Search Results (6,092)

Global climate change has brought issues of pollution and environmental protection to the forefront of public attention. The energy consumption and carbon emissions of buildings have become critical issues in energy conservation and emission reduction, and are important for environmental protection. This article focuses on typical residential buildings in Wuhan, a representative of regions with hot summers and cold winters, to study the impacts of different layout design parameters on energy consumption and carbon emission intensity of building complexes. VirVil-HTB2 was used for modeling and simulating building complex layouts, while SPSS was used for data analysis. This study shows that solar radiation is an important indicator for predicting building energy efficiency, directly affecting energy consumption and carbon emissions. We also examined the impact of building orientation, building spacing, staggered spacing, and the layout of open spaces between buildings on heating energy consumption, cooling energy consumption, and carbon emissions. Building spacing was positively correlated with cooling energy consumption and negatively correlated with heating energy consumption and carbon emissions. The effect of staggered spacing on energy consumption is greater in the south–north direction than in the west–east direction. Additionally, setting the building orientation to 135° results in the lowest carbon emissions. Under the idealized simulation conditions of this study, the west–east dispersed open-space layout is a preferable configuration for reducing carbon emissions from residential neighborhood buildings. This study explores the impact of layout design parameters on energy consumption and carbon emissions of building complexes in hot summer and cold winter regions, providing references for energy optimization and environmental sustainability research. Full article

The aim of this paper is to conceptualize smart culture as an important yet under-researched dimension of smart cities, and to empirically demonstrate the extent to which cultural events in Vilnius’ public spaces align with the key principles of smart culture. The theoretical section of the article provides a definition of smart culture in a smart city, based on which four categories of analysis are identified: accessibility, the integration of technology into the cultural experience, engagement of the population, and promotion of community building. The methodology consists of an instrumental case study, analysis of secondary sources, and directed content analysis. The research findings reveal that Culture Night festival events in Vilnius not only reduce social and geographical barriers to culture but also create spaces for active participation of the population, fostering community and the application of technological solutions in cultural activities. Culture Night represents a clear example of smart culture, highlighting the importance of this dimension in smart city policies. The study shows that the identified characteristics of smart culture may support inclusive and sustainable urban development trends associated with SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities). Full article

Reliable, explainable prediction of the maternal mortality ratio (MMR) is challenging in global health because country-level indicators are heterogeneous and missingness is often informative rather than random. This study aims to develop and validate a missingness-aware TabNet (MA-TabNet), an attention-based framework that treats absence patterns as learnable signals while maintaining a stable feature space for country-level MMR forecasting and interpretation. We build a country–year panel from a publicly available global nutrition and health dataset and predict MMR using socioeconomic and health indicators to test whether missingness patterns add predictive signal beyond observed covariates. The model applies a distribution-aware selective masking strategy, adding missingness indicators only for variables with high missing rates; remaining gaps are handled by median imputation, with indicators retained to explicitly encode reporting uncertainty. Country codes and regional groupings are encoded as learnable embeddings, and entmax-based sequential attention is used to improve feature selection via sparse, competition-style masks under correlated determinants. Hyperparameters are tuned using Bayesian optimization, and evaluation follows a temporally realistic protocol (train on earlier years; test on a future held-out year). MA-TabNet achieves a mean absolute error (MAE) of 21.05, root mean squared error (RMSE) of 36.24, and R² of 0.9739, outperforming strong tree-based baselines and improving on the original TabNet while avoiding the training instability observed in some transformer-style tabular models. For transparency, we report attention-derived global and local importance, compare original versus missing-mask features in model importances, and complement these with permutation-based Shapley additive explanation summaries, permutation importance (MAE drop), partial dependence plots for top drivers, and continent-stratified residual analyses to clarify how structural reporting gaps shape predictions and to support trustworthy maternal health monitoring. Overall, these findings suggest that modeling missingness as a measurable reporting signal can yield accurate, auditable forecasts that are better aligned with temporally realistic SDG 3.1 monitoring than “fill-and-forget” preprocessing. Full article

