Search Results (991)

Background: Foam rolling is a popular self-myofascial release (SMR) technique, yet empirical evidence regarding its long-term impact on cycling endurance remains inconclusive. This study investigated the effects of chronic SMR on cardiorespiratory capacity, metabolic kinetics, and mechanical performance in road cyclists. Methods: We conducted a six-month randomized controlled trial (RCT) with 32 male recreational cyclists. Both an intervention group (IG) and a control group (CG) followed a standardized training protocol. The IG additionally applied a Blackroll^® foam roller immediately after cycling training sessions. Outcomes included maximum oxygen uptake (VO₂max), submaximal heart rate, lactate slope, and relative mechanical power (W/kg) at aerobic and anaerobic thresholds. Data were analyzed using linear mixed-effects models (LMM), with age included as a fixed-effect covariate to control for baseline imbalances between groups. Effect sizes were determined via marginal and conditional R². Additionally, model robustness was verified through Shapiro–Wilk tests and Q–Q plots of conditional residuals. Results: No significant effects were observed for VO₂max or submaximal heart rate. In contrast the IG demonstrated significant improvements in metabolic kinetics, evidenced by a reduced lactate slope (p = 0.004). Furthermore, foam rolling yielded a statistically significant positive effect on relative mechanical performance at both the aerobic (p = 0.031) and anaerobic (p = 0.007) lactate thresholds. Sensitivity analyses confirmed that these effects were independent of the age difference between groups. Conclusions: Foam rolling did not enhance all endurance-related variables but showed positive effects on metabolic kinetics and mechanical performance. While it did not shift systemic cardiorespiratory limits, SMR appeared to optimize performance through improved metabolic economy and mechanical efficiency, suggesting it is a valuable supplemental tool for recovery and long-term performance maintenance in cycling. Full article

Accurate delineation of rural settlements at large spatial extents is fundamental to territorial spatial governance, rural revitalization, and the improvement of human living environments. However, in medium-resolution remote sensing imagery, rural settlement patches are typically small, morphologically complex, and easily confused with other impervious surfaces. As a result, existing products still fall short in characterizing these features. Here, we propose a lightweight Transformer-based semantic segmentation model, SELPFormer, and develop a multi-source fusion automatic labeling pipeline that integrates Global Impervious Surface Dynamics dataset, OpenStreetMap spatial priors, and nighttime lights constraints. Built upon SegFormer as the backbone, SELPFormer introduces a lightweight pyramid pooling module at the deepest feature level to aggregate multi-scale global context and embeds an SCSE channel–spatial attention mechanism into deep features to suppress background interference. In addition, it incorporates an efficient local attention module into multi-scale lateral connections to enhance boundary and texture representations, thereby jointly improving small-object recognition and fine boundary preservation. We evaluate the proposed method using Landsat multispectral imagery covering five provinces on the North China Plain. SELPFormer achieves IoU = 74.23%, mIoU = 86.43%, F1 = 85.21%, OA = 98.69%, and Kappa = 0.8452 under a unified training and evaluation protocol, yielding IoU gains of +1.44, +3.98, and +12.35 percentage points over SegFormer, U-Net, and DeepLabV3+, respectively. SELPFormer has 15.44 M parameters and attains a parameter efficiency of 3.93% IoU per million parameters and an ROC-AUC of 0.993, indicating strong threshold-independent discriminative capability. These results indicate that the proposed method can effectively extract rural settlements from medium-resolution imagery and provides a generic “global–channel–local” collaborative framework for model design and data construction. Full article

