Search Results (92)

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

This study aimed to quantify the absolute and relative data across a typical microcycle (MC) in female professional soccer players utilizing a match day minus (MD-) approach. Ten players (24.7 ± 2.6 years) from an elite female Portuguese team participated in this case study. Data was analyzed in absolute or relativized values (per minute) and included the following metrics: duration, total distance, high-speed running distance (HSR, >15 km/h), number of accelerations (ACC, >1–2 m.s⁻² [ACC1]; >2–3 m.s⁻² [ACC2]; >3–4 m.s⁻² [ACC3]; >4 m.s⁻² [ACC4]) and decelerations (DEC, <1–2 m.s⁻² [DEC1]; <2–3 m.s⁻² [DEC2]; <3–4 m.s⁻² [DEC3]; <4 m.s⁻² [DEC4]). Total distance showed a significant difference between MD-4 and MD-2 (p = 0.047, moderate effect), which presented the lowest value of all MC days, while MD presented the highest value of HSR compared to all training days (p < 0.001, large to very large effect) for both absolute and relativized data. Relative data showed higher values for MD-5 with significant differences during MD-2 for ACC1, ACC2, DEC1, and DEC2 (p < 0.01, large to very large effect), while absolute data showed higher values during MD-4 for ACC2, DEC1, and DEC2 (p < 0.01, large to very large effect). Absolute ACC3 was higher during MD-3, denoting significant differences from MD-2 (p = 0.002, large effect). This study highlighted that it is possible to train, in specific sessions, with identical loading patterns of match play, specifically for ACC and DEC metrics. However, HSR distance was found to be higher during MD, while training values were significantly lower. Full article

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

Purpose: This study assessed the adaptations resulting from implementing an experimental, integrated training program tailored to sex-specific traits. The aim was to enhance motor abilities, aerobic capacity, and metabolic variables in female and male rugby sevens athletes. Methods: Employing a combined observational and experimental design, initial and post-intervention assessments were conducted over three months (March–June 2023) with 24 elite professional players, divided equally by sex (12 females, 12 males). The protocol consisted of 12 micro-cycles, each lasting 7 days and comprising 12 training sessions. The evaluations included sprint and jumping tests, as well as functional assessments such as resting metabolic rate and cardiopulmonary exercise testing. Results: Using one-way repeated measures ANOVA, significant improvements were noted across all performance parameters (p < 0.001), with effect sizes ranging from small to very large. Sex-specific differences were evident, with females demonstrating consistent improvements in aerobic capacity and jumping ability, while males excelled in explosive power and longer sprints. Despite initial performance disparities, both sexes improved in short-distance sprints (10 m and 40 m). Cardiovascular efficiency improved as indicated by reduced maximum heart rates and lower respiratory quotients. Conclusions: Males showed superior progress in strength and explosive power tests, reflecting distinct physiological traits. These findings underscore the need for individualized and sex-specific training programs to optimize performance in high-intensity sports, such as rugby sevens. Full article

This study explored the influence of game-specific on-court drills on external load in junior and professional male tennis players. Using wearable inertial technology, a total of 345 drills performed during a training microcycle were analyzed. Drills were classified according to the usual tennis game situations (i.e., serve, return, baseline, net play, and all-court), and load was quantified through distance covered, explosive distance, accelerations, decelerations, and Player Load. Significant differences were found in load across playing situations, with baseline and all-court drills producing the highest demands, especially in distance and Player Load. Serve drills consistently showed the lowest external load, while acceleration and deceleration values remained stable. Age group comparisons revealed that juniors covered more distance and experienced higher overall load in return and baseline situations, while professionals showed greater acceleration and deceleration values. These findings highlight the relevance of adapting training load to the specific demands of the game situations, the developmental stage, and the skill level of players. Coaches and sports scientists can use these insights to better plan, monitor, and individualize training programs for injury prevention and performance optimization in high-performance tennis. Full article

