Search Results (468)

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

Background: The longitudinal RisCoin study investigated risk factors for COVID-19 vaccination failure among healthcare workers (HCWs) and patients with inflammatory bowel disease (IBD) at a University Hospital in Germany. Since the hospital served as the study sponsor and employer of the HCW, we implemented a custom mobile application. We aimed to evaluate the implementation, adherence, benefits, and limitations of this study’s app. Methods: The app allowed secure data collection through questionnaires, disseminated serological results, and managed bidirectional communication. Access was double-pseudonymized and irreversibly anonymized six months after enrollment. Download frequency, login events, and questionnaire submissions between October 2021 and December 2022 were analyzed. Multivariable logistic regression identified factors associated with app adherence. Results: Of the 3979 participants with app access, 3622 (91%) used the app; out of these, 1016 (28%) were “adherent users” (≥12 submitted questionnaires). App adherence significantly increased with age. Among HCW, adherent users were more likely to be non-smokers (p < 0.001), working as administrators or nursing staff vs. physicians (p < 0.001), vaccinated against influenza (p < 0.001), and had not travelled abroad in the past year (p < 0.001). IBD patients exposed to SARS-CoV-2 (p = 0.0133) and those with adverse events following the second COVID-19 vaccination (p = 0.0171) were more likely adherent app users. Despite technical issues causing dropout or non-adherence, the app served as a secure solution for cohort management and longitudinal data collection. Discussion: App-based cohort management enabled continuous data acquisition and individualized care while providing flexibility and anonymity for the study team and participants. App usability, technical issues, and cohort characteristics need to be thoroughly considered prior to implementation to optimize usage and adherence in clinical research. Full article

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted. Full article

Ocean-going vessels rely on marine diesel engines, referred to as the main engine, to carry the vessel’s load and ensure safe travel. These engines play a critical role, as their operation impacts on all aspects of the vessel’s functionality. To meet increasing demands for extended run times while maintaining reliability, it is essential to address the risks of main engine failure. Previous studies have highlighted numerous accidents resulting from such failures. Consequently, the reliability of the main propulsion engine is a crucial component of safe vessel operation. This study addresses the lack of methodologies for predicting engine reliability using failure running hours (FRHs). A data-driven model was developed using FRH data collected from marine engineers during on-board maintenance operations. Additionally, fault tree analysis (FTA) was employed to calculate the reliability of individual subsystems and the overall main propulsion engine. The findings indicate that the lube oil system, freshwater cooling system, scavenge system, and fuel system reach 0% reliability at approximately 2000 h, 14,000 h, 2500 h, and 1400 h of operation, respectively. Additionally, the reliability of the main propulsion engine drops to 0% after around 900 h of operation. By incorporating this prediction model, ship operators can better schedule maintenance, significantly enhancing engine reliability and reducing maritime accidents. This approach contributes to safer and more efficient operations for commercial marine systems. This study represents a vital step toward improving the reliability of ocean-going vessels. Full article

The aim of this cross-sectional study was to evaluate the sleep characteristics and behaviours of senior male, senior female, and under 21 (U21) male elite soccer players using athlete-specific questionnaires. During the preseason/early season period, 74 players from the English Premier League (n = 26, age 26 ± 5 y), Women’s Super League (n = 22, age 25 ± 5 y), and English Premier League 2 (n = 26, age 19 ± 1 y) completed the validated Athlete Sleep Screening Questionnaire (ASSQ) to obtain a sleep difficulty score (SDS) and the Athlete Sleep Behaviour Questionnaire (ASBQ) to obtain a global score and individual behaviours. We found that sleep difficulty scores were higher in senior females (5.9 ± 1.9) than senior males (4.1 ± 1.7) and U21 males (4.3 ± 1.2) (p ≤ 0.006), but no severe clinical problems were noted. Global sleep behaviour scores from the ASBQ were worse in senior males (37.9 ± 6.5) and senior females (40.6 ± 7.1) than male U21 players (33.6 ± 4.7) (p ≤ 0.021). Senior players consumed more alcohol and stimulants and felt that travel disrupted sleep; females went to bed thirstier, woke more for the bathroom, and ruminated more prior to sleep (all p < 0.05). In conclusion, senior female players reported more sleep difficulties than male senior and male U21 players. Behaviours such as pre-bed rumination, nutrition, and travel plans could be targeted to improve sleep quality in soccer players. Study limitations include data drawn from a single club in the off-season. Full article

