Search Results (7,897)

Nanoparticle-modified concrete can exhibit improved mechanical performance, yet its residual compressive strength (Fc) after fire-like thermal exposure is difficult to predict because the response depends on both mixture design and heating conditions. Building on recent advances in explainable machine learning (ML) for cementitious materials, this study compiles 218 literature datapoints of post-heating Fc from 100 mm concrete cubes incorporating carbon nanotubes (CNTs) and nano-alumina (NA), exposed to 20–800 °C for up to 2 h. Seven input variables are used: cement-to-total aggregate ratio, CNT-to-cement ratio, NA-to-cement ratio, coarse-to-fine aggregate ratio, water-to-cement ratio, peak temperature, and exposure duration at temperature. Two particle-swarm-optimized ensemble regression models, Extreme Gradient Boosting (XGB-PSO) and Random Forest (RF-PSO), were developed and evaluated using a 70/30 train–test split with K-fold cross-validation on the training set. SHAP, individual conditional expectation (ICE), and partial dependence plots (PDPs) were employed to study the individual and combined effects of each input parameter on Fc prediction. The results demonstrated that the XGB-PSO model provides the best predictive performance (training R² = 0.9983; testing R² = 0.9434; testing MAE = 1.3168 MPa). Model interpretability was assessed using SHAP, ICE, and PDP analyses, revealing that temperature and exposure duration dominate strength loss, while CNTs and NA contribute positively within dose-dependent regimes. The highest predicted strengths occur for CNTs of 0.05% to 0.15% and NA of 0.65 to 2.71% (by cement mass) under moderate temperature exposure. A Python-based graphical user interface is provided to support rapid what-if assessment of CNT–NA mixtures under elevated-temperature scenarios. Full article

This study investigates the elastic anisotropy and thermodynamic properties of the L1₂-type ScAl₃ phase under extreme conditions (0–1500 K and 0–50 GPa) using first-principles calculations. The elastic constants were determined using a precise stress–strain method, with polycrystalline moduli derived via the Voigt–Reuss–Hill (VRH) approximation. A systematic analysis was conducted to characterize the elastic anisotropy of Young’s modulus, shear modulus, and Poisson’s ratio. Results demonstrate that ScAl₃ is mechanically stable and exhibits near-perfect elastic isotropy (A^U = 0.0001). Thermodynamic analysis via the quasi-harmonic Debye–Grüneisen model reveals that the phase maintains its structural integrity and significant heat resistance up to 1500 K, despite thermal softening. These findings provide theoretical insights into the physical nature of ScAl₃ intermetallics and offer quantitative guidance for the design and thermal treatment of Sc-reinforced aluminum alloys in high-temperature aerospace applications due to their superior combination of strength and toughness. Full article

To overcome the limited high-temperature capability of silica-based fiber Bragg gratings (FBGs) and the accuracy degradation of gold-coated FBGs induced by residual stress, a temperature sensor based on a gold-coated FBG with high-temperature alloy packaging is proposed and fabricated. By introducing a high-temperature annealing pretreatment to the gold-coated fiber, residual stress is effectively relieved, enabling high-precision temperature measurement in high-temperature environments. Within the range of 20–800 °C, the annealed sensor achieves an accuracy of 0.72% F.S., a sensitivity of 9.65 pm/°C, and a linearity of 0.9997, in close agreement with theoretical predictions. After ambient vibration and high-temperature thermo-vibration tests, the maximum center wavelength shifts are 13 pm and 46 pm, corresponding to temperature variations of approximately 1.35 °C@24 °C and 4.77 °C@800 °C. These results demonstrate stable sensor performance under high-temperature testing conditions. In addition, a fitting formula applicable to different center wavelengths is proposed, significantly reducing calibration effort. The sensor features a simple structure, easy installation, and reliable performance, providing an effective solution for temperature sensing in extreme environments. Full article

