Search Results (1,829)

Background/Objectives: Neck pain caused by myofascial pain syndrome (MPS) is a highly prevalent musculoskeletal condition. Repetitive peripheral magnetic stimulation (rPMS) is a promising treatment option; however, its therapeutic effect and optimal treatment frequency remain unclear. This study aimed to investigate the therapeutic effect and duration of effect of rPMS in patients with MPS of the neck. Methods: In this randomized, sham-controlled, crossover trial, 27 patients with neck MPS and baseline visual analog scale (VAS) scores ≥ 40 were enrolled. The mean age was 43.8 ± 9.1 years, and 63% were female. Participants were randomly assigned to receive either an initial rPMS treatment (a 10 min session delivering 3900 pulses at 5–10 Hz) or sham stimulation. After 7 days, groups crossed over. Pain intensity (VAS), disability (Neck Disability Index; NDI), and analgesic use were recorded daily for seven consecutive days. A linear mixed-effects model was used for analysis. Results: At baseline, the VAS and NDI scores were 61.8 ± 10.5 and 26.0 ± 6.3, respectively. rPMS produced a significantly greater reduction in both VAS and NDI scores, with the greatest differences observed on Day 4: the differences were −24.1 points in VAS and −8.5 points in NDI compared to the sham group. There was no significant difference in analgesic use between the two groups. Conclusions: A single rPMS session provides short-term improvement in pain and disability in neck MPS. Based on the observed therapeutic window, more frequent sessions (e.g., twice weekly) may provide sustained benefit and should be explored in future studies. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Rhubarb, a medicinal herb in Gansu Province, China, undergoes significant quality changes during sun-drying. This study investigated color changes, drying kinetics, anthraquinone (AQ) content, metabolic profiles, and enzyme activity during the process. Results showed that drying induced enzymatic browning, with the browning index (BI) progressively increasing over extended drying periods (4–16 h) and with greater slice thickness (2–8 mm). Catalase (CAT) activity first decreased and then increased, while polyphenol oxidase (PPO) activity decreased throughout drying. Slice thickness significantly affected AQ content, with the highest in 2 mm slices and the lowest in 4 mm slices. The drying process followed a logarithmic model (R² = 0.99418, RMSE = 0.02310, and χ² = 0.0005). Metabolomics analysis identified 631 differential metabolites, with 8 key metabolites linked to flavonoid biosynthesis, phenylalanine biosynthesis, and tyrosine metabolism. Fifteen enzymes were involved in metabolite synthesis and decomposition, though some enzyme activity trends contradicted metabolite changes. This study provides insight into rhubarb drying mechanisms and a basis for optimizing the drying process. Full article

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

Multivalent lectin–glycan interactions (MLGIs) are vital for viral infection, cell-cell communication and regulation of immune responses. Their structural and biophysical data are thus important, not only for providing insights into their underlying mechanisms but also for designing potent glycoconjugate therapeutics against target MLGIs. However, such information remains to be limited for some important MLGIs, significantly restricting the research progress. We have recently demonstrated that functional nanoparticles, including ∼4 nm quantum dots and varying sized gold nanoparticles (GNPs), densely glycosylated with various natural mono- and oligo- saccharides, are powerful biophysical probes for MLGIs. Using two important viral receptors, DC-SIGN and DC-SIGNR (together denoted as DC-SIGN/R hereafter), as model multimeric lectins, we have shown that α-mannose and α-manno-α-1,2-biose (abbreviated as Man and DiMan, respectively) coated GNPs not only can provide sensitive measurement of MLGI affinities but also reveal critical structural information (e.g., binding site orientation and mode) which are important for MLGI targeting. In this study, we produced mannuronic acid (ManA) coated GNPs (GNP-ManA) of two different sizes to probe the effect of glycan modification on their MLGI affinity and antiviral property. Using our recently developed GNP fluorescence quenching assay, we find that GNP-ManA binds effectively to both DC-SIGN/R and increasing the size of GNP significantly enhances their MLGI affinity. Consistent with this, increasing the GNP size also significantly enhances their ability to block DC-SIGN/R-augmented virus entry into host cells. Particularly, ManA coated 13 nm GNP potently block Ebola virus glycoprotein-driven entry into DC-SIGN/R-expressing cells with sub-nM levels of EC₅₀. Our findings suggest that GNP-ManA probes can act as a useful tool to quantify the characteristics of MLGIs, where increasing the GNP scaffold size substantially enhances their MLGI affinity and antiviral potency. Full article

