Search Results (9,069)

This study assessed the accuracy of the EXERGEN TAT-5000 temporal scanner (T_EXERGEN) (EXERGEN, Watertown, MA, USA) for estimating core body temperature (T_CORE) during rest, progressive cycling exercise, and post-exercise recovery in a hot environment. Fourteen healthy adults (7 men and 7 women) completed a laboratory protocol consisting of 10 min of rest, 60 min of cycling, and 25 min of recovery at an ambient temperature of 32 °C and a relative humidity of 60%. Gastrointestinal temperature (T_Gi), measured via telemetry capsules, served as the criterion method. A total of 5376 paired measurements were analyzed. Throughout the protocol, T_EXERGEN systematically underestimated T_CORE compared with T_Gi, with mean biases between −0.35 °C and −1.15 °C. The overall 95% confidence intervals ranged from ±0.91 to ±1.43 °C, demonstrating poor precision. Limits of agreement were wide (from −2.00 to 0.87 °C), and concordance correlation coefficients (CCC) indicated poor agreement (CCC < 0.90 in all conditions). The underestimation was more pronounced during exercise and recovery, when T_CORE remained high according to T_Gi but decreased according to T_EXERGEN. These results indicate that T_EXERGEN does not monitor T_CORE accurately under heat stress or during rapid metabolic changes. Therefore, the use of this device is not recommended during and after exercises under environmental heat stress. Full article

Aiming at the insufficient capture of temporal dependence and weak coupling of external factors in multivariate load forecasting, this paper proposes a Transformer model integrating timestamp-based positional embedding and convolution-augmented attention. The model enhances temporal modeling capability through timestamp-based positional embedding, optimizes local contextual representation via convolution-augmented attention, and achieves deep fusion of load data with external factors such as temperature, humidity, and electricity price. Experiments based on the 2018 full-year load dataset for a German region show that the proposed model outperforms single-factor and multi-factor LSTMs in both short-term (24 h) and long-term (cross-month) forecasting. The research results verify the model’s accuracy and stability in multivariate load forecasting, providing technical support for smart grid load dispatching. Full article

A dew-point evaporative air cooler incorporating a novel segmented heat exchange design, demarcated according to the humidity state of moist air, is proposed. The system employs a porous fibrous material to create a wetted evaporative surface, which is continuously maintained in a moistened condition through a self-wicking water supply mechanism to enhance latent heat transfer. Circular fins are installed on the heat exchanger’s partition surface once the moist air reaches saturation, thereby improving sensible heat exchange between the dry and wet channels. The performance of a prototype was evaluated under controlled conditions in a standard enthalpy chamber. Experimental results indicate that, under typical summer conditions (inlet dry-bulb and wet-bulb temperatures of 33.8 °C and 25.4 °C, respectively), with an air mass flow ratio of 0.7 and an air velocity of 1.5 m/s, the wet-bulb effectiveness reaches 114.4% and the dew-point effectiveness achieves 84.8%. The maximum temperature reduction occurs in the sensible heat exchange section, reaching up to 6.1 °C, demonstrating its substantial sensible heat recovery capability. The device exhibits an energy efficiency ratio (EER) ranging from 9.1 to 31.8. The proposed compact configuration not only enhances energy efficiency but also reduces material costs by approximately 15.4%, providing a valuable reference for the future development of dew-point evaporative cooling systems in residential buildings. Full article

In the southwestern region of Asturias (Northern Spain) lies one of the few mountainous viticulture areas in the world, representing only 5% of global viticulture. The complex topography and differences in altitude, slope, and orientation of mountainous viticulture areas create highly variable microclimates even among nearby plots, with distinct mean temperatures, relative humidity, and solar radiation. These factors strongly influence grape and wine quality, as well as polyphenol concentration. Several production parameters and basic chemical characteristics of must were analyzed over multiple years, along with polyphenol content, in grapes from the same clones of Albarín Blanco and Verdejo Negro (autochthonous genotypes of this viticultural area), grown in geographically close vineyards with different topographies and microclimates. The results revealed significant differences in all analyzed parameters. Both varieties showed polyphenol concentrations slightly higher than those reported in the scientific literature, which may be related to the typical conditions of mountain viticulture or intrinsic genetic factors of these varieties. The best grape and must quality, regardless of variety, was obtained in plots located in sunny, well-ventilated areas with steep slopes and low-fertility soils. These plots exhibited higher potential alcohol content and greater concentrations of anthocyanins, hydrocarbons, and total polyphenols. When comparing varieties, Verdejo Negro showed the highest levels of anthocyanins, flavonols, and total polyphenols, whereas Albarín Blanco exhibited the highest concentrations of total phenolics and hydrocarbons. Full article

