Performance Optimization Research Articles

The Role of Digital Smart Wearable Technology in VO2MAX Measurement to Support Athlete Performance and Public Health

This study employed the systematic literature review to investigate to what extent digital smart wearable technology was used in measuring VO2MAX to support athlete performance and public health. To achieve the goal, data were obtained from articles searched through Google Scholar. The search was conducted in September 2024 using the following keywords: (Smart Wearable OR Wearable Technology AND VO2MAX OR VO2 Max OR Cardiorespiratory Fitness AND Athlete Performance OR Sports Performance OR Athletic Performance AND Public Health OR General Population OR Health Monitoring). The articles were selected by considering predetermined inclusion and exclusion criteria. For example, to be included in this review, the articles should be published in Scopus-indexed journals. Additionally, the publication period should be from 2019 to 2024. Based on this timeframe, 1,110 articles were found. Furthermore, they were screened again in multiple stages, and 16 articles that fit the theme and met the inclusion requirements were selected. During the article selection, this study followed the PRISMA guidelines. The results confirmed that wearable technology played an important role in athlete performance optimization through real-time monitoring of VO2MAX. The device enables exercise adjustments based on metrics such as heart rate and diurnal variations, which can improve exercise effectiveness and reduce the risk of injury. Interestingly, it also contributes significantly to improving public health by promoting active lifestyles, monitoring health parameters, and supporting self-health management, especially for the elderly and individuals with chronic diseases. Nonetheless, technical challenges, such as measurement accuracy, data synchronization issues, and user education needs, must be addressed for wearables to be optimally utilized. With proper development, wearable technology has great potential to improve athlete performance and public health. Keywords: Role, Digital Technology, Smart Wearable, VO2MAX, Athlete Performance, Public Health

Basketball player motion detection and motion mode analysis based on biomechanical sensors

Basketball player motion detection and analysis are crucial for optimizing performance and preventing injuries. Traditional methods often rely on visual observation and video analysis, lacking precision and real-time feedback. In this study, a unique novel Intelligent Bayesian tuned-augmented Support Vector Machine (IB-ASVM) was proposed for predicting basketball players’ motion modes and performance analysis using the biomechanical sensor data. Advancements in biomechanical sensors such as accelerometers, gyroscopes, and force sensors are deployed into ESP32 to build a player’s wearable gadget. This gadget provides dynamic players with real-time sensing data. Data are transmitted to the cloud via Wi-Fi 7.0 for motion analysis and this model is stimulated using Arduino IDE. The Kalman Filter reduces noise and smoothens sensor data such as acceleration, and angular velocity. Then, the filtered data is employed in the Discrete Wavelet Transform (DWT) to capture time-frequency characteristics of motion signals, making it ideal for extracting relevant features. The featured data are utilized in the ASVM model to classify and detect the motion modes of the basketball players via IB optimization. The Tensor Flow software is used to implement the IB-ASVM model. The result demonstrates that IB-ASVM most accurately predicts the jump shot, layup, dribbling, running, pivoting, passing, free throw, and motion states of the basketball players. The IB-ASVM model accurately classifies basketball motion states using biomechanical sensor data, enhancing performance optimization and injury prevention through precise motion detection.

Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.

Adaptive Neural Delay Feedback Control of a Typical Robot on a Slope

ABSTRACTIn this article, an adaptive neural delay feedback control strategy is proposed for the motion control of a two‐wheel self‐balancing robot (TWSBR) on a slope with an input delay. Based on the Lagrange equation, the dynamic model of the robot is derived. Given a target trajectory vector, the corresponding error system is obtained. An input delay and uncertainties are considered. First, the system with an input delay is transformed into a delay‐free one. Second, a quadratic performance optimization criterion is defined, and a linear quadratic regulator (LQR) controller is designed to minimize the error between the system's state vector and the target vector. Thirdly, an integral sliding mode controller is added to the LQR controller to enhance the system's robustness. To reduce the “chattering” phenomenon, an adaptive neural delay feedback control scheme is developed based on the adaptive learning of the uncertainties' upper bound by a radial basis function neural network (RBFNN). It is demonstrated that a relaxation process exists at the beginning of the upslope motion. As the input delay increases, the swing range of the center of gravity of the pendulum expands. The angle can converge to the steady state more quickly, but the relaxation time is still 0.031 s which has nothing to do with the input delay. An example of the back‐and‐forth motion of a TWSBR on a slope can verify an excellent agreement between theoretical analysis and numerical results.

