Search Results (781)

The Carboniferous coal seams in Northeast Kazakhstan remain insufficiently investigated, with a lack of comprehensive mineralogical and geochemical assessments necessary to understand the geological processes controlling coal quality. This study examines 15 coal samples from the Karagandy Coal Formation (KCF) at the Saradyr and Bogatyr mines using proximate and ultimate analyses, FTIR, XRD, SEM–EDS, ED-XRF, and ICP-OES, providing the first detailed comparison of mineralogical and geochemical characteristics—including depositional signals and inorganic constituent distribution—between these mines within the KCF. The coals exhibit an average ash yield of 24.1% on a dry basis, volatile matter of 21.6% on a dry and ash-free basis, and low moisture content of 1.1% (air-dry), with low sulfur levels of 0.7% in whole coal across both mines. Mineralogical composition is dominated by quartz and clay minerals, with minor pyrite, apatite, chalcopyrite, and rutile. Major oxides in the coal ash average 68.2% SiO₂ and 19.5% Al₂O₃, followed by Fe₂O₃, K₂O, and TiO₂ (3–12.1%). Among the 24 identified trace elements, Sm is the most abundant at 6.3 ppm with slight enrichment (CC = 2.8), Lu remains at normal levels (CC < 1), and most other elements are depleted (CC < 0.5). The Al₂O₃/TiO₂ ratios (3.8–10.8) indicate contributions from intermediate to mafic parent materials. The detrital mineralogy, parting compositions, and elevated ash content indicate significant accommodation space development during or shortly after peat accumulation, likely within a vegetated alluvial plain depression. These findings provide new insights into the depositional environment and coal-forming processes of the KCF and contribute to regional assessments of coal quality and resource potential. Full article

Drying is a crucial postharvest preservation step, particularly for fruits and vegetables, due to their high moisture content. Physical, sensory, and storage qualities after drying are of interest to food engineers; however, for medicinal purposes, such as nutraceuticals or functional foods, the retention of pharmacological or bioactive compounds is of great interest. This review discusses conventional novel/modern drying technologies and their impact on pharmacological compounds of MEFVs. Conventional drying techniques (sun drying and hot air drying) are cost-effective but slow and usually induce significant losses of thermolabile pharmacological compounds. In contrast, novel/modern drying techniques (solar drying, vacuum drying, freeze drying, microwave drying, infrared drying, heat pump, refractance window, and electrohydrodynamic drying) can accelerate faster moisture removal, but their impact on the pharmacological compounds varies. Current trends in drying research emphasize process optimization, technology hybridization, pretreatment methods, real-time monitoring, and green energy integration to enhance pharmacological compound retention while ensuring sustainability. Full article

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of $R^2=0.697$ and $R^2=0.794$ for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops. Full article

This study developed a process for producing prebiotic-fortified instant rice macaroni to diversify rice-based convenience foods. Resistant starch (RS) rice flour from three varieties—IR504 and two pigmented, anthocyanidin-rich rice cultivars (Huyet Rong and MS2019)—was blended with wheat flour and fixed ingredients (tapioca starch, salt, and vegetable oil at a ratio of 9g:1g:1g), together with hot water. The instant rice macaroni with the highest RS content (11.64%) was obtained using IR504 RS and wheat flour (44:6), gelatinized at 100 °C for 20 min, microwaved at 36 W/g for 30 s, retrograded at 4 °C for 24 h, and sterilized at 115 °C for 15 min. For anthocyanidin-containing macaroni, the combination of Huyet Rong RS and wheat flour (39:11) yielded 9.47% RS under similar retrogradation and sterilization conditions, but with a shorter gelatinization step (100 °C, 15 min) and longer microwave treatment (50 s at 27 W/g). The other optimized colored-RS formulation was based on MS2019 RS and wheat flour (21:29) processed under similar conditions. All optimized formulations exhibited lower estimated glycemic index (eGI) values of 64.1, 65.7, and 68.2, which were significantly lower than those of the control instant rice macaroni (78.2–85.9, p < 0.05). This study confirms the potential of developing instant rice macaroni rich in RS to enhance prebiotic effects that support the growth of beneficial intestinal bacteria, strengthen immune function, and improve nutritional quality through the incorporation of anthocyanidin-rich rice varieties and a processing procedure combining heat–moisture treatment with microwave heating. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article

