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Comparative efficacy of an 8-week core stability program versus foot-ankle strengthening on pain, function, and distal structural parameters in individuals with knee osteoarthritis: a randomized controlled trial
Human activity recognition using CNN–BiLSTM with attention on hip-mounted wearable sensors
Abstract Human activity recognition (HAR) is the identification of daily human activities using wearable sensor data. In this study, we evaluate a deep learning–based HAR framework utilizing hip-mounted accelerometer and gyroscope signals from the USC-HAD dataset, which contains readings from healthy participants only. The proposed pipeline integrates convolutional feature extraction, bidirectional long short-term memory modeling, and an additive attention mechanism to capture temporal dependencies in the sensor data. The model is evaluated using performance matrices and leave-one-subject-out cross-validation (LOSO-CV) to assess subject-independent generalization. Performance is reported using accuracy, precision, recall, F1-score, and 95% confidence intervals, and statistical significance testing. Our experimental results show that under subject-exclusive splitting, the proposed model achieves 98% accuracy. Under strict LOSO-CV, the model achieves a performance of 78% ± 0.1130, providing a more realistic assessment of subject-independent generalization across unseen individuals. The dataset does not include clinical or patient populations. The findings are limited to non-clinical settings and should be interpreted within this scope. The results primarily contribute methodological insights into wearable-based HAR systems. The potential of this work for healthcare applications is discussed as a direction for future research, subject to validation on clinically representative datasets.
Heterogeneous traffic flow theory fundamental diagram model considering vehicle queuing characteristics and lane changing behavior
Period increase and amplitude modification in the kink oscillations of a small-scale EUV loop
VLAN-aware hierarchical multi-agent protection framework for robust fault isolation and coordination in active distribution networks
Neuromuscular control deficits are independently associated with recurrent ankle sprains in professional male soccer players across a ten-year retrospective cohort
Determinants of household food security during seasonal flooding in Aweil West County, South Sudan
Abstract Seasonal flooding poses a major threat to household food security in vulnerable rural communities in South Sudan. Despite increasing flood impacts, empirical evidence on flood-related food insecurity and household resilience remains limited. This study assessed household food security status, determinants, and coping strategies among flood-affected households in Aweil West County, South Sudan. A cross-sectional study was conducted among 218 households in flood-prone communities in Aweil West County. Household food security was assessed using Food Insecurity Experience Scale indicators, and severity levels were estimated using the Rasch model. Households were classified as food secure or food insecure based on severity scores. Adjusted logistic regression was used to identify the determinants of household food security, while coping strategies were assessed using chi-square tests. The Rasch model showed that 74.3% of households were food insecure. Adjusted logistic regression identified several significant predictors of household food insecurity during seasonal flooding. Dinka households had higher odds of food insecurity than Luo households (AOR = 3.00; 95% CI: 1.10–8.20). Households with 7–9 members (AOR = 2.30; 95% CI: 1.10–4.80), ≥ 10 members (AOR = 3.10; 95% CI: 1.40–6.90), ≥ 4 adults (AOR = 4.50; 95% CI: 1.20–16.90), and ≥ 6 children (AOR = 2.30; 95% CI: 1.10–4.90) were significantly more likely to experience food insecurity. Living ≥ 6 km from the nearest market (AOR = 2.60; 95% CI: 1.20–5.80), lack of household savings (AOR = 3.80; 95% CI: 1.60–4.70), and absence of social network, government, or NGO support were also associated with increased odds of being food insecure. Conversely, households earning 100,001–150,000 SSP (AOR = 0.58; 95% CI: 0.31–1.09) and ≥ 150,001 SSP (AOR = 0.35; 95% CI: 0.14–0.86) were significantly less likely to experience food insecurity than those earning ≤ 50,000 SSP. Key coping strategies included food rationing, reducing meal frequency, reliance on social networks, emergency assistance, and livelihood-support activities. Flooding significantly undermined household food security in Aweil West County. Strengthening livelihood diversification, savings mechanisms, access to credit, and social protection programmes, including food assistance and cash-for-work initiatives, is essential for improving household resilience to recurrent flood shocks.
