Search Results (190)

This paper focuses on geophysical internal waves with centripetal forces in the f-plane approximation, presents Pollard’s solution, and constructs the exact solution of the system in different layers. On this basis, this paper further develops the structure of Pollard’s solutions that incorporate current terms in the horizontal, meridional, and vertical directions. Findings show that away from the equatorial regions, neither the transverse meridional current nor the vertical current exists, while at the equator, the vertical current vanishes along with the Coriolis parameter f, and the meridional current term is constant. Full article

With the rapid growth in the number of short-form video users, short-form video addiction (SVA) has drawn increasing attention due to its significant impact on individuals’ daily lives. This study examines the relationship between short-form video addiction and sleep quality, as well as the mediating mechanisms of social anxiety and self-control. Using the College Student Short-Form Video Addiction Scale, the Interaction Anxiousness Scale, the Brief Self-Control Scale, and the Pittsburgh Sleep Quality Index, we surveyed 677 Chinese college students. Correlation and mediation analyses were conducted using SPSS and the PROCESS macro. The findings revealed that SVA was negatively associated with sleep quality both directly and indirectly, with higher social anxiety and lower self-control mediating the indirect effect. By showing that addictive behavior is linked to its risk factors through affective, cognitive, and executive pathways, this study provides preliminarily elucidates how addictive behavior feeds back to reinforce its own risk factors proposed in the Interaction of Person–Affect–Cognition–Execution (I-PACE) model. Full article

Background/Objectives: There is a critical disparity between donor organs and recipients awaiting liver transplantation. Normothermic machine perfusion (NMP) has emerged as a promising strategy with which to address this shortage. This study aimed to evaluate the association between NMP and short-term outcomes following liver transplantation using a large, multivariable-adjusted national dataset. Methods: A retrospective analysis of the de-identified United Network for Organ Sharing (UNOS) database was conducted for adult liver transplant recipients between January 2020 and July 2024. Standard and deceased donor data were merged according to donor identification number. Multivariable logistic and Cox regression models were used to evaluate patient mortality, graft failure, and hospital length of stay (LOS). Results: Among 34,115 patients, adjusted regression demonstrated lower one-year patient mortality (OR 0.68, CI 0.54–0.86, p = 0.001) and graft failure (OR 0.72, CI 0.60–0.87, p = 0.001) with NMP compared to static cold storage. NMP was also associated with reduced 30-day (OR 0.67, CI 0.47–0.95, p = 0.03) and 90-day mortality (OR 0.72, CI 0.54–0.94, p = 0.02) and shorter LOS (HR 1.06, CI 1.01–1.12, p = 0.02). Kaplan–Meier and Cox analyses showed no significant differences in overall patient mortality or graft failure. Conclusions: NMP was associated with improved short-term survival in time-independent analysis; however, it failed to reach significance in time-dependent Cox regression. These findings suggest that NMP could play a role in improving short-term outcomes and expanding the donor pool for liver transplant candidates. Additional studies are needed to fully elucidate the impact of NMP on short-term survival outcomes. Full article

As digital technology becomes increasingly embedded in higher education, assessing students’ confidence in digital tasks is essential for understanding their adaptation to digital learning environments. This study adapted the Digital Self-Efficacy Scale (DSES) into Chinese and evaluated its psychometric properties among 1502 first-year college students in China. Participants were randomly split into two subsamples for item analysis and exploratory factor analysis, and structural validation respectively. All 25 items demonstrated satisfactory discrimination and homogeneity. Although parallel analysis indicated a four-factor exploratory solution, seven competing models were compared in the confirmatory stage. The Bifactor-ESEM model yielded the best combination of statistical fit and substantive interpretability, suggesting that the Chinese DSES primarily captures an overarching digital self-efficacy dimension, with domain-specific factors retaining limited reliable variance beyond the general factor. Total scores were positively associated with digital maturity (r = 0.642, p < 0.001); however, external validity is limited given that both measures were self-reported and concurrently collected. Gender measurement invariance analyses supported configural, metric, and scalar invariance. Overall, the Chinese DSES demonstrates promising preliminary psychometric properties. The total score is recommended as the primary interpretive unit, with subscale scores used as supplementary descriptive information only. Full article

