Individual Load Research Articles

Experimental study on the behavior of small-scaled circular and square RC columns under various loading conditions and corrosion effect

Reinforced concrete columns are vital load-bearing elements, responsible for transferring vertical loads and resisting lateral forces. While numerous factors govern column performance, this study pinpoints cross-sectional shape, transverse reinforcement configuration, and the corrosive threat as its primary areas of investigation. Thus, 20 small-scaled (1/8th scale) RC columns (square and circular, with ties or spirals) were tested under various loading conditions. Columns were divided into five groups. A preliminary test was conducted to determine individual axial load capacity through failure under axial compressive loading. The main test then explored their behavior under combined axial load and variable lateral drift compared to their behavior at two corrosion levels 15% and 25%. While corrosion levels were roughly achieved, uneven distribution due to the accelerated process concentrated pitting near construction joints, impacting reinforcement more than rebar. Results confirmed the efficacy of spirals in enhancing capacity, particularly for circular columns where they also increased lateral load capacity. However, spirals also amplified corrosion vulnerability in circular columns, suggesting a steeper decline in performance at higher corrosion levels. Two key models were derived from analyzing the recorded data: normalized total dissipated energy and normalized absolute peak load. Future research should prioritize exploring column behavior under a wider range of combined loads and corrosion levels to refine and validate these models.

CLEMD, a circuit-level electrical measurements dataset for electrical energy management

Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition.

Correlation of Blade Loading with SpinnerLidar-Measured Inflow

The Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) project’s main objective was collecting data for validation of aerodynamic and aeroelastic codes for large, flexible rotors. These data come from scanning lidars of the inflow and wake, met tower, profiling lidar, blade deflection from photogrammetry, turbine SCADA data (including root bending loads), and hub-mounted SpinnerLidar inflow measurements. The goal of the present work is to analyze various methods to align the SpinnerLidar inflow data in time and space with individual blade loading. These methods would prove a way of analyzing turbine response while estimating the flowfield at each blade and provide a way of improving turbine response understanding using field data in real time, not just from simulations. The hub-mounted SpinnerLidar measures the inflow in the rotor frame meaning the locations of the blades relative to the measurement pattern do not change. The present work outlines some methods for correlating the SpinnerLidar inflow measurements with root bending loads in the rotor frame of reference accounting for both changes in wind speed and rotor speed from the measurement location one diameter upstream to each blade.

A VMD-deep learning approach for individual load monitoring and forecasting for residential buildings energy management

This paper presents a comprehensive solution for Multi-Individual load disaggregation and Multi-individual load Forecasting in residential energy systems. The approach combines Variable Mode Decomposition (VMD) and deep learning algorithms, incorporating feature selection using Random Forest Regressor and parameter optimization through the Salp Swarm Algorithm (SSA). Correlation analysis is used to identify the most relevant Intrinsic Mode Functions (IMFs) for each appliance, while self-correlation is used to determine the past window for forecasting appliances’ usage a day ahead. The robustness of VMD against noise contributes to the improved accuracy of appliance designation. The load disaggregation strategy yields significant results, indicating the high performance of the disaggregation tasks of these models. Specifically, the proposed disaggregation models exhibit an average RMSE and MAPE of 4.10 W and 4.14 %, respectively. Furthermore, we conduct a comparison among three deep learning architectures (GRU, LSTM, 1DCNN) based Multi-Individual load forecasting models, revealing that the GRU-based MILF model outperforms the other two models, it shows an average performance of RMSE and MAPE of 9.54 W and 8.10 %. The research provides valuable insights and a practical solution for accurate load disaggregation and forecasting, contributing to enhanced energy efficiency, cost savings, and sustainable power consumption in residential settings, and give detailed energetic bill with the aim to analyze the occupant consumption behavior, and gives depth insights about power consumption and cost of each appliance and their operation time.