This study aims to determine the model parameters of proton exchange membrane fuel cells (PEMFC) by employing the Sea Horse Optimization (SHO) algorithm, a novel metaheuristic approach inspired by natural behaviors. Although conventional algorithms in the literature have achieved considerable success in parametric modeling accuracy, many of them suffer from inherent drawbacks, such as premature convergence and entrapment in local minima. The SHO algorithm, with its adaptive and dynamic nature, is designed to overcome these limitations. To further evaluate its performance, a detailed parametric sensitivity analysis is conducted on SHO-specific control parameters. In this work, experimental polarization data from a Ballard Mark V PEMFC is used as a reference to estimate the equivalent circuit model parameters ${ϵ}_1$, ${ϵ}_2$, ${ϵ}_3$, ${ϵ}_4$, $\beta$, $\lambda$, $R_c$. The SHO algorithm achieved a mean absolute error (MAE) of 0.001079 and a coefficient of determination (R²) of 0.999791, with a model-to-experiment fit ratio of 99.92%. Compared to similar studies reported in the literature, the results indicate that the SHO algorithm offers competitive performance. Moreover, the average convergence time is recorded as 1.74 s for 5000 iteration, highlighting the algorithm’s rapid convergence and low computational cost. Overall, the SHO algorithm is demonstrated to be an efficient, robust, and promising alternative to conventional methods for parameter identification in PEMFC modeling. Full article

The sustainable development of higher education exhibits a strong and measurable association with the level of regional innovation capacity. Drawing on panel data covering 31 provincial-level administrative regions in China from 2010 to 2022, we construct evaluation frameworks for both higher education and regional innovation capacity using the entropy weight method. These are complemented by kernel density estimation, spatial autocorrelation analysis, Dagum Gini coefficient decomposition, and the Obstacle Degree Model. Together, these tools enable a comprehensive investigation into the spatiotemporal evolution and driving mechanisms of coupling coordination dynamics between the two systems. The results indicate the following: (1) Both higher education and regional innovation capacity indices exhibit steady growth, accompanied by a clear temporal gradient differentiation. (2) The coupling coordination degree shows an overall upward trend, with significant inter-regional disparities, notably “higher in the east and low in the west”. (3) The spatial distribution of the coupling coordination degree reveals positive spatial autocorrelation, with provinces exhibiting similar levels tending to form spatial clusters, most commonly of the low–low or high–high type. (4) The spatial heterogeneity is pronounced, with inter-regional differences driving overall imbalance. (5) Key obstacles hindering regional innovation include inadequate R&D investment, limited trade openness, and weak technological development. In higher education sectors, limitations stem from insufficient social service benefits and efficiency of flow. This study recommends promoting the synchronized advancement of higher education and regional innovation through region-specific development strategies, strengthening institutional infrastructure, and accurately identifying and addressing key barriers. These efforts are essential to fostering high-quality, coordinated regional development. Full article

Despite extensive prevention strategies in Spain since the 1980s, gender-based violence, including among adolescents, remains prevalent, as observed in the Romance SUCC-ED Project (R&D&I Operating Programme ERDF Andalusia 2014–2020). This research study investigates the dimensions, meanings, relationships, and practices shaping the culture of romance in digital Andalusian adolescence (12–16 years) and its potential impact on school trajectories in Compulsory Secondary Education. Based on the premise that equality-focused relationship education is key to preventing gender violence, the study employs an ethnographic methodology with 12 Andalusian school case studies (4 out of them are located in rural areas) and 220 in-depth interviews (126 girls, 57.3%; 94 boys, 42.7%). This article aims to empirically explain gender violence in early adolescence by analysing the culture of romance as an explanatory factor. Findings reveal an interconnected model where dimensions (love, couple, sexuality, pornography, social networks, and cultural references), meanings (constructed by adolescents within each of them), relationships (partner), and practices (control and jealousy) reinforce romanticised femininity and dominant masculinity, thus explaining the high incidence of gender-based violence among students in the study. Full article