Fixed-Time Control with an Improved Sparrow Search Algorithm for Robotic Arm Performance Optimization

This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications.

3D printing for sodium batteries: From material design to integrated devices

Additive manufacturing, commonly known as 3D printing, is an innovative technique for fabricating batteries with arbitrary architectures. Understanding the intricacies of 3D printing designs in sodium battery materials is crucial for optimizing their electrochemical properties and unlocking the full potential of 3D printed sodium batteries. This review provides a comprehensive overview of the key aspects involved in the fabrication of 3D printed sodium batteries, encompassing material selectivity criterion, design considerations, and optimization strategies. Challenges and prospects for the fabrication of high-performance 3D printed sodium batteries are outlined, aiming to provide valuable insights into new conception and theoretical guidance for the design and performance optimization of composites by 3D printing for the practical application of sodium batteries in the future.

Three-dimensional turning force sensor based on a decagonal ring structure: crosstalk mitigation design and performance optimization

Abstract Real-time data acquisition using high-precision three-dimensional turning force sensors is crucial for intelligent manufacturing. However, existing sensors face challenges such as low integration levels and poor crosstalk resistance, rendering them inadequate for high-precision measurements. Theoretical analysis has demonstrated that a fully centered decagonal ring structure can effectively mitigate the effects of eccentric loading. This study focuses on designing a fully centered turning force sensor with minimal crosstalk. To achieve a balance between sensitivity and stiffness, multi-objective optimization was conducted using GWO-BP and TOP algorithms, resulting in a twofold increase in the sensitivity of the decagonal ring. The sensor design incorporates metallic strain gauges, instrumentation amplifiers, and peripheral circuits into the ring arms of the decagonal structure. Experimental results show that the sensor’s amplified sensitivities in the Fc, Ff, and Fp directions are 11.78 mV N−1, 10.31 mV N−1, and 1.78 mV N−1, respectively. The first three natural frequencies in an unconstrained state are 2518.5 Hz, 2537.5 Hz, and 4256.4 Hz. The sensor’s Fc-Fy crosstalk ranges from 0.18% to 0.88%, with noise limits for the Fc, Ff, and Fp directions of 0.035 N, 0.03 N, and 0.23 N, respectively. Cutting experiments have demonstrated that under varying spindle speeds and cutting depths, the sensor effectively detects surges in cutting force caused by rapid tool retraction, as well as other common faults.

BTIP: Branch Triggered Instruction Prefetcher Ensuring Timeliness

In CPU microarchitecture, caches store frequently accessed instructions and data by exploiting their locality, reducing memory access latency and improving application performance. However, contemporary applications with large code footprints often experience frequent Icache misses, which significantly degrade performance. Although Fetch-Directed Instruction Prefetching (FDIP) has been widely adopted in commercial processors to reduce Icache misses, our analysis reveals that FDIP still suffers from Icache misses caused by branch mispredictions and late prefetch, leaving considerable opportunity for performance optimization. Priority-Directed Instruction Prefetching (PDIP) has been proposed to reduce Icache misses caused by branch mispredictions in FDIP. However, it neglects Icache misses due to late prefetch and suffers from high storage overhead. In this paper, we proposed a branch-triggered instruction prefetcher (BTIP), which aims to prefetch Icache lines that FDIP cannot efficiently handle, including the Icache misses due to branch misprediction and late prefetch. We also introduce a novel Branch Target Buffer (BTB) organization, BTIP BTB, which stores prefetch metadata and reuses information from existing BTB entries, effectively reducing storage overhead. We implemented BTIP on the Champsim simulator and evaluated BTIP in detail using traces from the 1st Instruction Prefetching Championship (IPC-1). Our evaluation shows that BTIP outperforms both FDIP and PDIP. Specifically, BTIP reduces Icache misses by 38.0% and improves performance by 5.1% compared to FDIP. Additionally, BTIP outperforms PDIP by 1.6% while using only 41.9% of the storage space required by PDIP.

