Search Results (4,871)

Public building-attached green spaces are increasingly important urban carbon sinks, yet their carbon sequestration potential remains poorly understood and underutilized. This study quantified vegetation carbon storage across three attached green space typologies (green square, roof garden, and sunken courtyard) at a representative public building in Wuhan, China, using field surveys and species-specific allometric equations. Total carbon storage reached 19,873.43 kg C, dominated by the green square (84.98%), followed by a roof garden (12.29%) and sunken courtyard (2.72%). Regression analysis revealed strong correlations between carbon storage and morphological traits, with diameter at breast height (DBH) showing the highest predictive power for trees (r = 0.976 for evergreen, 0.821 for deciduous), while crown diameter (CD) best predicted shrub carbon storage (r = 0.833). Plant configuration optimization strategies were developed through correlation analysis and ecological principles, including replacing low carbon sequestering species with high carbon native species, enhancing vertical stratification, and implementing multi-layered planting. These strategies increased total carbon storage by 131.5% to 45,964.00 kg C, with carbon density rising from 2.00 kg C∙m⁻² to 4.63 kg C∙m⁻². The findings provide a quantitative framework and practical strategies for integrating carbon management into the design of building-attached green spaces, supporting climate-responsive urban planning and advancing sustainable development goals. Full article

Understanding visitors’ outdoor activities in urban public spaces and their relationship with the physical environment is essential for improving the precision of public space design. This study, set in the context of Yangzhou, China, focuses on physical activity and other wellbeing behaviors in meso-scale waterfront public spaces, aiming to explore the characteristics of visitor behavior. A professional behavioral observation protocol was employed, combined with object detection and multi-object tracking algorithms, to systematically code visitor activities in the waterfront area. Subsequently, agent-based modeling (ABM) and three-dimensional isovist analysis (3D isovist) were introduced to construct a quantitative framework for assessing visual accessibility. The results reveal a significant positive correlation between facade Visual Exposure Time (seen from the observer) and isovist field area (seen from the object), providing strong evidence that visual accessibility is a primary causal driver of pedestrian behavior—independent of other causality. Based on these findings, this study proposes actionable design guidelines: “Prioritize small-scale, high-density waterfront building facade layouts to maximize visual efficiency” and “Leverage topographical variation along the waterfront by introducing cross-river visual corridors at intervals of ≤45 m”. The integrated analytical toolkit developed in this study—combining behavioral simulation with spatial–visual analysis—provides not only a theoretical foundation but also clear practical guidance for the fine-grained renewal and design of waterfront public spaces. Full article

The construction sector contributes significantly to global greenhouse gases, accounting for 39% of worldwide emissions. Multi-story wood buildings (MSWBs) present a sustainable alternative to traditional emissions-intensive construction materials like concrete and steel. However, only a few studies have investigated how potential customers perceive MSWBs, which influences their acceptance and demand. This study uses a concept-driven scoping review to explore perceptions and concerns about living in MSWBs and to understand barriers to their adoption. Through a narrative synthesis of 20 peer-reviewed articles, this study uncovered five key themes: environmental sustainability, fire safety, human well-being, structural durability, and costs. These findings highlight opportunities and challenges for MSWBs’ market growth and inform future communication strategies to enhance public acceptance and promote sustainable construction and the built environment. Full article

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, p > 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones. Full article

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems. Full article

Raising awareness and understanding of climate change among younger generations is crucial for building a sustainable future. The Laboratory of Atmospheric Physics (LAP) within the School of Physics of the Aristotle University of Thessaloniki (AUTh) supports this goal by developing innovative educational activities centered on atmospheric processes and climate science. Drawing on its expertise in atmospheric monitoring and remote sensing, LAP makes complex scientific concepts accessible to school students through interactive workshops, hands-on experiments, and data-driven projects using real-time environmental measurements. By integrating research-grade tools and open-access satellite data from ESA, NASA, and EUMETSAT, LAP bridges academic research and public understanding. These activities foster critical thinking, environmental responsibility, and student engagement with real-world climate monitoring practices. Moreover, LAP contributes to the ACTRIS network, offering high-quality data and expertise at both national and European levels. Through these efforts, LAP serves as a hub for climate education, turning awareness into action and inspiring future climate-conscious citizens. Full article

