Search Results (1,212)

Background: Percussive massage therapy (PMT) with handheld massage guns is widely used to support recovery and flexibility, but the short-term behavior of skeletal muscle contractile properties and the relative contribution of application duration versus frequency remain unclear. This study investigated the 10 min post-intervention time course of tensiomyography (TMG)-derived contractile properties of non-fatigued vastus medialis (VM) after clinically realistic PMT protocols and examined whether longer duration is associated with persistent deviations from baseline than frequency. Methods: In a two-session, within-subject repeated-measure design, 32 participants completed four PMT conditions to the VM (35 Hz–3 min, 35 Hz–6 min, 45 Hz–3 min, and 45 Hz–6 min). TMG parameters (Td, Tc, Ts, Tr, and Dm) were recorded at baseline and repeatedly over 10 min post-intervention. Linear mixed-effect models with frequency and duration as fixed factors and time as continuous and categorical were used to characterize temporal patterns, with emphasis on effect sizes and consistency across parameters. The fixed protocol order (35 Hz in session one, 45 Hz in session two, 3 vs. 6 min assigned to contralateral legs) means that frequency was confounded with session and duration with leg side. Results: Compared with the 3 min protocols, the 6 min protocols were associated with slightly higher Td and Ts, a modest increase in Tr and a slightly greater Dm (e.g., Dm + 0.55 mm), whereas Tc showed no clear duration effect. Across conditions, Td increased immediately after PMT, Tc remained elevated for most of the first 8 min, Ts increased from mid to late post-intervention, Tr changed inconsistently, and Dm was reduced relative to baseline for most of the 10 min period. Differences between 35 and 45 Hz were small and non-significant for all TMG parameters. Conclusions: Clinically realistic PMT protocols at 35–45 Hz in non-fatigued muscle induce small but statistically detectable, duration-sensitive changes in TMG-derived contractile behavior over approximately 10 min. Within the constraints of the fixed, non-randomized design and the small effect sizes observed, these findings support viewing massage gun use as a recovery-oriented adjunct that subtly modulates contractile dynamics, rather than as a strong, standalone performance-enhancing stimulus. Full article

Laser-based Powder Bed Fusion (LPBF) enables the fabrication of complex metal components but often results in high porosity and microdefect densities, compromising fatigue performance despite acceptable static properties. Standard fatigue characterisation methods are time-consuming and costly and yield scattered results due to defect-induced brittleness and residual stresses. This study investigates the application of thermographic techniques as a rapid alternative for evaluating the intrinsic fatigue behaviour of tensile coupons fabricated by LPBF employing AISI 316L steel. By monitoring surface temperature during stepwise static monotone and fatigue loading, thermographic methods aim to detect early hints of heat dissipation associated with microdamage initiation. Approaches based on temperature harmonic analysis have been implemented, allowing near-real-time and full-field mapping of stress distribution and damage development. Results show that harmonic metrics correlate with the material state and effectively track the thermoelastic effect-induced temperature changes. Some evidence is found regarding the onset of intrinsic heat dissipation, which needs to be confirmed by more focused and extensive experimental tests. Full article

Background/Objectives: Radiation therapy is a common treatment modality for non-small-cell and small-cell lung cancer that can be associated with considerable side effects, mainly reactions of healthy tissues in the radiation field. Radiation therapy may lead to significant fatigue, which can potentially be mitigated by maintaining or increasing physical activity during treatment. Since achieving this goal may be a challenge for patients, they may benefit from a mobile application reminding them daily to perform a predefined number of steps. Such a reminder app will be investigated prospectively in a phase 2 trial. The current APART-LUNG study (NCT07380815) is a mandatory study for designing the prospective trial. Methods: The main objective of the APART-LUNG (exploratory non-interventional) study is to report patterns of physical activity during radiation therapy for lung cancer patients and generate hypotheses based on our findings. Our primary endpoint is the within-patient difference in weekly average steps per wear hour of the smartphone (week 5 minus week 1 of radiation therapy), and our secondary aim is to estimate differences in operational measures (wear time of the smartphone) between week 5 and week 1. The sample size of approximately 20 patients (full analysis set) allows us to detect a moderate-to-large standardized within-patient difference and is driven by feasibility and the intent to obtain preliminary estimates of effect size and variability. The results of the APART-LUNG study will be very important for appropriately designing a phase 2 trial. Full article

