Search Results (306)

Background: Men treated with androgen deprivation therapy (ADT) for prostate cancer are at risk for cognitive decline. Patient genetics and endocrine state may shape gut microbiome features that relate to cognition. Methods: We studied a subsample of 79 prostate cancer survivors with prior ADT exposure previously enrolled in a randomized controlled exercise trial comparing three training modalities (strength training, Tai Chi training, or stretching control) who completed an additional food-frequency questionnaire and remote Montreal Cognitive Assessment (MoCA) and provided saliva and stool for APOE genotyping, salivary testosterone, and 16S rRNA sequencing. We used beta regression for MoCA (scaled 0–1), linear models for testosterone, alpha diversity regressions, PERMANOVA for beta diversity, and DESeq2 for genus-level differential abundance, with false-discovery correction. Results: Compared to post-stretching control, post-strength training testing was associated with higher MoCA scores whereas post-Tai Chi testing was not. APOE ε4 carriers exhibited a greater testosterone increase with strength training than non-carriers. Testosterone, and its interactions with exercise modality and APOE ε2 status, was related to presence/absence-based community structure; APOE ε4 interacted with exercise intervention to influence alpha diversity. At the genus level, exercise was linked to lower levels of Bacteroidota taxa (including Muribaculaceae) and higher levels of Enterobacteriaceae; APOE ε4 status was linked to higher Megamonas and lower Rikenellaceae RC9 levels; and higher salivary testosterone levels were linked to higher Prevotellaceae taxa and Succinivibrio levels. Higher MoCA scores were associated with lower abundances of several Firmicutes genera. Conclusions: Endocrine state and APOE genotype may condition the gut microbiome’s response to exercise intervention in ADT-treated prostate cancer survivors, with downstream associations with cognition. These findings could inform precision survivorship strategies pairing strength training with genotype- and hormone-informed microbiome monitoring to optimize cognitive performance. Full article

With the increasing demand for indoor location-based services, magnetic-fingerprint-based positioning has emerged as a promising complementary solution in scenarios lacking WiFi coverage. However, the dynamic nature of indoor environments, architectural complexity, and variations in pedestrian walking speeds can lead to stretching, compression, and distortion of magnetic fingerprint sequences, making it challenging for traditional sequence-matching algorithms to maintain stable positioning performance. To address these challenges, this paper proposes a magnetic-fingerprint-based positioning model that integrates residual networks (ResNet), transformer, and LSTM, referred to as Res-T-LSTM. Within the overall architecture, the ResNet module extracts deep local spatial features of magnetic fingerprints, and its residual connections effectively mitigate gradient attenuation during deep network training. The transformer module leverages self-attention mechanisms to model long-range dependencies and global contextual information, adaptively emphasizing key magnetic variations to enhance the discriminability of the feature representations. The LSTM module further captures the dynamic temporal evolution of magnetic sequences, improving robustness to variations in walking speed and sequence stretching or compression. Experimental results show that the proposed model achieves excellent performance across four smartphone-carrying postures, yielding an average positioning error of 0.21 m. Full article

Lyme disease, caused by the Borrelia burgdorferi bacterium and transmitted through black-legged (deer) tick bites, is becoming increasingly prevalent globally. According to data from the Lyme Disease Association, the number of cases has surged by more than 357% over the past 15 years. According to the Infectious Disease Society of America, traditional diagnostic methods are often slow, potentially allowing bacterial proliferation and complicating early management. This study proposes a novel hybrid deep learning framework to classify Lyme disease rashes, addressing the global prevalence of the disease caused by the Borrelia burgdorferi bacterium, which is transmitted through black-legged (deer) tick bites. This study presents a novel hybrid deep learning framework for classifying Lyme disease rashes, utilizing pre-trained models (ResNet50 V2, VGG19, DenseNet201) for initial classification. By combining VGG19 and DenseNet201 architectures, we developed a hybrid model, SkinVisualNet, which achieved an impressive accuracy of 98.83%, precision of 98.45%, recall of 99.09%, and an F1 score of 98.76%. To ensure the robustness and generalizability of the model, 5-fold cross-validation (CV) was performed, generating an average validation accuracy between 98.20% and 98.92%. Incorporating image preprocessing techniques such as gamma correction, contrast stretching and data augmentation led to a 10–13% improvement in model accuracy, significantly enhancing its ability to generalize across various conditions and improving overall performance. To improve model interpretability, we applied Explainable AI methods like LIME, Grad-CAM, CAM++, Score CAM and Smooth Grad to visualize the rash image regions most influential in classification. These techniques enhance both diagnostic transparency and model reliability, helping clinicians better understand the diagnostic decisions. The proposed framework demonstrates a significant advancement in automated Lyme disease detection, providing a robust and explainable AI-based diagnostic tool that can aid clinicians in improving patient outcomes. Full article

