Search Results (3,647)

Generative artificial intelligence (AI) is rapidly reshaping higher education, yet the social pathways that trigger intensive use and may evolve into dependency remain insufficiently understood. This study examines how social drivers shape sustainable AI usage and the potential progression toward dependency. We surveyed 965 university students and analyzed the data using partial least squares structural equation modeling (PLS-SEM). The findings show that social factors, including fear of missing out, word-of-mouth, and subjective norms, primarily influence AI usage through trust in AI. AI usage has a limited direct effect on dependency, whereas dependency is more strongly associated with psychological evaluations of AI benefits, including perceived competence enhancement and perceived intelligence of AI systems. These results support a staged sociotechnical account of AI engagement and point to sustainability-relevant implications for responsible AI integration in higher education (Sustainable Development Goal 4), including trust calibration, competence building, and safeguards against over-reliance that may undermine long-term learning outcomes. Full article

Universities are increasingly seeking ways to build students’ sense of belonging. This paper reports a mixed-methods evaluation of BE At King’s, a seed-funded programme supporting grassroots, co-created initiatives to strengthen connection and inclusion across a large, multi-campus institution. Five projects—ranging from art clubs and community breakfasts to hackathons and writing retreats—were designed and delivered by students and staff, with evaluation embedded from the outset. Quantitative survey data (n = 202) showed high levels of belonging overall, with structured, interactive initiatives most strongly associated with meeting new people and feeling connected. Qualitative thematic analysis highlighted four themes—Refreshing Routines, Inclusive Conditions, Community Leadership, and Layered Engagement—revealing how belonging was fostered through predictable routines, psychologically safe spaces, and opportunities for shared ownership. Bringing findings together shows that grassroots initiatives can engage even less-connected students, but that careful design, inclusive outreach, and sustained facilitation are critical to their success. We argue that universities should embed belonging within the everyday fabric of institutional life through co-produced, flexible, and locally responsive approaches that combine institutional commitment with community leadership. Full article

Josip Vojnović (Omiš, 1929–Split, 2008) is a prominent Croatian architect, primarily known in professional circles for organising the construction of Split 3, the expansion of Split during the 1970s. His professional career began with the design of primarily residential buildings and concluded with his position as a university professor. This article analyses the URBS 1 standard residential buildings constructed during the 1960s, which were intended to address the housing shortage in post-war Split. These buildings—the most notable part of Vojnović’s design work—were built in several locations throughout Dalmatia. Even at the time of their construction, they were recognised as a significant example of designed and executed standardised residential architecture. This research is based on archival materials from the State Archives in Split, the Archive of the Urban Planning Institute of Dalmatia–Split, as well as research in situ. The article examines the design of the standard building, including a functional analysis of the residential unit and all the floors, as well as a formal and compositional analysis of the façade. The URBS-1 buildings are an illustrative example of housing construction, due to their number, distribution and architectural features shaped by the economic, technological, social and cultural context of the time. Full article

Indoor environmental comfort plays a central role in occupants’ well-being, learning outcomes, and productivity, especially in educational buildings characterized by high occupancy variability and diverse activities. This paper presents a real-world dataset collected at the Cesena Campus of the University of Bologna, aimed at supporting occupant-centric comfort analysis and prediction in classrooms and laboratories. The dataset integrates continuous environmental measurements, such as temperature, humidity, noise, air pressure, and CO₂ concentration, with subjective comfort feedback gathered from students during regular lectures. Data were collected using permanently installed ceiling sensors and additional control sensors placed near occupants, enabling both longitudinal monitoring and validation analyses. Furthermore, the dataset includes both repeated comfort perception reports and a one-time comfort definition phase capturing individual relevance weights for different comfort dimensions. By combining objective and subjective data in realistic academic settings, the dataset provides a valuable resource for developing, benchmarking, and validating data-driven models for smart campus applications, indoor comfort prediction, and human-centered building analytics. Full article