Background. This study aimed to quantify differences in the internal training load (ITL) of an elite rink hockey (RH) team across days within and between three types of microcycles: pre-season, in-season regular, and in-season congested, to provide insights to optimise microcycle scheduling. Methods. One international-level male RH team comprising seven outfielders (29.6 ± 4.7 years; height, 178.9 ± 2.3 cm; body mass, 77.8 ± 5.7 kg) and one goalkeeper (32 years; height, 180.4 cm; body mass, 83.6 kg) was monitored for 21 microcycles. The ITL was assessed using the session rate of perceived exertion (sRPE) and quantified as time based on a triphasic classification commonly utilised in team sports: low-intensity training (LIT, <80% heart rate maximum (HRmax)), medium-intensity training (MIT, 80–90% HRmax), and high-intensity training (HIT, >90% HRmax). Generalized estimating equations were used to examine differences across within-microcycle training days and between seasonal phases, with linear mixed models applied as sensitivity analyses. Results. Across all phases, significant day-to-day variations in ITL were observed within microcycles (all p < 0.001), with both subjective (sRPE) and objective (LIT–HIT) ITLs progressively decreasing as match days (MDs) approached, showing moderate-to-large population-averaged effects with 95% confidence intervals consistently not crossing zero. The pre-season exhibited the highest overall ITL (p < 0.001), characterised by a substantially greater sRPE and increased time spent across all intensity zones, with the largest magnitudes observed for LIT and MIT compared with the in-season phases. Conclusions. Findings suggest that an international-level RH team progressively reduced the ITL as MDs approached with the highest loads scheduled earlier within microcycles. Moreover, the pre-season had the highest ITLs. This ITL distribution may provide useful guidance for RH coaches and support staff in optimising microcycle planning. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

Monitoring the training load of elite soccer players is a common practice for clubs. However, limited information exists about the internal load experienced by elite soccer players. The heart rate (HR) exposure of 51 French elite soccer players was monitored using conductive vests incorporating ECG bands during two consecutive seasons using a three-zone intensity model. HR exposure was broken down into volume (i.e., total time in the three zones) and intensity (i.e., relative time in the three zones). The effect of playing position, as well as the period (monthly or daily), was assessed. Regarding seasonal exposure, a significant difference was observed between key periods of the season (i.e., preseason, in season, end-of-season) for both volume and intensity (p < 0.05). Noteworthily, monthly HR exposure was relatively constant across competitive period. For weekly exposure, a significant difference in HR volume and intensity was observed between matches and training sessions (p < 0.001) potentially highlighting gaps in players’ readiness. Note that there were small variations in terms of HR exposure between the three first training days (p < 0.05), especially for time and relative time over 90% of maximal HR (not significant). This study not only provides insight into typical HR exposure in elite football but also questions the current training periodisation. Full article

Background: This study aimed to analyze and compare the internal and external training load responses in U16 female basketball players participating in a micro-cycle with the U18 team from the same club. Methods: Twelve U16 and six U18 female basketball players completed two U18-team training sessions (MD-3 and MD-1; 90 min each). The internal load (heart rate metrics) and external load (accelerations, decelerations, speed, and distance) were measured using Polar Team Pro sensors. Differences between groups were analyzed using t-tests and Cohen’s d effect sizes. Results: No significant differences (p > 0.05) were found between age categories for either the internal or external load variables. U16 players showed slightly higher maximum heart rate percentages (96.5% vs. 94.7%, ES = 0.29) but similar average heart rate and time in heart rate zones. For the external load, both groups exhibited comparable values in total distance, average speed, and movement across speed and acceleration/deceleration zones. Effect sizes were mostly small, with moderate differences found in specific acceleration and deceleration zones. Conclusions: U16 players training with the U18 team experienced similar internal and external loads, suggesting that they can cope with the physical and physiological demands of older-age-group training. These findings support the inclusion of younger players in higher-age-group training environments as part of their long-term athletic development. Full article

Background/Objectives: To compare the external load (EL) of elite youth soccer players during official international matches between age categories and playing positions. Methods: The sample consisted of 42 elite youth soccer players categorized by age categories, U-15, U-17 and U-20 and playing positions: central defender (CD); fullback (FB); midfielder (MF); wide attacker (WA) and striker (ST). The Vector X7 (Catapult Sports) device was used for collecting the following EL variables: total distance traveled (TD), player load (PL) and distance traveled per velocity band 0 to 7 km/h (D7); 7 to 13 km/h (D13); 13 to 19 km/h (D19); 19 to 23 km/h (D23) and >23 km/h (HSR). Linear mixed-effect models were applied to analyze the differences. Results: Large differences were found between positions (p < 0.01) in TD (η²p = 0.48), PL (η²p = 0.30), D19 (η²p = 0.44), D23 (η²p = 0.68) and HSR (η²p = 0.53). Large differences were found according to category between U-15 and U-17 in TD (p = 0.006 and η²p = 0.25) and D13 (p = 0.003 and η²p = 0.27). Large interaction effects were found in DT (p = 0.014 and η²p = 0.44) and D23 (p = 0.002 and η²p = 0.51). Conclusions: This study concludes that there are differences in EL in official matches in elite youth players between age categories and playing position. These differences can be applied in practice to design individualized training by playing position and to monitor EL during microcycles. Full article

