Cailiao is the property of New Building Materials Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At medium to high gas content rates, the gas-liquid separation phenomenon and the stagnation behaviors of the gas masses occurring in the impeller channels are essential factors affecting the hydraulic performance of multiphase pumps. This paper designs a split trailing edge flap suitable for the multiphase pump to improve the hydraulic performance in the impeller by varying the flap deflection angle and length. The model's reliability was assessed by comparing the experimental outcome with the external characteristics of the experimental data. Based on the Eulerian-Eulerian multiphase model and the SST k‐ω turbulence model, the effects of different flap lengths on external characteristics, the turbulent kinetic energy and pressure distribution are analyzed. The study results show that when the length of the separated trailing edge flap is 0.25 l 0 and the deflection angle is 5°, the transporting efficiency of the multiphase pump is the highest at IGVF=50% with an efficiency increase of 3.40% compared to the original model. Its optimal flap length scheme effectively reduces the radial pressure gradient and the inverse pressure gradient in the impeller channel, which suppresses the gas blockage phenomenon to a certain extent. [ABSTRACT FROM AUTHOR]

AbstractIn this work, to enlarge the workspace, a novel 3-DOF planar parallel manipulator (PPM) with two identical RRR kinematic chains is proposed, and this manipulator has a significant advantage in orienting the moving platform (MP) across a wide range of angles. Resorting to the closed-loop vector approach, the inverse position equation and Jacobian matrix of the novel manipulator are established. Payload performance, dexterity performance, and singularity of the proposed manipulator are analyzed based on the Jacobian matrix. To investigate the distribution characteristics of the constant orientation workspace, orientation workspace, and reachable workspace of the proposed manipulator, a numerical discretization method is employed. Under the same working mode and structural parameters, constant orientation workspace and reachable workspace comparisons between the proposed manipulator and another two traditional PPMs are conducted. Focused on three kinds of performance indices, link lengths optimization of the proposed manipulator is carried out. A 4-DOF pick-and-place equipment is fabricated, and the rotational and translational capabilities are verified based on two examples. [ABSTRACT FROM AUTHOR]

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM} that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM} achieve 1.79× improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway, across eight representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Refrigeration is the property of Journal of Refrigeration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the non-equilibrium thermodynamics and energy and exergy analyses, a thermodynamic model of two-stage thermoelectric (TE) cooler (TTEC) driven by two-stage TE generator (TTEG) (TTEG-TTEC) combined TE device is established with involving Thomson effect by fitting method of variable physical parameters of TE materials. Taking total number of TE elements as constraint, influences of number distributions of TE elements on three device performance indictors, that is, cooling load, maximum COP and maximum exergetic efficiency, are analyzed. Three number distributions of TE elements are optimized with three maximum performance indictors as the objectives, respectively. Influences of hot-junction temperature of TTEG and cold-junction temperature of TTEC on optimization results are analyzed, and difference between optimization results corresponding to three performance indicators are studied. Optimal performance intervals and optimal variable intervals are provided. Influences of Thomson effect on three general performance indicators, three optimal performance indicators and optimal variables are comparatively discussed. Thomson effect reduces three general performance indicators and three optimal performance indicators of device. When hot- and cold-junction temperatures of TTEG and TTEC are 450, 305, 325 and 295 K, respectively, Thomson effect reduced maximum cooling load, maximum COP and maximum exergetic efficiency from 9.528 W, 9.043×10−2 and 2.552% to 6.651 W, 6.286×10−2 and 1.752%, respectively. [ABSTRACT FROM AUTHOR]

Abstract This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

An overview of thermodynamic energy and exergy analysis is provided in this work with the goal of developing, constructing, and improving a cascade cooling apparatus for extremely low-temperature managing goods and gas-efficient building operations. A review of the classes of cascade systems was carried out, stating the benefits and setbacks. Every part of the refrigeration mechanism cascades was identified, explained, and designed theoretically. Energy and exergy techniques were used to analyze every component of the system separately, model it, and evaluate it to minimize energy destruction. Necessary equations for calculating energy and exergy destructions were outlined to ensure effective modeling and optimization of the system processes. Process losses were identified, determined, and reduced using the application of exergy analysis. There was an improvement in comprehension of the process's deceptive efficiency and energetic effectiveness. The result aids in detecting the locations of energy degradation and mapping out the system's optimal performance. By lowering operational and process design costs and resolving energy- associated environmental issues, the analysis eventually contributed to sustainable growth. This offers a rationale for enhancing an exceptionally low-temperature freezer's functionality for the handling of susceptible-to-heat vaccines. The effective modeling and building of cascade refrigeration systems for zero energy destruction and high efficiency are made possible.

