Search Results (590)

As urban heat stress intensifies under rapid urbanization and climate change, urban parks are increasingly recognized as critical cooling infrastructure. However, conventional urban park planning has often emphasized the quantitative provision or spatially balanced distribution of parks, with limited attention to whether vulnerable populations can access parks with stronger cooling performance under spatial and mobility constraints. This issue is particularly important in aging societies, where older adults face greater heat vulnerability and more restricted walking mobility. This study proposes a decision-support framework that integrates park cooling performance, accessibility, and spatial equity assessment for age-sensitive urban green space planning. Using Seongnam City, South Korea, as a case study, park-level cooling performance was estimated using the InVEST Urban Cooling Model and incorporated into a Gaussian Two-Step Floating Catchment Area model. Focusing on older adults, with the working-age population as a comparative reference, the study assessed cooling-weighted park accessibility across multiple spatial scales. The results show that older adults experience lower and more unequal accessibility than the working-age population. In the Northern Living Zone, older-adult accessibility was only 35.2% of the Central Living Zone value, and 59.5% of older adults were exposed to low-accessibility hotspots. The framework provides practical evidence for prioritizing park provision, cooling-function enhancement, and heat-resilient pedestrian improvements. Full article

Accurate measurement of the temperature in the cutting zone is essential for closed-loop machining. However, it remains difficult due to the small size of the tool–chip contact area, its partial concealment by chips and the steep thermal gradients present. This study presents an integrated framework that combines a thin-film thermocouple (TFTC) on the rake face of a polycrystalline cubic boron nitride (PCBN) tool with a thermo-mechanical wear-coupled simulation in order to monitor cutting temperature and predict tool wear. The three-dimensional finite-element turning model includes a moving heat source that represents plastic and frictional heat at the tool–chip interface, as well as an Archard-type wear law that is enhanced by a temperature correction factor. The TFTC is fabricated by magnetron sputtering NiCr and NiSi films onto an insulating layer, after which it is embedded in the tool as a minimally intrusive in situ sensor. Turning experiments on AISI 1045 steel were performed at spindle speeds of 1000–3000 rpm, feeds of 0.05–0.20 mm/rev and depths of cut ranging from 0.3 to 1.0 mm under dry, wet (emulsion) and cryogenic (liquid nitrogen) cooling conditions. Simulated temperature fields reveal strong localisation at the tool–chip contact and a nonlinear increase in peak rake-face temperature with spindle speed, which fits a quadratic regression with R² = 0.99. The TFTC shows a response time of around 0.3 s with less than 5% overshoot, and its thermoelectric voltage is almost perfectly linear with temperature (R² = 1), with a sensitivity of approximately 12 µV/°C. During cutting, TFTC readings agree with infrared measurements within ±3 °C and demonstrate improved robustness in occluded zones. The coupled wear model replicates the observed wear growth trend with the compact expression VB = 0.0001·t^0.8. Sensitivity tests indicate that thermo-mechanical coupling increases wear rates compared to single-factor models, and that cooling reduces thermal loads by approximately 15% (wet) and 25% (cryogenic). Full article

Stainless steel pipes are critical components in industrial systems such as oil and gas transportation and nuclear power cooling. Surface defects can severely degrade their mechanical performance and operational safety. However, existing inspection methods still face challenges including difficult feature extraction, strong reflection interference, and limited accuracy in small-target detection. To address these issues, this paper proposes an improved detection algorithm termed YOLOv8s-BISW (incorporating BiFPN, SGE attention, and WIoU loss), which introduces multidimensional optimizations based on the YOLOv8s baseline. First, an image enhancement module combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE) is designed to mitigate uneven illumination and blurred defect imaging. Second, a Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to strengthen multi-scale feature fusion and improve adaptability to defects of different sizes. Meanwhile, a Spatial Group-wise Enhance (SGE) attention module is embedded into the backbone to enhance defect feature representation while suppressing background interference. Furthermore, the Wise Intersection over Union (WIoU) loss function replaces Complete IoU (CIoU) to improve bounding box regression for irregular defects. Experimental results show that the proposed model achieves an mAP of 0.979 on a self-constructed Stainless-steel Tube Flaw (STF) dataset. Compared with the original YOLOv8s, precision, recall, and mAP are improved by 0.007, 0.010, and 0.033, respectively, while the average detection time per image is only 3.7 ms, achieving a favorable balance between accuracy and real-time performance. Compared with mainstream algorithms such as SSD, YOLOv3, and Faster R-CNN, the proposed method demonstrates superior overall performance, providing reliable technical support for automated surface defect detection of stainless steel pipes and offering practical value for intelligent manufacturing quality control. Full article

