Power Performance Research Articles

Infantry training outcomes: are they improved with an initial reduction in load carriage mass and additional sprint intensity exercise?

IntroductionInfantry is a physically demanding trade that is associated with elevated rates of musculoskeletal injury. A 17-week longitudinal intervention assessed the effect of a progressive increase in load carriage mass...

A Multivariate Machine Learning Approach for the Prediction of Wind Turbine Blade Structural Dynamics

Wind turbine blade structural dynamics are crucial in the turbine structural design phase. Blade deflections and loads can affect the weight of the rotor as well as the power performance of a wind turbine if the deflections are extremely high. Predictions of the turbine’s blade deflections and loads can lead to informative decisions on optimizing the design of the blade. In this work, a multivariate machine learning (ML) approach is used to predict the blade’s dynamics based on the wind flow conditions and control actions of the turbine. Three different datasets were generated using the OpenFAST software tool for three different wind turbulence classes. Various ML algorithms were trained to predict the blade deflections at the tip and blade loads at the root in the edgewise and flapwise directions. The ML models were tested for generalization of the model to different flow conditions. A model is trained for one dataset with one of the turbulence classes and then used to predict the outputs of the other two datasets. The random forest ML algorithm gave the best accuracy for predicting the outputs for the dataset it was trained for, as well as the other two datasets. The accuracy of predictions was found to be higher in the edgewise direction for both load and deflection outputs. In the flapwise direction, the model could predict the outputs of the data it was trained for with an accuracy of around 99% and for the other two datasets with an accuracy of over 75%. While in the edgewise direction, the model trained on only one dataset gave a prediction accuracy above 95% for all three datasets.

Theta transcranial alternating current stimulation over the prefrontal cortex enhances theta power and working memory performance

IntroductionTranscranial alternating current stimulation (tACS) is a promising tool for modulating brain oscillations. This study investigated whether 5 Hz tACS could modulate neural oscillations in the prefrontal cortex and how this modulation impacts performance in working memory (WM) tasks.MethodIn two sessions, 28 healthy participants received 5 Hz tACS or sham stimulation over the left dorsolateral prefrontal cortex (DLPFC) while performing tasks with high and low WM loads. Resting-state EEG was recorded before and after stimulations for 5 minutes. EEG power was measured at electrodes surrounding the stimulation site.ResultsThe results showed that tACS significantly improved reaction time (RT) compared to sham stimulation. This effect was task-specific, as tACS improved RT for hit responses only in high WM load trials, with no impact on low-load trials. Moreover, tACS significantly increased EEG power at 5 Hz and in the theta band compared to pre-stimulation levels.DiscussionThese findings demonstrate that tACS applied over left DLPFC modulates post-stimulation brain oscillations at the stimulation sites – known as tACS after-effects. Furthermore, the results suggest that 5 Hz tACS enhances response speed by elevating task-related activity in the prefrontal cortex to an optimal level for task performance.ConclusionIn summary, the findings highlight the potential of tACS as a technique for modulating specific brain oscillations, with implications for research and therapeutic interventions.

Enhanced Performance of High-Power InAs/GaAs Quantum Dot Lasers Through Indium Flushing

InAs/GaAs quantum dots (QDs) appear promising for optoelectronic applications. However, the inhomogeneous broadening caused by natural strain and the non-uniform size distribution deteriorates the device performance based on multi-stacked QD layers. In this study, In-flush was incorporated during the epitaxy, and the photoluminescence (PL) linewidth was significantly narrowed to 26.1 meV for the flushed sample and maintained to 27.3 meV for the unflushed sample. The flushed sample shows better device performance in threshold current (0.229 to 0.334 A at 15 °C), power (1.142 to 1.113 W at 15 °C), and characteristic temperature (51 to 39 K in the range of 55~80 °C) compared with the unflushed sample.

Optimized Design and Applications of Arithmetic Logic Units: Addressing Power Efficiency and Performance in Diverse Computing Applications

In modern computer systems, the Arithmetic Logic Unit (ALU) is the core component of the central processing Unit (CPU) and performs basic tasks such as arithmetic operations, logic operations, and data transmission. With the development of information technology, the demand for computing is growing, the requirements for computing accuracy, speed and energy consumption are becoming more stringent. The design and optimization of ALU is directly related to the overall performance and energy efficiency of the system, so it becomes the focus of research and the key to technological breakthroughs. This paper focuses on summarizing and exploring the application scenarios, development, and optimization prospects of ALUs. This study reviews various application contexts, including embedded systems, quantum computing, and high-performance processors, highlighting how tailored ALU designs can meet the demands of each. In the field of optimization strategies, Gate Diffusion Input (GDI) technology, reversible logic, and Single Electron Transistor (SET) technology were discussed as a way to reducing power consumption and improving processing speeds.

