Search Results (321)

Enhancement behavior on saturation current of output characteristics in amorphous indium–gallium–zinc oxide (a-IGZO) thin-film transistors under positive bias stress (PBS) is investigated. The threshold voltage (V_th) of the a-IGZO TFT demonstrates a typical positive shift during PBS. Notably, the output current at an identical overdrive voltage (V_ov) initially rises with increasing bias stress duration. The observed phenomena are elucidated by the detrapping of positively charged defects situated at the interface between the dielectric and active layer due to electrons induced by bias stress during PBS, which diminishes carrier scattering at the channel interface. Following the full release of positive interface charge, additional electron trapping states emerge. The presence of trapped electrons intensifies carrier scattering at the channel interface, leading to a degradation in drive current. Low-frequency noise (LFN) measurements are conducted to verify the suggested mechanism of PBS instability in a-IGZO TFTs. Moreover, the energy distribution of PBS-induced traps is shown by C-V characterization to be exponential, dominated by shallow traps. Passivation greatly enhanced the PBS stability, which is attributed to hydrogen doping-induced defect passivation and the shielding of the back-channel interface from the air atmosphere. Full article

The objective of this study was to assess the performance of posture recognition systems based on artificial intelligence (AI) using deep learning (DL) and machine learning (ML) approaches for the prevention of work-related musculoskeletal disorders (WMSDs) in manufacturing. The study was conducted as a systematic review and meta-analysis following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four open-access databases were screened in May 2026 without date restrictions: PubMed/MedLine, Google Scholar, ScienceDirect, and IEEE Xplore. The selected studies had to be original, peer-reviewed studies written in English. The study had to evaluate the performance of an AI posture recognition system (ML or DL) for the prevention of WMSDs in manufacturing using at least one of the following parameters: accuracy, specificity, sensitivity, precision, or F1 score. The risk of bias for each included study was assessed using PROBAST (Prediction Model Study Risk of Bias Assessment Tool). A meta-analysis was conducted to pool the values of the five performance metrics separately. Logit and Freeman-Tukey transformations were applied prior to pooling, and the results were compared after back-transformation. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were applied to assess heterogeneity. Forest plots including pooled values with 95% confidence intervals were used to present the results. Subgroup analyses and meta-regressions were performed to test the effect of AI methods and ergonomic tools on performance and to explore potential causes of heterogeneity. Finally, publication bias (Egger’s test) and certainty of evidence (GRADE method—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to ensure the generalizability of the results. Ten studies were included: Among the 200 studies identified through database searches and the snowball method, 12 met the inclusion criteria and were selected. Two studies were excluded due to an insufficient number of participants, bringing the final number of studies considered to 10. The logit transformation yielded the best overall fit for the normality of the data distribution for the use of a random-effects model. High posture detection performance was observed, with pooled values ranging from 84.78% (95% CI: 80.23–88.19%, specificity with Freeman-Tukey) to 93.40% (95% CI: 89.57–95.89%, precision with logit). The values obtained with logit were higher than those obtained with Freeman-Tukey, with differences ranging from 2.75% to 4.75% across all performance metrics. Meta-regression showed that DL outperformed ML for all metrics, with differences ranging from 5% to 17%. RULA and REBA achieved better performance than other ergonomic tools. However, high heterogeneity (I² > 90%) and substantial inter-study variability were observed in all analyses, and a very low level of evidence was evidenced for all performance parameters. Consequently, the results should be interpreted with caution, particularly regarding the deployment of the systems in real-world settings. Future work could strengthen training and testing procedures on datasets, as well as external validation. These aspects are essential for effective use in manufacturing environments to ensure operator safety by reducing their exposure to WMSD risks associated with work postures. Full article

