Search Results (961)

This study examined the effects of two different intensities of running between repeated sprints and compared them with passive recovery. Thirteen professional soccer players performed two sets of six 30 m sprints on three randomly assigned occasions. A 5 min passive rest period separated the two sets, while sprints were interspersed with either passive standing, running at 95% of the first lactate threshold (MOD) and running at maximum aerobic speed (HIGH). Performance decrements were greater in HIGH than MOD at the last sprint in both sets (set 1: 5.8 ± 4.2% vs. 2.6 ± 3.2%, p = 0.07; set 2: 9.1 ± 4.5% vs. 4.0 ± 6.1%, p = 0.016). Acceleration (0–15 m) was more affected than maximal-speed running (15–30 m) (condition × sprint interaction: p < 0.001). Mean and peak heart rate were higher in both running conditions than passive (p < 0.05), with no difference between MOD and HIGH. Blood lactate showed a significant set × condition interaction (p < 0.001), peaking at 13.6 ± 2.7 mmol·L⁻¹ in HIGH, while blood lactate responses to passive and MOD were similar and peaked after the second set of sprints (10.7 ± 2.1 and 11.5 ± 2.8 mmol·L⁻¹, respectively). Between-sprint running intensity markedly influenced fatigue development during repeated-sprint exercise. The HIGH condition elicited greater metabolic strain and performance decrements than MOD or passive conditions. Within the present protocol, passive standing was associated with smaller decrements in repeated-sprint performance despite high heart rate and blood lactate responses. Full article

The escalating economic losses in agriculture due to deer intrusion, estimated to be in the hundreds of millions of dollars annually in the U.S., highlight the inadequacy of traditional mitigation strategies such as hunting, fencing, use of repellents, and scare tactics. This underscores a critical need for intelligent, autonomous solutions capable of real-time deer detection and deterrence. But the progress in this field is impeded by a significant gap in the literature, mainly the lack of a domain-specific, practical dataset and limited studies on the viability of deer detection systems on edge devices. To address this gap, this study presents a comprehensive evaluation of state-of-the-art deep learning models for deer detection in challenging real-world scenarios. We introduce a curated, publicly available dataset of 3095 annotated images with bounding box annotation of deer. Then, we provide an extensive comparative analysis of 12 model variants across four recent YOLO architectures (v8 to v11). Finally, we evaluated their performance on two representative edge computing platforms, the CPU-based Raspberry Pi 5 and the GPU-accelerated NVIDIA Jetson AGX Xavier, to assess feasibility for real-world field deployment. To ensure a standardized comparison, we established a framework-agnostic deployment pipeline using universal Open Neural Network Exchange (ONNX) runtimes. Results show that the real-time detection performance is not feasible on Raspberry Pi using universal runtimes, suggesting that while framework-agnostic runtimes facilitate portability, low-power CPU deployment requires hardware-specific optimization to achieve real-time thresholds. Conversely, NVIDIA Jetson provides greater than 30 frames per second (FPS) with ‘s’ and ‘n’ series models. This study also reveals that smaller, architecturally advanced models such as YOLOv11n, YOLOv8s, and YOLOv9s offer the optimal balance of high accuracy (Average Precision (AP) > 0.85) and computational efficiency (Inference Time < 34 milliseconds). Full article

The potential withdrawal of glyphosate necessitates a comprehensive evaluation of alternative weed control strategies that balances human health safety with environmental concerns. This study applied a decision-support grid to compare the impacts of glyphosate-based reference strategies against chemical and non-chemical alternatives across four Belgian case studies: pome fruit orchards, grassland renewal, arable weed patches, and railways. The assessment integrated twelve risk indicators including human, environmental and biodiversity risk, and life cycle assessment for global warming potential (GWP) into a Final Scenario Score (FSS). The results indicated that only one alternative strategy, the chemical alternative in local weed patch control, achieved the FSS threshold (<0.75) required to justify substitution (FSS = 0.70). Chemical alternatives in other case studies frequently shifted burdens; for instance, bio-herbicides in railways increased risks to residents and aquatic organisms compared to the reference. Conversely, mechanical and thermal alternatives eliminated chemical toxicity but resulted in GWP increases up to 32 times higher than glyphosate-based practices. These findings demonstrate that chemical substitutes often maintain toxicity risks while non-chemical strategies trade them for increased climate impacts. Consequently, a ban on glyphosate is currently unsupported by the environmental performance of available alternatives in these temperate high-intensity systems. Sustainable progress requires a transition period where optimized conventional strategies remain available within integrated weed management, while innovations in electrification and precision technology are accelerated to resolve current trade-offs. Full article

