Search Results (4,476)

The built environment is responsible for 40% of global energy demand, and, in line with urbanisation and population growth, this demand is expected to increase steadily. Urban areas are mostly composed of materials that can absorb energy from solar radiation and dissipate the accumulated energy in the form of heat. This study integrates a UAV-based Zenmuse XT S IR camera and handheld FLIR C5 thermal camera with ENVI-met microclimate simulation, providing quantitative insights for sustainable urban planning. From the 24 h experiment results, the characteristics of building surface materials are profiled for lowering energy use for internal thermal control during the operation stage of buildings. This study shows that building surface materials with the lowest solar reflectance and highest specific heat capacity reached a peak surface temperature of 73.5 °C in Jakarta (tropical hot climate) and 44.3 °C in Xiamen (subtropical late winter climate). In contrast, materials with the highest solar reflectance and lowest specific heat only reach a peak surface temperature of 58.1 °C in Jakarta and 27.9 °C in Xiamen. The peak surface temperature occurs at 2 PM in the afternoon. Moreover, we demonstrate the capability of an infrared drone to identify the peak surface temperatures of 55.8 °C at 2 PM in the study area in Xiamen. In addition, the ENVI-met validated model shows satisfactory correlation values of R > 0.9 and R2 > 0.8. This result demonstrates UAV-IR and ENVI-met simulation integration as a scalable method for city-level UHI diagnostics and monitoring. Full article

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

Thermal comfort and energy efficiency are two main goals of heating, ventilation, and air conditioning (HVAC) systems, which use about 40% of the total energy in buildings. This paper aims to predict optimal room temperature, enhance comfort, and reduce energy consumption while avoiding extra energy use from overheating or overcooling. Six Machine Learning (ML) models were tested to predict the optimal temperature in the classroom based on the occupancy characteristic detected by a Deep Learning (DL) model, You Only Look Once (YOLO). The decision tree achieved the highest accuracy at 97.36%, demonstrating its effectiveness in predicting the preferred temperature. To measure energy savings, the study used RETScreen software version 9.4 to compare intelligent temperature control with traditional operation of HVAC. Genetic algorithm (GA) was further employed to optimize HVAC energy consumption while keeping the thermal comfort level by adjusting set-points based on real-time occupancy. The GA showed how to balance comfort and efficiency, leading to better system performance. The results show that adjusting from default HVAC settings to preferred thermal comfort levels as well controlling the HVAC to work only if the room is occupied can reduce energy consumption and costs by approximately 76%, highlighting the substantial impact of even simple operational adjustments. Further improvements achieved through GA-optimized temperature settings provide additional savings of around 7% relative to preferred comfort levels, demonstrating the value of computational optimization techniques in fine-tuning building performance. These results show that intelligent, data-driven HVAC control can improve comfort, save energy, lower costs, and support sustainability in buildings. Full article

Outpatient neurosurgical oncology has expanded with advances in anesthesia, imaging, and minimally invasive techniques, enabling safe same-day discharge for selected patients undergoing procedures such as stereotactic biopsy and craniotomy. In this review, we find that across multiple international series, same-day discharge rates in several studies ranging from 85 to 95%, with low complication (3–6%) and readmission rates when structured pathways, including standardized selection criteria, enhanced recovery protocols, and routine 4-h postoperative CT imaging, are used. Studies on economic analyses demonstrate substantial cost savings driven by reduced inpatient bed utilization, with no increase in adverse events. Key challenges identified include medicolegal concerns amongst physicians, patient education, and limitations in organization adoption. Telemedicine and remote monitoring are increasingly incorporated to streamline preoperative evaluation and postoperative follow-up, improving access and continuity of care. Emerging technologies such as laser interstitial thermal therapy and focused ultrasound may further expand the outpatient neuro-oncology repertoire. Overall, current evidence supports outpatient neurosurgical oncology as a safe, efficient, and patient-centered model when applied with structured clinical pathways and patient selection. Full article

The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R²) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

Fluidized bed combustion of biomass requires maintaining stable properties of the inert bed material, which plays a key role in heat transfer, temperature stabilization and uniform fuel distribution in circulating fluidized bed (CFB) boilers. During long-term operation, quartz sand, i.e., the most commonly used inert material, undergoes physical and chemical degradation processes such as attrition, sintering and coating with alkali-rich ash, leading to changes in particle size distribution (PSD), deterioration of fluidization quality, temperature non-uniformities and an increased risk of bed agglomeration. This study analyzes quality management strategies for inert bed materials in biomass-fired CFB systems, with particular emphasis on the influence of PSD on boiler hydrodynamics and thermal behavior. Based on industrial operating data, sieve analyses and CFD simulations performed under representative operating conditions, a recommended mean particle diameter range of approximately 150–200 μm is identified as critical for maintaining stable circulation and uniform temperature fields. Numerical results demonstrate that deviations toward coarser bed materials significantly reduce solids circulation, promote segregation in the lower furnace region and lead to local temperature increases, thereby increasing agglomeration risk. The study further discusses practical approaches to bed material monitoring, regeneration and make-up management in relation to biomass type and ash characteristics. The results confirm that systematic control of inert bed material quality is an essential prerequisite for reliable, efficient and low-emission operation of biomass-fired CFB boilers. Full article

