Search Results (300)

Mexico City experiences severe water stress driven by aquifer overexploitation and recurrent droughts. Effective water management requires operational spatial monitoring systems capable of spatially reconstructing meteorological anomalies across multiple temporal scales. In this work we developed an explainable deep learning framework using Long Short-Term Memory (LSTM) networks to spatially reconstruct three drought indices—the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI)—across five accumulation scales (3, 6, 12, 18, and 24 months). To strictly isolate genuine meteorological deviations, we adopted a hybrid statistical approach: SPI was computed following the standard WMO methodology using Gamma distribution fitting, while SPEI and RDI were computed using empirical monthly standardized anomalies to ensure robustness in non-stationary urban climates without forcing distributional assumptions. Model generalization was evaluated using a leave-one-microsite-out validation strategy, training on two stations and testing on a spatially isolated third station, with inter-station distances ranging from 1.8 to 6.7 km, sufficient to capture urban microclimatic heterogeneity while remaining within the same regional climate zone. We quantified feature importance using SHapley Additive exPlanations (SHAP) to provide mathematical transparency. The LSTM achieved predictive performance at long-term scales by effectively capturing deep sequential memory, while short-term reconstructions reflected the inherent noise of urban convective precipitation. The framework demonstrates reliable intra-urban spatial generalization capacity, supporting the development of diagnostic tools for metropolitan water stress assessment. Full article

River ice products from optical satellites are still dominated by binary ice–water or ice–snow discrimination, leaving within-ice spectral heterogeneity largely unresolved. This study benchmarks how far river ice can be subclassified from multispectral reflectance alone on the Tibetan Plateau using Landsat 5/7, Landsat 8/9, and Sentinel-2 surface-reflectance imagery. We compiled 356 winter scenes acquired between 2000 and 2024 across eight Tibetan Plateau basins, delineated river ice using NDSI and RDRI, and extracted 24,674 pixel-level spectra. To define reproducible subclasses, we applied K-means clustering guided by the Silhouette Coefficient, Davies–Bouldin index, Calinski–Harabasz index, and Gap Statistic. Combined with stratified visual interpretation, this approach consistently supported four optical spectral subclasses: thin-snow-covered ice, thick ice cover, thin ice, and frazil ice. Within-sensor classification accuracy remained extremely high (overall accuracy ≥ 0.948; kappa ≥ 0.929), with the Backpropagation Neural Network (BPNN) and tree ensembles performing best. Crucially, evaluating the optimal BPNN architecture revealed exceptional multi-dimensional generalizability: a Leave-One-Basin-Out spatial cross-validation yielded a stable average OA > 99% with an average Kappa > 0.98, while a unified multi-sensor model achieved a robust OA of 90.14% and a Kappa of 0.86. The most stable discriminative cues were visible-band brightness, reflectance turnover near ~0.7 μm, and shortwave-infrared sensitivity to effective thickness and surface wetness. These results provide a sensor-aware benchmark for practical optical river ice spectral subclassification and clarify which multispectral bands most strongly constrain subclass separability. Full article

Wetland ecosystems have been a central focus of ecological research for an quite some time. Nevertheless, the degradation of wetland riparian zones has markedly accelerated due to anthropogenic activities, climate change, and habitat heterogeneity. The objective of this paper is to investigate the differences in functional traits of riparian plants under changing wetland environments on a karst plateau, as well as to elucidate the adaptive strategies of wetland plants across different habitats. This study examines the Caohai Wetland located on the Guizhou karst plateau, selecting the leaves of four dominant plant species (Phragmites australis, Onopordum acanthium, Galium odoratum, Paspalum distichum) in the Caohai Wetland lakeshore zone and analyzes the influence of soil factors on the variation of plant functional traits within the wetland riparian zone. The results reveal that: (1) significant differences exist in the functional traits of dominant plants in the riparian zones of karst plateau wetlands, with complex interrelationships among these traits; (2) the coefficients of variation for magnesium (Mg) and calcium (Ca) in the soil are notably high (79.53% and 67.21%, respectively), whereas soil oxidation-reduction potential (ORP) exhibits the lowest coefficient of variation (4.36%)—furthermore, the convergent variation in specific leaf area (SLA) and leaf dry matter content (LDMC) directly reflects the strong environmental filtering imposed by this habitat—and (3) redundancy analysis (RDA) indicates that leaf length (LL), specific leaf area (SLA), leaf area (LA), and plant carbon content (PCC) are particularly sensitive to environmental changes, while soil calcium (Ca), total nitrogen (TN), water-dispersible clay (WDR), soil organic matter (SOM), soil moisture content (SPMC), and total potassium (TK) constitute the principal soil factors influencing plant adaptive strategies in karst plateau wetlands. In conclusion, this study demonstrates that adaptation to karst wetland habitats is mediated through trade-offs in the allocation of photosynthetic products and the regulation of carbon (C), nitrogen (N), and phosphorus (P) nutrient balances under calcium-enriched and phosphorus-limited conditions, thereby reflecting the response characteristics of functional traits in karst plateau wetland plants to environmental changes. Full article

