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Resistive random-access memories have attracted significant attention in memory applications, while the physical mechanism behind their resistive switching behavior is still unclear. As a key issue, the migration of oxygen vacancies (VO) directly influences the performance of devices in the formation and rupture of conductive filaments (CFs). In this work, the distance of VO migration is performed as electric field dependent and affects the performance of the device. Sufficient distance of VO migration is essential for formation and rupture of CF completely, leading to, ideally, SET/RESET transition of the device. Based on this mechanism, an enhanced electric field is designed that optimizes the stability of the device significantly, which is demonstrated to be a promising approach to optimize the properties of the device. The investigation is helpful for guiding the design of device structures. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete’s physical fitness status. The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete’s physical fitness and overall performance to a considerable extent. The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

The Steam Generator (SG) is a crucial component of a Nuclear Power Plant (NPP), generating steam to transfer heat from the primary loop to the secondary loop. The control performance of the Steam Generator Level Control System (SGLCS) plays a crucial role in the normal operation of the SG. To improve the system's performance, the parameters of the control system should be optimized. However, the steam generator and its corresponding control system are highly complex, exhibiting nonlinearity and time-varying properties. Conventional parameter-setting methods mainly rely on engineers' experience, and are laborious and time-intensive. To tackle the aforementioned challenges, a Model-Free Optimization (MFO) method based on Knowledge-informed Historical Gradient-based Simultaneous Perturbation Stochastic Approximation (GK-SPSA) is applied to the performance optimization of the steam generator level control system. The GK-SPSA algorithm is a variant of the traditional SPSA algorithm. The fundamental idea of this revised algorithm is to maximize the utilization of historical gradient information generated during the optimization process of the SPSA algorithm, with the aim of enhancing overall algorithm performance in a model-free optimization context. Based on the effective utilization of historical gradient information, the GK-SPSA algorithm exhibits two improvements over the SPSA algorithm. The first improvement is related to the recognition of the online optimization progress, utilizing the state of the optimization progress to dynamically adjust the optimization step size. The second improvement is related to gradient estimation compensation, employing compensation rules to enhance the accuracy of gradient estimation, thus improving the optimization efficiency. Through simulation experiments, it can be observed that there is not much difference in the final iteration values among the GK-SPSA, IK-SPSA, and SPSA methods. However, the iteration count of GK-SPSA is reduced by about 20% compared to SPSA and by 11.11% compared to Knowledge-informed SPSA (IK-SPSA). The results indicate that this method can significantly improve the efficiency of parameter tuning for the liquid level control system of a steam generator. [ABSTRACT FROM AUTHOR]

Aiming at the problem of optimal design of the controller of electromagnetic levitation system, this paper proposes a gradient estimator-based controller design and optimization structure suitable for electromagnetic levitation system. The structure consists of a nominal controller, a residual generator, and a dynamic compensator. The controller structure can further improve the anti-disturbance and robustness of the electromagnetic levitation system on the premise of ensuring the stability and reliability. Considering the generality of electromagnetic levitation system, the proportional–integral–derivative (PID) controller is selected as the nominal controller, and the robustness of the system is improved by the dynamic compensator based on the residual generator. The parameter design and update strategy of dynamic compensator based on gradient estimator are proposed, and the expression of gradient estimator is designed and deduced. Taking the single-degree-freedom electromagnetic levitation experimental platform as an example, the simulation and experimental results show that the structure has strong robustness to shock disturbances. Compared with the PID controller, the range of the gap fluctuation is smaller after the instantaneous impact interference, and the anti-interference capability of the electromagnetic levitation system is improved. [ABSTRACT FROM AUTHOR]

Aquatic animals such as fish and cetaceans can actively modulate their body stiffness with muscle to achieve excellent swimming performance under different situations. However, it is still challenging for a robotic swimmer with bionic propulsion mode to dynamically adjust its body stiffness to improve the swimming speed due to the difficulties in designing an effective stiffness adjustment structure. In this paper, based on the special torque mode of a motor, we propose an active variable stiffness control method for a robotic dolphin to pursue better swimming speed. Different from a variable stiffness structure design, a torque control strategy for the caudal motor is employed to imitate the physical property of a torsion spring to act as the variable stiffness component. In addition, we also establish a dynamic model with the Lagrangian method to explore the variable stiffness mechanism. Extensive experiments have validated the dynamic model, and then the relationships between frequency and stiffness on swimming performance are presented. More importantly, through integrating the dynamic model and torque actuation mode-based variable stiffness mechanism, the online performance optimization scheme can be easily realized, providing valuable guidance in coordinating system parameters. Finally, experiments have demonstrated the stiffness adjustment capability of the caudal joint, validating the effectiveness of the proposed control method. The results also reveal that stiffness plays an essential role in swimming motion, and the active stiffness adjustment can significantly contribute to performance improvement in both speed and efficiency. Namely, with the adjustment of stiffness, the maximum speed of our robotic dolphin achieves up to 1.12 body length per second (BL/s) at 2.88 Hz increasing by 0.44 BL/s. Additionally, the efficiency is also improved by 37%. The conducted works will offer some new insights into the stiffness adjustment of robotic swimmers for better swimming performance. [ABSTRACT FROM AUTHOR]

