Search Results (50)

In the post-pandemic higher education context, multilingual students, particularly those from widening participation backgrounds, continue to face academic, linguistic, and socio-emotional challenges that can limit their participation and sense of belonging. This study examines the use of Reflective Video Diaries (RVDs) facilitated through Microsoft Flipgrid as an inclusive pedagogical approach to support reflective engagement, communication, and socio-emotional development among multilingual undergraduate students. Adopting a qualitative iterative action research approach, the study was conducted within a UK university module and involved three cycles of implementation, reflection, and pedagogical refinement, capturing students’ lived experiences rather than measuring causal effects. Multiple methods, including RVDs, end-of-module reflective reports, an anonymous survey, and lecturers’ field notes, were deliberately combined to provide complementary perspectives on students’ experiences, allowing triangulation of data and enhancing the validity and richness of findings. Thematic analysis of this longitudinal dataset collected across the three action-research cycles explored how students experienced RVDs as a space for reflection, peer support, and engagement with learning. Findings indicate that Flipgrid-mediated RVDs functioned as a low-anxiety, flexible, and dialogic learning environment that enabled students to articulate challenges, share progress, and develop reflective awareness, confidence, and a sense of connection with peers and lecturers. Improvements in participation and reflective depth were more evident in later cycles, suggesting that benefits emerged through iterative pedagogical adjustment rather than by video technology alone. Both positive experiences and challenges are reported, providing a balanced account of engagement with the RVDs. The study underscores the potential of inclusive digital pedagogies to inform curriculum planning and policy implementation, supporting equitable learning opportunities and socio-emotional development. By conceptualizing RVDs as relational and inclusive pedagogical practices rather than technological interventions, and by demonstrating how reflective engagement developed across successive action-research cycles, this research contributes to understanding how reflective digital practices can support multilingual learners’ academic and socio-emotional development within socially just higher education contexts. Practical implications for designing inclusive reflective learning environments are discussed. Full article

Sidewalk high-definition (HD) maps require centimetre-level representation of pedestrian barriers to support mobility assistance and barrier-free infrastructure management. This study evaluates six mobile light detection and ranging (LiDAR) platforms for sidewalk HD mapping: terrestrial laser scanning (TLS), a push-cart mobile mapping system (MMS), two backpack systems (GNSS/INS (Global Navigation Satellite System/Inertial Navigation System)-aided and SLAM (simultaneous localization and mapping)-based), and two handheld systems (GNSS/INS-aided and SLAM-based). Surveys were conducted at two sites with contrasting occlusion and GNSS conditions (park and dense downtown corridors). Point clouds were transformed to a common control network, with independent checkpoints for absolute accuracy. The reference dataset achieved a planimetric root mean square error (RMSE) of 0.017–0.049 m and vertical RMSE of 0.009–0.014 m across sites. Platforms were compared for positional accuracy, point density, and extractability of key accessibility attributes (effective width, step height, and longitudinal slope). Cart-mounted MMS provided stable geometry under occlusion, while SLAM-based handheld mapping improved robustness in GNSS-degraded areas; backpack SLAM performance depended on loop-closure opportunities and scene dynamics. We provide guidance on selecting pedestrian-scale LiDAR platforms for sidewalk HD mapping under different survey conditions. Full article

With the growing accessibility of tools such as online surveys and web scraping, longitudinal social network data are more commonly collected in social science research along with non-network survey data. Such data play a critical role in helping social scientists understand how relationships develop and evolve over time. Existing dynamic network models such as the Stochastic Actor-Oriented Model and the Temporal Exponential Random Graph Model provide frameworks to analyze traits of both the networks and the external non-network covariates. However, research on the dynamic latent space model (DLSM) has focused mainly on factors intrinsic to the networks themselves. Despite some discussion, the role of non-network data such as contextual or behavioral covariates remain a topic to be further explored in the context of DLSMs. In this study, one application of the DLSM to incorporate dynamic non-network covariates collected alongside friendship networks using autoregressive processes is presented. By analyzing two friendship network datasets with different time points and psychological covariates, it is shown how external factors can contribute to a deeper understanding of social interaction dynamics over time. Full article

