Search Results (426)

With the global economic restructuring and the consequent population mobility, urban shrinkage has become a common phenomenon. The Qinling–Daba Mountains, a zone with a key ecological function in China, have long experienced population decline and functional degradation. Clarifying the dynamics and influencing factors of urban shrinkage plays a vital role in supporting the sustainable development of the region. This study, using permanent resident population growth rates and nighttime light data, classified cities in the region into four spatial patterns: expansion–growth, intensive growth, expansion–shrinkage, and intensive shrinkage. It further examined the spatial characteristics of shrinkage across four periods (2005–2010, 2010–2015, 2015–2020, and 2020–2022). A Geographically and Temporally Weighted Regression (GTWR) model was applied to examine core influencing factors and their spatiotemporal heterogeneity. The results indicated the following: (1) The dominant pattern of urban shrinkage in the Qinling–Daba Mountains shifted from expansion–growth to expansion–shrinkage, highlighting the paradox of population decline alongside continued spatial expansion. (2) Three critical indicators significantly influenced urban shrinkage: the number of students enrolled in general secondary schools (X₅), the per capita disposable income of urban residents (X₇), and the number of commercial and residential service facilities (X₁₂), with their effects exhibiting significant spatiotemporal heterogeneity. Temporally, X₁₂ was the most influential factor in 2005 and 2010, while in 2015, 2020, and 2022, X₅ and X₇ became the dominant factors. Spatially, X₇ significantly affected both eastern and western areas; X₅’s influence was most pronounced in the west; and X₁₂ had the greatest impact in the east. This study explored the patterns and underlying drivers of urban shrinkage in underdeveloped areas, aiming to inform sustainable development practices in regions facing comparable challenges. Full article

This study investigates the influence of façade-design parameters on daylighting performance in hot arid climates, with a particular focus on Egypt. A total of nine façade configurations of a residential building were modeled and simulated using Autodesk Revit and Insight 360, varying three critical variables: glazing type (clear, blue, and dark), Window-to-Wall Ratio (WWR) of 15%, 50%, 75%, and indoor wall finish (light, moderate, dark) colors. These were compared to the Leadership in Energy and Environmental Design (LEED) daylighting quality thresholds. The results revealed that clear glazing paired with high WWR (75%) achieved the highest Spatial Daylight Autonomy (sDA), reaching up to 92% in living spaces. However, this also led to elevated Annual Sunlight Exposure (ASE), with peak values of 53%, exceeding the LEED discomfort threshold of 10%. Blue and dark glazing types successfully reduced ASE to as low as 0–13%, yet often resulted in underlit spaces, especially in private rooms such as bedrooms and bathrooms, with sDA values falling below 20%. A 50% WWR emerged as the optimal balance, providing consistent daylight distribution while maintaining ASE within acceptable limits (≤33%). Similarly, moderate color wall finishes delivered the most balanced lighting performance, enhancing sDA by up to 30% while controlling reflective glare. Statistical analysis using Pearson correlation revealed a strong positive relationship between sDA and ASE (r = 0.84) in highly glazed, clear glass scenarios. Sensitivity analysis further indicated that low WWR configurations of 15% were highly influenced by glazing and finishing types, leading to variability in daylight metrics reaching ±40%. The study concludes that moderate glazing (blue), medium WWR (50%), and moderate color indoor finishes provide the most robust daylighting performance across diverse room types. These findings support an evidence-based approach to façade design, promoting visual comfort, daylight quality, and sustainable building practices. Full article

Korea has a high population density, considering the size of its territory. Therefore, the importance of convenient and comfortable apartment buildings and high-rise residential–commercial complex buildings has been rising. In addition, because of the improvement in the standard of living along with continuous national economic growth, the interest in well-being and the expectation of a quiet life with a comfortable and pleasant residential environment have also been increasing. However, Koreans have a lifestyle involving sitting on the floor, so floor impact noise has been occurring more and more frequently. Because of this, neighborly disputes have been a serious social problem. And lately, damage and disputes from noise between floors have been increasing much more. The present work, therefore, used waste tire chips as a resilient material for reducing floor impact noise in order to recycle waste tires effectively. Also, a compounded resilient material, which combines EPS (expanded polystyrene), a flat resilient material on the upper part, with waste tire chips for the lower part, was developed. After constructing waste tire chips at a standardized test building, experiments with both light-weight and heavy-weight floor impact noise were performed. The tests confirmed that waste tire chips, when used as a resilient material, can effectively reduce both light-weight and heavy-weight floor impact noise. Full article