Accurate coastal bathymetry is critical for navigation, environmental monitoring, and marine resource management. This study presents a deep learning-based approach that fuses Sentinel-2 multispectral imagery with airborne hyperspectral-derived reference data to generate high-resolution satellite-derived bathymetry (SDB). To address the spatial resolution mismatch between Sentinel-2 (10 m) and LiDAR reference data (1 m), three interpolation methods—Inverse Distance Weighting (IDW), Natural Neighbor (NN), and Spline—were employed to resample spectral reflectance data to a 1 m grid. Two spectral input configurations were evaluated: the log-ratio of Bands 2 and 3, and raw RGB composite reflectance (Bands 2, 3, and 4). A Fully Convolutional Neural Network (FCNN) was trained under each configuration and validated using LiDAR-based depth. The RGB + NN combination yielded the best performance, achieving an RMSE of 1.2320 m, MAE of 0.9381 m, bias of +0.0315 m, and R² of 0.6261, while the log-ratio + IDW configuration showed lower accuracy. Visual and statistical analyses confirmed the advantage of the RGB + NN approach in preserving spatial continuity and spectral-depth relationships. This study demonstrates that both interpolation strategy and input configuration critically affect SDB model accuracy and generalizability. The integration of spatially adaptive interpolation with airborne hyperspectral reference data represents a scalable and efficient solution for high-resolution coastal bathymetry mapping. Full article

Background/Objectives: Communication disorders in childhood, including expressive, receptive, pragmatic, and fluency impairments, have been consistently linked to mental health challenges such as anxiety, depression, and behavioural difficulties. However, existing research remains fragmented across diagnostic categories and developmental stages. This scoping review aimed to synthesise empirical evidence on the relationship between communication disorders and mental health outcomes in children and adolescents and to identify key patterns and implications for practice and policy. Methods: Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and Arksey and O’Malley’s framework, this review included empirical studies published in English between 2000 and 2024. Five databases were searched, and ten studies met the inclusion criteria. Data were charted and thematically analysed to explore associations across communication profiles and emotional–behavioural outcomes. Results: Four interconnected themes were identified: (1) emotional and behavioural manifestations of communication disorders; (2) social burden linked to pragmatic and expressive difficulties; (3) family and environmental stressors exacerbating child-level challenges; and (4) a lack of integrated care models addressing both communication and mental health needs. The findings highlight that communication disorders frequently co-occur with emotional difficulties, often embedded within broader social and systemic contexts. Conclusions: This review underscores the need for developmentally informed, culturally responsive, and interdisciplinary service models that address both communication and mental health in children. Early identification, family-centred care, and policy reforms are critical to reducing inequities and improving outcomes for this underserved population. Full article

Material wear significantly impacts the clinical success and longevity of dental ceramic restorations. This in vivo study aimed to assess the wear behavior of IPS Empress^® glass-ceramic inlays and onlays over 14 years, considering the influence of different antagonist materials. Fifty-four indirect restorations of 21 patients were available for comprehensive wear analysis, with complete follow-up data for up to 14 years. Three-dimensional measurements relied on digitized epoxy resin models produced immediately post-insertion (baseline) and subsequently at 2, 4, and 14 years. The occlusal region on the baseline model was delineated for comparative analysis. Three-dimensional superimpositions with models from subsequent time points were executed to assess wear in terms of average linear wear and volumetric loss. Statistical analyses were conducted in R (version 4.4.1), employing Mann–Whitney U tests (material comparisons) and Wilcoxon signed rank tests (time point comparisons), with a significance threshold of p ≤ 0.05. During the entire study period, an increase in wear was observed at each assessment interval, gradually stabilizing over time. Significant differences in substance loss were found between the follow-up time points, both for mean (−0.536 ± 0.249 mm after 14a) and integrated distance (−18,935 ± 11,711 mm³ after 14a). In addition, significantly higher wear was observed after 14 years with gold as antagonist compared to other materials (p ≤ 0.03). The wear behavior of IPS Empress^® ceramics demonstrates clinically acceptable long-term outcomes, with abrasion characteristics exhibiting stabilization over time. Full article