Driven by the “dual carbon” goal and the strategy for cultivating new productive forces, China’s economy is undergoing a crucial transformation from high-speed growth to high-quality development. As a typical high-energy consumption and high-emission sector, the green and low-carbon transformation of the building materials industry directly affects the optimization of the national energy structure and the realization of ecological goals. However, traditional building material enterprises generally face practical challenges such as low resource utilization efficiency, insufficient digitalization and greening integration of the industrial chain, and weak green innovation momentum. The transformation actions of a single entity are difficult to break through systemic bottlenecks, and it is urgently necessary to establish a dynamic evolution mechanism involving multiple entities in collaboration. This paper aims to explore the evolutionary rules and stability of digital green (DG) transformation strategies of building materials enterprises (BMEs) under multi-agent interactions involving government, universities, and consumers. Centering on BMEs, a four-party evolutionary game model among the government, enterprises, universities, and consumers is constructed, and the evolutionary processes of strategic behaviors are characterized through replicator dynamic equations. Using MATLAB R2022 (Version number: 9.13.0.2049777) bnumerical simulations, this study investigates how key parameters, such as government subsidies, penalty intensity, and consumers’ green preferences, affect the transformation pathways of enterprises. The results reveal that the DG transformation behavior of BMEs is significantly influenced by governmental policy incentives and universities’ knowledge innovation. Stronger subsidies and penalties enhance enterprises’ willingness to adopt proactive DG strategies, while consumers’ green preferences further accelerate transformation through market mechanisms. Among multiple strategic combinations, active DG transformation emerges as the main evolutionarily stable strategy. This study provides a systematic multi-agent collaborative analysis framework for the transformation of BME DG, revealing the mechanisms by which policies, knowledge, and market demands influence enterprise decisions. Thus, it offers theoretical and decision-making references for the green and low-carbon transformation of the building materials industry. Full article

In order to solve the challenges of laboratory safety management in universities, such as insufficient supervision of high-frequency risk behaviors in responsibility traceability, a laboratory safety hazard perception and responsibility traceability system based on deep learning is proposed. Based on the YOLOv5s object detection model, the channel attention mechanism SE and NWD loss functions are introduced, with DeepSORT tracking to realize multi-target tracking and hidden danger perception in laboratory scenarios. Then, the responsibility matching algorithm and visual traceability mechanism are proposed to build a full-chain management system of “risk perception, analysis and tracking, and responsibility traceability”. Experiments show that the mean average precision (mAP) of YOLO-lab in the laboratory scene is 87.8%. Taking the experimenter not wearing a lab coat as an example, through the test of the laboratory scene, the multi-target tracking effect is excellent and the responsibility traceability report is generated, which solves the problem of “visible and uncontrollable behavior, traceable and unproven” in traditional supervision, and provides an intelligent technical path for laboratory safety governance. Full article

Low-level jets (LLJs) are common strong wind structures in the atmospheric boundary layer. They have important impacts on aviation safety, wind energy utilization and pollutant dispersion. However, the formation mechanisms of LLJs are complex. Traditional parameterization schemes and numerical models still show limitations in forecasting LLJ occurrence and resolving their structures. In this study, wind lidar, near-surface turbulence and gradient meteorological observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University are combined to construct a multi-source low-level dataset. Four processing modules are designed, including multi-source data fusion, turbulence preprocessing, turbulence intermittency metrics and LLJ identification, to overcome the constraints of single-platform observations. Six commonly used machine learning algorithms (LightGBM, XGBoost, CatBoost, K-nearest neighbors, Balanced Random Forest, and ExtraTrees) are compared. A two-stage classification–regression framework is then adopted. LightGBM is used for LLJ occurrence, and CatBoost is used for LLJ height and intensity, to build an LLJ-2Stage prediction system. The system performs automatic LLJ identification and predicts jet intensity and core height. For LLJ occurrence, the harmonic-mean F1-score of precision and recall reaches 0.820. The coefficient of determination R² is 0.643 for height prediction and 0.794 for intensity prediction. Both the classification and regression parts show good accuracy and stability. The SHAP method is further applied to assess model interpretability and to identify key predictors that control LLJ occurrence, height and intensity. Results indicate that thermal variables, such as net radiation (Rn) and sensible heat flux (H), dominate LLJ occurrence and structural changes. The strength of turbulence intermittency provides valuable supplementary information for locating the LLJ core height. Two representative nocturnal LLJ cases further show a consistent near-surface evolution during the LLJ period, with enhanced TKE and reduced H, followed by a gradual recovery after decay, while Rn remains persistently low, consistent with the SHAP-indicated effects. The proposed framework predicts LLJ occurrence and structural evolution and is of significance for improving understanding of boundary layer processes, air-pollution control, wind energy utilization and low-level aviation safety. Full article