Multiple additives are usually required to achieve better constructability for building gypsum-based materials. In this work, ternary contour was designed to determine the optimal proportions of Ca(OH)2 (CH), PCEs and citric acid (CA) by comprehensive considerations of water requirement, setting time, fluidity, strength, temperature, hydration products and microstructural characteristics. The effects of different additives on water requirement, setting time, fluidity, compressive strength, hydration products and microstructure were comprehensively investigated, and the associated mechanisms were revealed by X-ray diffraction (XRD), and scanning electron microscopy (SEM) tests. The results demonstrate the synergistic impact of PCEs and CH on decrease of the water demand and enhancement of fluidity and compressive strength. The incorporation of CH led to rapid setting, but compensated the strength reduction brought by CA. The optimum additives for building gypsum were obtained with a ternary mixture containing 0.1–0.9 % CH, 0.6–1.4 % PCEs and 0–0.08 % CA, since it guaranteed a longer setting time, lower water requirement and higher strength. The present work paves the way to optimize or predict the early hydration and mechanical properties of building gypsum in both technical and economic aspects by adjusting the proportions of the three additives.

The multistage thermoelectric cooler can provide a larger temperature difference, but its refrigeration and economic performance decreases rapidly with the increase of stages. Considering all kinds of internal effects of thermoelectric materials and the irreversibility of external heat transfer, a finite time thermodynamic model of air cooling multistage thermoelectric cooler is established. The calculation method for cooling capacity and coefficient of performance are given. In order to describe and analyze the performance of multistage thermoelectric cooler comprehensively, the index thermodynamic perfectibility is introduced, and the performance evaluation index of coordinated performance coefficient is proposed. The effects of working current, cross-sectional area of thermoelectric leg and temperature difference on cooling capacity, coefficient of performance, thermodynamic perfectibility and coordination performance coefficient are analyzed. With the cooling temperature difference of 87 ℃, when the current is 2.55 A and 1.30 A respectively, and the cross-sectional area of the thermoelectric arm is 2.2 mm2 and 3.0 mm2 respectively, the cooling capacity and coefficient of performance reach the maximum respectively. Considering the refrigeration and economic performance, when the current is 1.75 A, the best coordination performance of cooling capacity, power consumption, and refrigeration coefficient can be obtained.

This paper explores the significant impact of human proximity on antenna design evolution in mobile communication from GSM to LTE and future 5G technologies. It offers a comprehensive view of the challenges posed by human interactions in current antenna designs, alongside modern solutions to mitigate these issues. Central to our study is the crucial role of extensive data acquisition in enabling AI-driven methodologies. We emphasize the need for diverse and comprehensive datasets to refine AI models. Our research demonstrates notable achievements, with our deep neural network-based models reaching up to 90% accuracy in radiation pattern classification and 87.5% in angular delay profile categorization. These results underscore our proficiency in incorporating AI into antenna engineering. We trace the historical trajectory of user-induced antenna challenges and their current implications, illustrating a coherent progression. This narrative underscores the parallel evolution of antenna designs and user interactions, enhanced by our advanced model classification techniques. Our work presents an efficient method to address user effects using cutting-edge machine learning algorithms.

