Search Results (7,504)

Graduate students often experience difficulties in learning statistics, particularly those who have limited mathematical backgrounds. In recent years, Learning Management Systems (LMS) and Problem-Based Learning (PBL) have been widely adopted to support instruction, yet little research has explored how these tools relate to learning outcomes using mixed methods design. Limited studies have employed machine learning methods such as clustering analysis in Learning Analytics (LA) to explore different behavior of clusters based on students log data. This study followed an explanatory sequential mixed methods design to examine student engagement patterns on Canvas and learning outcomes of students in a graduate-level statistics course. LMS log data and surveys were collected from 31 students, followed by interviews with 19 participants. K-means clustering revealed two groups: a high-performing group with lower LMS engagement and a low-performing group with higher LMS engagement. Six themes emerged from a thematic analysis of interview transcripts: behavioral differences in engagement, the role of assessment, emotional struggle, self-efficacy, knowledge or skill gain, and structured instructional support. Results indicated that low-performing students engaged more frequently and benefited from structured guidance and repeated exposure. High-performing students showed more proactive and consistent engagement habits. These findings highlight the importance of intentional course design that combines PBL with LMS features to support diverse learners. Full article

Background: Musculoskeletal (MSK) pain is a frequent but under-addressed concern during pregnancy. In Bangladesh, challenges such as limited antenatal care (ANC) access and heavy maternal workloads make this issue particularly urgent for maternal health. This study aimed to determine the prevalence and predictors of MSK pain among pregnant women attending government ANC clinics in Bangladesh. Methods: A facility-based cross-sectional study was conducted among 300 pregnant women recruited from two government hospitals in Dhaka Division. Data were collected using structured interviewer-administered questionnaires covering patient characteristics, pain-related characteristics, and pregnancy-related characteristics. Pain was measured using the Numeric Pain Rating Scale (NPRS; mild <4, moderate 4–7, severe >7), and body mass index (BMI) was calculated based on self-reported height and weight. Descriptive statistics, chi-square tests, and multivariable logistic regression were employed to identify factors independently associated with MSK pain. Results: Overall, 67% of women reported MSK pain, most frequently in the lower back and lower abdomen. Women in later trimesters had about twice the odds of experiencing pain, while those with obesity had nearly six times higher odds compared to women with normal body mass index (BMI). Conclusions: MSK pain is common among pregnant women in Bangladesh and shows associations with later gestational stages and obesity. These findings suggest that integrating routine screening and non-pharmacological management into ANC may help support maternal health and reduce preventable complications in resource-limited settings. Full article

The Community and Civil Research Group of Eötvös Loránd University (Budapest) investigated sustainable community activities in Hungary and abroad to identify local responses to global challenges. Using qualitative research methods, focus groups and interviews, this research defined the concepts of community service, community practice and working methods by analysing nearly 80 practical examples and 65 interviews in Hungary. The practical examples were used to create a “sustainable community model” and a methodological guide for community developers on how to implement community services. The steps of the process presented in the model are based on building community involvement and participation, mobilising local resources and capacities, creating community-based services, building sustainability and self-sufficiency and consolidating innovative training and community working practices. The research resulted in the creation of an online Community Repository, which provides community responses to the 17 UN Global Sustainability Challenges and Goals —economic growth, social inclusion and environmental protection—by organising the collected community services, small community practices and working methods around seven community development perspectives: governance, place, sustainable livelihoods, culture (and the arts), identity (belonging and connection), human rights and resilience and engagement and knowledge. This study provides a methodological foundation for developing resilient community-based services that contribute to sustainability, inclusivity and innovation. Full article

Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R² of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms. Full article