Semitransparent perovskite photovoltaic (sPV) covers offer an attractive route for agrivoltaics, but their spectrally selective transmittance must be validated on plants cultivated under panel or in simulated conditions. Here, an AVA–MAPI perovskite module transmission profile was replicated using a programmable multi-channel LED platform and compared with a Reference McCree-adapted LED spectrum at identical photon flux density. Two lettuce cultivars (Lactuca sativa L.; ‘Canasta’ and ‘Trocadero’) were grown hydroponically in a light-sealed phytotron for 30 days (300 μmol m⁻² s⁻¹; 16/8 h photoperiod) under uniform temperature and humidity. Leaf gas exchange was quantified by fitting photosynthetic light-response curves, and plant performance was concurrently evaluated through growth metrics, biomass partitioning, and pigment-related traits (chlorophyll a/b, total carotenoids). The perovskite-emulated spectrum measurably reshaped net CO₂ assimilation across the PAR domain—yielding higher A_N at selected irradiances in post hoc contrasts—yet these physiological shifts did not translate into differences in leaf area, shoot or root biomass, or pigment concentrations—demonstrating spectral plasticity and agricultural compatibility of field-characterized perovskite transmission spectra. Overall, perovskite-emulated light sustained agronomically equivalent lettuce performance under moderate irradiance, supporting the feasibility of semitransparent perovskite PV covers, while underscoring the need for validation under natural sunlight. Full article

With the increasing number of highly airtight residences, concerns have risen that the negative pressure formed indoors during kitchen hood operation can reduce capture performance and cause unintended infiltration. This study experimentally and numerically (via CFD simulations) examined whether installing an air supply unit on the cooktop beneath a hood can stabilize hood performance and suppress infiltration in small residential spaces. Two cases were established depending on whether air was supplied: Case 1 (hood operation only) and Case 2 (simultaneous operation of the hood and the air supply unit). In the experimental setup, the hood exhaust flow rate, supply airflow rate, sink-drain infiltration rate, and temperature/humidity were measured. The period during which variations in measured values remained within 10% was defined as the steady state. In the CFD analysis, winter conditions were assumed, and the measured values were applied to the wall boundary, after which the temperature and velocity field were analyzed. In Case 2, by supplying 24.11 CMH of air, the hood flow rate remained stable at 75.72 CMH (98.8% of the initial level) throughout the test, and no infiltration was detected. The CFD analysis revealed that the air supply unit generated an “air curtain” effect, enabling rapid capture of hot airflow and reducing the high-temperature region. In conclusion, the interconnected operation of supply and exhaust systems was shown to be effective in enhancing hood exhaust stability, suppressing unintended infiltration, and improving capture reliability in highly airtight small residential buildings. Future studies should include further analyses, such as the effects of actual cooking behaviors and leakage path distributions. Full article

Temperature and relative humidity can significantly affect quality of paddy rice during storage. Limited studies established the link between storage time, environmental fluctuations, changes in grain and flour physicochemical properties, and culinary performances. In a West African context, IR 841 paddy rice variety was stored under humid–sub-humid (HSH), and dry (DRY) conditions for 12 months. Over 12 months, rice stored under DRY conditions experienced greater environmental fluctuations than rice stored under HSH conditions. Grain water absorption capacity (WAC) increased during storage under DRY conditions, rising from 3.3 ± 0.3 to 3.8 ± 0.3 g/g DM between 0 and 12 months. Flour amylose content and soluble solids remained relatively stable from month 0 to 6 in all conditions, and further under HSH conditions. The observed changes led to improved grain cooking performance after 6 months of storage under DRY conditions. After 12 months, a decrease in rice flour WAC and a peak in viscosity were observed, while mean particle size increased from 42 ± 1 to 67 ± 3 μm under HSH conditions and from 31 ± 3 to 83 ± 3 μm under DRY conditions. Storage time may reduce the breadmaking capacity of rice flour. Overall, environmental fluctuations under DRY conditions strongly affected rice grain and flour properties. Full article