The countermovement jump (CMJ) is widely used to monitor neuromuscular performance in sport, but its assessment is largely dependent on force platforms, which limits their use outside the laboratory due to their cost and limited portability. This work describes the development and validation of a fully custom wearable inertial measurement unit (IMU) system for CMJ assessment. The platform is based on a single IMU placed on the lower back and sampled at 1 kHz, and includes Bluetooth Low Energy (BLE) communication together with dedicated PC and smartphone applications. A new algorithm based on the derivative of vertical acceleration was implemented to identify take-off and landing instants. The system was evaluated using 119 CMJ trials performed by 19 participants and validated against a force platform used as the criterion reference. Different acceleration thresholds were tested, with 0.2 g providing the best compromise between detection robustness and the statistical quality of the measurements, yielding a detection rate of 97.43%. Agreement analysis showed a small systematic underestimation of flight time (bias = −0.0117 s), with moderate limits of agreement across the observed range. These results indicate that the proposed system may be suitable for practical, field-based CMJ monitoring, although the observed variability relative to force-platform measurements should be considered, particularly in applications requiring individual-level decision making. Full article

This study develops an integrated analytical framework to assess Russia’s green hydrogen demand potential and cost-competitiveness pathways across the steel production and road transport sectors. Using bottom-up sectoral analysis, we estimate Russia’s theoretical hydrogen demand potential at approximately 18.2 Mt/year. Three policy scenarios model demand realization trajectories under differentiated support regimes, calibrated to European alternative fuel vehicle diffusion patterns and Russian statistical data. A learning curve framework projects green hydrogen costs as an endogenous function of cumulative production, with learning rates of 5% and 10.1% representing conservative and optimistic technology development pathways. Results indicate that under realistic policy support and 10.1% learning rates, hydrogen costs decline from USD 15/kg to USD 7.23/kg by 2050, reaching the USD 10/kg competitiveness threshold by approximately 2035. However, Russia’s costs remain 2–4 times higher than global optimal-location projections due to scale disadvantages and infrastructure constraints. Policy recommendations emphasize front-loaded support mechanisms, export market integration with EAEU partners, and electrolyzer technology localization to accelerate learning effects and achieve cost competitiveness within mid-term planning horizons. Full article

Does the rapid expansion of the digital economy ultimately reduce or increase carbon emissions in tourism transportation? Its impact remains ambivalent, presenting both clear opportunities and unforeseen challenges. This study hypothesizes that while the digital economy increases total carbon emissions by expanding the scale of travel and driving up private car ownership, it concurrently reduces emission intensity. This study estimates tourism transportation carbon emissions across 30 Chinese provinces (2011–2021) using a bottom-up approach. By integrating fixed-effects, mediation, and threshold models, it systematically examines the digital economy’s direct, mechanistic, and nonlinear impacts on emission dynamics. The empirical findings provide strong support for initial hypotheses. Further, the threshold tests uncover the tipping points in how threshold variables influence tourism transportation carbon emissions. The effect of the digital economy transitions from accelerating to attenuating emission growth once these boundaries are crossed, revealing a shift from a scale-driven regime to an efficiency-driven equilibrium. These findings suggest that well-calibrated policies can harness digitalization to foster low-carbon transformation. Recommended measures include implementing tiered subsidy schemes for low-emission vehicles and fostering cross-regional collaboration to establish carbon-inclusive platforms. Full article