Blockchain technology can effectively solve the problem of trusted data sharing in the Internet of Things (IoT) scenario, but the main stream consensus algorithm may have a large delay, and it is difficult to process the mass heterogeneous data from multi-sources. To solve this problem, this paper proposed a dynamically multi-block double-round consensus algorithm (called DMDR). This algorithm split the two steps of transaction verification and packaging, and the way of transaction processing in advance and block generation in delay enabled the common node to take advantage of the waiting time to process the transaction and generate multiple blocks in a consensus, thus improving the data processing efficiency. This algorithm divided the nodes in the network into multiple clusters, and reduced the communication cost by the double-round consensus between each cluster and the whole system, and ensured the accuracy of the consensus results based on the mutual verification of the points in the cluster. The simulation results show that the throughput of the proposed algorithm is better than that of PoW and PBFT, and it still has a considerable transaction throughput in small delay. [ABSTRACT FROM AUTHOR]

This work describes the optimization of photovoltaic parameters of the inorganic perovskite solar cell (PSC) with RbGeX3 (X = I, Br, Cl) as the absorber layer, employing the DFT based absorption spectrum and band gap estimated with the help of WIEN2k software. For analysing the PSC using SCAPS 1D tool, IGZO and Cu2O are incorporated as the electron transporting layer and hole transporting layer respectively. Using Au as metal at the back electrode, the device configuration exhibits a peak PCE of 13.79% for RbGeI3 based PSC when the SCAPS conventional absorption spectrum for RbGeI3 is used for the study. However, when absorption spectrum obtained by DFT principles is employed for RbGeI3, device configuration exhibits a PCE of 6.49%. Our study shows that the absorption spectrum generated by DFT calculations is more reliable and accurate as it incorporates minute variations of absorption with quantum mechanical level of accuracy for different wavelengths of the incident light. When the halide ion I− of the perovskite structure, RbGeI3 is replaced by Br− or Cl−, the device configurations exhibited PCE values of 10.09% and 3.69% respectively with SCAPS default absorption spectrum, mainly due to the increase in bandgap of the resulting perovskite structures, RbGeBr3 and RbGeCl3 while a PCE of 2.87% and 1.48% are achieved for device configurations with DFT absorption spectra of RbGeBr3 and RbGeCl3 respectively. [ABSTRACT FROM AUTHOR]

This article explores the transformative potential of digital twin (DT) technology in the automotive sector, focusing on its applications in enhancing propulsion drive systems. DT technology, a virtual representation of physical objects, has gained momentum due to its real-time monitoring and analysis capabilities. Within the automotive industry, where propulsion systems dictate vehicle performance, DTs offer a game-changing approach. Propulsion drive systems encompass electric motors, transmissions, and related components, significantly impacting efficiency and power delivery. Traditional design and testing methods need help addressing these systems' intricate interactions. This article aims to investigate how DTs can revolutionize propulsion systems. The study examines various applications of DTs, ranging from predictive maintenance to performance optimization and energy efficiency enhancement. The article underscores the technology's potential by reviewing case studies and real-world implementations. It also outlines challenges tied to integration and validation. In unveiling the capabilities of DT technology for propulsion systems, this article contributes to a comprehensive understanding of its role in shaping a more data-driven and efficient automotive industry. [ABSTRACT FROM AUTHOR]

The air compressor for fuel cell system (FCS) is used to supply air for the cathode of fuel cell whose performance directly affects the efficiency of the whole system. Compared with the traditional air compressor, the special air compressor for vehicle fuel cell needs to meet the requirements of oil-free, high pressure ratio, high efficiency, low noise, low weight, small volume and fast dynamic response. In this study, through comparative analysis of various types of air compressors, it is clear that centrifugal air compressors have the best comprehensive effects on power density, isentropic efficiency, noise, volume and so on, and it will be the mainstream development direction of FCS special air compressors in the future. The current development and problems are systematically reviewed and analyzed, and the future development trend is also proposed. In addition, the durability test for centrifugal air compressors is also discussed, and the life test conditions of centrifugal air compressors are presented. This study is beneficial for the researchers to obtain a holistic view of the overall state of centrifugal air compressor and is of guidance to design a more efficient and commercial fuel cell system. • The performance, noise and cost of centrifugal air compressor for fuel cell are reviewed. • Air compressor efficiency in full operating conditions under the whole system should be optimized. • The noise reduction methods for centrifugal air compressor needs to be further explored. • Its cost needs to be further reduced to meet the target with $40/kWnet. • It is necessary to develop durability test technology with high acceleration coefficient. [ABSTRACT FROM AUTHOR]

Polyethylene oxide (PEO)-based solid-state electrolytes for lithium-ion batteries have garnered significant interest due to their enhanced potential window, high energy density, and improved safety features. However, the issues such as low ionic conductivity at ambient temperature, substantial ionic conductivity fluctuations with temperature changes, and inadequate electrolyte interfacial compatibility hinder their widespread applications. Electrospinning is a popular approach for fabricating solid-state electrolytes owing to its superior advantages of adjustable component constitution and the unique internal fiber structure of the resultant electrolytes. Thus, this technique has been extensively adopted in related studies. This review provides an overview of recent advancements in optimizing the performance of PEO solid-state electrolytes via electrospinning technology. Initially, the impacts of different lithium salts and their concentrations on the performance of electrospun PEO-based solid-state electrolytes were compared. Subsequently, research pertaining to the effects of various additives on these electrolytes was reviewed. Furthermore, investigations concerning the enhancement of electrospun solid-state electrolytes via modifications of PEO molecular chains are herein detailed, and lastly, the prevalent challenges and future directions of PEO-based solid-state electrolytes for lithium-ion batteries are summarized. [ABSTRACT FROM AUTHOR]

