Search Results (567)

Work-related musculoskeletal disorders (MSDs) are leading causes of disability and productivity loss globally, yet registry-based evidence from low- and middle-income countries remains limited. The study analyzed compensated work-related MSDs claims reported to the Workers’ Compensation Fund (WCF) in Tanzania between 2016 and 2022 to identify patterns and associated risk factors. A registry-based cross-sectional design was conducted using de-identified WCF data on demographics, occupation, industry, diagnosis, and recorded workplace exposures. Modified Poisson regression was used to estimate associations between work-related MSDs and risk factors. Among the 243 workers with work-related MSDs whose claims were accepted and compensated, 84% had low back pain (LBP), predominantly males (90%) and middle-aged workers (mean age 41.6 years). Mining and quarrying accounted for 50% of the cases, with drivers and mobile plant operators being the most affected. Whole-body vibration (WBV) exposure and work in mining and quarrying were significant predictors of LBP (adjusted PR = 1.25; 95% CI: 1.061.49 and PR = 1.21; 95% CI: 1.01–1.44, respectively). These findings highlight WBV and mining work as significant risk factors of work-related MSDs and underscore the need for targeted interventions alongside enhanced health surveillance systems for exposure documentation. Full article

Background/Objectives: Serum neurofilament light chain (sNfL) is an early and sensitive biomarker of polyneuropathy. This study compared the UmanDiagnostics enzyme-linked immunosorbent assay (ELISA), and Meso Scale Discovery (MSD) R-PLEX assay with the current gold-standard single-molecule array (Simoa) assay for sNfL measurement. Methods: sNfL levels were measured with Simoa, ELISA, and MSD R-PLEX in 330 serum samples from 73 individuals with a pathogenic transthyretin gene variant (TTRv) and in 165 healthy controls (HC) with ELISA and MSD R-PLEX. Results: Median sNfL levels, assessed in serum samples from TTRv individuals, differed across all assays (all p < 0.001). Passing–Bablok regression slopes were 1.01 (Simoa–ELISA), 1.00 (Simoa–MSD R-PLEX), and 1.02 (MSD R-PLEX-ELISA), with very strong correlations (all r > 0.8). Bland–Altman analysis showed mean differences of 0.1 ± 0.2 pg/mL (Simoa–ELISA), 0.7 ± 0.1 pg/mL (Simoa–MSD R-PLEX), and −0.6 ± 0.2 pg/mL (MSD R-PLEX-ELISA). In HC, sNfL levels positively correlated with age. Z-score normalization allowed for inter-assay comparison. Conclusions: The ELISA and MSD R-PLEX assays provide suitable alternatives for the Simoa assay to measure sNfL levels in carriers of a pathogenic TTR-gene variant. The differences in concentrations defined by the assays directly relate to the internal standard provided with the assays. Full article

This study investigated the bioactive potential of Cinnamomum camphora (L.) J. Presl (C. camphora) leaf extracts obtained using hydrothermal extraction (HE) and ultrasound-assisted extraction (UAE) with 30%, 50%, and 70% ethanol (v/v). Extracts were analyzed for their phytochemical composition and biological activities. UAE extracts, particularly with 70% ethanol, exhibited the highest total polyphenol (363.0 $\pm$ 1.40 mg GAE/g) and flavonoid (174.5 $\pm$ 0.42 mg QE/g) contents. This extract also demonstrated strong antioxidant activities (IC₅₀: 0.024 ± 0.001 mg/mL for DPPH; IC₅₀: 0.363 ± 0.002 mg/mL for ABTS; 3.080 $\pm$ 0.044 M Fe²⁺/g for FRAP) and potent enzyme inhibition (49.3 $\pm$ 0.35% for tyrosinase; 24.8 $\pm$ 0.34% for elastase; 94.5 $\pm$ 0.12% for α-glucosidase and 77.5 $\pm$ 1.11% for lipase). Antimicrobial activity was most effective against Gram-positive bacteria, notably against Cutibacterium acnes, showing the largest inhibition zone (23.0 mm at 10 mg/disc). Overall, antioxidant, enzyme inhibition and antimicrobial activities increased significantly with increasing ethanol concentration, particularly at 70% ethanol. GC–MSD analysis revealed that both HE and UAE extracts contained phenolic acids, terpenes, triterpenes, and sesquiterpenes. Collectively, these findings indicate that the extraction method (UAE) and solvent composition (70% ethanol) influence the bioactivity profile of C. camphora leaf extracts, supporting further investigation of their relevance for cosmeceutical and functional applications. Full article