To address the growing demand for sustainable road infrastructure development and resolve technical bottlenecks in reclaimed asphalt pavement (RAP) recycling, this study optimized the performance of recycled asphalt mixtures (RAMs) and validated their engineering applicability for field construction. RAM specimens were prepared using 5-year and 10-year aged RAP from Ningxia, with a constant RAP content of 30%. Laboratory tests including high-temperature rutting, moisture susceptibility, low-temperature cracking, dynamic modulus, and four-point bending fatigue were performed to determine the optimal mix proportion. Fourier Transform Infrared Spectroscopy (FTIR) and Thin-Layer Chromatography-Flame Ionization Detection (TLC-FID) were employed to reveal the regeneration mechanism of waste engine oil (WEO). Results showed that WEO modified the functional groups and four fractions of asphalt, optimizing its colloidal structure, while excessive WEO compromised high-temperature stability. The optimal WEO contents were 4% for RAP (5Y) and 8% for RAP (10Y), which significantly enhanced the overall performance of RAM to adapt to Ningxia’s climate. This study provides technical support for sustainable road infrastructure in arid and semi-arid regions. Full article

Background: Saudi Arabia has made significant progress toward malaria elimination; however, imported cases continue to occur, particularly in the southwestern regions. This study aimed to describe the clinical characteristics and outcomes of patients with malaria in the Aseer Region, Saudi Arabia. Methods: A retrospective observational study was conducted at Khamis Mushait General Hospital, Aseer Region, Saudi Arabia, including all patients with malaria from January 2022 to December 2025. Demographic, clinical, laboratory, and outcome data were extracted from the electronic medical records. Severe malaria was defined according to the World Health Organization criteria. Multivariate logistic regression using Firth’s penalized maximum likelihood estimation was performed to identify independent predictors of severe malaria (≥1 WHO criterion). Statistical analysis was performed using R software (version 4.2.1). Results: A total of 311 patients were included, predominantly male (90.0%), with a mean age of 28.8 ± 11.3 years. Ethiopian nationals comprised nearly half the cases (48.2%), followed by Saudi (16.4%) and Yemeni (15.1%) nationals. Plasmodium vivax was the most common species (51.1%), followed by Plasmodium. falciparum (40.2%). Fever was the most frequent symptom (89.4%), followed by fatigue (50.8%), chills (46.9%), and vomiting (39.5%). Low parasitemia (<1%) was the most frequent finding (33.8%), followed by moderate (27.3%) and mild (18.3%) levels, while high (4.2%) and very high parasitemia (1.9%) were uncommon. Severe malaria (≥1 criterion) was diagnosed at 43.7%, with severe anemia (26.0%) and jaundice (23.2%) being the most frequent WHO severity criteria. Notably, 84% of the cases occurred during 2024–2025, indicating a recent outbreak, with a sharp peak of 43 cases in October 2024. Multivariate logistic regression identified two independent predictors of having at least one WHO severity criterion: higher parasitemia level (adjusted OR = 1.70 per 1% increase, 95% CI: 1.40–2.11, p < 0.001) and non-Saudi nationality (adjusted OR = 2.40, 95% CI: 1.10–5.62, p = 0.027). Conclusions: Malaria in the Aseer Region predominantly affects young adult male expatriates, suggesting its imported nature. The predominance of P. vivax represents a shift from historical patterns. Parasitemia level and being of non-Saudi nationality independently predict severe malaria and may therefore support risk stratification and clinical decision-making. The dramatic case surge in 2024–2025 highlights regional vulnerability to outbreaks despite control progress. These findings support enhanced screening for at-risk populations, maintenance of clinical capacity for severe malaria management, and robust surveillance systems for early outbreak detection. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article