Strategic information planning and performance of SMEs: A structural equation modelling approach

BACKGROUND: The business environment is getting unstable due to which execution of decision support systems has become significant to maintain competitive advantage. Small and medium enterprises in particular confront issues, such as insufficiency of devising strategic planning, strategic decision-making, and information exchange, as well as difficulty increasing performance. Because functional strategic management and decision-making are required, strategic information systems planning is employed to accumulate information and assist decision-makers in developing and implementing the best strategy for gaining superior performance. Executives in information systems focus on technical difficulties while ignoring decisions to support strategic plans. OBJECTIVE: The purpose of this article is to investigate how strategic information systems planning contributes to increased performance in SMEs. METHODS: Data was gathered through questionnaires distributed to information system executives in SMEs, primarily in Jordan. The data was collected with the help of questionnaires adopted from prior studies. For Analysis after applying the diagnostic tests, structural equation modelling has been applied to test the framework developed based on the literature. RESULTS: A structural equation modeling (SEM) was conducted, the individual loadings of the SEM items are investigated, reliability and discriminant validity is tested, and path coefficients of hypothesized relationships of the developed model are tested. CONCLUSIONS: The findings suggested that managers should prioritize implementing strategic information systems planning so that they can gain superior performance with better agility in the future. This research not only enhances the current understanding of the significance of strategic information systems planning but also assists supervisors to improve the procedure.

Correlation between cytomegalovirus (CMV) viral load and mortality rate in individuals with acquired immunodeficiency syndrome (AIDS)

AIDS (acquired immunodeficiency syndrome) stems from immune deficiency caused by HIV (human immunodeficiency virus). CMV (cytomegalovirus), opportunistic in HIV/AIDS patients, is associate with mortality at elevated viral loads in individuals with AIDS. Objective: accomplish an integrative review to analyze the relation between CMV viral load and mortality rate in patients with AIDS. Methods: Usage of full review was made by following the said steps: theme identification and research questions structuring; definition of inclusion criteria; selection of articles based on scientific data; evaluation and analysis of selected studies; result interpretation and display of the final full review; insert method description. Result: High CMV viral loads are associated with elevated mortality in individuals with AIDS. Conclusion: It confirms the presence of a correlation between an increase in CMV viral load and an increase in the mortality rate of immunosuppressed patients, associates with viral loads ≥ 1000 copies/ml, marking this as a risk cutoff.

Interactions between the gut bacterial community of Exopalaemon carinicauda and infection by Enterocytozoon hepatopenaei

To explore the relationship between the intestinal flora of Exopalaemon Carinicauda and infection by Enterocytozoo Hepatopenaei (EHP), we analyzed the species and richness of gut microbiota in infected individuals in different EHP load groups [i.e., control (C), high load (H), and low load (L)] using gene sequencing after infection. The results showed that the abundance of intestinal flora in the high-load EHP group was significantly lower than that in the healthy group. Based on the UPGMA cluster tree and PCoA analysis, with comparisons to healthy shrimp, the gut microbiota of the EHP high load and low load groups were clustered into one branch, which indicated that EHP infection changed the composition of the gut microbiota of infected shrimps. The heat map analysis of species abundance clustering revealed that the dominant bacteria in the low EHP load group and the control group were beneficial genera such as Lactococcus, Ligilactobacillius, and Bifidobacterium, but the dominant bacteria in the high EHP load group were harmful genera such as Pseudomonas, Photobacterium, and Candidatus hepatincola. The functions of the intestinal flora predicted that most genes related to metabolism were more abundant in healthy shrimp, most genes related to metabolism and the organisms’ system were more abundant in the low EHP load group, and most genes related to diseases and environmental information processing were more abundant in the high EHP load group. After separation and purification, the dominant bacteria (Bifidobacterium animalis in healthy shrimp and Lactococcus garvieae in the low EHP load group) and the non-dominant bacteria (Macrococus caseolyticus in the low EHP load group) were obtained. Each of these isolated strains were used together with EHP to infect E. carinicauda, and the results showed that Bifidobacterium animali and Lactococcus garvieae significantly reduced the EHP load in EHP-infected individuals. At the same time, the morphology and structure of the hepatopancreas and intestinal tissue of EHP-infected E. carinicauda were improved. No improvement was seen in tissue that was infected with Macrococus caseolyticus.