In this study, a low-cost AS7265x-based multispectral electronic tongue system was developed for estimating milk composition and adulteration indicators and supported with an explainable artificial intelligence (XAI) framework. Experimental analyses were conducted on 190 augmented commercial milk samples, where fat, protein, solids-not-fat (SNF), density, freezing point, and added water ratio were treated as target variables. Sensor data were modeled as RAW, DERIVED, and FUSION feature sets, and regression performance was compared using Random Forest, Gradient Boosting, AdaBoost, KNN, and XGBoost. Model validation was carried out with both five-fold cross-validation and Leave-One-Out (LOO) strategies to assess field-level generalizability. Results showed that a narrow-band, low-cost optical sensor platform can estimate not only fat and protein but also SNF, density, and freezing point with high accuracy. Within the XAI framework, permutation-based importance analysis and SHAP were used to identify critical spectral bands for each target parameter, enabling data-driven recommendations for band-oriented sensor design optimization. The study presents a scalable methodology that integrates low-cost sensor design, multi-parameter quality estimation, and explainable modeling beyond traditional fat–protein-focused approaches. Across all six targets, the XAI analysis consistently identified the near-infrared channel at 860 nm (asIR_3) as the most informative band, reflecting the combined effect of water absorption and Mie scattering by fat globules; the visible channel at 680 nm (asVIS_4) emerged as a secondary band, reflecting dissolved-matter scattering. These bands are therefore the natural starting point for cost-reduced versions of the sensor. Among the compared feature sets (RAW, DERIVED, FUSION), the 18-band RAW configuration provided the most balanced performance across all six targets. Full article

Jasmonates (JAs) are a diverse group of jasmonic acid (JA)-linked metabolites, including the biosynthetic intermediate 12-oxophytodienoic acid (OPDA). Although changes in JAs have been associated with plant responses to abiotic stress, the involvement and kinetics of specific forms such as JA, JA-Ile and OPDA require further clarification. This study analyzed jasmonate profiles in roots and leaves of two oat genotypes differing in drought tolerance. Jasmonates were quantified using UPLC-MS/MS, expression of key biosynthetic genes was assessed by qRT-PCR, and JA/OPDA treatments were applied to evaluate their effects on physiological and morphological responses to drought. Drought induced contrasting jasmonate dynamics in roots and leaves, with overall JA levels increasing in leaves and decreasing in roots, with genotype- and compound-specific differences. JA and JA-Ile ((+)-7-iso-jasmonoyl-L-isoleucine) showed similar trends, whereas OPDA displayed a distinct pattern. The tolerant genotype exhibited an early and marked reduction in root OPDA, while the susceptible one showed minimal change. Exogenous OPDA increased drought symptoms, reduced leaf relative water content and strongly decreased root length by limiting the formation of new thin roots. In contrast, JA application alleviated drought symptoms, reflected in a lower area under the drought progress curve, without affecting root length. Results suggest that under water deficit, reduced OPDA, likely due to its conversion into JA and JA-Ile, is associated with the development of small-diameter roots essential for maintaining water status in oat. Together, these results highlight tissue-specific differences in jasmonate dynamics during drought and show that OPDA and JA treatments lead to distinct drought-related responses in both leaves and roots. Full article