Chromatic Crescendo: Night light colours boost Chrysanthemum growth and bloom

This innovative study, conducted at the Research Farm, Department of Floriculture and Landscaping, Punjab Agricultural University, Ludhiana, from 2018 to 2019, investigated the effects of coloured LED night breaks on Chrysanthemum morifolium Ramat. growth and flowering. Ten diverse cultivars were subjected to red, green, and blue light treatments from mid-August to late October, followed by natural day length exposure. Our findings reveal a nuanced interplay between light colour and genotype, significantly influencing plant architecture, flowering dynamics, and pigment production. Red light emerged as a maestro of stem elongation, producing the tallest plants (mean height 100.97 cm), while blue light conducted a powerful performance in stem thickening (mean diameter 7.81 mm) and stomatal development (up to 54.66 stomata per mm²). Green light accelerated flowering, with bud appearance as early as 55.79 days in ‘Thai Chin Queen’. Vase life peaked at 17.57 days under red light for ‘Snow Ball’, while flower size reached 19.24 cm in ‘Snow Ball’ under blue light. Anthocyanin content was highest (2.19 mg g-1) in ‘Thai Chin Queen’ under blue light, and carotenoids peaked (97.18 mg g-1) in ‘Pusa Centenary’ under blue light. This research unveils the potential for tailored light recipes in chrysanthemum cultivation, promising enhanced control over growth, flowering, and quality traits. Our results pave the way for innovative, sustainable practices in ornamental horticulture, offering a spectrum of possibilities for precision floriculture.

Clinical decision instruments for predicting mortality in patients with cirrhosis seeking emergency department care.

Clinical decision instruments (CDIs) could be useful to aid risk stratification and disposition of emergency department (ED) patients with cirrhosis. Our primary objective was to derive and internally validate a novel Cirrhosis Risk Instrument for Stratifying Post-Emergency department mortality (CRISPE) for the outcomes of 14- and 30-day post-ED mortality. Secondarily, we externally validated the existing Model for End-Stage Liver Disease (MELD) scores for explicit use in ED patients and prediction of the same outcomes. A cohort of 2093 adults with cirrhosis, at 16 sites in a statewide health system, was analyzed for 119 candidate variables available at ED disposition. LASSO with 10-fold cross-validation was used in variable selection for 14-day (CRISPE-14) and 30-day (CRISPE-30) logistic regression models. Area under the receiver operating characteristic curve (AUROC) was calculated for each variant of the CRISPE and MELD scores and compared via Delong's test. Predictions were compared to actual ED disposition for predictive value and reclassification statistics. Median (interquartile range [IQR]) characteristics of the cohort were age 62 (53-70) years and MELD 3.0 13.0 (8.0-20.0). Mortality was 4.3% and 8.5% at 14 and 30 days, respectively. CRISPE-14 and CRISPE-30 outperformed each MELD variant, achieving AUROC of 0.824 (95% CI: 0.781-0.866) and 0.829 (0.796-0.861), respectively. MELD 3.0 AUROCs were 0.724 (0.667-0.781) and 0.715 (0.672-0.781), respectively. Compared to ED disposition, CRISPE-14, CRISPE-30, and MELD 3.0 significantly improved positive and negative predictive value and net reclassification index at multiple cutoffs. Applying CRISPE-30 (cutoff 4.5) favorably reclassified one net ED disposition for mortality for every 12 patients, while MELD 3.0 net reclassified one disposition per 84 patients. CDIs may be useful in risk-stratifying ED patients with cirrhosis and aiding disposition decision making. The novel CRISPE CDI showed powerful performance and requires external validation, while the existing MELD 3.0 score has moderate performance and is now externally-validated in an ED population for short-term mortality.

Effects of Half-Time Re-Warm-Up With Core Strength Exercises on Subsequent Agility and Power Performance in Basketball Players.