This paper proposes a novel parametric output-only modal identification method, termed parametric complexity pursuit (PCP), for accurate identification of modal parameters in civil engineering structures. The proposed method extends the complexity pursuit (CP) algorithm through a system representation. Unlike the standard CP approach, which extracts modes individually, the PCP algorithm transforms CP into a parametric formulation that enables global identification of all modal parameters of interest. This parametric framework provides a rigorous theoretical foundation aligned with structural dynamics and explicitly accounts for mutual influences among different modes, thereby enhancing both accuracy and robustness. Numerical experiments on a simulated 3-DOF system under various damping conditions and closely spaced modes demonstrate that PCP maintains consistent identification accuracy across all damping levels, exhibiting true damping-independent performance. In contrast, conventional CP suffers from marked accuracy degradation as damping increases and fails to properly identify the closely spaced modes. Application to the HCT building further confirms the practical effectiveness of PCP, with comparative analysis demonstrating its superiority over existing output-only techniques in modal separation capability and identification accuracy. The proposed PCP method offers a robust and theoretically grounded framework for output-only modal identification, particularly advantageous in challenging scenarios involving high damping and closely spaced modes. Full article

Graph-based spatial analysis formalizes relations among spatial units, but the formation of these units and their boundary correspondences remains under-specified. This study defines the structural stage preceding relational abstraction and establishes the conditions under which spatial units and boundary correspondences become analytically determinate. It then develops a layered spatial articulation procedure that derives spatial objects from plan-encoded architectural information by differentiating topographic substrate, building frame, spatial enclosure, and relational boundary conditions. These are organized into a base spatial graph. The topology of this graph is fixed by articulation, and its edges encode admissible relational mode combinations. Using traditional Korean housing (hanok) as an illustrative reference for the proposed methodology, the study shows that heterogeneous spatial conditions can be consistently articulated into a unified structural domain prior to relational abstraction. The resulting base spatial graph defines a finite but combinatorially extensive space of admissible relational configurations. Within this domain, graph-domain operations act without expanding the articulated structure, while certain operations may reduce it through structural transformation. The study shows that spatial units cannot be treated as pre-given entities but must be structurally constituted. By formalizing this prior stage, the study establishes explicit structural preconditions for graph-based spatial analysis and provides a consistent analytical domain for subsequent spatial interpretation. Full article

Promoting the green development of large public buildings is a crucial pathway toward environmental sustainability. As a type of public building characterized by both high energy consumption and high public engagement, green stadiums provide an important setting for examining whether building-embedded green features are visible, understandable, and valued by users. In this sense, green stadium consumption intention is treated in this study as a building-related outcome that reflects user acceptance of green building spaces and services rather than as a generic green marketing preference alone. This study examines the effects of Green Facility Visibility, Perceived Green Communication, and Green Interactive Experience on Millennials’ Green Stadium Consumption Intention, while investigating the parallel mediating roles of Green Self-Efficacy and Future Orientation. A sample of 976 millennial users was surveyed. The hypothesized model was tested using covariance-based structural equation modeling (CB-SEM), and Bootstrapping was employed to validate the significance of the mediating effects. Findings reveal that: (1) Green Facility Visibility and Perceived Green Communication significantly and positively influence Green Stadium Consumption Intention, whereas the direct effect of Green Interactive Experience is insignificant; (2) Green Self-Efficacy mediates the relationships between Green Facility Visibility, Perceived Green Communication, and consumption intention; and (3) Future Orientation similarly mediates the relationships between Green Facility Visibility, Perceived Green Communication, and consumption intention. Rather than proposing a major theoretical breakthrough, this study offers a context-specific extension of green consumption research by introducing Green Self-Efficacy and Future Orientation as parallel mediators in a stadium setting. The findings show how building-related green cues and user cognition jointly shape the acceptance of green stadiums, thereby providing evidence relevant to the design, operation, and evaluation of public-facing green buildings. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article

