Abstract. The combined use of independent soil moisture data from radar and radiometer measurements in data assimilation (DA) systems is expected to yield synergistic performance gains due to their complementary strengths. This study evaluates the impact of simultaneously assimilating soil moisture retrievals from ASCAT (Advanced SCATterometer) and SMAP (Soil Moisture Active Passive) into the Korean Integrated Model (KIM) using a weakly coupled DA framework based on the National Aeronautics and Space Administration's Land Information System (LIS). The Noah land surface model (LSM) within LIS, which is the same as that used in KIM, is used to simulate land surface states and assimilate soil moisture retrievals. The impact of soil moisture DA is evaluated using independent reference datasets, assessing its influence on soil moisture analysis and numerical weather prediction performance. Overall, assimilating single-sensor soil moisture data, ASCAT or SMAP, into the LSM improves global soil moisture analysis accuracy by 4.0 % and 10.5 %, respectively, compared to the control case without soil moisture DA, achieving the most significant enhancements in croplands. Relative to single-sensor soil moisture DA, multi-sensor soil moisture DA yields more balanced skill enhancements for both specific humidity and air temperature analyses and forecasts. The most pronounced synergistic improvements by simultaneously assimilating both soil moisture products are observed in the 2 m air temperature analysis and forecast, especially when both soil moisture products have a positive impact. Precipitation forecast skill also improves with multi-sensor soil moisture DA, although the improvements are not consistent across regions and events. This paper discusses remaining issues for future studies to further improve the weather prediction performance of the KIM-LIS multi-sensor soil moisture DA system.

This study focused on the fouling of two seawater reverse osmosis (SWRO) membranes at the Beni Saf Water Company desalination plant in Algeria, which has a daily capacity of 200 000 m3 and a recovery rate of 45% using 17 920 membranes. Approximately 3 234 membranes are replaced annually due to fouling. A detailed study of the fouling agents of the two membranes was conducted using various analytical techniques, such as moisture analysis, loss on ignition (LOI), determination of calcium carbonate (CaCO3) content, x-ray fluorescence (XRF), x-ray diffraction (XRD), and Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR). Surface characterization was also performed using scanning electron microscopy equipped with energy-dispersive x-ray spectroscopy (SEM-EDS), FTIR-ATR, and XRD. The LOI analysis indicated that more than 30% of the fouling material was organic in nature. FTIR-ATR identified the presence of –OH groups, phenolic C–O groups, and amide bonds, suggesting the accumulation of organic substances such as proteins, humic substances, and polysaccharides. Additionally, SEM-EDS, XRF, and XRD revealed relatively high concentrations of silica, primarily in the form of quartz, confirming the formation of an organo-inorganic complex on the membrane surface. Based on these findings, a sequential chemical cleaning protocol was developed, incorporating alkaline (NaOH), metal chelator (EDTA), surfactant (SDS), oxidant (H2O2), and sulfuric acid (H2SO4), each followed by rinsing with deionized water (DI). This cleaning regime effectively removed fouling from the membrane surface, resulting in an average weight loss of 15% for one membrane and 14% for the other.

Background: Dry skin affects 50%–80% of Indonesia. Data from the Geriatric Department of the Dermatology and Venereology Clinic at Dr. Cipto Mangunkusumo Hospital (RSCM) in Jakarta between 2008 and 2013 indicate that dry skin is one of the ten most prevalent illnesses. Objective: Regarding skin moisture, this study tries to determine the impact of vitamin C on young adult participants. Methods: This is an observational analytical study with a quasi-experimental design carried out at Maranatha Christian University in Bandung. Medical students between 19 and 29 years old at Maranatha Christian University's Faculty of Medicine served as the study participants. Based on gender, sixty participants were split into two groups. TEWL levels were measured using the Skin Moisture Analyzer FCM-1. Vitamin C intake was obtained from a food frequency questionnaire containing food sources of vitamin C, which was then analyzed using the Nutrisurvey-2007 program, as well as vitamin C from supplementation. Results: Employing paired t-tests, the p-value for both male and female groups was <0.001, suggesting a substantial change in skin moisture levels before and after vitamin C intake. Conclusion: Young adults' skin moisture levels are affected by vitamin C intake.