Objective: This study explores the associations between parent–child exercise and children’s mental health from the perspective of family physical education. Methods: In total, 527 valid questionnaires were collected from students in grades four to six of three primary schools in Yuhang District, Hangzhou City, including a survey of the status of children’s exercise and family sports and the SCL-90 symptom self-measurement scale. Based on an analysis of practical challenges in family sports engagement and children’s mental health status, the data were analyzed and modeled using structural equation modeling to obtain a model of children’s mental health promotion, with individual children’s exercise as the primary factor and parent–child exercise as the mediator. Results: Both individual children’s exercise and parent–child exercise were significant predictors of children’s mental health promotion. Parent–child activities show a more significant negative correlation with symptoms of anxiety and depression than individual exercise alone. They also partially mediated the relationship between individual exercise and depression/anxiety symptoms. The indirect effects had confidence intervals of [−0.008, −0.001] for depression and [−0.007, −0.001] for anxiety. The direct effects of individual exercise on mental health (depression: β = −0.115; anxiety: β = −0.127) were stronger than the indirect effects and significantly positively correlated with parent–child exercise (β = 0.444, p < 0.05), suggesting that individual exercise may encourage more parent–child exercise. Conclusions: We propose a relational pathways model incorporating parent–child exercise as a mediating variable and individual exercise as the primary activity. This model is more closely aligned with real-life conditions and practical feasibility than approaches lacking such a family-based component. Full article

Regenerative medicine holds significant promise for addressing diseases and irreversible damage that are challenging to treat with conventional methods, making it a prominent research focus in modern medicine. Research on stem cells, a key area within regenerative medicine due to their self-renewal capabilities, is expanding, positioning them as a novel therapeutic option. Stem cells, utilized in various treatments, are categorized based on their differentiation potential and the source tissue. The term ‘stem cell’ encompasses a broad spectrum of cells, which can be derived from embryonic tissues, adult tissues, or generated by reprogramming differentiated cells. These cells, applied across numerous medical disciplines including cardiovascular, neurological, and hematological disorders, as well as wound healing, demonstrate varying therapeutic applications based on their differentiation capacities, each presenting unique advantages and limitations. Nevertheless, the existing literature lacks a comprehensive synthesis examining stem cell therapy and its cellular subtypes across different medical specialties. This review addresses this lacuna by collectively categorizing contemporary stem cell research according to medical specialty and stem cell classification, offering an exhaustive analysis of their respective benefits and constraints, thereby elucidating multifaceted perspectives on the clinical implementation of this therapeutic modality. Full article

This umbrella review provides a comprehensive synthesis of the available evidence on the relationship between digital device use and adolescent sleep. It summarizes results from systematic reviews and meta-analyses, presenting the magnitude and direction of observed associations. A total of seven systematic reviews, including five qualitative reviews and two meta-analyses, were included, comprising 127 primary studies with a combined sample of 867,003 participants. The findings suggest a negative impact of digital device use on various sleep parameters, including sleep duration, bedtime procrastination, and sleep quality. Devices such as smartphones and computers were found to have a greater adverse effect, while television use showed a weaker association. The most significant disruptions were observed in relation to social media and internet use, with problematic usage leading to delayed bedtimes, shorter sleep duration, and increased sleep onset latency. The review also highlights the role of timing and duration of device use, with late-night use particularly contributing to sleep disturbances. Biological, psychological, and social mechanisms are proposed as potential pathways underlying these effects. Despite moderate evidence supporting the negative impact of digital media on sleep, there is considerable heterogeneity across studies, and many relied on self-reported data, which may limit the generalizability of the findings. Future research should aim to standardize exposure and outcome measures, incorporate objective data collection methods, and explore causal relationships through longitudinal studies. This umbrella review underscores the importance of developing targeted public health strategies, parental guidance, and clinical awareness to mitigate the potential adverse effects of digital device use on adolescent sleep and mental health. Full article

Pulmonary fibrosis (PF) is a progressive and fatal lung disease characterized by irreversible alveolar destruction and pathological extracellular matrix (ECM) deposition. Currently approved agents (pirfenidone and nintedanib) slow functional decline but do not reverse established fibrosis or restore functional alveoli. Multifunctional bioscaffolds present a promising therapeutic strategy through targeted modulation of critical cellular processes, including proliferation, migration, and differentiation. This review synthesizes recent advances in scaffold-based interventions for PF, with a focus on their dual mechano-epigenetic regulatory functions. We delineate how scaffold properties (elastic modulus, stiffness gradients, dynamic mechanical cues) direct cell fate decisions via mechanotransduction pathways, exemplified by focal adhesion–cytoskeleton coupling. Critically, we highlight how pathological mechanical inputs establish and perpetuate self-reinforcing epigenetic barriers to regeneration through aberrant chromatin states. Furthermore, we examine scaffolds as platforms for precision epigenetic drug delivery, particularly controlled release of inhibitors targeting DNA methyltransferases (DNMTi) and histone deacetylases (HDACi) to disrupt this mechano-reinforced barrier. Evidence from PF murine models and ex vivo lung slice cultures demonstrate scaffold-mediated remodeling of the fibrotic niche, with key studies reporting substantial reductions in collagen deposition and significant increases in alveolar epithelial cell markers following intervention. These quantitative outcomes highlight enhanced alveolar epithelial plasticity and upregulating antifibrotic gene networks. Emerging integration of stimuli-responsive biomaterials, CRISPR/dCas9-based epigenetic editors, and AI-driven design to enhance scaffold functionality is discussed. Collectively, multifunctional bioscaffolds hold significant potential for clinical translation by uniquely co-targeting mechanotransduction and epigenetic reprogramming. Future work will need to resolve persistent challenges, including the erasure of pathological mechanical memory and precise spatiotemporal control of epigenetic modifiers in vivo, to unlock their full therapeutic potential. Full article

