Search Results (465)

The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution observations of sea-surface height. However, their direct use in ocean circulation studies is complicated by the presence of small-scale unbalanced motion signals and instrumental noise, which hinder accurate estimation of geostrophic velocities. To address these limitations, we developed an adaptive convolutional neural network (CNN)-based filtering technique that refines SWOT-derived sea-level observations. The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. By combining satellite and in-situ data with machine learning, this work demonstrates the potential of an adaptive CNN-based filtering approach to enhance the accuracy and reliability of SWOT-derived sea-level and velocity estimates, providing a valuable tool for global oceanographic applications. Full article

Streptococcus iniae (S. iniae) is a globally significant aquatic pathogen responsible for severe economic losses in aquaculture. While the S. iniae infection often exhibits distinct seasonal patterns strongly correlated with water temperature, there is limited knowledge regarding the temperature-dependent immune evasion strategies of S. iniae. Our results demonstrated a striking temperature-dependent virulence phenotype, with significantly higher A. latus mortality rates observed at high temperature (HT, 33 °C) compared to low temperature (LT, 23 °C). Proteomic analysis revealed temperature-dependent upregulation of key virulence factors, including streptolysin S-related proteins (SagG, SagH), antioxidant-related proteins (SodA), and multiple capsular polysaccharide (cps) synthesis proteins (cpsD, cpsH, cpsL, cpsY). Flow cytometry analysis showed that HT infection significantly reduced the percentage of lymphocyte and myeloid cell populations in the head kidney leukocytes of A. latus, which was associated with elevated caspase-3/7 expression and increased apoptosis. In addition, HT infection significantly inhibited the release of reactive oxygen species (ROS) but not nitric oxide (NO) production. Using S. iniae cps-deficient mutant, Δcps, we demonstrated that the cps is essential for temperature-dependent phagocytosis resistance in S. iniae, as phagocytic activity against Δcps remained unchanged across temperatures, while NS-1 showed significantly reduced uptake at HT. These findings provide new insights into the immune evasion of S. iniae under thermal regulation, deepening our understanding of the thermal adaptation of aquatic bacterial pathogens. Full article

Water scarcity and spatial variability in soil fertility are key constraints to stable grain production in the Huang-Huai-Hai Plain. However, the interaction mechanisms between regulated deficit irrigation and soil fertility influencing yield formation and water-nitrogen use efficiency in winter wheat remain unclear. In this study, a two-year field experiment (2022–2024) was conducted to investigate the effects of two irrigation regimes—regulated deficit irrigation during the heading to grain filling stage (D) and full irrigation (W)—under four soil fertility levels: F1 (N: P: K = 201.84: 97.65: 199.05 kg ha⁻¹), F2 (278.52: 135: 275.4 kg ha⁻¹), F3 (348.15: 168.75: 344.25 kg ha⁻¹), and CK (no fertilization). The results show that aboveground dry matter accumulation, total nitrogen content, pre-anthesis dry matter and nitrogen translocation, and post-anthesis accumulation significantly increased with fertility level (p < 0.05). Regulated deficit irrigation promoted the contribution of post-anthesis dry matter to grain yield under the CK and F1 treatments, but suppressed it under the F2 and F3 treatments. However, it consistently enhanced the contribution of post-anthesis nitrogen to grain yield (p < 0.05) across all fertility levels. Higher fertility levels prolonged the grain filling duration by 18.04% but reduced the mean grain filling rate by 15.05%, whereas regulated deficit irrigation shortened the grain filling duration by 3.28% and increased the mean grain filling rate by 12.83% (p < 0.05). Grain yield significantly increased with improved fertility level (p < 0.05), reaching a maximum of 9361.98 kg·ha⁻¹ under the F3 treatment. Regulated deficit irrigation increased yield under the CK and F1 treatments but reduced it under the F2 and F3 treatments. Additionally, water use efficiency exhibited a parabolic response to fertility level and was significantly enhanced by regulated deficit irrigation. Nitrogen partial factor productivity (NPFP) declined with increasing fertility level (p < 0.05); Regulated deficit irrigation improved NPFP under the F1 treatment but reduced it under the F2 and F3 treatments. The highest NPFP (41.63 kg·kg⁻¹) was achieved under the DF1 treatment, which was 54.81% higher than that under the F3 treatment. TOPSIS analysis showed that regulated deficit irrigation combined with the F1 fertility level provided the optimal balance among yield, WUE, and NPFP. Therefore, implementing regulated deficit irrigation during the heading–grain filling stage under moderate fertility (F1) is recommended as the most effective strategy for achieving high yield and efficient resource utilization in winter wheat production in this region. Full article

