Search Results (6,209)

Infrastructure development is a key pillar in realising the Sustainable Development Goals. Yet implementing sustainable practices across the various stages of infrastructure development remains suboptimal. This study aims to identify significant barriers to sustainability implementation in infrastructure projects in Zimbabwe and to develop targeted interventions to overcome them. A quantitative research approach was adopted, in which 246 structured questionnaires were distributed online to construction professionals in consultancy firms, contractors, and government and private property developers in Zimbabwe. The data were analysed through a multi-analytical approach using mean score, exploratory factor analysis (EFA), and fuzzy synthetic evaluation. This study identified 31 barriers that hinder the implementation of sustainable construction in infrastructure projects. The top five factors are resistance to change, lack of funding, lack of sustainable construction policies, inadequate building regulations, and the perceived high cost of sustainable projects. EFA revealed five dimensions that are ranked as follows: ‘enforcement and policy-related’, ‘government support, regulations and standards-related’, ‘financial, market and attitude-related’, ‘knowledge, skill and ability-related’, and ‘technical capacity’. All dimensions tend to have a high level of impact on the implementation of sustainable practices in Zimbabwean infrastructure projects. The results highlight the need to enhance awareness and provide adequate financial information on the economic benefits of investing in sustainable infrastructure projects. The provision of financial incentives, funding initiatives, and appropriate policies, regulations, and standards can help to enhance the implementation of sustainable practices in Zimbabwe. Construction stakeholders can utilise the results of this study to improve the implementation of sustainability across infrastructure projects. Full article

This study examines how artificial intelligence (AI) contributes to the formation of new quality productive forces (NQPF) at the employee level. While prior research has largely treated AI as an external technological driver, this study investigates whether AI becomes embedded within employees’ capabilities through confidence and skill transformation. Using survey data from 303 employees in Vietnamese commercial banks, the study applies exploratory factor analysis and regression models to analyze the relationships among AI confidence, skill transformation, work experience, and NQPF. The results show that AI confidence has a significant positive effect on NQPF, and this relationship is strengthened by skill transformation. However, work experience weakens this effect, suggesting uneven adaptation across employee groups. These findings indicate that the impact of AI on productive transformation depends not only on technological deployment but also on workforce capability development. The study contributes to the literature by providing micro-level evidence on how AI may be internalized within labor processes in emerging economies. Full article

The undergraduate nursing students’ final year project, commonly called a ‘dissertation’ is an important component of the bachelor’s nursing programme. It can take the form of a literature review and proposal for a research or service improvement project. While crucial for developing research competence and evidence-based practice skills in preparation for their future careers, nursing students often find the dissertation process highly stressful. An online qualitative survey comprising open-ended questions was used to elicit nursing students’ rich, reflective accounts of the dissertation process at a university in the Northeast of England (hereafter referred to as the study site) from those who have recently completed their dissertations. The data obtained from 24 pre-registration nursing students who responded to the survey were thematically analysed. The findings revealed that critical relationships and essential support systems were key mediators of the challenges students faced, particularly a lack of readiness for the dissertation module, but they ultimately achieved transformative outcomes of an effective learning experience. Their navigational challenges can inform curriculum design and practices to better support students in their dissertation journey. Full article