Selection and integration of adjustable load in multiple scenarios

In the context of the “carbon peaking and carbon neutrality goals”, the new electricity systems are evolving towards source-load friendly interactions. At the same time, the increasing necessity for adjustable loads to participate in the power balance of the new electricity systems is highlighted. Consequently, a large-scale source-grid-load design scheme is proposed. It employs three independent communication channels to facilitate communication between users and intelligent grid-load terminals. Based on analysis of the individual loads and equipment characteristics of each user, principles for user shunt load access principle and adjustable loads are formulated by combining the maximum duration, frequency constraints, and compliance costs. Furthermore, it proposes construction plans for both the customer side in a single distribution room and the customer side distributed across different locations. According to the experimental analysis of 245 users, the response time of the experimental system is less than 600 ms. These advancements enhance the emergency response speed and responsiveness, contributing to the advancement of precise, real-time, and lean management of the demand side of the power grid.

Optimal control of a multi-scale immuno-influenza A transmission model with viral load-dependent infection

Influenza A is a highly contagious respiratory illness that spreads globally and results in millions of cases worldwide. The transmission rate and mortality rate of influenza in the population are determined by the load of the influenza A virus. In this study, we have developed a multi-scale immuno-influenza model considering incomplete vaccine immunity. We have calculated the basic reproduction numbers for both immunological and epidemiological models. Additionally, we have examined the stability of the disease-free equilibrium and the existence of the endemic equilibrium at the population level. To assess the cost-effectiveness of antiviral therapy at an individual level and vaccination at a population level, we have formulated an optimal control problem. The objective of this problem is to minimize both the number of influenza cases and the cost of vaccination. To solve this optimal control problem, we have employed the Ekeland variational principle. Furthermore, we have designed a global forward and backward sweep algorithm to estimate the impact of antiviral treatment on reducing influenza transmission among humans. Our findings indicate that the use of antiviral treatment significantly reduces viral loads in individuals. However, it may also increase the potential risk at the population level. Therefore, vaccination against influenza is the most effective option from a public health perspective.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

Individual household load forecasting using bi-directional LSTM network with time-based embedding

Accurate individual household load forecasting is essential for effectively managing energy demand and promoting efficient energy consumption. This study evaluates the performance of various deep learning models for individual household load forecasting, specifically using the Smart Grid Smart City (SGSC) dataset. Feature engineering is conducted, producing two distinct sets of features, and the models’ accuracy in predicting individual household loads is assessed using both basic and derived feature sets. The results demonstrate that the T2VBiLSTM model outperforms other models, achieving an average mean absolute percentage error (MAPE) of 74.90% and a root mean square error (RMSE) of 0.433 kW. The incorporation of a wider range of features, including maximum load, minimum load, and load range over time, is emphasized to enhance forecasting accuracy. These features capture long-term load behavior, enabling the models to comprehend complex energy consumption patterns and generate more accurate and reliable predictions. Moreover, limitations in using MAPE as a loss function for load forecasting at the individual customer level are revealed, due to its sensitivity to scale, asymmetry, outliers, and uniform weighting assumption. Alternative loss functions like mean absolute error (MAE) are recommended, as they treat all errors equally and better capture data peaks. While this study focuses on the SGSC dataset, the findings have broader implications for efficiently managing energy demand and promoting energy consumption on a larger scale.

Mechanoreceptor sensory feedback is impaired by pressure induced cutaneous ischemia on the human foot sole and can predict cutaneous microvascular reactivity.