Accurate lung tumor segmentation using computed tomography (CT) scans is needed for efficient tumor treatment. However, the development of deep learning models is often constrained by strict patient privacy regulations that limit direct data sharing. This work presents a system that enables multi-institutional collaboration while training high-quality lung tumor segmentation models without requiring access to sensitive patient data. The proposed framework features the AuraViT suite, which includes the standard AuraViT—a hybrid model with 136 million parameters that combines a Vision Transformer (ViT) encoder, Atrous Spatial Pyramid Pooling (ASPP), and attention-gated residual connections—and the Lightweight AuraViT (LAURA) family (Small, Tiny, and Mobile). These variants are designed for resource-constrained environments and potential edge deployment scenarios. Training is conducted on publicly available datasets (MSD Lung and NSCLC) in a simulated five-client federated learning setup that emulates collaboration among institutions while ensuring patient privacy. The framework uses a federated learning setup with FedProx, adaptive weighted aggregation, and a dynamic virtual client strategy to handle data and system differences. The framework is further evaluated through ablation studies on model architecture and feature importance. The results show that the standard AuraViT-FL achieves a global mean Dice score of 80.81%, while maintaining performance close to centralized training. Additionally, the LAURA variations show a better trade-off between accuracy and efficiency. Notably, the Mobile variant with ∼5 M parameters reduces model complexity by over 96% while maintaining competitive performance (82.96% Dice on MSD Lung). Full article

To address the performance degradation of traditional adaptive filtering algorithms in environments with correlated input signals and non-Gaussian noise, this paper proposes a complex-valued affine projection algorithm based on the $\alpha$-Sigmoid cost function ($\alpha$-CSAP). The algorithm leverages the nonlinear characteristics of the $\alpha$-Sigmoid function and implicitly achieves variable step-size updates by introducing a normalization factor, which effectively suppresses impulsive noise interference and avoids matrix inversion, thereby reducing computational complexity. Theoretical analysis derives the steady-state mean square deviation (MSD) expression for the algorithm. Simulation results demonstrate that the proposed $\alpha$-CSAP algorithm exhibits superior performance compared to traditional complex adaptive filtering algorithms in both system identification and beamforming application scenarios. Full article

Musculoskeletal disorders remain a persistent concern in automotive repair, yet empirical evidence on task-specific ergonomic risks in Middle Eastern contexts is limited. This study provides a detailed ergonomic risk profile of auto body specialists in Jeddah, Saudi Arabia, using a mixed-method approach that integrates the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and a validated Nordic Musculoskeletal Questionnaire. Twenty-five specialists across diverse tasks including installation, weighing, painting, cutting, and lifting were systematically evaluated to identify both postural and self-reported risk patterns. Results showed a high prevalence of discomfort in the lower back (64%), shoulders (52%), and wrists (48%). Ergonomic assessment revealed that the evaluated tasks were predominantly classified as moderate-to-high-risk, with RULA scores ranging from 6 to 7 and REBA scores ranging from 8 to 11. Beyond confirming the physical strain inherent to auto body work, the study highlights contextual factors such as prolonged static postures, limited use of mechanical aids, and constrained workshop layouts that exacerbate ergonomic risks. Importantly, the findings inform multi-level recommendations ranging from workshop practices to industry standards and policy considerations ensuring that interventions are both practical and scalable. By situating locally grounded results within the broader discourse on musculoskeletal risk prevention, the study offers region-specific evidence while providing globally relevant lessons for labor-intensive industries. Full article