Background/Objectives: Dental Practice-Based Research Networks (DPBRNs) bridge the gap between academic research and private dental practice, addressing questions relevant to everyday medical care. Despite their growing scientific output, little research has explored the experiences of practitioners engaged in these networks. Our study therefore aims to investigate these practitioners’ perspectives in order to identify strategies for improving investigator recruitment, training and data quality in future DPBRN studies. Methods: The qualitative methodology was chosen, and our study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines. Semi-structured interviews were conducted with dentists who had participated in a DPBRNs study and transcribed before being thematically analysed using Braun and Clarke’s framework. MaxQDA 2022 software was used to facilitate coding of the verbatim quotes. Results: Three major themes emerged: (1) obstacles to participation, including time constraints, difficulties in patient recruitment, and a perceived disconnect between academia and private practice; (2) facilitators of engagement, such as strong leadership, logistical support, and a collaborative research environment; and (3) personal benefits, such as skill development, breaking professional routines, and counteracting stereotypes about private practitioners’ involvement in research. Conclusions: The findings align with existing literature on medical Practice-Based Research Networks (PBRNs), highlighting logistical and motivational barriers while also emphasizing the importance of social and professional benefits. Notably, although financial compensation or credits for continuing professional development are frequently cited as motivators for research participation, these were not significant concerns for our participants. This study sheds light on the experiences of health practitioners in PBRNs, offering recommendations to overcome challenges through strategies such as accessible training, practical incentives and collaboration opportunities. Full article

This mixed-methods study explores the views and experiences of 55 English-language teacher candidates in Chile who designed gamified project-based lessons aimed at fostering inclusive learning and social justice in culturally diverse classrooms. Data were collected through lesson plans, semi-structured interviews, and a Likert-scale survey, and were analysed using inductive content analysis and descriptive statistics. The findings reveal that participants valued gamification for enhancing student engagement, collaboration, and critical thinking, and they perceived gains in their ability to integrate social justice themes into language teaching. However, discrepancies emerged when participants had to plan lessons that had a social justice orientation because they perceived they did not have enough competence to approach equity-oriented themes. This study adopts a justice lens that foregrounds power, agency, and digital equity in teacher candidates’ lesson-planning skills to examine how they can redistribute voice, recognise situated knowledges, and expand their capacity to act within and against structural constraints. The study underscores the need for teacher education programmes to move beyond technical and motivational uses of gamification and digital tools. From their lesson plans, teacher candidates were not simply adopting digital tools at a technical level but seem to be designing an integrated pedagogical ecosystem that aligned gamification and project-based learning. However, it is inconclusive whether they are able to design gamified PBL environments that do not reproduce existing social and educational inequalities and ensure that access and participation are carefully scaffolded. Full article

Severe wintertime particulate pollution (PM₁₀ and PM_2.5) affects the Santiago Metropolitan Region in Chile and is intensified by basin topography and frequent thermal inversions. Local authorities rely on the Critical Episodes Management (CEM) forecasting system, yet its predictive performance is variable. This study assesses CEM to identify operational vulnerabilities and propose data-driven improvements for urban air-quality governance. About ~1.2 million hourly meteorological and air-quality records (2017–2022) were analyzed using Generalized Additive Models (GAMs) to characterize key nonlinear relationships, and we evaluated the operational skill of the Cassmassi-1 PM₁₀ model and the WRF-Chem-based PM_2.5 forecasting component used by the system. Cassmassi-1 missed more than 50% of critical episodes and showed a false-alarm rate above 60%, consistent with limitations associated with static or incomplete emission representations. By contrast, the WRF-Chem-based component achieved episode prediction accuracy above 70%. GAM results indicate that wind speeds below 2 m s⁻¹, high diurnal temperature range, and relative humidity below 65% are strongly associated with extreme events. Considering the results, we recommend transitioning to nonlinear forecasting approaches that explicitly incorporate these meteorological thresholds and vertical stability indicators to improve alert reliability, strengthen urban resilience, and reduce population exposure. Full article

