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Influences of TiO2 and SiO2 nanofillers on the hardness and surface roughness of maxillofacial silicone elastomers after two years of outdoor tropical weathering: An in vitro study
The properties of maxillofacial silicone elastomers (MFSE) are far from ideal and still require reinforcement. The incorporation of nanofillers (NFs) at high levels still requires further investigation. This study evaluated the influence of different percentages (3 and 10% w/w) of titanium dioxide (TiO 2 ) and silica (SiO 2 ) NFs on the roughness and hardness of A-2000 silicone elastomer after two years of natural weathering. A total of 80 accepted specimens were divided into two groups: weathered and non-weathered. Each group was equally subdivided into 5 subgroups: non-filled, filled with 3% SiO2, 10% SiO2, 3% TiO 2 , and 10% TiO 2 . Surface roughness was quantitatively measured using a profilometer and qualitatively using a Scanning Electron Microscope (SEM). The hardness was measured using a digital Shore-A durometer. The chemical interaction between NFs and elastomers was examined using Fourier Transform Infrared (FTIR) spectroscopy. The data were exported to SPSS, and the analysis was done using 1-way ANOVA and an independent t-test. Adding 10% SiO 2 and TiO 2 NFs significantly decreased the surface roughness of A-2000 MFSE, with no significance among all concentrations after 2-year natural weathering ( P = 1). 10% SiO 2 was associated with the highest hardness of A-2000 MFSE (32.7 ± .34; P < .001). The hot, humid natural weather conditions demonstrate a significant decrease in surface roughness and an increase in the hardness of A-2000 MFSE ( P < .001). FTIR analysis confirmed that there were no changes in the chemical interactions between the silicone matrix with and without NFs.
Dynamic-SQL: an adaptive NL2SQL framework with multi-path fusion reasoning and execution feedback correction
Abstract Current NL2SQL systems degrade sharply when confronted with practical constraints such as limited prompt length and the inability to fine-tune large language models (LLMs). Performance drop is especially pronounced in complex databases, where inaccurate schema linking, vague value conditions, and weak self-correction dominate the error surface. We propose Dynamic-SQL, an adaptive framework that couples multi-path chain-of-thought fusion with execution-based feedback correction. A dense–sparse hybrid vector space is first constructed to dynamically retrieve relevant schema elements, and an LLM is leveraged to generate an explicit schema subgraph. Real-value and few-shot exemplars are then injected to enrich the prompt and sharpen value conditioning. Multiple candidate SQL statements are produced via diverse reasoning paths; their chains of thought are fused to cover latent semantic interpretations, and execution feedback is exploited for iterative self-correction until convergence. On the BIRD benchmark, Dynamic-SQL, powered by the open source qwen2.5-coder-32b-instruct, reduces the average prompt length by 50.83% , raises strict schema-linking recall from 72.63% to 90.66% , and achieves 63.23% execution accuracy. By systematically addressing schema linking, exemplar augmentation, multi-path fusion reasoning, and self-correction, the framework offers a transferable paradigm for deploying LLMs in complex database querying scenarios.
Psychometric validation of the English version of the digital wellbeing questionnaire for young adults
Background Digital technologies increasingly impact daily life. Digital wellbeing is emerging as a multidimensional construct encompassing emotional regulation, autonomy, social connection, and respectful online interactions. Objective This study aimed to validate the English version of the 13-item Digital Wellbeing Questionnaire (DWBQ) in young adults. Methods: Data were collected via quota sampling on Prolific, an online crowdsourcing platform. The final sample comprised 1,853 young adults (US n = 933, UK n = 920), including 892 women, 871 men, and 90 non-binary participants., aged 18–25. Confirmatory factor analysis (CFA), measurement invariance testing (across gender and country), reliability analysis, and convergent validity assessments (with the Digital Flourishing Scale and the Digital Stress Scale) were conducted. Results CFA confirmed the four-factor structure (emotional resilience, agency, social connection, and communion), with good model fit (CFI = .969, TLI = .959, RMSEA = .058, SRMR = .038). The DWBQ showed strong internal reliability (α ≥ .80) and measurement invariance across both gender and country. Convergent validity was supported by positive correlations with digital flourishing and negative associations with digital stress. Sociodemographic factors and smartphone use patterns were significantly associated with DWBQ subscales, with gender and relationship status particularly influencing agency and emotional resilience Conclusions The English version of the 13-item DWBQ is a valid and reliable measure of digital wellbeing among young adults in the US and UK. It offers a concise tool for research, educational, and clinical use.