Thermoelectric generators (TEGs) are widely recognized as clean energy solutions that can convert low-grade waste heat into electricity through a temperature gradient. Despite their significant potential, challenges such as low conversion efficiency and high costs have limited their practical applications. In this paper, we present an innovative metamaterial design concept for TEGs with significantly improved efficiency. A Finite Element Model is validated using Bi0.5Sb1.5Te3 bulk samples fabricated via the drop-cast method. This model can predict open-circuit voltage and output power as a function of an arbitrary metamaterial design using the commercial software ANSYS®. Four different metastructure designs, including 2D Triangular Honeycomb, Re-entrant, body-centered cubic (BCC), and triply periodic minimal surface (TPMS) structures, are systematically investigated. Through experiments and numerical analysis, the effects of annealing temperature, porosity, and unit cell numbers (UCNs) on the performance of TE legs are explored. It is found that 2D Triangular Honeycomb and BCC structures outperform other configurations due to their capacity to maintain a higher thermal gradient. Optimizing their porosity and UCNs can further enhance the output power. Compared to the traditional designs with bulk TE legs, implementing a 2D metastructure design with 30 % porosity and UCNs of 4 × 4 × 4 can lead to approximately a 100 % increase in power output.

This paper investigates the development of pulse width modulation (PWM) schemes for three-phase quasi-Z-source inverters (qZSIs). These inverters are notable for their voltage boost capability, built-in short-circuit protection, and continuous input current, making them suitable for low-voltage-fed applications like photovoltaic or fuel cell-based systems. Despite their advantages, qZSIs confront challenges such as increased control complexity and a larger number of passive components compared to traditional voltage source inverters (VSIs). In addition, most existing PWM schemes for qZSIs lack the capability for independent control of the amplitude modulation index and duty cycle, which is essential in closed-loop applications. This study introduces innovative space-vector PWM (SVPWM) schemes, addressing issues of independent control, synchronization, and unintentional short-circuiting in qZSIs. It evaluates several established continuous and discontinuous PWM schemes, and proposes two novel decoupled SVPWM-based schemes that integrate dead time and in which the shoot-through occurrence is synchronized with the beginning of the zero switching state. These novel schemes are designed to reduce switching losses and improve qZSI controllability. Experimental validation is conducted using a custom-developed electronic circuit board that enables the implementation of a range of PWM schemes, including the newly proposed ones. The obtained results indicate that the proposed PWM schemes can offer up to 6.8% greater efficiency and up to 7.5% reduced voltage stress compared to the closest competing PWM scheme from the literature. In addition, they contribute to reducing the electromagnetic interference and thermal stress of the related semiconductor switches. [ABSTRACT FROM AUTHOR]

This paper endeavors to utilize the numerical modeling method to evaluate the energy, economic, and environmental performances of a new hybrid PV-FC system for green hydrogen and electricity production. The proposed system consists of photovoltaic panels, fuel cells, an electrolyzer, a converter, and a hydrogen storage tank. A robust techno-enviro-economic (3E) analysis is conducted through comprehensive modeling for the system components using MATLAB/Simulink®. In this validated model, the essential parameters have been calculated: PV plant power, area and efficiency, electrolyzer efficiency, flow rate and power, stack power, area and efficiency, total LCOE of the integrated components, and CO 2 emission reduction. Moreover, the NSGA-II coupled with TOPSIS decision-making approach and Gaussian Process Regression machine learning method with selection kernel function are also utilized as a novel inclusion for the prediction and optimization of the 3E performances of this hybrid system. To obtain a multidimensional view of the optimization, six key decision variables of total stack power, fossil fuel-based generator energy, total CO 2 emissions coming from hydrogen production, total FC system voltage, module area, and number of PV modules have been adopted. The optimization problem encompasses maximizing the total fuel cell stack power and carbon emission reduction, while simultaneously minimizing the total stack area and levelized cost of energy. The simulation outcomes reveal that the stack can reach its maximum output power of 350 kW when operating temperatures are between 40 °C and 55 °C and there are more than 380 cells in the stack. Also, the LCOE was found to be less than $2/kWh for solar radiation above 250 W/m2 and PV outputs reaching 100 W. Further, Increasing FCs from 10 to 400 reduces CO 2 emissions by roughly 13% at 100 °C. Ultimately, the optimal configuration of the system yields stack power of 1589 kW, a total stack area of 269.9 m2, and total CO 2 emission reduction of 1268 ton CO2 , respectively. • Robust 3E analysis using MATLAB-based techno-environmental-economic study. • ML tool using GPR and GA optimization method used for performance optimization. • LCOE is less than $2/kWh for solar radiation above 250 W/m2 and 100 W PV output. • The PVFCEHST model exhibits a high degree of reliability and a robust integrated model. • FC power efficiency reaches 60% when operational temperature is between 40 and 55 °C. [ABSTRACT FROM AUTHOR]

