Search Results (772)

Background: This study explored the observations and experiences of athletic trainers (ATs) regarding food security constraints and drivers among collegiate student-athletes through the framework of the social determinants of health (SDOH). Methods: Seventeen ATs from nine public Mountain West Division I schools participated in one of six 90 min, virtual focus groups conducted in July–December 2023. A phenomenological design and constructivist theoretical framework were employed to analyze transcripts, compile themes, infer food security constraints and drivers, and map them to SDOH domains. Results: Emerging constraints and drivers mapped to seven of the eight SDOH domains (education, employment, health systems and services, housing, income and wealth, physical environment, and social environment); no themes related to the public safety domain. Conclusions: ATs identified a considerable number of food security constraints and drivers; however, based on the findings of other studies, their observations were not comprehensive. Incorporating the findings of those other studies, food security constraints and drivers exist across all eight SDOH domains. Four concepts spanned multiple domains: student-athlete normative expectations, nutrition/food security prioritization, financial disparities, and residing off campus. To ensure food security among collegiate student-athletes, AT perceptions suggest that structural changes facilitating the implementation of necessary food drivers may be warranted. Full article

In the campus planning of severe cold regions, indoor pedestrian corridors have emerged as an important strategy for mitigating harsh weather and maintaining pedestrian network continuity. These enclosed systems provide thermal comfort and physical convenience for faculty and students. However, reduced visual contact with outdoor natural environments may be associated with less favorable affective experiences. Existing research primarily emphasizes functional connectivity and spatial efficiency, with limited quantitative evidence on the visual characteristics of indoor corridors and their relationship with emotional perception. Drawing upon environmental psychology, this study examines the associations between corridor visual characteristics and five AI-predicted affective perception dimensions. Using deep-learning-based computer vision, semantic segmentation, and statistical analysis, environmental features and predicted perception scores were analyzed. A local human validation showed moderate overall agreement between human consensus ratings and AI-predicted scores, supporting their use as exploratory perception proxies. The findings indicate that indoor-corridor-related visual features were negatively associated with predicted beauty scores and positively associated with predicted depression scores, while predicted boringness was positively associated with wall enclosure. Predicted safety and liveliness showed weaker associations with individual visual features. These results provide exploratory evidence for balancing thermal protection, circulation efficiency, and psychological comfort in future campus corridor design. Full article

The integration of sensing systems, Artificial Intelligence (AI), and data analytics technologies is enabling the development of digital twins for monitoring and managing complex built environments. University campuses represent suitable scenarios for the deployment and evaluation of these technologies due to their diversity of facilities, heterogeneous operational systems, and dynamic usage patterns. This paper presents the design and deployment of a campus-scale operational digital twin developed for the University of Alicante Smart Campus. The proposed environment integrates multiple data sources including indoor environmental sensors, electricity and water consumption monitoring systems, photovoltaic generation data, irrigation networks, and WiFi connectivity information used as a proxy for occupancy estimation. Through a continuously updated and spatially synchronized digital representation of the campus, the platform supports environmental comfort monitoring, occupancy analysis, environmental quality assessment, anomaly detection, alert generation, and AI-enabled analytical services. A distinguishing characteristic of the proposed approach is its long-term real-world deployment. The system currently manages several years of historical data comprising hundreds of millions of time-series measurements collected from distributed monitoring systems across the campus. The results demonstrate the feasibility of maintaining a campus-scale digital twin operating as an institutional monitoring and decision-support environment. Full article