Since ancient times, clays have been used to protect plants from insects and excessive sunlight. Today, their potential use is being re-evaluated as a tool to mitigate the effects of climate change and to manage emerging pests. This review synthesizes and compares findings from studies conducted in different regions of the world. Kaolin forms a reflective film on leaves and fruits, lowering tissue temperature. In warm climates, this temperature reduction can contribute to improved physiological parameters including net assimilation and water use efficiency; however, these responses are strongly influenced by additional factors. It may also affect some oenological characteristics of grapes (acidity, pH, and phenol content, particularly anthocyanins), thereby improving the overall chemical composition of grapes and wines, particularly in terms of acidity, pH and phenolic content. In addition, kaolin has been shown to reduce damage caused by the grape leafhopper (Empoasca vitis, Jacobiasca lybica, among others) to levels comparable to those achieved with synthetic pesticides. However, responses vary depending on different factors, such as application timing, dose, cultivar and climate. Overall, kaolin represents a sustainable strategy for mitigating climate change effects on fruit quality and for supporting ecological pest management. Full article

Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop status by detecting deficiencies in essential nutrients. This study evaluated the effects of N and P deficiency on field grown broccoli (Brassica oleracea L. var. italica Plenk) using a plant-induced electrical signal (PIES) sensor, in which needle electrodes are inserted into the stem to measure electrical conductivity reflecting plant water and ion status. Four treatments were established, including the control (N100P100) with sufficient N and P supply, N deficiency (N0P100), P deficiency (N100P0), and combined N–P deficiency (N0P0). For sufficient supply, urea and fused phosphate (FP) were applied at rates of 122 kg N ha⁻¹ and 71 kg P ha⁻¹, respectively. Soil, stem, and leaf nutrient contents, growth parameters, and stress related indicators were analyzed and their relationship with PIES values were evaluated. PIES was highest in control (N100P100) and lowest under N–P deficiency (N0P0). Higher PIES values were observed during the vegetative stage, whereas values declined during the reproductive stage, reflecting changes in physiological activity. Growth parameters such as shoot and root weight and stem diameter were generally superior in the control (N100P100) treatment, while leaf calcium (Ca), magnesium (Mg), and potassium (K) concentrations showed no significant differences among treatments. Total N content in leaves was higher in N fertilized treatments (control and P deficiency). Photosynthesis-related parameters, including soil plant analysis development (SPAD), Fv/Fm, and chlorophyll content, were lowest under N–P deficiency, which was reflected in the PIES. Principal component analysis (PCA) showed that the PIES was closely associated with growth and photosynthetic parameters and clearly distinguished N sufficient treatments (control and P deficiency) from N deficient treatments (N0P100, N0P0). Overall, these findings suggest that PIES monitoring can serve as a sensitive physiological indicator of nutrient stress and may be applied as an early diagnostic tool before visible growth inhibition occurs in broccoli cultivation. Full article

Hyperspectral remote sensing provides essential biochemical and structural information for crop disease monitoring, yet its application to cotton Verticillium wilt has largely focused on single-period evaluations or multi-temporal classifications. Such approaches overlook the progressive nature of this vascular disease, whose pigment, water, and mesophyll responses evolve over time, making temporal hyperspectral information critical for reliable severity estimation but still insufficiently utilized. To overcome this limitation, we conducted daily time-series observations on cotton leaves and collected 2895 hyperspectral reflectance measurements and 770 high-resolution RGB images together with disease severity records, generating a temporally dense spectral-severity dataset spanning symptom-free to severe stages. Five categories of disease-related vegetation indices were derived and organized into 5-day spectral–temporal slices. Based on these features, we introduce a dual-branch Transformer-TCN model that integrates global temporal dependencies captured by self-attention with local temporal variations resolved by dilated causal convolutions for severity inversion. The model delivers the strongest performance with an R² of 0.8813, exceeding multiple single and hybrid time-series alternatives by 0.0446–0.1407 in R², equivalent to a relative improvement of 5.33–19.00%. Temporal spectral features also outperform their non-temporal counterparts, highlighting that disease progression dynamics captured by time-series spectra are critical for reliable severity retrieval. Feature contribution analysis indicates that the blue red index BRI provides the highest contribution, consistent with the single-index time-series modelling results. Photosynthesis- and water-related indices provide secondary but complementary support. Collectively, our results demonstrate that the dual-branch Transformer-TCN model can capture complex spectral–temporal relationships between cotton Verticillium wilt and disease severity, providing methodological support for crop disease monitoring and evaluation. Full article

