Search Results (1,841)

A finite element simulation was conducted to analyze the thermal damage caused by a 532nm nanosecond pulsed laser on a CCD detector. A three-dimensional model was developed to study the temperature field variations within the detector. The simulation was centered on the laser-induced temporal progression of thermal damage in the CCD. Results showed that higher laser fluence led to increased heat accumulation, resulting in the expansion of the thermal damage area. Different thermal damage patterns were observed in the light sensor region and the light-shielded region. In the light sensor region, the melting of the silicon substrate expanded more in the transverse direction compared to the longitudinal direction with increasing laser fluence, while damage in the light-shielded region extended from the edges towards the center as laser fluence increased. These distinct damage patterns were attributed to different energy deposition patterns and structural differences between the light sensor region and the light-shielded region. Full article

Background/Objectives: Ecballium elaterium is a widely distributed species and is one of the earliest recorded in traditional medicine. With global temperatures rising, this study aimed to investigate the changes in E. elaterium plantlets subjected to thermal stress. The goal was to understand how thermal stress affects morphology, physiology, and bioactive metabolite production, both for ecological adaptation and potential therapeutic applications. Methods: Seedlings were cultivated under controlled conditions and subjected to either the control temperature (22 °C) or the heat stress temperature (35 °C) for one week. Morphological and anatomical traits were assessed, along with physiological parameters such as chlorophyll content, malondialdehyde (MDA), hydrogen peroxide (H₂O₂), L-proline, soluble sugars, and total phenolic content. Methanolic leaf extracts from both groups were analyzed via LC-HRMS/MS and examined in vitro for cytotoxic activity against three human cancer cell lines: MCF-7 (breast), DU-145 (prostate), and SH-SY5Y (neuroblastoma). Results: Heat stress reduced dry mass and stomatal density but increased the diameter of the root transition zone, indicating anatomical adaptation. Leaves exhibited elevated oxidative stress markers and altered metabolite accumulation, while the roots showed a more integrated stress response. LC-HRMS/MS profiling revealed significant shifts in Cucurbitacin composition. Extracts from heat-stressed plants displayed stronger cytotoxicity, particularly toward DU-145 and SH-SY5Y cells, correlating with higher levels of glycosylated Cucurbitacins. Conclusions: E. elaterium demonstrates organ-specific thermotolerance mechanisms, with heat stress enhancing the production of bioactive metabolites. These stress-induced phytochemicals, especially Cucurbitacins, hold promise for future cancer research and therapeutic applications. Full article

Urban thermal environments critically impact human settlements and sustainable urban development. In this study, a multi-index framework integrating Landsat TM/ETM+/OLI observations (2004–2019) is developed to quantify the contributions of “source–sink” landscapes to urban heat island (UHI) dynamics in Guangzhou, China, with direct implications for advancing sustainable development. Urban–rural gradient analysis was combined with emerging spatiotemporal hotspot modeling, revealing the following results: (1) there were thermal spatial heterogeneity with pronounced heat accumulation in core urban zones and improved thermal profiles in northern sectors, reflecting a transition from “more sources, fewer sinks” in the southwest to “fewer sources, more sinks” in the northeast; (2) UHIs were effectively mitigated within 25–35 km of the city center, with the landscape effect index (LI > 1) indicating successful sink-dominated cooling; (3) spatiotemporal hotspots were observed, including persistent UHIs in old urban areas contrasting with environmentally vulnerable coldspots in suburban mountainous regions, highlighting uneven thermal risks. This framework provides actionable strategies for sustainable urban planning, including optimizing green–blue infrastructure in UHI cores, enforcing cool material standards, and zoning expansion based on source–sink dynamics. This study bridges landscape ecology and sustainable development, offering a replicable model for cities worldwide to mitigate UHI effects through evidence-based landscape management. Full article

