Search Results (888)

Understanding the coupling mechanisms between vegetation phenology and carbon productivity is essential for assessing ecosystem responses to climate change and guiding sustainable grassland management. This study focuses on stable alpine grasslands on the southern slope of the Qilian Mountains from 2001 to 2020, a climatically sensitive but relatively under-investigated transition zone on the northeastern Tibetan Plateau. We utilized MODIS NDVI time-series (MOD13Q1) and the latest PML V2 gross primary productivity (GPP) product at 500 m resolution to quantify changes in the start (SOS), end (EOS), and length (LOS) of the growing season. A pixel-wise linear regression approach was applied to evaluate the sensitivity of GPP to phenological metrics, explicitly characterizing how much GPP changes in response to unit shifts in SOS, EOS and LOS. Compared with previous studies that mainly described large-scale correlations between phenology and GPP or relied on coarser GPP products, this study provides a pixel-level, sensitivity-based assessment of phenology–carbon coupling in alpine grasslands using a long-term, phenology–GPP dataset tailored to the Qilian alpine region. The results revealed trends of earlier SOS, delayed EOS, and extended LOS, accompanied by a gradual increase in GPP. However, phenology–GPP coupling exhibited notable spatial heterogeneity. In mid- and low-altitude areas, extended growing seasons enhanced GPP, whereas high-altitude zones showed limited or even negative responses, likely due to climatic constraints such as cold stress and thermal–moisture mismatches. To better understand these spatial differences, we constructed a three-dimensional phenology–GPP sensitivity space and applied k-means clustering to delineate three ecological functional zones: (1) high carbon sink potential, (2) ecologically fragile regions, and (3) neutral buffers. This sensitivity-based functional zonation moves beyond traditional correlation analyses and provides a process-oriented and spatially explicit framework for ecosystem service assessment, carbon sink enhancement and adaptive land-use strategies in sensitive mountain environments. Full article

To reduce mold costs in composite forming, multi-point tooling technology has been integrated into the hot diaphragm forming process. However, this approach still faces several challenges, including time-consuming prepreg layup, high energy consumption, and poor surface quality. This study proposes a heating pad-assisted multi-point thermoforming process: the prepreg is embedded in the thermal functional layers, placed on the lower mold, and formed via the downward movement of the upper mold to accomplish mold closure. Instead of the conventional rectangular array, this study adopted multi-point tooling with a hexagonal pin arrangement. Compared to traditional configurations, this hexagonal layout increases the punch support area by 9.8%, while its dense punch arrangement improves the accuracy of the molded curved surface. Taking a saddle-shaped surface as the target, a prototype part was fabricated. Subsequent analysis of the part’s surface quality identified three defects: dimples, fiber distortion, and ridge protrusions. The surface dimples were eliminated by adjusting the distance between the upper and lower molds. Notably, ridge protrusion is a defect unique to the hexagonal pin arrangement. We conducted a detailed analysis of its causes and solutions, finding that this defect arises from the combined effect of the pin arrangement and the saddle-shaped surface. Through a series of height compensation experiments, the maximum deviation at the ridges was reduced from 0.46 mm to approximately 0.35 mm, which is consistent with the deviation of defect-free areas. This work demonstrates that the multi-point hot-pressing process provides a potential, efficient, and low-cost method for manufacturing double-curvature composite components, whose effectiveness has been verified through the saddle-shaped case study. Full article

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems. Full article

Lithium-ion batteries are pivotal in powering modern technology, from electric vehicles to portable electronics. However, their safety is challenged by the risk of thermal runaway, a critical failure mode leading to catastrophic consequences such as fires and explosions. This study presents a machine learning framework for the early detection of thermal runaway events using sensor data from over 210 open-source battery tests. The framework utilizes voltage, temperature, and force measurements from experimental mechanical indentation tests, with force data providing additional predictive value beyond standard BMS sensors. Key features such as the rate of temperature change and voltage change were engineered from raw time-series data. An XGBoost classifier was trained to detect critical patterns up to 20 s in advance, with lead-time shifting applied to simulate real-time warnings. Critical conditions were operationally defined as temperature exceeding 80 °C or voltage dropping below 3.0 V. The model achieved an F1-score of 0.98 on a test set of 734k data points from 42 independent mechanical indentation battery tests (natural class distribution: 45% critical, 55% normal). SHAP analysis revealed that low voltage (below 3.0 V) and rapid temperature rise (above 80 °C/s) were the most influential features. The system identified patterns 5–10 s before threshold crossing, with a mean detection of 8.3 s. This research demonstrates the potential for machine learning-enhanced battery safety, providing a foundation for future advancements in the field. Full article