Toxoplasmosis is a widely distributed zoonosis caused by the protozoan parasite Toxoplasma gondii. Infection during pregnancy is a major public health concern due to its potential impact on both maternal health and fetal development. Early detection of maternal infection is critical to prevent adverse outcomes; however, maternal signs are often subtle, non-specific or absent, complicating timely diagnosis. This scoping review aimed to map and synthesise existing evidence on early maternal signs, pregnancy and foetal outcomes, frequently assessed risk factors, and diagnostic approaches of toxoplasmosis in expectant mothers in Africa. The review was done in accordance with the PRISMA-ScR guidelines. A literature search of PubMed, Scopus, ResearchGate, and Google Scholar was performed to identify studies published between 2000 and 2025. Retrieved records were managed using Zotero (version 8.0.4) for deduplication and screening. Only English-language studies conducted in Africa and reporting relevant maternal or clinical data were included. A total of 28 cross-sectional studies were included. Lymphadenopathy (25.0%) was the most frequently reported maternal early sign, followed by flu-like illness, asymptomatic infection, low-grade or mild fever, and fatigue or malaise (each 10.7%). Congenital anomalies (50.0%) and miscarriage or spontaneous abortion (42.9%) were the most commonly reported foetal and pregnancy outcomes. Frequently reported risk factors were exposure to cat faeces (57.1%) and ingestion of undercooked or raw meat (42.9%). Diagnostic approaches were commonly enzyme-based immunoassays (78.6%), with limited use of RDTs and molecular methods. These findings suggest the need for improved early detection and prevention strategies in high-risk, low-resource African settings. Enhancing routine screening, health education, and access to appropriate diagnostics are considered. Future studies should consider adopting standardised reporting and integrating sensitive, affordable, rapid diagnostic approaches to enhance early detection and reduce the burden of congenital toxoplasmosis. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Stress fractures (SFs) are a common overuse injury in athletes and represent the severe end of the bone stress injury (BSI) continuum. They result from repetitive mechanical loading exceeding the bone’s capacity for adaptation and are associated with impaired performance, prolonged time away from sport, and risk of recurrence if not appropriately managed. This narrative review provides a clinically oriented synthesis of current evidence on the epidemiology, pathophysiology, risk factors, diagnosis, management, and prevention of SFs in athletes. Particular emphasis is placed on modifiable contributors, including training load errors, neuromuscular fatigue, and low energy availability within the framework of Relative Energy Deficiency in Sport (RED-S). Diagnostic evaluation is discussed using a stepwise clinical approach integrating history, physical examination, targeted laboratory assessment, and imaging, with magnetic resonance imaging (MRI) as the reference standard for early detection and severity grading. Management is presented through a risk-based framework combining MRI severity and anatomical site classification to guide treatment decisions and return-to-sport pathways. While most low-risk SFs respond to conservative strategies, high-risk lesions require closer monitoring and, in selected cases, early surgical consideration. This review proposes a practical clinical framework to support decision-making in athletes with suspected or confirmed SFs, aiming to improve early diagnosis, optimize management, and reduce recurrence risk in sports medicine practice. Full article

While existing research has established that air pollution-induced “avoidance behavior” significantly drives the growth of online food delivery volumes, the Average Order Value (AOV) remains unexplored. This study utilizes micro-transactional data provided by the store owner and employs machine learning algorithms to detect the impact of air quality (measured by the AQI) on online food delivery AOV and analyze the underlying consumer behavior. The findings indicate that: (1) Air quality deterioration significantly drives up the AOV. The global average response coefficient is 0.0053, showing a 2.4-fold acceleration effect once the AQI crosses the median (66). (2) Crucially, this growth stems from a directional divergence in consumer decision-making. Air pollution leads to the simultaneous occurrence of a reduction in average item quantity (impact coefficient: −0.0014) and a surge in Average Item Price (AIP) (impact coefficient: 0.0066). (3) Causal analysis further identifies a “substitution mechanism.” Specifically, every one-unit decrease in average item quantity induces a CNY 1.098 jump in average item price. These findings suggest a plausible behavioral logic where environmental stress may induce psychological fatigue but does not necessarily trigger “defensive frugality.” Instead, the observed pattern is consistent with a “decision avoidance” mode where consumers streamline item quantities; simultaneously, to hedge against potential experience risks resulting from simplified choices, they appear to utilize saved cognitive resources to target high-value “signature” items. Theoretically, this study fills the gap in environmental stress research regarding the price dimension of online consumption and reveals a behavioral evolution from “pure avoidance” to “value-oriented selection.” Practically, it provides empirical support for online food delivery merchants to optimize product selection, differentiate pricing, and implement precision marketing in dynamic environments. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