Background: Volleyball requires explosive jumps, agility, and upper and lower limb coordination. Dynamic stretching (DS) and core stability (CS) protocols are often used separately in training sessions, but little is known about their combined effects on the performance in adolescent players. This study aimed to investigate the impact of a 12-week integrated DS and CS program (StretCor), in addition to standard training, on physical performance in U-16 volleyball players. Methods: Twenty-one volunteer players (15.1 ± 0.6 years) were randomly assigned to the Intervention Group (IG; n = 12) or Control Group (CG; n = 9). IG performed the StretCor protocol four times a week for twelve weeks in addition to standard volleyball training; CG continued standard volleyball training. Physical performance assessment included Countermovement Jump (CMJ), Vertec jump with run-up, isometric shoulder strength (ASH-I), dynamic balance (mSEBT), and agility (t-test) tests. Results: Significant group × time interactions (p < 0.05, η² ranged: 0.20–0.90) were found for CMJ height and peak power, Vertec jump, ASH-I, mSEBT scores, and t-test performance. Post hoc analyses showed improvements in IG for CMJ height (+16.5%), Vertec jump (+10.2%), shoulder strength (+11–14%), balance across directions (+8–12%), and agility (−5.7% t-test time). No significant changes were observed in CG. Conclusions: The present study suggests that a 12 weeks of StretCor protocol training improves jump performance, agility, dynamic balance, and upper limb strength in U-16 volleyball players. These findings also support that StretCor protocol may be beneficial for the performance when incorporated into regular training programs for adolescent athletes. Full article

This study investigated the effects of lightweight wearable resistance on the kinetics and kinematics of squat jumps (SJ) and countermovement jumps (CMJ) with 2%, 4%, and 6% body mass (BM). Twenty male athletes (age: 18.05 ± 0.6 years; weight: 76.4 ± 7.6 kg; height: 182.4 ± 5 cm) were assessed on a force plate. Key variables included jump height (JH), concentric (ConT) and eccentric (EccT) phase durations, concentric impulse (CI), mean force (CMF), mean velocity (CMV), mean power (CMP), and relative metrics. Elastic utilization ratios (EUR) were calculated to quantify stretch-shortening cycle enhancement. Load led to decrements in both jumps but with varying sensitivity. With 2% BM the CMJ significantly reduced JH (−8.6%), EccT (−7%), CMV (−4.1%), rCI (−4.1%), rPP (−4.4%), and velocity at PP (−4.8%), whereas variables in the SJ were non-significant until 4–6% BM. EURs observed the greatest differences with 2% BM with JH, CMV, rCMP, and VPP all significantly decreasing (p < 0.05). The varying sensitivity to load across variables observed in the two jumps supports the hypothesis that SJ and CMJ offer distinct diagnostic insights due to varying MTU contraction dynamics and neural factors. This has implications for WR use in training. Further, absolute metrics showed limited load sensitivity. However, when accounting for body mass, relative metrics revealed substantial declines. This indicates absolute values can misrepresent the effects of WR loading. Full article

The diagnosis of faults in induction motors, such as broken rotor bars, is critical for preventing costly emergency shutdowns and production losses. The complexity of this task lies in the diversity of induction motor operating regimes. Specifically, a change in load alters the signal’s frequency composition and, consequently, the values of fault diagnostic features. Developing a reliable diagnostic model requires data covering the entire range of motor loads, but the volume of available experimental data is often limited. This work investigates a data augmentation method based on the physical relationship between the frequency content of diagnostic signals and the motor’s operating regime. The method enables stretching and compression of the signal in the spectral domain while preserving Fourier transform symmetry and energy consistency, facilitating the generation of synthetic data for various load regimes. We evaluated the method on experimental data from a 0.37 kW induction motor with broken rotor bars. The synthetic data were used to train three diagnostic models: a Multilayer Perceptron (MLP), a Convolutional Neural Network (CNN), and a hybrid CNN-MLP model. Results indicate that the proposed augmentation method enhances classification quality across different load levels. The hybrid CNN-MLP model achieved the best performance, with an F1-score of 0.98 when augmentation was employed. These findings demonstrate the practical efficacy of physics-guided spectral augmentation for induction motor fault diagnosis. Full article