Samples of medium-carbon low alloyed steel (0.45 wt% C, 2.61 wt% Mn, 1.57 wt% Si) with bainite microstructure were welded using the cold metal transfer method. A series of single welding “dots” was made to produce welding joints using austenitic welding wires. The heat input was adjusted to the minimal possible level of 500–800 J per “dot”. Tensile tests of welded samples demonstrated that quality welds were obtained. All samples were broken via welded metal, showing tensile strength 530–670 MPa, which is inherent to the material of the welding wires. It was determined that the time required for phase transformations in the heat-affected zone during the thermal cycle is an order of magnitude greater than the time of temperature flash during producing a single welding “dot”. The results of extensive hardness measurements of material in the heat-affected zone, along with macro- and microstructure investigations, are presented. It has been demonstrated that cold metal transfer welding technology can be successfully used for welding steel with high carbon equivalent and bainite microstructure without preheating and with minimal deterioration of properties in the heat-affected zone. Full article

Urban heat not only affects thermal comfort but also constrains the sustainable development of cities, underscoring the necessity of understanding the temporal response of land surface temperature (LST) to urban characteristics over time. Most existing studies rely on single-overpass satellite observations or daily averages, failing to capture continuous diurnal variability and the time-dependent influence of different drivers. In this study, we reconstructed seasonal hourly LST series for Beijing using an improved diurnal temperature cycle (DTC) model (${\mathrm{GEM}}_{\eta }$) based on MODIS data, and employed a random forest framework to quantify the relative contributions of natural, urban morphological, and anthropogenic factors throughout the diurnal cycle. Unlike previous studies that rely on traditional DTC models and machine learning for largely static or single-scale assessments, our research provides a unified, time-explicit comparison of LST driver dominance across seasons, hourly diurnal cycles, and urban–rural contexts. The results indicate that persistent urban heat island (UHI) effects occur in all seasons, with the maximum intensity reaching approximately 5.0 °C in summer. Generally, natural factors exert a cooling influence, whereas urban morphology and human activities contribute to warming. More importantly, the dominant drivers show strong temporal dependence: a nature-dominated regime prevails in summer, where vegetation exerts an overwhelming cooling effect. Conversely, during transition seasons and winter, LST variability is governed by a mixed-driven mechanism characterized by an hourly-resolved diurnal handoff, in which the dominant contributors shift hour by hour between surface physical properties and anthropogenic proxies. Our findings challenge the static view of urban heat drivers and provide quantitative evidence for developing time-sensitive and seasonally adaptive mitigation strategies, thereby supporting sustainable urban planning and enhancing climate resilience in megacities. Full article

A comprehensive numerical analysis has been conducted to investigate unsteady natural convection (UNC), bifurcation behavior, and heat transfer (HT) in a rectangular enclosure containing thermally stratified air. The enclosure comprises a uniformly heated bottom wall, thermally stratified vertical sidewalls, and a cooled top wall. To assess thermal performance, square and rectangular cavities with identical boundary conditions and working fluid are considered. The finite volume method (FVM) is used to solve the governing equations over a wide range of Rayleigh numbers (Ra = 10¹ to 10⁹) for air with a Prandtl number (Pr) of 0.71. Flow dynamics and thermal performance are analyzed using temperature time series (TTS), limit point–limit cycle behavior, average Nusselt number (Nu_avg), average entropy generation (S_avg), average Bejan number (Be_avg), and the ecological coefficient of performance (ECOP). In the rectangular cavity, the transition from steady to chaotic flow exhibits three bifurcations: a pitchfork bifurcation at Ra = 3 × 10⁴–4 × 10⁴, a Hopf bifurcation at Ra = 3 × 10⁶–4 × 10⁶, and the onset of chaotic flow at Ra = 9 × 10⁷–2 × 10⁸. The comparative analysis indicates that Nu_avg remains nearly identical for both cavities within Ra = 10⁵ to 10⁷. However, at Ra = 10⁸, the HT rate in the rectangular cavity is 29.84% higher than that of the square cavity, while S_avg and Be_avg differ by 39.32% and 37.50%, respectively. Despite higher HT and S_avg in the rectangular enclosure, the square cavity demonstrates superior overall thermal performance by 13.52% at Ra = 10⁸. These results offer significant insights for optimizing cavity geometries in thermal system design based on energy efficiency and entropy considerations. Full article