Thermally induced deformation is a major source of dimensional error in end-milling, especially under high-speed or high-load conditions. Direct measurement of workpiece deformation during machining is impractical, while temperature signals can be obtained with good stability using embedded thermocouples. This study proposes an indirect method for predicting milling-induced thermal deformation based on temperature measurements. A three-dimensional thermo-mechanical finite element model is established to simulate the transient temperature field and corresponding deformation of the workpiece during milling. The numerical model is validated using cutting experiments performed under the same boundary conditions and machining parameters. Based on the validated results, the relationship between deformation at critical machining locations and temperature responses at candidate monitoring points is analyzed. To improve applicability to complex workpieces, a statistical prediction model is developed. Temperature monitoring points are optimized, and significant temperature–deformation correlations are identified using multiple linear regression combined with information-criterion-based model selection. The final model is constructed using simulation-derived datasets and provides stable deformation prediction over the entire milling process. Full article

Accurate estimation of hourly near-surface air temperature (NSAT) is critical for climate analysis, environmental monitoring, and urban thermal studies. However, existing temperature datasets remain constrained by coarse spatial resolution and limited hourly accuracy. This study systematically evaluates four widely used land surface temperature (LST) datasets—MODIS, ERA5-Land, FY-2F, and CGLS—and five machine learning models (RF, MDN, DNN, XGBoost, and GTNNWR) for NSAT estimation across two contrasting regions in Shaanxi, China: a complex-terrain region in southwestern Shaanxi and the urban area of Xi’an. Results demonstrate that single-source LST inputs outperform multi-source LST stacking, largely due to compounded systematic biases across heterogeneous datasets. MODIS provides the best performance in the mountainous region, while CGLS excels in the urban environment. Among all models, GTNNWR—which explicitly captures spatiotemporal non-stationarity—consistently achieves the highest accuracy, reducing RMSE by 44.8% and 44.2% relative to the second-best model in the two study areas, respectively, whereas the remaining four models exhibit broadly comparable performance. This work identifies effective data–model configurations for generating high-resolution hourly NSAT products and provides methodological insights for climate and environmental applications in regions with complex terrain or strong urban heterogeneity. Full article

Background: 5-Azacytidine (5-Aza) is a clinically important DNMT inhibitor with the potential to modulate cardiac remodeling by epigenetically reprogramming human cardiac fibroblasts (HCFs). However, its clinical utility is limited by rapid hydrolytic degradation. Nanoparticle (NP) encapsulation offers a strategy to mitigate this instability. This study evaluated the physical and chemical stability of free 5-Aza and 5-Aza-loaded lipid nanoparticles (5-Aza-NP) under different storage temperatures and examined their effects on DNA methylation-related gene expression in HCFs. Methods: Hyaluronic acid-stabilized lipid NPs were prepared using a solvent displacement method. Particle size, polydispersity index (PDI), and zeta potential were monitored over four days at −20 °C, 4 °C, and 30 °C. Chemical stability was assessed using HPLC and first-order kinetic modeling. Functional activity was evaluated by treating HCFs with free 5-Aza or 5-Aza-NP stored for 96 h and measuring DNMT1, DNMT3A, and DNMT3B expression by RT-qPCR. Results: 5-Aza-NP remained physically stable at 4 °C, while −20 °C induced aggregation and 30 °C caused thermal variability. Free 5-Aza degraded rapidly at 30 °C (6.56% remaining at 72 h), whereas 5-Aza-NP preserved 11.54%. Kinetic modeling confirmed first-order degradation, with consistently longer half-lives for the NP formulation. Functionally, 5-Aza–NP preserved its ability to suppress DNMT1 expression following 96 h of storage at 4 °C, whereas free 5-Aza showed reduced activity. In contrast, DNMT3A and DNMT3B levels remained low and unchanged across all treatments. Conclusions: NP encapsulation enhances the physicochemical stability of 5-Aza and preserves its DNMT1-inhibitory activity, while DNMT3A/B remain unaffected. These findings support NP-based delivery as a promising strategy to stabilize labile epigenetic drugs such as 5-Aza. Full article

The barrier fluid system in subsea boosting pumps primarily serves to seal and cool the pumps, representing a critical auxiliary system in subsea oil and gas development. Throughout their entire service life, these pumps experience both steady-state and transient operating conditions, making the monitoring of key parameters in the barrier fluid system essential. However, existing sensor configurations are relatively limited, hindering comprehensive monitoring of various components of the system, which constrains performance evaluation and optimal design. To address the sealing and cooling requirements of subsea boosting pumps, this paper establishes a system-level simulation model of the barrier fluid system based on the AMESim 2021.1 platform. The model captures the flow and pressure relationships among different components and incorporates the pump’s cooling circuit to investigate the thermal management efficiency of the barrier fluid system. Furthermore, integrated control algorithms enable automatic valve operation. The model’s accuracy is validated against measured data, and it can be used for parametric optimization to improve design and enhance overall system performance. Based on the analysis results, the model can identify optimal parameters for the subsea boosting pump barrier fluid system, providing a theoretical foundation for preventing potential issues in subsea boosting operations. Full article