Background: Nurse-manager competencies shape workforce stability, yet role-based perception gaps between managers and staff may influence staff nurses’ turnover cognitions. Objectives: To (1) compare nurse managers’ self-ratings with staff nurses’ ratings of the same managers on the Nurse Manager Competency Inventory (NMCI); (2) compare both groups’ perceptions of staff nurses’ turnover intention (EMTIS); (3) examine domain-specific links between perceived competencies and perceived turnover intention; and (4) explore demographic influences (age, education, experience) on these perceptions. Methods: Cross-sectional dual-rater study with 225 staff nurses and 171 nurse managers in two tertiary hospitals in Saudi Arabia. Data were collected from August to November 2024. Managers completed NMCI self-ratings, and staff nurses rated their managers on the same NMCI domains; both groups rated staff nurses’ turnover intention using EMTIS. Between-group differences were tested with one-way ANOVA (two-tailed α = 0.05), and associations were examined with Pearson’s r (95% CIs). Findings: Managers consistently rated themselves higher than staff rated them across all nine NMCI domains; the largest descriptive gaps were in Promoting Staff Retention, Recruit Staff, Perform Supervisory Responsibilities, and Facilitate Staff Development (e.g., overall NMCI: managers M = 3.67, SD = 0.61 vs. staff M = 3.04, SD = 0.74; F = 0.114, p = 0.73)with comparatively smaller divergence for Ensure Patient Safety and Quality. Managers and staff did not differ significantly on EMTIS (overall EMTIS: managers M = 3.16, SD = 1.28 vs. staff M = 3.00, SD = 1.15; F = 21.32, p = 0.173). Specific competency domains—retention, supervision, staff development, safety/quality leadership, and quality improvement—showed small inverse correlations with EMTIS facets (typical r ≈ −0.11 to −0.19; p < 0.05), whereas the global NMCI–overall EMTIS correlation was non-significant (r = −0.077, p = 0.124). Effect sizes were modest and should be interpreted cautiously. Conclusions: Actionable signals reside at the domain (micro-competency) level rather than in global leadership composites. Targeted, continuous, unit-embedded development in human- and development-focused competencies—tracked with dual-lens (manager–staff) measurement and linked to retention KPIs—may help nudge turnover cognitions downward. Key limitations include the cross-sectional, perception-based design and two-site setting. Findings nonetheless align with international workforce challenges and may be transferable to similar hospital contexts. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

Postoperative adhesion remains a major clinical challenge, often leading to chronic pain, functional disorders, and recurrent surgeries. Herein, we developed a multifunctional gelatin–polyphenol hydrogel (GPP20) featuring rapid gelation (within 5 min), strong tissue adhesion (lasting > 24 h under physiological conditions), and intrinsic wound healing capacity to achieve integrated prevention of postoperative adhesion. GPP20 was fabricated via dynamic crosslinking between gelatin and tea polyphenol, endowing it with injectability, self-healing, biodegradability, and excellent mechanical properties (shear stress of 14.2 N). In vitro studies demonstrated that GPP20 exhibited effective ROS scavenging (82% ABTS scavenging capability), which protects cells against oxidative stress, while possessing excellent hemocompatibility and in vivo safety. Notably, GPP20 significantly reduced postoperative cecum–abdominal wall adhesions through both physical barrier effects and modulation of inflammation and collagen deposition, demonstrating a comprehensive integrated prevention strategy. Furthermore, in full-thickness wound models, GPP20 accelerated tissue regeneration (85% wound closure rate on day 10) by promoting macrophage polarization toward the M2 phenotype and stimulating angiogenesis, thereby enhancing collagen deposition and re-epithelialization. Collectively, these findings demonstrate that GPP20 integrates anti-adhesion efficacy with regenerative support, offering a facile and clinically translatable strategy for postoperative care and wound healing. Full article