Abstract The high sensitivity of neutrons to hydrogen, together with the key role of moisture in wood utilisation, makes wood–water interactions a central topic in neutron imaging studies. However, neutron imaging alone does not enable direct quantification of dry-weight-based moisture content (MC), thereby limiting its application in moisture analysis. This study presents a combined neutron and X-ray imaging method to monitor temporal changes in wood moisture with detailed anatomical resolution. Scots pine sapwood specimens with initial MCs ranging from green state to approximately 24 %, 12 %, and 0 % were subjected to unidirectional drying or wetting under controlled relative humidity decreasing from about 85 % to 30 % at 60 °C. Eight specimens were imaged simultaneously, and image-registration techniques were applied to compensate for shrinkage and swelling during moisture changes. Wood was modelled as discrete layers of compact timber, water, and void space. While neutron imaging provided high sensitivity to water distribution, combining both imaging modalities enabled estimation of MC and improved anatomical visualisation. The method allows investigation of wood–water interactions across entire cross-sections down to individual growth-ring features. By enabling detailed tracking of moisture dynamics under controlled climatic conditions, the approach supports improved understanding of wood drying behaviour and moisture-related material performance.

Background. In marker-assisted rice breeding, the accelerated development of varieties with superior grain quality traits through advanced biotechnological approaches requires the generation of segregating rice populations followed by phenotyping of genotypes for traits of interest. These segregating populations are used to identify genetic loci (QTLs) associated with complex traits, including rice grain quality, based on phenotypic data. Purpose. The goal of the work was to evaluate experimental BC3 populations of rice based on physical characteristics of grain: size, vitreousity, fracturing, in order to carry out work on targeted selection based on phenotyping and genotyping data of promising plants - prototypes of varieties with specified traits in marker-assisted rice breeding. Materials and methods. The study involved hybrids of 15 combinations of parental forms. The seeds were sown in vessels on the vegetation site of FSBSI Federal Scientific Rice Centre, Pryanishnikov's mixture was used as the main fertilizer; as they ripened, the seeds were harvested manually. High-tech methods of phenotyping the breeding material were used to conduct the research. The grain size was estimated by the mass of 1000 absolutely dry grains using a moisture analyzer, an air-heat unit, and an automatic seed counter; the vitreousity and grain fracturing were estimated in transmitted light using a diaphanoscope. Results. Genotypes were differentiated and distributed into groups for each trait. As a result of the quality study of the obtained BC3 samples, lines combining high technological grain quality traits were identified using phenotyping data. The mass of 1000 absolutely dry grains was in the range of 23.2-30.2 g in the group of medium-weight samples, the indices of vitreousity and fracturing were 62-93% and 1-9%, respectively. Conclusion. As a result of the comparative analysis of hybrids and parental forms, combinations were noted for which the heterosis effect was typical for grain quality traits. Sponsorship information. The research was carried out with the financial support of the Russian Science Foundation and Kuban Science Foundation grant № 25-16-20103, https://rscf.ru/project/25-16-20103/. Federal State Budgetary Scientific Institution «Federal Scientific Rice Centre». EDN: MWACYE

Moringa (Moringa oleifera L.) is a member of the Moringaceae family that is rich in flavonoids and phenolics, especially in the leaves, with potential as an antioxidant and skin moisturizer. Serum is a preparation made with low viscosity so that it can deliver a thin film of active substances to the skin surface and has a high water content so that it can hydrate the skin. This study aims to optimize the formula of a face serum based on ethanol extract of moringa leaves. The extract was obtained using the maceration method and tested at concentrations of 2%, 4%, and 6%. The highest antioxidant activity was shown at a concentration of 6%, which was used as the optimal concentration in formulations with variations of 5%, 10%, and 15% glycerin. Antioxidant activity was tested in vitro using the DPPH method with UV-Vis spectrophotometry, while skin moisture was tested using a moisture skin analyzer. The results showed that all formulations met physical quality standards and had moderate to strong antioxidant activity with IC₅₀ values of F0 (36,772 ppm), F1 (26,966 ppm), F2 (25,300 ppm), and F3 (23,591 ppm). Increased concentrations of extract and glycerin were associated with decreased IC₅₀ values and increased moisturizing effects, with formula 4 producing the most optimal performance.

This study evaluated the cytotoxic potential of the coelomic fluid from the Californian earthworm Eisenia foetida in vitro, along with its physicochemical and molecular characteristics, to assess its feasibility for treating giardiasis. Bromatological [moisture, pH, ash] and electrophoretic analyses were performed on fresh and frozen samples, and antiprotozoal activity against Giardia lamblia was tested at concentrations of 5, 10, and 20%. The results indicated that the fluid maintained its protein quality after more than two and a half years of storage and showed significant anti-Giardia activity even at low concentrations, providing the first experimental evidence of its antiparasitic effect.

