Search Results (6,376)

To address head-end three-phase current unbalance and terminal power-quality deterioration caused by uneven three-phase load allocation in a low-voltage transformer area (LVTA), this paper proposes a DC-link-voltage-control-based phase-wise unbalanced power compensation strategy for a head-to-tail flexible interconnection structure embedded in the LVTA. The proposed structure consists of two three-phase four-leg converters sharing a common DC bus and connected to the head end and tail end of the LVTA, respectively. Different from conventional phase-wise compensation methods in which the DC side mainly acts as a power-transfer channel, the proposed strategy uses the DC-link voltage control of the head-end converter as the core of compensation power generation. Specifically, the outer DC-link voltage loop generates the total active compensation power, which is then allocated among the three phases according to the measured phase-power unbalance of the LVTA, thereby yielding the phase-wise compensation current references. Combined with phase-wise quasi-proportional-resonant current control, the compensation currents of different phase legs can be regulated without explicit positive-, negative-, and zero-sequence decomposition. Meanwhile, the tail-end converter adopts PQ control to support terminal power regulation and improve the terminal voltage quality of the LVTA. To provide a theoretical basis for the proposed method, a switching-cycle averaged model of the three-phase four-leg converter is established, and the leg-level phase-wise control characteristics are analyzed under the assumptions of a stiff DC link and symmetrical converter parameters. A control-oriented equivalent LVTA model is developed in MATLAB/Simulink. The proposed strategy is validated under steady-state unbalanced, RL load, load-disturbance, and equivalent feeder-impedance conditions. In addition, a conventional positive-, negative-, and zero-sequence compensation method is introduced as a benchmark for quantitative comparison. The simulation results demonstrate that the proposed method can effectively suppress the head-end three-phase current unbalance, maintain the DC-link voltage around its reference value, and improve the terminal voltage quality of the LVTA. Compared with the conventional sequence-component-based compensation method, the proposed strategy achieves effective unbalance mitigation while avoiding explicit sequence extraction and reducing the complexity of the compensation-current generation process. This study provides a feasible control framework for three-phase unbalance mitigation in flexible low-voltage transformer areas. Full article

Background/Objectives: Semaglutide and tirzepatide demonstrate substantial efficacy in obesity treatment, yet individual responses vary markedly. The incretin system—comprising glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP)—is frequently dysregulated in obesity, but whether fasting incretin levels predict differential pharmacological responses remains unexplored. We investigated whether combinatorial fasting GLP-1/GIP tertile profiles predict the initial weight-loss response to semaglutide versus tirzepatide in patients with severe obesity. Methods: This prospective, parallel-group, open-label pilot study enrolled 90 treatment-naïve patients with BMI > 40 kg/m² (mean 42.5 ± 3.5 kg/m²) at the University of Catania, Italy. Fasting serum GLP-1 (0.8–50 pg/mL) and GIP (1–16 ng/mL) were measured by chemiluminescence immunoassay and distributed into tertiles, generating nine combinatorial profiles (P1–P9; n = 10 per profile). Within each profile, five patients were randomly assigned to semaglutide (escalated to 2.4 mg/week) or tirzepatide (escalated to 15 mg/week). Primary outcome was pharmacological response category at six months: low (<5% body weight reduction), intermediate (5–15%), or optimal (>15%). Results: Baseline characteristics were balanced across profiles (age 48 ± 8 years, BMI 42.5 ± 3.5 kg/m², waist circumference 134 ± 12 cm, HOMA-IR 8.5 ± 3.0; all p > 0.05). Tirzepatide achieved optimal response in profiles with low GIP tertile regardless of GLP-1 level (P1, P6, P8), while semaglutide achieved optimal response when GLP-1 was low and GIP was intermediate-to-high (P4, P5). Both drugs showed low response in the high GLP-1/high GIP profile (P3). Mean weight loss in optimal-response groups was 18.2 ± 2.1% for tirzepatide and 16.8 ± 1.9% for semaglutide. Waist circumference reductions paralleled weight loss patterns. HOMA-IR decreased significantly in all optimal-response groups (mean reduction 4.2 ± 1.1 units). Conclusions: In this hypothesis-generating pilot study, fasting GLP-1/GIP combinatorial profiling, obtained from a single fasting blood sample, was associated with differential pharmacological responses to semaglutide and tirzepatide in severe obesity. Low GIP levels were tentatively associated with optimal tirzepatide response; low GLP-1 with intermediate-to-high GIP was tentatively associated with optimal semaglutide response. These preliminary findings provide proof-of-concept for incretin-guided personalised obesity pharmacotherapy but require confirmation in larger, adequately powered randomised trials before any clinical recommendations can be made. The inability to discriminate incretin secretory deficiency from receptor resistance using fasting measurements alone, the absence of a placebo control, and the six-month follow-up (shorter than the 12–18 months at which maximal efficacy is typically observed) remain critical limitations. Full article

