Search Results (8,555)

The heating load of residential buildings is closely related to the local microclimate. However, there is a lack of quantitative indicators for assessing the impact of the outdoor microclimate on building heating loads in Lhasa residential buildings. This study established an analytical relationship between surface temperature and building heating load through theoretical derivation. Simulations of the outdoor microclimate and building surface temperatures were conducted using Phoenics2019 and Ladybug1.8.0 tools. Statistical models were developed to correlate outdoor microclimate parameters with the surface temperatures of both transparent and opaque building envelopes. Ultimately, these individual models were integrated to form a comprehensive framework for directly calculating heating loads from microclimate data. The model validation results indicate that the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) is 12.87%, which meets the ASHRAE Guideline 14 international standard requirement of ≤30% for hourly data. The Normalized Mean Bias Error (NMBE) is –9.76%, also satisfying the ASHRAE Guideline 14 criterion of ±10% for hourly data. These results suggest that the model exhibits a minor underestimation, which is acceptable from an engineering perspective. The proposed model can provide a quantitative reference to a certain extent for the comprehensive evaluation of outdoor microclimate environmental performance in residential buildings in Lhasa. Full article

Spatial openness affects the subjective evaluation of soundscape, landscape, and thermal perceptions, leading to various restoration effects and recreational behaviors. However, the literature lacks studies investigating the effects of multisensory interactions under different levels of spatial openness in plazas on users’ behaviors in urban greenways. Thus, this study contributes to the enhancement of recreational experiences and the environmental design of urban greenways by examining the interaction between multisensory evaluations and recreational behaviors in greenway plazas with different levels of spatial openness. Three types of plazas (enclosed, semi-enclosed, open) were selected along an urban greenway to analyze interactions through in situ measurements, questionnaires, and behavior observation. The results showed that people rated the environment as the quietest and coolest in enclosed plazas, although the sound pressure level of these plazas was the highest. Furthermore, the visual evaluation (VE) was mostly correlated with acoustic evaluation (AE) in plazas with high openness, while the correlation effect between AE and thermal evaluation (TE) was only significant in enclosed plazas. In other words, AE was the key factor targeting the improvement in comfort in greenway plazas. Secondly, improving AE was more effective for stimulating the frequency of interactive activities in enclosed plazas, compared to improving TE. However, AE had a negative effect on the time that people were willing to spend on interactive activities in semi-enclosed plazas. Finally, these findings provide corresponding strategies for creating comfortable audio, visual, and thermal environments in greenway plazas with different levels of openness, as well as strategies for enhancing the recreational experiences of visitors. Full article

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article

Reliable detection of safety helmets is essential for ensuring personnel protection in large-scale outdoor operations. However, recognition becomes difficult when monitoring relies on low-resolution or compressed video streams captured by fixed or mobile platforms such as UAVs—conditions commonly encountered in intelligent transportation and urban surveillance. This study proposes a super-resolution-enhanced detection framework that integrates video super-resolution with ROI-guided inference to improve the visibility of small targets while reducing computational cost. Focusing on a single, carefully selected VSR module (BasicVSR++), the framework achieves an F1-score of 0.904 in helmet detection across multiple low-quality surveillance scenarios. This demonstrates the framework’s effectiveness for robust helmet monitoring in low-resolution and compressed surveillance scenarios. Full article

With growing community awareness of greenhouse gas emissions and their environmental consequences, distributed rooftop photovoltaic (PV) systems have emerged as a sustainable energy alternative in residential settings. However, the high penetration of these systems without effective operational strategies poses significant challenges for local distribution grids. Specifically, the estimation of surplus energy production from these systems, closely linked to complex outdoor weather conditions and seasonal fluctuations, often lacks an accurate forecasting approach to effectively capture the temporal dynamics of system output during peak periods. In response, this study proposes a recurrent neural network (RNN)- based forecasting framework to predict rooftop PV surplus in the context of micro-residential communities over time horizons not exceeding 48 h. The framework includes standard RNN, long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. In this context, the study employed estimated surplus energy datasets from six single-family detached houses, along with weather-related variables and seasonal patterns, to evaluate the framework’s effectiveness. Results demonstrated the significant effectiveness of all framework models in forecasting surplus energy across seasonal scenarios, with low MAPE values of up to 3.02% and 3.59% over 24-h and 48-h horizons, respectively. Simultaneously, BiLSTM models consistently demonstrated a higher capacity to capture surplus energy fluctuations during peak periods than their counterparts. Overall, the developed data-driven framework demonstrates potential to enable short-term smart energy scheduling in micro-residential communities, supporting electric vehicle charging from single-family detached houses through efficient rooftop PV systems. It also provides decision-making insights for evaluating renewable energy contributions in the residential sector. Full article