Yang, Y-R, Chen, C, Pan, C-H, Yen, S-Y, and Cheng, C-F. Effects of half-time re-warm-up with core strength exercises on subsequent agility and power performance in basketball players. J Strength Cond Res 39(1): 62-69, 2025-This study investigated the effects of half-time re-warm-up (RW) comprising core strength exercise (CSE) on basketball players' subsequent power and change of direction (COD) performance. Twelve male collegiate basketball players were recruited to perform 3 conditions--CSE RW on a stable platform (STA) or an unstable platform (USTA) and passive rest (control, CON)--in randomized, counter-balanced order after a modified Loughborough intermittent shuttle test (LIST). The COD and power performance tests were administered before the LIST and after the interventions. Heart rate (HR) was continuously measured during each trial. Results showed significantly lower changes in T-test time in STA compared with CON (p < 0.05). Changes in countermovement jump height in STA were significantly higher than those in CON (p < 0.05). During the isometric midthigh pull test, changes in the maximum rate of force development (RFD) (p < 0.05), RFD in the range of 0-90 milliseconds (p < 0.05), 0-150 milliseconds (p < 0.05), 0-200 milliseconds (p < 0.05), and 0-250 milliseconds (p < 0.05), in STA were significantly higher than those in CON. In addition, RFD in the range of 0-150 milliseconds (p < 0.05) and 0-250 milliseconds (p < 0.05) in USTA was significantly higher than that in CON. The mean HR during USTA was significantly higher than that in STA and CON (p < 0.05). These findings indicated that CSE RW during halftime might prevent power and COD performance decline in the second half of a game, with practical implications for coaches and players.

Artificial Intelligence-Based Direct Power Control for Power Quality Improvement in a WT-DFIG System via Neural Networks: Prediction and Classification Techniques

This paper discusses the improvement of power quality injected into the AC grid. This approach is achieved by enhancing the quality of injected power signals and mastering the active and reactive power exchanged between the DFIG based wind turbine (WT-DFIG) and the electrical grid, resulting in an improvement of the overall system performance and efficiency. This study includes all WT-DFIG operating modes, successively and continuously, as well as all local reactive power compensation modes. Therefore, novel control strategies are proposed in this paper for wind energy conversion systems based on artificial intelligence techniques. These techniques include Neural Network Prediction (PNN-DPC) and Classification (CNN-DPC). They aim to eliminate the drawbacks and difficulties associated with conventional Direct Power Control (C-DPC), while retaining its advantages. The paper also provides a thorough explanation of the mathematical models for neural network techniques and WT-DFIG system models. The MATLAB/Simulink environment is used to investigate the performance of the proposed techniques under different conditions and operating modes related to different scenarios. The results reveal a significant reduction in the ripple of the generated active power and the compensated local reactive power, better quality of the generated signal currents and a remarkable reduction in the current total harmonic distortion (THD). Furthermore, compared to C-DPC, PNN-DPC achieves a reduction of 72.07% in active power ripples, 77.07% in reactive power ripples, and 76.79% in current Total Harmonic Distortion (THD). CNN-DPC shows similar improvements with 72.04%, 77.13%, and 76.54% of reductions respectively. In addition, CNN-DPC slightly outperforms PNN-DPC. Nevertheless, both proposed control techniques show significant improvements in all characteristics compared to other methods. Consequently, the proposed control strategies indicate that artificial intelligence has the potential to improve the power quality and performance of wind power conversion system.

The Role of Carbon in Lead-Acid Batteries: Applications, Challenges, and Future Opportunities

The incorporation of various forms of elemental carbon into lead-acid batteries has the potential to significantly enhance battery performance. Carbon materials are commonly used as additives to the negative active material, particularly to improve cycle life and charge acceptance under high-rate partial state-of-charge (HRPSoC) conditions, which are prevalent in hybrid and electric vehicles. Carbon nanostructures and composite materials may also offer similar benefits. However, the impact of carbon on the positive active material is generally more limited compared to its influence on the negative side. Additionally, carbon can serve as a mesh current collector for both negative and positive plates. This advanced technology boosts energy storage efficiency by increasing the battery’s specific energy and optimizing active mass utilization. Such batteries, featuring a more robust active mass structure, promise extended cycle life. Recently, another important application of carbon in secondary batteries is its use in supercapacitor electrodes, which can either replace the negative plate or be connected in parallel with the lead plate. These innovative approaches enhance overall battery efficiency by improving specific power and HRPSoC performance. Furthermore, integrating carbon-based technologies into the production of lead-acid batteries can significantly enhance their performance, giving them a competitive advantage over other battery systems. These advancements also hold substantial potential for delivering more environmentally friendly and cost-effective energy storage solutions.