Optimal performance design of bat algorithm: An adaptive multi‐stage structure

AbstractThe bat algorithm (BA) is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness, which can be used to find the globally optimal solutions for various optimisation problems. Knowing the recent criticises of the originality of equations, the principle of BA is concise and easy to implement, and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing. In this research, the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues. In terms of operation effect, BA has an acceptable convergence speed. However, due to the low proportion of time used to explore the search space, it is easy to converge prematurely and fall into the local optima. The authors propose an adaptive multi‐stage bat algorithm (AMSBA). By tuning the algorithm's focus at three different stages of the search process, AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed. Therefore, AMSBA can achieve solutions with better quality. A convergence analysis was conducted to demonstrate the global convergence of AMSBA. The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm‐based algorithms. The results verify the effectiveness and superiority of AMSBA. AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020, while this experiment is carried out on five different dimensions of the objective functions respectively. A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance. AMSBA was also applied to the multi‐threshold image segmentation of Citrus Macular disease, which is a bacterial infection that causes lesions on citrus trees. The segmentation results were analysed by comparing each comparative algorithm's peak signal‐to‐noise ratio, structural similarity index and feature similarity index. The results show that the proposed BA‐based algorithm has apparent advantages, and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level. Based on a comprehensive study, the authors think the proposed optimiser has mitigated the main drawbacks of the BA, and it can be utilised as an effective optimisation tool.

Quality Certification and Business Performance: The Portuguese Large Firms’ Case

Objective: Observe the existence of a relationship between quality certification, financial performance and bankruptcy prediction, as well as the supposed superiority of the quality certified Portuguese large companies. Theoretical Reference: In the continuous pursuit for quality, as well as its certification, the International Standard Organization (ISO), suggests, from a theoretical perspective, a possible relationship of the last one with the going concern, contributing to the companies’ economic and financial sustainability. Method: We selected Portuguese large companies (according to recommendation 2003/361/EC) with Economic Activity Codes (NACE) D and G, certified with ISO 9000 or 9001, totalling 91 companies, along with computed average company from each of these sectors. We used univariate and multivariate techniques (MDA), models of business failure prediction, considered in previous studies as the most effective for the Iberian economy (Peres and Antão, 2018; Peres and Antão, 2019), aiming to assess the companies’ performance, as well as the supposed superiority of those quality certified companies. Results and Discussion: In neither of the analysis, univariate and multivariate, was observed a superior performance of the certified companies compared to others, indicating that quality certification is not effectively related to the companies’ economic and financial performance optimization. Implications of the Research: Uncover the influence of quality certification on the economic-financial position of companies, allowing for deeper investigation into bankruptcy, sustainability and value creation. Originality/Value: The specificities of the sectors and business size, crises and the increasing of business closures make it vital to identify what will influence performance and sustainability, as no studies have been identified on this topic, focusing on Portuguese Large companies, certified via ISO 9000 or 9001.

Optimization of Thermoelectric Performance of Ag2Te Films via a Co-Sputtering Method

Providing self-powered energy for wearable electronic devices is currently an important research direction in the field of thermoelectric (TE) thin films. In this study, a simple dual-source magnetron sputtering method was used to prepare Ag2Te thin films, which exhibit good TE properties at room temperature, and the growth temperature and subsequent annealing process were optimized to obtain high-quality films. The experimental results show that films grown at a substrate temperature of 280 °C exhibit a high power factor (PF) of ~3.95 μW/cm·K2 at room temperature, which is further improved to 4.79 μW/cm·K2 after optimal annealing treatment, and a highest PF of ~7.85 μW/cm·K2 was observed at 200 °C. Appropriate annealing temperature effectively increases the carrier mobility of the Ag2Te films and adjusts the Ag/Te ratio to make the composition closer to the stoichiometric ratio, thus promoting the enhancement of electrical transport properties. A TE device with five legs was assembled using as-fabricated Ag2Te thin films. With a temperature difference of 40 K, the device was able to generate an output voltage of approximately 14.43 mV and a corresponding power of about 50.52 nW. This work not only prepared a high-performance Ag2Te film but also demonstrated its application prospects in the field of self-powered electronic devices.

Parameter Study and Optimization of Static Performance for a Hybrid Cable-Stayed Suspension Bridge

The hybrid cable-stayed and suspension (HCSS) bridge is known for its stability and cost-effectiveness, with significant application potential. This study examined the static performance of an HCSS bridge with a 1440 m main span. A finite element model (FEM) was developed to assess key parameters, such as the span-to-rise ratio, cable-to-hanger ratio, pylon stiffness, steel–concrete interface, and cable stiffness. Through FEM analysis and parameter optimization using the zero-order and first-order optimization methods in an ANSYS module, key design variables were optimized. The results show that an inappropriate span-to-rise ratio negatively impacts mid-span girder forces, while increasing the cable-stayed area enhances the overall stiffness. Main cable stiffness plays a crucial role in load-bearing and deformation control. Significant force differences were observed between stay and hanger cables, with axial force in the main girder increasing from the side span to the pylon under dead load. Bending moments in the transition region varied widely under combined loads. Optimizing parameters, such as the span-to-rise and cable-to-hanger ratios, significantly improved the mechanical performance of HCSS bridges, offering valuable insights for future designs.