Moringa oleifera leaves are recognized as a nutrient-dense plant material of compositional and nutritional interest. This study aimed to characterize the nutritional and physicochemical properties of M. oleifera dried leaves through nutritional assessment and spectroscopic fingerprinting. Amino acid profiling, antioxidant activity assessment using ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and oxygen radical absorbance capacity (ORAC) assays, chromatographic analysis of organic acids and sugars, color measurement, techno-functional characterization, and vibrational spectroscopy including Fourier Transform infrared with attenuated total reflectance (FT-IR/ATR) and Raman were employed. The crude protein content was 16.13 ± 0.43%. Moringa leaves contained all essential amino acids, with notably high tryptophan content (amino acid score, AAS = 200.00%). The amino acids limiting the nutritional value of the protein were primarily sulfur-containing amino acids (AAS = 49.57%) and lysine (AAS = 49.79%). Histidine, leucine, and valine also showed levels below the reference protein. Antioxidant activity exhibited solvent-dependent patterns: the 80% ethanolic extract demonstrated significantly higher FRAP activity (27.05 ± 1.05 mg Trolox Equivalent (TxE)/g dry matter (DM)) and ORAC values (107.24 ± 6.80 mg TxE/g DM), while no statistically significant differences between extracts were observed for DPPH, ABTS, or total phenolic content. Chromatographic profiling identified fructose and glucose as the predominant sugars, alongside citric, succinic, lactic, and acetic acids. The leaves exhibited favorable techno-functional properties, including high water holding capacity and water solubility index. Spectroscopic analysis revealed bands consistent with proteins, lipids, carbohydrates, and glycoside-related structures, while the preserved green-yellow coloration (hue angle 101.68°) indicated retention of pigment-related features during processing. These findings provide compositional and physicochemical characteristics of Moringa leaves relevant to their evaluation as a plant-derived food material. Full article

Accurate dry weight assessment is crucial for hemodialysis (HD) fluid management, yet traditional clinical methods often lack precision. A significant scientific gap exists in the availability of a standardized multimodal framework for integrating objective tools, leaving clinicians without clear guidance on combining results from multiple devices. To address this gap, this narrative review provides a qualitative clinical synthesis of bioimpedance spectroscopy (BIS), lung ultrasound (LUS), and inferior vena cava diameter (IVCD). A structured literature search was conducted across PubMed, Scopus, and CINAHL for English-language studies published between 2012 and 2024. Studies focusing on dry weight assessment using these tools in adult HD patients were included, and findings from 22 core studies were synthesized narratively. BIS and LUS are valuable tools for identifying fluid overload. BIS assesses systemic fluid distribution across compartments, whereas LUS allows non-invasive detection of extravascular lung water. In contrast, IVCD primarily reflects intravascular volume status. While the integrated use of these tools shows potential clinical utility, individual methods, particularly IVCD, require further validation owing to interpatient variability. A multimodal approach that integrates these objective methods with clinical judgment offers a comprehensive evaluation of dry weight. Integrating these assessment strategies may improve outcomes and decision-making in nephrology care. Full article