Pastoral activities are an essential part of the cultural and ecological landscape of Central Italy. This traditional practice supports local economies, maintains biodiversity, and contributes to the sustainable use of natural resources. Understanding livestock behavior in response to environmental variability is essential for improving grazing management and animal welfare and ensuring the sustainability of these systems. This study evaluated the movement patterns of sheep grazing on pastures with differing vegetation indices in the Sibillini Mountains. Twenty lactating ewes foraging on two different pastures were monitored from June to October 2023 using GPS collars and accelerometers. GPS tracks were segmented using the Expectation Maximization Binary Clustering (EmBC) method to characterize movement behaviors, such as foraging, traveling, and resting. The NDVI was used to characterize vegetation dynamics, showing notable differences between the two pastures and across the grazing season. Additive mixed models were used to analyze data, accounting for individual variability and temporal autocorrelation in the sample. The results suggest that variations in the NDVI influence grazing behavior, with sheep in areas of lower vegetation density exhibiting increased movement during foraging. These findings provide valuable insights for optimizing grazing practices and promoting sustainable land use. Full article

Background: This study investigates the survival outcomes of individuals with HIV receiving different treatment regimens compared to a control group. Utilizing a cohort dataset with demographic and clinical information, this research aims to assess the impact of various factors, including age, education, and travel time, on survival while controlling for confounding effects using propensity score adjustment. Methods: A total of 380 patients with HIV were included in this study, categorized into an intervention group receiving a specific treatment regimen and a control group. The primary outcome measured was the time to death or censoring. Survival analysis was performed using the Cox proportional hazards model, adjusted for potential confounders, including treatment (intervention and control), age, education, travel time, and gestational age at enrollment. Propensity scores were also incorporated to adjust for treatment selection bias. Results: The Cox model revealed a significant protective effect of the intervention on survival (hazard ratio (HR) = 0.583, p = 0.045), indicating that the treatment improved survival outcomes compared to the control group. After adjusting for propensity scores, the relationship between the intervention and survival remained significant (HR = 0.631, p = 0.106), suggesting the robustness of the treatment’s effect even after accounting for confounding variables. Other covariates, such as age, education, and travel time, did not show significant independent effects on survival, likely due to their correlation with the treatment variable. Conclusions: This study highlights the crucial role of the intervention in enhancing survival among individuals with HIV. The use of propensity score adjustment improves the validity of these findings by mitigating confounding bias in observational data. These results highlight the importance of ART (antiretroviral therapy) in HIV management and demonstrate the utility of statistical methods like propensity scores in clinical research. Further studies with diverse populations and advanced methodologies are recommended to validate these findings across different settings. Full article

Recruiting participants for clinical trials targeting specific populations, like patients with chronic motor complete spinal cord injuries (SCIs), is challenging. The RESTORES trial evaluated spinal cord stimulation (SCS) combined with robotic neurorehabilitation for motor recovery in this population. This feasibility study enrolled three participants to assess SCS implant safety, synergistic effects of SCS and robotic rehabilitation, and clinical outcomes. Key recruitment barriers included the small patient pool, stringent eligibility criteria, patient skepticism, and logistical and emotional challenges. Strategies to address these challenges encompassed multidisciplinary collaborations with clinical centers, SCI associations, and patient support groups, including pre-surgical counselling and transparent communication. A dedicated clinical research coordinator ensured ethical compliance, logistical support, and participant engagement. Travel reimbursements, family involvement, and peer advocacy fostered accessibility and trust. Of the 115 patients screened, only 3 met the strict eligibility criteria, due to high screening failure rates and participant apprehension. Peer testimonials and family support helped enhance motivation and adherence. Ethical safeguards, like a data safety monitoring board, ensured participant safety and transparency. The RESTORES trial underscores the complexity of recruiting for pioneering interventions while highlighting the importance of tailored, patient-centric strategies. Insights gained will inform future trials and contribute to advancing SCI rehabilitation, offering hope for enhanced neurological recovery and quality of life for individuals with chronic motor complete SCI. Full article

