Load Variance Research Articles

Revolutionizing load harmony in edge computing networks with probabilistic cellular automata and Markov decision processes

In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP. Taking advantage of the stochasticity of PCA classification our model describes interactions between neighboring nodes in terms of a local load thus allowing for a distributed, self-organizing approach to load balancing. The MDP framework then determines each node’s decision-making with the focus on load offloading policies that are aligned with rewards that promote per node balance and penalties for offloading a larger load than it can handle.These models are then incorporated into our proposed PCA-MDP system to achieve dynamic load balancing with low variability in resource usage among nodes. By conducting a large number of experiments, we prove that the proposed PCA-MDP model yields a higher efficiency in the distribution of the load, higher stabilities of the reward function, and a faster convergence speed compared to the existing approaches. Key performance parameters, such as load variance, convergence time, and scalability, validate the robustness of the proposed model. Besides optimizing resource exploitation, load harmony in edge computing networks helps provide efficient work progression and minimize latency, thereby contributing to the advancement of the field with respect to real-time applications such as self-driving vehicles and the Internet of Things. The presented work offers an excellent foundation for the next-generation edge-computing load-balancing solution that can be easily scaled up.

Relationship between antihistamine residues in wastewater and airborne pollen concentrations: Insights into population-scale pollinosis response.

Pollinosis is the most prevalent allergic disorder. Assessing the impact of real-world pollen exposure on symptoms remains challenging due to extensive patient-level efforts required. This study explores the potential of wastewater-based epidemiology (WBE) to investigate the relationship between airborne pollen concentrations and antihistamine residues in wastewater as an indicator of pollinosis symptom treatment at the population-scale. In Zurich (Switzerland), 279 wastewater samples were collected during 2021-2023. Each sample represents a 24-h period with excreta from approximately 471,000 individuals. Eleven antihistamine markers were analyzed in the samples using liquid chromatography high-resolution mass spectrometry. The relationship between antihistamine loads in wastewater and airborne pollen concentrations (47 taxa and miscellaneous pollen) was investigated by determining seasonal components of antihistamine loads and exploring pollen-specific contributions using Non-Negative Least Squares (NNLS) optimization. Four antihistamines were detected in quantifiable amounts in wastewater. The first-generation antihistamine, diphenhydramine, demonstrated rather constant levels. In contrast, the three second-generation antihistamines - bilastine, cetirizine, and fexofenadine - showed pronounced day-to-day variation with strong correlations among each other. For fexofenadine, which was investigated in detail for its correlation with airborne pollen concentrations, approximately 50% of the annual wastewater loads were associated with acute pollen exposure. Another 20% related to baseline consumption during the pollen season, while the remaining 30% seems unrelated to pollen. Birch, grasses, hazel, hornbeam, plane, and plantain explained most of the variance in wastewater loads (R2=0.82), with grass pollen alone accounting for a quarter of the annual loads. Increased fexofenadine loads during periods without elevated concentrations of common allergenic pollen suggests the presence of additional triggers for allergy symptoms, potentially yew pollen. Our study demonstrates that WBE can effectively reveal substantial day-to-day variation in antihistamine use related to pollen exposure. Thus, WBE presents an objective and questionnaire-independent method for investigating pollinosis symptom treatment at a population-scale.

Functionally-informed fine-mapping identifies genetic variants linking increased CHD1L expression and HIV restriction in monocytes

Human Immunodeficiency Virus Type 1 (HIV) set-point viral load is a strong predictor of disease progression and transmission risk. A recent genome-wide association study in individuals of African ancestries identified a region on chromosome 1 significantly associated with decreased HIV set-point viral load. Knockout of the closest gene, CHD1L, enhanced HIV replication in vitro in myeloid cells. However, it remains unclear if HIV spVL associated variants are associated with CHD1L gene expression changes. Here we apply a heuristic fine-mapping approach to prioritize combinations of variants that explain the majority of set-point viral load variance and identify variants likely driving the association. We assess the combined impact of these variants on CHD1L regulation using publicly available sequencing studies, and test the relationship between CHD1L expression and set-point viral load using imputed CHD1L expression from monocytes. Taken together, this work characterizes genetically regulated CHD1L expression and further expands our knowledge of CHD1L-mediated HIV restriction in monocytes.