For a long time, low efficiency in the transfer of rural collective land use rights and the ambiguous attribution of collective land property rights have not only restricted the mobility of rural labor factors but have also hindered the release of vitality in the rural collective economy. This has resulted in lagging growth in the income that rural residents obtain from collective economic factors, contributing to the persistent widening of the urban/rural income gap. As an important institutional innovation to address these issues, the effects of the reform of the rural collective property rights system urgently need to be clarified. The reform of the rural collective property rights system constitutes a major initiative in the transformation of the rural land system. Centered on asset verification and valuation, as well as the demarcation of membership rights and the restructuring towards a shareholding cooperative system, it aims to establish a collective property rights regime characterized by clearly defined ownership and fully functional entitlements. This study takes the national pilot reform of rural collective property rights launched in 2016 as a quasi-natural policy experiment, systematically examining the impact of this pilot policy on the internal income gap within households and its spillover effects on the urban–rural income gap. Based on microdata from the China Household Finance Survey (CHFS) and the China Longitudinal Night Light Data Set (PANDA-China), this study constructs a five-period balanced panel dataset covering 2304 rural households across 25 provinces. A relative exploitation index based on the Kawani index is constructed, and empirical analysis is conducted using a combination of multi-period difference-in-differences (Multi-period DID), discrete binary models, and propensity score matching-difference-in-differences (PSM-DID) models. The results show that: First, the pilot reform significantly reduced the level of income inequality within rural areas in the pilot regions, and its policy benefits further generated positive spillovers via market-driven factor allocation mechanisms, effectively bridging the urban–rural income gap. Second, institutional reforms activated the potential of rural non-agricultural economic factors, establishing new channels for a two-way flow of urban and rural factors, becoming an important path to achieve the goal of common prosperity. Third, the policy effects exhibited significant heterogeneity, specifically manifested in the attributes of major grain-producing regions, initial household income levels, and the human capital characteristics of household heads having significant moderating effects on reform outcomes. This study not only provides theoretical support and empirical evidence for deepening rural property rights reforms under the new rural revitalization strategy, but it also reveals the driving role of institutional innovation in factor mobility, thereby influencing the transmission mechanism of income distribution patterns. This finding offers a China-based solution for developing countries to address the imbalance in urban–rural development and the widening income gap. Full article

Wetlands have been recognized as nature-based solutions to the climate crisis. This study evaluates the state of standardization in nationwide inland wetland survey datasets and analyzes terrestrial vertebrate patterns by integrating datasets with public environmental data. Species richness data for amphibians/reptiles (432 wetlands), birds (1183 wetlands), and mammals (72 wetlands) were compiled from 134 reports published between 2000 and 2021. Using generalized linear models (GLMs) and generalized additive models (GAMs), we assessed how 15 explanatory variables (climate, topography, wetland information, land use, and water quality) relate to species richness. Model families were chosen for each taxonomic group, and variables were selected using the Akaike information criterion (AIC) and ecological plausibility. Deviance explained was 55.5% for amphibians/reptiles, 60.1% for birds, and 52.4% for mammals. Wetland area and Normalized Difference Vegetation Index (NDVI) were positively associated with species richness across all groups. Despite the large volume of survey data, inconsistent reporting formats and limited metadata constrain longitudinal and time series analyses. Standardized protocols and metadata management are therefore needed to build a systematic national database that can support wetland ecological modeling and conservation policy. Full article

