Search Results (388)

Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart EV charging. The proposed architecture combines upper-level receding horizon optimization with lower-level priority-based dispatch, enabling cost-efficient energy allocation and fair distribution among EVs. The system was deployed at the Risø campus of the Technical University of Denmark (DTU) and tested over two days under realistic operational conditions, including heterogeneous EV behavior and limited grid capacity. The control system demonstrated autonomous operation, responsiveness to price signals, and effective coordination between control layers. High energy delivery rates were achieved, nearly 100% on the first test day and close to 90% on the second, despite operating under a constrained energy budget. The study also documents practical challenges encountered during deployment, such as charger communication faults and EV-side issues, and proposes adaptation strategies. These results confirm the feasibility of distributed smart charging in real-world conditions and provide actionable insights for future implementations. Full article

This paper investigates within-scene path loss prediction at 1.8 GHz in a smart-campus micro-urban environment using multivariate machine-learning (ML) models. We leverage an open measurement campaign from Covenant University (Nigeria) comprising three routes with per-sample geospatial predictors—longitude, latitude, altitude, elevation, Tx–Rx distance, and clutter height—and train Random Forests (RF), Gradient Boosting (GB), Support Vector Regression (SVR), Gaussian Processes (GP), and a shallow neural network (NN). A unified pipeline with 5-fold cross-validation (CV), seeded reproducibility, and Optuna-driven hyperparameter search is adopted; performance is reported as RMSE/MAE/R² (mean ± sd). To contextualize feature reliability, we include Pearson correlation heatmaps and Variance Inflation Factors (VIFs), a systematic ablation of predictors, and TreeSHAP beeswarm analyses on held-out splits. We also evaluate spatially aware validation (blocked CV within route and leave-one-route-out checks) to mitigate optimism due to spatial autocorrelation. Results show that multivariate ML consistently outperforms classical empirical formulas (COST-231, ECC-33) in this campus setting, with RF achieving the lowest errors across routes (RMSE ≈ 2.14/2.16/2.95 dB for X/Y/Z, respectively), while GB ranks second and kernel methods (SVR/GP) and the NN trail closely behind. Ablation confirms that distance plus coordinates drive the largest gains, with terrain/clutter providing route-dependent refinements. SHAP analyses align with these findings, highlighting stable, interpretable contributions of geospatial covariates. Spatial CV increases absolute errors moderately but preserves model ranking, supporting the robustness of conclusions. Overall, scenario-aware, multivariate ML yields material accuracy gains for smart-campus planning at 1.8 GHz. Full article

University campus teaching areas are essential spaces for students’ daily learning and recovery, in which soundscapes play a crucial role in shaping restorative experiences. This study aimed to explore the restorative effects of soundscapes in campus teaching areas and the factors influencing these effects. Field surveys, psychological assessments, and physiological experiments were conducted to evaluate restorative perceptions, which were characterized by three dimensions: Attractiveness, Coherence, and Being Away. The findings indicate that both the visual environment and acoustic characteristics significantly shaped restorative outcomes. Natural landscapes, particularly green areas and waterscapes, enhanced the restorative potential of soundscapes, while natural sounds, such as birdsong, fountain sound, and rustling leaves, were perceived as more restorative. In contrast, traffic noise, crowd noise, and class bell sound reduced restorative perceptions. Furthermore, the signal-to-noise ratio (SNR) played a critical role, with the higher SNR values of birdsong relative to traffic noise being associated with stronger restorative effects. These results suggest that campus soundscape design should prioritize green landscapes, introduce or amplify natural sounds, and optimize the SNR of restorative sounds. Overall, this research provides both theoretical support and practical guidance for designing healthier campus environments that foster students’ recovery and well-being. Full article