Milk production and quality are increasingly affected worldwide by rising ambient temperatures associated with climate change, with heat stress (HS) representing one of the major environmental challenges for dairy cattle. HS alters physiological and metabolic processes, leading to significant changes in milk composition, particularly in regions exposed to prolonged summer heat. The Temperature–Humidity Index (THI) is widely used to assess the degree of thermal discomfort and its impact on dairy performance. This study investigated the effects of heat stress on milk quality parameters in a dairy herd managed under identical conditions, comprising Romanian Black Spotted (RBS, Holstein strain) and Romanian Spotted (RS, Simmental strain) cows. Descriptive statistics were performed using the SAVC for Windows program, while differences between means were evaluated using the t-test in GraphPad Prism 9. Milk quality traits were significantly affected when THI values exceeded 73, with a consistent decline observed from early summer onwards. In the RBS breed, milk protein content decreased significantly compared with spring values, reaching 3.25% (p < 0.0001) in 2023 and 3.35% (p < 0.01) in 2024. Similar trends were recorded in the RS breed, with minimum protein values of 3.10% (p < 0.0001) and 3.19% (p < 0.0001). Fat content, casein concentration, and milk urea levels also showed highly significant HS-related changes (p < 0.0001). Overall, heat stress negatively affected milk quality, while the RS breed appears less affected under the studied conditions than the RBS breed. Full article

Understanding mechanisms of asphalt in the process of natural aging is crucial for predicting its long-term durability and optimizing performance under diverse environmental conditions. Despite its importance, the microstructural and micromechanical changes induced by natural aging remain poorly understood, particularly under varying climatic influences. This study addresses this gap by analyzing the effects of natural aging on asphalt’s microscopic properties and identifying key indicators that govern its degradation. Asphalt samples were subjected to natural aging across five climatically distinct regions over 6, 12, and 18 months. Atomic force microscopy (AFM) was employed to characterize surface roughness, adhesion forces, and DMT modulus, while correlation analysis and principal component analysis (PCA) were used to identify relationships among micromechanical indicators and streamline the dataset. The results reveal that natural aging induces irreversible transformations in asphalt’s microstructure, driven by the combined effects of temperature, UV radiation, humidity, and oxygen. These processes promote the evolution of “Bee structures,” increase surface roughness, and accelerate phase separation, alongside chemical modifications such as oxidation and polymerization, leading to progressive material hardening and stiffness. Significant regional and temporal variations in adhesion forces and DMT modulus were observed, reflecting the cumulative impact of environmental factors on asphalt’s aging dynamics. Correlation analysis demonstrated strong associations between surface roughness and “Bee structure” area, while mechanical properties such as stiffness and adhesion were largely decoupled from morphological features. Environmental factors interact in complex ways to drive asphalt aging. Humidity enhances adhesion and stiffness via water-induced capillary forces, while temperature reduces surface roughness and adhesion through molecular reorganization. UV radiation accelerates oxidative degradation, promoting surface erosion and stiffness loss, while altitude modulates these dynamics by influencing temperature and UV exposure. Full article

Southern blight, a disease caused by the soil-borne fungus Sclerotium rolfsii (S. rolfsii), is favored by the hot and humid conditions of the southeastern United States, posing a significant challenge to hemp production in Tennessee. Black plastic mulch (BPM), commonly used for weed control, can exacerbate the disease. There is limited information on the effects of straw mulch (SM), known to moderate soil temperatures and moisture, or planting time in disease management. Field studies were conducted in 2022 and 2023 at Tennessee State University to evaluate the effects of planting time, mulch type, and biofungicide application on disease severity, weed suppression, plant growth, and cannabinoid production in floral hemp. SM significantly reduced southern blight severity and moderated soil temperature, while BPM increased both. Early planting reduced disease severity by 28% in 2022 and by 53% and 34% in 2023 for first and second planting dates. SM lowered soil temperature by 6%, enhanced chlorophyll content by 30%, and increased plant height and biomass by 20% and 25%, respectively. Early planting increased cannabidiol (CBD) concentration by 0.4%, while late planting increased tetrahydrocannabinol (THC) by 0.25%. These findings demonstrate that integrating straw mulch with early planting can reduce disease severity, stabilize soil microclimate, and enhance hemp productivity under warm, humid conditions. Full article