TwinArray Sort is a non-comparison integer sorting algorithm designed for non-negative integers with relatively dense key ranges, offering competitive runtime performance and reduced memory usage relative to other counting-based methods. The algorithm introduces a conditional distinct-array verification mechanism that adapts the reconstruction strategy based on data characteristics while maintaining worst-case time and space complexity of O(n + k). Comprehensive experimental evaluations were conducted on datasets containing up to 10⁸ elements across multiple data distributions, including random, reverse-sorted, nearly sorted, and their unique variants. The results demonstrate consistent performance improvements compared with established algorithms such as Counting Sort, Pigeonhole Sort, MSD Radix Sort, Spreadsort, Flash Sort, Bucket Sort, and Quicksort. TwinArray Sort achieved execution times up to 2.7 times faster and reduced memory usage by up to 50%, with particularly strong performance observed for unique and reverse-sorted datasets. The algorithm exhibits good scalability for large datasets and key ranges, with performance degradation occurring primarily in extreme cases where the key range significantly exceeds the input size due to auxiliary array requirements. These findings indicate that TwinArray Sort is a competitive solution for in-memory sorting in high-performance and distributed computing environments. Future work will focus on optimizing performance for wide key ranges and developing parallel implementations for multi-core and GPU architectures. Full article

Background: Musculoskeletal disorders (MSDs) are highly prevalent among healthcare workers, particularly nurses, with reported prevalence rates ranging from 57% to 93%. Perfusionists are highly specialized healthcare professionals responsible for operating heart–lung machines during cardiac surgery. To date, the prevalence of MSDs in this professional group has not been systematically investigated. To address this gap, perfusionists in Germany were surveyed regarding MSDs. Methods: The German version of the Nordic Musculoskeletal Questionnaire (NMQ) was administered. Pearson’s correlation and chi-square tests were applied. Results: A total of 287 perfusionists (72 female, 215 male; age 42.6 ± 11.9 years, professional experience 13.5 ± 10.9 years) from 45 German cardiac centers participated. Overall, 86% reported MSDs, with the lower back (65.5%) and neck (58.9%) being the most frequently affected regions, and 4.5% to 36.6% in other body regions. Increasing age was significantly associated with a higher prevalence in six body regions, and greater professional experience was associated in five regions. Occupational risk factors previously identified in nursing are assumed to apply to perfusion practice. Conclusions: This study demonstrates a high prevalence of MSDs among perfusionists in Germany. These findings underscore the need for preventive measures, coping strategies, and further research to reduce work-related musculoskeletal strain among perfusionists. Full article

To address the challenges of accurately extracting features from elongated scratches, irregular defects, and small-scale surface flaws on high-precision linear guide rails, this paper proposes a novel instance segmentation algorithm tailored for guide rail surface defect detection. The algorithm integrates the YOLOv8 instance segmentation framework with deformable convolutional networks and multi-scale feature fusion to enhance defect feature extraction and segmentation performance. A dedicated guide rail surface Defect (GSD) segmentation dataset is constructed to support model training and evaluation. In the backbone, the DCNv3 module is incorporated to strengthen the extraction of elongated and irregular defect features while simultaneously reducing model parameters. In the feature fusion network, a multi-scale feature fusion module and a triple-feature encoding module are introduced to jointly capture global contextual information and preserve fine-grained local defect details. Furthermore, a Channel and Position Attention Module (CPAM) is employed to integrate global and local features, improving the model’s sensitivity to channel and positional cues of small-target defects and thereby enhancing segmentation accuracy. Experimental results show that, compared with the original YOLOv8n-Seg, the proposed method achieves improvements of 3.9% and 3.8% in Box and Mask mAP₅₀, while maintaining a real-time inference speed of 148 FPS. Additional evaluations on the public MSD dataset further demonstrate the model’s strong versatility and robustness. Full article