Conidiation and stress tolerance are pivotal traits in entomopathogenic fungi, critically influencing their production costs and environmental tolerance. While the transcription factor high-mobility group protein (HMG), characterized by a conserved HMG-box domain, has been extensively studied for its role in sexual development, its functions in entomopathogenic fungi remain largely unexplored. This study employed gene knockout to investigate the role of MaHMG in Metarhizium acridum. The deletion of MaHMG delayed conidiation initiation and caused a highly significant 58% reduction in conidial yield versus that of the wild type (WT) after 15 days. Furthermore, the conidiation pattern on microcycle induction medium (SYA) shifted from microcycle to normal conidiation. The ΔMaHMG mutant exhibited decreased conidial germination rates and markedly reduced tolerance following UV-B irradiation and heat-shock treatments, alongside increased sensitivity to the cell wall perturbant calcofluor white (CFW). RNA-seq analysis during this conidiation shift identified 88 differentially expressed genes (DEGs), with functional annotation implicating their predominant association with hyphal development, cell wall biogenesis, cell cycle progression, and conidiation. In conclusion, MaHMG functions as a critical positive regulator governing both conidiation and stress tolerance in M. acridum, underscoring its fundamental role in fungal biology and potential as a target for enhancing biocontrol agent performance. Full article

The allostatic load index (AL_index) measures the cumulative physiological burden on the body due to stress. This prospective cohort study examined the relationships between certain molecular biomarkers, physical variables, and psychometric variables during deload and overload microcycles to contribute to developing an AL_index in professional team-sport athletes. Twelve elite male basketball players (18.3 [0.9] years; 77.2 [5.7] kg; 185 [9.0] cm) were monitored during two microcycles (deload and overload). Blood creatine kinase (CK) and urea levels, countermovement jump (CMJ), session-RPE (RPE × session duration [min], its exponentially weighted moving average [EWMA]), and a cumulative wellness score (sleep, stress, fatigue, muscle soreness, and mood) were assessed at different time points. Bayesian and robust statistics (Cohen’s ξ) were employed. CK rose from 222 U/L (deload) to 439 U/L (overload; +98%, large effect ξ = 0.65), while session-RPE load more than doubled (270 [269] AU to 733 [406] AU, ξ > 0.8). No difference was found in urea and wellness scores (cumulative or other components). CK levels showed moderate positive correlations with both EWMA of session-RPE (ρ = 0.346, p = 0.002) and reduced sleep quality (ρ = 0.25, p = 0.018). Bayesian modeling identified the EWMA of session-RPE as the strongest predictor of jump-defined fatigue (β = 0.012, 95% HDI [0.004, 0.021]), while CK demonstrated a small negative association (β = −0.009, HDI [−0.016, −0.001]). Finally, a principal component analysis (PCA) revealed that CK and the EWMA of session-RPE were robust indicators of physiological stress. A parsimonious index based on PCA loadings ([0.823 × CK] + [0.652 × EWMA of session-RPE]) demonstrated strong discriminative validity between microcycle phases (overload: 515, 95% HDI [442, 587] versus deload: 250, 95% HDI [218, 283], BF₁₀ > 100,000). CK and session-RPE may serve as sensitive biomarkers for inclusion in the AL_index for team sport athletes. Full article

Accelerations and decelerations are critical components of soccer performance, reflecting mechanical load and injury risk, with understanding positional and temporal variations essential for optimizing training prescription. This study analyzed acceleration and deceleration demands in professional soccer players across playing positions and training microcycle phases. Twenty-five professional soccer players (26.6 ± 4.50 years) from a Spanish Second Division team were monitored using 18 Hz GPS STATSports (Newry, UK) devices during 16 training sessions and 4 official matches over four weeks. Accelerations and decelerations were categorized into six intensity zones (Z1–Z6, 0.5–1 to 5–10 m/s²), with players grouped by position: central defenders (CD), full-backs (FB), central midfielders (CM), attacking midfielders (AM) and forwards (FW). Match day (MD) significantly affected all variables (F > 4.75; p < 0.001, ω_p² = 0.13–0.42), with accelerations showing higher values at MD-2 for Z1, MD for Z2, MD-4 and MD for Z3–Z4, consistently reaching lowest values at MD-1. Decelerations peaked at MD across Z2–Z6, with MD-1 showing minimal preparation values. Positionally, FB exceeded other positions in low-intensity accelerations and decelerations (Z1–Z2), while CM dominated high-intensity decelerations (Z4–Z6). Total accelerations differed significantly by position (FB: 579 ± 163 vs. AM: 494 ± 184 events, p < 0.05). Training acceleration loads adequately replicate match demands, but deceleration preparation remains insufficient, representing a potential injury risk. Position-specific protocols should emphasize deceleration conditioning, particularly for CM and FB. Full article