Learning efficiency in a university context is predicated on a conducive learning environment. This in turn requires settings offering thermal comfort. In this study, we experimentally explored the relationship between the thermal environment of colleges and universities in hot-summer and cold-winter regions on the thermal comfort and learning efficiency of Chinese college students. Findings are intriguing in that temperatures delivering optimal thermal comfort and optimal learning efficiency differ. Specifically: (1) Students generally feel most comfortable when the room temperature is approximately 24 °C; (2) Combined studies comparing temperature on thermal comfort and learning efficiency found that college students learn better in slightly colder environments; (3) Based on the comprehensive value of satisfying the best thermal comfort and high learning efficiency, the optimal temperature range is 20.6 °C to 22.2 °C. Full article

In the production of clad rolled plates from asymmetric sandwich-type slab for pipeline applications, achieving both target mechanical properties and high geometric flatness remains a critical challenge due to differential thermal stresses between the dissimilar steel layers during accelerated cooling. This study aims to develop an optimal cooling schedule for a 25 mm thick clad plate, comprising a X70-grade steel base layer and an AISI 316L cladding, to ensure required strength and minimal bending. A comprehensive approach was employed, integrating a 3D finite element model (Ansys) for simulating thermoelastic stresses with a CatBoost machine learning model trained on industrial data to predict heat transfer coefficients accurately. A parametric analysis of cooling strategies was conducted. Results showed that a standard cooling strategy caused unacceptable bending of plate after cooling exceeding 130 mm. An optimized strategy featuring delayed activation of the lower cooling headers (on the cladding side) created a compensating thermoelastic moment, successfully reducing bending to approximately 20 mm while maintaining the base layer’s requisite mechanical properties. The findings validate the efficacy of the combined FEM-machine learning methodology and propose a viable, industrially implementable cooling strategy for high-quality clad plate production. Full article

Sustainable management of indoor and outdoor air quality is essential for protecting public health, enhancing well-being, and supporting resilient urban environments. Low-cost air quality sensors enable continuous, real-time monitoring of key pollutants and, when combined with data analytics, provide scalable and cost-effective insights for smart building operation and environmental decision-making. This pilot study evaluates an indoor–outdoor air quality monitoring system deployed at the Faculty of Electrical Engineering and Information Technologies in Skopje, with a focus on: (i) PM2.5 and PM10 concentrations and their relationship with meteorological conditions and human occupancy; (ii) sensor responsiveness and reliability in an educational setting; and (iii) implications for sustainable building operation. From January to March 2025, two indoor sensors (a classroom and a faculty hall) and two outdoor rooftop sensors continuously measured PM2.5 and PM10 at one-minute intervals. All sensors were calibrated against a reference instrument prior to deployment, while meteorological data were obtained from a nearby station. Time-series analysis, Pearson correlation, and multiple regression were applied. Indoor particulate levels varied strongly with occupancy and ventilation status, whereas outdoor concentrations showed weak to moderate correlations with meteorological variables, particularly atmospheric pressure. Moderate correlations between indoor and outdoor PM suggest partial pollutant infiltration. Overall, this pilot study demonstrates the feasibility of low-cost sensors for long-term monitoring in educational buildings and highlights the need for adaptive, context-aware ventilation strategies to reduce indoor exposure. Full article