The MERS-CoV (Middle East respiratory syndrome coronavirus) is a zoonotic virus with a high mortality rate and a lack of antiviral drugs, underscoring the need for effective therapeutic methods. Viral entry depends on interactions between viral surface proteins and human receptors, with Dipeptidyl Peptidase-4 (DPP4), a transmembrane glycoprotein, acting as the receptor for MERS-CoV. We employed Molecular Dynamics (MD) Simulations to identify critical interface residues under a high-performance computing (HPC) workflow for accelerated results. Target residue pairs were identified through analysis of salt bridge and hydrogen bond occupancy. The stability of these residues was confirmed through three independent MD Simulations at human body temperature and constant pressure. Additionally, binding affinity predictions were calculated to determine the interaction strength between the virus and human receptors. Applying the scientific threshold criteria, we narrowed our results to seven key interaction pairs; two of the identified pairs (Asp510-Arg317, and Arg511-Asp393) are consistent with findings published in previous research studies, and five novel interactions are proposed for future experimental studies with our active collaborators in Pharmacology. The results provide a molecular basis for targeted mutation-based experiments and support the rational design of structure-based inhibitors aimed at disrupting the MERS-CoV-DPP4 complex, thereby facilitating the translation of computational findings into antiviral drug discovery. Full article

3 × 3 basketball is a high-intensity intermittent sport practiced by both professional and recreational athletes. However, the use of predefined absolute thresholds to quantify external load may overlook meaningful inter-individual differences in movement intensity. This study examined internal and external load demands during official 3 × 3 match play using individualized, performance-based load zones. Seventeen male players were monitored across 38 valid match observations during a two-day tournament. External load was collected via inertial measurement units, while internal load was assessed through continuous heart-rate monitoring. Raw triaxial accelerometer data were processed in Python to remove gravitational components and reconstruct speed–acceleration profiles, allowing identification of individual acceleration, deceleration, and jump events. Statistical analyses were conducted using linear mixed-effects models with Bonferroni-adjusted post hoc comparisons to evaluate differences between absolute and individualized zones. Players sustained high physiological strain, operating at approximately 85–90% of HRmax, and performed frequent high-intensity mechanical actions. Individualized acceleration, deceleration, and jump zones yielded a more even dispersion of events across low-, moderate-, and high-intensity categories. In contrast, predefined absolute thresholds classified over 90% of events as low intensity, masking meaningful variability. These findings highlight substantial inter-individual differences in 3 × 3 match demands and support the use of individualized load profiling for accurate monitoring, performance evaluation, and training prescription. Full article

ALS is a multistep disease, in which (epi)genetic, environmental, and age-related processes, including senescence, converge over decades to reduce resilience resulting in self-sustaining symptomatic disease. The multistep model visualizes five to six impactful events in sporadic ALS, but fewer in those carrying high-penetrance mutations, such as SOD1, FUS, or C9orf72 expansions. The timing, duration, and cumulative effects of specific steps are presumed to have individual variability but, the steps themselves are inferred since they have not been observed and remain agnostic as to biological identity. Nevertheless, the model gives an opportunity to integrate genetics, aging, environmental exposures, and systems-level vulnerability into a single framework. Acting as step modifiers, environmental exposures including trauma lower the threshold for step acquisition, accelerate the accumulation of steps, influence the anatomical site of disease onset, and unmask preclinical disease. Because ALS emerges from the gradual collapse of multiple layers of biological robustness, tackling a single pathway will be insufficient and the multistep model forces a reconsideration of therapeutic timing and strategies. Protection against early-life insults, anti-aging, and anti-senescent therapies may curtail step accumulation preventing ALS from exceeding threshold and disease manifestation. Full article

