Search Results (17,603)

To enhance the mechanical performance and surface hydrophobicity of flat thin-sheet materials, we have developed a facile, environmentally benign, and low-cost synthesis strategy for fabricating a robust waterborne superhydrophobic coating with excellent mechanical reinforcement, via simple spray coating using a non-fluorinated material system (waterborne silicone–acrylic copolymer and silica sol). The functional coating exhibited excellent hydrophobicity (water contact angle: 150°) regardless of the compound of the substrates, which is primarily ascribed to the presence of abundant low-surface-energy methyl groups on the coating’s surface, along with the three-dimensional hierarchical network structure formed via the cross-linked silica network. Owing to the stable cross-linked structure and strong interfacial bonding between the acrylic polymer and silica network, the composite coating exhibited exceptional mechanical reinforcement, coupled with ultrahigh mechanical and chemical stability. Specifically, the maximum flexural fracture load of the modified materials increased from 119 N to 192 N, representing a 62.7% enhancement; similarly, the moisture-induced deflection of the samples had a significant increase from −14.5 mm to −3.01 mm, which confirmed that the mechanical properties of the modified sample and its deformation resistance under high humidity conditions have been significantly enhanced. Notably, the coating retained superior hydrophobicity and mechanical performance even after 50 abrasion cycles, as well as exposure to high-intensity UV radiation and corrosive acidic/alkaline environments. Furthermore, the composite functional coating demonstrated excellent self-cleaning and anti-fouling properties. This functional composite coating offers significant potential for large-scale industrial application. Full article

Background: Patients with diabetes mellitus are at increased risk of foot ulcerations. Prevention and timeous treatment of diabetic foot ulcer requires a multidisciplinary team, including patients and healthcare workers. The study investigated levels of knowledge regarding foot care and prevention of diabetic foot ulcer in patients with diabetes mellitus. Methods: We conducted a cross-sectional descriptive study using structured face-to-face interviews of patients seen at the outpatient department. Interviews covered socio-demographic and clinical characteristics, and foot care practices. Statistical analysis was performed using STATA 15. Results: The study involved 245 participants with a mean age of participants was 53.7 (SD) years, and 69% were female. The majority, 77.6%, had Type 2 diabetic mellitus (DM). Mean score of appropriate knowledge on diabetic foot ulcer (DFU) was 73.5%, lower scores were 44.9% and 45.7% for moisturizing between toes and wearing slippers outdoors, respectively. Knowledge of foot care was significantly influenced by age (p = 0.0033), duration of DM (p = 0.047), treatment type (p = 0.002), prior education on foot care (p < 0.0001), and existence of foot complications (p = 0.030). Conclusions: Younger patients and those with Type 1 diabetes mellitus had less knowledge of appropriate foot care and prevention of DFU. We recommend the implementation of structured and targeted educational interventions. Full article

Particle histories critically influence product quality in spray drying processes, encompassing statistical data on particle dynamics and behavior inside the chamber, including temperatures, moisture levels, wall impacts, and residence times. This study presents the first systematic parametric assessment of how chamber geometry influences particle histories in spray drying, extending previous work on airflow dynamics. A design of experiments (DOE) methodology combined with cost-efficient CFD simulations was employed to establish quantitative parameter–response relationships. The results reveal two distinct classes of particle responses: (i) residence time, moisture content, and wall temperature, which are primarily governed by chamber aspect ratio and drying air flow rate, and (ii) particle–wall impact behavior, which is dominated by chamber topology. Inlet swirl modulates all particle histories, differentially impacting final product quality and energy efficiency. These findings provide predictive guidelines for chamber design and operation, while the methodology offers a general framework for scale-up analyses and parametric CFD studies of particle-laden multiphase processes. Full article

Background: We investigate the influence of environmental variables and edge-interior gradients on the diversity and composition of anuran assemblages in four forest fragments in the southwestern Brazilian Amazon. Methods: A total of 590 individuals from 40 species and eight families were recorded, with Leptodactylidae being the most abundant family. Results: The Humaitá Forest Reserve (RFH) exhibited the highest species richness and diversity, while the Raimundo Irineu Serra Environmental Protection Area (APA) had the lowest. Species composition varied significantly among fragments and along the edge-interior gradient, with edges showing higher species richness. Redundancy analysis (RDA) revealed that temperature, humidity, and litter depth were the most important environmental variables structuring anuran communities. Conclusions: Edge habitats supported disturbance-tolerant species, whereas forest interiors harbored moisture-dependent specialists. These findings underscore the importance of conserving larger, less disturbed fragments and implementing management strategies that account for environmental heterogeneity. This study provides critical insights into the factors shaping anuran distribution in fragmented Amazonian landscapes, offering valuable guidance for biodiversity conservation in the region. Full article