Work-related musculoskeletal disorders (MSDs) remain a global occupational health priority, with recognized limitations in current point-in-time assessment methodologies. This research extends prior computer vision ergonomic assessment approaches by implementing biological proprioceptive feedback principles into a continuous, real-time monitoring system. Unlike traditional periodic ergonomic evaluation methods such as “Rapid Upper Limb Assessment” (RULA), our bio-inspired system translates natural proprioceptive mechanisms—which enable continuous postural monitoring through spinal feedback loops operating at 50–150 ms latencies—into automated assessment technology. The system integrates (1) markerless 3D pose estimation via MediaPipe Holistic (33 anatomical landmarks at 30 FPS), (2) depth validation via Orbbec Femto Mega RGB-D camera (640 × 576 resolution, Time-of-Flight sensor), and (3) proprioceptive-inspired alert architecture. Experimental validation with 40 adult participants (age 18–25, n = 26 female, n = 14 male) performing standardized load-lifting tasks (6 kg) demonstrated that 62.5% exhibited critical postural risk (RULA ≥ 5) during dynamic movement versus 7.5% at static rest, with McNemar test $p<0.001$ (Cohen’s $h=1.22$, 95% CI: 0.91–0.97). The system achieved 95% Pearson correlation between risk elevation and alert activation, with response latency of $42.1\pm 8.3$ ms. This work demonstrates technical feasibility for continuous occupational monitoring. However, long-term prospective studies are required to establish whether continuous real-time feedback reduces workplace injury incidence. The biomimetic design framework provides a systematic foundation for translating biological feedback principles into occupational health technology. Full article

This study aims to investigate the influencing factors and mechanisms of barium sulfate (BaSO₄) scaling under low-permeability reservoir conditions, providing a scientific basis for water quality selection during water injection. The effects of key scaling ions and flow conditions on scaling behavior were examined through integrated experimental core flooding tests and molecular dynamics (MD) simulations. Experiments were conducted using synthetic cores simulating the ultra-low permeability Chang-8 Reservoir of the Jiyuan Oilfield, analyzing the impact of ion concentrations (Ba²⁺, SO₄²⁻, Na⁺, Ca²⁺, HCO₃⁻), flow velocity, and injection pressure. MD simulations were performed based on an interfacial SiO₂(010)–BaSO₄ solution model constructed in Materials Studio to elucidate the micro-mechanisms. Results indicate that increasing concentrations of Ba²⁺ and SO₄²⁻ significantly promote scaling. High Ca²⁺ concentration (>8000 mg/L) inhibits BaSO₄ deposition via competitive adsorption. High Na⁺ concentration (>70,000 mg/L) reduces Ba²⁺ activity due to ionic strength effects. When HCO₃⁻ concentration exceeds 600 mg/L, CaCO₃ coprecipitation occurs, reducing effective SO₄²⁻ concentration and thus inhibiting BaSO₄ scaling. Increased flow velocity enhances scaling, whereas elevated injection pressure suppresses deposition. MD simulations reveal that increased ion concentrations decrease the mean square displacement (MSD) of Ba²⁺ and SO₄²⁻, weakening diffusion and enhancing scaling tendency. Elevated temperature promotes ion diffusion and inhibits scaling, while pressure shows negligible effect on ion diffusion at the molecular scale. This study provides theoretical insights for scaling prevention in low-permeability sandstone reservoirs. Full article

