Search Results (8,277)

Surface soil moisture forecasting is important for drought monitoring, irrigation management, and land–atmosphere process analysis but remains challenging because near-surface soil moisture is jointly influenced by antecedent precipitation, atmospheric drying, soil properties, vegetation conditions, and irregular multi-source observations. This study proposes an Antecedent-Precipitation-Informed Surface Soil Water Balance and Time-Aware Mamba–Mixture-of-Experts (API-SWB-Mamba-MoE) framework for forecasting in situ volumetric soil moisture at approximately 5 cm depth using only information available before the target time. The framework combines a process-guided API-SWB physical prior, a time-aware Mamba temporal encoder, a context-conditioned MoE residual decoder, and gated residual fusion. The U.S. source-domain stations were evaluated using five independently repeated station-level random holdout splits, with approximately 70%, 15%, and 15% of the stations assigned to training, validation, and testing in each repetition, respectively. The five resulting U.S.-trained models were further applied without target-domain retraining or fine-tuning to six German and French stations for zero-shot transfer evaluation. Across the U.S. test prediction–observation pairs pooled from the five repetitions, the proposed model achieved a Pearson correlation coefficient (R) of 0.934, a root mean square error (RMSE) of 0.035 cm³ cm⁻³, a Kling–Gupta efficiency (KGE) of 0.922, and a mean bias error (MBE) of −0.001 cm³ cm⁻³. Pooled zero-shot predictions yielded RMSE values of 0.036 and 0.038 cm³ cm⁻³ and KGE values of 0.885 and 0.917 for Germany and France, respectively. The pooled U.S. test RMSE was 7.9–22.2% lower than that of the ablation variants and 20.5–32.7% lower than that of the benchmark models. These results suggest that combining a process-guided prior with time-aware sequence modeling and context-conditioned expert routing offers a promising approach for station-scale soil moisture forecasting under irregular multi-source observations. The external results provide preliminary evidence of zero-shot transferability at the six selected sites, although broader regional validation remains necessary. Full article

The preparation of reliable input data for groundwater flow modeling remains a persistent bottleneck in data-scarce regions, where parameter estimation relies heavily on subjective expert judgment. This study introduces ALAES, a novel object-oriented expert system that codifies formal and heuristic knowledge to guide hydrogeologists through the entire pre-modeling workflow. The knowledge base was developed through structured interviews with 20 international experts and formalized using the KOD methodology within the Kappa-PC shell. The system comprises 258 production rules, 114 classes, and 1136 instances. Validation on the data-scarce Rhis-Nekor aquifer in Morocco showed that ALAES recommended MODFLOW and diagnosed modeling feasibility as challenging. A comparative assessment revealed substantial improvements over a baseline model developed without ALAES guidance: spatial resolution increased by a factor of four in critical zones, steady-state water balance consistency improved from 87% to 94%, and mean absolute errors were reduced by over 50% under ±20% perturbations. The system guided parameter estimation, reducing porosity uncertainty by over 40%, and achieved strong transient calibration (R² = 0.99). Three future management scenarios were evaluated, enabling formulation of a recommended exploitation strategy. By bridging the gap between data availability and modeling requirements, ALAES provides an explicit, reproducible decision-support tool for sustainable groundwater management in data-limited environments worldwide. Full article

Drought and water scarcity are threatening the food security in irrigation districts around the world, urging water-saving and highly efficient irrigation scheduling. However, the increasing uncertainty jointly caused by changing environment and human activities makes it much more difficult and unreliable. In this study, a chance-constrained multi-objective robust programming framework was proposed to optimize irrigation scheduling of multi-cropping systems, while dealing with hybrid uncertainty and multiple objectives simultaneously. It integrates deficit irrigation theory, soil water movement and reservoir regulation and was applied in a seasonal drought region of Southwest China with multi-cropping systems. The results show that: (1) the optimal irrigation schemes can substantially mitigate the seasonal drought and reveal the influence of uncertainty from parameters on the objectives; (2) compared with the current practice, the optimized irrigation scheduling can help save water for wet-season crops and reflect the response of crops to various water demand and supply scenarios; (3) the risk preferences, goal preferences, the expected objectives interval, and the preferences for robust penalty of decision makers were investigated to show their interactive influence on the results. Although there are still limitations, the developed model can help improve drought resistance, save water and realize sustainable development of agriculture. Full article