The integration of high-penetration renewable energy creates new requirements for cross-timescale peak shaving and for system robustness under extreme meteorological conditions. This study develops a dual-timescale capacity allocation method for pumped hydro storage (PHS), combining 8760 h chronological production simulation with monthly typical-day retrospective analysis. The model represents the operating limits of conventional units, nuclear power, hydropower, wind power, photovoltaic generation, tie-line exchange, and PHS energy shifting. On this basis, a stepwise capacity-sensitivity framework is established to minimize annualized comprehensive system cost while controlling renewable energy curtailment within a predefined planning threshold, rather than treating zero curtailment as an unconditional monthly hard constraint. Using long-term planning data from a coastal provincial power grid in southeastern China, the study compares the 2035 and 2040 planning scenarios. The results show that isolated typical-day models tend to overestimate PHS requirements because they disconnect chronological continuity and cross-day reservoir buffering. In 2035, the system presents a two-level seasonal capacity structure: 15,000 MW can support normalized operation in stable months, whereas the rigid boundary rises to 19,000 MW under extreme autumn high-wind conditions. In 2040, wind and photovoltaic capacity increase by approximately 20.01 GW compared with 2035, deepening low-net-load valleys and compressing seasonal regulation margins. Under the assumed planning boundary, the recommended PHS capacity converges to 23,000 MW. The proposed framework provides a practical reference for flexible resource planning in coastal power grids with deep renewable energy integration. Full article

Limited by the computational performance limits of the CPU(Central Processing Unit), the traditional Spark architecture struggles to achieve high throughput and low latency under the dual pressure of a large data scale and real-time requirements in centralized control systems. This work uses a publicly available CNC(Computer Numerical Control) milling dataset as a functional validation proxy for time-series data processing, then extends validation to a large-scale synthetic power transmission grid dataset. Furthermore, Spark-GPU(Graphics Processing Unit) collaboration suffers from load balancing failure due to heterogeneous resource scheduling and communication overhead, thus failing to unleash its performance potential. This paper proposes a Spark-GPU fusion acceleration technology path. The path consists of three key components: first, it integrates the RAPIDS accelerator; second, it designs a GPU-aware partitioning and task co-scheduling strategy; and third, it optimizes the zero-copy data path. Together, these components realize an integrated collaboration of heterogeneous resources. Validation on real-world datasets yields the following results. In real-time aggregation scenarios, the proposed solution improves throughput by a factor of 3.7 over the pure CPU baseline and reduces end-to-end latency by 62%. Compared with the basic GPU solution, GPU utilization rises from 51.7% to 72.3%, representing a relative improvement of 39.8%. Furthermore, the solution meets industrial-grade high availability requirements. This research significantly improves the processing throughput and reduces end-to-end latency in typical centralized control scenarios, thus providing a feasible technical route for demanding concurrent centralized control scenarios such as electric power industry manufacturing with high real-time demands. Full article

Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. Full article

Analog in-memory computing (AIMC) has emerged as a promising approach to mitigate the Von Neumann bottleneck in matrix operations, which are common in deep learning applications. However, the practical implementation of resistive crossbar arrays is limited by challenges in signed weight representation, conductance quantization, and device nonlinearity. This paper presents a differential mixed-signal architecture for accurate signed matrix–vector multiplication (MVM), integrated with a RISC-V microcontroller for edge inference applications. A structured digital-to-analog mapping framework encodes quantized neural network weights into programmable conductance values while preserving arithmetic correctness. The design employs voltage-mode input encoding, differential current summation, and transimpedance-based readout followed by analog-to-digital conversion, enabling single-cycle signed accumulation without duplicating crossbar resources. A 32 × 16 dual-layer prototype crossbar was fabricated and experimentally characterized. Measurements demonstrate a mean absolute percentage error (MAPE) below 1% within the linear operating region and below 4% over the full-scale conductance range. These results validate the robustness of the proposed mapping methodology and confirm the feasibility of hybrid analog–digital acceleration for edge AI systems. Consequently, this discrete prototype serves as a physical verification platform for the AIMC approach, providing valuable insights for more efficient mixed-signal computing integrated circuit (IC) designs. Full article