Aims: This study investigates the osmotic dehydration behaviour of pink radish (Raphanus sativus), an inherently high-moisture root vegetable with a very short ambient shelf life, under varying salt concentrations and temperatures. This research is significant for developing low-cost preservation techniques to reduce post-harvest losses and create value-added radish products. Study Design: Laboratory-based experimental study. Place and Duration of Study: Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India, during 2023–2024. Methodology: Fresh pink radish slices (0.5 cm thickness) were immersed in sodium chloride (NaCl) solutions of 5%, 15% and 25% (w/w). Osmotic dehydration was conducted at temperatures of 40°C, 50°C and 60°C for 4 h, maintaining a radish-to-solution ratio of 1:5. All experiments were carried out in two replications. Samples were withdrawn at 0.5, 1, 2, 3 and 4 h intervals, blotted, weighed, and analyzed for moisture content using an infrared moisture analyzer. Mass transfer parameters—water loss (WL), solid gain (SG), and weight reduction (WR)—were computed using standard equations. Results: Moisture content reduced from 95.7% w.b. to as low as 66.04% after 4 h in 25% NaCl at 60°C. WL and SG increased with rising temperature and concentration, with most mass exchange occurring during the first 2–2.5 h. Maximum WL values ranged from 0.122–0.349, and SG ranged from 0.027–0.254 across treatments. From the results, it was observed that at 50 °C with 25% concentrated salt solution the water loss, solid gain and weight loss were highest, reaching peak values of 0.349 and 0.254, respectively. Conclusion: Higher osmotic concentrations and temperatures significantly enhanced WL and SG. Osmotic dehydration is an efficient and gentle moisture reduction technique for pink radish, improving its storability and suitability for further processing.

In the context of sanctions pressure, a search for ways to substitute imported materials and technologies with Russian ones is underway. This trend is typical of all industries, including import substitution in the field of medicine, pharmacy, and the production of cosmetics, oils, and consumables. In this regard, an important role is played by assessing the capabilities of Russian manufacturers to replace raw materials from foreign producers – Germany, Turkey, and other countries. It is crucial for this process to occur without loss of quality of the final product. Our study is devoted to the evaluation of white oils from Russia's largest petrochemical manufacturer for the production of cosmetic products, as well as their widespread use not only in dermatology but also in various medical procedures. Objective methods of skin moisture analyzer control were utilized to assess the effectiveness of the tested samples, making it possible to record skin indicators such as fat content, hydration, and softness. During the testing of the prototypes, a database with multiple measurements of each subject was obtained. The resulting database was subjected to statistical processing by regression and correlation analysis. The survey assessed the potential demand for innovative products and the economic feasibility of the project.

Machine learning for soil moisture analysis: a survey and emerging perspectives

Objective – to present the development and application of an educational prototype based on artificial intelligence for visual soil analysis, focused on interactive learning and sustainability.Methodology – the application was developed in Python (Streamlit), integrating modules for color, texture, structure, moisture, and root analysis, supported by Munsell visual references and soil science literature. The process included support from generative AI tools (ChatGPT, OpenAI) and was tested with Environmental Engineering students at UNESP – Sorocaba. Originality/Relevance – the proposal introduces a bilingual interface (Portuguese and Spanish) combining technology and environmental education. It highlights how AI can empower non-programmers to create innovative sustainability tools. Results – the tests revealed high student engagement and didactic potential. The app proved effective for guided interpretation of soil characteristics, though color variation under different lighting and camera conditions suggests future technical refinements. Theoretical/Methodological Contributions – presents a replicable approach to developing educational tools with AI support, emphasizing human–machine collaboration in environmental learning. Social and Environmental Contributions – promotes knowledge democratization and awareness about soil conservation among students and small farmers, contributing to SDGs 4, 11, and 15.

Abstract Landscaping is an important aspect in our culture and plays an essential function in the environment quality. This research aims to analyse the reality of green spaces on campus using spatial analysis tools and geographic information system (GIS) technologies, as well as thermal and topographic imagery analysis and vegetation cover. The research was based on the analysis of soil characteristics and found moderate salinity and high sodium with low organic content, which necessitates improvements in management and irrigation. Topographic analyzes have shown a variation in elevations that affects the humidity and suitability for planting. Thermal analysis reveals the phenomenon of heat islands in urban areas, while moisture analysis has shown a positive relationship between rainfall and vegetation cover. The NDVI indicator revealed a discrepancy in the abundance of vegetation, with scarce areas in need of Reclamation and afforestation. The study recommends adopting sustainable environmental planning and increasing green spaces to reduce climate and environmental challenges on campus.