Sustainability concerns are driving greater interest in valorizing agri-food by-products as ingredients in meat products. This study aimed to optimize a cooked turkey sausage formulation incorporating apple pomace powder (APP), turkey skin (TS), and iced water (IW) and to evaluate their effects on physicochemical, textural, and sensory properties. Response surface methodology (RSM) was applied with APP (1–5%), TS (20–30%), and IW (15–30%) as independent variables. The responses evaluated were pH, cooking yield, color parameters (L*, a*, and b*), hardness, and overall acceptability. Numerical optimization identified a formulation containing 4.45% APP, 23.19% TS, and 15.73% IW, with predicted values of pH 5.63, cooking yield 87.21%, lightness (L*) 73.78, redness (a*) 4.81, yellowness (b*) 11.35, hardness 51.21 N, and overall acceptability 6.59. Experimental validation showed that the measured responses were generally consistent with the RSM predictions, although a larger deviation was observed for b*. Principal component analysis (PCA) showed that PC1 and PC2 explained 51.66% and 28.89% of the total variance, respectively, accounting for 80.55% cumulatively. PCA provided an exploratory representation of relationships among the measured responses, with PC1 primarily contrasting pH and lightness with cooking yield, redness, and yellowness, while hardness and overall acceptability were more strongly associated with PC2. Overall, the results demonstrate the feasibility of incorporating APP and TS into cooked turkey sausage and identify an optimized formulation that balances by-product incorporation with physicochemical, textural, and sensory responses. Full article

Glass wool provides a lightweight fibrous framework with inherent thermal-insulation capability, yet its direct use in hydrophobic monolithic aerogels has received comparatively limited systematic investigation. Here, glass wool fiber (GWF)/poly(vinyl alcohol) (PVA) aerogels were fabricated by freeze-drying followed by vapor-phase methyltrimethoxysilane (MTMS) modification. A composition matrix of 1.0–3.0 wt.% GWF and 0.10–1.00 wt.% PVA was evaluated to determine composition-dependent changes in density, calculated porosity, wettability, compressive response, thermal conductivity, crude-oil absorption, uptake kinetics, and cyclic reusability. The aerogels exhibited densities of 0.014–0.046 g/cm³, calculated porosities of 97.68–99.39%, water contact angles of 131.0–141.3°, thermal conductivities of 32.1–38.5 mW/m·K, and compressive stress at 50% strain up to 146.20 kPa. Crude-oil absorption, defined here as predominantly physical uptake and retention within the porous fibrous network, ranged from 18.86 ± 1.50 to 55.12 ± 1.57 g/g. At 0.25 wt.% PVA, samples containing 1.0–3.0 wt.% GWF reached 81–91% of equilibrium uptake within 10 s. The pseudo-second-order model provided the better empirical fit without implying chemisorption. Overall, composition influenced the balance among oil uptake, mechanical resistance, cyclic reuse, and thermal insulation. Full article

To improve the high-value utilization of recycled concrete powder (RCP) and its carbon sequestration potential, this study developed a low-carbon recycled powder-based binder (LCRPB) incorporating RCP, silica fume (SF), granulated blast-furnace slag (GBFS), and fly ash (FA). A five-factor, five-level orthogonal design was used to evaluate workability, mechanical properties, hydration behavior, wet carbonation performance, and post-carbonation reusability. The water-to-binder ratio showed the greatest relative influence on flowability and compressive strength, whereas RCP content had a greater influence on 28-day flexural strength and wet carbonation behavior. At favorable dosage levels, 2% SF, 7.5% GBFS, and 10% FA increased the 28-day compressive strength by 17.43%, 12.43%, and 11.83%, respectively. Wet carbonation showed the highest efficiency at a liquid-to-solid ratio of 5 and a CO₂ flow rate of 3 L/min, with CaCO₃ identified as the main carbonation product. The carbonated powder retained reuse potential. Matrix analysis identified a balanced formulation of W/B = 0.40, 0% SF, 7.5% GBFS, 10% FA, and 10% RCP. The selected formulation reduced the material-related carbon footprint by 26.6% relative to the 100% OPC reference. Full article