In-flight icing presents a critical safety hazard for unmanned aerial vehicles (UAVs), resulting in ice accumulation on propeller surfaces that compromise UAV aerodynamic performance and operational integrity. While hybrid anti-/de-icing systems (i.e., combining active heating with passive superhydrophobic coatings) have been developed recently to efficiently address this challenge, conventional active heating sub-systems utilized in the hybrid anti-/de-icing systems face significant limitations when applied to curved geometries of UAV propeller blades. This necessitates the development of innovative self-heating superhydrophobic coatings that can conform perfectly to complex surface topographies. Carbon-based electrothermal coatings, particularly those incorporating graphite and carbon nanotubes, represent a promising approach for ice mitigation applications. This study presents a comprehensive experimental investigation into the development and optimization of a novel self-heating carbon nanotube (CNT)-based superhydrophobic coating specifically designed for UAV icing mitigation. The coating’s anti-/de-icing efficacy was evaluated through a comprehensive experimental campaign conducted on a rotating UAV propeller under typical glaze icing conditions within an advanced icing research tunnel facility. The durability of the coating was also examined in a rain erosion test rig under the continuous high-speed impingement of water droplets. Experimental results demonstrate the successful application of the proposed sprayable self-heating superhydrophobic coating in UAV icing mitigation, providing valuable insights into the viability of CNT-based electrothermal coatings for practical UAV icing protection. This work contributes to the advancement of icing protection technologies for un-manned aerial systems operating in adverse weather conditions. Full article

This study investigates the uplift and exhumation history of the southern segment of the western margin of the Ordos Basin using low-temperature thermochronology, including zircon (U-Th)/He (ZHe), apatite fission-track (AFT), and apatite (U-Th)/He (AHe) data, combined with thermal history modeling. The study area exhibits a complex structural framework shaped by multiple deformation events, leading to the formation of extensively developed fault systems. Such faulting can adversely affect hydrocarbon preservation. To better constrain the timing of fault reactivation in this area, we carried out an integrated study involving low-temperature thermochronology and burial history modeling. The results reveal a complex, multi-phase thermal-tectonic evolution since the Late Paleozoic. The ZHe ages (291–410 Ma) indicate deep burial and heating related to Late Devonian–Early Permian tectonism and basin sedimentation, reflecting early orogenic activity along the western North China Craton. During the Late Jurassic to Early Cretaceous (165–120 Ma), the study area experienced widespread and differential uplift and cooling, controlled by the Yanshanian Orogeny. Samples on the western side of the fault show earlier and more rapid cooling than those on the eastern side, suggesting a fault-controlled, basinward-propagating exhumation pattern. The cooling period indicated by AHe data and thermal models reflects the Cenozoic uplift, likely induced by far-field compression from the rising northeastern Tibetan Plateau. These findings emphasize the critical role of inherited faults not only as thermal-tectonic boundaries during the Mesozoic but also as a pathway for hydrocarbon migration. Meanwhile, thermal history models based on borehole data further reveal that the study area underwent prolonged burial and heating during the Mesozoic, reaching peak temperatures for hydrocarbon generation in the Late Jurassic. The timing of major cooling events corresponds to the main stages of hydrocarbon expulsion and migration. In particular, the differential uplift since the Mesozoic created structural traps and migration pathways that likely facilitated hydrocarbon accumulation along the western fault zones. The spatial and temporal differences among the samples underscore the structural segmentation and dynamic response of the continental interior to both regional and far-field tectonic forces, while also providing crucial constraints on the petroleum system evolution in this tectonically complex region. Full article

Abnormally warm winters in recent years have accelerated flowering in fruit trees, increasing their vulnerability to late frost damage. To address this challenge, this study aimed to evaluate and compare the performance of three phenology models—the development rate (DVR), modified DVR (mDVR), and Chill Days (CD) models—for predicting full bloom dates of ‘Niitaka’ pear, using image-derived phenological observations. The goal was to identify the most reliable and regionally transferable model for nationwide application in South Korea. A key strength of this study lies in the integration of real-time orchard imagery with automated weather station (AWS) data, enabling standardized and objective phenological monitoring across multiple regions. Using five years of temperature data from seven orchard sites, chill and heat unit accumulations were calculated and compared with observed full bloom dates obtained from orchard imagery and field records. Correlation analysis revealed a strong negative relationship between cumulative heat units and bloom timing, with correlation coefficients ranging from –0.88 (DVR) to –0.94 (mDVR). Among the models, the mDVR model demonstrated the highest stability in chill unit estimation (CV = 6.3%), the lowest root-mean-square error (RMSE = 2.9 days), and the highest model efficiency (EF = 0.74), indicating superior predictive performance across diverse climatic conditions. In contrast, the DVR model showed limited generalizability beyond its original calibration zone. These findings suggest that the mDVR model, when supported by image-based phenological data, provides a robust and scalable tool for forecasting full bloom dates of temperate fruit trees and enhancing grower preparedness against late frost risks under changing climate conditions. Full article