Green exposure in public spaces plays a critical role in promoting mental health and well-being, especially for populations experiencing elevated psychological pressure in academically demanding environments such as university campuses. However, existing research remains largely limited to coarse-scale assessments, often overlooking the nuanced and nonlinear interactions between visual environmental characteristics and restorative quality across diverse spatial typologies. Addressing this gap, this study proposes a fine-scale, data-driven framework that integrates computer vision (CV) with perception-based evaluations using Shandong Jianzhu University as a case study. A semantic segmentation model (Mask2Former) was employed to extract 14 visual environmental elements from 193 campus photographs, while 426 students provided 4260 image-based evaluations of Perceived Restorative Quality (PRQ). Ridge regression optimized through cross-validation was used to identify key environmental correlates of PRQ, K-means clustering was applied to classify Campus Restorative Space (CRS) typologies, and Generalized Propensity Score Matching (GPSM) was employed to examine nonlinear dose–response relationships between sensory dimensions and restorative quality. The results indicate that natural elements, particularly vegetation and water-related features, are generally associated with higher PRQ scores; however, restorative responses do not consistently follow linear patterns. Several sensory dimensions exhibited threshold effects and diminishing marginal returns, suggesting that restorative benefits depend on balanced environmental composition rather than the simple accumulation of green elements. Furthermore, three CRS typologies—Greenery-Dominant Spaces, Open Landscape Spaces, and Courtyard Spaces—demonstrated distinct restorative pathways, indicating that the effects of environmental characteristics vary across spatial contexts. These findings support a context-dependent understanding of restorative environments and highlight the importance of spatial typology, environmental composition, and nonlinear responses in shaping restorative experiences. The study proposes a transferable analytical framework for assessing campus restorative environments and offers practical implications for evidence-based campus planning and design. Full article

The concept of Smart Campus has emerged as a strategic approach for the digital transformation of higher education institutions through the integration of intelligent technologies and data-driven systems. This study presents a systematic literature review aimed at analyzing Smart Campus initiatives in Higher Education, focusing on technological infrastructures, architectural frameworks, sustainability dimensions, and educational implications. The review was conducted following a structured protocol across IEEE Xplore, ScienceDirect, Scopus, and SpringerLink, resulting in a final corpus of 45 peer-reviewed open-access articles published between 2015 and 2026. The findings reveal that Smart Campus ecosystems are primarily supported by Internet of Things, artificial intelligence, Big Data, and distributed computing architectures, enabling real-time monitoring, predictive analytics, and improved institutional decision-making. While environmental sustainability is widely addressed through energy management systems, smart grids, intelligent tutoring systems and digital twins, social, governance, and cultural dimensions remain less developed. Additionally, challenges related to interoperability, data governance, and system integration persist across the literature. Based on these results, this study proposes the Smart Sustainable University City (SSUC) model as an integrative conceptual framework that reconceptualizes the university as a sustainable and intelligent micro-city ecosystem. The SSUC model integrates technological infrastructures, multidimensional sustainability, and educational innovation, providing a holistic approach to Smart Campus development. This research contributes to the advancement of Smart Campus studies by identifying key trends, gaps, and future directions for the design of scalable and sustainable intelligent educational environments. Full article

As student mental health concerns intensify, campuses are exploring wellness initiatives including equine-assisted intervention (EAI). Because maintaining equines on campus is not always feasible, virtual reality (VR) has emerged as an alternative method for delivering equine experiences. Although equine environments alone may reduce stress, limited research has examined how environmental setting influences outcomes during VR EAI. Therefore, the objective of this exploratory study was to compare stress levels, as observed through heart rate and cortisol concentrations, for students participating in VR EAI within equine and classroom settings. Students participated in one of the following on-campus VR EAI programs: (1) classroom (n = 10) or (2) equine (n = 14) environment. Although both environmental types demonstrated a significant difference between minimum-maximum heart rate during the session (p < 0.05), only the classroom led to a significant reduction in heart rate post-session (p = 0.02). Neither environmental type observed a significant difference between pre-post cortisol concentrations (p > 0.05). Environmental type was an influencing factor for cortisol concentrations (p = 0.04). These findings suggest that environmental context may influence physiological responses during VR EAI; however, further research is warranted that includes larger samples, more controlled designs, and integrated subjective and physiological measures. Full article