Macrobenthic seaweeds are key species in temperate and boreal nearshore rocky ecosystems, and their interspecific interactions profoundly affect the species composition, spatial distribution and dynamic succession of the community. This paper systematically reviews the mechanisms of interspecific competition and facilitative effects among macrobenthic seaweeds. We also examine their environmental regulation and impacts on community phase transitions. Competition mainly centers on three major resources: light, space and nutrients. It manifests as an asymmetric hierarchical structure, in which canopy-forming species exert a disproportionate repressive effect on lower algal layers through light competition. Facilitative effects are realized through several mechanisms. These include canopy shading to mitigate heat stress and water loss, physical structure building to create complex habitats, and nutrient retention with chemical microenvironment regulation. Moreover, the relative strengths of facilitation and competition shift systematically across environmental stress gradients. Multi-trophic level regulation further shapes community diversity maintenance mechanisms through the suppression of dominant species by vegetative predators and the cascading effect of top predators on vegetative predators. In the context of global change, physiological asymmetries driven by oceanic heat waves are tilting the competitive balance in favor of opportunistic turf algae. These algae form a lock-in state through the triple positive feedbacks: physical barriers, biogeochemical deterioration, and chemosensory exclusion. This significantly decreases the resilience of algal field recovery. Tropical coral-macroalgae phase transition is highly consistent with the degradation of temperate algal fields in terms of driving logic. They follow the law of coupling resource competition imbalance, downward regulatory relaxation, and positive feedback lock-up. This paper suggests several priorities for future research. Studies should focus on the nonlinear response of multifactor interaction, the quantitative correlation between key functional traits and community resilience, and the establishment of a cross-ecosystem comparison framework. These efforts will provide theoretical support for predicting and adaptively managing offshore ecosystems under global change. Full article

Water color is an important optical proxy for trophic status and water quality, but its integration into regulatory assessment frameworks is still limited. This study assesses the potential of the Forel–Ule Index (FUI) derived from Sentinel-2 as a proxy indicator to support the assessment of the ecological status of reservoirs under the European Union’s Water Framework Directive (WFD). Seventeen reservoirs located in semi-arid Mediterranean climate agricultural basins in southern Portugal (Sorraia, Sado, and Guadiana) were analyed, combining 4316 FUI observations (2017–2024) with in situ water quality data and official WFD ecological status classifications. The results showed that the values on the FUI scale (which ranges from 1 to 21) fell, for the most part, between 12 and 18 and with marked spatial and seasonal contrasts, particularly between more transparent reservoirs and persistently turbid ones, probably eutrophicated reservoirs. Principal component analysis showed that the first component (PC1, 39.5% of variance) represents a trophic gradient dominated by turbidity, chemical oxygen demand and chlorophyll-a, and is positively, albeit moderately, correlated with FUI (Spearman’s ρ = 0.439, p < 0.001), while the second component, dominated by nitrogen, showed no significant association. To make the Water Framework Directive (WFD) regulations compatible with the structure of the available dataset, ecological status was dichotomized into “Satisfactory” and “Deterioration”. Binary logistic regression showed that increasing FUI values were significantly associated with a lower probability of classification as “Satisfactory” (β = −0.682, p = 0.0137; odds ratio = 0.51, 95% CI: 0.29–0.87). The model performance was moderate (balanced accuracy = 0.682; AUC = 0.754), with better identification of “Deterioration” conditions than “Satisfactory” conditions. The Mann–Whitney U test confirmed that mean FUI values differed significantly between the two ecological status groups (U = 65, p = 0.0166), with lower values associated with reservoirs meeting the “Good” threshold. Overall, FUI proved to be a low-cost and temporally flexible screening and early-warning tool, particularly useful for identifying departures from favorable ecological conditions. However, the index is not a direct substitute for the official ecological classification and is best applied in combination with physicochemical and biological metrics when assessing changes in water quality over a shorter period of time. Full article