Semantic segmentation of metallographic micrographs is a key task for quantitative microstructural analysis in additive manufacturing, yet it remains challenging due to phase heterogeneity, complex morphologies, and the scarcity of annotated data. The MetalDAM dataset, composed of 42 labeled scanning electron microscopy images of steel microstructures, has been widely adopted as a benchmark, with U-Net commonly reported as the strongest supervised baseline. Nevertheless, the encoder–decoder structure of U-Net imposes architectural constraints that hinder the precise delineation of heterogeneous and irregular phase boundaries under severe data limitations. To address this limitation, this paper investigates a Fully Convolutional Network (FCN)-based architecture as an alternative approach for semantic segmentation on the MetalDAM dataset. The FCN is trained and evaluated under the same experimental protocol as the U-Net baseline, enabling a direct and fair comparison. Performance is assessed using multiple evaluation metrics, including Intersection over Union (IoU), precision, recall, and mean Average Precision at an IoU threshold of 0.5. The results show that the FCN achieves comparable overall IoU values (0.75) while delivering substantial improvements at the class level, particularly for minority and morphologically complex phases, with gains of up to 25–30% in class-specific IoU. Additional metrics confirm enhanced robustness, with consistently higher precision, recall, and mAP@0.5 values. These findings demonstrate that FCN-based architectures constitute a competitive and robust alternative to U-Net for metallographic segmentation in additive manufacturing scenarios characterized by limited annotated data. Full article

Non-crimp fabric (NCF) composites are increasingly adopted for structural components due to their high mechanical performance and processability. Like other fibre-reinforced plastics, NCFs remain vulnerable to in-service damage from tool drops or unintended collisions, which can substantially reduce load-bearing capacity. This study aimed to develop a validated numerical model capable of simulating damage initiation and post-impact behaviour through an integrated experimental–numerical approach. The mechanical properties of a representative unidirectional NCF composite were first experimentally established. Then, tubular NCF subcomponents were fabricated and tested under a two-phase loading protocol. In the first phase, damage was introduced using quasi-static indentation or controlled low-velocity impact. In the second phase, the residual load-bearing capacity of the damaged subcomponents was assessed under four-point bending. To support the research objective, a finite element model was developed in LS-DYNA to simulate both phases, using the MAT_ENHANCED_COMPOSITE_DAMAGE (MAT54) material formulation. Non-measurable input parameters, including stress limit factors and erosion strain thresholds, were calibrated via parameter estimation, sensitivity analysis, and iterative refinement. The final model showed close agreement with experiments in predicted damage location, deformation mode, and residual strength. X-ray computed tomography was used to validate delamination predictions. The findings support the development of reliable and cost-effective numerical tools for damage assessment in advanced composite structures. Full article

Intrusion and anomaly detectors trained on Internet Protocol (IP) traffic are increasingly deployed in heterogeneous IoT environments where Bluetooth Low Energy (BLE) links coexist with IP networks. We quantify the cross-protocol domain gap in an IP → BLE transfer setting under unsupervised domain adaptation (UDA), where target labels are unavailable for training and model selection. Using 14 lightweight window-level statistics and leakage-aware splits, we benchmark classical baselines and alignment methods (CORAL and MMD) against domain-adversarial neural networks (DANNs). Under random window splits, DANNs can yield modest target gains but exhibit strong seed sensitivity and non-monotonic domain confusion. We propose R3, a domain-aware checkpoint rule that combines near-best source validation with domain discriminator accuracy as a proxy for alignment, improving the target ROC-AUC by ~+0.053 across three representative seeds and producing more consistent AP gains over 20 seeds. However, under a stricter capture-wise leave-one-capture-out (LOCO) protocol, UDA collapses to near-chance ranking and can underperform simple baselines, highlighting the risk of optimistic random splits. Finally, we show that transferring a source-tuned threshold can trigger unsafe operating points (micro-FPR = 1.0 on benign-only captures), motivating PR-based metrics and calibration/operating-point audits. We have released derived feature tables, split definitions, and scripts to support reproducibility under restricted raw data access. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