Conventional thermoplastic polyurethane (TPU) films are commonly used in the field of waterproof and moisture-permeable textiles because of their excellent mechanical properties and flexibility. However, the high water absorption of TPU films limits their application in sophisticated waterproof and moisture-permeable products, particularly in extremely humid environments, where it may compromise the waterproof performance of textiles and negatively affect the wearing comfort. Therefore, to enhance the durability of these films, TPU was hydrophobically modified with end-hydroxy polydimethylsiloxane (PDMS). Because of its unique low-surface-energy properties and excellent hydrophobicity, PDMS substantially reduces the surface energy of the films and provides them with excellent water repellency, effectively addressing the excessive water absorption issue of TPU films. On this basis, a microporous film featuring waterproof and moisture-permeable properties is produced using phase conversion technology. Compared with that of the unmodified sample, the surface energy of silicone-modified TPU (Si-TPU) decreased by 10.56 mJ/m². Furthermore, the water contact angle increased from 83° to 105°, whereas the water absorption rate considerably reduced after the modification. Moreover, Si-TPU was employed for the fabrication of a microporous membrane, which displayed exceptional moisture permeability (8651.34 g/(m²⸱24 h)). Full article

An effective understanding of sediment deposition and erosion in river basins, particularly floodplains, is critical for modeling geomorphic evolution, managing flood risks, and maintaining ecological integrity. However, most related studies have been limited to hydraulic or hydrodynamic modeling approaches. Therefore, this study integrated Sentinel-1 differential interferometric synthetic aperture radar (DInSAR) coherence, Sentinel-2 normalized difference vegetation index, and soil surface moisture index data with one-dimensional hydraulic modeling to assess flood-induced sediment deposition and erosion in the Gamcheon River basin under non-flood, short flood, and long flood scenarios. The DInSAR deformation analysis revealed a clear pattern of upstream erosion and downstream deposition during flood events, indicating a total depositional uplift of 0.33 m during the long flood scenario but dominant erosion with a total measured surface lowering of −2.03 m during the non-flood scenario. These results were highly consistent with the predictions from the hydraulic model and supported by the hysteresis curves for in situ suspended sediment concentration. The findings of this study demonstrate the effectiveness of the proposed integrated approach for quantifying floodplain sediment dynamics, offering particular application value in data-scarce or inaccessible floodplains. Furthermore, the proposed approach provides practical insights into sediment management, flood risk assessment, and ecosystem restoration efforts. Full article

Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Drought Index, Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index) were derived from MODIS datasets, while the Precipitation Condition Index was calculated from CHIRPS precipitation data. Composite indicators included the Scaled Drought Composite Index, integrating vegetation, temperature, and precipitation factors, and the Soil Moisture Condition Index derived from reanalysis soil moisture data. Results revealed recurrent moderate drought with strong seasonal and interannual variability, with 2008 identified as the driest year and 2009 and 2012 as wet years. Summer was the most drought-prone season, with precipitation averaging 5.5 mm, PCI 1.1, SDCI 15.6, and SMCI 38.4, while winter exhibited recharge conditions (precipitation 197 mm, PCI 40.9, SDCI 57.3, SMCI 89.6). Interannual extremes were detected in 2008 (severe drought) and wetter conditions in 2009 and 2012. Vegetation stress was also notable in 2016 and 2018. The integration of multi-source datasets ensured consistency and robustness across indices. Overall, the findings improve understanding of agricultural drought dynamics and provide practical insights for irrigation modernization, efficient water allocation, and drought-resilient planning in line with Türkiye’s National Water Efficiency Strategy (2023–2033). Full article