The Standardised Precipitation-Evapotranspiration Index (SPEI) is widely used for drought monitoring, but reanalysis datasets like ERA5-Land contain biases that affect its reliability. Conventional bias correction, which adjusts variables independently, can violate the physical relationship between precipitation and PET. We introduce a Physics-Guided Neural Network (PGNN) that corrects both variables simultaneously while enforcing a water-balance constraint within the loss function. Using a rigorous temporal split (training 1950–2010, validation 2011–2018, testing 2019–2025), the PGNN framework shows improved performance compared with quantile distribution mapping (QDM). On the validation period (2011–2018), the correction reduces precipitation RMSE by 32% (from 40.4 to 27.6 mm month⁻¹) and increases PET R² from −2.820 to 0.890, compared with QDM’s 0.326. For the 3-month agricultural drought index, PGNN reduces RMSE by 27% (from 0.440 to 0.320), increases R² from 0.737 to 0.857, and improves correlation from 0.890 to 0.943, whereas QDM shows only marginal improvement (R² = 0.715). Consistent gains are observed across all timescales, with PGNN-corrected SPEI achieving R² exceeding 0.78 for the 1-, 3-, and 6-month agricultural indices. At longer timescales, PGNN shows improved skill, achieving R² = 0.101 and 0.113 for 12- and 24-month SPEI, respectively—outperforming QDM (R² = 0.043 and 0.032). The period-specific analysis reveals two distinct degradation mechanisms: error accumulation in the water balance explains the decline from short to long timescales, while climate non-stationarity explains the degradation from the validation period to the test period. In the independent test period (2019–2025), both methods show substantial degradation: PGNN precipitation RMSE increases from 27.6 to 32.0 mm month⁻¹ (+16%), PET R² drops from 0.890 to 0.458 (−49%), and longer timescales (9–24 months) yield negative R² values. Categorical metrics for extreme drought detection show mixed results on the validation period: the Critical Success Index (CSI) for Extreme Dry conditions (3-month SPEI) decreases from 0.269 (raw) to 0.164 for PGNN, while QDM achieves 0.277. This unexpected behaviour is attributed to the differing event distribution in the validation period. However, PGNN improves detection for severe and moderate dry classes, and Cohen’s Kappa increases from 0.552 (raw) to 0.646 (PGNN) compared with QDM’s 0.493. The water-balance constraint ensures climatic consistency and preserves the annual cycle (PET peaks in October at 148.1 mm month⁻¹, close to the CRU reference of 152.2 mm month⁻¹), while QDM introduces a two-month phase shift. The framework offers a potential scalable pathway for operational drought monitoring, but the test-period degradation suggests a need for adaptive correction methods that can track evolving climate baselines. Full article

This study investigates the water–energy nexus in vineyards adopting innovative practices, focusing on water management through rainwater harvesting systems and the installation of photovoltaic panels for renewable energy production. The research focuses on two regions in Greece, Nemea in Corinthia and Nea Anchialos in Magnesia, using historical time series of meteorological data to establish water and energy balances. The study aims to examine the practical use of these technologies to improve water and energy efficiency in grape and wine production, which are important parts of the country’s primary sector. A daily water balance model is applied to estimate the required storage capacity of rainwater tanks for irrigation use in vine cultivation, using daily rainfall and evapotranspiration data over 20 hydrological years (2001/02–2020/21). Additionally, the installation of photovoltaic panels covering a specific percentage of the total utilized area in the study parcels is examined. The analysis showed that the use of a rainwater collection system with a catchment area of 500 m² for crop areas from 500 to 10,000 m² and using rainwater tanks from 10 to 200 m³ can ensure demand coverage rates from 60% to 95%. The production of green energy through the panels ranges from 149 to 156 MWh per year. Full article

Selective lithium pre-leaching can separate an accessible lithium fraction before complete black-mass dissolution while limiting the co-dissolution of accompanying elements. However, comparative evidence across compositionally different, industrially pyrolyzed black masses under a consistent operating framework remains limited. To address this gap, three such black masses were systematically investigated using water and dilute inorganic and organic acids (0.1–0.5 mol·L⁻¹, 25–80 °C) without an external reducing agent. Water-leaching experiments were additionally performed to quantify the readily soluble lithium fraction. Lithium extraction after 120 min varied strongly with black mass origin and leaching conditions. Water leaching extracted 12–16% Li from BM1, 24–28% from BM2, and 36–47% from BM3. Under the most favourable water-leaching conditions, transition-metal dissolution remained negligible, resulting in substantially higher selectivity than in most acidic systems. Dilute acids increased lithium extraction in selected experiments, reaching up to 98%, but this was generally accompanied by increased dissolution of Co, Ni, Mn, Cu, and Al. The results demonstrate that lithium accessibility is governed more strongly by feed-specific phase composition and material history than by total lithium content. Selective pre-leaching should therefore be considered a feed-dependent conditioning step whose operating conditions must balance lithium removal against preservation of the transition-metal-rich solid fraction. Full article