Through a comprehensive analysis of urban vegetation summer seasonal and interannual patterns in the Bucharest metropolis in Romania, this study explored the response of urban vegetation to heat waves’ (HWs) impact in relation to multi-climatic parameters variability from a spatiotemporal perspective during 2000–2024, with a focus on summer HWs periods (June–August), and particularly on the hottest summer 2024. Statistical correlation, regression, and linear trend analysis were applied to multiple long-term MODIS Terra/Aqua and MERRA-2 Reanalysis satellite and in situ climate data time series. To support the decline in urban vegetation during summer hot periods due to heat stress, this study found strong negative correlations between vegetation biophysical observables and urban thermal environment parameters at both the city center and metropolitan scales. In contrast, during the autumn–winter–spring seasons (September–May), positive correlations have been identified between vegetation biophysical observables and a few climate parameters, indicating their beneficial role for vegetation growth from 2000 to 2024. The recorded decreasing trend in evapotranspiration from 2000 to 2024 during summer HW periods in Bucharest’s metropolis was associated with a reduction in the evaporative cooling capacity of urban vegetation at high air temperatures, diminishing vegetation’s key function in mitigating urban heat stress. The slight decline in land surface albedo in the Bucharest metropolis due to increased urbanization may explain the enhanced air temperatures and the severity of HWs, as evidenced by 41 heat wave events (HWEs) and 222 heat wave days (HWDs) recorded during the summer (June–August) period from 2000 to 2024. During the severe 2024 summer heat wave episodes in the south-eastern part of Romania, a rise of 5.89 °C in the mean annual land surface temperature and a rise of 6.76 °C in the mean annual air temperature in the Bucharest metropolitan region were observed. The findings of this study provide a refined understanding of heat stress’s impact on urban vegetation, essential for developing effective mitigation strategies and prioritizing interventions in vulnerable areas. Full article

The growing demand for high-efficiency photovoltaic (PV) technologies has intensified interest in advanced cell architectures, including hybrid passivated back contact (HPBC) solar cells. Conventional solder-based interconnection processes require high thermal budgets, which can induce thermomechanical stress and lead to performance degradation in thin back-contact cell structures. In this study, electrically conductive adhesive (ECA) interconnection is investigated as a low-thermal-budget, solder-free alternative for HPBC solar cells. The curing behavior of an acrylic-based, silver-filled ECA is systematically examined by controlling the upper lamp temperature and the welding time during the interconnection process. Electrical performance is evaluated through current–voltage characterization, fill factor, and series resistance analysis, while interfacial microstructural evolution is examined using scanning electron microscopy. The results identify a well-defined processing window in which adequate curing enables stable electrical contact formation. In contrast, both insufficient curing and excessive curing result in degraded electrical performance. To assess practical applicability, HPBC modules with an industry-relevant size of ~1000 × 1160 mm² are fabricated and evaluated using electroluminescence imaging and I–V measurements. By identifying a robust curing window and demonstrating its successful transfer from string-level interconnections to full-size HPBC modules, this study establishes a practical, low-thermal-budget, solder-free interconnection strategy for advanced back-contact PV architectures. Full article

The separation of rare earth elements (REEs) with similar chemical properties remains a relevant challenge today, most often addressed using liquid–liquid and solid-phase extraction with various chelating agents. Excellent complexing agents for REEs are 1,3-diketones and their analogs. We have for the first time proposed a method for preparing a material consisting of a covalently immobilized 3-acyl-4-hydroxycoumarin ligand on silica. For its synthesis, we employed a strategy based on the “click” reaction of 3-azidopropyl silica with a propargyl-containing coumarin–chalcone conjugate—this approach is the most tolerant and does not affect the coordinationally active fragment of the ligand. The material was characterized by thermal analysis, IR spectroscopy, and ¹³C NMR. The potential of the synthesized material for REE preconcentration was demonstrated at pH 5–5.5: high extraction efficiency for Gd(III), Dy(III), Er(III), Eu(III), Sm(III), and Yb(III) was observed, with fast adsorption kinetics (30 min) and extraction degrees of ~98%. Under unified conditions of static and dynamic extraction for Gd(III), Dy(III), Er(III), Eu(III), Sm(III), and Yb(III), affinity series toward the surface were obtained as a function of the distribution coefficient. It was shown that 10-fold molar excesses of Fe(III), Al(III), Cu(II), Ni(II), and Co(II) allow retention of more than 95% extraction for Dy(III) and Er(III). After adsorption of Dy(III) and Er(III), shifts in the carbonyl group absorption bands are visible in the IR spectra of the material, indicating a chelating mechanism of sorption. Additional studies are required for implementation in analytical and preparative REE separation schemes; however, preliminary data show that the material is a highly active adsorbent. Full article