In recent years, Location-Based Augmented Reality (LAR) systems have been increasingly implemented in various applications for tourism, navigation, education, and entertainment. Unfortunately, the LAR content creation using conventional desktop-based authoring tools has become a bottleneck, as it requires time-consuming and skilled work. Previously, we proposed an in-situ mobile authoring tool as an efficient solution to this problem by offering direct authoring interactions in real-world environments using a smartphone. Currently, the evaluation through the comparison between the proposal and conventional ones is not sufficient to show superiority, particularly in terms of interaction, authoring performance, and cognitive workload, where our tool uses 6DoF device movement for spatial input, while desktop ones rely on mouse-pointing. In this paper, we present a comparative study of authoring performances between the tools across three authoring phases: (1) Point of Interest (POI) location acquisition, (2) AR object creation, and (3) AR object registration. For the conventional tool, we adopt Unity and ARCore SDK. As a real-world application, we target the LAR content creation for pedestrian landmark annotation across campus environments at Okayama University, Japan, and Brawijaya University, Indonesia, and identify task-level bottlenecks in both tools. In our experiments, we asked 20 participants aged 22 to 35 with different LAR development experiences to complete equivalent authoring tasks in an outdoor campus environment, creating various LAR contents. We measured task completion time, phase-wise contribution, and cognitive workload using NASA-TLX. The results show that our tool made faster creations with 60% lower cognitive loads, where the desktop tool required higher mental efforts with manual data input and object verifications. Full article

Burnout among students is a widely recognized concern, yet little research has focused on the experiences of disabled students and even less on the social factors at and off campus that contribute to their burnout. To address this gap, we surveyed 91 undergraduate students enrolled in a critical disability studies course at a Canadian university using an online qualitative survey approach. These students, many of whom see themselves as allies of disabled people, were asked whether they believe disabled students are at risk of burnout and what they perceive to be the causes. The majority viewed disabled students as being at significant risk, and 92% identified the social environment as the contributor. Attitudinal inaccessibility, including discrimination, stigma, and exclusion, has emerged as the leading factor in both university settings and broader societal contexts. These findings suggest that interventions to reduce burnout must address both educational environments and the wider lived realities of disabled students. This study contributes to the limited literature on burnout among disabled students and highlights the importance of ally perspectives in understanding and addressing systemic contributors to burnout. Full article

As the core space for students’ daily living and learning, the quality of the indoor wind environment and air quality in dormitory buildings is particularly critical. However, existing studies often neglect natural ventilation optimization under local climatic conditions and the multidimensional evaluation of health benefits, leaving notable gaps in dormitory design. Under the Healthy China Initiative, the indoor wind environment in university dormitories directly impacts students’ health and learning efficiency. This study selects dormitory buildings in Xuzhou as the research object and employs ANSYS FLUENT 2020 software for computational fluid dynamics (CFD) simulations, combined with orthogonal experimental design methods, to systematically investigate and optimize the indoor wind environment with a focus on healthy ventilation standards. The evaluation focused on three key metrics—comfortable wind speed ratio, air age, and CO₂ concentration—considering the effects of building orientation, corridor width, and window geometry, and identifying the optimal parameter combination. After optimization based on the orthogonal experimental design, the proportion of comfortable wind speed zones increased to 44.6%, the mean air age decreased to 258 s, and CO₂ concentration stabilized at 613 ppm. These results demonstrate that the proposed optimization framework can effectively enhance indoor air renewal and pollutant removal, thereby improving both air quality and the health-related performance of dormitory spaces. The novelty of this study lies in integrating regional climate conditions with a coordinated CFD–orthogonal design approach. This enables precise optimization of dormitory ventilation performance and provides locally tailored, actionable evidence for advancing healthy campus design. Full article

Outdoor thermal comfort in tropical cities is increasingly threatened by rapid urbanization, high humidity, and insufficient climate-sensitive planning. Despite numerous studies on urban heat mitigation, there is a lack of empirical and numerical research that evaluates the synergistic application of bioclimatic strategies under humid tropical conditions. This paper addresses this gap by analyzing the combined effect of arborization, dry mist systems, water bodies, and sprinklers on outdoor thermal comfort at the Víctor Levi Sasso Campus of the Technological University of Panama. We hypothesized that synergistic application of these strategies provides greater thermal comfort improvements than isolated interventions. The central research question guiding this study was: To what extent can combined bioclimatic strategies enhance outdoor thermal comfort compared to individual strategies in humid tropical environments? To answer this, a hybrid methodology was employed, integrating ENVI-met dynamic simulations with in situ measurements and thermal comfort surveys based on the physiological equivalent temperature (PET) index and subjective comfort scales. The results demonstrate that combined strategies achieve superior reductions in mean radiant and surface temperatures while improving subjective comfort perceptions, highlighting their potential for context-sensitive urban design in tropical regions. Full article