Background/Objectives: Estimating the post-mortem interval (PMI) of human skeletal remains remains challenging because bone undergoes structural, molecular and optical changes that evolve differently over time and are strongly influenced by taphonomic conditions. Rather than assuming that a single multimodal classifier performs equally well across all PMI intervals, this study aimed to determine which imaging or spectroscopic modality is most informative for specific forensic decision tasks and to derive an exploratory task-specific diagnostic decision-support framework based on internally evaluated sample-level analyses. Methods: Human femoral bone samples were assigned to five PMI classes ranging from 0–2 weeks to >100 years and examined using micro-computed tomography, hyperspectral imaging, handheld and microscopic Raman spectroscopy, and NIR-ONE spectroscopy. Repeated acquisitions were aggregated at the physical-sample level. Four targeted diagnostic contrasts were defined for the present reanalysis: archaeological class 5 versus classes 1–4, early classes 1 + 2 versus later classes 4 + 5, classes 1 + 2 versus class 4 within the Raman-compatible forensic range, and class 1 versus class 2. In addition, an exploratory direct pairwise comparison of class 4 versus class 5 was performed to specifically assess the boundary between the latest forensic interval and archaeological material. Parameter-wise ROC analysis, bootstrap confidence intervals, exploratory operating points at approximately 95% specificity, and repeated stratified cross-validation were used. All preprocessing for multivariate modelling was performed within the respective cross-validation folds. Results: Diagnostic performance was strongly task-dependent. Archaeological class 5 was distinguished from classes 1–4 by micro-CT Mean2 (AUC 0.984), NIR-ONE reflectance at 1944 nm (AUC 0.980), and HSI-derived tissue water index (TWI; AUC 0.961). In the additional exploratory direct C4-versus-C5 analysis, micro-CT Mean2 showed complete separation of the available samples (AUC 1.000), while NIR-ONE reflectance at 1944 nm retained excellent discriminatory performance (AUC 0.963). In contrast, HSI-derived TWI showed only moderate direct C4-versus-C5 discrimination (AUC 0.742). For early classes 1 + 2 versus later classes 4 + 5, the device-derived HSI StO₂ index showed the highest univariate performance (AUC 0.940), followed by TWI (AUC 0.894) and the NIR spectral slope between 1550 and 1950 nm (AUC 0.860). Within the Raman-compatible forensic range, HSI remained highly informative, while Raman carbonate/phosphate and crystallinity parameters provided complementary molecular information. Class 1 versus class 2 discrimination remained moderate. Corrected five-class modelling achieved only moderate balanced accuracy and did not consistently improve upon NIR-ONE alone. Conclusions: Diagnostic performance was strongly task-dependent. In this internally evaluated cohort, micro-CT Mean2 and high-wavelength NIR-ONE features showed the strongest discrimination of archaeological-compatible C5 material, whereas HSI-derived optical indices were most informative for the separated early-versus-later contrast and Raman spectroscopy provided complementary molecular information within C1–C4. Because C5 comprised only six unique physical samples and archaeological context was confounded with chronological age, the very high C5-related AUC estimates should be regarded as exploratory, hypothesis-generating estimates rather than validated measures of chronological PMI. The proposed decision-support framework is likewise exploratory and requires independent external validation before forensic implementation. Full article