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

To address the urgent need for electronics operable in extremely high-temperature environments, this paper presents a novel three-terminal, dual-gate, lateral 4H-SiC n-channel depletion-mode junction field effect transistor (JFET) without a backside electrode. Featuring a fully planar electrode layout, the device eliminates the back-gate effect and significantly improves integration compatibility. Experimental results demonstrate stable DC operation up to 500 °C, with an intrinsic gain of 9.79 at room temperature and 6.01 at 500 °C. Comparison with TCAD simulations confirms excellent agreement in the key physical trends of threshold voltage drift and mobility degradation, though quantitative discrepancies are observed and attributed to process-induced parasitic effects such as non-ideal ohmic contacts and interface states. Analysis shows that the new structure broadens the channel depletion layer by optimizing the depletion profile, thereby suppressing channel-length modulation and improving both output resistance and gate control. This work not only provides an effective device platform for high-temperature 4H-SiC analog integrated circuits (ICs) but also deepens the understanding of process-performance correlations, offering clear guidance for process-oriented device optimization. The proposed structure serves as a foundation for developing fully planar, high-temperature 4H-SiC analog ICs with promising potential in aerospace, automotive, and energy exploration systems. Full article

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

Low temperature and low irradiance (LTLI) stress severely limits strawberry growth and productivity during winter protected cultivation. This study investigated the physiological responses of the short-day strawberry cultivar ‘Benihoppe’ to individual and combined LTLI stress and developed a quantitative damage evaluation index. Seedlings were exposed to four treatments for 20 d: control (25/15 °C, 600 μmol m⁻² s⁻¹), single low temperature (LT: 15/5 °C), single low irradiance (LI: 100 μmol m⁻² s⁻¹), and combined stress (LTLI: 15/5 °C, 100 μmol m⁻² s⁻¹). Compared to isolated stress factors, combined LTLI treatment exhibited a statistically verified synergistic damaging effect (two-factor ANOVA, LT × LI p < 0.01) on leaf structure and function. LTLI-treated plants showed severe reductions in leaf area, palisade tissue thickness, chlorophyll content, and net photosynthetic rate (Pn), alongside elevated malondialdehyde (MDA) accumulation. Chlorophyll a fluorescence (OJIP) analysis revealed that LTLI stress strongly blocked the electron transport chain at the PSII acceptor side, increasing the J-step relative variable fluorescence (Vj) and suppressing the performance index (PI). To quantify these impacts, a Low Temperature and Low Irradiance Damage Index (LTLDI) was derived from 12 core physiological and morphological variables. The LTLDI scores demonstrated that LTLI induced severe damage by day 10 (score: 0.69) and extremely severe damage by day 20 (0.87), which were substantially higher than the damage caused by LT (0.58 at 20 d) and LI (0.63 at 20 d) alone. The index reliability was confirmed by its strong correlation (r > 0.9) with key stress markers (Fv/Fm, Pn, and MDA). Overall, combined LTLI stress exacerbates structural degradation and PSII photoinhibition in strawberry leaves. The proposed LTLDI offers a practical, standardized tool for evaluating stress severity, facilitating timely environmental management in greenhouse strawberry production. Full article

Accurate temperature prediction is essential for understanding climate variability and hydrological extremes. In this context, Long Short-Term Memory (LSTM) networks have become a widely adopted tool for temperature forecasting; however, their performance strongly depends on hyperparameter selection. This study proposes a combinatorial optimization framework that integrates the Simulated Annealing (SA) algorithm with LSTM networks to enhance long-term temperature forecasting performance. To evaluate the proposed approach, monthly temperature data (1927–2024) from the Turkish State Meteorological Service (MGM) were used. A spatial hold-out strategy (57 training and 24 testing provinces) was employed to assess generalization performance. Model performance was evaluated using MAE, RMSE, R², and NSE. Results indicate that the SA-LSTM model significantly improves prediction accuracy compared with the conventional LSTM configuration. The optimized model achieved lower prediction errors (MAE = 2.56; RMSE = 3.42) and higher agreement metrics (R² = 0.856; NSE = 0.848) on the independent testing dataset. These findings demonstrate that combinatorial hyperparameter optimization enhances the robustness and predictive capability of deep learning models for large-scale temperature forecasting and provides a robust and reliable tool for climate and hydrological modeling. Full article