The BERT model is regarded as the cornerstone of various pre-trained large language models that have achieved promising results in recent years. This article investigates how to optimize the BERT model in terms of fine-tuning speed and prediction accuracy, aiming to accelerate the execution of the BERT model on a multi-core processor and improve its prediction accuracy in typical downstream natural language processing tasks. Our contributions are two-fold. First, we port and parallelize the fine-tuning training of the BERT model on a multi-core shared-memory processor. We port the BERT model onto a multi-core processor platform to accelerate the fine-tuning training process of the model for downstream tasks. Second, we improve the prediction performance of typical downstream natural language processing tasks through fine-tuning the model parameters. We select five typical downstream natural language processing tasks (CoLA, SST-2, MRPC, RTE, and WNLI) and perform optimization on the multi-core platform, taking the hyperparameters of batch size, learning rate, and training epochs into account. Our experimental results show that, by increasing the number of CPUs and the number of threads, the model training time can be significantly reduced. We observe that the reduced time is primarily concentrated in the self-attention mechanism. Our further experimental results show that setting reasonable hyperparameters can improve the accuracy of the BERT model when applied to downstream tasks and that appropriately increasing the batch size under conditions of sufficient computing resources can significantly reduce training time. [ABSTRACT FROM AUTHOR]

The incorporation of biologically active metallic elements into nano/micron-scale coatings through micro-arc oxidation (MAO) shows significant potential in enhancing the biological characteristics and functionality of titanium-based materials. By introducing diverse metal ions onto titanium implant surfaces, not only can their antibacterial, anti-inflammatory and corrosion resistance properties be heightened, but it also promotes vascular growth and facilitates the formation of new bone tissue. This review provides a thorough examination of recent advancements in this field, covering the characteristics of commonly used metal ions and their associated preparation parameters. It also highlights the diverse applications of specific metal ions in enhancing osteogenesis, angiogenesis, antibacterial efficacy, anti- inflammatory and corrosion resistance properties of titanium implants. Furthermore, the review discusses challenges faced and future prospects in this promising area of research. In conclusion, the synergistic approach of micro-arc oxidation and metal ion doping demonstrates substantial promise in advancing the effectiveness of biomedical titanium and its alloys, promising improved outcomes in medical implant applications. [ABSTRACT FROM AUTHOR]

Circadian rhythms play a pivotal role in governing various physiological processes, including physical performance. However, in individuals deprived of light perception, such as the blind, these circadian rhythms face disruption. This study aimed to explore the influence of disturbed circadian rhythms on short-term maximal physical performance in children and adolescents with visual impairment. Forty-five volunteers participated in this study, comprising 17 blind, 13 visually impaired, and 15 sighted participants. The participants underwent a series of tests assessing maximal isometric strength performance across two days. To mitigate the influence of morning session fatigue on the evening results, each participant group performed in two separate testing sessions (i.e. in the morning (7:00 h) and in the evening (17:00 h)) on non-consecutive days in a randomized and counterbalanced setting, with approximately 36 h of recovery time between sessions. To mitigate the impact of inter-individual differences on mean values and to account for the influence of age and sex on the studied variables, data were normalized. The outcomes revealed a significant diurnal variation in maximal isometric strength performance among sighted individuals, with peak performance observed in the evening. This pattern aligns with their well-entrained circadian rhythm. In contrast, blind and visually impaired individuals did not display significant diurnal variation, signaling disrupted circadian rhythms due to the absence of light perception. These findings emphasize the crucial consideration of circadian rhythms in assessments of physical performance, especially among participants with visual impairments. [ABSTRACT FROM AUTHOR]

One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies' strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments. [ABSTRACT FROM AUTHOR]