Self-Locking Domino Logic Pipelines: Application in RISC-V Architectures in FPGA

This paper presents the design and implementation of a self-locking domino logic pipeline controller for a RISC-V processor implemented on an FPGA. The emphasis is on asynchronous circuit design, which offers advantages such as enhanced resilience to supply voltage fluctuations, optimized power efficiency, and the elimination of clock-related issues such as skew and single-point failures. By leveraging the asynchronous Globally Asynchronous Locally Synchronous (GALS) systems and domino logic, the controller ensures hazard-free operation while maintaining race-free processing. The asynchronous approach, integrated into a 32- bit RISC-V processor, allows for flexible and energy-efficient operation, thereby demonstrating its potential for performance-critical applications. This paper high- lights the contrasts between the asynchronous design and the traditional synchronous multicycle processor, demonstrating the benefits of asynchronous systems in terms of power consumption and performance. A significant contribution of this design is the pipeline’s completion detection mechanism, which ensures that each processing stage locks until valid results are obtained, thereby markedly enhancing system stability. Furthermore, the paper investigates the parallelization of domino gates and introduces an asynchronous Arithmetic Logic Unit (ALU), which further optimizes performance through self-locking mechanisms. The power, performance, and area (PPA) analysis of the design demonstrates considerable improvements in throughput (up to 10%) and reduced latency per instruction in comparison to its synchronous counterpart, while maintaining moderate resource utilization on an FPGA. The results indicate that asynchronous domino logic pipelines may offer a promising approach for achieving energy-efficient and high-performance processors in future computing architectures.

Power and emission estimation of plastic waste pyrolysis-derived fuel blends in internal combustion engines

Energy, especially from fossil fuels, is essential for everyday life, while plastic waste is an increasing environmental threat. Plastic waste disposal methods such as landfilling and burning cause pollution. Therefore, a process is needed that converts plastic waste into fuel. The object of the study is the engine performance. The problem to be solved is the relationship between the use of a mixture of fossil fuels and pyrolysis fuel on the performance of internal combustion engines. This research uses a systematic data collection process to obtain accurate and reliable results. The necessary equipment, including a dynamometer and gas analyzer, was prepared, and the engine was warmed up to a stable operating temperature of 80 °C. The motorbike is then positioned on the dynamometer with the rear tires aligned and the front tires secured to prevent movement. Data collection was carried out at engine speeds of 2000, 3000, 4000, 5000, and 6000 rpm, using three fuel mixtures: 10 % plastic pyrolysis fuel with 90 % RON 90, 20 % plastic pyrolysis fuel with 80 % 90 RON, and 30 % plastic pyrolysis fuel with 70 % RON 90. Each test was repeated three times, with the output power measured using a dynamometer and exhaust emissions (CO and HC levels) recorded using a gas analyzer. The test results show that the optimal fuel mixture to produce maximum engine power is a PE-RON 90 mixture with a ratio of 20:80, providing the best performance at medium to high engine speeds (3000–6000 rpm) with low CO emissions. The highest power output (1.05) occurs at 4000 rpm, while the PE-RON 90 30:70 alloy produces the best power performance at 6000 rpm (0.78 % CO). Additionally, the pyrolysis fuel blend significantly reduces CO and HC emissions, with the PE-RON 90 30:70 blend showing the lowest CO (0.78 % at 6000 rpm) and consistently reducing HC emissions across the rpm range

Experimental optimization of the SG6043 airfoil for horizontal axis wind turbine using Schmitz equations

This study presents the experimental optimization of the SG6043 airfoil for horizontal axis wind turbines (HAWTs) using the Schmitz equation, focusing on enhancing power output and elucidating the surface flow structure. Two blade models, M1 (conventional) and M2 (optimized), were designed and tested at rotational speeds of 400 rpm and 600 rpm across a range of tip speed ratios (TSR). The M2 model, optimized using Schmitz equations, demonstrated significantly improved performance compared to the M1 model at both rotational speeds. At 400 rpm, the maximum power coefficient (CP) for M1 was 0.274, while M2 reached 0.419, indicating a 52.91% improvement. At 600 rpm, M1 achieved a maximum CP of 0.293, whereas M2 attained 0.458, representing a 56.31% enhancement. The M2 model also showed superior performance at higher TSRs, with the highest percentage increase in CP recorded at 4.9 TSR, reaching 574.54%. Additionally, dynamic surface oil-flow visualization experiments were conducted to examine flow behavior on the blade surfaces. Results indicated better flow attachment in the M2 blade due to its optimized twist angle and chord length, particularly in the mid-section, leading to delayed flow separation. The reattachment observed on the suction side of the M2 model, following the laminar separation bubble (LSB), which was absent in the M1, contributed to its higher aerodynamic efficiency and overall power performance. These findings confirm that the optimized SG6043 airfoil design, guided by Schmitz equations, offers significant improvements in HAWT performance, particularly under varying operational conditions.