Objectives: This study aimed to develop and validate an explainable machine learning (ML) model to predict the risk of sarcopenia in older European adults with arthritis, providing a practical tool for early and precise screening in clinical settings. Methods: We analyzed data from the English Longitudinal Study of Aging (ELSA) and the Survey of Health, Aging and Retirement in Europe (SHARE). The final analysis included 1959 participants aged ≥65 years. The ELSA dataset was divided into a training set (n = 1371) and an internal validation set (n = 588), while the SHARE dataset (n = 1001) served as an independent external test cohort. From an initial pool of 33 variables, nine core predictors were identified using ensemble feature selection techniques. Six ML algorithms were compared, with model performance evaluated using the Area Under the Curve (AUC) and calibration analysis. Model interpretability was enhanced via SHapley Additive exPlanations (SHAP). Results: The Decision Tree model demonstrated the optimal balance between performance and interpretability. It achieved an AUC of 0.921 (95% CI: 0.848–0.988) in the internal validation set and maintained robust generalizability in the external SHARE cohort with an AUC of 0.958 (95% CI: 0.931–0.985). The nine key predictors identified were stroke history, BMI, HDL, loneliness, walking speed, disease duration, age, recall summary score, and total cholesterol. SHAP analysis visualized the specific contribution of these features to individual risk. Conclusions: This study successfully developed a high-performance, explainable, lightweight ML model for sarcopenia risk prediction. By inputting only nine readily available clinical indicators via an online tool, individualized risk assessment can be generated. This facilitates early identification and risk stratification of sarcopenia in older European arthritis patients, thereby providing valuable decision support for implementing precision interventions. Full article

Using deep learning in automated pavement distress detection has shown huge improvements for transport infrastructure, but a noticeable challenge remains in distinguishing sealed cracks from active ones, which are more evident in high-resolution aerial imagery of airport pavements. Misclassifying sealed cracks, an indicator of maintenance intervention, as structural distress leads to false positives that cause overestimation in distress metrics and, ultimately, inaccurate Pavement Condition Index (PCI) scores. This study tries to address this limitation by investigating whether explicitly labeling sealed cracks as a separate class during training can improve model performance. In this regard, aerial orthophotos of taxiways from one selected airport, as a case study, were collected via Unmanned aerial vehicle (UAV) surveys, and three instance segmentation models based on YOLOv11 (version 11 from You Only Look Once family) were trained on different datasets: one excluding sealed cracks (including only longitudinal and transvers cracks), one including sealed cracks without explicit labeling, and one treating sealed cracks as a separate class. Validation against ground-truth field surveys revealed that the model trained with explicit sealed crack annotations achieved significantly lower error rates, with a 56.7% reduction for longitudinal cracks and a 75.2% reduction for transverse cracks with respect to traditional detection methods. This improvement led to fewer false positives and a more reliable quantification of both longitudinal and transverse cracking. The results demonstrate that tailored annotation strategies, which in this study means distinguishing sealed cracks, substantially improve the accuracy of deep learning models for real-world pavement condition assessment. Full article

Archaeological excavations today generate extensive datasets across survey, excavation, and analysis activities, especially when they are conducted in collaborative structures such as field schools. Working across such activities, data archivists contribute to the goals and research outcomes of the dig by establishing data practices that are participatory and educational (two pillars of data literacy) as they permanently record information about the archaeological results. At the Venus Pompeiana Project (VPP), a collaborative archaeological investigation of the Sanctuary of Venus in Pompeii, both provenance and provenience data are recorded into a database at the trenches’ edge, which optimises the accuracy of the data by allowing direct input and review by the data creators and archaeological site experts. When legacy data about work conducted decades or even centuries earlier are brought into the data picture, scholars stand to gain a deeper understanding of the geographic locations of key interest over time. Yet, the integration of analogue legacy and digital archival datasets is collaborative and longitudinal work. In this paper, we bring together experiential reflections on data archiving conducted at both the excavation site and in the physical archives of the Pompeii Archaeological Park. We then provide an integrative analysis of the outcomes of such data curation, highlighting what each data archiving contributor “discovered” about the site as a whole or a specific artefact, feature, or data category. Our findings contribute deeper insights into what data archiving and format-specific curation activities are most effective for learning experiences, archaeological scholarship, and professional practices. Full article