Genetic inbreeding load and its individual prediction for milk yield in French dairy sheep

BackgroundThe magnitude of inbreeding depression depends on the recessive burden of the individual, which can be traced back to the hidden (recessive) inbreeding load among ancestors. However, these ancestors carry different alleles at potentially deleterious loci and therefore there is individual variability of this inbreeding load. Estimation of the additive genetic value for inbreeding load is possible using a decomposition of inbreeding in partial inbreeding components due to ancestors. Both the magnitude of variation in partial inbreeding components and the additive genetic variance of inbreeding loads are largely unknown. Our study had three objectives. First, based on substitution effect under non-random matings, we showed analytically that inbreeding load of an ancestor can be expressed as an additive genetic effect. Second, we analysed the structure of individual inbreeding by examining the contributions of specific ancestors/founders using the concept of partial inbreeding coefficients in three French dairy sheep populations (Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse). Third, we included these coefficients in a mixed model as random regression covariates, to predict genetic variance and breeding values of the inbreeding load for milk yield in the same breeds.ResultsPedigrees included 190,276, 166,028 and 633,655 animals of Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse, respectively, born between 1985 and 2021. A fraction of 99.1% of the partial inbreeding coefficients were lower than 0.01 in all breeds, meaning that in practice inbreeding occurs in pedigree loops that span several generations backwards. Less than 5% ancestors generate inbreeding, because mating is essentially between unrelated individuals. Inbreeding load estimations involved 658,731, 541,180 and 2,168,454 records of yearly milk yield from 178,123, 151,863 and 596,586 females in Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse, respectively. Adding the inbreeding load effect to the model improved the fitting (values of the statistic Likelihood Ratio Test between 132 and 383) for milk yield in the three breeds. The inbreeding load variances were equal to 11,804 and 9435 L squared of milk yield for a fully inbred (100%) descendant in Manech Tête Noire and Manech Tête Rousse. In Basco-Béarnaise, the estimate of the inbreeding load variance (11,804) was not significantly different from zero. The correlations between (direct effect) additive genetic and inbreeding load effects were − 0.09, − 0.08 and − 0.12 in Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse.ConclusionsThe decomposition of inbreeding in partial coefficients in these populations shows that inbreeding is mostly due to several small contributions of ancestors (lower than 0.001) going back several generations (5 to 7 generations), which is according to the policy of avoiding close matings. There is variation of inbreeding load among animals, although its magnitude does not seem enough to warrant selection based on this criterion.

Leveraging Machine Learning for Enhanced Cloud Computing Load Balancing: A Comprehensive Review

Popularized especially throughout the last decade, cloud computing has significantly changed the approach to the use of computational resources. But load balancing still persists as a very important issue of concern to achieve the right balance in order to optimize the various resources. This review paper aims at discussing the various Machine Learning (ML) techniques that can be used for the improvement of load balancing in cloud computing systems. Therefore, the analysis of the current methods is intended to reveal the advantages of the modern approaches to load balancing and put focus on the shortcomings of the traditional algorithms and the contribution of new machine learning approaches. ML techniques offer dynamic load balancing solutions which can handle the variance of the system load. These techniques mainly involve the use of historical data and applying statistical analysis to arrive at the best decisions thus reducing the time response, server usage, and resource utilization. Further, this paper presents how the state-of-the-art machine learning models can be implemented in the context of cloud environments focusing on the complexity and overhead, data protection, and scalability questions. It continues the review of the most advanced machine learning algorithms in the context of load balancing. Adding several examples of the current case studies and experiment results from the published researches of the recent years, the effectiveness of these techniques has been illustrated in the real cloud context. This paper is concluded with the generalization for the future works where the emphasis is placed for the improvement of load balance of machine learning for more demanding and secured cloud computing service.

Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network

Currently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization. Therefore, a new time of using electricity price optimization method is proposed that takes into account the losses of distributed photovoltaic access to the distribution network. Considering the topology structure of the distribution network after the integration of distributed photovoltaic, this paper calculates the comprehensive losses generated by the operation of the distribution network. Also, this paper constructs a time of use electricity price optimization mathematical model with the objectives of minimizing network loss, minimizing load variance, minimizing peak valley difference of equivalent load, and maximizing user satisfaction. And refer to the basic requirements for electricity pricing in the distribution network, set a series of constraints for optimizing electricity prices. Applying an improved imperialist competition algorithm this paper integrates Tent chaotic reverse learning to solve a multi-objective optimization model and obtain an optimized time of use electricity pricing plan. The experimental results show that after the implementation of this optimization method, the peak valley difference of the daily power load curve of the distribution network is only 350 MW, demonstrating superior peak shaving and valley filling effects.

The road to a sustainable energy system in the Guadeloupe archipelago: Challenges and opportunities.

The Guadeloupe archipelago, situated in the eastern Caribbean Sea with a population of approximately 400,000 inhabitants, faces distinctive challenges in realizing a sustainable and resilient energy transition owing to its nature being a distributed island system. These unique challenges include the absence of energy interconnections, limited domestic energy resources, substantial dependence on fossil fuels, and considerable load variance. These factors necessitate specialized attention in the study and analysis of the energy system transition. In this study, the authors initially delineate storylines tailored to the specific requirements of Guadeloupe's energy system, with a primary focus on the reliance on fossil fuel imports. Two distinct energy transition roads, aiming for self-sufficiency and independence for Guadeloupe by 2040 and 2050, are established. Leveraging an advanced open-source investment and dispatch tool, the Global Energy System Model (GENeSYS-MOD), these storylines are implemented to ascertain the least-cost, sustainable, and resilient energy transition for the Guadeloupe archipelago. The ensuing results are comprehensively analyzed to offer insights into Guadeloupe's trajectory towards a sustainable energy system, comparing the proposed energy transition roads for achieving sustainability. The findings reveal that substantial investments in wind and photovoltaic power plants, coupled with electricity storage devices, are imperative for achieving energy independence in the future. Additionally, the availability of biomass emerges as a crucial factor facilitating a seamless transition across all sectors, encompassing power, heat, and transportation.

Hydro–Wind–PV–Integrated Operation Optimization and Ultra-Short-Term HESS Configuration

In order to address the challenges associated with optimizing multi-timescale operations and allocating ultra-short-term energy storage for HWP integration, this study takes into account both the economic and reliability aspects of the HWP integration base. It proposes a model for optimizing operations and allocating energy storage capacity, achieving optimization across long-term, short-term, and ultra-short-term operations for an MECB. Initially, operation optimization is implemented for an entire group of terraced hydropower plants by regulating them with annual regulating capabilities on a long-term timescale. The objectives are to maximize the daily average minimum output and annual power generation. Subsequently, short-term operation optimization focuses on maximizing HWP power feed-in, minimizing new energy power curtailment, and reducing residual load standard deviation while ensuring the guaranteed output optimization results for the long term. Finally, to mitigate ultra-short-term fluctuations in new energy, a HESS with specified capacity and power is configured with the goal of minimizing comprehensive costs. Additionally, to address the challenge of smoothing negative fluctuations, which is hindered by charging and discharging efficiency limitations, a variable baseline is introduced, deviating from the conventional 0 MW baseline. A simulation study based on data from the hydro–wind–PV hybrid project in the Beipanjiang River Basin, China, demonstrates the following: (1) after long-term system optimization, the total power generation capacity of the system increases by 9.68%, while the peak-to-valley difference in output is significantly reduced; (2) short-term system optimization significantly reduces both the average variance in residual loads and the amount of power curtailed over five representative days; (3) the system incorporates 398.62 MWh of lithium-ion battery storage with a power of 412.47 MW and 51.09 MWh of supercapacitor storage with a power of 223.32 MW, which, together, completely smooth out the ultra-short-term fluctuations in new energy output.