Mechanical coffee drying is an energy-intensive stage of postharvest processing that directly affects product quality and production costs. This study evaluated the technical and economic feasibility of using expanded polystyrene (EPS) as a thermal insulation material to improve the performance of a mechanical coffee dryer and to demonstrate its potential for sustainable reuse. Experiments were conducted using a total of 210 kg of wet parchment coffee (Coffea arabica L. var. Cenicafé 1) per treatment, corresponding to three experimental replicates of 70 kg each, dried at 50 ± 2 °C, comparing an EPS-insulated dryer (0.02 m thickness) with a non-insulated control. A theoretical model based on steady-state heat transfer through series resistances estimated energy losses and system efficiency for different insulating materials. Theoretical results indicated that EPS, polyethylene foam, and cork reduced heat losses by 58.1%, 54.3%, and 50.9%, respectively. Experimentally, EPS reduced drying time by 7.82%, fuel consumption by 13.9%, and energy demand by 9.5%, while increasing overall efficiency by 6.7% and reducing wall heat losses by 37.7%. Improved temperature stability enhanced heat retention and moisture migration behavior. Economically, EPS reduced operating costs, yielding annual savings of USD 81.5, a 0.45-year payback period, and an annual return on investment (ROI) of 10.86, confirming its viability as a cost-effective and sustainable solution for improving energy efficiency in mechanical coffee drying. 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

Stromboli’s volcanic activity fluctuates in intensity and style, and periods of heightened activity can trigger hazardous events such as crater collapses and lava overflows. This study investigates the volcano’s explosive behavior surrounding the 19 May 2021 crater-rim failure, which primarily affected the N2 crater and partially involved N1, by integrating high-frequency thermal imaging and high-resolution unmanned aerial system (UAS) surveys to quantify eruption parameters and vent morphology. Typically, eruptive periods preceding vent instability are characterized by evident changes in geophysical parameters and by intensified explosive activity. This is quantitatively monitored mainly through explosion frequency, while other eruption parameters are assessed qualitatively and sporadically. Our results show that, in addition to explosion rate, the spattering rate, the predominance of bomb- and gas-rich explosions, and the number of active vents increased prior to the collapse, reflecting near-surface magma pressurization. UAS surveys revealed that the pre-collapse configuration of the northern craters contributed to structural vulnerability, while post-collapse vent realignment reflected magma’s adaptation to evolving stress conditions. The May 2021 events were likely influenced by morphological changes induced by the 2019 paroxysms, which increased collapse frequency and amplified the 2021 failure. These findings highlight the importance of integrating quantitative time series of multiple eruption parameters and high-frequency morphological surveys into monitoring frameworks to improve early detection of system disequilibrium and enhance hazard assessment at Stromboli and similar volcanic systems. Full article

Brake squeal is an undesirable high-frequency noise caused by vibrations induced by friction in disc brake systems. The noise is strongly affected by temperature, as this influences the material properties of the friction pair and the dynamic behaviour of the brake components. This study investigates the effect of temperature changes on the squeal characteristics of a disc brake system under different operating conditions. Experiments are carried out using a laboratory-scale test setup comprising a rotating disc, pneumatically actuated callipers, and precise measurement equipment. A series of test combinations is performed by systematically varying three parameters: disc surface temperature (40, 55, 70, 85, 100 °C), brake pressure (4.0 bar), and disc rotational speed (50, 100, 150, 200 rpm). Acceleration data are acquired using an accelerometer mounted directly on the calliper, while sound pressure data are measured with a fixed-position microphone located 0.5 m from the disc surface. The collected data are analyzed in the time and frequency domain to identify squeal events and their dominant frequencies. The effect of temperature on brake squeal noise and vibration varies with operating conditions, showing different patterns at low and high disc speed at constant brake pressure. This highlights the importance of considering both thermal and mechanical factors together when addressing brake squeal. Full article

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models. Full article