Background/Objectives: The incorporation of peer support within mental health services has shown benefits for service users’ recovery and engagement, yet implementation is often hindered by role ambiguity and limited institutional recognition. The aim of this study is to explore the experiences of workers in a programme that provides peer support within a personal assistance model. The focus is on how they perceive the shaping of their professional role and their integration within care teams, rather than on evaluating service outcomes or effectiveness. Methods: An interpretive qualitative methodology with an exploratory approach was used. The study was conducted in a single organisational setting and focused on the self-reported experiences of personal assistants. Fieldwork was conducted in 2025 with ten personal assistants. Data were obtained through individual semi-structured interviews and one focus group with the same participants. A thematic content analysis combining inductive and deductive coding strategies was conducted using MAXQDA (version 24.11). Results: Findings indicate that the Personal Assistant role was perceived as reducing some of the ambiguity commonly associated with peer support, due to a clearer contractual framework and a more explicit delineation of functions. However, tensions persisted in relation to its hybrid professional identity, experiences of task overload, and ongoing gaps in coordination with traditional professional roles. Key facilitators included institutional support, accessible coordination, a supportive culture of care, and informal peer networks. Perceived benefits were reported for service users, including increased trust, hope, and autonomy, as well as for assistants, who described enhanced professional purpose and progress in their own recovery, alongside risks of emotional strain. Conclusions: Analysing the perspective of participants, the personal assistance model may represent a promising framework for the professionalisation of peer support through functional clarity, continuous supervision, and recognition of experiential knowledge. Further progress requires strengthening internal communication, expanding training opportunities, and enhancing the structural participation of personal assistants in decision-making. The study contributes an exploratory qualitative perspective to the growing literature on integrating lived-experience professionals into mental health services. Full article

Smart campuses employ advanced digital technologies and intelligent communication systems to enhance educational, operational, and living environments. This study investigates stakeholder perceptions of smart campus priorities in Saudi Arabia through a structured questionnaire administered to students and faculty. The study considered King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran as a case study in this regard. The survey examined 22 smart campus aspects grouped into six domains: smart education, smart mobility, smart energy and waste management, smart buildings and work environment, smart safety and security, and smart open spaces. The results indicated strong consensus regarding the importance of all domains, with an overall mean rating of 4.3 out of 5.0 and Relative Importance Index (RII) values ranging from 0.77 to 0.91. The highest-ranked aspects included IoT-enabled cooling energy optimization, smart public transportation, smart lighting systems, smart workflow management, e-libraries, and fire prevention and detection systems, reflecting a pronounced emphasis on infrastructure quality, energy efficiency, and operational effectiveness. The findings suggest that smart campus development in Saudi Arabia should prioritize high-impact, user-valued initiatives that align with Vision 2030 objectives including digital transformation. Strategic early investments in smart buildings, energy management, and mobility systems can deliver measurable benefits in this regard. Further research is recommended to consider additional case studies in the Saudi context to ensure that smart campuses remain contextualized and responsive to user needs. Full article

