Search Results (618)

Energy efficiency retrofits are central to climate policy, yet their implications for indoor environmental quality (IEQ) and occupant health remain underexplored. This study investigates IEQ outcomes following staged retrofits in Irish social housing, where achieving Building Energy Rating (BER) targets is the primary performance metric. Four dwellings, three retrofitted and one control, were monitored over six weeks during the heating season. Built in the 1980s, these homes represent the typical social and private housing stock of that era. Continuous measurements of carbon dioxide, temperature, relative humidity, and thermal performance were complemented by analyses of vapour pressure excess and ventilation rates. While all retrofitted homes achieved BER improvement targets, persistent IEQ challenges were identified. Elevated pollutant concentrations and increased condensation/mould risk occurred in the presence of inadequate ventilation. Thermal anomalies and cold bridging were associated with cavity wall insulation, whereas external wall insulation provided more stable surface temperatures and reduced moisture-related risks. These results underscore the complex interplay between retrofit measures, occupancy patterns, and ventilation performance. The study highlights the need for retrofit strategies that integrate energy efficiency with occupant health objectives. At scale, retrofit programmes risk embedding systemic vulnerabilities unless ventilation and moisture control are prioritised, with implications that extend to health, wellbeing, and long-term building resilience. Full article

Semiconductor manufacturing relies heavily on automatic test equipment (ATE), and yet thermal aging poses a critical risk to equipment reliability. This study proposes a novel anomaly detection framework for ATE driver boards by integrating cumulative degradation time (CDT)—derived from accelerated life testing (ALT)—with artificial intelligence models. Specifically, the approach quantifies the cumulative effects of thermal stress as CDT and utilizes it as a key input feature to enable the early detection of degradation under prolonged high-temperature conditions. The proposed framework successfully demonstrates the capability to diagnose real-time anomalies before critical CDT thresholds are reached. Consequently, this approach allows for efficient management, significantly contributing to reduced maintenance costs, minimized downtime, and enhanced equipment reliability, serving as a foundational strategy for condition-based maintenance (CBM) strategies in semiconductor manufacturing. Full article

The southeastern Iberian Peninsula, particularly the Águilas Arc within the Neogene Volcanic Province (NVP), represents a promising geothermal domain with complex tectonics and geology. The Palomares Fault Zone (PFZ), a key shear structure initiated during the Late Miocene, acts as a conduit for fluid migration, promoting mineralization and potential anomalies of rare and critical metals through fluid–rock interaction. This study investigates such interactions in the southernmost Águilas Arc, focusing on the El Arteal fault segment within the eastern PFZ strand. Mineralogical, geochemical, and hydrogeological analyses were performed using XRD, SEM, and ICP-MS techniques. Results reveal six mineral assemblages (MA) within the fault segment where the fault gouge samples were characterized by cataclastic textures and the occurrence of authigenic minerals, including halite, kaolinite, illite, paragonite, goethite, hematite, gypsum, barite, celestine, and quartz. Geochemical data indicate enrichment signatures in large-ion lithophile elements (LILE) and minor chalcophile and light rare-earth elements (LREE). Two thermal hydrofacies with alkaline metals enrichment were identified in wells and mine shafts: (1) Na⁺SO₄²⁻ and (2) Na⁺Cl⁻, where the latter exhibits high Na⁺ and Cl⁻ concentrations toward deeper sectors. These findings suggest multiple stages of fluid–rock interaction controlled by temperature: an early phase dominated by epithermal mineralization, followed by late-stage circulation of hypersaline fluids. This evolution provides an abnormal geochemical signature that is unique in the Aguilas Arc Geothermal System. Full article

The Xinding Basin is located in the high-heat-flow geothermal anomaly zone in the north-central part of China. Revealing the geothermal origin mechanism of the basin is of great significance for filling the measurement gap in heat flow values in China and providing a scientific basis for the evaluation and utilization of regional geothermal resources. Based on the hydrogeochemical characteristics of thermal reservoirs and borehole data in the Xinding Basin, this paper analyzes water–rock interaction process between geothermal water and heat reservoirs and discusses the types of geothermal systems in the basin. The results indicate that the fault structures in the basin are well-developed. The hydrochemical type of typical geothermal fields is dominated by the Cl·SO₄-Na type. Geothermal water is mainly immature water and receives recharge from shallow cold water with relatively rapid circulation. The discovered magma intrusion residues in the basin indicate that sections of the upper mantle with a shallow burial depth serve as the dynamic heat sources for regional thermal reservoirs. Intense extensional stretching in the Cenozoic Era resulted in high terrestrial heat flow values and an upward arching phenomenon of the Curie isothermal surface in the basin. Neotectonic movement is active in the basin. The regional geothermal reservoirs in the Xinding Basin occur in the glutenite beds of the Cenozoic Erathem and the rock formations of the New Archaean Erathem. The thick-layered Cenozoic loose sediments serve as the thermal cap rocks in this area. An efficient heat-convergent geothermal system integrating a heat source, heat channel, thermal reservoir, and cap rock (the “four-in-one” system) has promoted the formation of geothermal resources in the Xinding Basin. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