Background/Objectives: Phospholipase C zeta (PLCZ1; PLCζ) is a sperm-specific enzyme responsible for the Ca²⁺ oscillations required for oocyte activation, and altered PLCζ expression has been associated with fertilization failure in assisted reproductive technologies, particularly intracytoplasmic sperm injection (ICSI). This study aimed to develop and analytically validate a flow cytometry–based protocol for PLCζ quantification in human spermatozoa. Methods: The assay was established using normozoospermic samples and included validated positive and negative technical controls. Antibody specificity was confirmed by Western blot analysis. A defined gating strategy was used to assess linearity between fluorescence intensity and PLCζ expression. Analytical performance was evaluated for precision, reproducibility, stability, and sensitivity, including applicability to low sperm concentrations. Results: A linear relationship between fluorescence intensity and PLCζ expression was demonstrated. The assay showed high precision, reproducibility, and stability, with consistent results in samples stored up to 24 h at room temperature or up to one week post-fixation at 4 °C. Sensitivity testing confirmed suitability for low sperm concentrations. Conclusions: This work provides a standardized and analytically validated framework for PLCζ quantification using flow cytometry. Although the assay measures protein expression rather than functional competence or subcellular localization, it establishes a solid analytical basis for future studies to define clinically relevant PLCζ thresholds and assess its value as a biomarker of fertilization capacity. Full article

Schoolbags represent a common source of physical strain for school-aged children and may influence posture during critical years of growth. This systematic review synthesizes evidence published since the previous review (1995–2014), which mainly focused on load thresholds and did not consider postural health as a multidimensional outcome. The review protocol was prospectively registered in PROSPERO (CRD420251080328). PubMed, Web of Science, and Google Scholar were systematically searched up to 11 June 2024. Of 1013 screened articles, 12 studies met the inclusion criteria. Schoolbag weight was the most frequently investigated factor, followed by postural and spinal alterations, particularly scoliosis. Excessive loads were consistently associated with trunk inclination, postural asymmetry, lumbar strain, and musculoskeletal discomfort. Several studies reported demographic differences, with girls showing greater susceptibility to postural deviations and boys carrying heavier loads. Compared with earlier evidence, more recent studies employed improved postural assessment methods and broader outcome frameworks incorporating ergonomic and behavioral factors. Despite methodological heterogeneity, findings support limiting schoolbag weight to 10–15% of body weight. This review highlights the importance of integrating ergonomic design, carrying behaviors, and demographic modifiers into school health guidelines and provides practical recommendations for clinicians, educators, and policymakers. Full article

Background/Objectives: Delayed cerebral ischemia (DCI) represents a major cause of morbidity and mortality after aneurysmal subarachnoid haemorrhage (aSAH). Early identification of developing cerebral ischemia is essential for timely prevention of DCI. Near-infrared spectroscopy (NIRS) provides continuous, non-invasive bedside monitoring of regional cerebral oxygen saturation (rSO₂); however, its clinical value in patients with aSAH has not yet been fully established. The primary objective of this study was to investigate whether NIRS-detected rSO₂ desaturation can serve as an early indicator of cerebral vasospasm (CV) and predict the occurrence of DCI. Secondary objectives were to examine the associations between rSO₂ changes and other cerebral deterioration events, length of intensive care unit stay, functional outcome, and in-hospital mortality. Methods: This prospective, single-centre study included 30 patients with aSAH admitted to the intensive care unit (ICU) of Riga East University Hospital between January 2019 and January 2023. Bilateral frontal near-infrared spectroscopy (NIRS) monitoring (Covidien INVOS™ 5100C-PB) was initiated within 72 h after ictus and continued for up to 7 days. Cerebral desaturation was defined as a >20% reduction from baseline (BL) or an absolute regional cerebral oxygen saturation (rSO₂) value < 50% lasting ≥30 min. CV and DCI were diagnosed according to established clinical and radiological criteria. Receiver operating characteristic (ROC) analysis was performed to evaluate the sensitivity and specificity of rSO₂ thresholds for the detection of CV, DCI, and other cerebral deterioration events. Results: CV occurred in 10 patients (33%); however, only four cases were detected during the NIRS monitoring period. NIRS demonstrated very high sensitivity (97.5%) but extremely low specificity (6%) for the early detection of CV. In contrast, diagnostic accuracy for DCI was high. An absolute rSO₂ cut-off value of 52% yielded a sensitivity of 97.5% and a specificity of 95%, whereas a decrease of ≥26% from baseline (BL) demonstrated a sensitivity of 98% and a specificity of 93%. Significant rSO₂ reductions were also observed during aneurysm re-rupture, hydrocephalus, cerebral edema, and postoperative ischemia; however, the sensitivity of NIRS for detecting these events was negligible. Patients with ≥20% desaturation tended to have longer ICU stays, and lower mean rSO₂ values as well as greater desaturation were associated with poorer functional outcomes as assessed by the modified Rankin Scale. Patients who died exhibited more pronounced rSO₂ decreases and less recovery compared with survivors. Conclusions: In this cohort, NIRS demonstrated limited specificity for the early detection of CV but showed strong associations with DCI and neurological outcome. NIRS may be useful as a non-invasive adjunct to multimodal neuromonitoring rather than as a stand-alone diagnostic tool for cerebral vasospasm. Larger, prospective studies incorporating standardized imaging protocols and optimized rSO₂ thresholds are required to more clearly define the role of NIRS in the management of aSAH. Full article