Photovoltaic (PV) systems are important for sustainable energy infrastructure, but their rapid deployment introduces complex fire dynamics that current regulations fail to address adequately. While existing standards focus on the electrical safety of individual components, they often neglect the risks arising from the interaction between the PV array and the building envelope. This review synthesizes current research on ignition mechanisms, thermal behavior, and the aerodynamic propagation of smoke to evaluate these overlooked hazards. A primary finding is that the interstitial space between the panel and the roof functions as a “heat trap,” significantly altering airflow patterns and accelerating flame spread even across fire-rated materials. The analysis further highlights that standard testing protocols do not sufficiently account for the urban dispersion of toxic combustion byproducts, such as hydrogen fluoride and volatile organic compounds. By evaluating recent advancements in Computational Fluid Dynamics (CFD) and helium-based surrogate testing, this paper demonstrates that accurate prediction of pollutant transport requires coupled modeling of wind effects and thermal buoyancy. The study concludes that ensuring urban fire resilience demands an evolution from component certification to integrated system assessments that include installation geometry, ventilation strategies, and environmental impact. Full article

Existing buildings with large glazing ratios within subtropical Mediterranean climates face substantial challenges for thermal and visual control of their indoor environment. Previous research by the same authors has already identified the potential of incorporating both solar–PDLC (polymer-dispersed liquid crystal) and SPD (suspended particle device) switchable films within facades exposed to high solar insolation to provide a wide dynamic range of visual transparencies. This paper identifies a novel application for switchable laminates within a dynamic external shading device that permits the casting of a shadow on demand onto existing fenestration. This study compares the degree of glare within an enclosed space attained by a conventional opaque overhang over a window to that achieved with glass shading overhangs incorporating two types of switchable films. Using a scale model in a field test setting, indoor illumination and glare measurements are investigated under different states of switchable films and compared to those provided by conventional static glazing, with and without ordinary external overhangs under identical field test conditions. Results show that switchable overhangs in their transparent/bleached state can allow the ingress of daylight without creating excessive glare, whereas in their translucent/tinted state, switchable shades can deliver a level of glare protection similar to that provided by an opaque shading overhang. Full article

In the era of digital economy, manufacturing industry transcends geographical space to build virtual networks. Revealing the complex network characteristics and driver paths of virtual agglomeration is of great significance for accelerating the digitalization of manufacturing. First, this paper explains the formation mechanism and proposes the model of virtual agglomeration; moreover, the paper identifies complex network characteristics. Finally, this paper constructs a driving path framework based on the “Technology–Organization–Environment” theory, and uses fuzzy set qualitative comparative analysis to identify paths. The results show that the technological platform foundation plays a core role in enhancing the level of virtual agglomeration. Differentiated combinations of organizational and environmental conditions also have a positive impact. This study provides theoretical support and practical reference for cities to accelerate virtual agglomeration according to local conditions. Full article

Urban green space (UGS) is widely recognized as a core component of sustainable urban development in megacities. The study of the synergistic relationship between high-intensity urban development and daily accessibility of UGS, however, remains insufficient. This paper therefore critically assesses the inherent correlation between building intensity and the UGS daily accessibility in a typical megacity context. The analysis is twofold: What is the inherent correlation between building intensity and the daily accessibility of UGS in megacities? And, if such a correlation exists, how can daily accessibility be improved by integrating building intensity into the UGS planning process? Using a case study in Beijing, methods of multi-source data integration, GIS spatial analysis, and statistical correlation models are used to address the issues. Results indicate that building intensity exhibits a statistically positive spatial association with the Daily Accessibility Index (DAI). Mere expansion of the total UGS area does not necessarily lead to improved daily accessibility for residents. The findings include a clarified dual-effect mechanism of high-intensity development on UGS services, as well as evidence-based planning strategies for sustainable UGS layout in dense megacities. Full article

Under the background of highway ecological green construction and traffic-tourism integration, tourist highways in world natural heritage sites bear the dual responsibilities of heritage ecological protection and regional economic boosting, yet existing routes prioritize connectivity over ecological and economic values, damaging heritage integrity and failing to drive surrounding township development. This study aims to build a dual-coordinated route selection framework balancing ecological protection and economic development, taking Mount Fanjing as the case. Adopting literature research, field survey and spatial analysis, and grounding in road ecology, point-axis system and tourism space competition theories, it constructs a four-part framework covering township tourism potential evaluation, ecological suitability assessment, binary matrix model and route generation. Empirically, nine townships including Minxiao and Taiping are screened as core tourism service nodes, and the optimal layout of the ring Mount Fanjing tourist highway is determined via ecological suitability matching. The findings reveal the prominent contradiction between heritage protection and regional development in current heritage tourist highway construction, and the proposed dual coordination model effectively balances heritage conservation and local economic growth, providing a feasible planning reference for sustainable tourist highway layout in world natural heritage sites. Full article