The critical role of network traffic measurement and analysis extends across a range of network operations, ensuring quality of service, security, and efficient resource management. Despite the ubiquity of flow-level measurement, the escalating size of flow entries presents significant scalability issues. This study explores the implications of adaptive gradual flow aggregation, a solution devised to mitigate these challenges, on flow information distortion. The investigation maintains flow records in buffers of varying aggregation levels, iteratively adjusted based on the changing traffic load mirrored in CPU and memory utilization. Findings underscore the efficiency of adaptive gradual flow aggregation, particularly when applied to a specific buffer, yielding an optimal balance between information preservation and memory utilization. The paper highlights the particular significance of this approach in Internet of Things (IoT) and contrasted environmentstringent resource constraints. Consequently, it casts light on the imperative for adaptability in flow aggregation methods, the impact of these techniques on information distortion, and their influence on network operations. This research offers a foundation for future studies targeting the development of more adaptive and effective flow measurement techniques in diverse and resource-limited network environments. [ABSTRACT FROM AUTHOR]

The basic mathematical library is one of the fundamental software libraries in a computer system, and its performance is one of the key factors affecting the efficiency of higher-level applications. The existing RISC-V basic mathematical library can achieve correct calculations, but it contains a large number of memory access instructions and redundant instructions in its source code, which leads to suboptimal function performance. Additionally, the assembly code for RISC-V mathematical functions is large and involves complex branch conditions, which increases the difficulty of direct optimization. To address these issues, this paper follows a local-to-global optimization approach from and proposes a method for automatically detecting critical paths in RISC-V mathematical functions. This method focuses on solving the problem of other branch registers being easily modified when optimizing critical branches. By using a queue-based register allocation strategy, the registers within the same path are reallocated, which improves register utilization and minimizes the number of memory access instructions. Furthermore, redundant instructions are also combined and functionally refactored. Experimental results show that the average execution cycles for 67 RISC-V mathematical functions have been optimized from 144 cycles to 85 cycles, resulting in an average performance improvement of 29.61%.Abstract:The basic mathematical library is one of the fundamental software libraries in a computer system, and its performance is one of the key factors affecting the efficiency of higher-level applications. The existing RISC-V basic mathematical library can achieve correct calculations, but it contains a large number of memory access instructions and redundant instructions in its source code, which leads to suboptimal function performance. Additionally, the assembly code for RISC-V mathematical functions is large and involves complex branch conditions, which increases the difficulty of direct optimization. To address these issues, this paper follows a local-to-global optimization approach from and proposes a method for automatically detecting critical paths in RISC-V mathematical functions. This method focuses on solving the problem of other branch registers being easily modified when optimizing critical branches. By using a queue-based register allocation strategy, the registers within the same path are reallocated, which improves register utilization and minimizes the number of memory access instructions. Furthermore, redundant instructions are also combined and functionally refactored. Experimental results show that the average execution cycles for 67 RISC-V mathematical functions have been optimized from 144 cycles to 85 cycles, resulting in an average performance improvement of 29.61%. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless power transmission (WPT) technology and the demand for a wide range of applications, the common two-coil system is becoming increasingly mature. However, the monotonous structure of two-coil systems limits their development. By incorporating relay coils, the distance of power transmission, efficiency, and electromagnetic field (EMF) leakage can be effectively improved. In this paper, the three-coil system is analyzed, and the relay coil parameters are optimized by an evolutionary algorithm. After that, based on the coil optimization, the compensation capacitance of the relay coil is analyzed and an analytical formula of the compensation capacitance of the optimized relay coil is obtained. The results show that the parameter-optimized system reduces current by 86.9%, increases efficiency by 10%, and suppresses EMF leakage by 18 dB. Meanwhile, the capacitor optimization further improves the transmission efficiency of the system. [ABSTRACT FROM AUTHOR]

The solar air heater (SAH) is a flow device of various forms. The presence of rib turbulators is one of the basic characteristics of heat transfer changes. Two-dimensional numerical simulation of heat transfer provided by a SAH with inclined ribs at different angles (60°, 90°, 120°, 150°, 180°, and 210°) is investigated to determine the optimum angle of the inclined ribs leading to the maximum qualification of the SAH. The model is validated by comparing computed results with experimental data obtained under the same conditions. A good agreement using the standard k-epsilon model is assigned. Additionally, different Reynolds numbers (from 3800 to 18,000) are used to evaluate its effects on Nusselt number and friction factor, providing insights into the optimal operating conditions of the SAH. The design of inclined ribs significantly affects the thermal performance of SAHs. Results indicate that the thermal performance is improved when using an angle of the ribs greater than 90°. Specifically, an angle of 180° for the ribs was found to provide the optimum thermal performance. Results also demonstrate that the optimum angle is 180° for a Reynolds number of 18,000 where the Nusselt number and the friction factor are improved by 25% and 93% respectively, compared to its value obtained for an angle of 90°. The findings revealed the effect of increasing the angle of the inclined ribs on the thermal hydraulic performance parameter that the optimal angle of 180° enhances convection heat transfer and improves the efficiency of the system. The variation of the Reynolds numbers can impact the performance of the SAH, providing useful insights for the optimal operation. [ABSTRACT FROM AUTHOR]

