Search Results (2,088)

Vision-based fire detection models are often limited by the insufficient diversity of annotated fire and smoke images, particularly in terms of fire location, flame scale, smoke density, ignition cause, and indoor scene context. This study investigates whether generative AI-based synthetic images can expand fire-image diversity and improve object detection-based fire detection performance. Real fire images were combined with conventional augmented images and synthetic images generated using ChatGPT-4.o and ChatGPT-5.5. The generated images were constructed using multivariable prompts considering fire location, scale, and cause, and unsuitable samples were screened using a pretrained fire detection model. YOLOv8n, YOLOv11n, and RT-DETR were trained under 48 dataset–detector conditions and evaluated using fixed validation and test datasets. The results showed that generated-image-based training generally maintained or improved detection performance compared with the original and conventional augmentation conditions. In particular, selected ChatGPT-4.o-based YOLOv11 conditions showed statistically supported improvements over matched augmentation conditions, with increases of +0.052 in Precision, +0.031 in Recall, +0.065 in mAP@0.5, and +0.038 in mAP@0.5:0.95. LPIPS and t-SNE analyses indicated that the generated images formed structured perceptual and feature-space distributions relative to real fire images. Scenario-based inference using location-specific video frames also showed stable model responses in several complex indoor fire environments. These findings suggest that validated generative AI-based images can supplement the limited visual diversity of real fire datasets and improve the robustness of vision-based fire detection models. Full article

Within the subway systems of megacities, individual passenger transfer experiences have long been marginalized due to an overemphasis on macro-level, systemic, and functional performance, positioning low transfer efficiency as a pervasive bottleneck in enhancing the overall network efficacy. Adopting an individual passenger perspective, this study takes the Changping Line of the Beijing Subway as an empirical case. By using walking speed to evaluate transfer efficiency and through field survey, behavioral experiment, and quantitative model analysis, this paper reveals the key built environment factors influencing transfer efficiency and their underlying impact mechanisms and also provides empirical evidence for the synergistic optimization of transfer efficiency and the built environment in megacity subway systems. The findings indicate that the built environment impacts transfer efficiency in macro-non-linear and micro-linear ways, specifically manifesting across six specific mechanisms: the geographic location mechanism, the pressure mechanism of high-density development, the spatial exclusivity mechanism of regional transport hubs, the topological penalty mechanism of transfer paths, the bottleneck constraint mechanism of node facilities, and the compensatory mechanism of information guidance. Furthermore, as a medium affecting transfer efficiency, the shaping of the built environment is essentially determined by the city’s subway planning and construction institutions, the external technical conditions of the particular stations, and localized tactical governance to manage the dynamic daily traffic mobility. Based on these findings, this study suggests that improving the transfer efficiency of megacity metro systems like the Changping Line should implement systemic strategies from four aspects: tailored TOD at the macro-spatial planning phase, the micro-spatial integration of indoor and outdoor built environments during the station design phase, differentiated collaborative governance to alleviate station-external intermodal transfer competitions during the operation phase, and digitally empowered transfer guidance to proactively manage transfer demand across three scenarios. Full article

Indoor radon is a key indoor air pollutant and a major contributor to population exposure to natural ionizing radiation, with relevance in school buildings where children and staff spend extended periods indoors. This study investigated indoor radon concentrations in two public schools located in a radon-prone area of northern Portugal using a combined short- and long-term monitoring strategy. In the first phase, active RadonEye detectors were deployed for continuous winter measurements in four ground-floor rooms across two schools, with three rooms located in School S1 and one in School S2, providing hourly data to characterize temporal variability. In the second phase, CR-39 passive detectors were installed in the same spaces to obtain integrated long-term concentrations. Active monitoring revealed pronounced spatial and temporal variability, with mean concentrations ranging from 140 to 228 Bq/m³ and peak values reaching 586 Bq/m³. Radon levels generally declined during occupied periods and increased at night and on weekends, indicating the importance of ventilation and occupancy patterns in shaping indoor radon dynamics. Passive measurements ranged from 290 to 300 Bq/m³ in school S1 and from 84 Bq/m³ to 300 Bq/m³ in school S2, confirming higher long-term concentrations in S1. Although most measurements remained below the reference level of 300 Bq/m³, several rooms approached or temporarily exceeded this threshold. These findings highlight the value of combining active and passive techniques to improve radon assessment in school environments and support targeted ventilation and mitigation strategies to reduce exposure and improve indoor air quality. Full article