Cardiometabolic diseases such as obesity, prediabetes (PreD), and type 2 diabetes (T2D) are global health challenges linked to metabolic dysfunction. While probiotics show promise, postbiotics offer advantages in stability, safety, and food incorporation. This study evaluates the postbiotic pA1c^®HI, a heat-inactivated form of the probiotic pA1c^®, for its potential in modulating glucose and lipid metabolism in Caenorhabditis elegans, compared to its live form. Worms were supplemented with pA1c^®HI and live pA1c^® in glucose-enriched media. Fat accumulation, gene expression, oxidative stress, and lifespan were measured using Nile Red and DHE staining, qPCR, and longevity assays. pA1c^®HI significantly reduced glucose-induced fat accumulation, achieving fat reduction comparable to the anti-obesity drug orlistat and showing superior efficacy compared to the live probiotic form. It modulated the expression of genes associated with lipid oxidation (acox-1, cpt-2), fatty acid synthesis (fat-5), insulin signaling (daf-2, daf-16), and oxidative stress response (skn-1). Synergistic combinations with chromium picolinate (PC) and zinc (Zn) further enhanced metabolic outcomes. Importantly, pA1c^®HI retained efficacy after thermal treatment (121–135 °C), supporting its potential for use in processed foods. pA1c^®HI is a stable, effective postbiotic that modulates key pathways associated with obesity, PreD, and T2D in C. elegans, with superior performance to the live probiotic and added benefits when combined with PC and Zn. Full article

Drought, a complex and frequent natural hazard in the context of global change, poses a major threat to key forest ecosystems in the carbon cycle. However, current research lacks a systematic and quantitative analysis of the multi-factor drivers of drought sensitivity based on lagged and accumulative effects. To address this gap, a drought sensitivity model was established by integrating both lagged and accumulative effects derived from long-term remote sensing datasets. To leverage both predictive power and interpretability, the XGBoost–SHAP framework was employed to model nonlinear associations and identify the threshold effects of driving factors. In addition, the Geodetector model was applied to examine spatially explicit interactions among multiple drivers, thereby uncovering the coupling effects that jointly shape forest drought sensitivity across China. The results reveal the following: (1) Drought had lagged and accumulative effects on 99.52% and 95.55% of forest GPP, with evergreen broadleaf forest showing the strongest effects and deciduous needleleaf forest the weakest. (2) Evergreen needleleaf forests have the highest proportion of extremely high drought sensitivity (16.94%), while deciduous needleleaf forests have the least (1.02%), and the drought sensitivity index declined in 67.12% of forests over decades. (3) Temperature and precipitation are the primary drivers of drought sensitivity, with clear threshold effects. Evergreen forests are mainly driven by climatic factors, while forest age is a key driver in deciduous needleleaf forests. (4) Interactive effects among multiple factors significantly amplify spatial variations in drought sensitivity, with water–heat coupling dominating in evergreen forests and structure–climate interactions prevailing in deciduous forests. Full article

Rising global temperatures are increasingly affecting plant performance, leading to reduced growth, altered metabolism, and compromised membrane integrity. Although plant growth-promoting bacteria (PGPB) show promise in enhancing thermotolerance, the underlying mechanisms remain insufficiently explored. Therefore, this study investigated the effects of PGPB inoculation on Zea mays under control (26 °C) and heat stress (36 °C) conditions. Maize plants were inoculated with two thermotolerant bacterial strains and their effects were compared to non-inoculated plants through morphometric, biochemical, and lipidomic analyses. Heat stress negatively affected germination (−35.9%), increased oxidative stress (+46% for LPO, +57% for SOD, +68% for GPx), and altered leaf lipid composition, particularly fatty acids, glycerolipids, and sphingolipids. Inoculation with Pantoea sp. improved germination by 15% for seeds exposed to heat stress, increased growth (+28% shoot and +17% root), enhanced antioxidant defenses (+35% for CAT and +38% for APx), and reduced membrane damage by 65% compared with the control. Lipidomic profiling revealed that inoculation mitigated temperature-induced lipid alterations by reducing triacylglycerol accumulation and preserving the levels of polyunsaturated galactolipids and hexosylceramides. Notably, Pantoea sp.-inoculated plants under heat stress exhibited lipid profiles that were more similar to those of control plants, suggesting enhanced heat resilience. These results underscore the importance of specific plant–microbe interactions in mitigating heat stress and highlight PGPB inoculation as a promising strategy to enhance crop performance and resilience under projected climate warming scenarios. Full article

Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. The main objective of this work is to predict heat stress in livestock from shade-seeking behavior captured by computer vision, combined with some climatic features, in a completely non-invasive way. To this end, we evaluate two soft computing algorithms—Random Forests and Neural Networks—clarifying the trade-off between accuracy and interpretability for real-world farm deployment. Data were gathered at a commercial dairy farm in Titaguas (Valencia, Spain) using overhead cameras that counted cows in the shade every 5–10 min during summer 2023. Each record contains the shaded-cow count, ambient temperature, relative humidity, and an exact timestamp. From here, three thermal indices were derived: the current THI, the previous-night mean THI, and the day-time accumulated THI. The resulting dataset covers 75 days and 6907 day-time observations. To evaluate the models’ performance a 5-fold cross-validation is also used. The results show that both soft computing models outperform a single Decision Tree baseline. The best Neural Network (3 hidden layers, 16 neurons each, learning rate $={10}^{-3}$) reaches an average RMSE of $14.78$, while a Random Forest (10 trees, depth $=5$) achieves $14.97$ and offers the best interpretability. Daily error distributions reveal a median RMSE of $13.84$ and confirm that predictions deviate less than one hour from observed shade-seeking peaks. Although the dataset came from a single farm, the results generalized well within the observed range. However, the models could not accurately predict the exact number of cows in the shade. This suggests the influence of other variables not included in the analysis (such as solar radiation or wind data), which opens the door for future research. Full article

Efficient irrigation management is essential for optimizing yield and quality in specialty crops like hot peppers (Capsicum chinense), particularly under controlled greenhouse environments. This study employed a novel sensor-based system integrating soil moisture and sap flux monitoring to evaluate water use dynamics in Capsicum chinense, a species for which such applications have not been widely reported. Three cultivars—Habanero, Helios, and Lantern—were grown under three volumetric soil moisture contents: low (15%), medium (18%), and high (21%). Water uptake was measured at leaf (transpiration, stomatal conductance) and plant levels (sap flux via heat balance sensors). Photosynthesis, fruit yield, and capsaicinoid concentrations were assessed. Compared to high irrigation, medium and low irrigation increased photosynthesis by 16.6% and 22.2%, respectively, whereas high irrigation favored greater sap flux and vegetative growth. Helios exhibited an approximately 8.5% higher sap flux as compared to Habanero and about 10% higher as compared to Lantern. Helios produced over 30% higher fruits than Habanero and Lantern under high irrigation. Habanero recorded the highest pungency, with a capsaicinoid level of 187,292 SHU—exceeding Lantern and Helios by 56% and 76%, respectively. Similarly, nordihydrocapsaicin and dihydrocapsaicin accumulation were more cultivar-dependent than irrigation-dependent. No significant interaction between cultivar and irrigation was observed, indicating genotype-driven water use strategies. Our study contributes to precision horticulture by integrating soil moisture and sap flux sensors to reveal cultivar-specific water use strategies in Capsicum chinense, thereby demonstrating the potential of an integrated sensor-based irrigation system for efficient irrigation management under increasing water scarcity in protected environments. As a preliminary greenhouse study aimed at maintaining consistent irrigation throughout the growing season across three volumetric soil moisture levels, these findings provide a foundation for subsequent validation and exploration under diverse soil moisture conditions including variations in stress duration, stress frequency, and stress application at different phenological stages. Full article