Low back pain (LBP) is the leading cause of years lived with disability globally, and a major rehabilitation priority, yet nursing education often does not translate burden evidence into first-contact actions across care settings. This article aimed to develop a nursing education and practice framework that converts global LBP evidence into competencies for assessment, education, medication safety, referral, follow-up and occupational prevention. A focused, Global Burden of Disease-informed narrative review and practice-oriented educational synthesis was conducted. PubMed/MEDLINE, CINAHL, Scopus, Google Scholar, and organisational sources were searched for burden estimates, clinical guidelines, pain education scholarship, nursing occupational health evidence and nurse-led self-management literature. Sources were selected for relevance to nurse-deliverable actions and curriculum assessment; systematic-review methods, meta-analysis and formal risk-of-bias appraisal were not used. Evidence was mapped iteratively to six nursing practice domains: red-flag triage and escalation; functional, psychosocial and contextual assessment; brief therapeutic education; medication safety and low-value-care reduction; follow-up and referral; and occupational prevention. The revised framework adds an explicit evidence-to-competency map, implementation guidance and evaluation indicators for undergraduate education, postgraduate training, continuing professional development, clinical supervision and quality improvement. LBP should be taught as a nursing-sensitive disability and rehabilitation priority, supporting nurses to screen for danger, validate pain, reduce threat, promote graded activity, monitor medicines, coordinate continuity and prevent occupational harm. Full article

Background/Objectives: Low back pain (LBP) is a leading cause of disability worldwide and is influenced by multiple biological and functional factors. While pain intensity is a well-established determinant of disability, the contribution of body composition and relative skeletal muscle mass remains insufficiently understood. This study aimed to investigate the associations between body composition parameters, Muscle-to-Weight Ratio (MWR), pain intensity, and functional disability in patients with LBP and to explore sex-related differences in these variables. Methods: A cross-sectional observational study was conducted in 98 adults with chronic non-specific LBP recruited from a rehabilitation department in Romania. Anthropometric and body composition measurements were obtained using bioelectrical impedance analysis (BIA). Pain intensity was assessed using the Visual Analogue Scale (VAS), and functional disability was evaluated using the Roland–Morris Disability Questionnaire (RMDQ). The MWR was calculated as skeletal muscle mass divided by body weight and expressed as a percentage. Sex comparisons, correlation analyses, and multiple linear regression analyses were performed. Results: The mean age of participants was 61.9 ± 10.0 years, and 53.1% were female. Female participants reported significantly higher pain intensity than males (60.5 ± 31.0 vs. 44.2 ± 35.5; p = 0.021) and demonstrated a different distribution of disability severity categories compared with males (Fisher–Freeman–Halton exact p = 0.021). Pain intensity showed the strongest positive correlation with disability (r = 0.56, p < 0.001), whereas MWR was negatively correlated with disability (r = −0.34, p = 0.001). In multivariable regression analysis, pain intensity (β = 0.49, p < 0.001), age (β = 0.22, p = 0.013), and MWR (β = −0.27, p = 0.018) remained significantly associated with disability after adjustment for sex and body fat percentage. The model explained 43.2% of the variance in RMDQ scores (R² = 0.432). Conclusions: Pain intensity, age, and MWR were significantly associated with functional disability in patients with chronic non-specific LBP. Lower MWR values were associated with greater disability after adjustment for age, sex, body fat percentage, and pain intensity. These findings suggest that assessment of body composition and MWR may provide additional information when evaluating patients with chronic non-specific low back pain. However, the cross-sectional design precludes conclusions regarding causality. Full article