Gypsum ecosystems are constrained by extreme nutrient and water scarcity, where cyanoprokaryota interact with gypsophyte rhizospheres, influencing plant performance and soil biogeochemistry. This study examines three gypsophytes—Herniaria fruticosa, Helianthemum squamatum, and Teucrium libanitis—during winter and spring to characterize rhizospheric and bulk soil properties, assess enzymatic activity and nutrient cycling, identify cyanoprokaryota communities, and determine bioelement accumulation patterns in both Microcoleus chthonoplastes and gypsophytes. Physical, chemical, microbiological, and microscopic analyses were conducted across seasons. β-glucosidase activity showed species-specific responses to water pulses, particularly in Helianthemum squamatum. Seasonal differences in water-soluble C and N distinguished rhizospheres of Teucrium libanitis and Helianthemum squamatum. Key soil drivers included water-holding capacity to −1500 kPa, total and organic carbon, and Cr content. Cyanoprokaryota exhibited both rhizosphere-specific (Gloeocapsa novacekii, Pseudocapsa dubia) and ubiquitous taxa, with Microcoleus chthonoplastes reflecting bioaccumulation strategies. Bioelement accumulation differed between leaves and roots, especially for K, Mn, Zn, Na, Ni, C, and V, while the Sr:Ca ratio emerged as a potential indicator, especially in Herniaria fruticosa. These findings highlight the role of cyanoprokaryota in regulating nutrient availability and enzymatic activity, supporting gypsophyte adaptation and the ecological sustainability and resilience of gypsum ecosystems, and informing conservation and restoration strategies in these neglected ecosystems. Full article

Fluid seepages and seabed pockmarks are widely observed on continental margins worldwide in hydrate- and non-hydrate-bearing sediment. Subsurface gas chimneys connecting seafloor pockmarks to underlying gas reservoirs are commonly revealed by seismic reflection data, indicating pathways of past and present fluid migration. Fluid seepage occurs when the seal of a gas reservoir is breached, allowing fluids to migrate upward and vent at the seafloor, forming pockmarks. In hydrate-bearing settings, gas reservoirs beneath hydrate layers typically consist of coexisting water and gas phases. However, quantitative constraints on gas saturation in free-gas zones beneath hydrates inferred from pockmark morphology remain limited. In this study, a two-phase pockmark model was developed to investigate gas-chimney growth and pockmark formation, and to estimate gas saturation in free-gas zones below hydrates using pockmark depth and gas-zone thickness as key parameters. The model was applied to the Storegga Slide region off Norway, where hydrates, pockmarks, and chimney-like seismic anomalies have been documented. Here, the application is intended to represent localized near-threshold (pre-seepage) conditions leading to pockmark initiation, rather than the present-day post-venting state. Model results for the initiation (near-threshold, pre-venting) stage indicate that the effective gas saturation in the free-gas reservoir beneath the hydrates was approximately 1.36–1.58% for gas-zone thicknesses of 50–100 m, and that the corresponding chimney-propagation timescale during initiation was on the order of ~200 years. These estimates represent threshold conditions required for seal breach and pockmark formation rather than present-day seepage states. During venting, methane gas may form hydrates within the chimney inside the hydrate stability zone, while authigenic carbonates precipitate in pockmarks and shallow sediments. These secondary hydrates and carbonates eventually seal the chimney, leaving behind a residual gas chimney in the subsurface sediment. Full article

Drought is a highly destructive natural disaster that inflicts severe economic losses. Its formation mechanisms are complex, yet existing studies have often focused on single driving factors, leaving the synergistic effects of multiple factors insufficiently explored. Based on multi-source data from Xinjiang spanning 1981–2020, this study systematically examined the combined impacts of atmospheric circulation, underlying surface conditions, and human activities on drought, using the multi-temporal-scale Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI), along with partial correlation analysis, spatial autocorrelation, and principal component analysis. The results show that Xinjiang experienced a pronounced drying trend over the past 40 years, with the seasonal SPEI and SSI both exhibiting significant declines. Drought intensity was higher in northern Xinjiang than in the south. Correlations between drought indices and circulation indices, such as Atlantic Multidecadal Oscillation (AMO), were relatively weak, indicating a limited regulatory influence of large-scale circulation on regional drought under the dual constraints of topography and an inland setting. Among underlying surface factors, slope significantly influenced drought spatial patterns. Mountainous areas and basin interiors showed positive spatial correlations, characterized respectively by high–high clustering (high slope and high drought index) and low–low clustering (low slope and low drought index). In contrast, basin margins exhibited low–high clustering (low slope surrounded by high drought index), reflecting negative spatial correlation. Aspect showed no significant effect. Vegetation cover displayed clear seasonal coupling with drought, with strong negative correlations in spring due to intensified water stress. Human activities also played a prominent role. Since the mid-1990s, the expansion of built-up land and increased agricultural water use have shifted drought–land use relationships toward low–high clustering (low drought index surrounded by high land-use intensity) in southern Xinjiang oases, and toward low–low clustering (low drought index and low land-use intensity) in eastern Xinjiang. Meanwhile, ecological restoration projects promoted a transition from low–high to high–high clustering (high drought index and high land-use intensity) in some areas, alleviating local drying trends. Principal component analysis further revealed a shift in the dominant driver: land-use change was the primary factor before 2005, whereas vegetation cover became the key driver thereafter. By clarifying the mechanisms underlying multi-factor interactions in drought in Xinjiang, this study provides scientific support for integrated water resource management, ecological conservation, and climate adaptation strategies in arid regions. Full article