With China’s rapidly aging population, enhancing the safety and age-friendliness of existing residential communities has become a pressing need in the context of urban renewal. Based on empirical analysis of 146 questionnaires collected from aging communities in Jiangsu Province, this study examines how built environment factors influence safety risks and perceived security among older adults. The results show that public seating (F3), pedestrian pathways (F11), staircases (F1), lighting (F5), landscaping (F10), and outdoor animals (F12) significantly affect both actual safety risks and perceived safety. Insufficient lighting, uneven pathways, unstable seating, and unsafe staircases are the primary causes of falls, collisions, and abrasions, while issues such as standing water, overgrown vegetation, and stray animals further reduce residents’ sense of security. The findings indicate that improving elderly safety relies more on environmental visibility, accessibility, and spatial maintenance than on compensating for individual physical limitations. Therefore, interventions such as enhancing lighting, maintaining pedestrian routes, providing stable seating, and strengthening community management can effectively reduce risks and enhance perceived security. This study offers empirical evidence to guide age-friendly community renewal and provides policy insights for promoting safe, inclusive, and sustainable development in aging cities. Full article

Solar ultraviolet radiation (UVR) is classified as a Group 1 carcinogen and poses a significant occupational hazard to outdoor workers. Despite preventive guidelines, adherence to protective measures remains inconsistent. This systematic review identified the protective measures adopted by healthy outdoor workers and assessed their adherence to and the effectiveness of these measures. Following the PRISMA 2020 statement, the review searched Scopus, Web of Science, and PubMed for peer-reviewed studies published between 2015 and 2025. Eligible studies included at least 100 healthy participants and evaluated preventive or protective measures against solar UVR. Independent reviewers extracted data and assessed risk of bias using the McMaster Critical Review Form. From 17,756 records, 51 studies met the inclusion criteria after screening and a subsequent snowballing process. The identified protective strategies clustered into physical, behavioural, and organisational categories. Adherence ranged from low to moderate, with structured interventions and employer support improving compliance. Sunscreen use remained low due to perceived inconvenience and lack of provision. Overall, the evidence revealed substantial variability in implementation and effectiveness across occupations. Strengthened regulations and integrated interventions combining education, personal protective equipment, and organisational measures are essential. Future research should prioritise longitudinal designs and objective indicators such as biomarkers and dosimetry. Full article

To address traffic safety hazards from asphalt pavement icing in Xinjiang’s cold regions and inefficiencies of conventional deicing and imprecise geothermal deicing systems, this study focused on local asphalt surfaces. Using “outdoor qualitative screening and indoor quantitative verification”, key variables were identified via controlled tests and their coupling effects on the time to complete icing were quantified through an L16(4⁴) orthogonal test (a 4-factor, 4-level design encompassing 16 test groups). A Backpropagation (BP) neural network model (3 inputs, 5 hidden neurons, and a learning rate of 0.7) optimized with 64 datasets was established to predict the time to complete icing of asphalt pavements, achieving a prediction accuracy (PA) of 90.7% for the time to complete icing and a mean error of merely 0.71 min. Dynamic icing risk thresholds (high/medium/low) were established via K-means clustering and statistical tests, enabling data-driven precise activation and on-demand regulation of geothermal deicing systems. This resolves energy waste and deicing delays, offering technical support for efficient geothermal utilization in cold-region transportation infrastructure, and provides a scalable “factor screening + model prediction” framework for asphalt pavement anti-icing practice. Full article