Biostimulant-based nutrient management for energy-efficient and low-carbon mustard cultivation: a life cycle assessment approach for sustainable development
Staged Lithiation/Delithiation of Silicon Anode in All-Solid-State Batteries Revealed by High-Stack-Pressure Operando NMR Spectroscopy
Unmet supportive care needs of head and neck cancer survivors: A scoping review
Aim To investigate the unmet supportive care needs, the existing tools to screen these unmet needs, and the factors associated with them among head and neck cancer survivors. Methods A systematic search was conducted across eleven databases, including Web of Science and PubMed. The search covered the period from the inception of each database up to August 20, 2025. Identified records were screened for relevance, followed by data extraction and analysis. Results A total of 4074 articles were identified, of which 12 were included. 7 assessment tools were identified. Among these, 3 offered a relatively comprehensive scope, 2 focused specifically on disease-functional needs, and 1 incorporated the disease -lifestyle needs. Head and neck cancer survivors reported unmet supportive care needs across health system/information, psychological, patient care/support, physical/daily living, sexuality, disease-specific functioning and disease-specific life. Health system/information, patient care/support, psychological needs are the top three unmet needs. Some demographic, clinical, and psychological factors are associated with these unmet needs. Conclusion Head and neck cancer survivors experience considerable unmet supportive care needs in health system/information, psychological and patient care/support. Existing assessment tools lack comprehensiveness, failing to integrate both universal and disease-specific needs. Future development of a multidimensional, integrated tool is essential. Such instruments will enable multidisciplinary teams to deliver personalized support care informed by assessment results.
A synergistic nano-brine formulation for enhanced oil recovery through interfacial engineering in carbonate reservoirs
A High-Purity Ethylene Epoxide Stream Produced Using a Supported Electrocatalyst
Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach
Gait is a key indicator for assessing an individual’s mobility and overall health, and accurate detection of gait cycle phases is essential for precise gait analysis. Most existing gait phase detection approaches rely on multiple wearable sensors, increasing system complexity and limiting real-world applicability. Moreover, rule-based methods, traditional machine learning models, and convolutional neural network (CNN)-based approaches often fail to capture complex temporal dependencies, resulting in limited robustness and real-time performance. And approaching gait phase detection as a classification method enables automatic phase recognition directly from sequential signals, simplifying the processing pipeline and supporting stable performance. In this study, we utilized an end-to-end supervised classification learning approach using a state-of-the-art Transformer model. We utilized the publicly available NONAN GaitPrint dataset, time-series gait recordings from 35 healthy young adults who walked along a 200-meter indoor level-ground track, to train deep learning models for gait phase detection. Using the three-axis acceleration and three-axis angular velocity signals from the shank, we trained and evaluated a variety of classification models, including a one-dimensional CNN, a hybrid long short-term memory and gated recurrent unit (Hybrid LSTM+GRU) model, and a Transformer. The results showed that the Transformer model achieved the F1-score of approximately 92.99%, while the CNN and Hybrid LSTM+GRU models demonstrated comparable performance, indicating no substantial differences among the models. These findings demonstrate that clinically and practically feasible gait phase detection can be achieved using shank-mounted IMUs with an end-to-end learning approach.