Accurate building performance forecasting is critical for the design and renovation of energy-saving structures, but existing methods face four key challenges: heterogeneous data fusion (sensor streams, design parameters, and environmental sequences), non-stationary physical time series, model interpretability, and sample efficiency (e.g., limited commissioning data). To address these challenges, this paper proposes Fusion-Grounded Forecasting (FGF), which is a framework integrating a gated adaptive fusion layer, deterministic trend-season decomposition, an additive predictor with component decomposition, and Bayesian regularization. This framework is designed for next-hour forecasting broadcast to hourly resolution using hourly sensor data and monthly design parameters. The dataset covers 36 months (approximately 25,920 h). In addition to the combination of existing modules, the novelty lies in the integrated architecture, in which interpretable constraints can adjust the fusion layer in both directions, with decomposition prediction alignment supporting component attributes. The framework is verified on a proprietary 36-month dataset from institutional buildings using standard prediction metrics (MAE, RMSE, MAPE, and directional accuracy) and ablation studies for comparison against 10 baselines: SARIMAX, GPR, LSTM, XGBoost, N-HiTS, Informer, Autoformer, NAM, a physics-informed hybrid, and TFT. FGF achieves a 3.1% MAPE and 92.5% directional accuracy in hourly cooling load forecasting. Ablation confirmed the contribution of each module: removing gated fusion increased the MAPE to 6.8%. Compared with manual feature engineering, the speed of the framework is increased by 1680 times, and the cost is reduced by 99.6%. The explanatory index (counterfactual reliability: 0.95; Stability of functional importance: 0.11) is in compliance with audit requirements. These results indicate that FGF connects descriptive physics with quantitative prediction. However, this study is limited to a single institutional building; transferability to residential, commercial, or industrial buildings requires further verification. While waiting for this verification, FGF has demonstrated its potential as a transparent and efficient tool to build performance models. Full article

Small Mediterranean islands relying on the sun–sea–sand (3S) tourism model face growing climate risks that threaten their tourism-dependent economies. This study evaluates climate suitability for 3S tourism on the Island of Vis by integrating the Climate Index for Tourism (CIT) with land- use and land-cover (LU/LC) spatial analysis. The integration is operationalized by overlaying CIT-derived seasonal suitability windows with LU/LC-based spatial vulnerability maps, enabling identification of micro-zones where natural buffers (forest cover and elevation) can offset thermal discomfort during peak heat stress periods. Observed data reveals declining ideal 3S conditions from July to October, with the island already exceeding 50 days per year of Physiologically Equivalent Temperature (PET) above 35.1 °C, increasing by 0.7 days per year. Regional climate models tend to exhibit a cold bias over small Adriatic islands, largely related to their limited spatial horizontal resolution (12.5 km grid spacing). However, they robustly reproduce the direction of recent and projected warming trends. Future projections indicate that the annual number of strong heat stress days with PET above 35.1 °C increase from approximately one per year in the reference period to six under RCP4.5 and nine under RCP8.5, with both scenarios reducing ideal peak-summer conditions while extending favorable periods into transitional seasons. Spatial analysis shows that coastal zones have higher sealed surfaces and less forest cover, reducing natural shade and cooling capacity, while the island interior offers higher elevations, forest buffers, hiking trails, and a UNESCO Global Geopark. Drawing on social–ecological resilience theory, we conceptualize the island’s tourism system as an adaptive unit whose long-term viability depends on spatially diversified resource use and temporally extended seasonality. The integrated analytical framework identifies not only when conditions deteriorate but where alternative tourism resources exist, enabling more targeted adaptation planning and supporting diversification toward outdoor tourism forms. The novelty of this study lies in the systematic spatial integration of bioclimatic suitability assessments (CIT and PET) with LU/LC analysis at the micro-island scale. Such an approach moves beyond temporally focused climate–tourism indices to produce actionable, location-specific adaptation strategies. Full article