Hierarchical Optimal Dispatching of Electric Vehicles Based on Photovoltaic-Storage Charging Stations

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

The strong effect of flexible capacities on achievable WIP levels and throughput times: a simple model and its implications

Setting WIP levels at the workstations of a production is an important task: WIP levels affect throughput times as well as utilisation rates. Consequently, they also impact the delivery times to customers and the productivity. Queueing theory provides profound knowledge regarding the factors for determining suitable WIP levels. However, the existing models have been neglecting the important influence of capacity flexibility. This paper suggests an extension of the well-known Kingman equation to model the impact of flexible capacities on the required WIP levels. Simulation experiments have shown that the model is able to accurately forecast the utilisation level of a workstation for different factor levels of capacity flexibility, WIP and load variance. Companies can use the model to set consistent target levels for these factors.

MILP model for short-term peak shaving of multi-grids using cascade hydropower considering discrete HVDC constraints

The thriving of high-voltage direct current transmission for hydropower tackles supply-demand imbalances but complicates grids safety, especially concerning the related challenges of varying numbers of hydroelectric units operating, including the effects of discrete rectifier station altering current and vibration zones with minimum numbers of hydroelectric units operating. Here, a mixed integer linear programming model considering high-voltage direct current safety constraints for daily peak-shaving of cascade hydropower plants serving multi-grids is proposed to address these challenges. First, the model integrates the altering current constraint across multiple intervals to ensure stable operation following the passive transition of the direct current terminal into islanded system state. Second, a power distribution strategy considering load differences is adopted to compensate for shortcomings of traditional peak shaving treatments for multiple grids to achieve effective results. Finally, a method for linearizing the segmented vibration zones of hydropower station with minimum numbers of units operating is adopted during model solving to ensure that the model meets safe specifications. The case study indicates that besides guaranteeing the proposed security constraints, the model reduces the average distance by 30.4 % and 30.3 % during the dry and flood seasons, respectively, with better calculation time and variance of the residual load.

Coupling Analysis between the Transonic Buffeting Flow and a Heaving Supercritical Airfoil Based on Dynamic Mode Decomposition

The coupling between a transonic buffeting flow and a supercritical airfoil with harmonic heave motion was studied. A parametric space of the heave frequency and amplitude was investigated using a verified fluid–structural interaction framework. The spatial-temporal flow pattern around the transonic airfoil was studied using dynamic mode decomposition (DMD) to unveil the physical coupling mechanism. The results show three types of flow responses under the heave motion: (I) A buffet frequency response with a λ-shape shock wave structure and recirculation zone at the shock foot. The aerodynamic performance was alike the scenario in the flow past the stationary airfoil. (II) A transitional response with a weakened shock and enhanced boundary layer. The aerodynamic performance deteriorated sharply at f=fbuffet and recovered after the frequency was past the buffet frequency. The flow pattern was characterized by a double-shock structure that interacted with the enhanced boundary layer. (III) A heave frequency response with the dominant heave motion. The variance in the aerodynamic loading increased significantly at f>fbuffet and there were higher heave amplitudes in this stage. The driving motion of the airfoil transferred the energy of the buffet mode to the boundary layer with a more even energy balance according to the energy contribution analysis of the DMD modes.

Innovative design for enhancing transient stability with an ATFOPID controller in hybrid power systems

In order to make multi-area power systems more efficient and stable, this study aims to create a new intelligent controller design based on adaptive property. The integrated power grid becomes more unstable as renewable energy sources become more prevalent, and system inertia decreases. Resultant, the penetration of renewable and load variance causes frequency changes to be more pronounced. Keeping frequency stable is essential for the electrical energy transmission system to work reliably and efficiently. This paper presents an adaptive design based on the tilt fractional order proportional integral derivative (TFOPID) controller by using an optimized property of bio inspired tunicate search algorithm (TSA). The gains of the TFOPID controller are continuously updated by utilizing the ancillary TPID controller, which is dependent on changes in area frequency. The suggested control system is implemented in the northern and southern regions, which consist of photovoltaic (PV), wind, redox flow battery (RFB), hydro, and thermal power plants. Therefore, this study contributes in the creation of more resilient and efficient power grids, which are critical for incorporating renewable energy sources and satisfying the increasing need for environmentally friendly power solutions.

Frailty Assessment Scale for Heart Failure: Development and Psychometric Validation.

