Search Results (1,186)

Exit location can influence evacuation efficiency without changing the number of exits, yet its mechanism lacks quantitative characterization. Using a complex single-floor hospital outpatient department floor plan with 186 occupants as the case study, based on a direction-aware cellular automaton (CA) model, this study constructed two exit layout scenarios within the same complex building floor plan and independently repeated 50 simulations for each scenario under identical occupant population and model parameters. A mechanism-oriented analysis was conducted from the perspectives of evacuation efficiency, structural fairness, behavioral fairness, and structure–behavior deviation. The results showed that, in this case, exit relocation shortened the total evacuation time by approximately 20% ($p<0.001$) and significantly reduced the concentration of exit utilization, whereas the service area distribution changed only slightly, and local peak density did not increase significantly. This indicates that exit location improves evacuation efficiency by restructuring the crowd-splitting structure rather than by a simple balancing of structural service coverage. This study provides quantitative evidence for performance-based evacuation design and sustainable safety optimization in complex spaces. Full article

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and tourism potential circulation characteristics. Meanwhile, the industry confronts practical challenges, including an unbalanced layout between high-end and local brands, intense competition in core areas, and distinct service coverage blind spots in non-core areas. To fill these research gaps, this study takes the Macau Special Administrative Region as the research scope, integrates POI kernel density estimation, Voronoi diagram analysis, and space syntax to construct a three-dimensional analytical framework encompassing agglomeration intensity, service scope, and tourism flow matching, and systematically investigates the spatial clustering pattern of jewelry stores and its coupling mechanism with tourism potential circulation. The study reveals the following findings: (1) Jewelry stores exhibit a dual-segment, four-core clustering pattern. Among these, 38 high-end brands are concentrated in casino complexes and their surrounding areas, 34 comprehensive brands are evenly distributed across core and residential areas, and 300 local brands are mainly scattered in residential areas of the Macau Peninsula. (2) The service scope of jewelry stores is negatively correlated with agglomeration density. The Voronoi diagram area in core areas is 62% smaller than that in non-core areas, accompanied by a high degree of overlap—35% for high-end brands—and intense competition. In contrast, non-core areas have coverage blind spots accounting for 18% of Macau’s total land area. (3) Under a 300 m walking radius, high-integration paths identified by space syntax demonstrate an 85% matching degree with tourist routes, and the four core areas form differentiated coupling types. This study is the first to quantify the differentiated coupling mechanism between multi-level jewelry brands and tourism potential circulation. It further improves the GIS analysis framework for the coupling between commercial agglomeration and tourist behavior. The revealed negative correlation between service scope and agglomeration density, and the adaptive principle between brand spatial layout and regional functional attributes, provide universal references for similar business formats in tourist cities, including cultural and creative retail and characteristic catering. In practice, this research optimizes the spatial layout of Macau’s jewelry industry and increases the coverage rate of service blind spots to over 85%. It also provides scientific support for tourism route planning and the coordinated development of tourism and commerce in high-density tourist destinations. Full article

With the wave of new campus construction gradually receding, the focus of green campus planning and design is shifting toward the low-carbon retrofitting of the existing built environment. University campuses often face challenges such as dispersed land use, inadequate spatial planning, disorganized road layouts, suboptimal landscape design, and low energy efficiency. Grounded in a review of current research on campus carbon emissions, this study integrates green technology indicators with planning and design approaches to establish a multi-scale, context-adaptive planning framework for carbon control, spanning five dimensions: intensive land use, spatial layout, transportation systems, landscape development, and facility integration. Employing a combined approach of bibliometric analysis and case studies, this research examines and compares typical university campuses both domestically and internationally to validate the effectiveness of the synergistic “technology-system-behavior” pathway in mitigating high-carbon lock-in. Through a systematic comparative analysis of representative low-carbon campuses, the synthesized results indicate that under optimal operational conditions, the clustered reorganization of functional zones demonstrates the potential to reduce transportation carbon emissions by approximately 25%; comprehensive retrofitting of building envelopes can decrease building energy consumption intensity by an estimated 30%; a multimodal coordinated transport system can increase the share of non-motorized travel to around 65%; establishing high carbon-sequestration plant communities can enhance carbon sink capacity by up to 30%; and smart facility integration can reduce overall campus carbon emissions by a projected range of 25–40%. It should be noted that these quantitative outcomes represent high-probability potential ranges, with actual performance subject to behavioral and operational fluctuations. This study provides theoretical support and practical pathways for achieving “near-zero carbon campuses” and underscores the important demonstrative role that higher education institutions can play in addressing climate change. Full article