One potential strategy to mitigate the detrimental effects of heat stress on corals is upwelling, which brings deep, cold, nutrient-rich water to the reef surface, creating transient cooling. However, cold temperatures can also stress corals, and it is, therefore, important to understand the mechanisms of both cold and heat stress responses in corals. Similar to how mammals activate thermogenic and adaptive metabolic pathways, corals may also regulate energy and redox metabolism under fluctuating environmental conditions. Guanosine monophosphate reductase (GMPR), a conserved enzyme in purine metabolism, plays a critical role in maintaining intracellular adenine and guanine nucleotide balance. To study this enzyme in corals, we expressed Pocillopora damicornis (PD) GMPR heterologously in Escherichia coli and purified the recombinant protein using nickel–NTA affinity chromatography. SDS-PAGE analysis showed a single band corresponding to the expected molecular weight, indicating high purity. Sequence alignment revealed ~70% identity with mammalian GMPR2 orthologs, suggesting evolutionary conservation of function. Structural modeling and phylogenetic analysis positioned PD GMPR between the GMPR1 and GMPR2 clades, suggesting it may represent an ancestral or functionally intermediate variant. Kinetic analysis determined Km values of 33.76 ± 6.44 μM for GMP and 17.71 ± 0.99 μM for NADPH under fixed substrate concentrations. This study provides the first biochemical characterization of GMPR, which may open the door to uncovering mechanisms of cold tolerance in corals and inform strategies to enhance coral resilience in the face of climate change. Full article

Thermal pollution in underground spaces is one of the current challenges faced by subway tunnels. Energy tunnel technology based on heat pumps can not only solve the problem of thermal pollution but also realize the resource utilization of waste heat. However, the influence mechanisms of the tunnel air environment on the heat transfer characteristics of energy segments are still insufficiently studied. Taking the shield energy tunnel as the research object, this study proposed an energy segment model based on a capillary heat exchanger and established a fluid-thermal coupled numerical model on the COMSOL 6.4 simulation platform. Then, the effects of tunnel air temperature and speed on the heat transfer performance of the energy segment were systematically investigated. The results indicate that an increase in the temperature differential between the tunnel air and the inlet water of the capillary heat exchanger significantly enhances the heat transfer rate of the energy segments. Specifically, a 5 °C rise in air temperature corresponds to a 60.7% increase in the heat extraction rate of the CHE during the heating season, whereas it results in a 58.8% decrease in the heat release rate of the CHE during the cooling season. An increase in tunnel air speed enhances the overall heat transfer coefficient by strengthening convective heat transfer between the tunnel air and the energy segment. Although the enhancement of convective heat transfer is limited, the system already demonstrates relatively optimal heat transfer performance at a wind speed of 4.61 m/s. The study further reveals that increasing these two parameters not only enhances heat exchange but also exacerbates the non-uniformity of temperature distribution across the segment. This study conducts an in-depth analysis of how tunnel environmental parameters impact the thermal performance of energy segments, thereby offering a theoretical foundation for the optimized design of these energy segments in shield tunnels. Full article

The indoor environmental quality of primary and secondary school classrooms is crucial for students’ health and learning efficiency, yet enhancing comfort often leads to high energy consumption. Efficiently balancing the complex relationship between daylighting, visual comfort, and energy consumption during the early design stage presents a significant challenge for architects. To address the design optimization of standard classrooms in primary and secondary schools in the cold region of Zhengzhou, this paper proposes an efficient multi-objective optimization method based on metamodels. This method integrates physical performance simulation (EnergyPlus and Radiance), Latin Hypercube Sampling (LHS), an artificial neural network (ANN) metamodel, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Using Useful Daylight Illuminance (UDI), Discomfort Glare Index (DGI), and Cooling Energy Use Intensity (cEUI) as optimization objectives, ten design parameters, including classroom spatial form and envelope structure, were optimized. The aim is to replace time-consuming traditional simulation calculations and rapidly generate a Pareto optimal solution set. A case study of a typical south-facing classroom in Zhengzhou demonstrates that this method can substantially improve daylighting performance while moderately reducing cooling energy. Compared to the baseline model, the optimized schemes show an average increase in UDI of 42.9% (maximum 50.5%), an average reduction in DGI of 8.4% (maximum 9.6%), and an average reduction in cEUI of 4.7% (maximum 7.7%). Because the study focuses on summer cooling energy only, the reported cEUI improvement should not be interpreted as an annual energy reduction. Through K-means clustering and sensitivity analysis, the study further identifies different design strategies from the Pareto solution set and clarifies the key design variables affecting each performance indicator. This provides an evidence-based reference and preliminary design guidelines for the early-stage design of primary and secondary school classrooms in the region. Full article