A long-term, consistent dataset of blowing snow (BLSN), a common phenomenon over Antarctica, is essential for ice sheet mass balance analysis. While space-borne lidar missions such as Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Ice, Cloud, and Land Elevation Satellite (ICESat-2) have provided valuable continental-scale BLSN observations, their temporal resolutions and spatial reaches present notable limitations. Using CALIPSOs for training, a machine learning approach has been developed to generate hourly Antarctic BLSN data on the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) grid, extending back to the 1980s. ICESat-2 observations are used to assess the post-CALIPSO period; the results demonstrate greater bias with BLSN layer heights and better agreement with BLSN fraction and optical depth. When compared with ground-based observations, MERRA-2 exhibits a comparable and lower BLSN fraction and lower layer heights than those recorded by ceilometers. This data record represents the longest continuous BLSN data record to date. This study provides an overview of key BLSN properties, including occurrence, height, and optical depth. The results highlight strong seasonal patterns: BLSN occurrence peaks during Antarctic winter, while both height and optical depth are higher in summer with no statistically significant trend. Spatially, East Antarctica exhibits higher BLSN occurrence than West Antarctica. Full article

Old film restoration remains challenging because archival footage often combines scratches, blotches, flicker, blur, and temporal artifacts, disrupting the balance among structural recovery, regional texture repair, and fine artifact suppression. We propose Multi-Granularity Feature Decomposition with Ranking-Aware Regularization for Old Film Restoration (MgfrOFR), a multi-granularity extension of a Mamba-based old film restoration backbone. MgfrOFR first decomposes propagated features into coarse-to-fine restoration branches and feeds branch summaries back through channel-wise recalibration so different degradation scales can be represented without competing in a single feature stream. It then introduces a ranking-aware triplet regularizer that penalizes correlated temporal-similarity rankings across branches during training while preserving the baseline inference interface. Experiments on the Synthetic/Real-World Old Video (SRWOV) benchmark show statistically significant fidelity gains on synthetic clips, favorable no-reference naturalness on real-world archival footage, and a bounded perceptual-quality trade-off. These results indicate that representation-level branch balance is an effective inductive bias for old film restoration under heterogeneous and asymmetric degradation. Full article

The Box–Cox transformation is widely used to improve normality, stabilize variance, and enable Gaussian-based modelling in a transformed scale. After model fitting, conditional summaries are often mapped back to the original scale by applying the inverse transformation. This paper shows that this transform–fit–inverse procedure has a structural limitation: nonlinear inverse transformations do not, in general, preserve conditional expectations. Equivalently, conditional expectation and nonlinear inversion do not commute. Within the Box–Cox Gaussian framework, the admissible domain of the inverse transformation leads naturally to a truncated normal formulation in the transformed scale. Under this formulation, we derive a second-order decomposition showing that the original-scale conditional mean differs from the inverse-transformed truncated conditional mean by a curvature-driven correction term depending on the truncated conditional variance. The usual untruncated Gaussian expression is recovered as a local approximation when the inadmissible probability is negligible. A numerical sensitivity analysis, focused on $0\le {\lambda }_Y\le 1$, illustrates how the distortion depends on the transformation parameter, correlation, and conditional dispersion. A real-data illustration using medical insurance charges further shows that the discrepancy can be visible in an applied regression setting and is not removed by changing the transformation of the explanatory variable. The results distinguish this structural invariance problem from classical retransformation bias and show that inverse-transformed fitted curves should be interpreted as transformation-induced structural curves, not automatically as conditional mean functions on the original scale. Full article

The inherent genomic plasticity of RNA viruses, particularly influenza viruses and SARS-CoV-2, poses a major obstacle to the establishment of durable herd immunity. This challenge is further compounded by immune imprinting, whereby prior antigenic exposures bias subsequent responses toward previously encountered epitopes at the expense of effective recognition of antigenically drifted variants. In this review, we delineate the mechanistic basis of immune imprinting, with emphasis on the competitive dominance of cross-reactive memory B cells (MBCs). We discuss how the rapid “back-boosting” of these pre-existing clones can limit de novo priming of naïve B cells—through epitope masking and competition for antigen and T follicular helper cell support—thereby diverting germinal center selection and affinity maturation away from variant-specific de novo epitopes and promoting viral immune escape. To address this challenge, this article further reviews the characteristics of immune imprinting responses in influenza viruses, coronaviruses, and dengue virus, as well as corresponding countermeasures, providing a theoretical basis and new avenues for intervention to address immune imprinting induced by rapidly mutating RNA viruses. Full article