The foot sole endures high magnitudes of pressure for sustained periods which results in transient but habitual cutaneous ischemia. Upon unloading, microvascular reactivity in cutaneous capillaries generates an influx of blood flow (PORH: post-occlusive reactive hyperemia). Whether pressure induced cutaneous ischemia from loading the foot sole impacts mechanoreceptor sensitivity remains unknown. Pressure induced ischemia was attained using a custom-built-loading device that applied load to the whole right foot sole at 2 magnitudes (15 or 50% body weight), for 2 durations (2 or 10 minutes) in thirteen seated participants. Mechanoreceptor sensitivity was assessed using Semmes-Weinstein monofilaments over the third metatarsal (3MT), medial arch (MA), and heel. Perceptual thresholds (PT) were determined for each site prior to loading and then applied repeatedly to a metronome to establish the time course to return to PT upon unload, defined as PT recovery time. Microvascular flux was recorded from an in-line laser speckle contrast imager (FLPI-2, Moor Instruments Inc.) to establish PORH peak and recovery rates at each site. PT recovery and PORH recovery rate were most influenced at the heel and by load duration rather than load magnitude. PT recovery time at the heel was significantly longer with 10 minutes of loading, regardless of magnitude. Heel PORH recovery rate was significantly slower with 10minutes of loading. The 3MT PT recovery time was only longer after 10 minutes of loading at 50% body weight. Microvascular reactivity or sensitivity was not influenced with loading at the MA. A simple linear regression found that PORH recovery rate could predict PT recovery time at the heel (R2=0.184, p<0.001). In populations with degraded sensory feedback, such as diabetic neuropathy, the risk for ulcer development is heightened. Our work demonstrated that prolonged loading in healthy individuals can impair skin sensitivity, which highlights the risks of prolonged loading and is likely exacerbated in diabetes. Understanding the direct association between sensory function and microvascular reactivity in age and diabetes related nerve damage, could help detect early progressions of neuropathy and mitigate ulcer development.

Functional Morphologic Changes of the Heel Fat Pad and Plantar Fascia in Patients With Heel Pain During Weightbearing and Nonweightbearing.

This study aimed to investigate the thickness changes of the heel fat pad and the plantar fascia associated with loading and unloading in healthy individuals and patients with heel pain and reveal the differences between them. The study included adult male participants with (n = 9) and without (n = 26) heel pain. The participants placed their right foot on an evaluation apparatus with a polymethylpentene resin board (PMP), while their left foot was positioned on a weighing scale used to adjust the loading weight. The heel fat pad was differentiated into superficial Microchamber and deep Macrochamber layers. These layers and plantar fascia thickness were measured using an ultrasonographic imaging device at loading phase ranging from 0% to 100% of their body weight and unloading phase from 100% to 0%. Additionally, the study examined the thickness change ratios of the superficial and deep heel fat pad layers when the load increased from 0% (unload) to 100% (full load). In healthy individuals and patients with heel pain, no significant thickness changes were observed in the Microchamber layer of the heel fat pad or the plantar fascia during loading and unloading evaluations. However, significant thickness changes were observed in the Macrochamber layer of the heel fat pad, and the pattern of change differed between the loading and unloading phases. Additionally, patients with heel pain showed differences in the thickness change and thickness change ratios of the microchamber and macrochamber layers of the heel fat pad during both loading and unloading phases. The thickness of the plantar fascia did not show significant differences between both groups. Compared with healthy individuals, in our relatively small study, patients with heel pain had greater deep fat pad compression in loading and less recovery after load removal. This finding suggests that these patients have different intrinsic fat pad function and related morphology than those without heel pain. Level III, retrospective cohort study.

Novel wireless laser doppler flowmeter-based investigation of earlobe vascular dynamics in cardiopulmonary exercise testing.