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements. Full article

This study presents a coupled building simulation framework that evaluates thermal and daylight performance concurrently within a unified multi-objective decision space. Unlike conventional sequential workflows, where daylight metrics are assessed after energy optimization or used primarily for compliance verification, the proposed approach embeds EnergyPlus and Radiance simulations directly within the same optimization loop. This structure enables a systematic exploration of non-linear interactions between Energy Use Intensity (EUI), cooling loads, Spatial Daylight Autonomy (SDA), and Annual Sunlight Exposure (ASE) during early-stage façade design. The framework is demonstrated through a medium-rise office building in India’s National Capital Region, a composite climate characterized by strong seasonal and directional variability. Parametric variation in façade orientation, window-to-wall ratio, and external shading configurations was explored using a multi-objective genetic algorithm to identify Pareto-optimal performance regimes. The results reveal distinct orientation-dependent trade-off structures between solar exposure, cooling demand, and daylight availability that are not evident in rule-based or sequential simulation approaches. In particular, a transitional East-facing façade regime emerges in which balanced shading and glazing proportions achieve near–North-facing cooling performance while maintaining high daylight autonomy under controlled sunlight exposure. Rather than proposing a single optimal solution, the study demonstrates how tightly coupled thermal–daylight simulation can function as a knowledge-discovery tool, enabling the extraction of transferable façade response patterns from simulation outputs. The findings highlight the limitations of prescriptive orientation hierarchies in composite climates and illustrate the value of integrated simulation workflows for performance-driven early-stage design across diverse climatic contexts. Although the study references thermal performance, the optimization objectives are limited to peak cooling load and annual Energy Use Intensity (EUI). Occupant comfort indices such as Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) were not explicitly simulated. Therefore, results are interpreted as energy–daylight performance optimization rather than direct thermal comfort optimization. Full article

Background/Objectives: Global climate change has increased occupational heat exposure, posing significant risks to outdoor workers. Automotive maintenance workers face high temperatures, radiant heat from machinery, and physically demanding tasks; however, their awareness and preventive behaviors regarding heat-related illness remain insufficiently understood. This study examined heatstroke awareness and preventive behaviors among automotive maintenance workers in Japan. Methods: A cross-sectional web-based survey was conducted among 371 automotive maintenance workers. Self-reported heat-related illness experience was assessed based on subjective judgment without formal medical diagnosis. Associations between heat-related illness experience and behavioral, physical, and health-related factors were analyzed using chi-square tests with Bonferroni correction and multivariable logistic regression. Results: Approximately 39.6% of participants reported experiencing heat-related illness during summer work. In multivariable analysis, headache (OR: 2.66, 95% CI: 1.25–5.64), dizziness (OR: 2.06, 95% CI: 1.12–3.80), obesity (OR: 1.86, 95% CI: 1.06–3.27), and lower self-perceived health (OR: 2.19, 95% CI: 1.36–3.55) were independently associated with heat-related illness experience. Some preventive behaviors, including wearing cooling garments and frequent hydration, showed associations in the multivariable analysis; however, these findings should be interpreted with caution due to possible reverse causation, small cell sizes, and residual confounding. Conclusions: Behavioral and individual health-related factors, particularly symptom recognition and self-perceived health, are associated with heat-related illness experience among automotive maintenance workers. Interventions focusing on early symptom awareness, risk perception, and self-monitoring may be important components of workplace-based heat illness prevention. Future studies incorporating objective environmental and physiological measurements are needed to clarify causal relationships. Full article