This study explores the use of a homemade mung bean protein extract solution (MP) as the moisture source in high-moisture extrusion to produce pea–mung bean composite textured protein (PMP). Single-factor experiments assessed the effects of MP addition amount (30–70%), screw speed (140–220 rpm), and extrusion temperature (140–180 °C) on the textural, physicochemical, and structural properties, followed by optimization using response surface methodology (RSM). MP addition amounts between 50% and 60% promoted higher surface hydrophobicity, a higher disulfide bond content, more ordered secondary structures, and a higher intrinsic fluorescence, accompanied by improved water- and oil-holding capacities, bulk density, and texturization degree (p < 0.05). Screw speeds of 160–180 rpm enhanced texturization and texture via increased shear and reduced residence time, whereas higher extrusion temperatures darkened the color (Maillard browning) and reduced texturization and the bulk density. RSM found that the optimal conditions were 53% MP, 160 rpm, and 150 °C, yielding a theoretical maximum texturization degree of 1.55, which was experimentally validated (1.53 ± 0.02). These findings support MP as an effective green moisture source to tailor the structure and functionality of pea-based high-moisture extrudates. Future work will integrate calibrated SME, sensory evaluation, and application testing in meat-analog formats. Full article

In arid environments such as the Namib Desert, non-rainfall water sources—including dew and fog—constitute indispensable yet understudied components of the regional hydrological cycle. These moisture inputs play a critical role in sustaining ecological functionality and biogeochemical processes, but remain among the least quantified facets of desert ecohydrology. The present study investigates multi-year trends in morning dew formation within the Namib Desert, utilizing observations from the Gobabeb–Namib Research Institute between 2015 and 2022. Meteorological data from the Southern African Science Service Centre for Climate and Adaptive Land Management (SASSCAL), in conjunction with direct field observations of dew, were used to develop an empirical equation to estimate dew occurrence. A sensitivity analysis verified the robustness of this formulation, and subsequent validation using field data confirmed its reliability (84.84% accuracy). During this eight-year period, the annual number of days with morning dew decreased from 170 in 2015 to 140 in 2022, representing an overall decline of approximately 18%. However, the total daily dew occurrence across 24 h remained relatively constant, indicating that the observed decline is confined primarily to morning condensation events. Dew formation was most prevalent during the wet season (December–May). Both monthly and annual analyses revealed a discernible declining trend in morning dew occurrence across this hyperarid ecosystem (p < 0.05). This decline corresponded with a gradual increase in both air and soil temperatures (approximately +0.03 °C yr⁻¹) and a slight but consistent decrease in relative humidity (approximately −0.26% yr⁻¹) between 2015 and 2022. The principal drivers of this decline include rising soil and air temperatures and decreasing atmospheric humidity. The analysis further identified an inverse relationship between air temperature and dew formation, implying that climatic warming intensifies evaporative demand and thereby suppresses dew condensation. Random forest analysis identified soil temperature, air temperature, and relative humidity as the most important predictors influencing dew occurrence, whereas wind speed and direction played lesser roles. Collectively, these findings underscore the vulnerability of dew-dependent ecosystems to anthropogenic climate change and highlight the imperative to continue investigating non-rainfall moisture dynamics in desert environments. Full article

Soil moisture (SM) is crucial for ecosystems and agriculture. Since the root systems of plants absorb water at different depths with different intensities, monitoring multi-layer SM can better respond to the water demand of plants and offer a crucial technical backing for drought monitoring and precision irrigation. Synthetic aperture radar (SAR) and multispectral (MS) have been widely used in SM estimation; however, their combined application for multi-layer SM profiling remains underexplored. Existing research based on these two data types has primarily focused on surface soil moisture (SSM), with limited investigation into estimating SM at deeper or varying depths. Therefore, the aims of this research are to integrate Sentinel-1 SAR and Sentinel-2 MS data and employ machine learning algorithms to estimate multi-layer SM in the Shandian River Basin. The results showed that (1) MS + SAR-based SM estimation significantly outperformed single-source data (MS or SAR alone). Specifically, MS data performed better in the root-zone estimation, while SAR data showed superior performance in SSM estimation. (2) The BKA-CNN estimation accuracy significantly outperformed RF and XGBoost. The results of its five-fold cross-validation are as follows: R² = 0.768 ± 0.011 at 3 cm, R² = 0.777 ± 0.013 at 5 cm, R² = 0.799 ± 0.011 at 10 cm, R² = 0.792 ± 0.01 at 20 cm, and R² = 0.782 ± 0.011 at 50 cm. (3) The BKA-CNN model performed better in grassland than in farmland. These findings indicate that the BKA-CNN model proposed in this study effectively improves the estimation precision of multi-layer SM by fusing SAR and MS data, demonstrating considerable generalization ability and robustness. It holds potential application value in ecological protection and agricultural water resource management. Full article