Background/Objectives: Non-specific low back pain (NSLBP) is the leading cause of disability worldwide. Artificial intelligence (AI) technologies are increasingly being integrated into healthcare interventions for NSLBP, yet their effectiveness remains uncertain. This systematic review and meta-analysis aimed to evaluate the effectiveness of AI-based Physical therapy (PT) interventions on pain intensity and disability outcomes in patients with NSLBP. Methods: We conducted a comprehensive search across six electronic databases. Randomised controlled trials (RCTs) evaluating AI-based interventions for NSLBP were only included. Mean differences (MD) with 95% confidence intervals (CIs) were calculated using random-effects models. Heterogeneity was assessed using I² statistics and Cochran’s Q test. Results: Five RCTs (n = 1939) met the inclusion criteria for systematic review. Three RCTs (n = 594 participants) provided data for meta-analysis. AI-based interventions significantly reduced pain (pooled MD −0.721, 95% CI −1.047 to −0.395; z = −4.34, p < 0.001; I² = 9.5%). Disability also significantly improved (pooled MD −1.031, 95% CI −2.020 to −0.042; t(2) = −4.48, p = 0.046; I² = 0%). Neither effect reached the minimal clinically important difference (1.0 for pain, 2–4 for disability). No serious adverse events were reported. Conclusions: AI-based PT interventions produce statistically significant but clinically small improvements in pain and disability for NSLBP. Certainty of evidence is low due to risk of bias and imprecision. Larger, blinded RCTs with standardised outcomes are needed. Full article

In this work, ZnO, WO₃, and MgO were doped into InSnZnO (ITZO) films via co-sputtering to enhance the mobility and stability of ITO-based thin film transistors (TFTs). ITZO, InSnWO (ITWO) and InSnMgO (ITMO) films were fabricated, and the effect of cation dopants on the oxygen stoichiometry in ITO films was investigated. We further discussed their influence on the electrical parameters of corresponding TFTs, including threshold voltage ($V_{\mathrm{th}}$), subthreshold swing (SS), and field-effect mobility (${\mu }_{\mathrm{FE}}$). Additionally, the positive and negative bias stress stability of these devices was evaluated. The results demonstrate that ITWO TFTs exhibit superior stability despite a reduction in mobility. This is attributed to the high electronegativity of W⁶⁺ and the strong W-O bonding, which effectively mitigate the formation of oxygen vacancies and suppress the adsorption of impurities at the back channel. The findings provide valuable insights for the material design of high-performance TFTs. Full article

Background/Objectives: Chronic non-specific low back pain is a leading cause of disability. Although therapeutic exercise and manual therapy are commonly recommended, their relative effects are often interpreted using broad therapeutic categories. This network meta-analysis aimed to compare the relative effectiveness of specific therapeutic exercise and manual therapy techniques on post-treatment self-reported disability in adults with chronic non-specific low back pain. Methods: A systematic review and frequentist random-effects network meta-analysis were conducted according to Cochrane recommendations and PRISMA-NMA guidance. The protocol was registered in PROSPERO (CRD42022331411). Randomized controlled trials including adults aged 18–65 years with chronic non-specific low back pain were searched in CENTRAL, PubMed, PEDro, Google Scholar, and SciELO up to 31 March 2026. Disability was assessed using the Roland–Morris Disability Questionnaire or Oswestry Disability Index. Effects were synthesized as standardized mean differences. Risk of bias was assessed with RoB 2, and confidence in network estimates was evaluated using CINeMA. Results: Forty-five studies were included. Compared with control/placebo, the largest favorable estimates were observed for equipment-based Pilates, stabilization with motor control, stabilization exercise, soft tissue manipulation, and Pilates Mat. Equipment-based Pilates showed the largest favorable estimate with moderate-confidence evidence, and soft tissue manipulation also showed moderate-confidence evidence. However, heterogeneity was substantial, and confidence in most favorable exercise estimates was low. Conclusions: Specific exercise and manual therapy techniques may reduce post-treatment disability in adults with chronic non-specific low back pain. Equipment-based Pilates and soft tissue manipulation showed favorable signals supported by moderate-confidence evidence. However, the findings do not support a definitive hierarchy of efficacy or categorical superiority of therapeutic exercise over manual therapy. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