With advancements in solar-induced fluorescence (SIF) observation technology and the evolution of vegetation radiative transfer models, SIF signals can now be more effectively interpreted and leveraged from a mechanistic perspective. This, in turn, facilitates a deeper understanding of the mechanistic link between SIF and photosynthesis. Considering the impact of water stress on terrestrial ecosystems, this paper simulated SIF and gross primary productivity (GPP) values using the STEMMUS-SCOPE model at half-hour scales from 2017 to 2023 at the Daman site. The simulation results were compared and validated against flux tower observations and SCOPE model outputs. Taking advantage of irrigation events in the semi-arid irrigated farmland, we assessed the accuracy of STEMMUS-SCOPE in simulating SIF and GPP under drought stress, as well as its capability to quantitatively analyze the impacts of water stress on SIF and GPP. The results show that the accuracy of the SIF and GPP values simulated by the STEMMUS-SCOPE model is higher than that of the SCOPE model. The averaged R² and RMSE between the SIF simulated by STEMMUS-SCOPE model and the observed SIF values are 0.66 and 0.29 mW m⁻² nm⁻¹, and the averaged R² and RMSE between the GPP simulated by the STEMMUS-SCOPE model and the observed GPP values from 2017 to 2023 are 0.88 and 4.93 µmol CO₂ m⁻² s⁻¹, respectively. Especially under relatively drought conditions, the R² between the SIF simulated values and observed values is 0.84, and the R² between the GPP simulated values and observed values is 0.96. By further combining soil moisture content (SMC) and canopy conductance (Gs) analyses, we found that the response of the STEMMUS-SCOPE simulations under water stress was consistent with previous findings on the impacts of water deficits, thereby confirming the model’s reliability for drought conditions. Under drought stress, the decline in fluorescence emission efficiency (ΦF) with decreasing Gs and SMC was smaller than that of the light use efficiency (LUE). Therefore, the STEMMUS-SCOPE model is promising for investigating the SIF–GPP relationship under drought stress. Full article

This work presents the development and characterization of laminated composite panels produced from açaí residues and fibers, incorporated into a castor oil-based vegetable polyurethane matrix. The study aimed to evaluate the potential of these Amazonian agro-industrial residues as lignocellulosic reinforcement in sustainable materials. The manufacturing process was carried out by manual lamination and cold pressing, following the recommendations of ABNT NBR 14810-2:2018. The physical (moisture, density, and swelling) and mechanical (perpendicular tensile and static flexural) properties of the resulting panels were analyzed. The results revealed an average moisture content of 6.23% and a 24 h swelling of 2.76%, which are values within and well below the regulatory limits, respectively. The perpendicular tensile strength (0.49 N/mm²) exceeded the minimum required value, indicating good interfacial adhesion and internal cohesion. However, the flexural strength and modulus of elasticity (2.4 N/mm² and 1323 N/mm²) were below the standards due to the absence of oriented fibers and density heterogeneity. It is concluded that the composite has high potential for indoor applications with low structural stress, standing out for its lightness, dimensional stability and environmental viability in the use of açaí residues. Full article

Underground gas storage (UGS) facilities may cause ground displacements as a result of the cavern convergence or regular gas injection (alternate ground uplift and subsidence). The occurrence and scale of displacements are strongly dependent on the storage time and cavern capacity. At an early stage of facility operation, displacements can be difficult to detect in the presence of wetlands. The main objective of this study was to describe the global and local relationships between vertical ground displacements observed over a small and relatively new Kosakowo UGS facility (Poland) from 2014 to 2024 (dependent variable) and selected topographic, hydrological, and mining factors (independent variables). The dependent variable was determined through SBAS-InSAR analysis of Sentinel-1 SAR data, while the independent variables were developed using passive Sentinel-2 imagery and open geospatial data. The global relationships between variables were described using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR) models, while the Geographically Weighted Regression (GWR) model was utilized to analyze local relations. The results obtained indicate that ground displacements were characterized by seasonal fluctuations between 4 mm and 10 mm. The factors that had, both globally and locally, the strongest influence were soil moisture, vegetation water content, and the flora condition, indicating that the environmental hydrogeology had the greatest impact on the phenomenon under study. None of the considered models identified underground gas storage as a significant contributing factor to the observed ground displacements. The results confirm that the presence of wetlands can be a significant obstacle to an accurate description of the impact of gas storage on the ground movements, especially in UGS areas at an early stage of operation. Full article