In this paper, we propose a 1-bit algorithm using spatial information to improve the accuracy of Kalman filter (KF)-based location estimation. The proposed algorithm aims to improve position estimation accuracy by re-estimating values outside a feasible region as being at the boundary of that region, based on the information that the user is present within that feasible region. This approach enhances position estimation accuracy without significantly increasing complexity. This paper discusses two methods for applying the 1-bit algorithm and verifies their performance by comparing Time of Arrival (ToA), the ToA-based KF, and the ToA-based KF with the 1-bit algorithm through simulations under three scenarios. Performance analysis was conducted from two perspectives: cumulative distribution function (CDF) and average position error (APE). The ToA-based KF with a 1-bit algorithm demonstrated the best performance. The proposed approach improved performance without high computational complexity and is suitable for real-time applications, making it applicable to indoor positioning, robot navigation, and wireless sensor networks that require high positioning accuracy. Full article

This study investigates an integrated ecological strategy to reduce electricity consumption in semi-collective housing located in the hot–arid climate of Biskra, Algeria, a region with high solar potential. The research combines photovoltaic (PV) electricity generation with passive thermal insulation using a locally sourced bio-based material derived from date palm fibers. The case study includes 104 dwellings within a residential complex of 350 units. Results show that monocrystalline PV panels (350 W) can produce approximately 479 kWh/panel/year. To meet the total annual electricity demand (504,712 kWh), around 1052 panels are required, corresponding to 1714 m² (13.8%) of the available building envelope. This installation area demonstrates the significant photovoltaic potential of the residential complex under hot–arid climatic conditions. Thermal analysis indicates that integrating a 5 cm palm fiber insulation layer increases thermal resistance from 2.06 to 2.62 m²·°C/W and reduces heat flux from 2.18 to 1.72 W/m². This improvement decreases conductive heat transfer through the envelope by approximately 21%, while numerical simulations indicate indoor temperature reductions of 4–8 °C during summer conditions. These findings demonstrate that combining PV systems with bio-based insulation significantly enhances energy efficiency and thermal comfort in residential buildings under desert climatic conditions. Full article

Freeze–thaw cycles cause mechanical deterioration and instability of slope rock masses in open-pit coal mines located in the cold regions of Northwest China. In this study, the research object is fine-grained sandstone from the Yan’an Formation in the Tarangole mining area of the Ordos Basin. Here, indoor freeze–thaw cycling, uniaxial compression, and triaxial compression tests were conducted to systematically analyze the deformation behavior, strength evolution, and failure modes of the sandstone under varying numbers of freeze–thaw cycles, freezing temperatures, and confining pressures, thereby revealing its freeze–thaw damage mechanism. The results show that the number of freeze–thaw cycles is the dominant factor affecting the elastic modulus. Freezing temperatures (especially between −5 °C and −15 °C) and the number of freeze–thaw cycles (particularly the first 10 cycles) significantly reduce peak strength. In addition, confining pressure can significantly enhance the resistance to deformation (under 15 freeze–thaw cycles, the elastic modulus increases by 181.8% as confining pressure rises from 0 to 2 MPa). Within the low confining pressure range (0–1.5 MPa), peak strain decreases monotonically with increasing confining pressure and is independent of the number of freeze–thaw cycles. Finally, the increase in the number of freeze–thaw cycles and the decrease in temperature jointly promote crack development, and the failure mode shifts from pure shear to a shear-tension composite mode. The underlying cause lies in the evolution of interparticle cementation within the soil skeleton and in the associated pore–crack structure. In addition, based on fracture damage mechanics and the modified Weibull distribution, a damage evolution equation and a constitutive model for sandstone considering freeze–thaw cycles and temperature effects were established and validated. Therefore, the research findings can provide a theoretical basis for slope support, freeze–thaw disaster prevention and mitigation, and stability assessment in the Tarangole mining area and other cold regions. Full article

This study presents an Indoor Environmental Quality (IEQ)-aware framework for residential valuation by integrating low-cost IoT sensing, transparent scoring, and hedonic price modeling. The analysis uses a dataset of 244 apartments across 12 districts in Tehran. It combines indicators of thermal comfort, particulate exposure, lighting, acoustics, stability, exceedance, and uncertainty with conventional housing covariates (area, age, bedrooms, floor level, renovation status, amenities, and accessibility proxies). Results show that pooled IEQ–price relationships are weak and confounded, whereas controlled specifications produce modest but consistent improvements in explanatory fit after IEQ features are introduced. Conventional location and structural attributes remain the dominant determinants of price per square meter. Still, IEQ contributes a non-redundant information layer that improves within-segment differentiation and interpretability for inspection and listing workflows. Methodologically, the framework extends beyond average comfort metrics by incorporating volatility, threshold exceedance duration, and sensor uncertainty, enabling uncertainty-aware reporting rather than single-point scoring. In practice, the workflow supports portable sensing, reproducible analytics, and privacy-preserving edge aggregation, suitable for PropTech deployment. The findings support a cautious but actionable conclusion: IEQ should be treated as an incremental valuation signal rather than a standalone pricing determinant. In this context, IEQ is conceptualized as a supplementary attribute block that may add explanatory value beyond conventional housing covariates rather than as a standalone pricing determinant. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