Reasonable adjustment of construction parameters is of great value to reduce surface settlement and ensure the safety of shield construction. A novel hybrid intelligent optimization framework based on combination weighting and gray correlation analysis methods (CWG), a long short-term memory (LSTM) model and a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (CPSOS) algorithm was proposed. The CWG method was employed to screen key construction parameters and determine the weights of various influencing factors of surface settlement, thereby constructing a CWG-LSTM prediction model for surface settlement. On this basis, the prediction model served as the objective function for optimizing construction parameters, and the CPSOS algorithm was used for the optimization of shield construction parameters. Based on the Qingdao Metro Line 4 in China, sample sets were collected to verify the performance of the developed framework. The CWG-LSTM model achieved coefficients of determination (R²) of 0.92 and 0.91 on the train and test sets, respectively, along with root mean square errors (RMSE) of 1.29 and 1.03, and mean absolute percentage errors (MAPE) of 15.60% and 17.18%. Its prediction ability surpasses that of other comparison models, such as the Gated Recurrent Unit, Random Forest, Transformer, and Multiple Linear Regression, demonstrating higher accuracy. Optimized construction parameters derived from the CWG-LSTM-CPSOS facilitated shield tunneling in the unconstructed section. All surface settlement monitoring results recorded during excavation fell within the safety threshold, demonstrating that the proposed hybrid intelligent optimization framework effectively manages surface settlement during shield tunneling and serves as a reliable optimization approach for construction parameters. Full article

Flat spots on railway wheels critically threaten operational safety, accelerating track damage, noise pollution, and energy waste. Their repetitive, high-magnitude impacts dissipate mechanical energy as ground vibration and noise, directly reducing traction efficiency. This paper presents a comprehensive review of recent vibration and acoustic detection methods, comparing onboard and stationary wayside systems. The literature from 2019 to 2025 shows a trend toward machine learning and deep learning, often reaching nearly 100% accuracy in laboratory or simulated settings. However, most studies lack real-world validation with naturally occurring defects. Bridging this gap requires industry–academic collaboration for operational data and testing. Crucially, systems must classify defect severity in line with maintenance thresholds rather than focus on minor, non-actionable faults. Integrating these technologies into condition-based maintenance and predictive digital twins will enable optimization of scheduling and work orders. Future efforts should leverage edge computing for real-time analysis, federated learning for data scarcity, and energy harvesting for sensor autonomy. The goal is to develop field-validated, integrated systems that enhance safety, reduce energy loss, and improve reliability. Full article

Head injuries remain one of the major health concerns in contact sports such as boxing. Despite the widespread use of protective gloves and helmets, their biomechanical effectiveness in mitigating head acceleration and reducing brain injury risk remains uncertain. This study aims to biomechanically assess available boxing equipment solutions and identify the brain–skull system’s response to physical forces from a boxing punch. A dedicated experimental setup was developed using mini triaxial accelerometers and a high-speed camera to measure head accelerations in a Primus unbreakable dummy. Tests were performed using gloves of different masses (0 oz, 10 oz, and 16 oz) and three head protection configurations: no helmet, rugby helmet, and boxing helmet. The resultant accelerations were analyzed and compared across test conditions. Peak wrist accelerations ranged from 195.00 to 271.77 m/s², while head accelerations did not exceed biomechanical injury thresholds. The boxing helmet, composed of multilayer polyurethane foam, did not consistently decrease acceleration; in some cases, it produced higher overloads due to increased head mass and moment of inertia. A rugby helmet made of open-cell EVA (ethylene vinyl acetate) foam with lower density exhibited more favorable energy-dissipation characteristics under low-impact conditions. Glove mass also influenced acceleration differently between male and female participants, likely due to variations in punch velocity and force generation. This work is a pilot study using two trained adult volunteers to validate the combined IMU–video measurement framework. The results serve as hypothesis-generating mechanistic observations rather than population-level effect estimates. Protective effectiveness in boxing depends on a complex interaction between material properties, geometry, and user biomechanics. Optimal equipment design should balance energy absorption and mass to minimize both linear and rotational accelerations. Future studies should integrate advanced material modeling and finite element simulations to support the development of adaptive, lightweight protective systems. Full article