Visual cues in urban street environments shape residents’ perceived safety, and these perceptions often differ across social groups. Using Shanghai as a case study, this research focuses on two vulnerable populations: older adults and migrants. In the context of rapid urban transformation and increasingly fine-grained governance, perceived safety not only reflects environmental experience but also relates to whether different social groups can receive equitable perceptual support and access to opportunities for public-space use. We trained a deep learning model and rated perceived safety using over 160,000 street-level images, integrated with demographic census data at the neighborhood level, to systematically examine inequalities in visual environment perception and underlying group-specific mechanisms. However, existing studies have largely relied on small-sample surveys or average-effect analyses, and systematic evidence remains limited that can simultaneously characterize city-scale inequalities in perceived safety, disparities in group exposure, and group-specific mechanisms, while translating findings into actionable guidance for targeted governance. Firstly, we quantified spatial inequality in perceived safety using the Gini coefficient and the Theil T index. Decomposition results indicate that the remaining disparity is primarily associated with between-group differences linked to social structure. Nonparametric tests and multiple linear regression further identified significant interactions between demographic characteristics (the share of older adults and the migrant proportion) and visual environmental features, confirming group-differentiated responses to comparable streetscape conditions. In addition, we developed a priority governance index that combines perceived safety scores with vulnerability indicators to spatially identify neighborhoods requiring targeted interventions. Results suggest relatively low overall spatial inequality in perceived safety at the city scale, while decomposition analyses reveal clear group-structured disparities between central and peripheral areas and between local residents and migrants. Migrants are more frequently concentrated in neighborhoods with lower perceived safety. Priority intervention areas are primarily older-adult communities in central districts and migrant settlements in peripheral areas, characterized by lower perceived safety and higher demographic vulnerability. These findings underscore the need to shift urban renewal from uniform improvements toward differentiated strategies that account for perceptual equity and social identity. Our main contribution is not the development of a new network architecture but the alignment of image-based perception estimates with demographic vulnerability at the neighborhood scale. By combining inequality decomposition with tests of interaction mechanisms, we provide governance-relevant evidence for identifying priority intervention areas and advancing fine-grained renewal decisions oriented toward visual justice. Full article

Neurodegenerative diseases, including Alzheimer’s disease, are marked by cholinergic dysfunction, oxidative stress, and reduced neurotrophic support, which drives the quest for multifunctional therapeutic agents. This pilot study presents four novel monoterpene–aminoadamantane conjugates (MACs 1–4) designed to combine the antioxidant and neuromodulatory characteristics of monoterpenes with the neuroprotective properties of aminoadamantane derivatives. Their physicochemical characteristics, blood–brain barrier permeability, and binding affinity to human acetylcholinesterase (AChE) were evaluated using molecular docking and in silico descriptor analysis. In vivo, the neuroprotective efficacy of the MACs was investigated in a scopolamine-induced dementia model in rats, employing behavioral tests. Biochemical assays conducted in the hippocampus and prefrontal cortex assessed AChE activity, antioxidant enzyme performance, lipid peroxidation levels, total glutathione content, and BDNF concentrations. The findings indicate that MAC1, MAC3, and MAC4 demonstrate favorable calculated blood–brain barrier permeability, strong predicted affinity for AChE, and significant in vivo alleviation of scopolamine-induced memory deficits, in conjunction with improvement of key markers of oxidative stress and cholinergic function. These results show that the structural hybridization of myrtenal with aminoadamantane frameworks produces promising multifunctional ligands that are relevant for Alzheimer’s-type neurodegeneration. Full article

Background: Communication with patients and families at the end of life is key to quality care, allowing for informed decisions and emotional support. This study explores the experience of nursing students in clinical simulations, analyzing their emotions, perceptions of the family role, the impact on their communication skills, and their reflection on the role of nursing in these contexts. Methods: This study was conducted at the Faculty of Health Sciences of UNIE University, Spain, with 44 first-year students enrolled in the Fundamentals of Psychology in Health Sciences course. Data were collected through focus groups and reflective narratives with open-ended questions between January and February 2025. Following data collection, transcripts were generated and subjected to a thematic analysis following the COREQ checklist. Results: Five thematic blocks and their categories were identified: (T1) Family as a pillar of care; (T2) Relationship with the family; (T3) Communication as a therapeutic tool; (T4) Emerging emotions; (T5) Learning through simulation. Conclusions: The family is a fundamental pillar at the end of life, providing emotional and practical support to the patient and the care team. Communication is key to building trust and facilitating acceptance of the process. Students experience diverse emotions that reflect the complexity of the accompaniment. Simulation at the end of life allows nursing students to develop communication skills, reflect on their professional role, and manage complex emotions. Full article