Although the hamstring muscles play an important role in running, very little is known about the individual contributions of each hamstring muscle (biceps femoris_{long head}, biceps femoris_{short head}, semitendinosus, and semimembranosus) toward running economy. As such, our study examined all the muscles in the hamstring to provide insight into which muscles contribute the most to running economy. Such information can provide insight in designing precise exercise training programs for enhancing running performance. Secondary analysis from our cross-sectional study conducted on 23 male recreational runners examined the relationships between stretch shortening cycle potentiation (via leg press throw), running net VO₂ (inverse of running economy) (at 11.3 km/h), and maximum cross-sectional area of biceps femoris_{long head}, biceps femoris_{short head}, semitendinosus, and semimembranosus was assessed via magnetic resonance imaging. We obtained significant correlations between the maximum cross-sectional area of the biceps femoris_{long head} and log₁₀running net VO₂ (r = −0.52; p < 0.05). Our multiple regression model showed that the maximum cross-sectional area of biceps femoris_{long head} but not stretch shortening cycle potentiation predicted log₁₀running net VO₂ (r = −0.52; p < 0.01). We found no other relationship between any other hamstring muscles and log₁₀running net VO₂. Our findings provide preliminary evidence of the importance of the biceps femoris_{long head} toward running economy. This may be due to the preferential activation of efficient slow twitch muscle fibers of the biceps femoris_{long head}. Additionally, we noted that the biceps femoris_{short head}, semitendinosus, and semimembranosus muscles were not related to running economy in recreational male runners. Full article

Background/Objectives: This study examined the effects of a six-week eccentric–reactive training program on neuromechanical markers of lateral explosiveness, asymmetry, and stretch-shortening cycle (SSC) efficiency in elite male youth table tennis players. Fourteen national-level athletes (mean age = 16.6 years) were assigned to either an experimental group (EG, n = 7) or a control group (CG, n = 7). EG performed flywheel squats and lateral depth jumps three times per week, while CG maintained regular training. Pre- and post-intervention testing included countermovement jumps, reactive strength index (RSI_DJ), force asymmetry, time-to-stabilization, SSC efficiency, and energy transfer ratio (ETR), measured via force plates, EMG, and inertial sensors. Methods: Multi-dimensional statistical analysis revealed coordinated improvements in explosive power and movement efficiency following eccentric training that were not visible when examining individual measures separately. Athletes in the training group showed enhanced neuromechanical control and developed more efficient movement patterns compared to controls. The analysis successfully identified distinct performance profiles and demonstrated that the training program improved explosive characteristics in elite table tennis players. Results: Univariate ANOVAs showed no significant Group × Time effects for RSI_DJ, ETR, or SSC_Eff, although RSI_DJ displayed a moderate effect size in EG (d = 0.47, 95% CI [0.12, 0.82], p = 0.043). In contrast, MANOVA confirmed a significant multivariate Group × Time interaction (p = 0.013), demonstrating integrated neuromechanical adaptations. Regression analysis indicated lower baseline CMJ and RSI_DJ predicted greater RSI improvements. Conclusions: In conclusion, eccentric–reactive training promoted multidimensional neuromechanical adaptations in elite racket sport athletes, supporting the use of integrated monitoring and targeted eccentric loading to enhance lateral explosiveness and efficiency. Full article

Recent studies have identified the hip joint as a central component of the human kinetic chain, playing a pivotal role in optimizing force transmission during movement. Enhancing its functional capacity represents an effective strategy for enhancing overall physical well-being and preventing injuries. This study investigates the effects of an eight-week hip joint functional training program on the health-related physical fitness, hip joint function, and factors associated with injury risk in university students from a track and field elective class. A total of 56 participants were randomly assigned to an experimental group (n = 28) or a control group (n = 28). The experimental group incorporated hip joint functional training, which comprising dynamic stretching and activation exercises, into their standard physical education (PE) class activities, while the control group continued with the regular physical education curriculum. Pre-intervention and post-intervention assessments included hip joint range of motion (ROM), functional movement screening (FMS), a 50 m sprint, standing long jump, sit-and-reach test, and spinal health evaluations. Results indicated that the experimental group demonstrated significant improvements in multi-directional hip range of motion (ROM), with examples including flexion increasing by 10° and external rotation by 9°. These improvements were accompanied by significant gains in functional movement screen (FMS) scores, with significant improvements in the Hurdle Step, whose median score increased to 3.0, Active Straight Leg Raise, and Rotary Stability components (all p < 0.05) compared to the control group. Furthermore, the training significantly reduced spinal asymmetry (axial trunk rotation reduced from 3.86° to 3.43°) and enhanced performance in the 50 m sprint (−0.26 s) and standing long jump (+0.08 m) (all p < 0.05). These objective improvements in functional movement patterns, postural alignment, and physical performance are associated with key biomechanical factors known to influence injury risk, such as the demonstrated gains in joint mobility and movement efficiency. Therefore, incorporating hip joint functional training into college physical education programs may effectively enhance students’ fundamental movement quality, improve joint stability, and promote postural health, thereby mitigating key biomechanical risk factors. This approach offers a practical strategy for educators to improve student physical health in general PE settings. Full article