Community water systems in the United States provide drinking water to more than 300 million people annually, making their reliability fundamental to public health. In regions with long histories of racial segregation and unequal infrastructure maintenance, water system failures can deepen existing environmental injustices. This study examines water quality conditions in the Jackson, Mississippi, metropolitan area following the 2022 distribution system collapse and a decade of repeated noncompliance with the Safe Drinking Water Act’s Lead and Copper Rule (LCR). Using the U.S. Environmental Protection Agency’s 2024 updated LCR tap sampling protocol, water samples from 29 sites were collected. Samples were analyzed for lead, copper, iron, zinc, chlorine, sulfate, pH, and total dissolved solids concentrations. Chlorine-to-sulfate mass ratios (CSMR) were also calculated to evaluate corrosion potential. Demographic surveys, statistical analyses, and geospatial visualizations were used to interpret neighborhood-level patterns. Our findings show that all sites met primary drinking water standards and complied with LCR action levels but exceeded secondary drinking water standards at 100% of study sites. Seven sites exhibited CSMR values above the threshold, indicating increased susceptibility to corrosion. These results highlight the need for targeted corrosion control, treatment optimization, and ongoing monitoring, particularly in historically marginalized communities. Full article

The reduction or elimination of sulphur dioxide (SO₂) in winemaking represents a major technological and sustainability challenge due to the central antimicrobial and antioxidant roles of this additive. This study evaluated the technological feasibility and chemical stability of a no-added-SO₂ vinification protocol applied under controlled winery conditions over four consecutive vintages, compared with a conventional sulphite-based protocol. The no-added-SO₂ protocol integrated closed-circuit operations, controlled inert gas management, temperature-regulated fermentation, strict hygiene practices, the addition of grape seed extracts as alternative antioxidant agents, and real-time monitoring of CO₂ production and O₂ availability via a smart tank. Across all vintages, wines produced using the no-added-SO₂ protocol showed regular alcoholic and malolactic fermentations and volatile acidity values consistently below the sensory perception threshold (1.2 g/L). Total SO₂ levels ranged between 0.3 and 86 mg/L and free SO₂ ranged between 0.4 and 16 mg/L, attributable exclusively to endogenous yeast production. Multivariate analysis confirmed that vintage was the dominant factor affecting most compositional parameters, particularly phenolic and anthocyanin profiles, whereas sulphur dioxide management represented a secondary but clearly identifiable source of variability. These findings indicate that sulphur dioxide-free vinification is technically feasible when supported by precise process control and continuous real-time monitoring. Rather than a universal replacement for conventional sulphite management, the no-added-SO₂ protocol should be regarded as a complementary and technologically contingent tool for sustainable SO₂ reduction within a precision oenology framework. Full article