The presence of people has a complex relationship with public safety—while it is often associated with increased natural surveillance, it can also attract specific types of crime under certain urban conditions. This exploratory study examines this dual relationship by integrating Jane Jacobs’s urban theories and the principles derived from them in Quito, Ecuador. Anchored in Jacobs’s concept of “eyes on the street,” this research assesses four morphological dimensions—density, land use mixture, contact opportunity, and accessibility through nine specific indicators. A binary logistic regression model is used to examine how these features relate to the incidence of street robberies against individuals. The findings indicate that urban form characteristics that foster “eyes on the street”—such as higher population density and a mix of commercial and residential uses—show statistically significant associations with lower rates of street robbery. However, other indicators of “eyes on the street”—such as larger block sizes, proximity to public transport stations, greater street lighting, and a higher balance between residential and non-residential land uses—correlate with increased crime rates. Some indicators, such as population density, block size, and distance to public transport stations, show statistically significant relationships, though the practical effect size compared to residential/non-residential balance, commercial and facility mix, and street lighting is modest. These results underscore the importance of contextualizing Jacobs’s frameworks and offer a novel contribution to the literature by empirically testing morphological indicators promoting the presence of people against actual crime data. Full article

Between approximately 725 and 518 Ma, a suite of specialized felsic plutons and granitic stocks were emplaced across the Arabian Shield, many of which are now recognized as highly mineralized prospects enriched in rare earth elements (REEs), rare metals, and radioactive elements bearing mineralizations. The current investigation focused on the radiological and geochemical characterization of naturally occurring radionuclides, specifically ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, within three strategically selected granitic prospects, namely, J. Tawlah albite granite (TW), J. Hamra (HM), and J. Abu Al Dod alkali feldspar syenite and granites (AD). Concerning the radioactivity levels of the investigated granitic stocks, specifically the activity concentrations of ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, the measured average values demonstrate significant variability across the TW, HM, and AD stocks. The average ²³⁸U concentrations are 195 (SD = 38.7), 88.66 (SD = 25.6), and 214.3 (SD = 140.8) Bq/kg for TW, HM, and AD granitic stocks, respectively. Corresponding ²²⁶Ra levels are recorded at 172.4 (SD = 34.6), 75.62 (SD = 25.9), and 198.4 (SD = 139.5) Bq/kg. For ²³²Th, the concentrations are markedly elevated in TW at 5453.8 (SD = 2182.9) Bq/kg, compared to 77.16 (SD = 27.02) and 160.2 (SD = 103.8) Bq/kg in HM and AD granitic stocks, respectively. Meanwhile, ⁴⁰K levels are reported at 1670 (SD = 535.9), 2846.2 (SD = 249.9), and 3225 (SD = 222.3) Bq/kg for TW, HM, and AD granitic plutons, respectively. Notably, these values exceed the global average background levels, indicating an anomalous enrichment of the studied granitic occurrences. The mean radiological hazard indices for each granitic unit generally exceed global benchmarks, except for AEDE_out in the HM and AD stocks, which remain below international limits. The geochemical disparities observed are indicative of post-magmatic alteration processes, as substantiated by the interpretation of remote sensing datasets. In light of the significant radiological burden presented by these granitic stocks, it is essential to implement a rigorous precautionary framework for any future mining. These materials must be categorically excluded from uses that entail direct human exposure, especially in residential construction or infrastructure projects. Full article