Material and texture decisions in bionic machinery installation art often remain intuition-dependent, limiting the reusability of empirical evidence for experience design. Building on the biomimetic content logic in biomimetic design theory, this study proposes a targeted framework—Texture Bionics—and operationalizes texture into four quantifiable perceptual dimensions: transparency, hardness, roughness, and surface texture, forming a controllable sample space of 12 plastic texture conditions. A case database encompassing 56 representative works (2000–present) was constructed to justify material selection; plastics were chosen for their tunable properties and feasibility for parameterized modulation. In a standardized viewing setup (≈500 lx illumination; 60 cm viewing distance), participants viewed a dynamic biomimetic mechanical wing module with interchangeable textured plastic surfaces. Subjective affect responses were captured using PAD ratings, and objective attention was assessed via wearable eye-tracking technology. Repeated-measures analyses showed robust main effects of texture on total fixation duration across all four dimensions, and selective effects on time to first fixation (significant/marginal for transparency, roughness, and surface texture but not hardness); pupillary response metrics provided no stable discrimination. PAD mappings further revealed functional “role types” (e.g., Key driver, Explore guide, Stable base), and a strong association between Arousal and inter-participant variability in fixation distribution, suggesting that high-arousal textures act as strategy amplifiers rather than uniformly increasing attention. Finally, findings were translated into an actionable Texture Design Toolkit using a three-question workflow—function label → attention goal → differentiation risk—to support evidence-based orchestration of installation narratives. Full article

Rolling bearing simulations are often too computationally expensive for early design decisions, because many simulations are required in a large design of experiments. Therefore, the aim of this systematic literature review is to provide an overview of how machine learning (ML) is used to integrate engineering knowledge in advance when simulations are the primary data source for supervised learning. In the 11 included studies, ML is mainly applied as regression models trained on simulation data to replace repeated solver calls. The applications can be classified into three domains—contact mechanics, lubrication, and dynamics—mostly linked to their domain specific outputs. In most cases, ML models replace the simulation once the model is trained and validated, followed by optimization, which is often performed on the surrogate using evolutionary algorithms. Surrogates have the potential to enable design-space exploration, sensitivity analysis, and uncertainty propagation, but this capability is not yet fully exploited in current practice. The purpose of this review article is to provide a summary of methodological building blocks and practical guidelines to assist researchers and engineers in selecting appropriate ML workflows for simulation-based analysis of rolling bearings in the areas of tribology, dynamics, service life, load capacity, and system-level investigations. Full article

Global climate change continues to intensify, leading to an increase in extreme meteorological disasters characterized by high intensity, frequency, and extensive impact. Chinese cities are facing increasingly severe flood disaster risks. As the fundamental unit of the urban system, scientifically quantifying a community’s post-disaster recovery capacity provides a crucial basis for formulating disaster prevention and mitigation strategies. Existing research has largely focused on either quantitative resilience assessment of communities or the functional recovery of specific systems within communities, falling short of meeting the quantitative needs for assessing community functional recovery after flood disasters. Given this, this paper aims to construct a community functional recovery model based on different land use types to precisely quantify the recovery trajectory of community functions. First, the MIKE 21 two-dimensional hydrodynamic model is employed to simulate 100-year and 200-year flood scenarios, obtaining dynamic inundation data at the community scale. Subsequently, a semi-Markov process is adopted to model the recovery of individual buildings, with the aggregated building functions within the community summarized to derive building recovery curves. A road network topology model is constructed using the Space L method, and network global efficiency is applied to quantify community road functionality. Green space functional loss is quantified based on the percentage of inundated areas. Finally, calculation is performed based on the proposed dual-layer computational framework consisting of a connectivity layer and a functional layer, and the overall community functional recovery curve after the disaster is generated, thereby achieving precise quantification of the recovery process. The research findings indicate that increased disaster intensity significantly amplifies functional losses and recovery delays. Concurrently, distinct land use types exert markedly different impacts on community recovery. This study quantitatively reveals the phased dominant roles of various land use types throughout the community recovery process, providing a scientific basis for formulating phased, prioritized resilience enhancement strategies. Full article