Background: Evidence comparing the effects of novel alternative dietary strategies on resting energy expenditure (REE) with a hypocaloric standard Mediterranean diet (MedDiet) with continuous caloric restriction remains limited. This study aimed to evaluate the effects of diets with varying ketogenic potentials—including a very-low-carbohydrate diet (ketogenic diet, KD), time-restricted eating (TRE), and modified alternate-day fasting (mADF)—on the REE of individuals with obesity compared to those of a standard MedDiet. Methods: This was a secondary post hoc sub-analysis of a three-month, parallel-arm, randomized clinical trial (RCT) including 160 adults with obesity (body mass index > 30 kg/m²). The participants were randomly assigned to one of five calorie-restricted dietary interventions: control (MedDiet), KD, early time-restricted eating (eTRE), late time-restricted eating (lTRE), or mADF. All interventions featured an individualized energy deficit of 600 kcal/day. In this sub-analysis, a total of 102 participants with valid baseline measures were included. The REE was assessed by indirect calorimetry, and longitudinal trajectories were evaluated using Linear Mixed Models (LMMs) in 98 participants to account for baseline variability and to maximize data retention. Results: The mean age of participants in this sub-analysis was 45.3 years (SD 10.8), and 73.1% were women. The longitudinal modeling confirmed no statistically significant differences in the adjusted REE trajectories among the five dietary groups over the 3-month intervention (Group × Time interaction, p = 0.506). Furthermore, the LMMs showed that total body weight (p < 0.001) and biological sex (p < 0.001) were the variables most strongly associated with REE within the model. No independent associations between circulating beta-hydroxybutyrate levels and REE trajectories were detected. Conclusions: Hypocaloric diets with varying macronutrient distributions and fasting windows did not show statistically significant differences in REE trajectories over the 3-month intervention. In this exploratory sub-analysis, the REE trajectories were more closely associated with individual biological characteristics, particularly body weight and sex, than with the specific dietary strategy employed. Given the modest sample size and exploratory nature of the study, these findings should be interpreted cautiously and require confirmation in larger, adequately powered prospective trials. Full article

Frequency instability caused by reduced system inertia in inverter-dominated islanded microgrids represents a critical challenge in renewable-integrated power systems. Conventional fixed-parameter controllers exhibit limited adaptability to uncertain and time-varying low-inertia conditions. This paper proposes an LSTM–MPC + VIC framework that embeds a Long Short-Term Memory (LSTM) surrogate predictor directly within a Model Predictive Control (MPC) optimisation loop, coordinated with a Virtual Inertia Controller (VIC) for immediate transient support. The LSTM provides data-driven frequency predictions without requiring precise analytical system modelling, while the VIC supplies reactive inertial damping within the same control cycle. The proposed controller is evaluated against Proportional–Integral–Derivative (PID), PSO-optimised PID, and standard MPC baselines on a 50 Hz islanded microgrid. Results demonstrate the lowest maximum frequency deviation of 0.009748 Hz, fastest settling time of 36.34 s, and minimum integral absolute error of 0.12283 Hz·s among all controllers. A Lyapunov-based Input-to-State Stability (ISS) analysis, incorporating the load disturbance term via Young’s inequality, confirms an ISS ultimate bound of 0.057866 Hz and an effective decay rate of 1.2952 s⁻¹. Robustness is further validated through multi-scenario testing, parametric sensitivity analysis, component ablation, and computational feasibility assessment, confirming suitability for real-time deployment in low-inertia microgrid systems. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