This paper proposes a main-auxiliary cooperative receiving coil (MA-coil) with a lower space occupation rate and a simple control based on the time-sharing working principle, which can effectively improve the anti-misalignment capability of a dynamic wireless charging (DWC) system. First, the structure and circuit topology of the MA-coil are designed. The two auxiliary coils (A-coil) are connected in reverse series and symmetrically placed on both sides of the main coil (M-coil). Second, the output performance of the MA-coil in the y-direction is calculated based on the time-sharing working principle. The A-coil works by itself and enhances the output power when side shift occurs. Third, the most suitable ratio of coil width wM and wA is determined. The anti-misalignment performance and the effective side shift range are compared through simulation between the MA-coil in this case and the square coil. Finally, an experimental prototype is built to verify the feasibility of the proposed structure, and experimental results obtained from the prototype are basically consistent with the theoretical analysis. The anti-misalignment capability of the MA-coil is more than 20% higher than that of the square coil. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In high-temperature proton exchange membrane fuel cells (HT-PEMFCs), the thickness of the gas diffusion layer (GDL) can affect the transport and distribution of hydrogen and oxygen within the cell, thereby affecting the performance of the fuel cell. While too thin GDL can result in uneven reactant gas distribution, an excessively thick GDL increases resistance and hinders the transport of the gas to the catalyst layer. To optimize GDL thickness and enhance cell performance, this study employs a genetic algorithm (GA), using net battery output power as the objective function. Following optimization, fuel cell performance improved by 6.2%, with cathode and anode GDL thicknesses set at 0.1963 mm and 0.1125 mm, respectively. Reducing both anode and cathode GDL thicknesses promotes the rapid transport of hydrogen and oxygen to the catalyst layer (CL), facilitating more efficient reactions and enhancing fuel cell performance. A thinner cathode GDL ensures sufficient oxygen storage in the cathode compartment, further improving reaction dynamics compared to a thicker anode GDL. [ABSTRACT FROM AUTHOR]

The thermoelectric generator (TEG) can convert heat to electricity and is widely used. To overcome the limitation for low efficiency of thermoelectric materials, optimizing the performance of TEG in given system is necessary. This study proposes a method to match individual TEG components to the system environment based on two boundary conditions—temperature and heat flux. With this method, TEG geometrical parameters can be determined to optimize the system performance by varying temperature as an independent variable. Using this method, the TEG in an undersea Radioisotope thermoelectric generator (u‐RTG) is optimized. The TEG system exhibits an output power of 14.10 W when powered by a 200 W heat source. The simulate result in the comprehensive system model of u‐RTG is 14.04 W, which has 0.45% deviation from the TEG optimization result. It is proved that the TEG optimized by this method matches the system, the optimization results are accurate. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the shell of twin screw air compressor produces large noise during operation, the noise spectrum of the shell of the twin screw air compressor was studied by using finite element analysis technology, and the noise spectrum diagram of the shell of the twin screw air compressor was obtained at 100 ~ 1 500 Hz. Noise reduction was carried out by adding a muffler, and the structure of the muffler was optimized to eliminate the resonance phenomenon. The designed muffler was installed at the suction port of twin-screw air compressor. It is found that the muffler has better noise reduction effect. The research results can play a certain reference role for noise reduction of twin-screw air compressor. [ABSTRACT FROM AUTHOR]

The influence of operating parameters on the hydrogen production performance of the proton exchange membrane electrolysis cell (PEMEC) is crucial. In this paper, we first investigate the effect of a single operating parameter on the performance of PEMEC, and find the law of single operation effect. After that, the orthogonal experimental methods are used to investigate the effect of cross-coupling of combined operating parameters on the performance of PEMEC and the optimal combination of levels of operating parameters is obtained. Finally, the LSTM neural network is used to predict the PEMEC performance under the multiple operating parameters. The results showed that the weights of voltage, temperature, water flow rate, and bolt torque on PEMEC performance were 68.72 %, 20.63 %, 6.17 %, and 4.48 %, respectively. The optimal combination of operating parameters for the experiment is a torque of 3.5 N m, temperature of 70 °C, voltage of 2.0 V, and the water flow rate of 40 mL/min. It is also found that the long short term mermory (LSTM) neural network is able to predict the performance of PEMEC well, and that the experimental data-driven approach is an effective method for predicting the experimental performance. • The single operating parameter influence law is discovered. • The optimal combination of operating parameters is derived. • The order and weights of operating parameters affecting PEMEC performance are found. • An effective experimental performance prediction method is proposed. [ABSTRACT FROM AUTHOR]