Objectives: This study aimed to examine the association between weekend catch-up sleep (CUS) and obesity among Korean workers. Methods: Data were derived from the 2016–2023 Korean National Health and Nutrition Examination Survey (KNHANES), a nationally representative dataset. The final analytic sample comprised 17,208 Korean workers aged 26 to 64 years. General and abdominal obesity were defined as body mass index (BMI) ≥ 25 kg/m² and waist circumference ≥ 90 cm for men and ≥85 cm for women, respectively. Sleep patterns were categorized into sufficient sleep, weekend CUS, and insufficient sleep. Multivariable logistic regression analyses were performed to evaluate associations between sleep patterns and obesity, adjusting for demographic, socioeconomic, and health-related variables. Results: Compared to individuals with sufficient sleep, those with weekend CUS showed increased odds of general obesity (adjusted odds ratio [AOR] = 1.21) and abdominal obesity (AOR = 1.18). The insufficient sleep group had even higher odds for both general obesity (AOR = 1.23) and abdominal obesity (AOR = 1.33). Conclusions: Insufficient sleep is significantly associated with increased risks of both general and abdominal obesity among Korean workers. While weekend CUS may offer partial mitigation of obesity risk, it should not be considered a substitute for regular, adequate sleep. Longitudinal studies are warranted to further explore causal relationships between sleep patterns and obesity in working populations. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

(1) Background: Cardiovascular disease (CVD) and edentulism are major public health challenges with shared risk factors and overlapping inflammatory pathways. This study investigates the association between complete tooth loss and CVD. (2) Methods: Data were analyzed from the 2015–2018 National Health and Nutrition Examination Survey (NHANES) dataset using Poisson regression analysis to examine the relationship between CVD and complete edentulism, adjusting for age, sex, education, family income-to-poverty ratio, race/ethnicity, diabetes status, and BMI. Of the 11,287 participants, 1763 individuals (15.62%) were completely edentulous, and 9524 (84.38%) retained some or all of their dentition. (3) Results: Individuals with cardiovascular conditions, including myocardial infarction (PR = 1.55; 95% CI: 1.23–1.98), coronary heart disease (PR = 1.44; 95% CI: 1.13–1.85), congestive heart failure (PR = 1.58; 95% CI: 1.22–2.07), and stroke (PR = 1.46; 95% CI: 1.13–1.90), demonstrated a higher prevalence of complete edentulism compared to those without these conditions, after adjusting for key demographic and health-related confounders (p < 0.01). (4) Conclusions: These findings suggest a statistical association between CVD and complete edentulism in U.S. adults. However, due to the cross-sectional nature of this study, causal relationships cannot be inferred, and further longitudinal studies are needed to understand the bidirectional mechanisms between CVD and complete edentulism. Full article

Background/Objectives: Traditional models like ordinary least squares (OLS) struggle to capture non-linear relationships in children’s subjective well-being (SWB), which is associated with physical activity. This study evaluated machine learning (ML) for predicting SWB, focusing on sports participation, and explored theoretical prediction limits using a global dataset. It addresses a gap in understanding complex patterns across diverse cultural contexts. Methods: We analyzed 128,184 records from the ISCWeB survey (ages 6–14, 35 countries), with self-reported data on sports frequency, emotional states, and family support. To ensure cross-country generalizability, we used GroupKFold CV (grouped by country) and leave-one-country-out (LOCO) validation, yielding mean R² = 0.45 ± 0.05, confirming robustness beyond cultural patterns, SHAP for interpretability, and bootstrapping for error estimation. No pre-registration was required for this secondary analysis. Results: XGBoost and LightGBM outperformed OLS, achieving R² up to 0.504 in restricted datasets (sensitivity excluding affective leakage: R² = 0.35), with sports-related variables (e.g., exercise frequency) associated positively with SWB predictions (SHAP values: +0.15–0.25; incremental ΔR² = 0.06 over demographics/family/school base). Using test–retest reliability from literature (r = 0.74), the estimated irreducible RMSE reached 0.941; XGBoost achieved RMSE = 1.323, approaching the predictability bound with 68.1% of explainable variance captured (after noise adjustment). Partial dependence plots showed linear associations with exercise without satiation and slight age decline. Conclusions: ML improves SWB prediction in children, highlighting associations with sports participation, and approaches predictable variance bounds. These findings suggest potential for data-driven tools to identify patterns, such as through physical literacy pathways, informing physical activity interventions. However, longitudinal studies are needed to explore causality and address cultural biases in self-reports. Full article