[Purpose] A new wireless laser Doppler blood flowmeter has facilitated easier, more stable measurement of skin perfusion during exercise. However, earlobe blood flow during the cardiopulmonary exercise test remains unascertained. This study aimed to clarify the characteristics of earlobe blood flow during incremental exercise load in healthy individuals. [Participants and Methods] Among 25 healthy males (age 23.6 ± 2.5 years), cycle ergometer-based symptom-limited cardiopulmonary exercise test, after 4 minutes of rest, was conducted with a 4-minute 20W warm-up and a continuous 2W-increase in the work rate every 6 seconds; earlobe blood flow was measured using a wireless laser Doppler blood flowmeter. [Results] Compared with that at rest, earlobe blood flow increased significantly from 50% of exercise peak intensity to a maximum of 1.7 times, but decreased immediately after exercise. The earlobe blood flow %change did not significantly correlate with hemodynamic parameters and its inflection point 36.4% Loadpeak was significantly lower than the anaerobic metabolic threshold 58.1% Loadpeak. [Conclusion] In healthy participants, earlobe blood flow during cardiopulmonary exercise test increased gradually with low-intensity exercise from approximately 1.5 times the resting rate and approached the anaerobic metabolic threshold with a maximum of 1.7 times the resting earlobe blood flow, but decreased quickly after exercise.

Signature of Altered Retinal Microstructures and Electrophysiology in Schizophrenia Spectrum Disorders Is Associated With Disease Severity and Polygenic Risk

BackgroundOptical coherence tomography and electroretinography studies have revealed structural and functional retinal alterations in individuals with schizophrenia spectrum disorders (SSDs). However, it remains unclear which specific retinal layers are affected; how the retina, brain, and clinical symptomatology are connected; and how alterations of the visual system are related to genetic disease risk. MethodsOptical coherence tomography, electroretinography, and brain magnetic resonance imaging were applied to comprehensively investigate the visual system in a cohort of 103 patients with SSDs and 130 healthy control individuals. The sparse partial least squares algorithm was used to identify multivariate associations between clinical disease phenotype and biological alterations of the visual system. The association of the revealed patterns with individual polygenic disease risk for schizophrenia was explored in a post hoc analysis. In addition, covariate-adjusted case-control comparisons were performed for each individual optical coherence tomography and electroretinography parameter. ResultsThe sparse partial least squares analysis yielded a phenotype-eye-brain signature of SSDs in which greater disease severity, longer duration of illness, and impaired cognition were associated with electrophysiological alterations and microstructural thinning of most retinal layers. Higher individual loading onto this disease-relevant signature of the visual system was significantly associated with elevated polygenic risk for schizophrenia. In case-control comparisons, patients with SSDs had lower macular thickness, thinner retinal nerve fiber and inner plexiform layers, less negative a-wave amplitude, and lower b-wave amplitude. ConclusionsThis study demonstrates multimodal microstructural and electrophysiological retinal alterations in individuals with SSDs that are associated with disease severity and individual polygenic burden.

The Role of Plant Defense Signaling Pathways in Phytoplasma-Infected and Uninfected Aster Leafhoppers' Oviposition, Development, and Settling Behavior.

In plant-microbe-insect systems, plant-mediated responses involve the regulation and interactions of plant defense signaling pathways of phytohormones jasmonic acid (JA), ethylene (ET), and salicylic acid (SA). Phytoplasma subgroup 16SrI is the causal agent of Aster Yellows (AY) disease and is primarily transmitted by populations of aster leafhoppers (Macrosteles quadrilineatus Forbes). Aster Yellows infection in plants is associated with the downregulation of the JA pathway and increased leafhopper oviposition. The extent to which the presence of intact phytohormone-mediated defensive pathways regulates aster leafhopper behavioral responses, such as oviposition or settling preferences, remains unknown. We conducted no-choice and two-choice bioassays using a selection of Arabidopsis thaliana lines that vary in their defense pathways and repeated the experiments using AY-infected aster leafhoppers to evaluate possible differences associated with phytoplasma infection. While nymphal development was similar among the different lines and groups of AY-uninfected and AY-infected insects, the number of offspring and individual female egg load of AY-uninfected and AY-infected insects differed in lines with mutated components of the JA and SA signaling pathways. In most cases, AY-uninfected insects preferred to settle on wild-type (WT) plants over mutant lines; no clear pattern was observed in the settling preference of AY-infected insects. These findings support previous observations in other plant pathosystems and suggest that plant signaling pathways and infection with a plant pathogen can affect insect behavioral responses in more than one manner. Potential differences with previous work on AY could be related to the specific subgroup of phytoplasma involved in each case.