Global warming-induced extreme heatwaves present a severe threat to global tomato yield and production stability. To elucidate the molecular regulatory mechanisms underlying heat stress tolerance in tomato (Solanum lycopersicum), this study utilized label-free quantitative proteomics to profile alterations in protein abundance in tomato leaves in response to heat stress. A total of 294 differentially expressed proteins (DEPs) were identified, with function enrichment in the systematic activation of core stress-responsive biological processes, including the mitogen-activated protein kinase (MAPK) signaling cascade, the endoplasmic reticulum protein processing, and glutathione metabolism. Among them, heat shock protein (HSP) family members exhibited the most significant changes, particularly two small heat shock proteins (sHSPs), designated as SlsHSP1 and SlHSP17.4. Functional validation showed that silencing either SlsHSP1 or SlHSP17.4 drastically impaired heat tolerance in tomato plants. Specifically, silenced lines displayed excessive reactive oxygen species (ROS) accumulation and reduced antioxidant enzyme activities, with SlsHSP1-silenced plants showing more severe heat-induced phenotypic damage. Subcellular localization assays further demonstrated SlsHSP1 was located in the ER and SlHSP17.4 in the nucleus. Collectively, this study unravels multiple heat defense regulatory networks in tomato, in which organelle-specific sHSPs like SlsHSP1 and SlHSP17.4 synergistically maintain protein homeostasis and cellular redox balance, conferring broad-spectrum stress resistance in plants under high-temperature stress. Full article

Global climate change and air pollution jointly threaten respiratory health. Asthma, a prevalent chronic inflammatory airway disease, is exacerbated by both traditional air pollutants such as particulate matter (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and emerging contaminants like microplastics (MPs) and per- and polyfluoroalkyl substances (PFAS), with effects amplified under extreme temperature conditions. In reality, individuals face complex combined exposures, yet the synergistic effects of these factors on asthma pathogenesis remain poorly understood. This narrative review synthesizes epidemiological and toxicological evidence. It aims to elucidate both the individual and the notably synergistic effects of these factors on asthma pathogenesis. The central mechanistic pathway is initiated by oxidative stress, which activates key inflammatory signaling pathways, thereby driving immune imbalance and airway inflammation. Our review underscores that the combined exposure to traditional pollutants, emerging pollutants, and extreme temperatures may pose a greater threat than individual factors. These findings underscore the critical need for an integrated perspective in asthma research and public health policy. Moving beyond single-pollutant approaches, we advocate for combinatorial risk assessment and synergistic intervention strategies to effectively mitigate the growing burden of asthma in a changing climate. Full article

Functionally graded materials (FGMs) effectively alleviate residual stress induced by physical property mismatch at dissimilar material interfaces through a graded transition in composition or structure. Among these, the matching of the coefficient of thermal expansion (CTE) is a core indicator for ensuring the service reliability of the joint. Traditional composition design relies on empirical trial-and-error, which makes it difficult to efficiently identify the optimal path in a high-dimensional composition space. This study proposes a data-driven, machine learning-assisted composition design method. Based on a high-precision dataset covering 15 elements and 747 CTE data points, six typical regression models were systematically evaluated. The results show that the random forest (RF) model achieves the best performance, with a coefficient of determination (R²) of 0.929 and a root mean square error (RMSE) of 0.658 on the test set. Using the SHapley Additive exPlanations (SHAP) method, the lattice constant (c), Young’s modulus (YM), and temperature (T) were identified as the key physical descriptors governing the thermal expansion behavior. Experimental validation shows that the CTE prediction deviation of the model for the high-performance Fe-based alloy Norem02 in the range of 20–300 °C is only 0.89%. Based on this framework, the composition of the 316L/Norem02 transition layer was successfully designed in this study. This effectively reduced the interfacial thermal expansion mismatch. Consequently, it provides a reliable theoretical basis for the rational design of dissimilar material interfaces under extreme service conditions. Full article

Early-spring ephemeral plants act as pioneer species on stabilized dunes in cold arid deserts; they are capable of rapid growth under extreme drought and low-temperature conditions while sustaining dune ecosystem functions. These species are highly sensitive to climate change, yet their spatiotemporal dynamics and the mechanisms by which climatic factors regulate their growth remain poorly understood. In this study, we investigated the Gurbantunggut Desert, China, using long-term NDVI time series to extract phenological traits associated with their life cycle and developed a remote-sensing-based analytical framework to quantify the distribution patterns of early-spring ephemeral plants and their environmental drivers. We combined random forest (RF), structural equation modeling (SEM), and convolutional neural networks (CNN) to assess the relative importance and pathways of key climatic drivers and to predict future distribution changes. Our results indicate that: (1) the life cycle extraction method achieved a classification accuracy exceeding 80%, and from 2001 to 2022, the overall distribution of early-spring ephemeral plants exhibited an increasing trend; (2) snowend, snowday, and precipitation during the driest quarter were the primary drivers of ephemeral plant distribution, collectively explaining over 60% of the observed variation, and structural equation modeling further revealed that snow and precipitation had significant positive effects on their distribution; and (3) under future climate scenarios, Medium-NDVI areas are projected to expand northward and westward, with the potential emergence of new suitable habitats in northern localities by mid-century. Climate warming may facilitate the dispersal and latitudinal migration of early-spring ephemeral plants. Based on these findings, biodiversity conservation efforts should prioritize ecologically sensitive transitional zones and promote species migration and establishment under climate change through the construction of ecological corridors. Full article