Urban overheating poses major challenges in Mediterranean cities, affecting public health and well-being. This study comparatively evaluates how alternative greening configurations influence urban microclimate and outdoor thermal comfort in a brownfield regeneration site in Imola, Italy, using ENVI-met simulations under a representative extreme summer condition. Eight scenarios with varying vegetation density, structure, and spatial arrangement were modelled on the hottest day of the year, and the Physiological Equivalent Temperature (PET) was evaluated at representative times. Results show that greening reduces heat stress, though its effectiveness varies over time and across configurations. No meaningful cooling occurred at 5:00 a.m., confirming that vegetation has a limited impact during nocturnal radiative processes. At 9:00 a.m., the medium-density scenario (S2b) achieved the greatest PET reduction (~2 °C), suggesting favorable evapotranspiration conditions under moderate radiation. At 4:00 p.m., the distributed high-density scenario (S3.2b) provided the strongest mitigation (~1.8–2 °C). Distributed layouts outperformed clustered ones, highlighting the non-linear nature of vegetation cooling. Zonal analysis showed the largest cooling in public green areas, followed by parking, building, and path zones, demonstrating the influence of surface type and shading geometry. Greening also produced modest improvements in surrounding neighborhoods (up to 0.8 °C in the morning), although impacts remained localized. Overall, results highlight how vegetation quantity, structure, and spatial distribution influence cooling performance under critical summer conditions, supporting climate-responsive urban regeneration design. These findings contribute to sustainable urban planning by supporting nature-based strategies for climate adaptation and improved environmental quality in regenerating urban districts. Future work should consider seasonal vegetation dynamics and multi-objective design optimization. Full article

Reliable and efficient low-voltage distribution networks are critical for scaling mini-grid deployment and advancing universal electricity access, yet prevailing design practices remain manual, heuristic, and difficult to scale. This study presents a fully automated workflow that integrates geospatial feature extraction, distribution network layout, conductor sizing, mixed-integer linear programming-based phase balancing, nonlinear AC power flow validation, and system costing to generate rapid, standard-compliant techno-economic designs for greenfield mini-grid sites. The methodology is demonstrated across 62 rural sites to confirm practicality for large-scale rural electrification planning. Designs were evaluated for single-phase, three-phase, and hybrid low-voltage configurations. When design constraints were relaxed, single-phase networks achieved the lowest median voltage drop (~0.8%) and technical losses (~0.6%); however, under realistic voltage-drop and ampacity limits, compliance relied on conductor oversizing, resulting in low utilization (median loading <20%) and substantially higher costs. Fewer than half of the sites met construction feasibility limits for parallel conductors, and single-phase designs were typically 3–4× more expensive than multi-phase alternatives. Multi-phase layouts delivered comparable technical performance at significantly lower cost. Phase-balancing optimization reduced voltage drop by 15–20% and current unbalance by ~50%, enabling loss reduction and increased load accommodation. Overall, the results demonstrate that automated low-voltage network design can replace manual drafting with scalable, data-driven workflows that reduce soft costs while improving technical performance, constructability, and investment readiness. Full article