Recent advancements in Large Language Models (LLMs), including ChatGPT, DeepSeek, and Claude, have facilitated their growing integration into computer science education, including data structure courses. Despite their widespread adoption, the association between sustained and informal LLM usage and students’ learning outcomes remains insufficiently understood. This study seeks to address this gap by empirically examining the association between LLM usage and undergraduate performance in data structure education. We conduct a twelve-week empirical study involving fifty-four undergraduate students, in which LLMs were made freely accessible but neither explicitly encouraged nor discouraged during coursework and assignments. Students’ LLM usage patterns are analyzed in relation to their academic performance across different task types. Findings reveal a significant negative association between extensive reliance on LLMs for cognitively demanding tasks and overall learning outcomes. Additionally, an inverse associative trend is observed between the frequency of LLM usage across some learning activities and academic performance. In contrast, the use of LLMs for supplementary purposes, including conceptual clarification and theoretical understanding, exhibits a notably positive association with final performance. These findings suggest a task-dependent associative relationship between LLM usage and learning outcomes: LLM usage for conceptual learning shows a positive association with the mastery of relevant knowledge when used as a supplementary learning tool, while excessive LLM usage shows a negative association with the development of fundamental analytical and problem-solving skills. This study highlights the importance of carefully integrating LLMs into data structure education to support learning while preserving students’ independent cognitive engagement. Full article

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. Full article

Lean production philosophy and sustainability approach have become a critical framework for efficiency improvement, waste reduction, and promoting sustainable manufacturing practices. In the age of artificial intelligence (AI), there is a synergy, which has now found new dimensions, data-driven decision-making, predictive analytics, and operational agility. AI technologies promise to transform industrial processes by converging lean production and sustainability principles, a synergy explored in this paper. AI APIs enable the use of AI to improve resource utilization, reduce environmental pressure, and maintain economic growth inherent to all business sectors while also fostering social accountability. In this study, a robust regression model is employed to study the role of AI in moderating the lean practices and sustainability outcomes relationship, using a sample of 528 manufacturing firms. The results show that the contribution of AI technologies to economic, ecological, and social sustainability is effectively multiplied by that of lean production. This research offers a framework to help practitioners and policymakers optimize production systems in line with Sustainable Development Goals. Finally, the study delivers actionable recommendations for navigating skill gaps and cybersecurity risks that were identified. In sum, this paper contributes to the rapidly emerging conversation by providing empirical evidence on AI’s moderating role in the lean–sustainability relationship and offering a strategic framework for practitioners. Full article

Though separate research has found that early experiences, parental beliefs, and cognitive skills all influence science learning, science remains an underexamined domain compared to math and reading, despite its policy and societal implications. We integrate and expand on previous research by examining culturally relevant variables in different subgroups of Asian American kindergartners (N = 894). Guided by the Opportunity-Propensity Model of Achievement, we conducted a multi-group path analysis with science scores as the outcome, and propensity (self-regulation, social skills, and prior knowledge), opportunity (e.g., parent and child reading, TV-watching routine), and antecedent variables (e.g., poverty, SES, number of siblings and close grandparents, parental expectations, primary language at home, immigrant status) as predictors. We expected that propensity and opportunity variables would mediate the effects of antecedent variables. We conducted a multi-group path analysis, in which we examined differences between subgroups (China, India, Vietnam, Other East, Other Southeast, Other). Although we did not find heterogeneity in science achievement among subgroups, we found various direct and indirect effects at the subgroup level. Findings suggest that Asian American children may generally benefit from enhanced self-regulatory skills and prior knowledge, though some subgroups may benefit specifically from having fewer TV-watching rules and non-structured activities. We also recommend further disaggregation and reporting of data to better support learners. Full article

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities. Full article

Stakeholder engagement is increasingly recognized as vital to developing interdisciplinary solutions to complex sustainability problems, such as phosphorus management. At the same time, several challenges and barriers may arise when engaging stakeholders in practice. This study identifies key challenges in engagement and explores how they may be addressed. Using an online survey of 121 researchers and practitioners engaged in sustainability work in the U.S., along with 10 interviewees, data were analyzed using an explanatory sequential mixed-methods design. Key results from this study identify two main sets of challenges and needs, as well as the relationships between them. First, participants identified “top down” challenges to engagement, including limited funding, resources, organizational support, and time, alongside “bottom up” challenges related to recruitment and retention, inclusive representation, trust-building, facilitation skills, and balancing stakeholder expectations. While prior studies have noted important factors and case-specific challenges, this study is the first to systematically document these challenges and needs across a range of fields and highlight interconnections between structural resource limitations and practitioners’ ability to build and sustain meaningful stakeholder relationships. Future research can build on these findings to enhance the field of engagement by advocating for more resources to conduct engagement and developing methods to better assess success of engagement practices. Full article