This study presents the development and deployment of a modular digital twin system designed to enhance sustainable facility management within a smart campus context. The system was implemented at the Faculty of Engineering, Chiang Mai University, and integrates 3D spatial modeling, real-time environmental and energy sensor data, and multiscale dashboard visualization. Grounded in stakeholder-driven requirements, the platform emphasizes energy management, which is the top priority among campus administrators and technicians. The development process followed a four-phase methodology: (1) stakeholder consultation and requirement analysis; (2) physical data acquisition and 3D model generation; (3) sensor deployment using IoT technologies with NB-IoT and LoRaWAN protocols; and (4) real-time data integration via Firebase and standardized APIs. A suite of dashboards was developed to support interactive monitoring across faculty, building, floor, and room levels. System testing with campus users demonstrated high usability, intuitive spatial navigation, and actionable insights for energy consumption analysis. Feedback indicated strong interest in features supporting data export and predictive analytics. The platform’s modular and hardware-agnostic architecture enables future extensions, including occupancy tracking, water monitoring, and automated control systems. Overall, the digital twin system offers a replicable and scalable model for data-driven facility management aligned with sustainability goals. Its real-time, multiscale capabilities contribute to operational transparency, resource optimization, and climate-responsive campus governance, setting the foundation for broader applications in smart cities and built environment innovation. Full article

This study assesses the biometeorological conditions in the Ancient Olive Grove Campus of the University of West Attica, near central Athens, to evaluate human thermal comfort in an urban green space. Hourly calculations of the Universal Thermal Climate Index (UTCI) and HUMIDEX were performed using BioKlima 2.6, based on data from the campus meteorological station (DAVIS Vantage Pro 2), covering July 2022 to April 2024. Results show diverse thermal comfort-discomfort conditions, with more extremes in warmer months. The study highlights how microclimatic factors influence thermal perception, supporting efforts to design climate-adaptive, user-friendly urban environments with historical and ecological value. Full article

The evidence base supporting practices to widen participation in higher education, such as campus visits and multi-intervention programs for younger students, remains limited. In order to address this gap, this exploratory study examines the impact of repeated university campus exposure on primary-aged children in the UK. We studied the influence of a campus tour on the views of a group of 78 primary school children who had visited the setting on a previous occasion. Our cohort (32M, 45F, aged 10–11) was drawn from schools with high populations of pupils from low-socioeconomic status backgrounds. Using a pre- and post-visit survey design, we assessed changes in perceptions following a second campus tour, building on a prior visit. We found that while one visit was enough to establish basic perceptions—for example, a university is big not small—a second visit allowed participants to see a different side of the university experience, adding nuance, expanding university-related vocabulary, and increasing comfort with the campus environment. Notably, repeat visits strengthened intentions to pursue higher education. We conclude that multiple campus visits benefit low-participation groups by fostering familiarity and exposing younger pupils to different motivations for university attendance. While this study provides a useful foundation from which to explore this area, further work is needed to address limitations such as the small sample size and the UK-specific context. Full article

Since statistics show a growing trend in blindness and visual impairment, the development of navigation systems supporting Blind and Visually Impaired People (BVIP) must be urgently addressed. Guiding BVIP to a desired destination across indoor and outdoor settings without relying on a pre-installed infrastructure is an open challenge. While numerous solutions have been proposed by researchers in recent decades, a comprehensive navigation system that can support BVIP mobility in mixed and unprepared environments is still missing. This study proposes a novel navigation system that enables BVIP to request directions and be guided to a desired destination across heterogeneous and unprepared settings. To achieve this, the system applies Computer Vision (CV)—namely an integrated Structure from Motion (SfM) pipeline—for tracking the user and exploits Building Information Modelling (BIM) semantics for planning the reference path to reach the destination. Audio Augmented Reality (AAR) technology is adopted for directional guidance delivery due to its intuitive and non-intrusive nature, which allows seamless integration with traditional mobility aids (e.g., white canes or guide dogs). The developed system was tested on a university campus to assess its performance during both path planning and navigation tasks, the latter involving users in both blindfolded and sighted conditions. Quantitative results indicate that the system computed paths in about 10 milliseconds and effectively guided blindfolded users to their destination, achieving performance comparable to that of sighted users. Remarkably, users in blindfolded conditions completed navigation tests with an average deviation from the reference path within the 0.60-meter shoulder width threshold in 100% of the trials, compared to 75% of the tests conducted by sighted users. These findings demonstrate the system’s accuracy in maintaining navigational alignment within acceptable human spatial tolerances. The proposed approach contributes to the advancement of BVIP assistive technologies by enabling scalable, infrastructure-free navigation across heterogeneous environments. Full article