Frailty is common in patients with heart failure. Specific scales, including the Clinical Frailty Scale, exist but have not undergone thorough psychometric validation among patients with heart failure. The aim of this study was to develop and validate the Frailty Assessment Scale for Heart Failure (FAS-HF) in patients with heart failure. This study included 3 phases. In phase I, conceptualization and item generation, the FAS-HF was initially developed through the analysis of qualitative research data and a literature review. In phase II, item selection and content validity, the Delphi method was used to gather experts' opinions and suggestions regarding the FAS-HF. In phase III, field testing and psychometric evaluation, a cross-sectional study of 184 patients with heart failure in northern Taiwan was conducted to test the reliability and validity of the FAS-HF. The 15-item FAS-HF included the physical, psychological, and social frailty domains. The total score ranged from 0 to 45, with higher scores representing frailer individuals. Exploratory factor analysis revealed that the cumulative loading variance was 57.39%. In the confirmatory factor analysis, the goodness-of-fit index (0.91), adjusted goodness-of-fit index (0.87), and root mean square error of approximation (0.06) were acceptable for model fit. Acceptable reliability was found, with a Cronbach α coefficient of 0.87 and a test-retest reliability of 0.99. Compared with the Fried frailty phenotype, the area under the receiver operating characteristic curve was 0.94, and the optimal cutoff score for frailty was 20. The FAS-HF has good reliability, validity, and discrimination and can be used as a frailty indicator for patients with heart failure.

Epistatic interaction between ERAP2 and HLA modulates HIV-1 adaptation and disease outcome in an Australian population.

A strong genetic predictor of outcome following untreated HIV-1 infection is the carriage of specific alleles of human leukocyte antigens (HLAs) that present viral epitopes to T cells. Residual variation in outcome measures may be attributed, in part, to viral adaptation to HLA-restricted T cell responses. Variants of the endoplasmic reticulum aminopeptidases (ERAPs) influence the repertoire of T cell epitopes presented by HLA alleles as they trim pathogen-derived peptide precursors to optimal lengths for antigen presentation, along with other functions unrelated to antigen presentation. We investigated whether ERAP variants influence HLA-associated HIV-1 adaptation with demonstrable effects on overall HIV-1 disease outcome. Utilizing host and viral data of 249 West Australian individuals with HIV-1 subtype B infection, we identified a novel association between two linked ERAP2 single nucleotide polymorphisms (SNPs; rs2248374 and rs2549782) with plasma HIV RNA concentration (viral load) (P adjusted = 0.0024 for both SNPs). Greater HLA-associated HIV-1 adaptation in the HIV-1 Gag gene correlated significantly with higher viral load, lower CD4+ T cell count and proportion; P = 0.0103, P = 0.0061, P = 0.0061, respectively). When considered together, there was a significant interaction between the two ERAP2 SNPs and HLA-associated HIV-1 adaptation on viral load (P = 0.0111). In a comprehensive multivariate model, addition of ERAP2 haplotypes and HLA associated adaptation as an interaction term to known HLA and CCR5 determinants and demographic factors, increased the explanatory variance of population viral load from 17.67% to 45.1% in this dataset. These effects were not replicated in publicly available datasets with comparably sized cohorts, suggesting that any true global epistasis may be dependent on specific HLA-ERAP allelic combinations. Our data raises the possibility that ERAP2 variants may shape peptide repertoires presented to HLA class I-restricted T cells to modulate the degree of viral adaptation within individuals, in turn contributing to disease variability at the population level. Analyses of other populations and experimental studies, ideally with locally derived ERAP genotyping and HLA-specific viral adaptations are needed to elucidate this further.