Accurate correction of daily satellite-derived precipitation estimates in data-scarce tropical regions remains a critical challenge for climate monitoring, agriculture, and public health. Satellite products such as CHIRPS offer broad spatial coverage but exhibit systematic biases relative to ground-based observations particularly in complex terrain under bimodal tropical regimes influenced by ENSO. We propose a Functional Generalised Additive Mixed Model (FGAMM) that corrects CHIRPS-derived precipitation estimates by treating the annual accumulated precipitation curve as a functional response and the satellite accumulation curve as a functional covariate, while incorporating station-level random effects and the Southern Oscillation Index. This functional formulation targets the systematic, slowly varying bias between satellite and ground-station accumulation, the quantity most relevant for water-balance applications such as reservoir management and agricultural planning rather than day-to-day storm nowcasting. Applied to 62 IDEAM stations in the Valle del Cauca department of Colombia (2012–2020), the FGAMM achieves a mean cross-validation RMSE of 0.68 mm/day (95% bootstrap CI: 0.61–0.75), a substantially lower error than linear regression, SVM, and Random Forest within this dataset, where the gap is statistically significant across all competing methods. This magnitude of advantage is not reproduced when applying the same fitting-and-differencing pipeline, via a simplified concurrent approximation, to an independent national-network dataset; we discuss the methodological factors that likely contribute to this discrepancy—including an inherent smoothness asymmetry between the penalised-spline FGAMM fit and the unconstrained benchmark models, and differences in validation design between the two checks—in the Discussion, and treat the true size of the FGAMM’s advantage as an open question pending a fully controlled comparison. Corrected estimates are currently restricted to the calibrated station locations; because CHIRPS provides near-global daily coverage from 1981 to the present, we discuss how the same modelling approach could in principle be applied to other tropical or subtropical regions with a sparse reference station network, including areas of Latin America, sub-Saharan Africa, and South Asia where station density is similarly limited. Full article

The escalating production of fetal bovine serum generates substantial quantities of fetal bovine hide as an underutilized byproduct. In this study, we extracted collagen from this source, characterized it as predominantly type I collagen with intact triple-helical features, and fabricated a series of composite sponge dressings by blending it with chitosan. The best-balanced formulation (COL₁/CS₁, 1:1 ratio) exhibited markedly superior physicochemical properties relative to pure collagen sponges, as evidenced by higher porosity (91.3%), water uptake (2010%), moisture retention (23.7%), and water vapor transmission rate (4169.02 ± 86.45 g/m²/day). We hypothesize that the intrinsically lower cross-linking density of fetal collagen may expose a greater abundance of carboxyl and hydroxyl moieties, thereby fostering electrostatic complexation and hydrogen bonding with chitosan’s amino groups. This molecular interplay appears to promote the genesis of a highly uniform, interconnective porous network. In vitro, the COL₁/CS₁ sponge elicited a hemolysis rate below 5%, a blood coagulation index as low as 7.02%, no cytotoxicity toward L929 and MRC-5 cells, and a pronounced capacity to stimulate cell proliferation and wound repopulation. In a murine full-thickness excisional wound model, the COL₁/CS₁ group achieved a 98.1% closure rate by day 14, significantly outpacing both the pure collagen and untreated controls. Histological examinations corroborated these findings, revealing accelerated granulation tissue deposition, robust neovascularization, and orderly collagen remodeling, with no overt toxicity observed in vital organs under the tested conditions. This work presents a viable valorization pathway for an agricultural byproduct into high-value biomedical constructs and provides insights into how source-dependent collagen attributes may influence the functional performance of biomaterials. Full article

Commercial polyolefin separators for lithium-ion batteries (LIBs) exhibit only inadequate wettability and thermal stability. In large-scale production, high electrolyte uptake and wetting are essential to enable rapid electrolyte filling during battery assembly to reduce costs. In addition, it is imperative to develop separators with enhanced thermal stability for improved performance and safety. The focus of this study is the structure–property–performance relationship of separator coatings. Platelet-shaped glass particles are utilized as inorganic coating material for polyethylene (PE) separators. Styrene–butadiene rubber (SBR) was selected as binder due to its high thermal stability. Hybrid separators are prepared using a colloidal coating technology employing a water-based slurry. As the excessive use of binder in the coating can block pores, precise control of the binder content was essential to maintain battery performance. The resulting separators with an optimized binder content of 1 wt.% in the coating demonstrate high porosity, instantaneous wetting with electrolyte, and a 25 K increase in onset temperature for shrinkage. The utilization of glass platelets with an aspect ratio of 10 as coating material provided the best balance among processability, coating homogeneity, thermal stability, ionic conductivity, and cycling performance under the investigated processing conditions. Full article