Amid expanding higher education and enhancing spatial quality, modern university campuses face challenges including inefficient space utilization and a disconnect from human-centered design. We developed a coupled model that integrates the analytic hierarchy process (AHP) with space syntax theory to identify and address functional fragmentation, limited accessibility, and diminished spatial vitality. The Delphi method was employed to determine weights on visual and traffic influence factors. Through spatial quantitative analysis using Depthmap software, we estimated spatial-efficiency discrepancies across 11 component types, including school gates, teaching buildings, and libraries. A case study was conducted at a university located in the hilly terrain of Conghua District, Guangzhou, China which revealed significant contradictions between subjective evaluations and objective data at components, such as the administrative building and gymnasium. These contradictions led to poor visual permeability, excessive path redundancy, and imbalanced functional layouts. Based on the results of this study, targeted optimization strategies were proposed, including permeable interface designs, path network reconfiguration, and the implementation of dynamic functional modules. These interventions were tailored to accommodate the humid subtropical climate, balancing shading, ventilation, and visual transparency. In this study, methodological support for the renovation of existing campus infrastructure was provided as theoretical and technical references for space renewal in tropical and subtropical academic environments and the enhancement of the quality and resilience of campus spaces. The results also broadened the application of interdisciplinary methods in university planning. Full article

Field workers require expeditious and pertinent access to information to execute their duties, frequently in arduous environments. Conventional document search interfaces are ill-suited to these contexts, while fully automated approaches often lack the capacity to adapt to the variability of situations. This article explores a hybrid approach based on the use of specialized small language models (SLMs), combining natural language interaction, context awareness (static and dynamic), and structured command generation. The objective of this study is to demonstrate the feasibility of providing contextualized assistance for mobile agents using an intelligent conversational agent, while ensuring that reasonable resource consumption is maintained. The present case study pertains to the supervision of illumination systems on a university campus by technical agents. The static and the dynamic contexts are integrated into the user command to generate a prompt that queries a previously fine-tuned SLM. The methodology employed, the construction of five datasets for the purposes of evaluation, and the refinement of selected SLMs are presented herein. The findings indicate that models of smaller scale demonstrate the capacity to comprehend natural language queries and generate responses that can be effectively utilized by a tangible system. This work opens prospects for intelligent, resource-efficient, and contextualized assistance in industrial or constrained environments. Full article

Against the backdrop of growing concerns over university students’ mental health worldwide, campus environments play a crucial role not only in shaping spatial experiences but also in influencing psychological well-being. However, the psychosocial mechanisms through which campus landscapes affect well-being remain insufficiently theorized. Drawing on survey data from 500 students across two Chinese universities, this study employs structural equation modeling (SEM) and interpretable machine learning techniques (XGBoost-SHAP) to systematically examine the interrelations among landscape perception, place attachment, perceived social acceptance, school belonging, and psychological well-being. The results reveal the following: (1) campus landscapes serve as the primary catalyst for fostering emotional identification (place attachment) and social connectedness (perceived social acceptance and school belonging), thereby indirectly influencing psychological well-being through these psychosocial pathways; (2) landscape perception emerges as the strongest predictor of well-being, followed by school belonging. Although behavioral variables such as the green space maintenance quality, visit frequency, and duration of stay contribute consistently, their predictive power remains comparatively limited; (3) significant nonlinear associations are observed between core variables and well-being. While the positive effects of landscape perception, place attachment, and school belonging exhibit diminishing returns beyond certain thresholds, high levels of perceived social acceptance continue to generate sustained improvements in well-being. This study advances environmental psychology by highlighting the central role of campus landscapes in promoting mental health and provides actionable strategies for campus planning. It advocates for the design of balanced, diverse, and socially engaging landscape environments to maximize psychological benefits. Full article

Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions. Full article