End-to-End Data Flows Management in the Decentralized 5G/6G Mobile Networks

The evolution of 5G and the anticipated emergence of 6G networks demand significant enhancements in optical backhaul infrastructure to support higher bandwidth, low latency, and increased reliability. Considering a wide range of feasible applications with different quality requirements, the key challenge for the underlying backhaul and optical transport network is in the high load variance in the optical switching nodes and complicated data flows management. In this paper, a novel approach is proposed for end-to-end data flows management in decentralized 5G/6G mobile networks, which are interconnected by the optical burst switching transport infrastructure. The key idea is to train a deep recurrent neural network over a real network statistic obtain at the different network segments and service slices. Then, predictions made by deep neural network are used for predictive resource allocation in each node of the optical burst switching network to ensure a target quality of service for each end-to-end data flow. The experimental results show that proposed approach provides 90% accuracy of predictions and allows to effectively utilize the resources of optical network.

Energy supply structure optimization of integrated energy system considering load uncertainty at the planning stage

Under the trend of global carbon neutrality, the integrated energy system with the characteristics of multi-energy scheduling and gradient utilizing will be widely constructed and applied in the future energy market. For the construction of energy systems in emerging building complex, this paper analyzes the load probability characteristics of regional building complex at the planning stage, and conducts an aggregation analysis of its multiple uncertainties, and obtains the conclusion that the load factor at time t obeys the normal distribution. In order to formulate this uncertainty, this paper combines the case study with the scenario analysis method containing scenario generation and reduction to transform the stochastic programming model into several deterministic models and analyses the discrepancies of the optimization results under different objectives and different load variances. The results show that after considering load-side uncertainty, the total system capacity increases by 7 %, the total investment under the minimum investment objective by 4 %, and the carbon emission under the minimum carbon emission objective by 3 %. In addition, the system needs to pay 32.8 % of the total investment increment to obtain 80 % carbon reduction when adopting carbon emissions as the objective.

Study on charge and discharge control strategy of improved PSO for EV

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.

Flexible resource demand response scheduling strategy under 5G-V2X

Electric vehicles (EV) and 5 G base stations, functioning as flexible load resources, contribute significantly to the provision of ample reserve capacity for the power grid. The emergence of 5 G Vehicle-to-Everything (V2X) technology has fostered a coupling between EV and base stations, further enhancing their roles within the power grid. Building upon this foundation, this paper proposes a scheduling strategy for demand response in flexibility resources under the 5 G-V2X framework. Initially, the dispatchable load of EV is examined in conjunction with the traffic network. Subsequently, we consider the communication power consumption resulting from the connected vehicle model, enabling us to devise path planning and charging navigation strategies for EV. Lastly, we establish a scheduling decision model that aims to minimize both the operation costs of base stations and the variance in grid load. The simulation is validated using real-world road conditions in the primary urban area of a city. The findings illustrate that the proposed dispatching strategy not only achieves a mutually beneficial outcome for EV users and aggregators, without disrupting EV charging and discharging processes, but also enhances the capability of demand response, thereby ensuring reliable grid operation.

Longitudinal Internal Training Load and Exposure in a High-Performance Basketball Academy.

Lever, JR, Duffield, R, Murray, A, Bartlett, JD, and Fullagar, HHK. Longitudinal internal training load and exposure in a high-performance basketball academy. J Strength Cond Res 38(8): 1464-1471, 2024-This study describes the longitudinal training exposure (session counts) and internal training load (Rating of Perceived Exertion [RPE] and Session Rating of Perceived Exertion [sRPE]) of youth basketball players at a high-performance academy, based on the training year, training term, and playing position. Historical internal training load and training exposure data were collated from 45 male high-performance youth basketball athletes between 2015 and 2019. Data included session duration, RPE, sRPE, training type, and date. Linear mixed models and pairwise comparisons were performed on the weekly means and categorized by training year (year 1, year 2, year 3), term (term 1, term 2, term 3, term 4), and playing position (Backcourt, Frontcourt). Linear mixed models indicate that the individual athlete had the greatest influence on variance in training load and exposure. Significant differences were observed for increased session count, duration, and sRPE ( p < 0.001) in year 2 compared with year 1. These measures also increased within each year whereby term 3 and term 4 ( p < 0.001) were significantly greater than term 1 and term 2. No significant differences were observed between playing position ( p > 0.05). Training exposure and internal training load increase in year 2 from year 1 for high-performance youth basketball academy athletes. Differences between training load and exposure for terms (i.e., training blocks) suggest the phase of season influences training prescription, while playing position has limited effect.