Biomarkers have emerged as valuable tools in the realm of elite sports, providing objective measures of athletes' physiological responses and potential for performance optimization. This article review delves into the significance and implications of biomarkers in elite sports, emphasizing their crucial role in supporting athletes' training, recovery, and overall success. The integration of biomarkers offers unprecedented opportunities for evidence-based decisions, personalized strategies, and injury prevention, revolutionizing the landscape of elite sports. Through comprehensive literature review and analysis, this article highlights the tremendous promise of biomarkers in optimizing athletic performance while also addressing the ethical considerations surrounding their implementation. As research continues to advance, biomarkers are poised to reshape the future of elite sports, enhancing performance and paving the way for the next generation of sporting legends. [ABSTRACT FROM AUTHOR]

The power outputs and hydrogen consumption of PEMFC are affected by its core operating parameters such as operating pressure, operating temperature and cathode stoichiometric ratio. In order to develop a high efficiency PEMFC, this paper first analyzes and designs a comprehensive optimization objective that takes into account both the power output and hydrogen consumption of the PEMFC. Based on the orthogonal experiment and significance analysis, a multi-factor and multi-level orthogonal experiment table for the optimization variables was designed subsequently, the PEMFC performance optimization variables were determined. Finally, the performance of PEMFC before and after optimization is analyzed and compared. The results indicate that working pressure, working temperature, cathode stoichiometric ratio and anode stoichiometric ratio are significant factors affecting the performance optimization of PEMFC. Under the step loading current of 80 A, 150 A, 130 A, 160 A, 230 A, 200 A and 250 A, the net output power of the optimized PEMFC increased by 1.32%, 2.55%, 3.30%, 3.71%, 9.47%, 7.57% and 10.55% respectively. The average hydrogen consumption rate decreased by 3.87% under the whole step loading current. The total hydrogen consumption is reduced by 2.37%. Under actual road conditions, the maximum efficiency and average efficiency of the optimized PEMFC are increased by 3.81% and 6.07%, respectively, and the hydrogen consumption is reduced by 2.61%. [ABSTRACT FROM AUTHOR]

Energy-efficient and reliable underwater acoustic communication attracts a lot of research due to special marine communication conditions with limited resources in underwater acoustic sensor networks (UASNs). In their final analysis, the existing studies focus on controlling redundant communication and route void that greatly influence UASNs' comprehensive performances. Most of them consider directional or omnidirectional transmission for partial optimization aspects, which still have many extra data loads and performance losses. This paper analyzes the main issue sources causing redundant communication in UASNs, and proposes a lightweight differentiated transmission to suppress extra communication to the greatest extent as well as balance energy consumption. First, the layered model employs layer ID to limit the scale of the data packet header, which does not need depth or location information. Second, the layered model, fuzzy-based model, random modeling and directional-omnidirectional differentiated transmission mode comb out the forwarders step by step to decrease needless duplicated forwarding. Third, forwarders are decided by local computation in nodes, which avoids exchanging controlling information among nodes. Simulation results show that our method can efficiently reduce the network load and improve the performance in terms of energy consumption balance, network lifetime, data conflict and network congestion, and data packet delivery ratio. [ABSTRACT FROM AUTHOR]

• Substituting perovskite-type materials for charge transport layers. • The interface defect layer is added, which can be closer to reality. • Compared with the previous simulation, the efficiency is greatly improved. In recent years, Ge-based perovskite solar cells (PSCs) have gradually become a shining star in the field of scientific research because of their high conductivity. However, the defects between battery layers seriously affect the transmission efficiency of carriers. Therefore, we doped CsGeI 3 instead of the original charge transport layer to passivate the defects of the battery. We created p-CsGeI 3 layer and n-CsGeI 3 layer on both sides of i-RbGeI 3 , which improved the energy level matching between i-RbGeI 3 layer and the contact materials on both sides, and then facilitated the carrier transmission. We also added two interface defect layers (IDL) in order to be able to quantify the various adverse reactions at the interface, which reduces the recombination of carriers. We compared different electrode materials, and found that the appropriate metal work function can maximize the carrier transmission efficiency. We designed a structure of CsGeI3 instead of charge transport layer, and optimized the basic parameters of the battery with SCAPS. Reasonable thickness display can not only improve the absorption rate of photons, but also significantly reduce the recombination rate. Excessive trapping density increases electron/hole recombination, which is not conducive to carrier transportation. The appropriate band gap improves the matching degree of energy levels of each layer, and then promotes the transport of carriers between interfaces. At high temperature, more strain is generated inside the battery, which causes the crystal structure of the battery to deform. The high doping concentration strengthens the internal potential and promotes the carrier migration. The efficiency of the battery reached 34.96%, and the efficiency of other batteries with the same structure was less than 20%. Our research proved the great potential of doping CsGeI3 to construct all perovskite solar cell structures. [ABSTRACT FROM AUTHOR]