Aeroponics has emerged as a key technology for sustainable and resource-efficient food production, particularly under intensifying constraints on water availability, land use, and greenhouse gas (GHG) emissions. This review synthesizes recent advances in water–energy–nutrient integration, highlighting operational parameters—humidity (50–80%), temperature (18–25 °C), nutrient solution pH (5.5–6.5), and electrical conductivity (1.5–2.5 mS cm⁻¹)—that critically influence system performance. Evidence indicates that closed-loop water recirculation and AI-assisted monitoring for environmental control and nutrient dosing can stabilize system dynamics and reduce water consumption by more than 90%. Reported yield improvements ranged from 45% to 75% compared with conventional soil-based cultivation. Moreover, systems powered by renewable energy demonstrated up to an 80% reduction in GHG emissions. Life-cycle assessment studies further suggest that aeroponics, coupled with low-carbon electricity in controlled-environment agriculture (CEA), can outperform traditional agricultural supply chains in climate and resource efficiency metrics. Additional technological innovations—including multi-tier vertical rack architectures, optimized misting intervals, and micronutrient-enriched fertigation formulations containing N, P, Ca, Mg, and K—were found to enhance spatial productivity and crop quality. Overall, aeroponics represents a promising pathway toward net-zero, high-performance agricultural systems. Full article

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Chronic obstructive pulmonary disease (COPD) is strongly associated with the inhalation of harmful particulate matter in ambient air. This study examined 786,290 COPD-related hospital admissions among patients aged 45–64 in Istanbul from 2013 to 2015, using a Generalized Linear Model (GLM) with meteorological variables included as covariates and air pollutant effects evaluated across lag days 0–9. Daily mean concentrations of PM₁₀, PM_2.5, and NO₂ were used as air pollution indicators, while average temperature and relative humidity were considered as meteorological variables. Relative risk (RR) and excess relative risk (ERR) estimates were calculated for a 10 μg/m³ increase in pollutant concentrations across the lag period. Significant associations were found between air pollution and COPD-related hospital admissions in overall analyses as well as seasonal assessments, especially for temperature-related effects. A 10 μg/m³ increase in PM_2.5 was associated with an ERR of 1.26% in females and 1.07% in males at lag 1, while NO₂ exposure showed ERRs of 1.31% in males and 1.30% in females. The effects of PM₁₀ were comparatively smaller, peaking at about 1.13% ERR at lag 5. Stronger associations were observed in both summer and winter seasons. PM_2.5 demonstrated the highest overall impact, particularly among females, with an excess risk of 1.7%. Pollutant effects were more pronounced at ambient temperatures around 0 °C and 25 °C. Full article

This study aims to optimize the estimation of reference evapotranspiration (ETo) in data-scarce regions by integrating ERA5-Land reanalysis data with machine learning (ML) models. Daily meteorological data from 33 stations across Turkey’s diverse climate zones (1981–2010) were utilized to train and validate three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM). The methodology involved rigorous quality control of ground-based observations, spatial correlation of ERA5-Land grids to station locations, and performance evaluation under various data-limited scenarios. Results indicate that while ERA5-Land provides highly accurate solar radiation (R_s) and temperature (T) data, variables like wind speed (U₂) and relative humidity (RH) exhibit systematic biases. Among the used models, XGBoost demonstrated superior performance (R² = 0.95, RMSE = 0.43 mm day⁻¹, and MAE = 0.30 mm day⁻¹) and computational efficiency. This study provides a robust, regionally calibrated framework that corrects reanalysis biases using ML, offering a reliable alternative for ETo estimation in areas where local measurements are insufficient for sustainable water management. Full article

Historic city centers host dense ensembles of heritage buildings where conservation goals must coexist with sustainable and smart urban development, yet the semi-outdoor “liminal” spaces of these complexes, such as cloisters, loggias and courtyards, are rarely included in microclimate monitoring networks. This study develops and tests the Liminal Environmental Monitoring System (LEMS), a flexible environmental data acquisition architecture designed for long-term monitoring in such spaces. The LEMS is based on a custom, low-cost data acquisition board able to handle multiple analogue and digital sensors, combined with a daisy-chain communication layout using the MODBUS RS485 protocol and a commercial datalogger as master, in order to meet the technical and visual constraints of historic buildings. Board calibration and sensor characterisation are reported, and the system is deployed in the cloister of Palazzo Costabili, a renaissance complex in the historic city center of Ferrara (Italy). This case study illustrates how the LEMS captures spatial and temporal variation in air temperature, relative humidity and solar irradiance and how an annual solar-shading indicator derived from 3D ray-tracing simulations supports the interpretation of irradiance measurements. The results indicate that the LEMS is a viable tool for heritage-compatible microclimate monitoring and can be adapted to other historic courtyards and loggias. Full article