Search Results (351)

The subjective perception of safety in public space is a crucial indicator of urban participation, shaping how people experience and navigate their surroundings. Urban fear spaces highlight how physical, social, and emotional factors unequally structure access to and use of public environments, linking spatial perception to social justice. This paper addresses the question: What opportunities and limitations does a mixed-methods approach—combining immersive Virtual Reality (VR), electrodermal activity (EDA) measurement, and semi-structured interviews—offer for examining subjective perceptions of urban fear? It offers a methodological reflection on an exploratory study of potential fear spaces on the campus of Ruhr University Bochum, hypothesizing that mixed-methods integration reveals non-conscious arousal patterns inaccessible via verbal data alone. We discuss methodological potentials and limitations in integrating physiological data within qualitative frameworks. The study design comprised VR simulation, physiological signal acquisition, and qualitative interpretation and triangulation. Findings show that combining immersive VR with EDA detects non-conscious physiological arousal patterns that would remain inaccessible through verbal data alone, while simultaneously revealing substantial interpretative challenges that necessitate qualitative contextualization. Integrating interviews proved vital for linking physiological patterns to subjective meaning. The reflection concludes with implications for applying such multimodal approaches in participatory urban planning and spatial research. Full article

Property development, roads, vehicles, and suburban sprawl cause biodiversity habitat fragmentation. Some herpetofauna are at risk from a conservation perspective. This phenomenon is simultaneously a road ecology and a public health problem. The article analyzes the impact of “campus-based growth machine” development on herpetofauna habitat fragmentation around various wetlands in uptown Albany, New York, U.S. This study fills an unresearched gap on the impact of the campus-based growth machine, roads, vehicles, and suburban sprawl on biodiversity habitat fragmentation. The research methods comprised both qualitative and quantitative assessments of property development inventories, wildlife observations, student engagements and biodiversity monitoring at the University at Albany, the cataloging of test-design and conservation measures, and the review of institutional planning regulations and roadway design features. The key finding is the need for more biodiversity conservation innovations to increase the continuity of habitats, uniform underground crossings, and the elimination of biodiversity road crossing deaths. The article presents research and management practice recommendations. The study shows a plausible association between university expansion and biodiversity reductions on campus grounds. It also identifies potential mitigation measures and opportunities for community service collaborations. Full article

Multi-campus AI data centers procure identical hardware and service SKUs across geographically heterogeneous locations, yet finance and operations require a single system-level benchmark (“world price”) per SKU for budgeting, chargeback, and capacity planning. Naive deployment-weighted aggregation preserves total cost but can induce Simpson-type aggregation bias, where heterogeneous location mixes reverse global SKU rankings and weaken managerial decision signals. This study formalizes the problem of location-robust, cost-preserving aggregation and develops two mathematically structured operators for production cost pipelines. The first operator applies a two-way fixed-effects decomposition to separate global SKU effects from campus-specific premia, followed by normalization to guarantee exact cost preservation. This yields an interpretable benchmark that performs well when campus coverage is sufficiently broad and location effects remain approximately additive. The second operator solves a constrained convex common-weight optimization, producing a unified set of non-negative campus weights that preserves total cost while providing the strongest protection against dominance reversals in the ordered setting. Simulation experiments and a semi-real calibrated AI datacenter OPEX illustration show that both operators substantially improve ranking stability relative to naive blending, while the convex operator serves as the more conservative safeguard under adverse heterogeneity. The resulting detect–correct–validate workflow provides a scalable decision-support framework for robust cost aggregation in distributed AI infrastructure and illustrates how symmetry-preserving aggregation operators can stabilize benchmarking in large heterogeneous systems. Full article

AI data-center (DC) growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI DC cooling, treating cooling as an energy service that can be supplied through grade matching rather than solely through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning objects by explicitly accounting for thermal attenuation, absorption-based conversion, and parasitic electricity associated with delivery and auxiliaries. Three results structure the analysis. First, a reference-case energy-service ledger shows how a representative regulated WtE plant with municipal solid-waste throughput of 1500 t/day and lower heating value of 10 MJ/kg yields ~78.1 MWth of exportable driving heat and, at a 20 km corridor, ~53.0 MWcool of delivered cooling and ~8.0 MWe of net avoided cooling electricity after parasitic debiting. Second, the coupled system is governed by operating regimes, not a single efficiency score. Under the baseline package, full thermal coverage is maintained up to ~20.9 km, the stricter quality-adjusted criterion remains positive to ~22.9 km, and the electricity–relief criterion remains positive to ~44.7 km. Third, deployment-scale translation for a 1 GW IT campus (u = 0.70, L = 5 km) implies a net grid relief of ~116.9–264.4 MW across scenario packages, while the required WtE footprint ranges from roughly three to 148 equivalent representative plants, or about 0.6–40 full-load-equivalent plants at a 25% displacement target. The contribution is a siting-ready planning framework that identifies when WtE-coupled cooling remains corridor-feasible, when it becomes hybrid and marginal, and when infrastructure scale rather than thermodynamic benefit becomes the binding constraint. It is intended as a screening tool for planning and comparison, not as a project-specific hydraulic or plant-cycle design. Full article