De-gendering and dehumanization in mental representations of autistic men’s and women’s facial appearance
Abstract Mental representations of autistic people’s character (i.e., what they are like) are often negative and even dehumanizing. Yet the potential role of gender in shaping such mental representations has rarely been considered, a pattern mirroring the dearth of autism research on autistic women. Using a reverse-correlation image-classification technique, we tested whether dehumanization is evident in visualizations of autistic (vs. neurotypical) men’s and women’s facial appearance (i.e., what they look like) generated by non-autistic university students from the United States ( N = 527). Results revealed that facial images of autistic men and women were mechanistically and animalistically dehumanized, but were not infantilized, more than facial images of neurotypical men and women (according to independent raters with no knowledge about whom the images depicted: N = 573), even for images generated by participants who explicitly disavowed such dehumanization. Visualizations of autistic men’s (women’s) faces were also ascribed fewer conventionally masculine (feminine) traits than neurotypical men’s (women’s) faces, and this de-gendering helped explain their greater dehumanization.
Disorientation patterns of loggerhead sea turtle (Caretta caretta) hatchlings in Pinellas County, Florida, USA
The disorientation of loggerhead sea turtle ( Caretta caretta ) hatchlings, primarily caused by artificial lighting, poses a significant threat to their survival, as they rely on environmental cues to reach the ocean. In this study, we analyzed data from nesting surveys and disorientation reports to identify factors contributing to disorientation events, including position on the beach and spatial location. Between 2018 and 2023, 1048 nests successfully had hatchlings emerge, with 377 (36%) of these emergences resulting in disorientation events. Nests located in the upper portion of the beach were significantly less likely to result in disoriented hatchlings compared to the middle portion. Spatiotemporal analysis identified areas of hot and cold spots of disoriented hatchlings across different beaches in Pinellas County, Florida, USA, with significant spatial variations of disorientations across years, with 2022 having peaks in spatial clustering and 2021 and 2023 having no clustering. Moonlight was found to play a mitigating role, with significantly more disorientation events occurring on nights with lower moonlight exposure. These findings underscore the need for improved lighting regulations and beach management strategies, such as enhancing natural dunes and beach profiles to reduce artificial light exposure in Pinellas County.
Children’s respiratory hospitalizations linked to short-term air pollution exposure in Peninsular Malaysia’s urban areas
Direct Polymer-on-Polymer Grafting of Polyolefins under Visible Light
Felis Catus Optimization (FCO): A novel nature‑inspired metaheuristic algorithm
This study introduces Felis Catus Optimization (FCO), a novel nature‑inspired metaheuristic algorithm modeled on the ecological and adaptive behavioral dynamics of urban domestic cats. FCO divides its population into explorer (male) and exploiter (female) agents to maintain a dynamic equilibrium between global search and local refinement. Male agents perform asynchronous triplet movements governed by adaptive exploration scaling, while female agents execute Gaussian‑based local exploitation and cooperative litter burst. A rejuvenation‑and‑noise ecological cycle replaces explicit renewal events, sustaining diversity and preventing stagnation through random reallocation and mild environmental perturbation. These mechanisms collectively achieve continuous exploration using direct position-update rules. Extensive experiments on CEC 2005 and CEC 2017 benchmarks confirmed FCO’s competitive behavior ranking among top optimizers and outperforming seven algorithms significantly under Holm’s post‑hoc procedure (p < 0.05). The critical‑difference (CD) analysis positioned FCO in the central, statistically equivalent cluster, validating its robust convergence pattern. Applications to three real‑world engineering design problems demonstrated consistent near‑optimal performance and low result variance. Overall, FCO exhibits stable convergence, reliable population renewal, and strong resilience against premature stagnation, establishing it as a scalable and dependable optimizer for continuous and constrained engineering problems.