An analysis of the minimum pressure coefficient on the suction side of the axialflow pump blades is presented as a design criterion. A Matlab code is used to improve the computer aided design process efficiency and quality. X-Foil software determines the blade profiles' lift and drag coefficients, and a computational fluid dynamics model is applied to certify the pump efficiency. The model is validated from the available experimental data in the literature. The finite volume method is used through the commercial software Ansys CFX, in order to solve the model equations. A case study is presented to design the axialflow pump for a large circulating water channel that will be used to test ships, naval structures, and hydrokinetic turbines. Particular attention is given to the pump cavitation conditions. The model evaluates the minimum pressure coefficient criterion and pressure coefficient distribution on the blade span, showing satisfactory performance for the pump at the design point and at variable speed. [ABSTRACT FROM AUTHOR]

Developing a just‐in‐time (JIT) compiler can be a daunting task, especially for a language as flexible as Python. While PyPy, powered with JIT compilation, can often outperform the official pure interpreter, CPython, by a noteworthy margin, its popularity remains far from comparable to that of CPython due to some issues. Given that an easier‐to‐deploy and better‐compatible JIT compiler would benefit more Python users, we have developed comPyler, a simple JIT compiler functioning as a CPython extension and intended to convert frequently interpreted CPython bytecode into equivalent machine code. Designed with good compatibility in mind, it does not alter CPython's internal data structures or external interfaces. Based on LLVM's mature infrastructure, it can be readily ported to almost all platforms. Compared with CPython, it achieved the highest speedup of 2.205, with an average of 1.093. Despite its relatively limited effect, comPyler incurs low development costs. As a baseline compiler, it also sheds light on the improvement attainable by optimizing solely the overhead of bytecode interpretation. Furthermore, as there is still a dearth of empirical research covering the multitude of JIT compilers available for Python, we have conducted a performance study that examines Jython, IronPython, PyPy, GraalPy, Pyston, Pyjion, and our comPyler. Our research takes into account not only the benchmark speed for various time windows but also the boot latency and memory footprint. Through this comprehensive study, our objective is to assist developers in gaining a better understanding of the effects of distinct JIT compilation techniques and to aid users in making informed decisions when choosing among different Python implementations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations. [ABSTRACT FROM AUTHOR]

Atomic layer deposition (ALD) has become the most widely used thin‐film deposition technique in various fields due to its unique advantages, such as self‐terminating growth, precise thickness control, and excellent deposition quality. In the energy storage domain, ALD has shown great potential for supercapacitors (SCs) by enabling the construction and surface engineering of novel electrode materials. This review aims to present a comprehensive outlook on the development, achievements, and design of advanced electrodes involving the application of ALD for realizing high‐performance SCs to date, as organized in several sections of this paper. Specifically, this review focuses on understanding the influence of ALD parameters on the electrochemical performance and discusses the ALD of nanostructured electrochemically active electrode materials on various templates for SCs. It examines the influence of ALD parameters on electrochemical performance and highlights ALD's role in passivating electrodes and creating 3D nanoarchitectures. The relationship between synthesis procedures and SC properties is analyzed to guide future research in preparing materials for various applications. Finally, it is concluded by suggesting the directions and scope of future research and development to further leverage the unique advantages of ALD for fabricating new materials and harness the unexplored opportunities in the fabrication of advanced‐generation SCs. [ABSTRACT FROM AUTHOR]