Machine learning-based inversion and sensitivity analysis of soil moisture in Hemerocallis cultivation

Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become essential for optimizing water utilization and promoting healthy crop development. While existing machine learning methods have demonstrated efficacy, they often lack the adaptability required to accommodate the dynamic conditions of agricultural environments. Prior research has identified soil moisture and chlorophyll content as key indicators of plant health and stress, with conventional models relying on simplistic algorithms for stress prediction. However, these models exhibit limitations in scalability, adaptability and interpretability. To overcome these challenges, this study employed sparse additive models with learning (SAM-L) algorithms, integrated with explainable artificial intelligence (XAI), to provide a flexible and transparent solution. In this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content. The SAM-L algorithm is a machine learning method that focuses on sparsely selecting relevant features through additive models. It aims to enhance model interpretability while maintaining high prediction accuracy by learning sparse representations of input data. The SAM-L algorithm enhances interpretability while preserving high predictive accuracy by learning sparse feature representations from input data. Additionally, XAI was incorporated to ensure interpretable decision-making, enabling farmers and stakeholders to comprehend the rationale behind irrigation recommendations. The model’s architecture incorporates a three-layer Long Short-Term Memory (LSTM) network to process sequential data effectively. The proposed framework achieved a high performance on publicly available dataset, yielding an overall accuracy of 89.2% on the multi-class classification task. Further analysis of the results across the three predefined stress categories (healthy, moderate stress, and high stress) revealed strong performance, with the model obtaining a macro F1-score of 0.88 and a macro recall of 0.88. The proposed framework not only can enhance prediction accuracy but also can promote sustainable farming practices by reducing water wastage and improving crop resilience.

This study explores the effect of date palm gum (DPG) as a novel functional ingredient for whole wheat bread (WWB) to enhance its physicochemical and textural properties. Herein, samples containing varying concentrations of DPG (0–3% w/w) were prepared and analyzed, out of which D2 (containing 1% w/w DPG) exhibited superior qualities. Microscopic studies showed that D2 exhibited improved crumb aeration, suggesting better fermentation than the others. Moisture analysis revealed that D2 retained a higher quantum of moisture (50.06 ± 0.41%). Further, the colorimetric study showed that increasing DPG concentration led to a corresponding decrease in L* values (46.69 ± 0.13) due to the combined effect of Maillard browning and the inherent color of DPG. Analysis of FTIR spectra confirmed stable interactions of DPG and starch–protein complexes in D2. Stress relaxation exhibited that D2 had the highest initial (F0; 162.95 ± 1.70 g) and residual (F60; 95.81 ± 3.94 g) forces, indicating that it maintained its structure under stress. In gist, DPG exhibited strong potential as a natural hydrocolloid that could be explored to develop functional bakery products.

Abstract Soil moisture strongly influences land–atmosphere interactions, yet regional-scale analyses of future changes and shifting environmental drivers for the subtropical East Asian region (STEA) remain under-represented compared to global studies. This study uniquely integrates multi-layer soil moisture analysis with machine learning-based driver attribution to reveal temporal shifts in climate controls across climate-vulnerable STEA in response to future warming, where springtime transitions are crucial for water security. Using outputs from 14 CMIP6 models, evaluated against ERA5-L, we find that 86.7% (73.3%) of models capture historical surface (total) soil moisture patterns, providing confidence in projections. Projected changes are assessed using the multi-model ensemble under SSP5-8.5 scenario. Our Random Forest Importance Score analysis reveals a critical hydrological regime transition: rainfall and runoff dominate historical (1995–2014) and mid-future (2041–2060) periods, while near-surface temperature becomes the dominant environmental control by far-future (2081–2100), demonstrating non-linear responses where temperature effects overwhelm rainfall changes. Regional projections indicate progressive drought vulnerability across STEA, with surface (total) soil moisture decreasing by 3.1% (2.0%) by mid-future and 9.1% (5.7%) by far-future, driven by this fundamental reorganization of the environmental drivers. This quantitative assessment provides essential insights for temperature-informed water management strategies, revealing that traditional rainfall-centric approaches become inadequate as warming intensifies across climate-sensitive STEA.

Dendrobium huoshanense flavone attenuates radiation enteritis by enhancing gut immunity to inhibit inflammation and fibrosis.