Target-less registration of UAV-LiDAR point clouds based on graph matching of tree locations in forest environments
Synchronic Assembly of Multilevel Micelles for Construction of Efficient Catalysts
Sociodemographic and geographic determinants of childhood immunization coverage and equity in Ghana: Analysis of the 2022 demographic and health survey
Background Childhood immunization remains a cornerstone of public health in sub-Saharan Africa, yet substantial gaps in coverage, equity, and schedule adherence persist. This study examines immunization coverage, sociodemographic disparities, predictors of completion, and policy implementation effectiveness in Ghana using nationally representative data. Methods Data from 3,788 children aged 12–35 months in the 2022 Ghana Demographic and Health Survey were analysed using stratified two-stage cluster sampling across 614 enumeration areas. Weighted coverage estimates were calculated for individual vaccines and composite indicators including full immunization, dropout rates, and timeliness. Design-adjusted chi-square tests identified bivariate associations between sociodemographic factors and immunization outcomes. Multivariate logistic regression models incorporated sociodemographic characteristics, healthcare utilization, and information access to identify independent predictors. Equity analysis employed concentration indices, rate ratios, and intersectional stratification. Findings Full immunization coverage reached 69.5% (95% CI: 67.3 to 71.7%), with 25.7% partial immunization and 4.8% zero-dose children. Substantial inequalities emerged across wealth (17.4 percentage point gap), maternal education (18.3 percentage point gap), and region (35.7 percentage point gap). The concentration index of 0.045 indicated modest pro-rich inequality. Dropout rates were concerning, particularly 28.9% attrition between measles-rubella first and second doses among age-eligible children. In adjusted models, facility delivery (aOR=1.43, 95% CI: 1.08 to 1.90), four or more antenatal care visits (aOR=1.55, 95% CI: 1.19 to 2.01), higher wealth (aOR=1.39, 95% CI: 1.04 to 1.87), and maternal secondary or higher education (aOR=1.34, 95% CI: 1.04 to 1.72) independently predicted full immunization. Interpretation Ghana’s immunization program achieves broad initial access but faces critical challenges in retention, timeliness, and equity. Policy priorities should emphasize completion-focused interventions including tracking systems, reminder mechanisms, and targeted outreach to disadvantaged populations.
Colorectal cancer incidence associated with dietary patterns in the Brazilian population
Reducing frailty in frail people with multiple sclerosis: Feasibility of a 6-week multimodal exercise training program
Background Frailty is increasingly recognized as a prevalent and debilitating condition in people with multiple sclerosis (MS) and is linked to poorer health outcomes. However, targeted interventions remain limited. The objective of this study was to examine the feasibility of a multimodal exercise training (MET) program to reduce frailty in frail people with MS. Materials and methods Sixteen frail people with MS (age = 55.0 ± 7.7 years, 81.3% female, Fried frailty score ≥3) participated in this pilot randomized controlled trial. Participants were randomly assigned to a 6-week MET program consisting of virtual reality treadmill training + resistance training (n = 8) or to a waitlist control group (n = 8). Feasibility outcomes included recruitment, retention, and adherence rates as well as safety and user engagement throughout the study (Study Participant Feedback Questionnaire—SPFQ). Exploratory outcomes were collected at baseline and 6 weeks and included the Evaluative Frailty Index for Physical Activity (EFIP), the 54-item MS Quality of Life questionnaire (MSQoL-54), the Modified Fatigue Impact Scale (MFIS), and the Physiological Profile Assessment (PPA). Results Fourteen participants, eight in the intervention group and six in the control group, completed the study. The recruitment rate was 0.33 participants/week, retention was 87.5%, and adherence was high, with participants completing 97.2% of planned training sessions. No adverse events or training-related pain were recorded. The data collection procedures were successfully implemented with complete outcome data. Participants agreed or strongly agreed with 95.7% of applicable SPFQ items, indicating high levels of engagement and satisfaction with the trial. Between-group differences in baseline to 6-week change were: EFIP −0.07 (95% CI: −0.14, −0.00); MSQoL-54 mental health +21.24 (95% CI: 7.32, 35.16); MSQoL-54 physical health +19.26 (95% CI: 5.61, 32.91); MFIS −11.46 (95% CI: −18.34, −5.13); and PPA −0.09 (95% CI: −1.19, 1.01). Conclusion The MET program was safe, feasible, and well-received by frail people with MS. These findings support the viability of MET for future larger-scale trials targeting frailty reduction in this population. Trial registration ClinicalTrials.gov NCT06042244