The contamination of drinking water by per- and polyfluoroalkyl substances (PFASs) presents a global concern due to their extreme persistence, driven by strong C–F bonds. This study investigated the potential of graphene as a filtration material for PFAS removal, focusing on six key compounds regulated by the U.S. EPA: PFOA, PFNA, GenX, PFBS, PFOS, and PFHxS. Using molecular simulations, adsorption energy, diffusion coefficients, and PFAS-to-graphene distances were analyzed. The results showed that adsorption strength increased with molecular weight; PFOS (500 g/mol) exhibited the strongest adsorption (−171 kcal/mol). Compounds with sulfonic acid head groups (e.g., PFOS) had stronger interactions than those with carboxylate groups (e.g., PFNA), highlighting the importance of head group chemistry. Shorter graphene-to-PFAS distances also aligned with higher adsorption energies. PFOS, for example, had the shortest distance at 8.23 Å (head) and 6.15 Å (tail) from graphene. Diffusion coefficients decreased with increasing molecular weight and carbon chain length, with lower molecules like PFBS (four carbon atoms) diffusing more rapidly than heavier ones like PFOS and PFNA. Interestingly, graphene enhanced PFAS mobility in water, likely by disrupting the water structure and lowering intermolecular resistance. These results highlight graphene’s promise as a high-performance material for PFAS removal and future water purification technologies. Full article

Gully erosion refers to the landform formed by soil and water loss through gully development, which is a critical manifestation of soil degradation. However, research on the spatio-temporal variations in erosion gullies at the county scale remains insufficient, particularly regarding changes in gully aggregation and their driving factors. This study utilized high-resolution remote sensing imagery, gully interpretation information, topographic data, meteorological records, vegetation coverage, soil texture, and land use datasets to analyze the spatio-temporal patterns and influencing factors of erosion gully evolution in Bin County, Heilongjiang Province of China, from 2012 to 2022. Kernel density evaluation (KDE) analysis was also employed to explore these dynamics. The results indicate that the gully number in Bin County has significantly increased over the past decade. Gully development involves not only headward erosion of gully heads but also lateral expansion of gully channels. Gully evolution is most pronounced in slope intervals. While gentle slopes and slope intervals host the highest density of gullies, the aspect does not significantly influence gully development. Vegetation coverage exhibits a clear threshold effect of 0.6 in inhibiting erosion gully formation. Additionally, cultivated areas contain the largest number of gullies and experience the most intense changes; gully aggregation in forested and grassland regions shows an upward trend; the central part of the black soil region has witnessed a marked decrease in gully aggregation; and meadow soil areas exhibit relatively stable spatio-temporal variations in gully distribution. These findings provide valuable data and decision-making support for soil erosion control and transformation efforts. Full article

Multistage centrifugal pumps, owing to their high head characteristics, are commonly applied in domains like subsea resource exploitation and groundwater extraction. However, the wear of flow passage components caused by solid particles in the fluid severely threatens equipment lifespan and system safety. To investigate the influence of solid-liquid two-phase flow on pump performance and wear, this study conducted numerical simulations of the solid-liquid two-phase flow within multistage centrifugal pumps based on the Euler–Lagrange approach and the Tabakoff wear model. The simulation results showed good agreement with experimental data. Under the design operating condition, compared to the clear water condition, the efficiency under the solid-liquid two-phase flow condition decreased by 1.64%, and the head coefficient decreased by 0.13. As the flow rate increases, particle momentum increases, the particle Stokes number increases, inertial forces are enhanced, and the coupling effect with the fluid weakens, leading to an increased impact intensity on flow passage components. This results in a gradual increase in the wear area of the impeller front shroud, back shroud, pressure side, and the peripheral casing. Under the same flow rate condition, when particles enter the pump chamber of a subsequent stage from a preceding stage, the fluid, after being rectified by the return guide vane, exhibits a more uniform flow pattern and reduced turbulence intensity. The particle Stokes number in the subsequent stage is smaller than that in the preceding stage, weakening inertial effects and enhancing the coupling effect with the fluid. This leads to a reduced impact intensity on flow passage components, resulting in a smaller wear area of these components in the subsequent stage compared to the preceding stage. This research offers critical theoretical foundations and practical guidelines for developing wear-resistant multistage centrifugal pumps in solid-liquid two-phase flow applications, with direct implications for extending service life and optimizing hydraulic performance. Full article