Background: Type 1 diabetes (T1D) is associated with metabolic and neuromuscular impairments that may influence fatigue mechanisms and limit exercise tolerance. Although previous investigations have characterized muscle performance in T1D, the peripheral fatigue threshold, defined as the maximal sustainable level of peripheral fatigue, remains poorly understood in this population. This study aimed to compare the amplitude of the maximal peripheral fatigue threshold between individuals with T1D and healthy controls to elucidate the effects of T1D on neuromuscular function. Methods: Twenty-two participants (11 with T1D and 11 healthy controls) completed two randomized experimental sessions. In each session, 60 quadriceps maximal voluntary contractions (MVCs) were completed, performed for 3 s with 2 s of rest between contractions. One session was conducted under a non-fatigued control condition (CTRL), and the other followed a fatiguing neuromuscular electrical stimulation (FNMES) protocol. Central and peripheral fatigue were evaluated from the pre- to post-exercise changes in potentiated twitch force (ΔPtw) and voluntary activation (ΔVA), respectively. Critical torque (CT) was calculated as the average torque produced during the last 12 contractions, whereas the curvature constant of the torque–duration relationship (W′) was quantified as the area above CT. Results: Although both groups exhibited a decline in pre-exercise Ptw following the FNMES condition, no significant within-group differences in ΔPtw were observed between sessions (T1D: p = 0.34; controls: p = 0.23). Nevertheless, the extent of peripheral fatigue was significantly lower in participants with T1D than in controls (ΔPtw = −38 ± 11% vs. −52 ± 17%; p < 0.05). Additionally, W′ values were reduced by 24% in the T1D group relative to controls during the CTRL condition (p = 0.02), and CT was significantly lower in T1D participants (262 ± 49 N) compared to controls (353 ± 71 N; p < 0.01). A significant positive correlation was observed between ΔPtw and W′ across groups (r² = 0.62, p < 0.001), suggesting a mechanistic link between peripheral fatigue tolerance and work capacity. Conclusions: The present results indicate that, although individuals with T1D retain the capacity to develop peripheral fatigue, their fatigue threshold and critical torque are markedly attenuated relative to those of healthy individuals. This reduction reflects impaired neuromuscular efficiency and diminished tolerance to sustained contractile activity. The strong relationship between peripheral fatigue and work capacity underscores the contribution of peripheral mechanisms to exercise intolerance in T1D. These results enhance current understanding of fatigue physiology in diabetes and emphasize the need for tailored exercise and rehabilitation strategies to improve fatigue resistance and functional performance in this population. Full article

Deception detection with low-channel wearable EEG requires protocols that generalize across people while remaining practical for portable devices. Using the public LieWaves dataset (27 subjects recorded with a five-channel Emotiv Insight headset), we evaluate to what extent five-channel head-mounted EEG can support lie–truth discrimination under both subject-independent and subject-dependent evaluations. For the subject-independent setting, we train a compact Residual Network with Squeeze-and-Excitation blocks (ResNet-SE) model on raw overlapping windows with focal loss, light data augmentation, and grouped cross-validation by subject; out-of-fold window probabilities are averaged per session and converted to labels using a single decision threshold estimated from the cross-validated session scores. For the subject-dependent setting, we adopt an overlapping short-window Residual Temporal Convolutional Network with Squeeze-and-Excitation and Attention (Res-TCN-SE-Attention) model that fuses raw EEG with discrete wavelet transform (DWT)-based spectral and handcrafted band-power and Hjorth features, using an 80/10/10 split at the recording/session level (stratified by session label), so that all windows from a given session are assigned to a single subset; because each subject contributes two sessions, the same subject may still appear across subsets via different sessions. The subject-independent model attains 66.70% session-level accuracy with an AUC of 0.58 on unseen subjects, underscoring the difficulty of person-independent generalization from low-channel wearable EEG. Because practical deployment requires generalization to previously unseen individuals, we treat the subject-independent evaluation as the primary estimate of real-world generalization. In contrast, the subject-dependent pipeline reaches 99.94% window-level accuracy under the overlapping sliding-window (OSW) setting with a session-disjoint split (no session contributes windows to more than one subset). This near-ceiling performance reflects the optimistic nature of subject-dependent evaluation with highly overlapping windows, even when avoiding within-session train–test overlap, and should not be interpreted as a meaningful indicator of deception-detection capability under realistic deployment constraints. These results suggest limited, above-chance separability between lie and truth sessions in LieWaves using a five-channel wearable EEG under the studied protocol; however, performance remains far from deployment-ready and is strongly shaped by evaluation design. Explicit reporting of both protocols, together with clear rules for windowing, aggregation, and threshold selection, supports more reproducible and comparable benchmarking. Full article