Honeycombed pomegranate-like sludge ceramsite particles: preparation with fly ash floating beads as the pore-forming template and performance optimization

Ceramsite used in construction and building are typically limited by high water absorption rates, the contradiction between lightweight and high strength, and severe performance fluctuations. This can be attributed to the fact that ceramsites are currently prepared by high-temperature sintering expansion, wherein viscosity and surface tension of the liquid phases shall perfectly match with the gas generation rate. On the other hand, sand washing sludge and fly ash are common solid wastes in construction and building and their environmentally friendly disposal are of great significance. In this study, honeycombed pomegranate-like sludge ceramsites with low density (packing density = 733.2-968.3kg/m3), high strength (cylinder compressive strength = 11.2-18.8MPa), and low water absorption rate (one-hour water absorption rate = 1.2-2.8%) were prepared with sand washing sludge and fly ash floating beads as the raw material and the pore-forming template, respectively. Also, the effects of particle size and content of fly ash floating beads on the structure and performance of the as-prepared ceramsite were investigated. The results indicated that the pore structure of the as-prepared ceramsite was directly dependent on size and content of the fly ash floating beads, suggesting that the performance of the as-prepared ceramsite can be customized by tuning size and content of the beads. Additionally, lightweight aggregate concrete prepared by using the as-prepared ceramsites exhibited improved 7-day and 28-day compressive strengths and reduced thermal conductivity compared with concrete prepared by using commercially available ceramsites. Overall, this study presents the preparation of ceramsite particles with both high strength and low density by using fly ash and sand washing sludge, both of which are abundant solid wastes in constructions, thus contributing to solid waste recycling in construction.

Quantitative Analysis of TPU Microstructure and Performance Optimization across Various Processing Conditions.

Thermoplastic polyurethane (TPU) is essential in resource exploration, healthcare, automotive, and high-end recreational sports. Despite extensive research on TPU's microstructures and their macroscopic properties, the impact of processing conditions like compression and injection molding remains underexplored. This study investigates the influence of processing conditions on TPU by preparing samples with varying hard segment contents using compression molding at 205 °C and injection molding at melt temperatures of 205, 210, 215, and 220 °C, followed by heat treatment at 120 °C for 12 h. Results indicate that injection-molded TPU at 205 °C exhibits lower hydrogen bonding, crystallinity, long period, interfacial thickness, and lamella thickness than compression-molded TPU, leading to higher Young's modulus but lower elongation at break. As melt temperatures increase, these microstructural parameters decrease, reducing Young's modulus and increasing elongation at break. Post heat treatment, microstructural parameters increase, aligning Young's modulus with that of compression-molded samples, while elongation at break surpasses them. This suggests that heat treatment enhances microphase separation by rearranging hard and soft segments. our research reveals a consistent pattern across TPUs with varying hard segment contents, indicating that adjusting processing parameters can effectively regulate microstructure and performance, offering valuable insights for developing high-performance polyurethanes.

Integrated respiratory toxicity of municipal wastewater to human bronchial epithelial cells and 3D bronchospheres

Respiratory symptoms have been reported in wastewater treatment workers and residents living close to sewage treatment plant. However, toxicological research about the respiratory hazards of municipal wastewater is scarce. The present study aims to gain insight into the comprehensive respiratory hazards induced by the contaminant mixtures in municipal wastewater. The integrated respiratory hazards of effluents from four secondary wastewater treatment plants (SWTPs), a tertiary wastewater treatment plant (TTP), and a constructed wetland (CW) were evaluated using normal human bronchial epithelial cells (NHBE) bioassay, and toxicity reduction efficiency of various treatment techniques was analyzed. Effluents caused cytotoxicity, oxidative damage, inflammation response with the increased levels of IL-6 and CXCL8, and impaired barrier integrity with decreased expressions of ZO-1 and occludin in NHBE. Further, the effluents inhibited the development of 3D bronchospheres, increased irregular surface and cell debris, and suppressed the formation of luminal structures. TTP E effluent significantly increased the expression of MUC5AC in bronchospheres. The integrated biomarker response (IBR) of the influent was removed by 40.2% at SWTPs, 18.2% at TTP, and 36.6% at CW, respectively. The IBR of the final effluents from SWTPs, TTP, and CW were 7.2, 7.7, and 7.7, respectively. Significant correlation with toxicity biomarkers was frequently observed for stearyl alcohol, o-cresol, phenanthrene, butylated hydroxytoluene, and dimethyl phthalate. The present study provided human relevant evidence concerning the adverse respiratory effects associated with discharge. The necessity for deep water treatment, performance optimization, and the potential means were suggested for improving water quality and protecting respiratory health.