This study evaluated the effectiveness of a youth-developed vaping intervention created by the Airdrie Board of Youth Affairs (ABYA) in changing Grade 7 and 8 students’ knowledge, attitudes, and harm perceptions regarding e-cigarette use. The vaping video trial followed a preceding local education program on smoking harms. A total of 107 students were randomly assigned to either the intervention group (ABYA video) or the control group (expert-developed video). Four instruments were used: the Video Survey, Personal Information Questionnaire, Knowledge and Attitudes Regarding E-cigarette Ingredients, Safety, and Addictive Properties (KAS), and the E-cigarette Harm Perception and Reduction (EHI). Pre- and post-intervention data were analyzed using t-tests and Mann–Whitney U tests. Compared with controls, the intervention group showed a significantly larger mean decrease in total EHI scores (mean change 9.05 vs. 2.06; t(105) = 3.34, p = 0.001; Cohen’s d = 0.65), indicating that the youth-developed video increased the perceived risk of vaping relative to cigarettes to a greater extent than the expert-developed video. Students also rated the ABYA video significantly higher on a 5-point scale for overall enjoyment (3.13 vs. 2.33; p < 0.001; Cohen’s d = 0.81) and for perceived increase in knowledge about vaping (3.34 vs. 2.84; p = 0.023; Cohen’s d = 0.44). The KAS instrument showed low internal consistency in this sample, so item-level KAS findings were treated as exploratory. Notably, unlike the expert-developed video, which explicitly acknowledged uncertainty, the ABYA video conveyed a clearer and more direct message, which may have contributed to its greater appeal while also carrying a risk of overstating absolute risk of e-cigarettes relative to cigarettes. Overall, these findings suggest that grassroots, youth-led interventions may be an effective approach for adolescent vaping education and may offer advantages over traditional expert-developed messaging in some contexts. Full article

This study advances the model of proactive caring to articulate the strategies and practices employed by higher education professionals to support the retention and graduation of underserved students. Employing an explanatory case study design and drawing upon multiple data sources—including semi-structured interviews, focus groups, and observations—collected from students and higher education professionals, we identified six core elements essential to proactive caring: (1) staff accessibility, (2) trust-building, (3) positive motivation, (4) reinforcement, (5) encouragement, and (6) student accountability. Our findings also reveal that higher educational professionals initiate support prior to students’ arrival on campus by assessing institutional environments to identify potential barriers and leveraging data and experiential knowledge to proactively address these challenges. This research contributes to ongoing efforts to promote access and equity in higher education, particularly in the context of increasing anti-diversity, equity, and inclusion (DEI) legislation. While the current study centers on Students of Color in STEM disciplines, the elements underlying the proactive caring model may be broadly applicable, which offers a framework for empathetic practitioners committed to supporting underserved students throughout their collegiate journey. Full article

Background/Objectives: The purpose of this study was to analyze the demographics and injury patterns of children with transportation-related non-fatal injuries occurring on public roads, streets and highways using a nationwide emergency department (ED) database. Methods: Data from the National Electronic Injury Surveillance System (NEISS) All Injury Program (AIP) 2005–2021 was used. Five transportation methods (motor vehicle occupant, bicyclist, pedestrian, motorcyclist, other) occurring on a public highway, street, or road were analyzed. Statistical analyses were performed with SUDAAN 11.0.01™ software to obtain national estimates. Results: There were an estimated 8,188,810 ED visits for traffic-related injuries in children; the median age is 14.3 years. Sex distribution was equal; 93.4% were discharged from the ED, and the head/neck was the most injured area (51.9%). The most common diagnoses were contusion (35.7%), strain/sprain (28.0%), internal organ injuries (13.3%), fracture (8.4%), lacerations (7.4%) and concussions (4.1%). Predictor variable of not being discharged from the ED was the presence of a fracture (OR = 119.7 [71.3, 200.7], p < 0.0001), injury to the trunk (OR = 3.2 [2.7, 3.8], p < 0.0001), a pedestrian (OR = 3.9 [2.8, 5.3], p < 0.0001), those <1.5 years old (OR = 4.3 [2.8, 6.6], p < 0.001), and males (OR 1.5 [1.4, 1.6], p < 0.0001). The greatest prevalence of head/neck fractures was in motor vehicle occupants (23.3%), upper extremity fractures in bicyclists (73.1%) and motorcyclists (49.2%), and lower extremity fractures in pedestrians (56.6%). Conclusions: This detailed study can be used to compare/contrast these injuries to other countries regarding road traffic injuries in children. This data can be used to assess the outcomes of prevention strategies introduced in the future. Full article