Refrigeration and air-conditioning systems are vital to the global economy but contribute significantly to greenhouse gas emissions by using high-global-warming potential synthetic refrigerants. As regulatory frameworks like the Montreal Protocol, the Kigali Amendment, and the EU’s F-gas Regulations tighten, the industry faces a mandatory transition toward environmentally benign alternatives. This perspective paper evaluates the technological and environmental implications of replacing synthetic fluids with natural refrigerants, specifically ammonia, carbon dioxide, and hydrocarbons. A comparative assessment reveals that natural refrigerants offer superior thermodynamic efficiency, zero ozone depletion potential, and ultra-low global warming potential. While technologies like transcritical CO₂ and low-charge ammonia systems may involve higher initial capital costs, they increasingly achieve life cycle cost parity through improved energy performance and regulatory stability. The analysis further explores advanced cycle configurations, such as ejectors and expanders, which mitigate efficiency losses. The transition to natural refrigerants is presented as a technologically feasible and environmentally friendly strategy to mitigate the risk that rising cooling demands further accelerate climate change. Ultimately, natural refrigerants are expected to become the default global standard within the shortest feasible timeframe, with policy, industry, and research aligned to support and accelerate this transition. Full article

The reduction in energy demand in buildings through the adaptation of energy-efficient strategies is attracting significant attention from the research community. In this context green building concepts can contribute towards achieving national sustainable development goals (SDGs) and NetZero targets. Given the substantial energy demand associated with heating and cooling in commercial and residential buildings, enhancing energy efficiency has become essential for achieving sustainable development, particularly amid ongoing global energy challenges. The Envelope Thermal Transfer Value (ETTV) model has been established as a simplified method of calculating building loads; however, its integration with green building elements remains limited, particularly in subtropical climates. Furthermore, the combined effects of living walls, green façades, and green roofs on building energy performance have not been comprehensively investigated. In this study, an extensive experimental investigation was conducted using prototype buildings under controlled conditions to evaluate the thermal performance of green elements. Modified ETTV formulations incorporating green envelope systems have been developed, and the thermodynamic effects of these green elements on the building energy performance have been analysed. The results demonstrate that integrating green elements significantly reduces thermal heat gain and cooling energy demand. Specifically, a combination of a living wall on a west facing wall and a green roof could reduce the thermal heat gain by up to 30%. Full article

AI infrastructure is entering a constraint-dominated regime in which power access, cooling, water conditions, reliability, and financing jointly shape cost, sustainability, and operational risk. Yet the metrics used to evaluate these systems remain fragmented across facility engineering, compute/workload performance, and economic or risk analysis, with definitions that often sit at different layers and under different boundaries. This fragmentation weakens cross-layer reasoning and makes decision-traceable trade-off analysis difficult. This paper proposes a structured, decision-oriented measurement architecture for AI infrastructure metrics. The framework combines a 6 × 3 taxonomy, which organizes metrics across six layers and three semantic domains, with a procedural workflow built around a problem card, variable registry, minimality gate record, activated-cell map, boundary log, metric ledger, and a results sheet with case-pack manifest. Within this protocol, the Metric Propagation Graph is used as a case-specific dependency representation for tracing decision-facing metrics back to minimal boundary-consistent inputs. It is introduced as a traceability layer within the framework rather than as a stand-alone graph-theoretic method. The paper is illustrated through one fully worked case and one scoped portability illustration. The first is a fully worked large-load planning case for the Northern Virginia data-center corridor within PJM’s Dominion zone, showing that a boundary-consistent integrated metric can reverse the ranking obtained under a simpler screening view. The second is a scoped portability illustration for hourly matching under dual Scope 2 boundaries. Its purpose is not to provide a second full empirical validation, but to show how the same dossier logic, boundary discipline, and traceable metric construction transfer to a distinct decision setting. Full article

As the core of digital infrastructure, the exceptionally rapid development of data centers (DCs) faces serious challenges due to their high electricity costs. Traditional approaches treat computational task scheduling separately from different physical control mechanisms, such as server group management, overlooking the synergistic potential between the two aspects. To address this problem, this paper proposes a computational–physical collaborative optimization model that realizes spatiotemporal task migration on the computational side and adaptive parameter regulation of IT equipment and cooling devices on the physical side. In response to the lack of global coordination in conventional distributed optimization, a two-layer partially observable Markov game (POMG) is constructed to unify global cooperative decision-making and local autonomous control. On this basis, the hierarchical multi-agent deep deterministic policy gradient (H-MADDPG) algorithm is designed by introducing task priority ranking and a variable-dimension action mask mechanism, which effectively handles the discrete–continuous hybrid action space and adapts to the dynamic variation in action dimensions caused by uncertain task arrivals. Comparative experiments with various benchmark schemes are conducted to verify the effectiveness and superiority of the proposed strategy in total cost, power usage effectiveness (PUE), resource utilization, and load balancing. Full article