Despite advances in deep learning for interior layout design, existing bubble diagram methods still rely on manual sketches or dataset retrieval. These methods use a binary functional relationship with simple adjacency logic, limiting end-to-end generation and integration of complex constraints. In contrast, bubble diagrams can represent more complex, overlapping relationships. This paper proposes a Functional Constraint Bubble Diagram (FCBD) framework for intelligent layout generation. Annotated interior data are encoded into structured representations capturing spatial boundaries, room functions, area constraints, and user preferences. A dual-branch Transformer is employed, where the Node Transformer learns function-aware room representations and the Edge Transformer models adjacency relationships under environment-aware constraints to enhance spatial coherence. Latent-space sampling enables multi-solution generation, while an interactive refinement mechanism supports real-time user adjustments. The generated bubble diagrams drive floor plan synthesis and are evaluated on layout rationality, functional compatibility, visual quality, and diversity. Experimental results show that FCBD achieves a functional accuracy of 92.0%, adjacency accuracy of 88.9%, the lowest room overlap of 0.038, and the highest layout diversity of 1.245. Compared to baselines, FCBD improves functional and adjacency accuracy by up to 10%, reduces room overlap by over 25%, and generates more diverse and well-connected layouts, significantly reducing manual design effort. The end-to-end experimental results verify the validity of the generated topology and the practical value of the FCBD framework in intelligent interior design. Full article

Public Open Spaces (POSs) on university campuses play a vital role in promoting student well-being, fostering social interaction, and enhancing academic engagement. Yet, in Indian technical institutions, these spaces are often underutilized due to poor design integration, lack of thermal comfort, and minimal user-centered planning. This study applies the Campus Open Space Index (COSI) to assess the functionality, inclusivity, and experiential quality of POSs at two premier Indian institutions, IIT Delhi and IIT Roorkee. COSI evaluates campus POSs across five dimensions: Physical Planning, Engagement, Need Perception & Behavior, Thermal Comfort, and Management. Through a mixed-methods approach involving surveys (n = 522), field observations, and spatial mapping, six open spaces from each campus were analyzed. The aspect-wise COSI results indicate that IIT Delhi performs better in Management (75.84%) and Thermal Comfort (60.56%), while IIT Roorkee performs better in Engagement (71.68%); both campuses show deficits in universal accessibility and climate responsiveness. The study reveals that POS effectiveness depends not only on spatial layout but also on user behavior, comfort, and perceived safety. COSI provides a replicable and scalable assessment model that supports data-driven decision-making for campus planners and administrators. This research advocates for participatory, student-centric planning approaches to transform campus POSs into more inclusive, responsive, and sustainable environments aligned with educational and social goals. Full article

As a typical example of a high-density city, Macau’s medical resource allocation system, a key component of the city’s complex socio-technical system, suffers from significant spatial imbalances, which restricts the overall effectiveness of the medical service system. Based on the perspective of systems science theory, regards the allocation of medical resources as a dynamic system with multiple coupled factors. It comprehensively utilizes systems research methods such as POI data mining and space syntax analysis and employs techniques such as kernel density analysis and spatial structure coupling models to systematically evaluate the spatial structure, resource accessibility, and service balance of Macau’s medical service system. It found that (1) the Macau Peninsula has concentrated core medical resources, such as the Conde de São Januário Hospital (CHCSJ) and Kiang Wu Hospital, which form a core subsystem with high service saturation. Excessive concentration of resources has led to high concentration of a certain type of facility. (2) Taipa Island and the Cotai Reclamation Area have created an extended subsystem of medical resources along with urban development. However, the northern area does not have enough facilities, and its internal structure is not balanced. (3) Coloane Island has only basic health stations remaining, forming a marginal subsystem with scarce medical resources, which has a significant hierarchical gap with the core and extended subsystems. This spatial pattern of “saturated Macau peninsula, expanded Taipa Island, and sparse Coloane Island” is essentially a concrete manifestation of the imbalance between the medical resource allocation system and the urban spatial development system. Therefore, based on system optimization theory, it proposes constructing a multi-level, networked spatial system for medical facilities to promote the coordinated operation of various regional medical subsystems and achieve overall functional optimization and a balanced layout for Macau’s medical service system. This research analyzes the imbalance mechanism of high-density urban public service systems using systems science methods, providing not only a scientific basis for the precise optimization of Macau’s medical resource allocation system but also a practical reference for the planning and governance of similar high-density urban public service systems under a systems thinking framework. Full article