Rural logistics faces persistent challenges such as high distribution costs, dispersed demand, and limited transport infrastructure, which hinder efficient last-mile delivery. To address these issues, this study proposes a bus–heterogeneous drone collaborative delivery system that integrates the fixed-route coverage of rural buses with the flexibility of multiple types of drones. The proposed system enables synchronized operations between buses and drones, where buses serve as mobile depots for drone launching and recovery along predefined routes. A mixed-integer programming (MIP) model is developed to jointly optimize bus schedules and drone routing under spatiotemporal synchronization constraints, considering drone endurance, payload capacity, energy consumption, and bus departure times. Due to the NP-hard nature of the problem, an Improved Genetic Algorithm (IGA) is designed, incorporating a three-layer encoding scheme, adaptive crossover and mutation operators, and a local search repair mechanism to enhance convergence and solution feasibility. A real-world case study from Baihe County, Shaanxi Province, China, is conducted to evaluate the performance of the proposed model and algorithm. Comparative experiments under the reported case-study setting show that the proposed bus–heterogeneous drone system achieves notable cost reduction and improved overall delivery performance. Sensitivity analyses further confirm the robustness of the model with respect to drone endurance, drone payload capacity, and bus stop quantity. This research contributes to the literature by bridging the methodological gap between truck–drone coordination and bus-based collaborative delivery, offering an innovative framework for sustainable rural logistics and multi-modal last-mile optimization. Full article

Introduction: Negative legal emotion refers to the affective component of negative orientations toward legal norms, institutions, and procedures. It is closely related to, but not synonymous with, the broader construct of legal cynicism, which more often emphasizes generalized skepticism toward the fairness, legitimacy, and effectiveness of the law. This study examined the association between negative legal emotion and prosocial behavior among university students, with peer attachment as a mediator and social exclusion as a moderator. Method: A total of 404 undergraduates from mainland Chinese universities were included in the final analysis after predefined data screening, including attention-check items and response-time cutoffs. Participants completed an online survey assessing negative legal emotion, prosocial behavior, peer attachment, and social exclusion. Descriptive statistics, correlation analyses, and Hayes’s PROCESS macro (Model 7) were used to test the moderated mediation model, controlling for gender, grade, and parental educational attainment. Results: Negative legal emotion was negatively associated with prosocial behavior. Peer attachment statistically accounted for this association, such that higher negative legal emotion was associated with lower peer attachment, which was in turn associated with lower prosocial behavior. Social exclusion moderated the first stage of the indirect pathway: the negative association between negative legal emotion and peer attachment was weaker at higher levels of social exclusion. Accordingly, the indirect association between negative legal emotion and prosocial behavior via peer attachment also varied across levels of social exclusion. Conclusions: The findings suggest that peer attachment is an important relational correlate linking negative legal emotion with prosocial behavior and that social exclusion is associated with variation in the strength of this indirect pathway. These results extend research on legal socialization and contribute to understanding prosocial behavior among university students. Full article