This study investigates the tribological behavior of selective laser-sintered (SLS) sliding bearings under dry-sliding operating conditions. These polyamide-12 bearings reinforced with glass beads (PA 3200 GF) were tested against a stainless-steel sleeve in three different pressure–velocity (PV) regimes that represent real operating conditions. The coefficient of friction (COF) and contact temperatures were monitored throughout the experiment, while the specific wear rate was quantified based on mass loss measurements. The evolution of surface topography was analyzed using roughness parameters of the Abbott-Firestone family. Scanning electron microscopy (SEM) analysis was performed to identify the dominant wear mechanism. The results show a pronounced running-in phase, after which a stable thermomechanical equilibrium occurs in all regimes. Heavy-loaded regimes increase temperature but accelerate surface adaptation and lower stable coefficients of friction. Lower load regimes have the lowest thermal load but higher friction due to lower real contact. The medium PV regime has a low COF and moderate temperature rise, while peak and core roughness metrics increase more significantly. These results provide an experimentally based insight into the influence of the load regime on the tribological behavior and topography of the SLS-made polymer sliding bearings, thus contributing to a deeper understanding of their operation in real dry-sliding conditions. Full article

This work examines resistance drift in FPGA I/O paths subjected to combined electrical and thermal stress, using a Xilinx Spartan-6 device as a representative platform. A multiplexed measurement approach was employed, in which multiple I/O pins were externally shorted and sequentially activated, enabling precise tracking of voltage, current, and effective series resistance over time, under controlled bias conditions. Two accelerated stress modes were investigated: high-temperature dwell in the range of 80–120 °C and thermal cycling between 80 and 140 °C. Both stress modes exhibited similar sub-linear (power-law) time dependence on resistance change, indicating cumulative degradation behavior. However, Arrhenius analysis revealed a strong contrast in effective activation energy: approximately 0.62 eV for high-temperature dwell and approximately 1.3 eV for thermal cycling. This divergence indicates that distinct physical mechanisms dominate under each stress regime. The lower activation energy is consistent with electrically and thermally driven on-die degradation within the FPGA I/O macro, including bias-related aging of output drivers and pad-level structures. In contrast, the higher activation energy observed under thermal cycling is characteristic of diffusion- and creep-dominated thermo-mechanical damage in package-level interconnects, such as solder joints. These findings demonstrate that resistance-based monitoring of FPGA I/O paths can discriminate between device-dominated and package-dominated aging mechanisms, providing a practical foundation for reliability assessment and self-monitoring methodologies in complex electronic systems. Full article

Limnological parameters were monitored in four highland reservoirs in the Czech Republic from 2022 to 2024 to evaluate the effects of management practices on water quality. Although the reservoirs share similar morphometry and all serve as drinking water sources, they differ in trophic status and management: Hubenov (HU, eutrophic) is stocked with piscivores, Nová Říše (NŘ, mesotrophic) undergoes hypolimnetic aeration, and Landštejn (LA, meso-oligotrophic) and Mostiště (MO, eutrophic) receive no targeted management interventions. Limnological data were collected monthly from April to October along vertical profiles in dam parts of the reservoirs. Comparisons were performed using graphical presentation and linear mixed-effects models. Analyses of abiotic (thermal, oxygen, and pH stratification, transparency, total phosphorus (TP) and nitrogen (TN) concentrations) and biotic (algae chlorophyll-a, cyanobacterial pigments, zooplankton density and composition) variables revealed that HU and MO exhibited the lowest transparency (on average 1.9 m in both in contrast to 2.2 m and 2.8 m in NŘ and LA, respectively) and highest seasonal algae chlorophyll-a concentrations (11.4 µg/L in HU and 15.1 µg/L in MO in contrast to 6.4 µg/L in NŘ and 5.5 µg/L in LA), indicating negligible improvement from biomanipulation. In contrast, NŘ demonstrated nutrient and chlorophyll-a levels comparable to LA (TP: 0.010 mg/L and 0.009 mg/L, TN: 1.591 mg/L and 0.419 mg/L, in NŘ and LA, respectively), despite higher nutrient input, and achieved the second highest transparency. Zooplankton densities were similar across reservoirs, supporting the hypothesis of bottom-up control or insufficient piscivore impact. These findings highlight the importance of reducing nutrient inputs to preserve water quality. Hypolimnetic aeration, which enhances sediment nutrient retention, appears more effective at mitigating eutrophication and controlling algal proliferation than fish stocking, a commonly applied biomanipulation approach. Full article

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article