Tactile maps play a critical role in supporting spatial learning and independent mobility for people with visual impairments, particularly in complex environments such as university campuses. This study evaluates the effects of two tactile map production methods, 3D printing and heat-sensitive embossed paper, through a user-centered mixed-methods experimental design. The research was conducted on the North Campus of Boğaziçi University and involved 15 university students with visual impairments. The experimental process consisted of two stages: (1) controlled tactile map perception and evaluation, and (2) real-world wayfinding and on-site navigation experience. Quantitative data were collected through structured questionnaires and analyzed using descriptive statistics, while qualitative data were obtained through observations, open-ended feedback, and thematic analysis. The findings suggest that tactile map production methods may influence tactile perception, mental map formation, and user confidence. Participants generally reported that 3D-printed maps provided clearer tactile spatial organization and supported mental mapping in complex environments, whereas embossed paper maps offered advantages in portability and rapid accessibility. Full article

Smart campus development is increasingly associated with the combined use of IoT technologies, artificial intelligence, cloud infrastructures, and large-scale data analytics in higher education. Despite this progress, many existing data models are not well-suited to the educational domain, particularly when interoperability and real-time analytical capabilities are required. To address this limitation, the study proposes a Smart Campus Education Data Model (SCEDM), which can be integrated into any FIWARE-based platform. The model is organized as a layered architecture that includes data acquisition, processing, and storage; analytics and decision support; application presentation; and security. The proposed model is not presented only at a conceptual level; it is also validated in a containerized FIWARE environment built around the Orion-ld Context Broker and NGSI-ld specifications. The SCEDM model is validated in a system that supports real-time state management across multiple campus domains. The model’s practical operation is validated across several experimental scenarios, including a simulation of a lecture process, classroom occupancy monitoring, and automated notifications to external platforms. In addition, the study compares five international case studies from different contexts. The comparison shows that, despite differences across local settings, similar benefits can be observed in campus operations and learning conditions. The study also recognizes several continuing challenges in the development of smart campuses, including interoperability, long-term scalability, data governance, privacy protection, stakeholder engagement, and financial sustainability. In response to these issues, the authors propose practical design guidelines alongside strategic recommendations for adoption at the institutional level. Full article

Though it has been well established that a subset of collegiate student-athletes in the United States (U.S.) are not food secure, efficacious solutions for mitigating this issue appear to be elusive. This study identified system-level barriers and enablers to food security among student-athletes through the lens of collegiate athletic professionals (athletic directors, athletic trainers, sports dietitians, and strength and conditioning coaches). Using a phenomenological-informed design and constructivist theoretical framework, researchers explored the experiences and insights of participants (n = 27, Division I, northwestern U.S.) via six online focus groups July–December 2023. Categorical themes for barriers to food security were finances, culture, and competing priorities; enablers were on-campus supplemental food assistance, referrals to community services, and educational programming. A gap analysis was performed; existing solutions enabling food security and aspirational solutions for addressing food insecurity were compared, and gaps identified. Based on the insights of athletic professionals, a lack of student-athlete-centric solutions to ensure food security emerged. The introduction of shared revenue programs in American college sports may address some of food security barriers; however, additional funds may not mitigate the contribution of competing priorities and scheduling issues experienced by student-athletes. System-level changes are required to enable food security among this population. Full article