Informal settlements are widely present and important urban spaces, providing valuable living spaces for many migrant populations, low-income groups, and indigenous peoples. However, urbanization faces a common challenge: the transformation of informal settlements often destroys social structures and belongingness. Despite its importance, few studies have examined how redevelopment is correlated with original residents’ belongingness. To address this gap, this study proposes a research framework of belongingness structured along the logical chain of “network–node–subject”. Social network analysis (SNA) is employed to identify the existing outdoor activity space network and its key nodes. Exploratory factor analysis (EFA) is then conducted to determine the key environmental features of outdoor activity spaces that influence original residents’ belongingness. Furthermore, K-means clustering is applied to explore the correlation mechanism between space and belongingness across different age groups. This study identifies differentiated and universal elements by analyzing the clustered conflict factors, in order to provide precise policy insights. The findings provide actionable insights for enhancing residents’ belongingness during the redevelopment of informal settlements in cities. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of building energy consumption and play a crucial role in maintaining indoor comfort. However, hidden faults in air-handling units (AHUs) often lead to energy waste and degraded performance, highlighting the importance of reliable fault detection and diagnosis (FDD). This study proposes a simulation-driven FDD framework that integrates a standardized prototype dataset and an independent evaluation dataset generated from a calibrated EnergyPlus model representing a target facility, enabling controlled experimentation and transfer evaluation within simulation environments. Training data were generated from the DOE EnergyPlus Medium Office prototype model, while evaluation data were obtained from a calibrated building-specific EnergyPlus model of a research facility operated by Company H in Korea. Three representative fault scenarios—outdoor air damper stuck closed, cooling coil fouling (65% capacity), and air filter fouling (30% pressure drop)—were systematically implemented. A Deep Belief Network (DBN) classifier was developed and optimized through a two-stage hyperparameter tuning strategy, resulting in a three-layer architecture (256–128–64 nodes) with dropout and regularization for robustness. The optimized DBN achieved diagnostic accuracies of 92.4% for the damper fault, 98.7% for coil fouling, and 95.9% for filter fouling. These results confirm the effectiveness of combining simulation-based dataset generation with advanced deep learning methods for HVAC fault diagnosis. The results indicate that a DBN trained on a standardized EnergyPlus prototype can transfer to a second, independently calibrated EnergyPlus building model when AHU topology, control logic, and monitored variables are aligned. This study should be interpreted as a simulation-based proof-of-concept, motivating future validation with field BMS data and more diverse fault scenarios. Full article

Vulnerability to heat and cold is influenced by many characteristics. This study analyzed determinants of vulnerability at the district level in the whole of Austria. Daily deaths (1970–2020) and daily temperatures per district were entered into time series models using negative binomial General Additive Models controlling for long-term and seasonal trends and for the day of the week. District-wise effect estimates of 111 districts in total were entered into linear meta-regression models seeking determinants of inter-district variation in heat and cold vulnerability. Generally, temperature effects on the daily number of deaths were highly significant in all districts, with higher death rates occurring when the same-day temperature exceeded a clear threshold and higher death rates with declining temperature averaged over the previous 14 days, in that case not showing any clear threshold effect. A higher heat vulnerability was observed for more densely populated areas, especially for the city of Vienna, for districts with a higher percentage of singles, of homeless people, of unemployed, and of migrants. Surprisingly, a higher percentage of outdoor workers seemed to be protective. Higher cold vulnerability was found for an increasingly autochthonous population, for districts with a higher employment rate, with more commuters, more agricultural workers, and more green spaces. Full article

The deployment of 5G networks has transformed the landscape of radiofrequency electromagnetic field (RF-EMF) exposure patterns, shifting from high-power macro base stations to dense networks of small, beamforming cells. This review critically assesses the validity, challenges, and research gaps of low-cost RF-EMF sensors used for 5G exposure monitoring. An analysis of over 60 studies covering Sub-6 GHz and emerging mmWave systems shows that well-calibrated sensors can achieve measurement deviations of ±3–6 dB compared to professional instruments like the Narda SRM-3006, with long-term calibration drift less than 0.5 dB per month and RMS reproducibility around 5%. Typical outdoor 5G FR1 exposure levels range from 0.01 to 0.5 W/m² near small cells, while personal device use can cause transient exposures 10–30 dB higher. Although mmWave (24–100 GHz) and Wi-Fi 7/8 (~60 GHz) are underrepresented due to antenna and component limitations, Sub-6 GHz sensing platforms, including software-defined radio (SDR)-based and triaxial isotropic designs, provide sufficient sensitivity for both citizen and institutional monitoring. Major challenges involve calibration drift, frequency band gaps, data interoperability, and ethical management of participatory networks. Addressing these issues through standardized calibration protocols, machine learning-assisted drift correction, and open data frameworks will allow affordable sensors to complement professional monitoring, improve spatial coverage, and enhance public transparency in 5G RF-EMF exposure governance. Full article