Research on soft skills in developing countries remains limited, and much of what we think we know comes from studies in very different educational systems. This paper uses data from 1006 Grade 9 students in Thailand, collected by the Equitable Education Fund (EEF), to examine whether gender, school type, and regional location are reflected across eight domains of soft-skill development. We combine simple descriptive comparisons with regression models and propensity score matching, mainly to see whether the broad patterns stay the same when the data are approached in different ways. The results challenge what many policy discussions anticipate. Gender differences are small and often disappear once controls are added. By contrast, the gaps linked to school context and region are substantial and persistent across analytical approaches. Students in opportunity-expansion schools record lower scores in several domains, and children in the Northeastern region show even wider shortfalls. These patterns are consistent across methods and substantially larger than any associations with gender. The analysis underscores that institutional conditions, particularly in less advantaged regions, play a larger role in shaping soft skills than gender-targeted initiatives. Full article

Background: Food literacy (FL) comprises the knowledge, skills, and motivation needed for food production, selection, preparation, intake, and waste management. This study examined whether socio-ecological correlates of FL differ across settlement contexts in China. Methods: Cross-sectional survey data from Chinese adults (N = 1145) were analyzed across four settlement tiers: tier-1 metropolitan cities (R1), provincial/secondary cities (R2), smaller prefecture-level cities (R3), and county/rural areas (R4). General linear models estimated associations between socio-ecological predictors and overall FL after adjustment for sociodemographics, health behaviors, chronic disease, and BMI. Significant interactions were probed using HC3-robust simple slopes and pairwise slope contrasts. Robustness checks included domain-specific measurement invariance, variance inflation factor (VIF) diagnostics, and a regional sensitivity analysis. Results: The fully adjusted model explained substantial variance in FL (R² = 0.629). Awareness showed the strongest association with FL, followed by family support, injunctive norms, and social norms. Moderation was modest and predictor-specific: dining preferences and family support were positively associated with FL across all regions, with the strongest effects in county/rural areas. Although the omnibus interaction for injunctive norms was statistically significant, follow-up slope contrasts were not, indicating limited substantive regional heterogeneity. Component analyses indicated that preference-related heterogeneity was concentrated in food intake and food choices/selection, whereas family-support heterogeneity was most pronounced for waste disposal. Domain-level invariance analyses supported broad cross-regional comparability of the FL structure, VIFs were all below 5, and the regional distribution of valid and invalid responses did not differ significantly. Conclusions: Socio-ecological correlates of FL were broadly robust across China, with limited context-specific variation driven mainly by stronger household-support effects in county/rural settings. These findings support region-sensitive FL strategies that strengthen household-based support while leveraging normative influences across regions. Full article

Youth-led STEM programming depends on skilled adult facilitators who can support authentic teen leadership, yet professional learning for developing these specialized skills remains understudied. Through three cycles of design-based research, we iteratively developed and studied a professional development model that trained informal educators from museums, libraries, afterschool programs, and schools to launch Teen Science Café programs—a youth-led model where teens organize STEM events. Analysis of data from trainer reflections, trainee interviews, trainee surveys, and implementation tracking across three iterative design cycles revealed six interconnected principles essential for effective professional development: focusing on a committed adult leader; personalized training characterized by mutual respect; learning by doing; establishing accountability that builds momentum; enabling learning from peers and near-peers; and recognizing success to nurture professional pride. Implementing these principles to prepare educators to center youth voice requires substantial, coordinated investment across stakeholders—commensurate with the complexity of developing youth agency and STEM identity in informal settings. From our findings, we contrast this approach with the “efficiency trap,” in which scaled training without sustained support wastes resources when many educators are trained but youth-centered programs fail to materialize. Full article