To investigate the sintering mechanism of magnesium aluminate (MgAl 2 O 4) spinel material. In this study, MgAl 2 O 4 spinel materials were synthesized by single-step reaction sintering method using light calcined magnesia, white corundum as raw materials, and YSZ (yttrium oxide stabilized zirconia) as additive. The effects of w (MgO + Al 2 O 3) content (20-80 wt%), molding pressure (100 MPa and 200 MPa), and YSZ additive (0-3 wt%) on the phase compositions, microstructures, sintering properties, mechanical properties, and thermal shock resistance were investigated. The results show that increasing the content of w (MgO + Al 2 O 3) from 20 wt% to 80 wt% and molding pressure from 100 MPa to 200 MPa are a benefit to the formation of second MgAl 2 O 4 spinel. Simultaneously this will lead to the increased apparent porosity due to the formation of point defects in MgO crystals during sintering. YSZ addition induced double vacancy defects in the MgAl 2 O 4 , which also facilitated the formation of the second MgAl 2 O 4 spinel. Besides, YSZ additives plays a pinning role, preventing the migration of grain boundaries. The positive effect of adding 1 wt% of YSZ is intuitively manifested in increasing the sintering and mechanical properties. The material with superior comprehensive performance can be obtained by adding 1 wt% YSZ additive, 80 wt% of w (MgO + Al 2 O 3), and forming under molding pressure of 200 MPa, followed by sintering at 1600 °C for 2 h. The final material exhibits the diameter shrinkage ratio of 3.32%, volume shrinkage ratio of 7.31%, apparent porosity of 14.21%, bulk density of 3.02 g cm−3, cold compressive strength of 176.88 MPa, and residual strength retention ratio of 84.92% after one thermal shock. [ABSTRACT FROM AUTHOR]

To meet the fast iteration speed and high computing power requirements of deep learning models, mainstream hardware vendors are increasingly inclined towards heterogeneous systems consisting of general-purpose processors and AI-specific accelerator cores. However, AI-specific accelerator ores only support certain core operators and do not have general programming capabilities. Therefore, how to efficiently deploy deep learning tasks on such heterogeneous architectures is worth further research. Based on the domestically developed FT-Matrix DSP+MatrixZone heterogeneous system platform, this paper designs and implements a deep learning programming framework, called KaiSa. KaiSa analyzes the input parameters of the deep learning model, identifies the operator type, and assigns it to the corresponding computing core. For complex operators, KaiSa automatically completes the optimal search for the block size based on a performance model, improving the performance of dual-core parallel computing. At the same time, KaiSa shields all low-level hardware details to provide users with a friendly programming environment for efficient program development. Experimental results show that KaiSa can achieve performance improvements of up to 39.0%. [ABSTRACT FROM AUTHOR]

Biomass‐derived carbon (BC) has attracted extensive attention as anode material for lithium ion batteries (LiBs) due to its natural hierarchical porous structure and rich heteroatoms that can adsorb Li+. However, the specific surface area of pure biomass carbon is generally small, so we can help NH3 and inorganic acid produced by urea decomposition to strip biomass, improve its specific surface area and enrich nitrogen elements. The nitrogen‐rich graphite flake obtained by the above treatment of hemp is named NGF. The product that has a high nitrogen content of 10.12% has a high specific surface area of 1151.1 m2 g−1. In the lithium ion battery test, the capacity of NGF is 806.6 mAh g−1 at 30 mA g−1, which is twice than that of BC. NGF also showed excellent performance that is 429.2 mAh g−1 under high current testing at 2000 mA g−1. The reaction process kinetics is analyzed and we found that the outstanding rate performance is attributed to the large‐scale capacitance control. In addition, the results of the constant current intermittent titration test indicate that the diffusion coefficient of NGF is greater than that of BC. This work proposes a simple method of nitrogen‐rich activated carbon, which has a significantly commercial prospect. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to improve autonomous flight performance of an unmanned aerial vehicle (UAV) having actively sweep angle morphing wing using simultaneous UAV and flight control system (FCS) design. Design/methodology/approach: An UAV is remanufactured in the ISTE Unmanned Aerial Vehicle Laboratory. Its wing sweep angle can vary actively during flight. FCS parameters and wing sweep angle are simultaneously designed to optimize autonomous flight performance index using a stochastic optimization method called as simultaneous perturbation stochastic approximation (SPSA). Results obtained are applied for flight simulations. Findings: Using simultaneous design process of an UAV having actively sweep angle morphing wing and FCS design, autonomous flight performance index is maximized. Research limitations/implications: Authorization of Directorate General of Civil Aviation in Turkey is crucial for real-time UAV flights. Practical implications: Simultaneous UAV having actively sweep angle morphing wing and FCS design process is so beneficial for recovering UAV autonomous flight performance index. Social implications: Simultaneous UAV having actively sweep angle morphing wing and FCS design process achieves confidence, high autonomous performance index and simple service demands of UAV operators. Originality/value: Composing a novel approach to improve autonomous flight performance index (e.g. less settling and rise time, less overshoot meanwhile trajectory tracking) of an UAV and creating an original procedure carrying out simultaneous UAV having actively sweep angle morphing wing and FCS design idea. [ABSTRACT FROM AUTHOR]

The pulse-jet cleaning process is a critical part of the bag filter workflow. The dust-cleaning effect has a significant impact on the operating stability of bag filters. Aiming at the multi-parameter optimization problem involved in the pulse-jet cleaning process of bag filters, the construction method of hybrid surrogate models based on second-order polynomial response surface models (PRSMs), radial basis functions (RBFs), and Kriging sub-surrogate models is investigated. With four sub-surrogate model hybrid modes, the corresponding hybrid surrogate models, namely PR-HSM, PK-HSM, RK-HSM, and PRK-HSM, are constructed for the multi-parameter optimization involved in the pulse-jet cleaning process of bag filters, and their objective function is the average pressure on the inner side wall of the filter bag at 1 m from the bag bottom. The genetic algorithm is applied to search for the optimal parameter combination of the pulse-jet cleaning process. The results of simulation experiments and optimization calculations show that compared with the sub-surrogate model PRSM, the evaluation indices RMSE, R 2 , and RAAE of the hybrid surrogate model RK-HSM are 9.91%, 4.41%, and 15.60% better, respectively, which greatly enhances the reliability and practicability of the hybrid surrogate model. After using the RK-HSM, the optimized average pressure F on the inner side wall of the filter bag at 1 m from the bag bottom is −1205.1605 Pa, which is 1321.4543 Pa higher than the average pressure value under the initial parameter condition set by experience, and 58.4012 Pa to 515.2836 Pa higher than using the three sub-surrogate models, verifying its usefulness. [ABSTRACT FROM AUTHOR]