This study develops a new concept of computer-assisted exercises (CAX) on constructive simulation systems and how the proposed concept affects the strategy and teaching methods. The current state of affairs in the field of defense and security, both in Europe and in the world, requires the acquisition of competencies (European Qualifications Framework—EQF: knowledge, skills, independence, and responsibility), i.e., the education and training of a significantly larger number of personnel in the field of defense and security than has been the case in the last 70 years. In addition, an important specificity of today is that students need to acquire some competencies that were almost unknown until recently. Most of these competencies are the result of the rapid development of technology, which has significantly changed human life in all areas. In order to respond to the modern requirements of conducting operations, where the transfer of information both horizontally and vertically is exponentially accelerated, current concepts of preparation and implementation of education and training, of which exercises are often the most important part, need to be replaced with new concepts, and one such concept is developed in this paper. New information introduced is mostly related to the new weapons that are being introduced (unmanned systems, hypersonic missiles, weapons based on microwaves and lasers, etc.), which all result in necessary changes to the traditional approach to conducting war, i.e., tactics, techniques, and procedures (TTP). This novel exercise concept allows for the simultaneous implementation of training for up to three or four hierarchical levels (e.g., TF Div, brigade, battalion, and company) in one exercise, while in most countries, including the NATO alliance, it is still common for such exercises to be conducted according to a concept that is over 20 years old and, as a rule, is focused on the implementation of exercises for one or two hierarchical levels. This approach allows key personnel from the headquarters of units from four hierarchical levels to be simulated in real time, which is not provided by current concepts for preparing and conducting exercises. The new concept was applied as a multi-level, computer-assisted exercise (CAX) on constructive simulation systems. In addition, significant advantages of the new concept relate to the flexibility and adaptability of the proposed concept to be applied in addition to operational units and in training institutions such as academies and higher education institutions. In addition to the above, the new concept requires a shorter planning period as well as fewer total resources needed for the preparation and implementation of the exercise. The management, organizational, and technological components of the proposed exercise concept are implemented in the CAX model. The hypotheses in this paper will be tested in an applied study, which was evaluated through an external evaluation body. The implemented CAX model was tested in Croatia on the example of using exercises at the Croatian Defense Academy. Full article

The rapid expansion of e-commerce and on-demand logistics has intensified the need for cost-effective and reliable urban distribution systems. This paper investigates an energy-aware routing problem for electric vehicle fleets operating from multiple depots under time-varying traffic conditions. We propose a novel multi-depot vehicle routing model that jointly incorporates time-dependent travel speeds, simultaneous pickup and delivery operations, and time window constraints. The model explicitly captures key operational realities, including battery capacity limitations, load- and speed-dependent energy consumption, synchronized pickup-delivery requirements, and soft time windows. The objective is to minimize total operational cost by simultaneously optimizing depot assignments, vehicle routes, and service schedules. Given the NP-hard nature of the problem, we develop a two-stage heuristic solution framework. In the first stage, a spatio-temporal clustering strategy is employed to assign customers to depots efficiently. In the second stage, route construction and improvement are performed using an enhanced Adaptive Large Neighborhood Search (ALNS) algorithm equipped with problem-specific destroy and repair operators. Computational experiments on adapted benchmark instances demonstrate that the proposed approach consistently produces high-quality solutions and exhibits robust convergence behavior. In addition, sensitivity analyses provide managerial insights, revealing an optimal range of vehicle energy capacity and an economically efficient speed band that balances travel time and energy consumption. Full article

This study sought to identify key factors in university students’ physical and mental health by using a combination of methods, including classical statistical analysis and machine learning techniques. Physical and mental health test data were collected from undergraduates of the 2020 to 2023 cohorts at a university. A self-designed questionnaire on factors associated with physical and mental health was also sent to randomly selected undergraduate students from the same university. The study data were analyzed by one-way analysis of variance, Pearson correlation analysis, hierarchical regression analysis, and a machine learning model. The results revealed that participation in school sports clubs (β = −0.111, p < 0.001) and amount of exercise (β = 0.182, p < 0.001) were significant predictors of physical health status. Difficult family economic situation (β = 0.162, p < 0.001), major satisfaction (β = −0.092, p = 0.02), the quality of a romantic relationship (β = −0.121, p = 0.003), the quality of interpersonal relationships (β = −0.157, p < 0.001), and an overprotective family parenting style (β = 0.109, p = 0.011) were significant predictors of mental health status. The results of regression analysis and application of the machine learning model identified that the amount of exercise, quality of interpersonal relationships, and family parenting style had consistent effects on both the physical and mental health of university students. Full article