Food insecurity is not only shaped by behavioral, socioeconomic, and demographic factors but is also determined by an individual’s ability to access food in their community. Analyzing new survey data from a large city located in the southwest USA, this research adds to current dialogues on food insecurity among older adults and individuals with disabilities in economically disadvantaged communities. Using logistic regression, the findings provide nuanced evidence to distinguish between two crucial determinants of food insecurity related to transportation access—the lack of service availability and transportation unaffordability. One-third of respondents missed grocery trips due to a lack of affordable transportation. For individuals who cannot drive or do not own vehicles, access to ride services is critical to overcome exacerbated risks owing to food insecurity compared with those who own personal vehicles. Those relying on community-based ride services are more likely to miss grocery trips due to inadequate services. Our research further provides evidence that inadequate services result in greater food insecurity for specific vulnerable subgroups, such as those with poor health, renters, and those with lower incomes. Our findings highlight the importance of understanding behavioral travel constraints and call for equity-focused improvements in transportation systems to mitigate food access barriers. Full article

Background: The availability of laboratory tests to screen and diagnose migrants and travellers for neglected and tropical parasitic diseases significantly impacts individual and public health. Italian scientific societies for parasitology, tropical diseases, and global health developed a survey to assess number and geographical localisation of laboratories able to carry out adequate diagnostics. Methods: An open-ended and multiple-choice questionnaire was constructed and sent to 752 members working in Italian microbiology laboratories via scientific societies’ mailing lists. Data concerning malaria, cystic echinococcosis, leishmaniasis, schistosomiasis, strongyloidiasis, and Chagas disease were included. Results: Members from 96 laboratories replied. At least one laboratory responded from 18 out of 20 Italian regions. Serological tests for Schistosoma spp., Strongyloides stercoralis, Trypanosoma cruzi, Echinococcus spp., and Leishmania spp. are performed in <50% of responding laboratories. Only 56.6% of labs provide all three recommended tests for malaria diagnosis in the emergency room. Direct identification methods availability varies for Schistosoma eggs (75–95.8%), S. stercoralis larvae (53.1%), trypomastigotes (59.4%), and Leishmania amastigotes (53.1%). Geographical differences (mainly northern versus southern regions) were evident. Conclusions: The survey underlines the need to improve diagnosis for neglected and tropical diseases, to define a network of reference laboratories for testing less prevalent diseases, and to share information, education, and training for both clinicians and microbiologists/parasitologists. Full article

Background/Objectives: Individuals with chronic stroke often experience various impairments, including poor balance, reduced mobility, limited physical activity, and difficulty performing daily tasks. In the context of the COVID-19 pandemic, telerehabilitation (TR) can overcome the barriers of geographical and physical distancing, time, costs, and travel, as well as the anxiety about contracting COVID-19. In this retrospective case-control study, we aim to evaluate the motor and cognitive effects of balance TR training carried out with a sensor-based non-immersive virtual reality system compared to conventional rehabilitation in chronic stroke patients. Methods: Twenty chronic post-stroke patients underwent evaluation for inclusion in the analysis through an electronic recovery data system. The patients included in the study were divided into two groups with similar medical characteristics and duration of rehabilitation training. However, the groups differed in the type of rehabilitation approach used. The experimental group (EG) received TR with a sensor-based VR device, called VRRS—HomeKit (n. 10). In contrast, the control group (CG) underwent conventional home-based rehabilitation (n. 10). Results: At the end of the training, we observed significant improvements in the EG in the 10-m walking test (10MWT) (p = 0.01), Timed-Up-Go Left (TUG L) (p = 0.01), and Montreal Cognitive Assessment (MoCA) (p = 0.005). Conclusions: In our study, we highlighted the potential role of sensor-based virtual reality TR in chronic stroke patients for improving lower limb function, suggesting that this approach is feasible and not inferior to conventional home-based rehabilitation. Full article