The rapid growth of express packaging waste (EPW) is creating increasing challenges for urban waste management and high-quality resource recovery, while reliable automated sorting remains difficult because of heterogeneous materials, deformable packaging, random stacking, and frequent partial occlusion. This study develops a system-oriented RGB-D sensing framework to improve the reliability of EPW sorting in conveyor-belt recycling operations. A synchronized and spatially registered RGB-D paired dataset, MEPWaste, was constructed containing 4528 paired samples (9056 RGB/depth images) from 10 representative categories of inner and outer packaging under multiple levels of stacking and occlusion. Based on this dataset, a multimodal detection framework was designed to improve feature reliability within each modality, reinforce spatial consistency between modalities, and enhance detection stability in densely stacked scenes with fragmented visible evidence. The proposed framework achieved 89.1% ${mAP}_{50}$ and 69.3% ${mAP}_{50:95}$, exceeding the RGB-only model by 5.3 and 5.8 percentage points, respectively, and the RGB-D baseline by 2.7 and 3.4 percentage points, with recall reaching 85.1%. A closed-loop sensing–inference–execution pipeline integrating synchronized RGB-D acquisition, real-time detection, robotic grasping, and PLC-based control was further implemented to examine engineering feasibility. Overall, the study provides a practical technical pathway for improving EPW sorting reliability and supporting intelligent resource recovery in waste management systems, with potential applicability to green campus management scenarios characterized by high-frequency parcel consumption and concentrated packaging waste generation. Full article

Pedestrian Spatial Experience PSE in urban spaces is a multi-faceted topic that requires the thematization of assessment methods due to their fragmentation across studies. Accordingly, this systematic review followed an inductive approach to define a framework of PSE assessment themes reflecting their evaluation in public and university campus spaces. This systematic review included open-access, accessible, peer-reviewed sources based on assessment-focused English research that followed defined frameworks on the effects of urban environments on adult PSE. Studies were excluded if they focused on non-pedestrians or vulnerable user groups, examined non-pedestrian-scale contexts, explored pedestrian experience in virtual environments, assessed interior spaces, lacked a structured attribute-based assessment framework, were review articles, did not specify how urban environments shape pedestrian experience, investigated non-urban or rural areas, or examined urban settings without clearly defined street or square infrastructure. The review relied on querying PSE-related bibliography from the Scopus and Web of Science databases on 12 October 2025; results were processed through a screening procedure according to the inclusion and exclusion criteria. The final sets of sources reviewed included 83 and 24 sources related to PSE assessment in public and university campus spaces, respectively. Risk of Bias (RoB) tools included the Joanna Briggs Institute (JBI) tool for cross-sectional studies, tailored for urban spatial studies, and the Prediction model Risk Of Bias Assessment Tool (PROBAST+AI), tailored for ABM studies. Using a data extraction sheet and codebook to identify the prominent codes in the included sources, in addition to reviewing frequent words and the methods of the included sources, clarified the main conceptual framework of PSE assessment themes. The thematic categorization of PSE studies was followed by analyses of the frequencies of the themes, the prevalence of themes across countries and cities, and the theoretical explorations within the themes over the years in both reviewed contexts. Subsequently, synthesizing both sets clarified the interrelations between themes, methods, and tools as an attempt to address gaps in PSE assessment methods. The main results of this review are the 11 themes of PSE assessment that were identified from the reviewed sources. Data analyses and syntheses indicated a high prevalence of quantitative methods relying on visual aspects, signifying the dominance of the Cognitive and Navigational Experience theme due to its frequent assessment by numerous and diverse sets of methods in both reviewed sets. Nevertheless, the Temporal Experience theme emerged as the least considered. The key limitations of this systematic review include its reliance on accessible articles from bibliographic databases, as well as its focus on adult populations as the common users of public and university campus spaces. This review decodes PSE in terms of its assessment themes through the methods followed and the applied tools within real environments. As an application of the introduced conceptual framework, this systematic review clarifies the comparison of the themes examined between public and university campus spaces. The findings of this systematic review provide a foundation for a comprehensive understanding of PSE, thereby informing the design of more user-centered environments. Full article