Vegetation heterogeneity supports biodiversity, while homogeneity limits it. In the Great Plains, fire and herbivory enhance ecosystem function by increasing spatial heterogeneity. However, quantifying their effects on plant functional traits and spectral diversity remains challenging due to landscape complexity and scaling limitations. Hyperspectral remote sensing offers a high-resolution approach to assessing these dynamics, improving the evaluations of post-fire recovery and vegetation function. This study examines the impact of fire on plant functional traits and spectral diversity within a savanna landscape in the Edwards Plateau, Texas, using airborne hyperspectral and multispectral imagery. Specifically, it aims to (1) quantify the spatial patterns of plant functional traits and spectral diversity, (2) assess fire’s effects on these patterns, and (3) evaluate how soil type, woody structure, and burn patterns mediate fire responses. High-resolution airborne images from 2018 (pre-fire) and 2020 (post-fire) were analyzed to classify burned and unburned areas, pre-fire woody cover, and derive spectral indices representing plant functional traits, β-diversity components, and spectral evenness. The results indicate that temporal patterns in spectral diversity were driven primarily by soil properties and weather, with limited evidence that fire altered spectral evenness or β-diversity across soils. In contrast, spectral indices showed clearer—but still soil-dependent—fire effects: declines in canopy structure, greenness, and chlorophyll content were less pronounced in burned areas, indicating that fire partially moderated late-season senescence. Fire had a substantial influence on spatial patterns of spectral evenness (but not β-diversity) and vegetation spectral functional traits, and fire and dry-down increased spatial heterogeneity in spectral evenness and in spectral indices indicative of biophysical and biochemical traits across scales. These findings demonstrate that environmental drivers, particularly soil–moisture interactions and interannual moisture variability, exert a stronger control over post-fire spectral diversity than fire alone. Hyperspectral imaging effectively captured these dynamics, supporting its role in monitoring post-fire vegetation responses. In addition to the use of hyperspectral imaging, fire management strategies should consider broader ecological drivers, including soil and weather interactions, to improve the assessments of ecosystem resilience and recovery. Full article

The excessive intake of saturated and trans fats is associated with several chronic disorders. Reformulating foods to reduce total and saturated fats has therefore become a global health priority. However, the structural and sensory roles of saturated fats often hinder direct reduction. Oil structuring technologies, such as gelled emulsions, have emerged as effective strategies to replace solid fats with liquid vegetable oils, improving nutritional quality. This study evaluated the effects of partially replacing pork backfat (33% and 66%) with oil-in-water gelled emulsions prepared using pumpkin seed oil and orange peel flour (PS-GE) in English breakfast sausages. Reformulated samples exhibited higher moisture contents, whereas fat and protein levels were reduced compared with the control. Increasing the proportion of PS-GE substitution led to a progressive rise in total unsaturated fatty acids accompanied by a decrease in total saturated fatty acids. Lipid oxidation was not affected by the reformulation in raw sausages. Sensory evaluation confirmed comparable acceptability among all samples, indicating that fat replacement did not negatively influence product quality. Overall, the use of orange peel flour and pumpkin seed oil as a gelled emulsion presents a promising strategy for producing healthier English breakfast sausages with enhanced nutritional profiles and maintained technological and sensory properties. Full article