The development of Global Positioning System (GPS) technology has contributed in various ways to improving the physical condition of modern football players by enabling the quantification of physical load. Previous studies have reported that the running demands of matches vary depending on playing position and formation. Over the past decade, despite the widespread use of GPS technology, studies that have investigated the running performance of young football players within the 1-4-3-3 formation are particularly limited. Therefore, the aim of the present study was to create the match running profile of playing positions in the 1-4-3-3 formation among high-level youth football players. An additional objective of the study was to compare the running performance of players between the two halves of a match. This study involved 25 football players (Under-19, U19) from the academy of a professional football club. Data were collected from 18 league matches in which the team used the 1-4-3-3 formation. Positions were categorized as Central Defenders (CDs), Side Defenders (SDs), Central Midfielders (CMs), Side Midfielders (SMs), and Forwards (Fs). The players’ movement patterns were monitored using GPS devices and categorized into six speed zones: Zone 1 (0.1–6 km/h), Zone 2 (6.1–12 km/h), Zone 3 (12.1–18 km/h), Zone 4 (18.1–21 km/h), Zone 5 (21.1–24 km/h), and Zone 6 (above 24.1 km/h). The results showed that midfielders covered the greatest total distance (p = 0.001), while SDs covered the most meters at high and maximal speeds (Zones 5 and 6) (p = 0.001). In contrast, CDs covered the least distance at high speeds (p = 0.001), which is attributed to the specific tactical role of their position. A comparison of the two halves revealed a progressive decrease in the distance covered by the players at high speed: distance in Zone 3 decreased from 1139 m to 944 m (p = 0.001), Zone 4 from 251 m to 193 m (p = 0.001), Zone 5 from 144 m to 110 m (p = 0.001), and maximal sprinting (Zone 6) dropped from 104 m to 78 m (p = 0.01). Despite this reduction, the total distance remained relatively stable (first half: 5237 m; second half: 5046 m, p = 0.16), indicating a consistent overall workload but a reduced number of high-speed efforts in the latter stages. The results clearly show that the tactical role of each playing position in the 1-4-3-3 formation, as well as the area of the pitch in which each position operates, significantly affects the running performance profile. This information should be utilized by fitness coaches to tailor physical loads based on playing position. More specifically, players who cover greater distances at high speeds during matches should be prepared for this scenario within the microcycle by performing similar distances during training. It can also be used for better preparing younger players (U17) before transitioning to the U19 level. Knowing the running profile of the next age category, the fitness coach can prepare the players so that by the end of the season, they are approaching the running performance levels of the next group, with the goal of ensuring a smoother transition. Finally, regarding the two halves of the game, it is evident that fitness coaches should train players during the microcycle to maintain high movement intensities even under fatigue. Full article

Background: Pre-season training is pivotal for optimizing athletic performance in collegiate basketball, yet the effectiveness of such programs in improving body composition (BC) under NCAA-mandated hourly restrictions remains underexplored. The aim of this study was to evaluate the impact of a four-week, NCAA Division II-compliant strength and conditioning (SC) program on BC in women’s basketball. Methods: Sixteen student athletes (20.6 ± 1.8 y; 173.9 ± 6.5 cm; 76.2 ± 20.2 kg) completed an eight-hour-per-week micro-cycle incorporating functional conditioning, Olympic-lift-centric resistance, and on-court skill development. Lean body mass (LBM) and body-fat percentage (BF%) were assessed using multi-frequency bioelectrical impedance on Day 1 and Day 28. Linear mixed-effects models were used to evaluate the fixed effect of Time (Pre, Post), including random intercepts for each athlete and covariate adjustment for age and height (α = 0.05). Results The LBM significantly increased by 1.49 kg (β = +1.49 ± 0.23 kg, t = 6.52, p < 0.001; 95% CI [1.02, 1.96]; R² semi-partial = 0.55), while BF% decreased by 1.27 percentage points (β = −1.27 ± 0.58%, t = −2.20, p = 0.044; 95% CI [−2.45, −0.08]; R² = 0.24). Height positively predicted LBM (β = +1.02 kg/cm, p < 0.001), whereas age showed no association (p > 0.64). Conclusions: A time-constrained, NCAA-compliant SC program meaningfully enhances lean mass and moderately reduces adiposity in collegiate women’s basketball athletes. These findings advocate for structured, high-intensity, mixed-modality training to maximize physiological readiness within existing regulatory frameworks. Future research should validate these results in larger cohorts and integrate performance metrics to further elucidate functional outcomes. Full article