The digital transformation of the construction industry is still lagging due to its incomplete implementation throughout the entire building lifecycle. One stakeholder in particular has been largely overlooked thus far: public administration. This study explores the potential integration of augmented reality (AR) into the processes of building authorities, with a particular focus on the review part of the permissions process, taking the City of Vienna as an example. As part of the EU-funded BRISE-Vienna project, an AR platform was developed and tested and an AR application was designed to enhance the transparency, stakeholder communication, and efficiency throughout the process. This study compares the proposed AR-based review process with the traditional plan-based approach, assessing both hard and soft factors. To this end, the durations of the individual process steps were measured, with a particular focus on the time spent by the officers (as a hard factor). In addition, qualitative surveys were conducted to gather the subjective impressions of the test participants (as soft factors). The key findings were a reduction in the officers’ workloads and an improvement in spatial understanding. While the overall review time remained similar, the use of AR reduced officers’ workload by over 40%. Additionally, the test participants stated that AR improved their spatial understanding and alleviated the time pressure within the process. This case study demonstrates the potential of AR in the permissions process and could serve as a model for other cities and countries. Full article

The environmental noise impact on sensitive buildings and residents, generated by urban rail transit systems, has attracted increasing attention from the public and various levels of management. Owing to the diversity of building types and the complexity of noise propagation paths, the accurate prediction of noise levels adjacent to structures through traditional experimental or empirical formula-based methods is challenging. In this paper, on-site multi-dimensional noise monitoring of the noise source affecting the sensitive buildings was first carried out, and a hybrid prediction method combining normative formulas, numerical simulations, and experimental research is proposed and validated. This approach effectively addresses the shortcomings of traditional prediction methods in terms of source strength determination, propagation path distribution, and accuracy of results. The results show that, while predicting or assessing the noise impact on sensitive buildings and interior residents, it is important to properly consider the impact of background noise (such as road traffic) as well as vibration radiation noise of bridge structures. The predicted results obtained by using this method closely match the measured results, with errors controlled within 3 dB(A). The noise prediction error in front of buildings is controlled within 2 dB(A), fully meeting the requirements for environmental noise assessment. Full article

With the rapid development of large-space buildings, their fire risk has become increasingly prominent. Conventional fire detection technologies are often limited by spatial height and environmental interference, leading to false alarms, missed detections, and delayed responses. This paper reviews 83 publications to analyze the limitations of conventional methods in large spaces and highlights the advantages of and current developments in image-based fire detection technology. It outlines key aspects such as equipment selection, dataset construction, and target recognition algorithm optimization, along with improvement directions including scenario-adaptive datasets, model enhancement, and adaptability refinement. Research demonstrates that image-based technology offers broad coverage, rapid response, and strong anti-interference capability, effectively compensating for the shortcomings of conventional methods and providing a new solution for early fire warning in large spaces. Finally, future prospects are discussed, focusing on environmental adaptability, algorithm efficiency and reliability, and system integration, offering valuable references for related research and applications. Full article

The urban heat island (UHI) effect has become a critical environmental issue affecting urban livability and public health, attracting widespread attention from both academia and society. Although numerous studies have examined the influence of urban characteristics on land surface temperature (LST), most have been restricted to single variables or single time points, and the traditional “urban–rural dichotomy” approach fails to capture intra-urban thermal heterogeneity. To address this limitation, this study integrates the Local Climate Zone (LCZ) framework with machine learning techniques to systematically analyze the diurnal variation patterns of LST across different LCZ types in Beijing and explore the interactive effects of urban characteristic variables on LST. The results show the following: (1) Compact building zones (LCZ 1–3) exhibit significantly higher daytime LST than open building zones (LCZ 4–6), with reduced differences at night; high-rise buildings cool daytime surfaces through shading but increase nighttime LST due to heat storage. (2) Blue–green space variables, such as NDVI and tree coverage (T_PLAND), substantially lower daytime LST through evapotranspiration, but their nighttime cooling effect is weak; cropland coverage (C_PLAND) plays a particularly important role in lowering nighttime LST. (3) Blue–green space and urban form variables exhibit significant interaction effects on LST, with contrasting impacts between day and night. (4) Population activity variables are strongly correlated with increased LST, especially at night, when their warming effects are more prominent. This study reveals the relative importance and nonlinear relationships of different variables across diurnal cycles, providing a scientific basis for optimizing blue–green space configuration, improving urban morphology, regulating human activity, and formulating effective UHI mitigation strategies to support the development of more sustainable urban environments. Full article