This study aimed to evaluate the impact of an 8-week reverse Nordic exercise training (RNET) program on physical fitness in male youth soccer players. A total of 35 players participated in the study and were divided into two groups: the RNET group (n = 19, age 16.39 ± 0.46 years) and the active control group (CG: n = 16, age 16.53 ± 0.48 years). To assess fitness changes, participants were tested on linear sprint speed (5, 10, and 20 m sprints), change-of-direction (CiD) speed (505-CiD), vertical jump (countermovement jump [CMJ]), horizontal jump (standing long jump [SLJ]), drop jump (20 cm drop jump [DJ-20]), and repeated sprint ability (RSA). Significant group-by-time interactions were observed (effect size, [ES] = 0.70 to 1.37), with substantial improvements in the RNET group across linear sprint, CiD, and jumping performances (ES = 0.61 to 1.47), while no significant changes were noted in the CG. However, no significant group-by-time interactions were observed for RSA parameters. Individual response analysis revealed that 63–89% of RNET group exhibited improvements exceeding the smallest worthwhile change (SWC_0.2) threshold. These results suggest that the RNET program is both effective and safe for enhancing physical fitness in male youth soccer players. Full article

Coastal communities and marine ecosystems face increasing risks due to changing ocean conditions, yet effective wave monitoring remains limited in many low-resource regions. This study investigates the use of seismic data to predict significant wave height (SWH), offering a low-cost and scalable solution to support coastal conservation and safety. We developed a baseline machine learning (ML) model and improved it using a longest-stretch algorithm for seismic data selection and station-specific hyperparameter tuning. Models were trained and tested on consumer-grade hardware to ensure accessibility and availability. Applied to the Sicily–Malta region, the enhanced models achieved up to a 0.133 increase in R² and a 0.026 m reduction in mean absolute error compared to existing baselines. These results demonstrate that seismic signals, typically collected for geophysical purposes, can be repurposed to support ocean monitoring using accessible artificial intelligence (AI) tools. The approach may be integrated into conservation planning efforts such as early warning systems and ecosystem monitoring frameworks. Future work may focus on improving robustness in data-sparse areas through augmentation techniques and exploring broader applications of this method in marine and coastal sustainability contexts. Full article

Nanotechnology has become a transformative field in modern science and engineering, offering innovative approaches to enhance conventional thermal and fluid systems. Heat and mass transfer phenomena, particularly fluid motion across various geometries, play a crucial role in industrial and engineering processes. The inclusion of nanoparticles in base fluids significantly improves thermal conductivity and enables advanced phase-change technologies. The current work examines Powell–Eyring nanofluid’s heat transmission properties on a stretched Riga plate, considering the effects of magnetic fields, porosity, Darcy–Forchheimer flow, thermal radiation, and activation energy. Using the proper similarity transformations, the pertinent governing boundary-layer equations are converted into a set of ordinary differential equations (ODEs), which are then solved using the boundary value problem fourth-order collocation (BVP4C) technique in the MATLAB program. Tables and graphs are used to display the outcomes. Due to their significance in the industrial domain, the Nusselt number and skin friction are also evaluated. The velocity of the nanofluid is shown to decline with a boost in the Hartmann number, porosity, and Darcy–Forchheimer parameter values. Moreover, its energy curves are increased by boosting the values of thermal radiation and the Biot number. A stronger Hartmann number M decelerates the flow (thickening the momentum boundary layer), whereas increasing the Riga forcing parameter Q can locally enhance the near-wall velocity due to wall-parallel Lorentz forcing. Visual comparisons and numerical simulations are used to validate the results, confirming the durability and reliability of the suggested approach. By using a systematic design technique that includes training, testing, and validation, the fluid dynamics problem is solved. The model’s performance and generalization across many circumstances are assessed. In this work, an artificial neural network (ANN) architecture comprising two hidden layers is employed. The model is trained with the Levenberg–Marquardt scheme on reliable numerical datasets, enabling enhanced prediction capability and computational efficiency. The ANN demonstrates exceptional accuracy, with regression coefficients $R\approx 1.0$ and the best validation mean squared errors of $8.52\times {10}^{-10}$, $7.91\times {10}^{-9}$, and $1.59\times {10}^{-8}$ for the Powell–Eyring, heat radiation, and thermophoresis models, respectively. The ANN-predicted velocity, temperature, and concentration profiles show good agreement with numerical findings, with only minor differences in insignificant areas, establishing the ANN as a credible surrogate for quick parametric assessment and refinement in magnetohydrodynamic (MHD) nanofluid heat transfer systems. Full article