An experimental and numerical study of the on-resistance degradation in AlGaN/GaN-based technology is presented. Back-bias measurements on transmission-line-method (TLM) structures were performed to investigate the mechanism underlying the current degradation. The observed TLM current collapse exhibits Arrhenius behavior, which is associated with traps in the buffer layers. Interestingly, the decay time of the collapse shows a decreasing trend with increasing applied bias, which is here investigated and newly interpreted as a signature of Poole–Frenkel bias-enhanced trap emission. An effective model is discussed and implemented in TCAD simulations to support the experimental findings. In addition to providing justification for the temperature and applied-voltage dependence of the observed degradation trends, the proposed mechanism can also explain the spread in the activation energies measured for acceptor traps in the buffer layers, as reported in the literature for AlGaN/GaN technologies. Full article

Introduction: Chronic non-specific low back pain (CNLBP) is associated with altered lumbopelvic mechanics and impaired hip mobility. This study examined whether changes in pain-provoking hip flexion are associated with changes in low back pain and assessed agreement between inertial measurement units (IMUs) and a markerless motion capture system. Methods: Thirty-six patients with CNLBP completed a longitudinal repeated-measures rehabilitation protocol consisting of approximately 13 physiotherapy sessions over a period of up to 6 weeks. Active hip flexion was assessed in the symptomatic limb (the limb provoking lumbar pain). Hip flexion was recorded during the same movement trial using IMUs and a markerless system. Pain and disability were assessed using the Visual Analogue Scale and Oswestry Disability Index. Results: Improvements in hip flexion were moderately associated with pain reduction (markerless: r = −0.52; IMU: r = −0.57), with negligible associations with disability. Markerless and IMU measurements showed a strong correlation (r = 0.87), while Bland–Altman analysis showed consistent underestimation by the markerless system (bias = −3.67°). Conclusions: Symptom-specific hip mobility is associated with pain reduction in CNLBP, highlighting the role of lumbopelvic biomechanics. IMUs demonstrated higher consistency, while markerless systems offered a more accessible alternative for clinically meaningful movement assessment. Full article

Reliable prediction of the factor of safety (Fs) of circular-failure soil slopes is critical to geotechnical practice. Data-driven models developed on small slope-stability datasets are, however, prone to overfitting, data leakage, and optimistic bias, which can lead to overestimated predictive performance. This study presents a small-sample-oriented, leakage-aware support vector regression (SVR) framework with a radial basis function (RBF) kernel for Fs prediction. A database of 80 published circular-failure slope cases was compiled, and six predictors were adopted: soil unit weight, slope height, pore pressure ratio, cohesion, internal friction angle, and slope angle. To improve reliability under limited-data conditions, preprocessing, hyperparameter tuning, and performance evaluation were all embedded within a repeated nested cross-validation framework. The proposed SVR model was benchmarked against the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models under identical validation partitions and evaluation settings. The results indicated that SVR achieved the best predictive performance among the three candidate models. For case-level illustration, a single representative hold-out split was reported in addition to the repeated nested cross-validation results, on which the SVR model attained an R² of 86.56%, an RMSE of 0.07497, an MAE of 0.0666, and an MRE of 5.29%. In this test subset, all SVR predictions exhibited relative errors below 10%, indicating more stable predictive behaviour than the benchmark models. The main contribution of this study is thus a validated SVR framework for small-sample conditions. Full article