One to four million annual premature deaths are associated with household air pollution. This indoor pollution is mainly generated by traditional biomass cookstoves. Thus, solar cooking can significantly reduce this toll. Its proliferation would also mitigate deforestation pressures. Additionally, for developing countries, it would alleviate the fuel collection workload, mainly borne by women responsible for fuel collection. Electric cooking provides a clean and controllable alternative to thermal cookers for indoor food preparation, sterilization and heating. This study presents a minimal, off-grid photovoltaic solar cooker that operates without batteries and power electronics. Such a cooker constitutes a low-cost and high-reliability solution for electrically decentralized locations. The system encompassing the cooker is conceived as an accessible entry point for household-level photovoltaic (PV) adoption. So, it offers the potential to catalyze the uptake of clean-energy technologies and to support sustainable development. The proposed design dissipates PV power into heat using commercial positive temperature coefficient (PTC) resistors operating near their Curie temperature. A simplified theoretical model is formulated to easily estimate the thermal power and heat-transfer conductances required for achieving cooking temperatures. An instrumented prototype allows for characterizing the transient temperature evolution during controlled heating and cooling experiments in the laboratory, facilitating development in an initial step avoiding the PV panel. The results demonstrate that the minimal PV configuration is technically feasible, robust, and compatible with low-resource settings. This encourages its adoption in communities experiencing energy poverty. Full article

Nowadays, closed-circuit television (CCTV) cameras are deployed worldwide to monitor movements of humans and other objects to improve the efficiency and safety of societies. Therefore, their proper placement is crucial for achieving effective surveillance coverage. Additionally, their proper placement is significantly important for maximizing visual coverage while reducing installation/management costs. For this task, digital twin is a useful technology, since it can simulate coverage and blind spots while freely changing camera locations. To implement digital twin, 3D modelling of a structure including a complex room is a key issue. In this paper, we propose a 3D indoor modelling method using 360° panoramic images and show its application to a CCTV camera placement optimization. This method constructs a structured 3D model of a target room from captured 360° panoramic images using a 3D Gaussian Splatting reconstruction method based on a visual simultaneous localization and mapping (VSLAM) framework. The Inertial Measurement Unit (IMU) is used together to improve the camera position estimation accuracy. The model construction is anchored using a GNSS/GPS reference to establish global spatial coordinates. As an application of the generated 3D model, optimal locations of a given number of CCTV cameras are determined by combining ray-casting visibility analysis and a greedy optimization algorithm in the virtual environment, maximizing visual coverage while minimizing blind spots and avoiding excessive overlap between camera views. For evaluations, we applied the proposed method to three rooms in Okayama University, Japan, and seven rooms in the Indonesian Institute of Business and Technology, Indonesia. After optimizing camera locations in the virtual environment, the cameras were actually installed in the rooms according to the recommended positions. The performance was evaluated using visibility coverage, blind spot reduction, and Root Mean Squared Error (RMSE) between the estimated and actual camera positions, where promising results were achieved. Full article

Radon (222Rn) is a naturally occurring radioactive gas. It is colourless, odourless, and tasteless, produced through the spontaneous decay of uranium in soil and rocks. Among school-aged children, exposure to radon is a major public health concern because, during school hours, learners spend an average of 6–8 h daily inside school buildings, often on the ground floor or in basement classrooms, where radon levels tend to be highest. This study aims to contextualize radon exposure among children in educational settings, with a focus on the associated risk factors. A systematic review of the literature on radon exposure in classrooms among schoolchildren was conducted, analysing associated risk factors and methods of radon measurement. A literature search was performed across reputable databases to ensure compliance with systematic review standards. The quality of the evidence was appraised using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. A total of 32 studies met the inclusion criteria and were analyzed. Radon levels measured in classrooms exhibit variability based on geographic location. Certain classrooms in Continental Europe and North America exceed the WHO reference limit of 100 Bq/m³, as well as regional thresholds, including the European Union limit of 300 Bq/m³ and the United States Environmental Protection Agency (EPA) limit of 148 Bq/m³. Indoor radon exposure in classrooms is a worldwide concern because children are particularly vulnerable during their formative years. Those attending daycare centers and kindergartens are at greater risk due to their nascent respiratory systems. Full article

