Search Results (87)

As the core carrier of cognitive construction, the design optimization of campus learning space is crucial to the improvement of education quality, but the existing research focuses on the analysis of behavioral preferences and lacks an in-depth analysis of the psychological dynamics of users. Through multimodal questionnaires and spatiotemporal tracking, we developed an ‘expectation–perception–behavior’ framework to quantify discrepancies between users’ visual expectations and actual experiences. The results showed that blue and wood tones significantly enhanced learning efficiency; however, there was a significant difference between facility usability and sound insulation. Based on this, dynamic environment adjustment, virtual reality preview, and modular flexible space strategies are proposed to optimize spatial performance through biophilic design and intelligent regulation. This study provides interdisciplinary methodological innovation for architecture, education, and environmental psychology and promotes the transformation of campus space, injecting new momentum into the transformation of global stock space, the construction of a sustainable education ecology, and contributing to the overall improvement of social cognitive performance. Full article

Photovoltaic (PV) energy production in Western countries increases yearly. Its production can be carried out in a highly distributed manner, not being necessary to use large concentrations of solar panels. As a result of this situation, electricity production through PV has spread to homes and open-field plans. Production varies substantially depending on the panels’ location and weather conditions. However, the integration of PV systems presents a challenge for both grid planning and operation. Furthermore, the predictability of rooftop-installed PV systems can play an essential role in home energy management systems (HEMS) for optimising local self-consumption and integrating small PV systems in the low-voltage grid. In this article, we show a novel methodology used to predict the electrical energy production of a 48 kWp PV system located at the Campus Feuchtwangen, part of Hochschule Ansbach. This methodology involves hybrid time series techniques that include state space models supported by artificial intelligence tools to produce predictions. The results show an accuracy of around 3% on nRMSE for the prediction, depending on the different system orientations. Full article

Educational institutions face growing challenges. Rising enrolment, limited budgets, and sustainability goals demand more efficient resource management and administrative decision-making. To address challenges like these, this work proposes a conceptual framework for smart campus management which integrates Artificial Intelligence (AI) and advanced wireless networks based on 5G. The framework’s design outlines layers for campus data collection (via sensors and connected devices), high-speed communication, and AI-driven analytics for decision support. By leveraging data-driven insights enabled by reliable wireless connectivity, institutions can make more informed decisions, use resources more effectively, and automate routine tasks. Envisioned AI capabilities include forecasting (for predictive maintenance and demand planning), anomaly detection (for fault or irregularity identification), and optimisation (for resource scheduling). Rather than reporting empirical results, the framework is illustrated through hypothetical scenarios (e.g., anticipating equipment maintenance, dynamically scheduling classrooms, or reallocating resources) to present potential benefits and tools for researchers. The discussion also highlights how the framework incorporates data privacy, security, and accessibility considerations to ensure inclusive adoption. Eventually, this conceptual proposal provides a roadmap for administrators and planners, guiding the adoption of AI and wireless innovations in educational management to enable more responsive, efficient governance and, ultimately, improve outcomes for students and staff. Full article

Recent advancements in artificial intelligence, particularly in generative technologies, have significantly redefined the design paradigm for community public cultural spaces, shifting from a traditionally designer-centric model to one that emphasizes multi-stakeholder co-creation. This paper focuses on the design of public cultural spaces at the community scale, proposing a generative dynamic workflow-based co-creation framework that integrates large language models (LLMs) with text-to-image technologies. The framework includes a natural dialogue-based needs-capturing module, a needs analysis module, and a needs expression text-to-image module. This study validates the proposed framework by developing a system prototype for renovating a public space in a student dormitory at Tongji University’s Jiading campus. The results show that the prototype demonstrates good usability and a relatively satisfactory capability in capturing user requirements. These findings indicate that this research helps address key limitations in traditional community design practices, such as limited resident participation, inefficient integration of diverse needs, and slow iteration processes. Full article

This paper reconceptualizes the role of the teacher in the university foreign language classroom in an age of generative AI chatbots and automatic translation tools. We call for a reconceptualization of this role based on two factors: the unique social interactivity of the university language classroom and the need for effective instruction on how to use Intelligent Computer-Assisted Language Learning (ICALL) tools outside of the classroom. We argue that the teacher must master and integrate these two different modes of teaching and learning. Interpersonal exchanges in class respond to the need for real-time human interaction and relatedness in language learning and so cannot, and should not, be wholly replaced by chatbots. Rather, these sorts of exchanges must form a cornerstone of on-campus foreign language pedagogy. In contrast, teachers must also be able to leverage the benefits of learner-facing AI tools, especially for use outside of the classroom, given the learning gains associated with them. We provide detailed examples of how this dual approach can be realized and propose a five-step approach for incorporating AI into university language pedagogy. Full article