The rise of SIM cloning, identity spoofing, and covert manipulation in mobile and IoT networks has created an urgent need for continuous post-registration verification. This work introduces an unsupervised deep learning framework for detecting behavioral anomalies in SIM-tagged network flows by modeling the intrinsic structure of benign behavioral descriptors (TTL, timing drift, payload statistics). A Temporal Deep Autoencoder (TDAE) combining Conv1D layers and an LSTM encoder is trained exclusively on normal traffic and used to identify deviations through reconstruction error, enabling one-class (label-free) training. For deployment, alarms are set using an unsupervised quantile threshold ${\tau }_{\alpha }$ calibrated on benign traffic with a false-alarm budget; ${\tau }^{*}$ is reported only as a diagnostic reference for model comparison. To ensure realism, a large-scale corpus of 3.6 million SIM-tagged flows was constructed by enriching public IoT traffic with pseudo-operator identifiers (synthetic SIM tags derived from device identifiers) and controlled anomaly injections. Cross-domain experiment transfer under SIM-grouped protocol: Training on clean Cassavia-like traffic and testing on attack-rich Guarascio-like flows yields a PR-AUC of 0.93 for the proposed Conv-LSTM Temporal Deep Autoencoder, outperforming Dense Autoencoder, Isolation Forest, One-Class SVM, and LOF baselines. Conversely, the reverse direction collapses to PR-AUC $\approx 0.5$, confirming the absence of data leakage and the validity of one-class behavioral learning. Sensitivity analysis shows that performance is stable around the unsupervised quantile operating point. Overall, the proposed framework provides a lightweight, interpretable, and data-efficient behavioral verification layer for detecting cloned or unauthorized SIM activity, complementing existing registration mechanisms in next-generation telecom and IoT ecosystems. Full article

Knee disorders are a major indication for musculoskeletal imaging, yet MRI reliability remains constrained by signal nonuniformity, motion artefacts, protocol variability, and reader-dependent effects. This study presents an integrated two-vector framework that couples (i) a fuzzy diagnostic control-risk model with (ii) a quantitative digital-maturity assessment to strengthen MRI-based diagnosis of knee pathology. The vertical vector characterizes organizational readiness through a weighted fuzzy aggregation of six capability agents (technical, information and analytical, mathematical/model, metrological, human resources, and software support). The horizontal vector estimates producer’s and consumer’s risks as misclassification probabilities relative to an acceptance boundary, driven by measurement/interpretation uncertainty, variability of the decision threshold, and the ratio of instrumental to physiological dispersion. Simulation results indicate that error probabilities increase sharply when threshold uncertainty exceeds 20–25% and rise by approximately 15–20% as the standard-deviation ratio approaches unity. To connect diagnostic reliability with downstream mechanics, a FE analysis of the tibial insert in TKA under $F=1150$ N at 0° flexion predicts a peak contact pressure of 85.449 MPa and a maximum UHMWPE von Mises stress of 43.686 MPa, identifying wear-critical contact zones. Overall, the proposed framework provides interpretable quantitative targets for QA, protocol refinement, and resource allocation in radiology services undergoing digital transformation, and offers a reproducible pathway for linking imaging reliability to biomechanical risk. Full article