The annealing effects on Pb0.97La0.03Sc0.45Ta0.45Ti0.1O3 (PLSTT) ceramics prepared by the solid-state reaction method are systemically investigated using experimental and theoretical techniques. Comprehensive studies are performed on the PLSTT samples by varying annealing time (AT) from t (=0, 10, 20, 30, 40, 50 and 60) h. The properties involving ferroelectric polarization (FP), electrocaloric (EC) effect, energy harvesting performance (EHP) and energy storage performance (ESP) are reported, compared and contrasted. All these features are seen to gradually improve with the increase in AT, and they all reach the climaxed-shaped values and then decrease by further increasing the AT. For t = 40 h, the maximum FP (23.2 µC/cm2) is attained at an electric field of 50 kV/cm, while the high EHP effects (0.297 J/cm3) and positive EC are achieved (for ΔT~0.92 K and ΔS~0.92 J/(K·kg)) at 45 kV/cm. The EHP value of the PLSTT ceramics increased by 21.7% while the polarization value was enhanced by 33.3%. At t = 30 h, the ceramics have attained the best ESP value of 0.468 J/cm3 with an energy loss of 0.05 J/cm3. We strongly believe that the AT plays a crucial role in the optimization of different traits of the PLSTT ceramics. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have an important place in today's technologies because of the solutions they bring to many critical problems in military and civilian application around the world. One of the important advantages of UAVs in aviation, particularly in the military field, is high observation, reconnaissance and pursuit activities. Fixed-wing UAVs, which have many configurations, stand out with their electric motor models owing to their efficiency and flight cost advantages. However, for reconnaissance and pursuit activities, this must be supported by a low cruise flight speed and high aerodynamic efficiency. In this study, it is aimed to reduce flight speed, to increase flight duration and aerodynamic efficiency by designing new modular wing based on flow analysis. Test flights are performed at different angles of attack and new parameters are determined. As a result of experimental studies, cruise flight speed is significantly reduced and pursuit capabilities of UAV is improved. This development has a positive effect on power consumption of electric motor by reducing current drawn by motor and its RPM value. Due to the reduction of power consumption, flight time increased by 36% and the average current drawn decreased by 45% which makes aerodynamic efficiency to be increased. [ABSTRACT FROM AUTHOR]

High-efficiency separation of low-grade collophane becomes an inevitable trend with the depletion of high-grade phosphate ore. The work innovatively introduced enhanced gravity separation technology into the pre-separation process of low-grade collophane and discussed the influences of various factors on the enhanced gravity pre-separation effect. The influential significances of parameters, parametric interactions, and performance optimization were investigated using the RSM (Response Surface Methodology). The results showed that the P2O5 grade of the low-grade collophane could be increased from 14.84% to 20.26% with a recovery of 79.55%, and the P2O5 grade of tailings could be reduced to 7.27% under the optimal conditions—f of 44.50 Hz, P of 0.02 Mpa, and V of 22.04 ml/s. Compared with conventional gravity separation, the P2O5 recovery significantly increased by nearly 30%, and the P2O5 grade of tailings has been reduced remarkably. The response surface analysis showed that the influential significances of parameters could be ranked as P > f > V, and parametric interactions could be ranked as fP > PV > fV. The results of verification experiments agreed with the optimization results, which proved the reliability of the prediction model. The enhanced gravity separation technology developed an excellent pre-separation effect of low-grade collophane. [ABSTRACT FROM AUTHOR]

Resonance optical fiber sensors are widely studied for their high sensitivity, small size, and anti‐electromagnetic interference. However, traditional resonance optical fiber sensors have encountered bottlenecks in the trace detection due to limited localized field intensity and low molecules affinity. With the development of materials technology, low‐dimensional materials such as quantum dots, graphene, and transition metal sulfides have excellent properties such as high carrier mobility, large specific surface area, and flexible structure controllable artificial technologies that can also be used to synthesize new materials with desired characteristics, which can enhance the performance of sensors fundamentally. The introduction of low‐dimensional materials can not only achieve the performance optimization of sensors, but also make sensors functional to realize the diversification of detection objects. This paper reviews the research progress of resonance optical fiber sensors based on low‐dimensional materials. The detection principle and performance indexes related to resonance optical fiber sensing are elaborated, and resonance optical fiber sensors modified by low‐dimensional materials are introduced. Furthermore, the role of low‐dimensional materials in resonance fiber sensing is summarized, the direction of performance optimization in the production process of resonance fiber sensors is analyzed, and the future prospect of new resonance optical fiber sensors is given. [ABSTRACT FROM AUTHOR]