This study investigated a plasma-sonic treatment that combines plasma-activated water (PAW) and ultrasound (US) as an alternative to conventional sodium hypochlorite (NaOCl), which may leave harmful chlorine residues and generate toxic by-products in fresh produce. The treatment was applied to kale to evaluate its decontamination efficiency and storage stability during 7 days at 4 °C. PAW was generated at 52 W and 14.4 kHz for 624 s, and US was applied at 20 kHz and 250 W for 624 s. The plasma-sonic treatment achieved microbial inactivation of indigenous bacteria by 3.2 log CFU/g, which is comparable to the 3.0 log CFU/g reduction achieved by NaOCl treatment. Moreover, the plasma-sonic treatment group exhibited the highest initial moisture content (89.42%) and maintained higher firmness during storage than the NaOCl-washed and untreated groups. Collectively, these findings indicate that the combined PAW and US washing method constitutes a promising non-chlorine-based intervention that enhances microbial stability while maintaining the physicochemical quality of fresh leafy vegetables. Full article

Leaf water content (LWC) is a vital physiological indicator reflecting crop water status, crucial for precision irrigation and water management. Traditional monitoring methods are labor-intensive and costly, while unmanned aerial vehicle (UAV) remote sensing offers an efficient alternative with high spatiotemporal resolution. This study developed an inversion model for winter wheat LWC based on a stacking ensemble learning framework integrating multispectral and texture features to improve estimation accuracy. UAV multispectral images collected at different growth stages were used to extract 17 vegetation indices (VIs) and 32 texture features (TFs). The top 10 features most correlated with LWC were selected to construct a fused dataset, and five machine learning models (SVM, RF, XGB, PLSR, RR) were combined within a base–meta stacking architecture. Results showed that: (1) Using only multispectral features yielded R² values of 0.526–0.718 and rRMSE of 22.795–29.536%, while texture-only models performed worse (R² = 0.273–0.425, rRMSE = 34.7–36.6%), indicating that single data sources cannot fully represent LWC variability. (2) Combining multispectral and texture features notably improved accuracy (R² = 0.748–0.815; rRMSE = 18.5–21.6%), demonstrating the complementary advantages of spectral and spatial information. (3) Stacking ensemble learning outperformed all single models, achieving the highest precision under fused features (R² = 0.865; rRMSE = 16.3%). (4) LWC distribution maps derived from the stacking model effectively revealed field-scale moisture differences and spatial heterogeneity during different periods. This study confirms that multi-source feature fusion combined with ensemble learning enhances UAV-based crop water estimation, offering a reliable and scalable approach for precision agricultural water monitoring. Full article

This research, carried out within the framework of the European Space Agency’s second Scout mission (HydroGNSS), seeks to utilize CYGNSS Level 1B products over land for soil moisture estimation. The approach involves a novel physically based algorithm, which inverts a semiempirical forward model of surface reflectivity proposed in the literature. An Artificial Neural Network (ANN) algorithm has also been developed. Both methods are implemented in the frame of the HydroGNSS mission to make the most of the reliability of an approach rooted in a physical background and the power of a data-driven approach that may suffer from limited training data, especially right after launch. The study aims to compare the results and performance of these two methods. Additionally, it intends to evaluate the impact of auxiliary data. The static auxiliary data include topography, Above Ground Biomass (AGB), land cover, and surface roughness. Dynamic auxiliary data include Vegetation Water Content (VWC) and Vegetation Optical Depth (VOD) from Soil Moisture Active Passive (SMAP), as well as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Moderate Resolution Imaging Spectroradiometer (MODIS), on enhancing the accuracy of retrievals. The algorithms were trained and validated using target soil moisture values derived from SMAP L3 global daily products and in situ measurements from the International Soil Moisture Network (ISMN). In general, the ANN approach outperformed the semiempirical model with RMSE = 0.047 m³ m⁻³ and R = 0.91. We also introduced a global stratification framework by intersecting land cover classes with climate regimes. Results show that the ANN consistently outperforms the semiempirical model in most strata, achieving around RMSE = 0.04 m³ m⁻³ and correlations above 0.8. The semiempirical model, however, remained more stable in data-scarce conditions, highlighting complementary strengths for HydroGNSS. 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

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