Background: Male college students are at risk for weight gain due to unhealthy dietary habits. This study assessed the dietary habits of undergraduate college males. Methods: Online cross-sectional survey (n = 235) of randomly sampled male undergraduate college students. Results: The mean age was 21.15 ± 3.21. Most were enrolled full-time (91.5%), lived off-campus (77.4%), upper class (59.6%), had a campus meal plan (52.8%), and white (51.9%). Mean body mass index (BMI) was 25.02 ± 4.86. Males gained an average of 10.81 ± 13.01 lbs while in college. Most ate one to two servings of fruits (67.1%) and vegetables (65.1%). Significant differences in weight gained during college and fruit consumed was significant [p = 0.02 *]. Male students reported on 20 different foods and drinks they consumed at least “a few times per week.” Most ate fresh fruits (76.1%), prepared a hot meal at home (72.7%), ate fresh vegetables (68.1%). Males also ate at fast-food restaurants (47.7%) and drank coffee (44.4%). Males that reported they drank alcohol (p = 0.03*), diet soda (p = 0.03 *), coffee (p = 0.01 *), and ate at fast-food restaurants (p = 0.02 *) “a few times per week” were found to have significantly gained more weight. Conclusions: Increased intake and consumption of alcohol, diet soda, and fast-food was associated with increased weight gain among college males. Full article

Hospitals in earthquake-prone regions must evacuate heterogeneous occupants rapidly while preserving operational continuity under disrupted conditions. However, many hospital-evacuation studies still rely on static routing assumptions or narrowly defined behavioral rules, which limits their value for building-level resilience planning. This paper develops a comparative hospital-campus evacuation framework that combines GIS-based geodesic routing, heterogeneous agent-based modeling, and reinforcement-learning-based decision policies. Puge County People’s Hospital in Sichuan, China, is used as the case study. Six algorithms are evaluated: three rule-based baselines—Shortest Path (SP), Random Walk (RW), and the Social Force Model (SFM)—together with a training-free density-aware heuristic, Density-Aware Gradient Routing (DAGR), and two reinforcement-learning approaches, Density-Aware Q-Learning (DAQL) and SARSA. Experiments cover three population scales ($N\in \{50,100,200\}$), normal daytime conditions, staffing-variation scenarios, and a blocked-exit disruption scenario, with 30 independent runs for each main condition. The results show that the rule-based and training-free methods remain the most reliable under full multi-agent evaluation: the SFM and RW achieve the highest completion ratios (approximately 100% and 93.5%, respectively), while DAGR provides the strongest balance between completion and evacuation efficiency among the non-trained methods. In contrast, the trained RL agents perform substantially worse in direct multi-agent deployment with DAQL reaching approximately 37% completion and SARSA approximately 17%, highlighting a train–evaluation distribution shift associated with independent Q-learning. The ablation analysis further shows that collision avoidance is the most critical reward component, whereas density-avoidance shaping can unintentionally induce collective deadlock when all agents execute the learned policy simultaneously. Among the enhanced variants, DAQL_RoleAware yields the best overall improvement, increasing the completion ratio to approximately 52% and reducing the 90th-percentile evacuation time to approximately 363 s. Overall, this paper clarifies both the promise and the present limitations of density-aware reinforcement learning for hospital evacuation while providing a more building-centred and reproducible basis for future coordination-aware evacuation design and emergency-planning research. Full article