In order to protect human safety, ensure the normal operation of precision instruments and improve national defense capabilities, it is necessary to develop electromagnetic waves absorbing materials to solve the issue of electromagnetic pollution. Carbon materials offer the characteristics of abundant species, low density, tunable conductivity, and outstanding mechanical property, enabling them to be used in a wide range of applications, and are of particular interest in the electromagnetic protection technology. In recent years, a large number of carbon-based absorbers have been synthesized and investigated in pursuit of the ideal goal with "thin thickness, light density, wide effective bandwidth and strong absorption" characteristics. In this review, it begins with a detailed description of the electromagnetic waves attenuation mechanisms. Next, a systematic summary of strategies to optimize the electromagnetic protection effect of carbon-based absorbers is provided. Thirdly, the multifunctional requirements of absorbing materials are discussed in depth when faced with practical application situations. Finally, we put forward the challenges and prospects of carbon-based protection materials, which can serve as a reference for the development of high-performance absorbers. In this review, the current research status of carbon-based electromagnetic absorbing materials in recent years is summarized, focusing on the electromagnetic wave attenuation mechanism, performance optimization strategy and the required versatility in the practical application. [Display omitted] [ABSTRACT FROM AUTHOR]

A simulation model based on Dymola modelling was developed to investigate the dynamic characteristics of automatic generation control (AGC) for variable-load thermal power units in this study. Specifically, a 300 MW unit from a power plant in northern China was used to verify the model's validity in steady-state processes and to analyze the behavior of the main thermal parameters under different rates of load changes. The economic performance of the unit under different rates of load changes is also analyzed by combining the economic indexes of "two regulations" in the power grid. Results indicate that as the rate of load changes increases, boiler output, main steam temperature, reheat steam temperature, main steam pressure, and working temperatures of various equipment fluctuate more intensely. Specifically, at a rate of load reduction of 2.0% Pe MW/min, the maximum deviation of the main steam temperature can reach 7.6 °C, with the screen-type superheater experiencing the largest heat exchange. To achieve a balance between safety and economics for the unit, the rate of load raising should not exceed 1.2% Pe MW/min, and the rate of load reduction should not exceed 0.8% Pe MW/min. This paper applies the covariance index and AGC assessment index of the thermal power unit load control system to the established dynamic simulation model to supplement the AGC assessment index in the "two regulations", and to provide a flexible and reasonable system evaluation result for field operators to refer to, so as to improve the economics of the system on the basis of safety. [ABSTRACT FROM AUTHOR]

To meet increasing data access performance demands of applications run on high-performance computing (HPC) systems, an efficient design of HPC storage file system is becoming more important. Nowadays, Lustre is the most commonly used distributed parallel file system that has been deployed on world's fastest supercomputers and currently supports two backend file systems: ldiskfs and ZFS. Although ZFS provides numerous performance optimization options, most of the supercomputer systems use Lustre with ldiskfs for higher performance. In this work, we analyze the root cause of low I/O performance on a ZFS-based Lustre file system and propose a novel ZFS scheme, dynamic-ZFS, which combines two optimization approaches. The experiment results show that our approach can improve the sequential I/O performance by 37% on average. We demonstrate that dynamic-ZFS can deliver an I/O performance comparable to that of ldiskfs-based Lustre while still providing a multitude of beneficial features. [ABSTRACT FROM AUTHOR]

Myotonometry is a relatively novel method used to quantify the biomechanical and viscoelastic properties (stiffness, compliance, tone, elasticity, creep, and mechanical relaxation) of palpable musculotendinous structures with portable mechanical devices called myotonometers. Myotonometers obtain these measures by recording the magnitude of radial tissue deformation that occurs in response to the amount of force that is perpendicularly applied to the tissue through a device's probe. Myotonometric parameters such as stiffness and compliance have repeatedly demonstrated strong correlations with force production and muscle activation. Paradoxically, individual muscle stiffness measures have been associated with both superior athletic performance and a higher incidence of injury. This indicates optimal stiffness levels may promote athletic performance, whereas too much or too little may lead to an increased risk of injury. Authors of numerous studies suggested that myotonometry may assist practitioners in the development of performance and rehabilitation programs that improve athletic performance, mitigate injury risk, guide therapeutic interventions, and optimize return-to-activity decision-making. Thus, the purpose of our narrative review was to summarize the potential utility of myotonometry as a clinical tool that assists musculoskeletal clinicians with the diagnosis, rehabilitation, and prevention of athletic injuries. [ABSTRACT FROM AUTHOR]

The study aimed to determine whether athletes who practice biofeedback are able to self-regulate by reaching resonance frequency and gaining physiological control quicker than if practice time integrates imagery or a rest period. Intervention effectiveness (e.g., intervention length, time spent training) was also explored. Twenty-seven university athletes were assigned to one of three groups: (a) biofeedback (i.e., continuous training), (b) biofeedback/imagery (i.e., interspersed with imagery), and (c) biofeedback/rest (i.e., interspersed with a rest period). Five biofeedback sessions training respiration rate, heart rate variability, and skin conductance were conducted. A repeated-measure analysis of variance showed a significant interaction between groups over time (p ≤.05) for respiration rate, heart rate variability, and skin conductance, indicating that resonance frequency and physiological control was regained following imagery or a rest period. Postmanipulation check data found intervention length and training time to be sufficient. Combining imagery with biofeedback may optimize management of psychophysiological processes. [ABSTRACT FROM AUTHOR]