Optimizing the Power Performance of Lithium‐Ion Batteries: The Role of Separator Porosity and Electrode Mass Loading

This study investigates the concealed effect of separator porosity on the electrochemical performance of lithium‐ion batteries (LIBs) in thin and thick electrode configuration. The effect of the separator is expected to be more pronounced in cells with thin electrodes due to its high volumetric/resistance ratio within the cell. However, the electrochemical analyses show similar power performance regardless of the separator porosity in the thin electrode configuration. In contrast, for cells with thick electrodes, separator porosity significantly impacts the direct current‐internal resistance (DC‐IR) and the capacity retention at a high rate. This behavior is attributed to ion concentration gradients in the upper regions of thick electrodes, while Li+ transfer to lower regions is hampered as the electrode thickness increases. These findings suggest that the intrinsic properties of individual cell components, such as separator porosity, are highly dependent on the overall cell design. Moreover, while high‐porosity separators enhance power performance, particularly in thick electrode configurations, they exhibit lower thermal stability and tensile strength. In conclusion, this study highlights the need for an integrated approach to optimizing separator characteristics, considering both electrochemical performance and safety trade‐offs in LIBs.

Investigating the correlation between EEG brainwave patterns and English reading proficiency using biosensors

Reading ability is a complex cognitive activity affected by various neural mechanisms. This research aims to examine the relationship between brain activity, as measured by non-invasive biosensors, specifically an electroencephalogram (EEG), and English reading ability. Specifically, the research investigates how different brainwave patterns (alpha, beta, and theta waves) relate to reading speed, comprehension, and accuracy across different levels of reading proficiency. Biosensors, in EEG devices, suggest a non-invasive means of monitoring brain functions in real-time, giving extensive vision into cognitive functions correlated with reading performance. Seventy native Chinese-speaking university students in China were selected as participants for this research. Based on their performance in word recognition, sentence comprehension and abstract reading, the participants are divided into high, intermediate, and low proficiency groups. While participants are performing the reading tasks, their brain activity was recorded using a 32-channel EEG system. The three main frequency bands for EEG set up are, theta waves, 4–7 Hz; alpha waves, 8–12 Hz; and beta waves, 13–30 Hz. The EEG-based biosensor system offers high resolution data, allowing very precise measurements of brainwave activity, which are directly correlated with cognitive states during reading tasks. Correlation analyses, one-way ANOVA, and multivariate regression models were applied to check the associations between brainwave power and reading performance in the context of proficiency groups. The outcome suggests that higher alpha and beta wave activity was related to greater reading proficiency, with higher beta waves being related to higher speeds as well as better comprehension, while theta waves were again more pronounced in low-proficiency readers on complex tasks. These results suggest that EEG-based biosensors assessments should be used to measure cognitive states related to reading proficiency and may offer a new avenue for personalized educational intervention.

A maximum statistic for the one-sided location-scale alternative in the two-stage design

AbstractAn increase in location is typically accompanied by an increase in variability. Subsequently, the heteroscedasticity can indicate a treatment effect. Therefore, it may be appropriate to perform a location-scale test. A common statistic for a location-scale test is the sum of a location and scale statistic. As demonstrated by Neuhäuser (Biometri J 43:809–819, 2001), weighting the sum increases the power. Although weights cannot usually be reasonably selected a priori, a weighting is possible in an adaptive design using the information obtained in an interim analysis. Here, we propose an adaptive statistic that increases and stabilizes the power. The power performance in various situations for continuous and discrete distributions is investigated using Monte Carlo simulations, which reveal that the proposed statistic increases and stabilizes the power, thus rendering it a strong competitor to existing location-scale statistics. The new statistic is illustrated using real data.