Mesoscale eddies play a key role in oceanic transport, yet their characterization in marginal seas like the Gulf of California remains challenging due to complex coastlines and bathymetry that hinder conventional detection methods. This study addresses this gap by presenting a robust hybrid framework—integrating dynamical (Okubo–Weiss), velocity geometry (Nencioli), and closed-contour (Chelton) criteria—applied to the high-resolution (${0.01}^{\circ }$) Neural Ocean Surface Topography (NeurOST) altimetry product (2010–2024). Temporal continuity is ensured through a cost-based tracking algorithm optimized to tolerate observational gaps and track quasi-stationary features. This census, representing the first systematic, high-resolution sea surface height anomaly (SSHA)-based characterization for this region, identified 344 persistent trajectories (≥14 days) and revealed a fundamental dichotomy in the Gulf’s dynamics: a transient, tidally dominated regime in the north (dominated by short-lived features) contrasting sharply with a persistent, topographically trapped regime in the south. Focusing on the long-lived population (lifetimes $>30$ days), our analysis confirms that multi-year, quasi-stationary cyclonic eddies are trapped in the southern basins, while a subset of energetic tracks exhibits coherent poleward propagation consistent with advection by the Mexican Coastal Current. Cyclonic structures dominate the ten longest-lived tracks (90%) and include two events with lifetimes confirmed to exceed 500 days. We also identify a robust seasonal decoupling between SSHA and sea surface temperature anomalies (SSTA) in spring, when surface heating masks the thermal signature of cyclones. This census, which documents multi-year structures and distinguishes the two regional regimes, establishes a new baseline for quantifying mesoscale transport and serves as a transferable framework for the new generation of satellite altimetry observations (i.e., the Surface Water and Ocean Topography, SWOT, mission). Full article

Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water-level fluctuations, and aqueous surface extent, influence the distribution and size of waterbird colonies (Ardeidae, Threskiornithidae, and Phalacrocoracidae) in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote-sensing-derived habitat metrics, in situ meteorological and hydrological measurements to model colony abundance dynamics. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of numerical population dynamics. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting an adaptive spatial response that was consistent with behavioral flexibility. We propose an adaptive management framework prioritizing sustainable solutions for maintaining minimum lacustrine water levels to preserve critical foraging zones. This integrative framework highlights the pivotal role of remote sensing in transitioning from reactive monitoring to predictive conservation of deltaic ecosystems. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Polylactic acid (PLA) is a promising biodegradable polymer whose widespread application is hindered by inherent brittleness. Polyethylene glycol (PEG) is a common plasticizer, but the effects of intermediate molecular weights, such as 4000 g/mol, on the coupled thermal, mechanical, and rheological properties of PLA remain insufficiently understood. This study presents a comprehensive analysis of PLA plasticized with 0–20 wt% PEG 4000, employing differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and rheology. DSC confirmed excellent miscibility and a significant glass transition temperature (T_g) depression exceeding 19 °C for the highest concentration. A complex, non-monotonic evolution of crystallinity was observed, associated with the formation of different crystalline forms (α′ and α). Critically, DMA revealed that the material’s thermo-mechanical response is dominated by its thermal history: while the plasticizing effect is masked in highly crystalline, as-cast films, it is unequivocally demonstrated in quenched amorphous samples. The core finding emerges from a targeted rheological investigation. An anomalous increase in melt viscosity and elasticity at intermediate PEG concentrations (5–15 wt%), observed at 180 °C, was systematically shown to vanish at 190 °C and in amorphous samples. This proves that the anomaly stems from residual crystalline domains (α′ precursors) persisting near the melting point, not from a transient molecular network. These results establish that PEG 4000 is a highly effective PLA plasticizer whose impact is profoundly mediated by processing-induced crystallinity. This work provides essential guidelines for tailoring PLA properties by controlling thermal history to optimize flexibility and processability for advanced applications, specifically in melt-processing for flexible packaging. Full article