This study investigates the prediction of carbon emissions from fly ash and ground granulated blast furnace slag-based geopolymer concrete (GPC) using advanced ensemble machine learning (ML) techniques. Although ML has been extensively utilized to model GPC’s mechanical performance, its application in estimating environmental impacts, specifically carbon emissions, is limited. The research employs six ensemble ML models, such as random forest, gradient boosting, extreme gradient boosting (XGB), CatBoost, and light gradient boosting machine (LGBM), including versions optimized using ant colony optimization (ACO). Among them, the ACO-enhanced XGB model demonstrated the highest predictive accuracy with a coefficient of determination (R²) of 0.97, with low prediction errors (MAE = 3.92, RMSE = 6.17). However, cross-validation and uncertainty analyses indicate that the performance differences among top models are relatively small. Conversely, LGBM exhibited the least predictive reliability. Feature importance analysis revealed that curing parameters, specifically initial curing time, curing temperature, and the dosage of dry sodium hydroxide, had the most influence on carbon emissions. To evaluate model robustness and interpretability, Monte Carlo simulation and Gaussian white noise analyses were conducted. Results confirmed that CatBoost and ACO–gradient boosting (ACO-GB) demonstrated greater stability under varying and noisy conditions, whereas XGB-based models, although highly accurate, were comparatively more sensitive to input variability. Overall, the research establishes a data-driven, efficient framework for quantifying carbon emissions in GPC, highlighting the importance of evaluating both predictive accuracy and model robustness, advancing sustainable material design through intelligent modelling. Full article

This research developed a ZrSi₂-TiB₂-modified alumina fiber/boron phenolic resin ceramizable composite intended to fulfill the criteria for high-temperature resistance, oxidation resistance, and structural load-bearing capacity in reusable thermal protection systems. The composite exhibits a low thermal conductivity of 0.405 W·m⁻¹·K⁻¹, a reduced density of 2.11 g·cm⁻³, and a high mass retention rate of 89.45% after heat treatment at 1200 °C in air. During thermal cycling at 1200 °C with a 30 min dwell time, it consistently demonstrates excellent stability, mass retention, and mechanical properties, indicating its potential for applications in reusable thermal protection systems. Following 20 cycles, the variation in length and width remains below 0.6%, the mass retention surpasses 80%, and the flexural strength remains above 20 MPa after 15 cycles. Microstructural evolution and thermodynamic analysis disclose that the in situ ceramization reaction of ZrSi₂ and TiB₂ consumes oxygen, inhibits oxygen diffusion, and fills pores and microcracks with oxidation products (SiO₂ and B₂O₃), thereby forming self-healing and densifying phases. This synergistic mechanism of self-healing and densification ensures the reusability of the composite. The research illustrates the performance evolution patterns and strengthening mechanisms of the composite under extreme thermal conditions, confirming its outstanding performance in repeated usage evaluations. Full article

With the rapid development of flexible electronics technology, flexible wearable sensors based on Lanthanum Strontium Manganese Oxide (La_1-xSr_xMnO₃) have garnered extensive attention in recent years due to their excellent multi-functional integration, environmental stability and biocompatibility. This review systematically analyzes the preparation methods, process optimization strategies, multi-performance integration technologies, and the expansion of the application field of La_1-xSr_xMnO₃-based flexible sensors. Firstly, the basic characteristics and sensing mechanism of the La_1-xSr_xMnO₃ material were presented, including its temperature sensitivity, strain response characteristics, and magnetoresistance effect. Secondly, the fabrication process of flexible sensors was elaborately discussed, with a focus on analyzing crucial technologies, such as laser induction and transfer printing technology. Subsequently, the strategies for regulating the electrical, thermal, and mechanical properties of materials through element doping, along with the multimodal sensing integration and signal decoupling methods, were expounded. Furthermore, the actual performance of this type of sensor in fields such as health monitoring, human–computer interaction, and extreme environment applications was summarized. Finally, the challenges and future development directions of La_1-xSr_xMnO₃-based flexible sensors are outlined, providing theoretical references for the design and optimization of next-generation flexible electronic devices. Full article