THE EFFECT OF FITNESS TRAINING ON THE MOTION PREPAREDNESS OF WOMEN 23-33 YEARS OLD

Modern fitness programs require a special individual load as an optimal health-promoting preventive effect of physical activity. At the same time, the interaction of training effects from loads that differ in their nature can be negative. In this connection, a problem arises when an individual wants to develop several different physical qualities as much as possible at the same time. The research objective: to substantiate the effectiveness of fitness exercises on the motor readiness of women aged 23-33. Research methods: analysis of scientific and methodological literature, pedagogical observation, pedagogical testing, pedagogical experiment, methods of mathematical statistics. 40 women aged 23-33, average age 28±1.3 years, took part in the study. The discussed issues of systematic fitness exerciseses contribute not only to increasing the level of muscle training and improving the condition of the cardiovascular system, but also help to gain confidence in one's own strength, improve the emotional background, and also gain good physical shape. In the training process, strength abilities, flexibility, and endurance were improved; the performance of the cardiovascular and respiratory systems increased. An effective training combination was considered to be two aerobic trainings and two statodynamic trainings per week. The duration of aerobic training should be 30-45 minutes, provided that ones are held on different days. A comfortable and effective training regimen that takes place on the threshold of comfort is when women feel the load, but at the same time breathe deeply through the nose and without shortness of breath. Fitness training can improve: health and mental state (minimize the risk of many diseases, increase the level of work capacity and activity); physical abilities (increase endurance, strength, flexibility) of women aged 23-33.

Low Complexity Energy Disaggregation Algorithm for Non-intrusive Load Monitoring

Energy Disaggregation is the efficient technique to detect the energy profile of individual electric load by disaggregating the overall power consumption. The benefits of energy disaggregation are not only limited to residents but also helps to improve the building efficiency through load identification process. The idea of this paper is to provide a widespread review of energy disaggregation and present a scheme for non-intrusive load monitoring at a building level model. As the scheme involves a simple regression model with better accuracy, it is less complex to achieve energy disaggregation for the real time load identification in educational institution. Various metrics involved in the assessment of model performance like accuracy, standard errors values and computation time were evaluated and demonstrated for validity. A broad investigation is done for building level energy disaggregation and probable elucidations were discussed for future research initiation.

Multi‐Task Residential Short‐Term Load Prediction Based on Contrastive Learning

AbstractLoad forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short‐term electricity load prediction for residential users, single‐task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi‐task residential short‐term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre‐training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi‐task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short‐term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real‐world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Testing in Intermittent Sports-Importance for Training and Performance Optimization in Adult Athletes.

Performance in intermittent sports relies on the interplay between multiple physiological systems determining the capacity to perform short explosive efforts as well as repeated intense actions with limited recovery over the course of an entire game. Testing should reflect these demands to allow for sport- and position-specific capacity analyses that eventually may translate into optimized training and improved performance. This may include individual load management and optimized training prescription, intensity targeting for specific positions or individual athletes, as well as benchmarking for monitoring of training progression and enhanced engagement of athletes. This review provides an overview of available tests in different exercise domains identified as relevant (from assessment of single explosive actions to intermittent endurance capacity), forming the basis for recommendations on how to compose a comprehensive yet feasible test battery that may be integrated into the seasonal competition and training plan. The test procedures should cover the performance spectrum of relevance for the individual athlete-also in team sports to account for positional differences. We emphasize the benefits of sport-specific tests, highlight parameters of importance for test standardization, and discuss how the applied test battery may be supplemented with secondary tests directed toward specific energy systems to allow for more in-depth analyses when required (e.g., in terms of an underperforming athlete). The synergy between testing and tracking of match performance (utilizing time-motion or global positioning systems) is highlighted, and although tracking cannot substitute for testing, combining the tools may provide a comprehensive overview of the physiological demands and performance during competition contextualized to the athletes' maximal exercise capacity.