Accurate real-time soil moisture monitoring is critical for optimizing water use and ensuring crop health and food security. This study aims to calibrate and integrate low-cost capacitive soil moisture sensors into a solar-powered sensor node for real-time soil moisture monitoring in a loamy sand soil. Three capacitive soil moisture sensors were calibrated in the laboratory under controlled volumetric water content conditions (0–40%) using a constrained linear regression approach. The system was tested in a limited pilot-scale in a drip-irrigated onion field at the IWAD farm, Yagaba (North-East Region, Ghana). The results showed good agreement of the sensor readings with the soil moisture obtained using the gravimetric method (R² of 0.92–0.94, RMSE of 0.40–0.52%, and MAE of 0.35–0.39%) demonstrating the successful transfer of the calibration functions to field conditions. Soil moisture data was successfully monitored and transmitted from the nodes to a LoRa gateway via LoRaWAN (433 MHz) and from the gateway to a Raspberry Pi edge server via Wi-Fi. Data was stored both locally in SQLite on the Raspberry Pi and on the InfluxDB cloud. These results suggest that the developed system, when extensively validated under field conditions, can be used to support decision-making for data-driven IoT-based irrigation scheduling. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

Background: In youth football, sprint performance depends on the capacity to produce and orient force horizontally during acceleration. Resisted sprinting may preferentially target the force end of the sprint force–velocity profile, whereas free sprinting may favour velocity-oriented adaptations. Purpose: To compare the effects of resisted versus non-resisted sprint training on sprint performance and sprint force–velocity variables in youth footballers, while monitoring countermovement jump (CMJ) as a secondary outcome. Methods: This parallel-group randomised controlled trial included 44 players from two age categories (U14, n = 21; Youth, n = 23). Within each category, players were randomly allocated to resisted sprint training (RST; U14 n = 11, Youth n = 12) or non-resisted sprint training (NRST; U14 n = 10, Youth n = 11). Both groups completed two supervised sessions per week for six weeks. Outcomes were CMJ and sprint-derived variables including maximal theoretical horizontal force (F0), maximal theoretical velocity (V0), maximal power (Pmax), measured maximal sprint velocity (Vmax), peak ratio of horizontal force (RFpeak), decrease in RF with increasing velocity (DRF), and force–velocity slope (FV). Results: CMJ remained essentially unchanged in both age categories. Sprint performance improved over time, with the pattern of adaptation generally favouring RST for force-oriented sprint mechanical variables (F0, Pmax and RFpeak), whereas improvements in Vmax were observed in both groups. In the Youth category, the FV slope differed between groups post-test (p = 0.002). Overall, resisted sprint training tended to produce larger improvements in acceleration-oriented mechanical qualities, while non-resisted sprint training was associated with more velocity-oriented adaptations. Conclusions: Low-volume resisted sprint training using a sled load of ~20% body mass was associated with more favourable adaptations in force-oriented sprint mechanical variables, whereas non-resisted sprint training tended to favour velocity-oriented characteristics. CMJ performance remained unchanged in both groups. These findings should be interpreted cautiously given the small age-stratified subgroup sizes and the single-club nature of the study. Trial registration: This study was retrospectively registered at ClinicalTrials.gov (NCT07418892). Full article

Femtosecond laser surface texturing offers a promising route to tailor the functionality of bio-based wood coatings, yet the interplay between coating composition and laser processing remains poorly understood. In this study, bio-based epoxy coatings with eventual micronized wax additives were textured using a femtosecond laser to investigate the effects of laser processing parameters on pattern formation and resulting hydrophobicity. The epoxy coatings containing PE, PE/PTFE, HDPE, and rice bran waxes at 1, 5, and 7 wt.-% were characterized in terms of morphology, roughness, wettability, and chemical stability, followed by systematic variation of pulse repetition rate and laser power. The results reveal that the ablation threshold strongly depends on intrinsic coating properties. Ablation resistance increases with surface roughness and wax melting enthalpy, reflecting the role of phase transition energy in laser–matter interaction. The wax-filled coatings exhibit a transition from melting-dominated behavior at low energy input to ablation-dominated behavior at a higher energy. Laser texturing enhances hydrophobicity in parallel with theoretical values calculated from the Cassie–Baxter wetting model, with the highest hydrophobicity achieved for coatings combining intrinsic hydrophobicity and stable pattern formation. Chemical analysis confirms limited degradation of the epoxy matrix without significant carbonization, while wax additives provide partial thermal shielding. Overall, this work demonstrates clear options for tailoring surface morphology and wettability of hydrophobic polymer coatings through controlled femtosecond laser processing. Full article