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

Ultrafast laser welding of glass/metal heterostructures has found extensive applications in sensors, medical devices, and optical systems. However, achieving high-stability, high-quality welds under non-optical contact conditions remains challenging due to severe internal damage within glass materials. This study addresses thermal management through synergistic control of thermal accumulation effects and material ablation thresholds. Using the sapphire/Invar alloy system as a model for glass/metal welding, we investigated thermal accumulation effects during ultrafast laser ablation of Invar alloy through theoretical simulations. Under a repetition rate of 1 MHz, the femtosecond laser raised the lattice equilibrium temperature by 700 K within 10 microseconds, demonstrating that high repetition rate femtosecond lasers can induce effective heat accumulation in Invar alloy. Furthermore, ablation thresholds for both materials were determined across varying repetition rates via the D² method, with corresponding threshold curves systematically constructed. Finally, based on the simulation and ablation threshold calculation results, laser parameters were selected for ultrafast laser single point welding of sapphire and Invar alloy. The experimental results demonstrate effective thermal effect mitigation, achieving a maximum shear strength of 63.37 MPa. Comparative analysis against traditional scan welding further validates the superiority of our approach in thermal management. This work provides foundational theoretical and methodological guidance for ultrafast laser welding of glass/metal heterostructures. Full article

This study analyzes the dynamics of temperature and moisture in a cylindrical silo with a conical roof and floor used for storing corn in the Bajío region of Mexico, considering conditions both with and without aeration. The model incorporates external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity through the sorption isotherm (water activity), and grain respiration to simulate real storage conditions. The model is based on continuity, momentum, energy, and moisture conservation equations in porous media. This model was solved using the finite element method (FEM) to evaluate temperature and interstitial humidity variations during January and May, representing cold and warm environmental conditions, respectively. The simulations show that, without aeration, grain temperature progressively accumulates in the center and bottom region of the silo, reaching critical values for safe storage. In January, the low ambient temperature favors the natural dissipation of heat. In contrast, in May, the combination of high ambient temperatures and solar radiation intensifies thermal accumulation, increasing the risk of grain deterioration. However, implementing aeration periods allowed for a reduction in the silo’s internal temperature, achieving more homogeneous cooling and reducing the threats of mold and insect proliferation. For January, an airflow rate of 0.15 m³/(min·ton) was optimal for maintaining the temperature within the safe storage range (≤17 °C). In contrast, in May, neither this airflow rate nor the accumulation of 120 h of aeration was sufficient to achieve optimal storage temperatures. This indicates that, under warm conditions, the aeration strategy needs to be reconsidered, assessing whether a higher airflow rate, longer periods, or a combination of both could improve heat dissipation. The results also show that interstitial relative humidity remains stable with nocturnal aeration, minimizing moisture absorption in January and preventing excessive drying in May. However, it was identified that aeration period management must be adaptive, taking environmental conditions into account to avoid issues such as re-wetting or excessive grain drying. Full article

This study examines how Multi-Scalar Nature-Based Regenerative Solutions (M-NbRS) can realign urban–industrial systems with planetary boundaries to mitigate Earth system destabilization. Using integrated systems analysis, we document three key findings: (1) global material flows show only 9% circularity amid annual extraction of 100 billion tons of resources; (2) Earth system diagnostics reveal 28 trillion tons of cryosphere loss since 1994 and 372 Zettajoules of oceanic heat accumulation; and (3) meta-analysis identifies accelerating biosphere integrity loss (61.56 million hectares deforested since 2001) and atmospheric CO₂ concentrations reaching 424.61 ppm (2024). Our Vicious Cycle Atlas of Fragility framework maps three synergistic disintegration pathways: metabolic overload from linear resource flows exceeding sink capacity, entropic degradation through high-entropy waste driving cryospheric collapse, and planetary boundary transgression. The M-NbRS framework counters these through spatially nested interventions: hyper-local urban tree canopy expansion (demonstrating 0.4–12 °C cooling), regional initiatives like the Heart of Borneo’s 24 million-hectare conservation, and global industrial controls maintaining aragonite saturation (Ωarag > 2.75) for marine resilience. Implementation requires policy innovations including deforestation-free supply chains, sustainability-linked financing, and ecological reciprocity legislation. These findings provide an evidence base for transitioning industrial–urban systems from drivers of Earth system fragility to architects of regeneration within safe operating spaces. Collectively, these findings demonstrate that M-NbRS offer a scientifically grounded, policy-actionable framework for breaking the vicious cycles of Earth system destabilization. By operationalizing nature-based regeneration across spatial scales—from street trees to transboundary conservation—this approach provides measurable pathways to realign human systems with planetary boundaries, offering a timely blueprint for industrial–urban transformation within ecological limits. Full article