Salmonella enterica serotype Mbandaka has emerged as a significant foodborne pathogen in poultry, posing increasing public health risks through its zoonotic transmission from poultry sources to humans, yet critical gaps remain in understanding its transmission inter-host transmission and antimicrobial resistance (AMR) mechanisms within the poultry industry. In this study, we addressed these knowledge gaps by conducting a comprehensive genomic analysis of 1813 S. Mbandaka genomes, including genotyping, phylogenetic reconstruction, and pangenome analysis. The results revealed that S. Mbandaka exhibits a global distribution pattern, with sequence type 413 (ST413) representing the dominant lineage. Phylogenetic analysis revealed frequent close genomic relatedness between human and poultry-derived strains (SNP ≤ 10), suggesting poultry as a potential major zoonotic reservoir for human S. Mbandaka infection. Furthermore, close genetic relationship was also detected among the human-derived strains, suggesting the potential community spread. In addition, genomic analysis indicated an increase over time in the number of antimicrobial resistance genes (ARGs) detected per genome, frequently associated with plasmids and insertion sequences (ISs). Notably, the ARGs significantly enriched in Chinese strains were primarily associated with the Col(pHAD28) plasmid. Comparative analysis demonstrated that the ARG profiles of S. Mbandaka were similar to those of other Salmonella serovars, suggesting the potential for cross-species transmission. In conclusion, these findings represent a large-scale retrospective genomic analysis of publicly available whole-genome sequences and elucidate the transmission dynamics and AMR mechanisms of S. Mbandaka in poultry, providing insights into its risks to poultry production and public health while guiding the development of targeted prevention strategies for the poultry sector. Full article

With the escalating demand for forest-derived ecological products, quantifying forest ecological product production capacity (EPC) has become essential for precise ecological governance. Addressing the methodological gaps and complexity in current assessments, this study develops a transferable Forest EPC theoretical framework integrated across four dimensions: environmental background, vegetation status, human pressure, and human investment. Using Henan Province as a case study, we established a multi-criteria evaluation model to characterize the spatial drivers and supply potential of Forest EPC. Our findings reveal that the provincial forest EPC stands at a moderate level (0.341). The spatial distribution is highly heterogeneous: the “medium” EPC grade dominates the landscape (36.69%), whereas “high-level” areas are critically scarce (3.76%). Notably, forest EPC exhibits a strong spatial gradient, with high-performance clusters in the southern and western highlands contrasting with lower values in the northern plains. The identification of significant spatial autocorrelation (Global Moran’s I = 0.71) highlights the necessity of regional collaborative management. This study provides a methodological reference that is adaptable to diverse regional contexts through the recalibration of local indicators and weights, offering a scientific benchmark for optimizing the spatial layout of ecological product supply. Full article

Rapid urbanization in coastal regions presents complex challenges for environmental management and public safety. Accurate, high-resolution wind field monitoring is critical for urban disaster mitigation, infrastructure resilience, and pollutant dispersion analysis in these densely populated areas. However, utilizing massive multi-source satellite remote sensing data for precise prediction remains difficult due to the spatiotemporal heterogeneity caused by the land–sea interface. To address this, this study proposes a novel lightweight Geospatial Artificial Intelligence (GeoAI) framework (DA-DSC-UNet) designed to predict wind fields in coastal urban environments (e.g., Fujian, China). We constructed a dataset by integrating multi-source satellite scatterometer products (including Advanced Scatterometer (ASCAT), Fengyun-3E (FY-3E), and Quick Scatterometer (QuickSCAT)) and buoy observations. The framework employs a UNet architecture enhanced with dual attention mechanisms (Efficient Channel Attention (ECA) and Convolutional Block Attention Module (CBAM)) to adaptively extract features from remote sensing signals, focusing on critical spatial regions like urban coastlines. Additionally, depthwise separable convolutions (DSCs) are introduced to ensure the model is lightweight and efficient for potential deployment in urban monitoring systems. Results demonstrate that our approach significantly outperforms existing deep learning models (reducing Mean Absolute Error (MAE) by 14–25.8%) and exhibits exceptional robustness against observational noise. This work demonstrates the potential of deep learning in enhancing the value of remote sensing data for urban resilience, sustainable development (SDG 11), and environmental monitoring in complex coastal zones. Full article