Background: High-intensity interval training (HIIT) is a time-efficient approach that has been recognized to enhance cardiometabolic health and aerobic capacity in adolescents. The purpose of this study was to investigate the effects of various HIIT durations on cardiometabolic health and aerobic ability in adolescent women aged 17 to 19 years. Methods: Participants were separated into two intervention groups: HIIT 1 (6 weeks) and HIIT 2 (8 weeks), along with a control group. Both HIIT regimens included two weekly sessions: warm-up (jogging, accelerated running, and dynamic stretching), major sets (2 × 6–9 bouts of 30 s training at 90–95% HR_max with active recovery), and cooldown. Pre- and post-intervention measurements included body mass, BMI, body fat percentage, lipid profile, blood pressure, fasting glucose, and VO_2max. Results: Both HIIT programs resulted in significant reductions in body weight, BMI, and body fat percentage (all p < 0.001), as well as improvements in total cholesterol, triglycerides, LDL cholesterol, and systolic and diastolic blood pressure (all p < 0.001), compared to the control group. The changes in glycemia (p = 0.078) and HDL cholesterol (p = 0.825) were not statistically significant. Both HIIT groups showed significantly higher VO_2max (p < 0.001). Conclusions: Adolescent women’s cardiometabolic health and aerobic capacity increased considerably following 6- and 8-week HIIT training. These findings emphasize HIIT as a practical and time-saving strategy for this population, highlighting its effectiveness in improving key health parameters within a relatively short period. Full article

Physical activity influences red blood cell (RBC) deformability and aggregation, which affect oxygen transport and performance. While regular training may enhance RBC properties, adaptations depend on exercise intensity, duration, and recovery. This study aimed to assess the impact of a 12-week core muscle training program on RBC deformability, aggregation, and aerobic capacity in military trainees. A total of 35 male volunteers were divided into a Training group (n = 17) and a Control group (n = 18). The intervention included dynamic stretching, core stabilization, and functional movement exercises. Spiroergometry tests, blood gas analysis, and hemorheological measurements were conducted before and after the program. Results showed no significant changes in body composition or aerobic capacity. RBC deformability slightly decreased after exercise in both groups, while RBC aggregation increased. Blood viscosity changes were more moderate in the Training group, suggesting potential adaptation. However, the training intensity may have been insufficient for significant hemorheological improvements. While regular physical activity can enhance RBC function, adequate intensity, recovery, and nutrition are essential for optimal adaptation. Individualized training strategies should consider these factors to maximize performance and hemorheological benefits. Full article

This study aimed to investigate the effect of antagonist static stretching applied between sets during resistance training on the number of repetitions of leg press exercise in young volleyball players. For this purpose, a total of 16 female active volleyball players (age 15.50 ± 0.52 years; height 167.25 ± 6.10; body mass 57.00 ± 5.98) participated voluntarily. The athletes participating in the study visited the laboratory five times. In the first session, anthropometric measurements were taken. In the second session, their 10 repetition maximums (RTs) were recorded, and in the third session, 10 control RTs were recorded. In the other two sessions, athletes were randomly assigned to two experimental protocol treatments in accordance with the crossover experimental design. In the traditional application, leg press exercise was performed as four sets with their own maximums and 2 min of passive rest between sets. In the experimental application, the participants performed four sets of leg press exercise with ten repetitions of their own maximums until concentric exhaustion, and static hamstring stretching was applied continuously for 30 s over 2 min between sets. All participants participated in both application protocols in different sessions. SPSS 20.0 package programed and GraphPad Prizm 8 graphics program were used for the analysis of all data. Data were analyzed at 0.05 significance level. In the findings obtained, Group* application interaction was found to be statistically significant according to the application and groups (F = 4.198, p = 0.016, η_p² = 0.219). In the leg press repetitions, statistical significance was found in favor of the experimental treatment in the third and fourth sets. This study shows that antagonist static stretching applied between sets positively affects resistance training performance by increasing the number of repetitions in leg press exercise in young female volleyball players. Full article