As large-scale continuous geological weak planes widely developed in deep mines, through-going discontinuities have a significant impact on the distribution of deep rock mass stress fields and the deformation and failure modes of surrounding rock and are the core factors restricting the safe and efficient extraction of deep resources. In this paper, indoor tests and numerical analysis methods were adopted to study the mechanical strength evolution characteristics and displacement changes of monitoring points of rock mass with prefabricated through-going discontinuities under horizontal and vertical pressure conditions, and the influence laws of different structural thicknesses and distances on the stress characteristics of engineering cavities were obtained. The results show that the introduction of through-going discontinuities can significantly reduce the peak strength and elastic modulus of rock specimens and change the circumferential strain. The thickness, distance, and relative position of through-going discontinuities jointly determine the strength degradation characteristics of the specimens. The larger the structural thickness (3 mm, 5 mm, and 10 mm), the weaker the strength degradation effect; when the through-going discontinuities are located above the cavity, the degradation effect is more significant. The relative position between the structure and the cavity determines the overall level of the stress field, while the structural thickness and distance (20 mm, 25 mm, and 30 mm) regulate the stress distribution and transfer path. When the through-going discontinuities are located above the cavity, the vertical stress of the surrounding rock decreases significantly with the increase of thickness, the horizontal stress remains basically stable, and the overall presents a high stress concentration characteristic. When the through-going discontinuities are located below the cavity, the vertical stress of the surrounding rock is generally low and changes gently; the horizontal stress increases gradually with the increase of thickness, and the overall stress level is much lower than that of the upper working condition. Full article

Repurposing shipping containers to construct modular buildings is an emerging trend that contributes to a more sustainable building sector. In the tourism sector, they enable low-impact, relocatable accommodation adaptable to diverse environments, reducing their ecological footprint. The feasibility of using this kind of structure for self-sufficient tourist accommodation has not yet been thoroughly explored. This work focuses on the case study of the Versatile Cabin, a modular building made from end-of-life shipping containers. It provides a comprehensive analysis of its thermal performance and the capability of maintaining comfortable indoor conditions without relying on the electricity grid. Using TRNSYS, the thermal demands of the dwelling are evaluated across 45 different Spanish locations, taking into account the climatic diversity of the country. Additionally, the study explores the integration of a photovoltaic system to supply power for the HVAC equipment, revealing potential for self-sufficiency, particularly in southern locations with lower heating demand. The results indicate that the PV system can meet between 88.5% and 99.9% of the dwelling’s electricity needs, with an average of 96.1%. Overall, the findings offer valuable insights into the thermal performance and self-sufficiency of modular buildings within the tourism sector, aligning with sustainable building practices and sustainable development goals. Full article

Decarbonizing HVAC in hot–arid regions is challenging for natatoriums because year-round cooling must be delivered alongside stringent dehumidification and occasional heating under high ambient temperatures. In this paper, a fully renewable system has been developed and evaluated for an indoor swimming pool located in Abu Dhabi with a 679 m² swimming pool hall designed to accommodate 200 pool users. The hybrid system includes a high-temperature linear Fresnel reflector (LFR) solar field, stratified thermal energy storage (TES), a single-effect LiBr–H₂O absorption chiller for cooling, a water-to-water heat pump as a backup system for the stability of cooling and heating rates, and a photovoltaic (PV) system to offset the ancillary equipment power input of the hybrid system. The system performance was simulated and validated by using hourly data from Abu Dhabi. Optimization of design/operation parameters was carried out by a multi-objective genetic algorithm to achieve the maximum coefficient of performance (COP) and the minimum levelized cost of cooling (LCOE). The initial COP and LCOE were 0.701 and 0.037 $/kWh, respectively. They were optimized to 0.825 and 0.0254 $/kWh, respectively. The annual energy balance revealed a synergistic operation of the solar field, TES, and heat pump. The lifecycle assessment was utilized to compare the proposed hybrid system with the conventional vapor-compression systems in terms of energy, cost, and CO₂ emissions, in which the proposed system proved superior over conventional systems with a positive net present value (NPV) and net zero carbon emissions. Full article

Personal exposure assessment is essential in environmental and epidemiological studies. However, conventional methods often do not adequately reflect individuals’ spatiotemporal activity characteristics. This study evaluates the suitability of personal exposure assessment methods using PM_2.5 as a case study, comparing measured personal exposure concentrations with three exposure estimation scenarios (S1–S3). S1 relies on fixed-site monitoring data, S2 incorporates location-based outdoor concentrations and a single indoor measurement, and S3 integrates individual location with microenvironment-specific concentrations. Using personal PM_2.5 measurements and time–activity data (TAD) from adults in the Seoul metropolitan area, exposure levels showed substantial variation depending on activity patterns and time spent in different microenvironments. Time-weighted average (TWA)-based estimates differed across scenarios; among them, the one integrating microenvironmental concentrations and TAD showed the closest agreement with measured exposure. In contrast, S1 and S2 generally overestimated exposure. Although S3 slightly underestimated short-term high-concentration events, it showed high correlation (r = 0.78) and low errors (RMSE = 4.79, MAE = 3.70), effectively capturing relative variability in personal exposure. These results suggest that integrating time–activity patterns with microenvironmental concentrations improves the accuracy and reliability of personal exposure assessment and is expected to further enhance the reliability of personal exposure assessment. Full article