Narrowband Internet of Things (NB-IoT) is widely used for connecting low-power devices that must operate for years without maintenance. To design reliable systems, it is essential to understand how much energy these devices consume under different conditions and which power sources can support long lifetimes. This study presents a detailed experimental evaluation of NB-IoT power consumption using a commercial System-on-Module (LMT-SoM). We measured various transmissions across different payload sizes, signal strengths, and temperatures. The results show that sending larger packets is far more efficient: a 1280-byte message requires about 7 times less energy per bit than an 80-byte message. However, standby currents varied widely between devices, from 6.7 µA to 23 µA, which has a major impact on battery life. Alongside these experiments, we compared different power sources for a 5-year deployment. Alkaline and lithium-thionyl chloride batteries were the most cost-effective solutions for indoor use, while solar panels combined with supercapacitors provided a sustainable option for outdoor applications. These findings offer practical guidance for engineers and researchers to design NB-IoT devices that balance energy efficiency, cost, and sustainability. Full article

As unique forms of intangible cultural heritage of Inner Mongolia, traditional musical instruments from the region have undergone significant changes alongside socioeconomic development and evolving performance styles. The performance environment has transitioned from early outdoor and non-fixed venues to professional concert halls. Existing research has demonstrated a correlation between the acoustic quality of performance halls and their objective architectural acoustic parameters. However, no studies have been conducted in China on the acoustic parameters suitable for the performance environments of traditional Inner Mongolian musical instruments. This study determined the optimal acoustic environment for performances of traditional musical instruments, unique to Inner Mongolia, by employing computer simulations and subjective listening experiments in representative performance spaces. Participants were asked to select preferred audio samples of different reverberation times, generated by convolving the impulse responses of simulated spatial models with dry recordings of the instruments. Statistical analysis of the results revealed that the optimal reverberation times for traditional Inner Mongolian instruments are 1.2 s and 1.4 s in a theater space, and 0.9 s and 1.1 s in a rectangular space. Furthermore, under the influence of different factors, the four instruments exhibited distinct preferences for optimal reverberation values in the sampled spaces. Full article

This study developed high-performance anti-flashover silicone coatings using sol–gel-synthesized CaCO₃/SiO₂ hierarchical fillers optimized via L₁₆(4⁵) orthogonal design. The optimal filler (Sample 5) was prepared under 70 vol% ethanol, with nTEOS:nCaCO₃ = 1:1 and 0.2 mol/L NH₃·H₂O, at 45 °C, for 18 h, featuring covalent Si-O-Ca bonding, a dual-scale microstructure (2–4 μm CaCO₃ cores + 20–40 nm SiO₂ nodules), a 14.44 m²/g specific surface area, and bimodal porosity (8–80 nm). Composite C7 (30 wt% filler, 3 wt% KH-570, 1:2 resin-to-filler ratio) achieved superhydrophobicity (a 153° contact angle via Cassie-Baxter stabilization), ultrahigh electrical insulation (3.20 × 10¹⁴ Ω·cm volume resistivity, 1.60 × 10¹³ Ω surface resistivity), and robust mechanical properties (Shore 3H hardness, 5B adhesion). Standardized IEC 60507:2020 tests showed that C7’s flashover voltages (14.8 kV for KMnO₄, 14.3 kV for NaCl/KMnO₄, 13 kV for NaCl) exceeded that of neat silicone resin (NSR) and conventional CaCO₃-filled composite (SR-CC) by >135%. Additionally, C7 retained superhydrophobicity after 500 h UV aging and maintained a 124° contact angle after 12 months of outdoor exposure. The superior performance stems from synergistic hierarchical topology, tortuous discharge paths, and interfacial passivation. This work establishes a microstructure-driven design paradigm for grid protection materials in harsh environments. Full article