The emergence of smart campuses marks a pivotal advancement in educational pedagogy, environmental quality, resource allocation, and administrative services. This study presents the conceptualization and implementation of the Nanxun Campus of Zhejiang University of Water Resources and Electric Power (ZJWEU), which serves as an exemplary smart water initiative. Adhering to the philosophy of integrating educational facilities within the campus infrastructure, the campus incorporates several specialized zones: a key protection area, the water conservation area, the ecological stability area, the living water spirit area, and the teaching and practice area. This study clarifies the unique attributes, design philosophies, and operational mechanisms of these distinct zones. Central to the campus’s identity is a water culture-centric strategy, with each building reflecting water-themed concepts and providing extensive real-world engineering teaching and practice environments. Since its opening in 2022, the campus has been admitting approximately 5000 students annually and has been a model of water culture campuses that integrate ecology, intelligence, humanism, and synergy. The insights and infrastructure provide a valuable reference and foundational support for the evolution of smart campuses, underscoring the potential to merge water culture with avant-garde educational practices. Full article

The concept of a smart campus is rapidly gaining traction worldwide, driven by the growth of artificial intelligence (AI) and the Internet of Things (IoT), along with the digital transformation of higher education institutions. While numerous initiatives have been undertaken to enhance the capability of smart campus systems to keep pace with AI advancements, there have been few attempts to develop a cohesive conceptual framework for the smart campus, and to date, there has been limited empirical research conducted to validate the framework. This study bridges this gap by providing the first in-depth assessment of a holistic smart campus conceptual framework. The paper uses a Delphi study approach to validate and consolidate a framework for assessing the robustness of the smart campus assessment framework for application in university settings. The framework consists of four domains, 16 categories, and 48 indicators, comprising a total of 68 items that were validated by experts across the globe. Two rounds of structured questionnaires were conducted to achieve consensus on the framework. The first round involved 34 experts from diverse geographic and professional backgrounds in the smart campus field. The second round included 21 of the earlier participants, which was sufficient to determine consensus. In total, seven of the forty-eight indicators were agreed upon after Round 1, increasing to forty-three after Round 2. The results indicate strong agreement among the experts, affirming the framework’s robustness. This study offers an expert-based, interpretive assessment of the development of the smart campus concept, with a particular focus on validating the smart campus framework. Full article

To address the issue of significant point cloud ghosting in the construction of high-precision point cloud maps by low-speed intelligent mobile vehicles due to the presence of numerous dynamic obstacles in the environment, which affects the accuracy of map construction, this paper proposes a LiDAR-based Simultaneous Localization and Mapping (SLAM) algorithm tailored for dynamic scenes. The algorithm employs a tightly coupled SLAM framework integrating LiDAR and inertial measurement unit (IMU). In the process of dynamic obstacle removal, the point cloud data is first gridded. To more comprehensively represent the point cloud information, the point cloud within the perception area is linearly discretized by height to obtain the distribution of the point cloud at different height layers, which is then encoded to construct a linear discretized height descriptor for dynamic region extraction. To preserve more static feature points without altering the original point cloud, the Random Sample Consensus (RANSAC) ground fitting algorithm is employed to fit and segment the ground point cloud within the dynamic regions, followed by the removal of dynamic obstacles. Finally, accurate point cloud poses are obtained through static feature matching. The proposed algorithm has been validated using open-source datasets and self-collected campus datasets. The results demonstrate that the algorithm improves dynamic point cloud removal accuracy by 12.3% compared to the ERASOR algorithm and enhances overall mapping and localization accuracy by 8.3% compared to the LIO-SAM algorithm, thereby providing a reliable environmental description for intelligent mobile vehicles. Full article

The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions. Full article

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today’s traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the “Campus de Las Llamas” at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model. Full article

This study investigates how students’ well-being relates to sustainable development and education for sustainable development at Prince Mohammad Bin Fahd University, Saudi Arabia. The authors endeavor to answer the question “How Does Students’ Well-Being Relate to Sustainable Development Via Education for Sustainable Development?” and four sub-questions. The authors used primary data collected by semi-structured interviews exploring the viewpoints of twenty-six female and male students (n = 26) aged between 18 and 25. These interviewees, who are Saudi senior business students, have played a crucial role in our study. As revealed in the content and thematic analysis results, their insights have identified additional well-being and education for sustainable development antecedents that influence sustainable education and, therefore, sustainable development. In addition to this study’s new well-being and education for sustainable development factors, this study also developed a theoretical model for the relationship between antecedents for education for sustainable development factors, education for sustainable development, and sustainable development in the Saudi context. Moreover, this study’s outcomes guide educational institutions to link students’ education for sustainable development to their inner well-being and that on campus, which requires instructors’ awareness and training to help them deliver knowledge, skills, and emotional intelligence that improve students’ well-being, and therefore, education for sustainable development. Full article