This paper addresses the question of how many autonomous aerial vehicles (UAVs or drones) can safely operate within a bounded three-dimensional airspace. First, we derive the absolute mathematical limits on drone density using geometric arguments from sphere packing and covering theory. Then, we verify these limits empirically by simulating a swarm controlled via model predictive control. We incrementally increase the number of drones until motion becomes impossible. Each drone is modeled as a double-integrator system with a bounded speed and acceleration and is surrounded by a radius spherical safety zone $r>0$. The drones are controlled via model predictive control with hard separation constraints. We formalize complete blockage as the loss of any feasible non-trivial trajectory set, either due to geometric crowding or dynamic limitations. Using tools from discrete geometry, we establish absolute upper bounds on a safe population via sphere-packing results and sufficient conditions for total immobilization via sphere-covering arguments. We extend these static bounds by incorporating dynamics through stopping-distance analysis, leading to an inflated exclusion radius that captures the effect of finite control authority. In addition, we prove min-cut style flow-capacity bounds that limit feasible throughput across bottlenecks and derive horizon-dependent conflict-graph conditions that capture MPC infeasibility at high densities. These results provide a rigorous theoretical framework for determining the transition from feasible multi-drone operation to inevitable gridlock, offering explicit quantitative thresholds that can inform airspace design, drone density regulation, and the tuning of predictive controllers. We evaluate our theoretical findings with a simulation environment. Full article

Background: Age-related hormonal changes in older women accelerate bone and muscle loss, leading to postural dysfunction and chronic musculoskeletal pain. This study aimed to evaluate the short-term effects of a physical therapy program combining elastic band exercises and vigorous walking on pain, thoracic mobility, and functional capacity in older adult women. Methods: A multicenter randomized controlled trial was conducted older adult women (60–80 years) with chronic non-specific musculoskeletal pain, allocated to an elastic band plus vigorous walking group (EBWG), a vigorous walking group (VWG), or a control group (CG). A total of 91 participants completed all of the assessments. Outcomes included pressure pain threshold (PPT), self-reported pain (VAS), thoracic mobility (UPC, LWC), functional capacity (5XSTS), and perceived improvement (PGIC), evaluated at baseline, after a 4-week intervention, and at 4-week follow-up. Results: The EBWG demonstrated greater improvements in PPT (+0.66 kg/cm² at T2), upper chest expansion (+1.00 cm), and 5XSTS performance (−1.7 s) compared to the control group. The VWG showed significant reductions in overall pain (−0.9 points) and lumbar pain (−1.7 points). Improvements in PPT and thoracic mobility in the EBWG exceeded MDC/MCID thresholds, indicating clinically meaningful changes. Vigorous walking alone improved self-reported pain but was less effective than the multicomponent program. Conclusions: A 4-week combined program of elastic band exercises and vigorous walking produced clinically relevant improvements in pain threshold, thoracic mobility, functional capacity, and perceived change compared to walking alone or usual activity. These findings support the clinical utility of short, feasible, multicomponent interventions for managing chronic musculoskeletal pain in older women. Full article

With the active modernization of power facilities and the increasing deployment of maneuverable combined-cycle gas turbines (CCGTs), the selection of rational start-up strategies becomes increasingly important from the perspective of power quality. Excessive acceleration of power ramp-up may lead to undesirable voltage deviations, particularly in transmission networks with limited grid stiffness. This study investigates the impact of CCGT start-up ramp rate on voltage dynamics and power quality indicators at a 110/220 kV grid node. A detailed model of the Almaty power hub was developed in MATLAB/Simulink, taking into account the network structure, generating units, transformers, and aggregated loads. Three start-up scenarios were analyzed: an existing combined heat and power plant, a 504 MW combined-cycle gas turbine unit, and a 560 MW combined-cycle gas turbine unit with fuel afterburning. Voltage dynamics were evaluated using RMS-based indicators and a stabilization criterion incorporating a 5 s sliding time window and an 80% admissibility threshold. The simulation results reveal a nonlinear relationship between the start-up ramp rate and voltage quality. Increasing the ramp rate reduces the voltage stabilization time; however, beyond approximately 0.05 MW/s, further acceleration does not lead to additional improvement in power quality. The results indicate the existence of an optimal range of start-up ramp rates that provides a compromise between start-up speed and voltage quality requirements. The proposed approach can be used in the development of start-up algorithms for modern combined-cycle power plants connected to 110/220 kV transmission networks. Full article