To address the misalignment between conservation–development policies and villagers’ needs in traditional villages, this study identifies core demands through a questionnaire survey in 112 villages across three counties in Hubei Province, China. An evaluation system encompassing public facilities, infrastructure, exterior environment, interior environment, and village culture (23 indicators) was analyzed using the Kano model and Better–Worse coefficients (211 valid questionnaires). Results reveal the primary needs ranking: village culture > exterior environment > interior environment > infrastructure > public services. Key findings show villagers prioritize traditional building conservation, cultural identity, and roof improvement, while certain public service investments (e.g., water supply, signage, education) yield lower satisfaction. Notably, villagers are indifferent to lighting improvements. This indicates a deviation from past government priorities and underscores the necessity of integrating villager perspectives into “top-down” decision-making for sustainable village development. The findings provide practical guidance for habitat improvement and precise policy formulation in Northeastern Hubei. Full article

The effectiveness of recently developed tools for detecting dangerous items is overestimated due to the low quality of the datasets used to build the models. The main drawbacks of these datasets include the unrepresentative range of conditions in which the items are presented, the limited number of classes representing items being detected, and the small number of instances of items belonging to individual classes. To fill the gap in this area, a comprehensive dataset dedicated to the detection of items most used in various acts of public security violations has been built. The dataset includes items such as a machete, knife, baseball bat, rifle, and gun, which are presented in varying quality and under different environmental conditions. The specificity of the constructed dataset allows for more reliable results, which give a better idea of the effectiveness of item detection in real-world conditions. The collected dataset was used to build and compare the effectiveness of modern models for detecting items belonging to the YOLO and Vision Transformer (ViT) architectures. Based on a comprehensive analysis of the results, taking into account accuracy and performance, it turned out that the best results were achieved by the YOLOv11m model, for which Recall = 88.2%, Precision = 89.6%, mAP@50 = 91.8%, mAP@50–95 = 73.7%, Inference time = 1.9 ms. The test results make it possible to recommend this model for use in public security monitoring systems aimed at detecting potentially dangerous items. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

This article examines the activities of the Bahá’í community in Iran after the Islamic Revolution, challenging the misconception that the community has remained disengaged from societal involvement which arises from a misinterpretation of its principle of non-involvement in partisan politics. Contrary to this belief, the Bahá’í community has been actively engaged in social change through a framework rooted in its principles, which emphasize constructive resilience and non-adversarial strategies. Informed by the Bahá’í teachings, the global Bahá’í experience, and contemporary theories of social change, the community has focused on translating its spiritual principles into practical actions, particularly in community building, social action, and participation in the prevalent discourse of society. These efforts, characterized by a commitment to unity and collaboration, differ from conventional adversarial activism and demonstrate the community’s significant yet often overlooked contribution to Iranian society. Despite severe persecution, the Bahá’í community has maintained a principled engagement with social change, challenging the narrative of disengagement and highlighting its ongoing involvement in the life of the nation. Full article

As society ages, there is a growing concern about the comfort and health of elderly people. Although populations around the world, including Slovenia, are rapidly aging, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. An increasing number of older adults live with chronic diseases, functional limitations, or frailty. In 2025, Slovenia launched the project Systemic Approach to Frailty with a Focus on Pre-Frailty for Healthy and Hight-Quality Ageing, within the European Cohesion Policy Programme 2021–2027, aiming to address frailty through multidimensional and community-based interventions. In addition to presenting the project framework, this paper provides an analytical preliminary review of existing literature, critically reflecting on research gaps in the field. The main aim of this paper is to explore the possibilities for creating healthy living environments that support the prevention and management of frailty. The project’s core innovation lies in the integration of public health principles into urban planning and design through a structured, community-based approach and the use of the Living Environmental Assessment (OBO) Tool. This tool enables urban planners, municipalities, and local communities to collaboratively evaluate and co-design living environments (e.g., optimizing walkability, green space access, barrier-free design, and social amenities) to build resilience and independence among older adults. Designing inclusive, accessible, and health-promoting environments can help to prevent frailty and improve well-being across all age groups. Full article