As urbanization accelerates in China, the protection and renewal of historical and cultural districts have become key issues. The Beiyuanmen Historical and Cultural District in Xi’an, with its long history and cultural significance, is a prime example. This study uses Beiyuanmen as a case study, employing Baidu heatmap data, Point of Interest (POI) data, and space syntax theory to examine the district’s spatial layout, crowd activity distribution, and functional structure. The purpose is to quantify its vitality and spatial characteristics, providing a basis for scientific planning. The methods involve analyzing spatiotemporal crowd activity intensity via heatmaps, assessing street network configuration through integration and choice values, and comparing POI data from 2014 and 2024 to track functional evolution. The research identifies the distinctive spatiotemporal patterns of crowd activity, revealing not only a southeast concentration correlated with urban functions but also distinct diurnal rhythms—a bimodal pattern on weekdays versus a sustained leisure-oriented pattern on weekends, underscoring a functional shift. It also explores the directed permeability of the spatial structure, identifying streets like Miaohou Street that form a highly integrated “cross-shaped backbone”. Analysis of POI data shows that commercial services dominate and have expanded outward, with the growth rate of POI density in the control area surpassing that of the core, indicating a trend of functional diffusion. Finally, the study highlights Miaohou Street, Beiguangji Street, Damai Market Street, Beiyuanmen, and Sajinqiao as key areas, and it concludes by proposing integrated planning recommendations that focus on four strategic aspects—spatial and crowd activity distribution management, functional zoning guidance, enhancement of public services and cultural displays, and alignment with broader urban policies—for prioritized landscape enhancement and tourism. Full article

As the development of High-Speed Railways (HSRs) shifts from scale expansion to quality and efficiency, high-density timetables face increasing challenges regarding operational stability. Traditional capacity metrics often prioritize volume over service quality, neglecting the economic and service implications of delays. To reconcile theoretical capacity with practical reliability, this paper proposes a novel Reliable Value (RV)-oriented framework for HSR timetabling. We construct a Reserve Capacity Incremental Heuristic Optimization Framework that employs a synergetic integrated stochastic optimization strategy. This methodology treats reserve capacity as a systematically varied analytical parameter rather than a static constant, integrating redundancy layout planning with dynamic recovery adjustments under stochastic delay scenarios. The RV metric quantitatively combines efficiency (Expected Running Time) and robustness (Indirect Capacity Loss). A case study on the Beijing–Shanghai high-speed railway corridor demonstrates a non-linear relationship between reserve capacity allocation and system value. The results identify an optimal saturation interval of 5 to 14 min, where the marginal gains in reliability maximize the overall system value without excessively compromising operational efficiency. These findings provide theoretical support for transitioning from static capacity planning to proactive, value-based resilience engineering through optimized redundancy allocation. Full article

Amid rapid urbanization, the rapid increase in urban vehicles has exacerbated parking scarcity, particularly in areas surrounding hospitals. As the core city of the Huaihai Economic Zone, Xuzhou’s medical institutions serve a broad region spanning 178,000 square kilometers. The pronounced mismatch between parking supply and demand in these areas severely impacts traffic efficiency and public service quality. To address this challenge, this study proposes a data-driven parking resource planning methodology for the identification and planning of informal/shared parking spaces (utilizing underutilized idle spaces) in hospital vicinities, integrating multi-source geospatial data from OpenStreetMap, remote sensing imagery, and field surveys. The methodology involves data preprocessing (e.g., format conversion, building boundary calibration), parking space identification and classification (e.g., buffer zone delineation, vacant land categorization, shape-based division), and layout optimization using a genetic algorithm combined with manual refinement. Applied within a 1 km radius around two hospitals in Xuzhou, the results demonstrate significant improvements in space utilization and provide a scientific basis for temporary parking facility planning. The results provide practical decision support for urban spatial management and temporary parking governance in high-demand public service areas. Full article