Sugarloaf chicory (Cichorium intybus var. porphyreum) represents a valuable crop for investigating metabolic responses to environmental stress. This study applied quantitative ¹H-NMR-based metabolomics to characterize the water-soluble metabolome and evaluate root metabolic adaptations under water-deficit (WD) conditions compared to well-watered (WW) conditions. A total of 44 compounds were identified across roots and leaves, with inulin being root-specific. To address the lack of aqueous NMR data for chicory sesquiterpene lactones (STLs), a solid-phase extraction and fractionation protocol was implemented. Comparison of ¹H-NMR and ¹³C chemical shifts with data from the literature, 2D NMR experiments (HSQC, HMBC), and spiking with standards confirmed that the major root STLs (lactucin, 8-deoxylactucin, and lactucopicrin) are 15-oxalate conjugates with enhanced water solubility. Under water deficit, root profiles revealed significant stress-induced alterations: sucrose, alanine, threonine and phospho-choline increased, whereas asparagine, glutamic acid, chiro-inositol, myo-inositol, and all three STL conjugates decreased markedly (−39% to −50%). These shifts reflect adaptive osmotic adjustments and carbon reallocation strategies under stress. As roots represent a remarkable source of bioactive STLs, these findings support their potential valorization as functional ingredients. This study establishes quantitative NMR metabolomics as a robust tool for assessing physiological responses to water deficit, providing insights into stress adaptation mechanisms and identifying roots as promising targets for alternative applications. Full article

Crop failures are common in rain-fed farming in sub-Saharan Africa, especially in water-scarce South Africa. Inadequate rainfall necessitates innovative solutions to enhance food production. Water-saving irrigation technologies can significantly reduce crop failures, particularly for smallholder farms with limited access to irrigation water. This study evaluated the effects of Self-Regulating, Low-Energy, Clay-Based Irrigation System (SLECI), subsurface (SDI) and surface drip (DI) on the performance of moringa (Moringa oleifera) and cowpea (Vigna unguiculata), cultivated either as mono (sole) crops or in intercropping systems, in an open experimental field in South Africa. The experimental design was a factorial Randomized Complete Block Design (RCBD) replicated three times. The main aim was to assess water productivity and yield performance in different irrigation systems over two growing seasons. The results showed that the SLECI irrigation system was more suitable for M. oleifera, while V. unguiculata performed best with standard drip irrigation. Moringa oleifera fresh leaf biomass was higher under SLECI with sand around the clay element and surface drip irrigation with 1.42 t/ha, followed by the SLECI treatment without sand with 1.25 t/ha, while the least yield was noted in subsurface drip irrigation treatment with 1.18 t/ha. Vigna unguiculata (a dual-purpose crop for grain and leaves) produced higher total fresh biomass yield under subsurface drip irrigation treatment with 66.26 t/ha, followed by the SLECI treatment without sand (61.51 t/ha), while drip and SLECI with sand showed similar yield with 52.34 and 52.31 t/ha, respectively. In M. oleifera, the irrigation water productivity (IWP) varied from 0.26 kg/m³ below the surface to 0.65 kg/m³ after the SLECI treatment with sand. IWP in V. unguiculata treatments ranged from 27.52 kg/m³ in SLECI without sand to 9.52 kg/m³ under surface drip irrigation. In addition, chlorophyll content and stem diameter were elevated under SLECI, reflecting enhanced nutrient and water availability. The findings have important implications for sustainable agriculture under water-limited conditions. Full article