This study uses a nationally representative longitudinal dataset in the US to examine the long-term association of volunteering for environmental, recycling, and conservation groups with a person’s (a) willingness to continue to volunteer later in life and (b) several measures of their mental and physical well-being including perceived social status, optimism, psychological stress, suicidal thoughts and attempts, depressive symptoms and general self-reported physical health. By using Add Health data, we match responses to an environmental volunteerism question in Wave III (2002) with subjective well-being responses in Wave V (2016–2018) to examine the long-term association between these variables. After excluding missing responses, the analysis sample consists of 9800 individuals. After using linear survey regression analyses and several techniques based on propensity scores (stratification, weighting, matching) two key results emerged: first, being involved in environmental groups and organizations early in life showed a significant positive association with more hours spent on volunteering or community service work later in life; and second, people who volunteer in early adulthood are more optimistic, more sociable, have a higher perceived social status, display less stress and depressive symptoms. Full article

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of student loans has not been thoroughly investigated. This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. Primary data were collected from 1024 university students through structured surveys covering academic, financial, and personal variables. The models were trained and tested on the same dataset and evaluated using a comprehensive set of classification and regression metrics. The findings reveal that each model exhibits distinct strengths. Deep Learning achieved the highest classification accuracy (85.55%), while random forest demonstrated robust performance, particularly in providing balanced results across classification metrics. Gradient Boosting was effective in recall-oriented tasks, and support vector machine demonstrated strong precision for the positive class, although its recall was lower compared to other models. The study highlights the importance of aligning model selection with specific application goals, such as prioritizing accuracy, recall, or interpretability. It offers practical implications for financial institutions and universities in developing machine learning and deep learning tools for student loan eligibility prediction. Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance. Full article

Background/Objectives: Depressive disorders are diagnosed using categorical definitions provided by DSM-5 and ICD-11. However, categorization for diagnostic purposes fails to account for the inherently dimensional nature of depression. Artificial categorization may impede research and obstruct the achievement of optimal treatment outcomes. Methods: The current study utilized a Canadian historical dataset called the National Population Health Survey (NPHS) to explore a simple alternative approach that does not depend on categorization. The NPHS collected complete data from 5029 participants through biannual interviews conducted in 1994–2010. Data collection included the K6 Distress Scale as well as the Composite International Diagnostic Interview Short Form for Major Depression. Data from the National Population Health Survey (NPHS) were used to quantify vulnerability to depressive symptoms through longitudinal K6 Distress Scale assessments. Variability of symptoms across this dimension of apparent vulnerability was quantified using ordinal regression, adjusting for age and sex. Results: Predicted probabilities from these models were used in simulations to produce a visualization of the epidemiology and to explore clinical implications. Conclusions: Consideration of these two dimensional factors (estimated overall level of vulnerability to depression and variability over time) is already a component of clinical assessment and is also accessible to repeated measurement in settings adopting measurement-based care. More formal consideration of these elements may provide a complementary approach to categorical diagnostic assessment and an opportunity for greater personalization of care and improved clinical outcomes. Future studies should validate these findings in diverse clinical settings to ensure their applicability in real-world contexts. Full article