Different interventions can independently improve athletic performance via different central or peripheral mechanisms, but little is known about their potential combination. We tested the effects of seven combined interventions (enhanced recovery package [ERP]) on performance during intermittent high‐intensity cycling compared with placebo. Sixteen trained men (maximal power output: 5.0 ± 0.5 W･kg−1) completed six 30‐s cycling sprints with 3‐min breaks in‐between under ERP, placebo, and control conditions. The ERP combined neck cooling, carbohydrate and caffeine mouth rinsing, carbohydrate and water ingestion, hyperventilation, hyperoxia, and potentiation maneuvers. Power output, heart rate, blood lactate concentration, rate of perceived exertion, and gas exchange were compared between the ERP and placebo conditions. Mean power output was higher during ERP compared with placebo (570 ± 74 W vs. 560 ± 71 W, p < 0.001, dz = 1.15). Heart rate was higher (+3 ± 4 bpm, p = 0.012) during ERP, as was breathing frequency (+2.4 ± 4.0 breaths･min−1, p = 0.028) and respiratory exchange ratio (+0.12 ± 0.06, p < 0.001). Oxygen uptake was 80 ± 109 mL･min−1 lower (p = 0.013) for ERP. No differences were found with regards to the rate of perceived exertion or blood lactate concentration. Performance gains with ERP were small in magnitude but consistent across individuals. The ERP might have prevented a loss of aerobic efficiency or increased reliance on anaerobic energy. Testing combined interventions might increase the chances to see beyond daily variability in practice. Highlights: Combining seven different ergogenic interventions boosted repeated sprint performance by approximately 1.8% compared with placebo in trained individuals, suggesting that gains from single interventions are not cumulative.Fifteen out of 16 participants showed improved performance with the combined interventions, suggesting that the benefit of the combination might reside in the consistency of the gains obtained across individuals.The increase in power output with the ergogenic interventions took place despite lower oxygen uptake, suggesting either a maintenance of aerobic efficiency or increased reliance on anaerobic energy.A top‐down approach might be interesting for coaches and athletes trying to identify a set of interventions that improve performance, which can then be tailored to remove the ones that are least practical or least appreciated. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is transforming telematics network management, providing advanced solutions to meet the challenges of the increasing complexity and demands of modern networks. This article explores how AI is revolutionizing network management through automation, performance optimization, and improved security. Network automation and orchestration, facilitated by machine learning algorithms, are enabling more efficient and accurate configuration and management. In addition, AI is playing a crucial role in detecting and responding to security threats, analyzing large volumes of data to identify anomalous patterns and prevent attacks. Network performance optimization is also benefiting from AI, with systems dynamically adjusting bandwidth and reducing latency to deliver a superior user experience. Another significant advance is predictive maintenance, which makes it possible to anticipate problems before they occur, minimizing the impact on network operation. Through specific case studies, this article demonstrates how AI implementation has significantly improved efficiency and security in various environments. Finally, the benefits and challenges associated with integrating AI into telematics network management are discussed, providing a comprehensive view of its current and future impact. [ABSTRACT FROM AUTHOR]

High-performance computing and deep learning domains have been motivating the design of domain-specific processors. Although these processors can provide promising computation capability, they are notorious for exotic programming paradigms. To improve programming productivity and fully exploit the performance potential of these processors, domain-specific compilers (DSCs) have been proposed. However, building DSCs for emerging processors requires tremendous engineering efforts because the commonly used compilation stack is difficult to be reused. Owing to the advent of multilevel intermediate representation (MLIR), DSC developers can leverage reusable infrastructure to extend their customized functionalities without rebuilding the entire compilation stack. In this paper, we further demonstrate the effectiveness of MLIR by extending its reusable infrastructure to embrace a heterogeneous many-core processor (Sunway processor). In particular, we design a new Sunway dialect and corresponding backend for the Sunway processor, fully exploiting its architectural advantage and hiding its programming complexity. To show the ease of building a DSC, we leverage the Sunway dialect and existing MLIR dialects to build a stencil compiler for the Sunway processor. The experimental results show that our stencil compiler, built with a reusable approach, can even perform better than state-of-the-art stencil compilers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Madenat Al-Elem University College / Magallaẗ Kulliyyaẗ Madīnaẗ Al-ʿAlam Al-Ğāmi'aẗ is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)