Crop production in Ethiopia is limited owing to water scarcity. Various technologies and management options are being used for efficient use of the available water resources in crop production. This study evaluated the performance of Soil Moisture (SM) and Evapotranspiration (ET) based irrigation scheduling methods on carrot yield and Water Use Efficiency (WUE), Water Productivity (WP) and field water use efficiency at water scarce areas of Arba Minch for two consecutive years/seasons of 2021 and 2022. The experimental design was a randomized complete block with three replicates. The treatments combined two scheduling techniques (soil moisture, SM, and evapotranspiration, ET). Water was delivered to furrows using an RBC flume, and data was analyzed with ANOVA at 5% significance level using SAS software and the graphs were drawn by Python. Across 2021 and 2022 seasons, SM-based irrigation consistently required less water than ET-based scheduling, achieving 5–5.4% water savings at full irrigation levels without reducing yields. Under moderate deficit irrigation (50–75%), both methods sustained comparable yields (42–43 t/ha), but SM-based treatments showed higher WUE, FWUE, and WP, with peak values at SM50% (WUE = 31.4 kg/m³; FWUE = 22.7 kg/m³; WP = 1.78 kg/m³) compared to ET50% (WUE = 25.8 kg/m³; FWUE = 18.1 kg/m³; WP = 1.78 kg/m³). Severe deficit irrigation (25%) drastically reduced yield and all efficiency indices in both methods. Economic analysis indicated that moderate irrigation levels (50%) maximized net benefits and cost-benefit ratios. Overall, SM-based irrigation was more efficient in water use, improved yield stability, and enhanced WUE, FWUE, and WP across irrigation levels. Thus, it demonstrated its suitability for sustainable carrot production under limited water resources. This saving is particularly relevant for Ethiopia where water scarcity limits crop production. It demonstrates a practical strategy for farmers to grow more food with less water while supporting sustainable resource management. Keywords: Soil moisture; Furrow Irrigation; Crop evapotranspiration; ET-Based irrigation; SM-based irrigation

This study presents a multi-drought metric dataset based on the Standardized Precipitation Index (SPI) for the Greater Antilles, covering Cuba, Jamaica, Puerto Rico and La Española. The SPI was derived from high-resolution (0.1°) Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation data for the 1980-2023 period. The MSWEP precipitation and SPI datasets were validated against observational and reanalysis datasets, providing good relationships. Temporal and spatial drought conditions were identified for the SPI temporal scales from 1 to 24 SPI temporal scales, while drought episodes were identified for 1, 3, 6, 12, 18 and 24 temporal scales, and characterised according to their duration, severity, affected area, persistence, onset speed, and recovery. A source-sink moisture analysis was also performed to support the attribution of dry conditions. We demonstrate possible uses of the database, and its consistency in representing past drought events that caused severe damage. Therefore, it proves to be a practical tool for understanding the phenomenon of drought in the Caribbean, managing water resources, agricultural development, and many other sectors.

Accurate profiling of deep (20–300 cm) soil moisture is crucial for precision irrigation but remains technically challenging and costly at operational scales. We systematically benchmark eight regression algorithms—including linear regression, Lasso, Ridge, elastic net, support vector regression, multi-layer perceptron (MLP), random forest (RF), and gradient boosting trees (GBDT)—that use easily accessible inputs of 0–20 cm surface soil moisture (SSM) and ten meteorological variables to non-invasively infer soil moisture at fourteen 20 cm layers. Data from a typical agricultural site in Wenxi, Shanxi (2020–2022), were divided into training and testing datasets based on temporal order (2020–2021 for training, 2022 for testing) and standardized prior to modeling. Across depths, non-linear ensemble models significantly outperform linear baselines. Ridge Regression achieves the highest accuracy at 0–20 cm, SVR performs best at 20–40 cm, and MLP yields consistently optimal performance across deep layers from 60 cm to 300 cm (R2 = 0.895–0.978, KGE = 0.826–0.985). Although ensemble models like RF and GBDT exhibit strong fitting ability, their generalization performance under temporal validation is relatively limited. Model interpretability combining SHAP, PDP, and ALE shows that surface soil moisture is the dominant predictor across all depths, with a clear attenuation trend and a critical transition zone between 160 and 200 cm. Precipitation and humidity primarily drive shallow to mid-layers (20–140 cm), whereas temperature variables gain relative importance in deeper profiles (200–300 cm). ALE analysis eliminates feature correlation biases while maintaining high predictive accuracy, confirming surface-to-deep information transmission mechanisms. We propose a depth-adaptive modeling strategy by assigning the best-performing model at each soil layer, enabling practical non-invasive deep soil moisture prediction for precision irrigation and water resource management.