Multi-objective student nurse allocation problem during training using exact and hybrid heuristic–metaheuristic methods
A novel dual-section geothermal–parabolic trough multigeneration system for sustainable energy supply optimized via Grey Wolf Optimizer
Influence of silicon nitride nanoparticle reinforcement on the structure and properties of surface treated hemp/basalt reinforced hybrid epoxy composites
Abstract The growing demand for sustainable lightweight materials has accelerated the development of natural fiber-reinforced polymer composites as alternatives to conventional synthetic composites. In this study, the physical, mechanical, and fracture behavior of hemp/basalt fabric-reinforced epoxy hybrid nanocomposites modified with silicon nitride (Si 3 N 4 ) nanoparticles was investigated. Hemp fibers were treated with 5 wt% NaOH, and basalt fibers were silane-treated to improve fiber–matrix interfacial adhesion. Hybrid nanocomposites containing 0.5–2 wt% Si 3 N 4 nanoparticles were fabricated through ultrasonication-assisted hand lay-up followed by vacuum bagging and compression curing. Fourier transform infrared spectroscopy confirmed successful fiber surface modification and enhanced interfacial interactions among the fibers, epoxy matrix, and nanoparticles. Scanning electron microscopy revealed improved nanoparticle dispersion and compact morphology up to 1.5 wt% Si 3 N 4 loading, whereas localized agglomeration was observed at higher filler contents. The composite containing 1.5 wt% Si 3 N 4 exhibited the best overall performance, achieving a tensile strength of 190.78 MPa and a tensile modulus of 11.11 GPa, corresponding to improvements of 13.41% and 23.31%, respectively, compared with the unfilled hybrid composite. The same composite also showed the highest impact strength of 16.4 kJ/m 2 and enhanced hardness due to improved stress transfer, crack deflection, and interfacial bonding. Excessive nanoparticle loading resulted in agglomeration and microvoids formation, causing a slight reduction in mechanical properties. The results demonstrate that the synergistic combination of alkali-treated hemp fibers, silane-treated basalt fibers, and Si 3 N 4 nanoparticles is an effective strategy for developing sustainable nanocomposites with enhanced strength, stiffness, toughness, and fracture resistance for lightweight structural and transportation applications.
Heavy metal contamination in soil and Indian mustard (Brassica juncea L. Czern.) irrigated with borewell water and paper mill effluent
Research on the water-sensitivity characteristics and consolidation mechanism of biopolymer-modified dispersive soils
Seroprevalence and associated risk factors of bovine brucellosis in the Dasenech and Gnangatom districts of the South Omo zone, southern Ethiopia
Sustained simvastatin delivery via poly(lactide) nanoparticles enhances early osteogenic-associated responses in human periodontal ligament stem cells
Abstract Simvastatin has recognized osteoinductive properties, but its application in regenerative strategies is limited by poor aqueous behavior and a narrow cytocompatible dosing window. Here, we developed simvastatin-loaded poly(lactide) nanoparticles and evaluated whether nanoparticle-mediated delivery improves cytocompatibility and early osteogenic-associated responses of human periodontal ligament stem cells (hPDLSCs) compared with free simvastatin. Nanoparticles were prepared by nanoprecipitation and characterized by dynamic light scattering, transmission electron microscopy, and nanoparticle tracking analysis, showing spherical morphology and a mean diameter of approximately 150 nm. Cellular internalization was confirmed using rhodamine-labeled nanoparticles and confocal microscopy, demonstrating efficient uptake with predominantly cytoplasmic localization. In hPDLSCs, nanoparticle-delivered simvastatin improved short-term cytocompatibility and enhanced mineralization together with increased periostin and osteocalcin secretion, with the most pronounced differences observed at day 14, whereas mineralization outcomes converged between delivery formats by day 21. Complementary clonogenic assays in osteoblasts demonstrated that nanoparticle-mediated delivery attenuated simvastatin-associated loss of long-term proliferative capacity relative to free simvastatin under the tested conditions. These findings indicate that poly(lactide) nanoparticles may improve the cytocompatible delivery profile of simvastatin and preferentially enhance early osteogenic-associated responses in vitro in hPDLSCs, supporting further investigation of controlled simvastatin delivery strategies for periodontal regenerative applications.
HybridSwingNet for explainable swing trading using multi-encoder deep learning and confidence-calibrated signal execution
A probilistic markov model-based framework for indoor localization
Ground vibration propagation under road traffic with different vehicle types and shallow foundation thicknesses
Abstract Ground vibrations induced by road traffic are among the most significant challenges in urban environments and can affect both residents’ comfort and the performance of adjacent structures. Despite numerous studies in this field, the evaluation of the effects of vehicle type and the role of shallow foundations in vibration propagation, as well as the investigation of the phenomenon of resonance, still face limitations. In this study, a 2.5D numerical model based on the finite element method was developed to simulate dynamic vehicle–pavement–soil interaction, accounting for road surface roughness. The dynamic loads generated by the passage of different vehicles were calculated using a half-car model. Following model validation, a sensitivity analysis was conducted for vehicle type, shallow foundation thickness, and distance from the loading axis. The results showed that the truck was the most critical source of vibration, and the peak particle velocity (PPV) generated by it at a distance of 15 m from the road was 12.7 times greater than that of a passenger car. Furthermore, the occurrence of the resonance phenomenon within the range of 14 to 22 m from the road led to a renewed increase in vibration amplitudes. Increasing the foundation thickness from 0.5 to 4 m increased the reduction in PPV at 13 m from 7.25 to 42.28% and reduced the spectral energy of vibrations by up to 64.70%. In addition, the 4-m-thick foundation resulted in an 11 dB reduction in the vibration level at the dominant frequency. The findings of this study demonstrate the decisive role of vehicle type and foundation characteristics in controlling traffic-induced vibrations. They can provide a basis for the design and optimization of structures located adjacent to heavily trafficked roads.