The structural origin of the germanate anomaly in glasses, which involves complex Ge–O coordination environments, is frequently studied using crystalline analogs. This study aims to provide reliable spectroscopic fingerprints by performing a detailed structural and thermal analysis of crystalline A₄Ge₉O₂₀ model systems with A = Li, Na, K. The compounds were synthesized via melt crystallization and characterized using powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), and Raman spectroscopy techniques. The results demonstrate clear cation-dependent crystallization pathways. The Li-containing system predominantly forms Li₂Ge₇O₁₅ in mixture with Li₄Ge₉O₂₀, indicating a preference for thermodynamically stable phases. The Na-system successfully yields the target Na₄Ge₉O₂₀ compound. In contrast, the K-system primarily produces the likely metastable K₂Ge₄O₉ phase with a significant amorphous fraction, highlighting the role of kinetic limitations. This comparative study demonstrates that the size of the alkali cation is a critical factor for controlling phase formation under identical stoichiometric and thermal conditions. Full article

To achieve energy savings, reduce consumption, and support the “dual-carbon” strategy in China, this study applies digital twin technology to investigate the centralized air-conditioning water system of a metro-station HVAC installation and develops a high-fidelity digital twin model to reveal the thermal delay and thermal attenuation characteristics of the pipeline network. Using the noncausal modeling approach of the Modelica language, a full digital twin representation of the centralized air-conditioning water network is constructed by covering chillers, cooling towers, pumps, terminal units, the pipeline network, etc. The model is validated against real operation data to ensure high fidelity. Validation shows the predicted chilled water flow rate of the digital twin model agrees well with the measured chilled water flow rate with an RMSE of 0.27 kg/s. Validation also shows the difference is about 0.3 °C between the digital twin prediction and the measurement in the main pipe. Based on the validation digital twin model, the thermal delay and thermal attenuation characteristics of the centralized air-conditioning water system are seriously evaluated. The results indicate that branch K3, due to its longest transport distance, exhibits a delay of 227 s. The overall thermal delay of the system reaches 7.5 min. The temperature attenuation of this water system is about 0.2 °C due to heat loss through pipe walls. The findings may offer theoretical support for the optimal regulation and control, fault detection, and anomaly identification of this centralized air-conditioning water system. Full article

Thermal runaway (TR) of lithium-ion batteries (LIBs) represents a critical safety challenge in EV applications. This study explores the potential of data-driven safety management strategies for mitigating TR risks in EVs. To minimize the impact of external environmental factors on the degradation of LIBs, experiments were conducted using an accelerating rate calorimeter (ARC). The intrinsic thermal behavior of six nickel–cobalt–manganese (NCM) cells at different states of health (SOH) and operating temperatures has been captured in created adiabatic conditions. Multiple sensors were deployed to monitor the temperature and electrochemical and environmental parameters throughout the degradation process until TR occurred. The results show that both the thermal and electrochemical stability of LIBs have been affected, exhibiting consistent thermal patterns and early electrochemical instability. Furthermore, even under adiabatic conditions, the degradation of LIBs show synergistic effects with environmental parameters such as chamber temperature and pressure. Correlation analysis further revealed the coupling relationships between the monitored parameters. Through calculating their correlation coefficients, the results indicate advantages of combining thermal, electrochemical, and environmental parameters as being to characterize the degradation of LIBs and enhance the identification of TR precursors. These findings stress the importance of considering the battery-environment system as a whole in safety management of EVs. They also provide insights into the development of data-driven safety management strategies, highlighting the potential for achievement and integration of anomaly detection, diagnosis, and prognostics functions in current EV management frameworks. Full article

Tropical cyclones typically weaken rapidly during poleward propagation due to decreasing sea surface temperatures and increasing vertical wind shear. Super Typhoon Oscar (1995) deviated from this pattern by maintaining Category-5 intensity at an anomalously high latitude. This study investigates the oceanic mechanisms driving this resilience by integrating satellite SST data with atmospheric (ERA5) and oceanic (HYCOM) reanalysis products. Our analysis shows that the storm track intersected a persistent marine heatwave (MHW) characterized by a deep thermal anomaly extending to approximately 150 m. This elevated heat content formed a strong stratification barrier at the base of the mixed layer (~32 m) that prevented the typical entrainment of cold thermocline water. Instead, storm-induced turbulence mixed warm subsurface water upward to effectively mitigate the negative cold-wake feedback. This process sustained extreme upward enthalpy fluxes exceeding 210 W m⁻² and generated a regime of thermodynamic compensation that enabled the storm to maintain its structure despite an unfavorable atmospheric environment with moderate-to-strong vertical wind shear (15–20 m s⁻¹). These results indicate that the three-dimensional ocean structure acts as a more reliable predictor of typhoon intensity than SST alone in regions affected by MHWs. As MHWs deepen under climate warming, this cold-wake mitigation mechanism is likely to become a significant factor influencing future high-latitude cyclone hazards. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels. Full article