Research on pediatric hand exoskeletons remains limited compared to that on devices for adults. This paper presents the design and experimental validation of a customizable pediatric finger module, part of a hand exoskeleton tailored to individual anatomical features. The module aims to assist finger flexion in children with mild spasticity during activities of daily living. A patient-specific design methodology was applied to the case of a 12-year-old child. The finger module integrates compliant dorsal structures and cable-driven transmission with rigid anchoring elements to balance flexibility and structural stability. Different geometries and thickness values were tested to optimize comfort and quantify mechanical performance. Additive manufacturing was adopted to enable rapid prototyping and easy replacement of parts. Tensile and bending tests were conducted to determine stiffness and cable travel. Results support the feasibility of the proposed finger module, offering empirical data for selection and sizing of the actuation system and paving the way for the advancement of new modular pediatric devices. Full article

In this study, we examined the role of intellectual humility (IH) as an antecedent of individual attitude toward sustainable tourism viewed from the lens of personality trait theory, virtue ethics theory, and regenerative tourism principles within a stakeholder framework. Data were collected via Qualtrics in an online survey of 233 adults in the United States. A series of confirmatory factor analyses (CFA) were applied to the data to test the measurement model. In addition, a bifactor CFA was found to have acceptable fit and appropriate in controlling for common method variance. A series of covariance-based structural equations models (SEMs) was estimated to test the hypothesized model while controlling for common method variance in addition to individual age and gender. Using the chi-square difference test for nested model comparison, we found that intellectual humility was a significant antecedent of the negative ecological impact of tourism (β = 0.14, p < 0.01) while its relationships with economic and social impacts of travel became non-significant after controlling for common method variance. Pro-social tendency, operationalized as HEXACO Honesty–Humility, was also a significant antecedent of the negative ecological impact (β = 0.17) and positive economic impact (β = −0.34) of tourism, after controlling for common method variance. Despite its limitations due to its cross-sectional design and use of self-report data in the U.S., this study was novel in introducing intellectual humility as an important virtue to be cultivated at the individual level to achieve a holistic approach to sustainable tourism, especially in shaping destination choices. In addition, the study highlights the need to detect common method variance in self-report data via bifactor CFA to avoid erroneous reporting of significant findings, hampering our collective research efforts to address climate change and its impact. Full article

In response to the lack of ability to handle multidimensional data in current research methods for shared bicycle travel patterns, and the fact that correlation analysis is only conducted on a single feature, this study investigates the travel pattern using tensor decomposition and a random forest model. Based on the riding data of dockless shared bicycles in Shenzhen, tensor decomposition is applied to extract three shared bicycle travel patterns: peak-high traffic pattern, steady traffic pattern, and off-peak high traffic pattern. Spatially, each pattern exhibits clustering, and the travel volume decreases from the center to the periphery. Based on this, with 13 built environment factors as feature variables, a random forest model is trained. Importance and interaction analyses are performed for both individual features and feature combinations. The results indicate that the random forest model demonstrates excellent fitting performance and accuracy. Furthermore, for the peak-high traffic pattern, the combination of the length of primary roads and the number of companies contributes the most, while for the steady traffic pattern, it is the combination of the number of malls and companies. Finally, for the off-peak high traffic pattern, the influence of the number of malls and interests is the most significant. Full article