ChirpArray: A low‐cost, easy‐to‐construct microphone array for long‐term ecoacoustic monitoring

Abstract Advances in passive acoustic monitoring (PAM) have highlighted the importance of recording devices and audio recognition techniques in ecosystem monitoring. This study introduces ChirpArray, a cost‐effective and easily assembled microphone array for long‐term ecoacoustic monitoring of outdoor ecosystems. The ChirpArray system features a four‐channel microphone array that estimates sound source directions, aids in identifying individual animals and provides a detailed behavioural analysis. Unlike previous microphone arrays, ChirpArray is low‐cost, low‐power and waterproof, making it ideal for extended‐field monitoring. It is a fully open source and is constructed from readily available materials, ensuring broad accessibility for applications ranging from local citizen projects to large‐scale landscape recordings. This study details the power, storage consumption and localization performance of ChirpArray. Current measurements show that a small solar‐power set‐up can continuously operate the system. Localization tests using loudspeakers have yielded promising results. ChirpArray is a compact, energy‐efficient microphone array designed for long‐term outdoor recording, offering considerable advantages in ecoacoustic monitoring. Its exceptionally low power consumption allows for efficient and flexible deployment, making it an ideal solution for extended‐field monitoring and large‐scale landscape recordings.

Effect of Green Tea Extract in Conjunction with Exercise Prices of Training on Anaerobic Performances in College Students’ Outcomes

To evaluate the effects of various interventions on anaerobic power, fatigue index, and overall performance in college students. Participants were 30 college students aged 18-22 years from Sichuan Vocational College of Health. Their height, weight, and anaerobic capacity were measured using the running-based anaerobic sprint test (RAST). We divided the students into three groups of 10 participants each, consisting of 3 males and 7 females, based on their RAST test scores. Group 1 received a placebo (500 mg) combined with 4 weeks of athletic training; Group 2 received a single dose of 500 mg of green tea extract with exercise; and Group 3 received 500 mg of green tea extract combined with 4 weeks of athletic training. Before-and-after-experimental data research designs were compared using a t-test analysis, while differences between groups were assessed. The results showed that statistically significant were not differences in anaerobic capacity for Group 1, while Group 2 displayed significant improvements in anaerobic capacity and mean power among female students (p&lt;.05). Group 3: students’ exercise training on anaerobic power, fatigue index, and mean power variables are differences, significant for both male and female students (p&lt;.05). Confirmation of the effect of green tea extract in conjunction with exercise prices of training on anaerobic performance in college students’ outcomes between each paired-group using one-way ANOVA, the results revealed that the four variables are differences, significant in anaerobic power, average power, and fatigue index across the groups, and Group 3 indicating the most notable improvements. Suggestions that, combining green tea extract with prolonged athletic training significantly improves anaerobic capacity, particularly in female participants. These results highlight the potential benefits of green tea extract in enhancing athletic performance, warranting further research to explore its long-term effects and optimize exercise interventions.

CEO Power and Carbon Emissions Management: Australian Evidence

ABSTRACTWe investigate how CEO power contributes to the emissions management of Australian companies. Constructing a CEO power index and employing firm‐level carbon emissions data, we document a significant negative relationship between CEO power and carbon emissions, suggesting that firms with powerful CEOs better manage their carbon emissions. Extending our analyses into different dimensions of power, we find that the CEO power–emissions management relationship mainly comes from structural power and expert power sources. Further analyses show that emissions management plays a significant mediating role in the association between CEO power and firm performance. A survey administered among sustainability managers of large Australian companies reveals corroborative evidence that the leadership of powerful CEOs is an essential element in managing carbon emissions and mitigating the risk associated with climate change. The findings of this study provide insights to policymakers, regulators and corporate top‐management teams regarding an issue that is under severe public scrutiny and social pressure.

Testing Spherical Symmetry Based on Statistical Representative Points

This paper introduces a novel chisquare test for spherical symmetry, utilizing statistical representative points. The proposed representative-point-based chisquare statistic is shown, through a Monte Carlo study, to considerably improve the power performance compared to the traditional equiprobable chisquare test in many high-dimensional cases. While the test requires relatively large sample sizes to approximate the chisquare distribution, obtaining critical values from existing chisquare tables is simpler compared to many existing tests for spherical symmetry. A real-data application demonstrates the robustness of the proposed method against different choices of representative points. This paper argues that the use of representative points provides a new perspective in high-dimensional goodness-of-fit testing, offering an alternative approach to evaluating spherical symmetry in such contexts. By leveraging the flexibility of choosing the number of representative points, this method ensures more reliable detection of departures from spherical symmetry, especially in high-dimensional datasets. Overall, this research highlights the practical advantages of the proposed approach in statistical analysis, emphasizing its potential as a powerful tool in goodness-of-fit tests within the realm of high-dimensional data.