Accurate satellite-based precipitation estimates are crucial for climate studies and water resource management, particularly in regions with sparse meteorological station coverage. This study evaluates the improvements of the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07 (V07) relative to the previous version (V06). The evaluation employed gridded data from the Brazilian Daily Weather Gridded Data (BR-DWGD) product and ground observations from 58 rain gauges distributed across the Parnaíba River Basin in Northeast Brazil. The analysis comprised three main stages: (i) an intercomparison between BR-DWGD gridded data and rain gauge records using correlation, bias, and Root Mean Square Error (RMSE) metrics; (ii) a comparative assessment of the IMERG Final V06 and V07 products, evaluated with statistical metrics (correlation, bias, and RMSE) and complemented by performance indicators including the Kling-Gupta Efficiency (KGE), Probability of Detection (POD), and False Alarm Ratio (FAR); and (iii) the application of cluster analysis to identify homogeneous regions and characterize seasonal rainfall variations across the basin. The results show that the IMERG Final V07 product provides notable improvements, with lower bias, reduced RMSE, and greater accuracy in representing the spatial distribution of precipitation, particularly in the central and southern regions of the basin, which feature complex topography. IMERG V07 also demonstrated higher consistency, with reduced random errors and improved seasonal performance, reflected in higher POD and lower FAR values during the rainy season. The cluster analysis identified four homogeneous regions, within which V07 more effectively captured seasonal rainfall patterns influenced by systems such as the Intertropical Convergence Zone (ITCZ) and Amazonian moisture advection. These findings highlight the potential of the IMERG Final V07 product to enhance precipitation estimation across diverse climatic and topographic settings, supporting applications in hydrological modeling and extreme-event monitoring. Full article

As global climate change intensifies, understanding drought mechanisms is crucial for managing water resources and agriculture. This study employs the Standardized Precipitation–Actual Evapotranspiration Index (SPAEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI) to analyze meteorological, hydrological, and agricultural droughts in the lower Songhua River basin. The PLUS model was used to predict future land types, with model accuracy validated using four evaluation metrics. The projected land cover was integrated with CMIP6 data into the SWAT model to simulate future runoff, which was used to calculate future SRI. Drought events were extracted using run theory, while drought occurrence probability and return period were calculated via a Copula-based joint distribution model. Bayesian conditional probability was employed to explore propagation mechanisms. The results indicate a significant increase in multidimensional drought risk, particularly when the cumulative frequency of univariate droughts reaches 25%, 50%, or 75%. Although increased duration and intensity enhance the likelihood of combined droughts, extremely high values cause a decline in joint probability under “OR” and “AND” conditions. Under different climate scenarios, the recurrence intervals of meteorological, hydrological, and agricultural droughts in the lower reaches of the Songhua River exhibit increased sensitivity with severity, demonstrating consistent propagation patterns across the meteorological–hydrological–agricultural system. Meteorological drought was found to propagate to hydrological and agricultural drought within ~6.00 months and ~3.67 months, respectively, with severity amplifying this effect. Propagation thresholds between drought types decreased with increasing intensity. This study combined SWAT and CMIP6 models with PLUS-based land-use scenarios, highlighting that land-use changes significantly influence spatiotemporal drought patterns. Model validation (Kappa = 0.83, OA = 0.92) confirmed robust predictive accuracy. Overall, this study proposes a multidimensional drought risk model integrating Copula and Bayesian networks, offering valuable insights for drought management under climate change. Full article