Dynamic muscular endurance, the ability to lift a submaximal load until task failure, is a common measure in both cross-sectional and training studies. However, the repeatability of low-load muscular endurance in the knee extensors has not been well established. Establishing reliability metrics is essential to ensure that observed differences reflect true physiological changes rather than measurement error. The purpose of this study was to quantify the repeatability of low-load dynamic knee extensions performed to task failure. Forty healthy adults completed three visits, each consisting of one set of knee extensions at 20%, 30%, and 40% of one repetition maximum (1RM) to assess relative muscular endurance, and three sets at 20% 1RM on the contralateral leg to assess the impact of fatigue within a single session (fatigue curve). Intraclass correlation coefficients (ICCs), standard error of the measurement, and smallest detectable difference (SDD) were calculated. Repeatability ranged from moderate to excellent across conditions (ICC = 0.77–0.94). Lower loads and later sets demonstrated reduced repeatability compared with heavier loads and earlier sets. These results indicate that researchers and practitioners should consider load and fatigue curve effects in protocol design and SDDs when interpreting the meaningfulness of individual changes in knee extension muscular endurance. Full article

Background/Objectives: Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper presents BrainTwin, a next-generation cognitive digital twin, as a patient-specific, constantly updating computer model that combines state-of-the-art MRI analytics for neuro-oncological assessment related to clinical study and management of tumors affecting the central nervous system (including their detection, progression, and monitoring) with real-time EEG-based brain health intelligence. Methods: Structural analysis is driven by an Enhanced Vision Transformer (ViT++), which improves spatial representation and boundary localization, achieving more accurate tumor prediction than conventional models. The extracted tumor volume forms the baseline for short-horizon tumor progression modeling. Parallel to MRI analysis, continuous EEG signals are captured through an in-house wearable skullcap, preprocessed using Edge AI on a Hailo Toolkit-enabled Raspberry Pi 5 for low-latency denoising and secure cloud transmission. Pre-processed EEG packets are authenticated at the fog layer, ensuring secure and reliable cloud transfer, enabling significant load reduction in the edge and cloud nodes. In the digital twin, EEG characteristics offer real-time functional monitoring through dynamic brainwave analysis, while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states, which are probabilistically inferred cognitive conditions derived from EEG spectral patterns. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The BrainTwin performs EEG–MRI fusion, correlating functional EEG metrics with ViT++ structural embeddings to produce a single risk score that can be interpreted by clinicians to determine brain vulnerability to future diseases. Explainable artificial intelligence (XAI) provides clinical interpretability through gradient-weighted class activation mapping (Grad-CAM) heatmaps, which are used to interpret ViT++ decisions and are visualized on a 3D interactive brain model to allow more in-depth inspection of spatial details. Results: The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/Recall/F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and a ViT++ MRI accuracy of 96%, outperforming baseline architectures. Conclusions: These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring. Full article

Mental fatigue (MF) has been hypothesized to contribute to injury risk in athletes, but observational studies have not directly investigated this relationship. Therefore, the current study evaluates potential relationships between mental fatigue and subsequent injury occurrence in basketball. Using an observational design, we monitored fourteen male semi-professional basketball players (age: 22 ± 4 years; stature: 192.6 ± 8.8 cm; body mass: 85.5 ± 9.1 kg; Tier 3) from a single team for 21 weeks throughout the competitive season. Each week, the players participated in 5 team-based training sessions, 2–4 individual training sessions, and 1–2 official games. Subjective MF ratings were collected using 100 mm visual analogue scales twice a week (the day before and after the official game) and then averaged. Time-loss injuries were registered, noting the body location, mechanism, and context (training and games). Generalized logistic mixed models were employed to evaluate whether MF levels were associated with injury occurrence in the subsequent 1, 3, and 5 days and 1, 2, 3, and 4 weeks of basketball activity. A total of 11 injuries were registered during the study (7.40 per 1000 h of basketball activity), with an average time loss of 12 ± 19 days. There were no associations between MF and injury occurrence in the following 1, 3, 5 days nor 1, 2, 3, 4 weeks (all p > 0.05, odds ratios: 1.00–1.28). In male semi-professional basketball settings, preliminary evidence indicates that MF might not be associated with injury occurrence. However, due to the dearth of injury events, the statistical power of this study is insufficient to detect potential small–medium effects. Therefore, the current results should be considered exploratory as opposed to a definitive rejection of the hypothesis. Future studies should evaluate the relationship between MF and injury risk in larger samples and among professional athletes. Full article

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel. Full article