Despite recent advances in deep learning for fire detection, much of the current research prioritizes model-centric metrics over dataset fidelity, particularly from a fire safety engineering perspective. Commonly used datasets are often dominated by fully developed flames, mislabel smoke-only frames as non-fire, or lack intra-video diversity due to redundant frames from limited sources. Some works treat smoke detection alone as early-stage detection, even though many fires (e.g., electrical or chemical) begin with visible flames and no smoke. Additionally, attempts to improve model applicability through mixed-context datasets—combining indoor, outdoor, and wildland scenes—often overlook the unique false alarm sources and detection challenges specific to each environment. To address these limitations, we curated a new video dataset comprising 1108 annotated fire and non-fire clips captured via indoor surveillance cameras. Unlike existing datasets, ours emphasizes early-stage fire dynamics (pre-flashover) and includes varied fire sources (e.g., sofa, cupboard, and attic fires), realistic false alarm triggers (e.g., flame-colored objects, artificial lighting), and a wide range of spatial layouts and illumination conditions. This collection enables robust training and benchmarking for early indoor fire detection. Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). The proposed model achieved 86.11% accuracy, 88.76% precision, and 84.04% recall, along with low false positive (11.63%) and false negative (15.96%) rates. Compared to its CBAM-free baseline, the model exhibits notable improvements in F1-score and interpretability, as confirmed by Grad-CAM++ visualizations highlighting attention to semantically meaningful fire features. These results demonstrate that effective early fire detection is inseparable from high-quality, context-specific datasets. Our work introduces a scalable, safety-driven approach that advances the development of reliable, interpretable, and deployment-ready fire detection systems for residential environments. Full article

There is a growing global interest in making existing buildings more energy-efficient. However, stakeholders seem to have differing views on the matter, especially in developing countries, thus raising the issue of awareness amongst key stakeholders at the operational stage of existing buildings. This study aimed to examine stakeholders’ awareness of the benefits of passive retrofit in residential buildings using a convergent mixed-methods approach. Quantitative data were collected from 118 property managers and 163 owners of residential buildings, and qualitative data were collected from six government officials in Lagos State, Nigeria. The quantitative data collected were analysed using fuzzy synthetic evaluation, which addresses the fuzziness in judgement-making on multi-criteria phenomena. The results revealed that property managers and owners had a moderately high level of awareness of the environmental, economic, and social benefits of the passive retrofitting of residential buildings. However, while property managers generally had a higher level of awareness than owners, a significant gap was found in their awareness of environmental benefits. Conversely, the qualitative analysis results showed that government officials demonstrated a strong awareness of environmental benefits (energy reduction, air quality, and natural lighting) and economic advantages (cost savings and lower implementation costs). In contrast, their awareness of social benefits was limited to health improvements. The findings have practical implications for policy development and awareness campaigns. Building agencies need to further reinforce their targeted awareness programmes for owners, who demonstrated fair awareness of environmental benefits while leveraging the intermediary role of property managers in promoting home retrofit practices. Economic benefits should also be an integral part of policy frameworks to drive wider adoption across all stakeholder groups. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

This study explores the reuse of high-resolution 3D spatial datasets for multiple urban analyses within a game engine environment, aligning with circular economy principles in sustainable urban planning. The work is situated in two residential test areas in Finland, where watershed analysis, lighting simulation, and change detection were conducted using data acquired through drone photogrammetry and terrestrial laser scanning. These datasets were processed and visualized using Unreal Engine 5.5, enabling the interactive, multitemporal exploration of urban phenomena. The results demonstrate how a single photogrammetric dataset—originally captured for visual or structural purposes—can serve a broad range of analytical functions, such as simulating seasonal lighting conditions, modeling stormwater runoff, and visualizing spatial changes over time. The study highlights the importance of capturing data at a resolution that satisfies the most demanding intended use, while allowing simpler analyses to benefit simultaneously. Reflections on game engine capabilities, data quality thresholds, and user interactivity underline the feasibility of integrating such tools into citizen participation, housing company decision making, and urban governance. The findings advocate for a circular data approach in urban planning, reducing redundant fieldwork and supporting sustainable data practices through multi-purpose digital twins and spatial simulations. Full article

Against the background of the global concern for environmental protection and the prevalence of the green building concept, the requirements for building design are increasing, as are the technological content and functional requirements. Meanwhile, the urgency to address challenges related to the ecological environment and performance requirements has become increasingly pronounced. Taking a dormitory building in China as an example. Autodesk Revit 2018 software is employed in this study to establish a building information modeling (BIM). Green building software (GBSWARE) simulates and analyzes outdoor wind environment, indoor thermal comfort, calculates building energy conservation, does daylighting analysis, and calculates building daylighting. Although the building’s energy-saving design aligns with the requirements, the lighting and indoor thermal comfort of the rooms do not meet the standards. Additionally, the outdoor wind environment has problems with the wind zone and a wind speed amplification coefficient that surpasses the limit. The thermal environment within the residential building fails to satisfy the requirements. This study leverages a BIM-based model for multifaceted applications, integrating tailored retrofit strategies that align with the building’s inherent characteristics and detailed analyses of its components. By harnessing the building’s energy-saving potential, it enhances energy use efficiency, offering a valuable reference for the conceptual design of green buildings and energy-efficient retrofits. Full article