Urban green spaces provide measurable cooling that can mitigate urban heat islands, yet few studies have quantified these effects over multiple decades. This study analyzed Landsat imagery from four epochs (1990, 2000, 2013, 2018) to derive land surface temperature (LST) and vegetation indices—NDVI for greenness and NDMI for moisture content—for four large urban parks in Krakow. Late spring/summer LST in parks was compared with that of urban areas within 0–150 m and 150–300 m of park boundaries. Statistical significance was evaluated using bootstrapped confidence intervals, long-term trends were assessed via the Mann–Kendall test, and correlation analysis was used to examine relationships between LST and each vegetation index. Results show a persistent park cooling effect, with park interiors ~2–3 °C cooler than adjacent urban areas in all years. Despite an overall city-wide LST rise of ~5–6 °C from 1990 to 2018, the park cool island intensity (temperature difference between park and city) remained stable (no significant long-term trend, p > 0.7). Bootstrapped 95% confidence intervals confirmed that each park’s cooling effect was statistically significant in each year analyzed. NDMI (vegetation moisture content) correlated more strongly with LST (r ~ −0.90) than NDVI (r ~ −0.7 to −0.9), highlighting the importance of vegetation moisture in park cooling. These findings demonstrate that well-watered urban parks can sustain substantial cooling benefits over decades of urban development. The persistent ~2–3 °C daytime cooling observed underscores the value of water-sensitive green space planning as a long-term urban heat mitigation strategy. Full article

The present study aims to compare the chemical composition of samples of jerked beef commercialized in Brasilia, Brazil, subjected to diverse desalting techniques (room temperature, refrigerated, and heat desalting). This experimental study was divided into five steps: determination of desalting techniques, chemical composition, determination of titratable acidity and pH, sodium analysis of the samples, and statistical analysis. The control samples showed high sodium levels (>6000 mg/100 g), confirming the need for desalting to ensure suitability for consumption. Desalting at room temperature was the most efficient, reducing sodium content by up to 76%, followed by refrigeration (67–74%) and the heat method (52–58%). It was also observed that the desalting technique significantly affects the chemical composition. Desalting at room temperature and under refrigeration increased moisture (54.12→73.82 g/100 g) and reduced proteins (23.50→18.70 g/100 g) and lipids (3.70→3.00 g/100 g) through a dilution effect, while desalting in heat concentrated solids, increasing protein (31.29 g/100 g), lipids (4.19 g/100 g), and lipid oxidation (TBARS = 91.79 µmol MDA/kg) in comparison to control samples (38.63 µmol MDA/kg). Acidity and pH showed minor variations but correlated with lipid oxidation processes. Although no technique eliminates excess sodium, the results reinforce that desalting at room temperature offers the best balance between sodium reduction and preservation of the product’s nutritional quality and oxidative stability, making it the most suitable method for use in restaurants and at home. Full article

Efficient water management is essential for optimizing agricultural productivity in water-scarce regions such as the Lis Valley, Portugal. In situ measurements of soil moisture content (SMC) and electrical conductivity (EC), together with Sentinel-2-derived vegetation indices, were used to assess the crop water status and evapotranspiration dynamics of pumpkin (Cucurbita moschata ‘Butternut’) during the 2020 growing season. SMC and EC were measured at depths of 10, 20, 30, 50, and 70 cm using a TDR sensor, with strong correlations observed in the upper layers, indicating that EC can complement direct SMC measurements in characterizing near-surface moisture conditions. Sentinel-2 imagery was acquired to compute NDVI, SAVI, EVI, and GCI. In addition, NDVI values obtained from both a GreenSeeker^® sensor and Sentinel-2 imagery were compared, showing a similar temporal pattern during the season. By replacing the standard FAO-56 K_c values with those derived from each vegetation index, ET_a was recalculated to incorporate actual crop condition variability detected via satellite. ET_a estimates from RS-assisted vegetation indices agreed with those obtained using the FAO-56 method; independent ET_a measurements were not available for validation. Although such agreement is partly expected due to calibration, its confirmation for Cucurbita moschata under Mediterranean conditions—where published references are scarce—reinforces the method’s practical applicability for water management in data-limited settings. Water Productivity (WP) was estimated as 8.32 kg m⁻³, and Water Use Efficiency (WUE FAO-56) was calculated as 0.64 kg m⁻³, indicating high water use efficiency under Mediterranean smallholder irrigation conditions. These findings demonstrate that integrating high-resolution RS with continuous soil moisture monitoring can enhance precision irrigation strategies, increase crop yields, and conserve water resources in the Lis Valley. Full article