The rehabilitation of aging educational buildings has become increasingly important in the context of sustainable campus development and adaptive reuse of existing infrastructure. This study proposes an integrated BIM-based framework for the rehabilitation of underutilized academic spaces through the combined application of Building Information Modeling (BIM), biophilic interior design principles, and Artificial Intelligence (AI) predictive modeling. The methodology was implemented in a case study involving non-functional areas within the Faculty of Aerospace Engineering at the National University of Science and Technology POLITEHNICA Bucharest. Autodesk Revit was employed to develop a parametric digital model of the existing structure, support spatial reconfiguration, and assess environmental and functional performance indicators throughout the rehabilitation process. To evaluate the effectiveness of the proposed framework, multiple performance criteria were considered, including spatial efficiency, daylight performance, material sustainability, acoustic quality, and user-perceived visual comfort. Furthermore, a synthetic dataset generated through parametric simulation was utilized to train and compare four machine learning algorithms (Multiple Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Networks) to predict user comfort based on spatial and environmental variables. The rehabilitation strategy resulted in an 18% increase in usable floor area, a 26% improvement in average daylight factor, a 25% increase in renewable material utilization, and a 38% reduction in estimated acoustic reverberation time. Simultaneously, the predictive modeling revealed that the Artificial Neural Network (ANN) provided the highest accuracy (R² = 0.91) in capturing the non-linear relationship between biophilic design elements and perceived interior quality. By integrating Gilbreth’s principles of cognitive ergonomics, the AI framework actively prevents the rigid, purely quantitative optimization associated with “Digital Taylorism, The findings demonstrate that the proposed BIM-integrated rehabilitation framework can support both technical optimization and user-centered environmental enhancement in educational facilities. The study contributes a transferable digital methodology for sustainable academic building transformation, combining geometric precision, predictive environmental performance assessment, and human-centered design principles within a unified rehabilitation workflow. Full article

This study addresses the limited campus-specific operationalization of attention restoration theory and the insufficient empirical validation of perceptual factors in university settings. It aims to identify key perceptual factors influencing attention restoration on campuses and to develop an evidence-based assessment framework for restorative campus design. An initial set of ten perceptual factors was synthesized from Attention Restoration Theory, Stress Recovery Theory, and Environmental Preference Theory. A two-round e-Delphi process involving 26 multidisciplinary experts from academia and design practice was conducted, followed by combination weighting to derive factor weights and construct a weighted assessment model. The model was validated through a student survey and tested for applicability in a representative campus space. The process achieved consensus on seven primary perceptual factors with the following weights: accessibility (0.187), comfort (0.150), visibility (0.143), sense of belonging (0.143), familiarity (0.142), peacefulness (0.118), and recognition (0.117). A practical assessment tool yielding a 0–10 restorativeness score was developed to quantitatively evaluate campus spaces. Student survey data confirmed the correspondence between model-derived scores and observed issues in the sample space. This study extends restorative environment theories to the hybrid natural-built contexts of university campuses and integrates an online Delphi procedure to develop a decision-support model. The resulting framework and tool enable planners and designers to diagnose deficiencies in campus environments and implement targeted interventions to enhance attention restoration. Full article

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article

Universities increasingly compete through value propositions, service processes, and communication ecosystems rather than through program supply alone. Yet evidence remains limited on how students’ perceptions of the educational marketing mix are associated with satisfaction in non-metropolitan higher education settings in Latin America. This study examines a private university branch campus in a Latin American emerging economy using a quantitative, cross-sectional, non-experimental design and a probabilistic stratified sample of 287 complete student responses. Educational marketing was operationalized as students’ perceived evaluation of the educational marketing mix—product, price, place, and promotion—whereas satisfaction was measured with SERVQUAL-informed, performance-oriented service-evaluation items. The findings show a strong positive association between the perceived educational marketing mix and student satisfaction, with promotional communication emerging as the most closely related dimension. Descriptively, both constructs were concentrated at intermediate levels, indicating an acceptable but non-distinctive institutional experience and pointing to service weaknesses in security, empathy, and responsiveness. The article contributes by problematizing the use of marketing-mix logic in higher education, clarifying that satisfaction is not a proxy for educational quality or belonging, and showing how perceived value communication and service delivery are connected with students’ satisfaction judgments in an emerging-economy context. Full article