This paper presents an occupant-centric load optimization framework for Smart Green Townhouses (SGTs). A hybrid Long Short-Term Memory and Convolutional Neural Network (LSTM-CNN) model is combined with real-time Internet of Things (IoT) data to predict and optimize energy usage based on occupant behavior and environmental conditions. Multi-Objective Particle Swarm Optimization (MOPSO) is applied to balance energy efficiency, cost reduction, and occupant comfort. This approach enables intelligent control of HVAC systems, lighting, and appliances. The proposed framework is shown to significantly reduce load demand, peak consumption, costs, and carbon emissions while improving thermal comfort and lighting adequacy. These results highlight the potential to provide adaptive solutions for sustainable residential energy management. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article

Photosynthetically Active Radiation (PAR) is critical for sustaining plant growth in the ground and on building surfaces, but how to accurately predict PAR availability in a complex urban environment can be a challenge. Using an advanced ray-tracing software (Radiance 4.0) and local weather data, this study presents a dynamic analysis of the effects of layout, façade orientation and height on PAR availability in four high density residential areas in Shanghai city, China. A metric system was also adopted using three light level requirements of outdoor plants (low, medium, high light levels). Key findings included: (1) the urban layout with the highest ratio of building height to north–south facing adjacent building separation achieved the higher levels of PAR availability for low/medium light level plants and the lower levels of PAR availability for high-light plants for middle and low façades and the ground, while high façades in all layouts could see similar PAR availability for all plants. (2) The PAR availability for low/medium-light plants decreased with the increasing façade height, while the PAR availability for high-light plants showed the opposite trend. (3) The north façade and its ground had higher levels of PAR availability for low/medium-light plants and lower levels of PAR availability for high-light plants than other façades. (4) All layouts offered more opportunities to apply high-light and medium-light plants at façades and the ground. Full article

Countries worldwide have implemented regulations on the green coverage ratio of new buildings to address the urban heat island effect. For example, Taipei City mandates that the green coverage rate of new buildings must be between 40% and 70%, while Singapore requires a green coverage rate of 100% or higher. Consequently, building greening is now a regulatory requirement rather than a preference. This study focuses on developing an indoor light-emitting-diode (LED) hydroponic inverted planting system to utilize ceiling space for expanding green areas in buildings. The light source of this system is suitable for both plant growth and daily lighting, thereby reducing electricity costs. The watertight planting unit does not require replenishment of the nutrient solution during a planting cycle for small plants, which can reduce water consumption and prevent indoor humidity. The modular structure allows various combinations, enabling interior designers to create interior ceiling scapes. Additionally, it is possible to grow aromatic plants and edible vegetables, facilitating the creation of indoor farms. Consequently, this system is suitable for high-rise residential buildings, office buildings, underground shopping malls, and indoor areas with limited or no natural light. It is also applicable to hospitals, clinics, wards, and care centers, where indoor plants alleviate psychological stress and enhance mental and physical health. Full article

Current research on aged housing prioritizes community planning and environmental enhancement over older adults’ needs, creating a retrofit mismatch amid population aging. To investigate the relationship between indoor environmental quality and residential satisfaction among elderly occupants, this study examines 72 households in aged residential buildings, analyzing four environmental indicators (thermal, lighting, acoustic environments, and air quality). The environmental measurements reveal that 81.9% of thermal environment parameters fall below the ASHRAE-55 comfort range, with winter average temperatures reaching only 13.94 °C. Insufficient illumination exists in kitchen and bedroom areas. Lifestyle patterns including infrequent air conditioning use (87%) and window ventilation substituting range hoods (32%) may deteriorate thermal comfort and air quality. An ordered logistic regression analysis demonstrates significant correlations between all four environmental indicators and elderly satisfaction levels. Thermal comfort emerges as the priority focus for aging-adapted retrofitting. Air quality improvement shows particularly significant potential for enhancing residential satisfaction. Although prolonged window opening (73%) exacerbates low-temperature/high-humidity conditions and noise exposure, it still contributes positively to overall satisfaction. This research provides crucial insights for aligning aged residential retrofitting with home-based elderly care requirements, promoting housing development that better accommodates the lifestyle patterns of older populations, thereby improving quality of life for aging-in-place residents. Full article