This study evaluates the resistance of existing reinforced concrete buildings to progressive collapse using the Tie-Force Method specified in UFC 4-023-03. Five multi-storey residential reinforced concrete buildings in different regions are analysed. In situ rebar scanning and Schmidt hammer tests revealed existing reinforcement layouts and concrete strengths (14–26 MPa). From the measured geometries, material properties and design loads, the required peripheral, longitudinal–transverse and vertical tie forces are calculated and converted into equivalent reinforcement areas. The results show that none of the investigated buildings satisfies all tie-force requirements with its current detailing. In particular, approximately 40% of the total Ø12 reinforcement required for the most critical peripheral ties in the other functional areas is concentrated in a single building. For longitudinal and transverse ties within the slab plane, additional Ø12 bars are required, especially along the most unfavourable grid lines in large-span panels. Vertical tie demands are modest and can generally be met with about 1–7 Ø16 bars in the selected columns. The findings indicate that, in the investigated sample, tie-force deficiencies appear to be governed more by design era and structural layout than by geographic location, and that strengthening slab-plane ties is critical for improving progressive-collapse resistance in the investigated buildings. In typical existing RC frame buildings, tie-force inadequacy is governed primarily by slab-plane ties rather than by vertical ties and the variation in required tie reinforcement across buildings is controlled more by design era and plan-geometry/floor-load characteristics than by geographic location. Full article

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on forecasting surpluses and deficits to achieve optimal decision making. However, both tasks, which in fact constitute a flow allocation problem across power networks, are subject to multiple peculiarities, arising from the nonlinear dynamics of the underlying processes, subject to numerous technical and operational constraints. Interestingly, a mutual problem emerges in water resource systems, also comprising network-type storage, abstraction and conveyance components. In this vein, triggered from well-established simulation approaches from the water domain, we introduce a generic (i.e., topology-free) and time-agnostic framework, the key methodological elements of which are: (a) the graph-based representation of the power fluxes; (b) the effective handling of energy uses and constraints through virtual nodes and edges; (c) the implementation of priorities via proper assignment of virtual costs across all graph components; and (d) the configuration of the overall problem as a network linear programming context, which allows the use of exceptionally fast solvers. Specific adjustments are required to address highly complex issues within HRESs, particularly the representation of conventional thermal and pumped-storage hydropower units, as well as the power losses across transmission lines. The modeling approach is stress-tested by means of configuring a hypothetical HRES in a non-interconnected Aegean island, i.e., Sifnos, Greece. Full article

Adaptive reuse projects frequently involve substantial plan-level reorganization; however, the reconfiguration of spatial hierarchy within interior layouts remains insufficiently examined at the building scale. Background: This study investigates how spatial hierarchy is restructured during the adaptive reuse of an industrial building converted into a hotel, focusing on configurational implications of program-driven design decisions within unchanged architectural boundaries. Methods: Visibility-based Space Syntax analyses were conducted using visual integration, connectivity, and mean depth measures. Rather than relying on floor-level averages, a control-point-based comparative protocol enabled systematic pre- and post-intervention comparisons linked to plan-level architectural interventions under identical analytical parameters. Results: The findings indicate selective amplification of spatial accessibility and visual integration at defined circulation nodes on the ground floor, while upper floors exhibit contraction of visibility fields and increased relational depth. These shifts indicate a floor-specific redistribution of spatial hierarchy rather than uniform configurational transformation. Conclusions: The results suggest that spatial transformation in adaptive reuse can be interpreted as a design-driven recalibration of configurational relationships within fixed architectural boundaries. Without pursuing statistical generalisation, the study proposes a case-bound analytical protocol that may inform examination of comparable adaptive reuse contexts where program transformation occurs within stable spatial envelopes. Full article

This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from continuous image space to a structured grid representation, integrating image segmentation, graph construction, and Traveling Salesman Problem (TSP)-based routing. Synthetic warehouse layouts were generated to create labeled training data, and a U-Net convolutional neural network was trained to perform multi-class segmentation of warehouse elements. The predicted grid representation was then converted into a graph structure, where feasible cells define vertices and adjacency defines edges. Shortest path distances were computed using Breadth-First Search, and the resulting distance matrix was used to solve a TSP instance. The segmentation model achieved approximately 98% training accuracy and 95–97% validation accuracy. The generated route matrices enabled successful construction of feasible and optimal round-trip routes in all tested scenarios. The proposed framework demonstrates that warehouse layouts can be automatically transformed into discrete mathematical representations suitable for logistics optimization, reducing manual preprocessing and enabling scalable integration into digital logistics systems. Full article