This paper proposes a complex fast recursive least-squares (FRLS) channel-estimation algorithm for single-carrier electromagnetic (EM) communications across the seawater–air interface, where severe attenuation and multipath cause strong SNR fluctuations. By redesigning the input-data structure and using forward–backward joint estimation, FRLS reduces the per-iteration complexity from the quadratic cost of classical RLS to a linear form (14L + 20 operations per iteration, where L is the channel length). Simulations under representative one- to three-path channels show that FRLS achieves the lowest steady-state mean-square deviation (MSD) at low SNR, outperforming LMS, IPNLMS, RLS, and PRLS. Offshore experiments further validate the practicality: after MMSE equalization, FRLS yields higher OSNR and improves the BER distribution, demonstrating an effective accuracy–complexity trade-off for hardware-constrained cross-medium EM links. Full article

We investigated the diffusive dynamics of a Lévy walk subject to stochastic resetting through combined numerical and theoretical approaches. Under exponential resetting, the process mean squared displacement (MSD) undergoes a sharp transition from free superdiffusive behavior with exponent ${\gamma }_0$ to a steady-state saturation regime. In contrast, power-law resetting with exponent $\beta$ exhibits three asymptotic MSD regimes: free superdiffusion for $\beta <1$, superdiffusive scaling with a linearly $\beta$-decreasing exponent for $1<\beta <{\gamma }_0+1$, and localization characterized by finite steady-state plateaus for $\beta >{\gamma }_0+1$. MSD scaling laws derived via renewal theory-based analysis demonstrate excellent agreement with numerical simulations. These findings offer new insights for optimizing search strategies and controlling transport processes in non-equilibrium environments. Full article

The walking accessibility of primary healthcare institutions (PHCIs) is a pivotal determinant of health equity. However, prior studies often lack a comprehensive assessment that integrates the spatiotemporal dynamics of both multi-faceted supply and multi-scenario demand. To bridge this gap, this study develops an enhanced two-step floating catchment area method (2SFCA-MSD) that concurrently incorporates multiple types of service supply and multiple temporal demand scenarios to quantify PHCI walking accessibility, with equity evaluated using the Gini coefficient and Lorenz curve. The results indicate that: (1) Both supply and demand exhibit pronounced spatiotemporal inequalities. (2) Walking accessibility varies substantially across scenarios; Health services for vulnerable groups (Service B) exhibit the highest walking accessibility across all three supply scenarios, while the morning work scenario demonstrates the best walking accessibility among the four demand scenarios. (3) Gini coefficients exceeding 0.5 across all scenarios reveal severe resource allocation inequity. By establishing a dynamic supply–demand integration framework, this research advances methodological precision in accessibility evaluation, uncovers critical spatiotemporal mismatch patterns, and provides actionable insights for optimizing PHCI planning to promote spatial justice in urban health. Full article

Pest detection in the field is crucial for realizing smart agriculture. Deep learning-based target detection algorithms have become an important pest identification method due to their high detection accuracy, but the existing methods still suffer from misdetection and omission when detecting small-targeted pests and small-targeted pests in more complex backgrounds. For this reason, this study improves on YOLO11 and proposes a new model called MSDS-YOLO for enhanced detection of small-target pests. First, a new dynamic multi-scale feature extraction module (C3k2_DMSFE) is introduced, which can be adaptively adjusted according to different input features and thus effectively capture multi-scale and diverse feature information. Next, a novel Dimensional Selective Feature Pyramid Network (DSFPN) is proposed, which employs adaptive feature selection and multi-dimensional fusion mechanisms to enhance small-target saliency. Finally, the ability to fit small targets was enhanced by adding 160 × 160 detection heads removing 20 × 20 detection heads and using Normalized Gaussian Wasserstein Distance (NWD) combined with CIoU as a position loss function to measure the prediction error. In addition, a real small-target pest dataset, Cottonpest2, is constructed for validating the proposed model. The experimental results showed that a mAP50 of 86.7% was achieved on the self-constructed dataset Cottonpest2, which was improved by 3.0% compared to the baseline. At the same time, MSDS-YOLO has achieved better detection accuracy than other YOLO models on public datasets. Model evaluation on these three datasets shows that the MSDS-YOLO model has excellent robustness and model generalization ability. Full article