Due to their aesthetic qualities and versatile applications, medicinal and aromatic plants are an important component of landscape systems. The diversity of color, shape, and texture observed in the vegetative and reproductive organs of these plants contributes to visual composition, while their medicinal and aromatic properties enhance their ecological and socio-cultural significance. However, many taxa are underrepresented in landscape planning applications. This study examined the diversity of medicinal and aromatic plant taxa identified at the Iğdır University Şehit Bülent Yurtseven Campus in Iğdır Province, Turkey, using a descriptive approach. Plant taxa were evaluated based on their families, life forms, leaf characteristics, flowering periods, and medicinal and aromatic properties. Multivariate analyses were conducted to examine phenological similarities among the taxa. A total of 98 plant taxa were identified; 66 taxa possess only medicinal properties, one taxon possesses only aromatic properties, and 31 taxa possess both. These findings reveal that the campus is home to a wide variety of medicinal and aromatic plant taxa, with characteristics relevant to planting layout and species selection. Consequently, this study provides a descriptive foundation for further research on how such taxa can be incorporated into campus planting designs and green space planning. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

Against the backdrop of globalization and rapid urbanization, the weakening of regional cultural identity has emerged as a significant challenge in contemporary architectural practice, particularly within the context of large-scale campus development. University architecture must navigate the complex task of balancing functional demands with long-term cultural and social sustainability. However, the prevalence of homogenized architectural forms in many newly constructed campuses often undermines local distinctiveness, leading to diminished place identity and reduced social sustainability. In response, this study takes the Yan’an University new campus in China as a representative case to explore how regional culture can be sustainably integrated into campus architecture through spatial organization, typological strategies, and symbolic translation. The study employs qualitative analysis and a life-cycle perspective, integrating architectural semiotics and typological methods to construct a multidimensional analytical framework of “space–material–culture”. This framework is systematically applied to examine how the loess culture, revolutionary heritage, and folk art of Yan’an are translated and expressed in a contemporary context. The findings reveal that achieving cultural sustainability does not rely on direct imitation of historical forms but rather on an adaptive spatial framework, modular architectural typologies, and a performance-integrated material system, which together shape a resilient and organically evolving campus entity. Specifically, the design employs strategies such as “symbolic translation from archetype to type”, “dialogue between traditional materials and contemporary craftsmanship”, and “spatial translation from enclosed courtyards to open landscapes”. These approaches facilitate the organic embedding of regional cultural genes, promote the continuity of collective memory, strengthen local identity, and enable phased development throughout the campus’s life cycle. By extending the concept of sustainability from environmental performance to cultural continuity, social cohesion, and spatial adaptability, this study provides actionable design pathways and theoretical references for campus development in regions with profound historical backgrounds. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

Background: Hepatitis B virus (HBV) reactivation in oncology patients receiving chemotherapy can cause severe hepatitis, including hepatic failure and death. Universal HBV screening before chemotherapy initiation can reduce HBV-related morbidity; however, screening practices vary widely, and guideline recommendations continue to evolve. Objective: The aim of this study was to evaluate the implementation of universal HBV screening in oncology patients and its effectiveness in identifying active infection, prior exposure, and individuals at risk for HBV reactivation. Methods: We implemented universal HBV screening at Rambam Health Care Campus in January 2018 for all patients initiating chemotherapy. This retrospective cohort study analyzed 1614 oncology patients who underwent chemotherapy between January 2018 and April 2024. Screening included testing for HBsAg, anti-HBc, and anti-HBs serology. HBV DNA testing was performed in patients with positive HBsAg and/or anti-HBc serology. Patients with known HBsAg positivity or those already receiving antiviral therapy were excluded. Results: Of the screened patients, 16 (1.0%) were HBsAg-positive, and 134 (8.3%) were HBsAg-negative/anti-HBc-positive. Detectable HBV DNA was identified in four patients (3%) within the latter group. One additional patient was classified as high risk for HBV reactivation based on the planned chemotherapy regimen. Overall, 21 patients met criteria for prophylactic antiviral therapy; however, prophylaxis was administered to only 17 patients. Notably, when applying the 2015 ASCO guidelines, only a single patient within the subgroup of HBsAg-negative, anti-HBc-positive, and HBV DNA-negative patients would have qualified for HBV serologic screening based on chemotherapy-related risk alone. Conclusions: Universal HBV screening prior to chemotherapy enables the identification of patients with active or prior HBV infection who would not have been detected using risk-based screening strategies alone. Our findings further support the implementation of universal HBV screening in oncology settings to prevent HBV reactivation and its potentially severe consequences. Full article