β-Hydroxy-β-methylbutyrate (HMB), a natural metabolite derived from the essential amino acid leucine, is primarily recognised for its anabolic and anti-catabolic effects on skeletal muscle tissue. Recent studies indicate that HMB may also play a role in influencing the structural organisation of extracellular matrix (ECM) components, particularly collagen, which is crucial for maintaining the mechanical integrity of connective tissues. In this investigation, bovine type I collagen was polymerised in the presence of two concentrations of HMB (0.025 M and 0.25 M) to explore its potential function as a molecular modulator of fibrillogenesis. The morphology of the resulting collagen fibres and their molecular architecture were examined using atomic force microscopy (AFM) and Fourier-transform infrared (FTIR) spectroscopy. The findings demonstrated that lower levels of HMB facilitated the formation of more regular and well-organised fibrillar structures, exhibiting increased D-band periodicity and enhanced stabilisation of the native collagen triple helix, as indicated by Amide I and III band profiles. Conversely, higher concentrations of HMB led to significant disruption of fibril morphology and alterations in secondary structure, suggesting that HMB interferes with the self-assembly of collagen monomers. These structural changes are consistent with a non-covalent influence on interchain interactions and fibril organisation, to which hydrogen bonding and short-range electrostatics may contribute. Collectively, the results highlight the potential of HMB as a small-molecule regulator for soft-tissue matrix engineering, extending its consideration beyond metabolic supplementation towards controllable, materials-oriented modulation of ECM structure. Full article

A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely on subjective human labels. To improve the early detection of asthma worsening in children in homecare setting, we trained and evaluated a Deep Learning model based on spirometry-labelled lung sounds recordings to detect asthma exacerbation. A single-center prospective observational study was conducted between November 2020 and September 2022 at a tertiary pediatric pulmonology department. Electronic stethoscopes were used to record lung sounds before and after bronchodilator administration in outpatients. In the same session, children also underwent spirometry, which served as the reference standard for labelling the lung sound data. Model performance was assessed on an internal validation set using receiver operating characteristic (ROC) curves. A total of 16.8 h of lung sound recordings from 151 asthmatic pediatric outpatients were collected. The model showed promising discrimination performance, achieving an AUROC of 0.763 in the training set, but performance in the validation set was limited (AUROC = 0.398). This negative result demonstrates that acoustic features alone may not provide sufficient diagnostic information for the early detection of asthma attacks, especially in mostly asymptomatic outpatients typical of homecare settings. It also underlines the challenges introduced by differences in how digital stethoscopes process sounds and highlights the need to define the severity threshold at which acoustic monitoring becomes informative, and clinically relevant for home management. Full article

We present a theoretical model to explain how tubulin dimers in neuronal microtubules might achieve collective quantum coherence, resulting in wavefunction collapses that manifest as avalanches within a self-organized criticality (SOC) framework. Using the Orchestrated Objective Reduction (Orch-OR) theory as inspiration, we propose that microtubule subunits (tubulins) become transiently entangled via dipole–dipole couplings, forming coherent domains susceptible to sudden self-collapse. We model a network of tubulin-like nodes with scale-free (Barabási–Albert) connectivity, each evolving via local coupling and stochastic noise. Near criticality, the system exhibits power-law avalanches—abrupt collective state changes that we identify with instantaneous quantum wavefunction collapse events. Using the Diósi–Penrose gravitational self-energy formula, we estimate objective reduction times $T_{\mathrm{OR}}=\hslash /E_g$ for these events in the 10–200 ms range, consistent with the Orch-OR conscious moment timescale. Our results demonstrate that quantum coherence at the tubulin level can be amplified by scale-free critical dynamics, providing a possible bridge between sub-neuronal quantum processes and large-scale neural activity. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article