In this study, the passive acoustic performance of Necmettin Erbakan University Köyceğiz Campus Masjid was investigated. Designed as the largest masjid of the city with a capacity of 15,000 people and a volume of 43,200 m³, the masjid, which has traces of Seljuk, Ottoman and Modern architecture. is built as a complex at a location overlooking the city in the Meram District of Konya City, Turkiye. The aim of the study is to determine the acoustic comfort conditions by considering all the activities in the masjids as a whole. Within the scope of the study, the acoustic performance of the masjid was evaluated by determining different source and receiver points for each mode of activity. As a method, the chosen masjid was simulated with ODEON Room Acoustics Software Ver. 14.04 software. Objective room acoustic parameters were analysed in three groups. These are sound energy ratio parameters (reverberation time (RT), early decay time (EDT), clarity (C50, C80), lateral fraction (LF80)), speech intelligibility parameters (definition (D50), speech transmission index (STI)) and sound strength parameters (strength (G)). The results obtained were compared with precedent studies in the literature. In comparison with the acoustic values obtained in other masjid/mosque buildings, it was reported that, while the speech intelligibility of other masjids/mosques was at a satisfactory level, the masjid under consideration was at a poor level in both fully occupied and unoccupied conditions. In the analysis made for reverberation time, it was seen that the masjid discussed in this study showed similar characteristics to other masjids/mosques globally. As a result, it was determined that the dimensions of the surfaces forming the mihrab, the minbar design and the depths of the mahfil overhangs are effective regarding the acoustics of the masjid, and the design of curved surfaces should be carried out in a way that does not cause focusing problems. In addition, suggestions that can give guidelines to modern masjid designs have been put forward. Full article

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings. Full article

This paper explores how participatory science can enhance building services on a higher education campus. The use of participatory science aims to involve students, faculty members, and technical teams in improving the management of the campus through their participation in data collection and evaluation of the building services. It represents a valuable alternative for campuses needing more building monitoring. The paper also shows how the performance of participatory science could be improved by combining digital technologies such as Building Information Modeling (BIM) and artificial intelligence (AI). The framework is applied to the Faculty of Engineering at An-Najah National University to improve the building services of the campus. A combination of users’ feedback and AI-generated synthetic data is used to explore the performance of the proposed method. Results confirm the high potential of participatory science for improving the services and quality of life on higher education campuses. This is achieved through students’ active participation and involvement in data collection and reporting on their individual experiences. Full article

This study explores the integration and optimization of battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs) within an energy management system (EMS), using Kangwon National University’s Samcheok campus as a case study. This research focuses on designing BESSs and HESSs with specific technical specifications, such as energy capacities and power ratings, and their integration into the EMS. By employing MATLAB-based simulations, this study analyzes energy dynamics, grid interactions, and load management strategies under various operational scenarios. Real-time data from the campus are utilized to examine energy consumption, renewable energy generation, grid power fluctuations, and pricing dynamics, providing key insights for system optimization. This study finds that a BESS manages energy fluctuations between 0.5 kWh and 3.7 kWh over a 24 h period, with battery power remaining close to 4 W for extended periods. Grid power fluctuates between −5 kW and 75 kW, while grid prices range from 75 to 120 USD/kWh, peaking at 111 USD/kWh. Hydrogen energy storage varies from 1 kWh to 8 kWh, with hydrogen power ranging from −40 kW to 40 kW. Load management keeps power stable at around 35 kW, and PV power integration peaks at 48 kW by the 10th h. The findings highlight that BESSs and HESSs effectively manage energy distribution and storage, improving system efficiency, reducing energy costs by approximately 15%, and enhancing grid stability by 20%. This study underscores the potential of BESSs and HESSs in stabilizing grid operations and integrating renewable energy. Future directions include advancements in storage technologies, enhanced EMS capabilities through artificial intelligence and machine learning, and the development of smart grid infrastructures. Policy recommendations stress the importance of regulatory support and stakeholder collaboration to drive innovation and scale deployment, ensuring a sustainable energy future. Full article