Accurate modeling of outdoor wireless propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity multipath insight; however they suffer from high computational cost and limited scalability in large 3D environments. This work proposes a hybrid framework combining MATLAB-based (MATLAB 2024b 24.2.0.2773142, 64-bit, 22 October 2024) ray tracing and Machine Learning for scalable Wi-Fi 7 channel analysis. A large dataset is generated over a realistic university campus across multiple frequency bands, transmit powers, and reflection/diffraction configurations. Several regression models are evaluated, with emphasis on transformer-based architectures. The FT-Transformer achieves a Mean Absolute Error (MAE) of $3.49$ dB, RMSE of $5.36$ dB, and an $R^2$ of $99.63\%$ for validation, reducing computation time from months of simulation to seconds at inference. The framework enables accurate and efficient surrogate modeling for network planning and digital twin applications. Full article

Accurately characterizing the kinematics of slow-moving urban landslides remains a major scientific and operational challenge, because no single monitoring technique can simultaneously provide spatially continuous deformation patterns and reliable three-dimensional displacement measurements. This study investigates the spatial and temporal evolution of a slow-moving landslide affecting the University of Azuay campus in Cuenca (Ecuador), where ongoing ground deformation has caused structural damage to several buildings. An integrated monitoring strategy combining GNSS measurements, Sentinel-1 multi-temporal DInSAR analysis, and geophysical investigations (ERT and seismic profiling) was adopted to characterize landslide kinematics and constrain subsurface conditions. GNSS observations revealed that the north–south displacement component was dominant, with cumulative displacements exceeding 20 cm during the monitoring period (from July 2021 to June 2024), while east–west displacements were on the order of 10 cm. MT-DInSAR analysis delineated the spatial extent of the unstable area and identified mean deformation rates of up to approximately −1.5 cm/year in the central sector of the landslide. The combined interpretation of geodetic and geophysical data indicates that slope instability is controlled by saturated fine-grained layers and mechanical contrasts, with the basal sliding zone associated with weak levels of the Mangan Formation. Overall, the results demonstrate the value of a multi-sensor, component-wise monitoring strategy for improving the reliability of deformation estimates and for supporting landslide risk assessment and land-use planning in complex urban environments. Full article

This study examines the campus-planning projects (1920–1937) of Kwan, Chu & Yang, Architects & Engineers (KCY), a major Chinese firm, against the backdrop of Sino-American cross-cultural knowledge transfer. It argues that their work exhibited a distinct compositional tendency derived from the partners’ U.S. Beaux-Arts education and contemporary American planning theory. Through historical analysis and case studies of four university projects, this research examines how composition-based spatial unity engaged with specific Chinese site conditions. The results indicate that early projects negotiated irregular boundaries, while later ones grappled with complex topography, such as historic gardens and hills. Although often unrealized, these grand schemes embodied a scientific planning methodology and served as aspirational blueprints. This study concludes that compositional practice was a significant part of China’s architectural modernization, representing both a professional design approach and a cultural response to the quest for modernity and national identity. Full article

Declining birth rates are reshaping higher education across East Asia, accelerating the large-scale underutilization and, in some contexts, partial abandonment of university campus assets. Although adaptive reuse has been widely discussed, campus transformation is often framed primarily as a programmatic or policy problem, with limited attention to the inherited spatial logic embedded in campus morphology. This study revisits Le Corbusier’s concept of unlimited growth as a generative framework for campus transformation. Rather than treating it as a museum-specific historical typology, the research reinterprets unlimited growth as a scalable spatial logic defined by modular continuity, circulation hierarchy, and open-ended sequencing. To enhance reproducibility and operational clarity, the study formalizes a typo-morphological decoding protocol—modules, circulation, and growth sequence—and applies it through plan-, section-, and diagram-based analysis. Through comparative examination of three museum precedents—Sanskar Kendra Museum, the National Museum of Western Art (Tokyo), and the Chandigarh Museum and Art Gallery—the study extracts a set of transferable spatial mechanisms: modular increment, circulation-centered ordering, directional displacement, and fifth-façade ecological continuity. These mechanisms are then translated into an operational right-sizing model and tested through a design-operational demonstrator on a single anonymized Taiwanese campus experiencing demographic contraction. The findings indicate that unlimited growth functions not merely as a formal principle but as a spatial governance logic that supports phased consolidation, adaptive recomposition, and system-level coherence under long-term uncertainty. Importantly, this framework contributes to sustainability by reducing land consumption through spatial consolidation, minimizing unnecessary new construction, enabling adaptive reuse of existing campus assets, and improving long-term resource-use efficiency through phased right-sizing and ecological continuity. This study further advances a reproducible, mechanism-based methodological framework for institutional spatial transformation, providing a transferable approach for large-scale campus restructuring under conditions of long-term demographic and environmental uncertainty. Full article