Biofeedback is among the various self-regulation techniques that mental performance consultants can utilize in their practice with athletes. Biofeedback produces psychophysiological assessments in real time to enhance awareness of thoughts and emotions. Quantitatively, research shows that biofeedback can facilitate self-regulation, allowing an athlete to gain control over psychophysiological responses that could be detrimental to performance. With technology becoming a widespread tool in monitoring psychophysiological states, an exploration of consultants' use of biofeedback, their perceptions of effectiveness, and limitations of their use was warranted to qualitatively evaluate efficiency of the tool. A qualitative descriptive approach was taken through semistructured interviews with 10 mental performance consultants. Inductive reasoning uncovered three themes: positive implications, practical limitations, and equipment options. With biofeedback, athletes have the ability to develop a deeper level of self-awareness and thereby facilitate the use of self-regulation strategies intended for optimal performance states and outcomes. [ABSTRACT FROM AUTHOR]

Heat pumps (HPs) are energy-efficient space heating devices that are key to global carbon reduction and carbon neutrality. However, current commercial HPs have performance issues in cold climates where space heating is needed most, including low COP and high energy consumption of defrosting. Aiming to tackle these issues, a novel two-stage heat recovery heat pump (THRHP) is therefore developed to enable heat recovery from exhaust air for improving COP and preventing dramatic energy use during winter defrosting. The performance of THRHP was optimized in the laboratory by investigating its expansion valve opening and exhaust air fans situation. Finally, the experiment results showed the prototype provided a heating capacity of 32.3 kW, generating 4 m3/h hot water of 55 °C with COP of 2.57 at outdoor temperature of 0 °C, achieving 20.1% higher COP than the commercial HPs, while the efficient and quick defrosting process only consumed 0.46 kW and 4 mins under outdoor temperature on −6 °C. The results gave more insights into the characteristics of THRHP and obtained the optimal control strategies of THRHP for better performance, thus promoting the wide deployment of HPs and achieving the ambitious carbon-neutrality targets. [ABSTRACT FROM AUTHOR]

To optimize the thermal resistance of thermoelectric generator for improving performance, the output performance variation of thermoelectric generator with different thermal resistance at both sides was investigated. The external ceramic substrates with different thermal conductivity and thickness were used to simulate the thermal resistance at both sides of thermoelectric generator. The variation of temperature difference, open-circuit voltage, output power and maximum output power of thermoelectric generator at different hot source temperatures and load resistances was analyzed. The results show that when the thermal resistance at both sides of the thermoelectric generator is the same, the temperature difference and the open-circuit voltage are increased with the increasing of hot source temperature. When the thermal resistance at both sides of thermoelectric generator is different, the open-circuit voltage and the maximum output power are increased with the increasing of hot source temperature. The effective temperature difference is increased with the increasing of load resistance, and the power is increased first with latter decreasing when the load resistance is increased. Compared to the performance of thermoelectric generator with the same thermal resistance at both sides, the ceramic substrate with smaller thermal resistance at the hot side is more beneficial to improve the output performance of thermoelectric generator, and the improvement effect is more obvious near the maximum power point. The open-circuit voltage, the maximum output power and the maximum effective temperature difference can be increased by 0.64 V, 0.87 W and 24.5 ℃, respectively. [ABSTRACT FROM AUTHOR]

In this study, we construct a three-dimensional heat- and mass-transfer model to determine the moisture loss and spatio-temporal distribution of pork carcass temperature during precooling. We consider how convection, evaporation, and radiation affect the heat- and mass-transfer process and compare and analyze how precooling conditions affect the precooling rate, uniformity, and moisture loss of pork carcasses. The results reveal nonuniform precooling between different parts of single carcasses, mainly because of the nonuniform heat transfer on the pork carcass surface. However, for the overall precooling space, no significant difference appears in the rate of temperature decrease between individual carcasses. In addition, the ambient humidity most strongly affects carcass moisture loss, and the precooling temperature most strongly affects temperature uniformity and precooling rate. Finally, by comprehensively evaluating the effects of different precooling conditions on carcass precooling performance, the optimal precooling conditions are determined, namely, the air velocity, precooling temperature, ambient humidity, and initial carcass temperature of 10 m/s, 0 °C, 95 %, and 37 °C, respectively. This study thus provides an important theoretical basis and a useful reference for the determination of optimal precooling conditions and the comprehensive evaluation and optimization of pork carcass precooling. [ABSTRACT FROM AUTHOR]

To minimize the impact of snowfall and ice formation on safety of transportation, salt is sprinkled on the asphalt every winter. However, the use of salt has economical as well as ecological disadvantages. To resolve these problems, road heating systems are used in the northern regions of Europe and America. Despite their widespread usage, considerable potential of the operational optimization is evident. The current systems are controlled under predefined weather conditions such as start of operation at 5 °C air temperature, even when snowfall is absent. Consequently, loss of energy input to heat the system is caused. To avoid unnecessary financial and energetic expense, this study presents CFD-based performance investigation as a basis for a novel predictive controller to increase the operational efficiency of hydronic road heating systems (HRS). The simulation model was developed based on a real operational HRS located in Ingolstadt and composed of bridges and ramps for a total surface of 1989 m2. Climate data of the years 2019–2020 from local weather stations were implemented in the simulation model for performance prediction on extreme climate conditions. This investigation identified that up to 70% of operational hours in terms of energy input can be saved by using a hypothetical predictive controller, thus making the HRS a more economically efficient and environmentally attractive alternate to conventional de-icing techniques. [ABSTRACT FROM AUTHOR]