Utilization of waste phosphogypsum in high-strength geopolymer concrete: Performance optimization and mechanistic exploration

Utilization of waste phosphogypsum in high-strength geopolymer concrete: Performance optimization and mechanistic exploration

Performance optimization and environmental assessment of novel alkali-activated binders containing carbonated recycled concrete powder

This study investigates the influence of carbonated recycled concrete powder (CRCP), alkali content (AC), and silicate modulus (SM) on the compressive strength, chemical composition, and environmental benefits of CRCP-blast furnace slag blended alkali-activated binders (AABs). The results show that increasing CRCP content reduces the compressive strength of AABs due to lower geopolymeric reactivity. Using response surface methodology, the optimal values for AC and SM in AABs containing 50 wt.% CRCP were determined as 14.28% and 0.92, respectively, resulting in predicted and actual strengths of 49.9 MPa and 46.8 MPa. Chemical analyses reveal that calcite and vaterite are the predominant crystalline phases in AABs, while both Si-Al gels and calcite from CRCP participate in geopolymeric reactions. The effects of AC and SM on AAB strength are further elucidated through the combined analysis of precursor dissolution amount, geopolymeric gel formation amount, and calcium carbonate decomposition amount. Moreover, CRCP demonstrates negative carbon emissions, with a global warming potential of -0.16 kg CO2 eq/kg. The optimally proportioned AAB containing 50 wt.% CRCP shows global warming potential and carbon intensity values significantly lower than those of Portland cement, at 54.4% and 50.8%, respectively. These findings emphasize that AABs containing 50 wt.% CRCP not only meet the 43-grade strength classification but also offer notable environmental benefits. In summary, this study contributes to the development of a novel 43-graded low-carbon binder containing high-volume CRCP.

Predictive Analytics in Solar Energy : Optimizing Efficiency with AI

This article explores the transformative role of predictive analytics and artificial intelligence in the rapidly growing solar energy sector. It examines how these technologies revolutionize solar farm operations by enabling equipment failure prediction, performance optimization, intelligent maintenance scheduling, and enhanced grid integration. The article delves into the key components of predictive analytics systems, including data collection via IoT sensors, advanced data processing, machine learning models, and actionable insights generation. It also discusses the technical implementation challenges, such as ensuring data quality, improving model interpretability, integrating with legacy systems, and addressing the industry skill gap. By leveraging these cutting-edge technologies, solar energy providers can significantly improve operational efficiency, reduce costs, and maximize energy production, ultimately accelerating the global transition to renewable energy.

Optimization of Hemolytic Performance for Blood Pump Based on Temperature Field Analysis and Three Control Strategies

Optimization of Hemolytic Performance for Blood Pump Based on Temperature Field Analysis and Three Control Strategies

Numerical simulation of micro gas turbine engine based on time-marching one-dimensional method

The advancement of gas turbine technology serves as a crucial indicator of high-tech and technological prowess, with widespread applications in aviation, marine vessels, power generation, and clean energy. The precision of the performance simulation model stands as a pivotal component in the development of gas turbines. The present study deals with the one-dimensional modeling and validation of gas turbines by utilizing a time-marching scheme, in order to establish the correlation between the engine components and full-engine performance. The flow through the turbomachinery was solved by the one-dimensional model. The combustor was modelled as a compressible actuator disk. The accuracy of the turbomachinery solver was validated using the KJ66 compressor and turbine, and the predicted results demonstrated consistency with the experimental data or the three-dimensional simulations. The characteristic interface method was developed based on Riemann invariants, in order to provide the flow properties on both sides of the combustor. The results indicated that compared to the component-by-component approach, the developed characteristic interface method significantly reduced the computational time. Finally, the developed one-dimensional method was employed to simulate the performance of the KJ66 micro gas turbine engine. The predicted performance for the KJ66 gas turbine engine was consistent with the trend in the experiment results. The novelty of the present study was its modeling approach, and its ability to eliminate the dependence on the general component map, and obtain meridional distributions of flow parameters, which cannot be achieved in traditional zero-dimensional thermal cycle calculations. These results can provide an accurate and powerful tool for the analysis and optimization of component and full-engine performance in the preliminary design stage.