The abnormal growth of smooth muscle cells (SMCs) contributes to the vascular remodeling associated with coronary artery disease, a leading cause of death in women. Estradiol (E2) mediates cardiovascular protective actions, in part, by inhibiting the abnormal growth (proliferation and migration) of SMCs through various mechanism. Since microRNAs (miRNAs) play a major role in regulating cell growth and vascular remodeling, we hypothesize that miRNAs may mediate the protective actions of E2. Following preliminary leads from E2-regulated miRNAs, we found that platelet-derived growth factor (PDGF)-BB-induced miR-193a in SMCs is downregulated by E2 via estrogen receptor (ER)α, but not the ERβ or G-protein-coupled estrogen receptor (GPER). Importantly, miR-193a is actively involved in regulating SMC functions. The ectopic expression of miR-193a induced vascular SMC proliferation and migration, while its suppression with antimir abrogated PDGF-BB-induced growth, effects that were similar to E2. Importantly, the restoration of miR-193a abrogated the anti-mitogenic actions of E2 on PDGF-BB-induced growth, suggesting a key role of miR-193a in mediating the growth inhibitory actions of E2 in vascular SMCs. E2-abrogated PDGF-BB, but not miR-193a, induced SMC growth, suggesting that E2 blocks the PDGF-BB-induced miR-193a formation to mediate its anti-mitogenic actions. Interestingly, the PDGF-BB-induced miR-193a formation in SMCs was also abrogated by 2-methoxyestradiol (2ME), an endogenous E2 metabolite that inhibits SMC growth via an ER-independent mechanism. Furthermore, we found that miR-193a induces SMC growth by activating the phosphatidylinositol 3-kinases (PI3K)/Akt signaling pathway and promoting the G1 to S phase progression of the cell cycle, by inducing Cyclin D1, Cyclin Dependent Kinase 4 (CDK4), Cyclin E, and proliferating-cell-nuclear-antigen (PCNA) expression and Retinoblastoma-protein (RB) phosphorylation. Importantly, in mice, treatment with miR-193a antimir, but not its control, prevented cuff-induced vascular remodeling and significantly reducing the vessel-wall-to-lumen ratio in animal models. Taken together, our findings provide the first evidence that miR-193a promotes SMC proliferation and migration and may play a key role in PDGF-BB-induced vascular remodeling/occlusion. Importantly, E2 prevents PDGF-BB-induced SMC growth by downregulating miR-193a formation in SMCs. Since, miR-193a antimir prevents SMC growth as well as cuff-induced vascular remodeling, it may represent a promising therapeutic molecule against cardiovascular disease. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

Background/Objectives: This study investigates the utility of multiparametric PET/MRI in delineating changes in physiologically distinct intratumoral habitats during trastuzumab-induced alterations in a preclinical HER2+ breast cancer model. Methods: By integrating diffusion-weighted MRI, dynamic contrast-enhanced MRI, [¹⁸F]Fluorodeoxyglucose- and [¹⁸F]Fluorothymidine-PET, voxel-wise parametric maps were generated capturing cellular density, vascularity, metabolism, and proliferation. BT-474 tumor-bearing mice have high expression of HER2 and, in response to trastuzumab, an anti-HER2 antibody, effectively show changes in proliferation and tumor microenvironment alterations that result in decreases in tumor volume through time. Results: Single imaging metrics and changes in metrics were incapable of identifying treatment-induced alterations early in the course of therapy (day 4) prior to changes in tumor volume. Hierarchical clustering identified five distinct tumor habitats, which enabled longitudinal assessment of early treatment response. Tumor habitats were defined based on imaging metrics related to biology and categorized as highly vascular (HV), hypoxic responding (HRSP), transitional zone (TZ), active tumor (ATMR) and responding (RSP). The HRSP cluster volume significantly decreased in trastuzumab-treated tumors compared to controls by day 4 (p = 0.015). The volume of ATMR cluster was significantly different at baseline between cohorts (p = 0.03). The TZ cluster, indicative of regions transitioning more to necrosis, significantly decreased in treated tumors (p = 0.031), suggesting regions had already transitioned. Multiparametric image clustering showed a significant positive linear correlation with histological multiparametric mapping, with R² values of 0.56 (HRSP, p = 0.013, 0.64 (ATMR, p = 0.0055), and 0.49 (responding cluster, p = 0.024), confirming the biological relevance of imaging-derived clusters. Conclusions: These findings highlight the potential utility of multiparametric PET/MRI to capture biological alterations prior to any single imaging metric which has potential for better understanding longitudinal changes in biology, stratifying tumors based on those changes, optimizing therapeutic monitoring and advancing precision oncology. Full article

This study investigated the fracture behavior of asphalt mixtures under indirect tensile loading by comparing the performance of homogenized and micromechanical finite element (FEMs) models based on the cohesive zone model (CZM). Five asphalt mixture types were tested experimentally, and both models were calibrated and validated using load–displacement curves from indirect tensile tests (IDTs). The micromechanical model, incorporating random aggregate generation and three-phase material definition, exhibited significantly higher predictive accuracy (R² = 0.86–0.98) than the homogenized model (R² = 0.66–0.77). The validated micromechanical model was further applied to quantify the impact of construction-related deficiencies—namely, increased air voids, non-continuous gradation, and aggregate segregation. The simulation results showed that higher void content (from 4% to 10%) reduced peak load by up to 35% and increased localized stress concentrations by up to 40%. Discontinuous gradation and uneven aggregate distribution also led to premature crack initiation and more complex fracture paths. These findings demonstrated the value of micromechanical modeling for evaluating sensitivity to mix design and compaction quality, providing a foundation for performance-based asphalt mixture optimization and durability improvement. Full article