This article documents the widespread absence of sessile species in bedrock intertidal habitats at the head of the St. Lawrence Estuary, a large macrotidal estuary located in eastern Canada. Extensive observations revealed that no seaweeds or sessile invertebrates occurred anywhere (including cracks and crevices) on substrate areas that become exposed to the air during low tides. Only one sessile species, a green filamentous alga, was found submerged in tidepools. The lack of truly marine sessile species is likely explained by the very low water salinity of this coast, while the absence of sessile freshwater species on intertidal substrates outside of tidepools likely responds to a combination of oligohaline conditions during high tides and daily exposures to the air during low tides, which freshwater species are typically not adapted to. Influences of winter ice scour and coastal suspended sediments are likely secondary. Experimental research could unravel the interactive effects of these abiotic stressors. Overall, this “intertidal desert” could be a useful model system to further explore the boundaries of life on our planet. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

The prevention of bubble coalescence is essential in various industrial processes, such as mineral flotation, where the stability of air–liquid interfaces significantly affects performance. The combined influence of multiple physicochemical parameters on bubble coalescence remains insufficiently understood, particularly under conditions relevant to flotation. This study explores the key factors that influence the inhibition of bubble coalescence in aqueous solutions containing methyl isobutyl carbinol (MIBC), providing a systematic comparative analysis to assess the effect of each variable on coalescence inhibition. An experimental method was employed in which two air bubbles were formed from identical capillaries and brought into contact either head-to-head or side-by-side, then held until coalescence occurred. This setup allows for reliable measurements of coalescence time with minimal variability regarding the conditions under which the bubbles interact. The study examined the effects of several factors: temperature, pH, salt concentration and type, bubble approach speed, contact area, and contact configuration. The results reveal that coalescence is delayed at lower temperatures, alkaline pH conditions, high salt concentrations, and larger interfacial contact areas between bubbles. Within the range studied, the influence of approach speed was found to be insignificant. These findings provide valuable insights into the fundamental mechanisms governing bubble coalescence and offer practical guidance for optimizing industrial processes that rely on the controlled stabilization of air–liquid interfaces. By understanding and manipulating the factors that inhibit coalescence, it is possible to design more efficient and sustainable mineral flotation systems, thereby reducing environmental impact and conserving water resources. Full article

Wide-range electric submersible centrifugal pumps (ESPs) are critical for offshore oilfields but suffer from narrow high-efficiency ranges and frictional losses under dynamic reservoir conditions. This study introduces bio-inspired dimple-type non-smooth surfaces on impeller blades to enhance hydraulic performance. A combined numerical-experimental approach was employed: a 3D CFD model with the k-ω turbulence model analyzed oil–water flow (1:9 ratio) to identify optimal dimple placement, while parametric studies tested diameters (0.6–1.2 mm). Experimental validation used 3D-printed prototypes. Results revealed that dimples on the pressure surface trailing edge reduced boundary layer separation, achieving a 12.98% head gain and 8.55% efficiency improvement at 150 m³/d in simulations, with experimental tests showing an 11.5% head increase and 4.6% efficiency gain at 130 m³/d. The optimal dimple diameter (0.9 mm, 2% of blade chord) balanced performance and manufacturability, demonstrating that bio-inspired surfaces improve ESP efficiency. This work provides practical guidelines for deploying drag reduction technologies in petroleum engineering, with a future focus on wear resistance in abrasive flows. Full article

Vegetables are usually thermally processed before consumption to improve their flavor and safety. In this work, the effect of boiling (BO), blanching (BL), steaming (ST), air-frying (AF), and pan-frying (PF)on the nutritional value and bioactivity of broccoli (Brassica oleracea var. italica) heads was investigated, including a comparative analysis of the tissue and the cooking water remaining after the treatments. Using spectrophotometric methods, AF broccoli was found to have the highest levels (p ≤ 0.05) of hydroxycinnamic acids (1.58 ± 0.71 mg CAE/g fw), total glucosinolates (3.76 ± 2.09 mg SinE/g fw), carotenoids (6.73 ± 2.89 mg/kg fw), and lycopene (0.91 ± 0.19 mg/kg fw). Steamed and AF broccoli had the highest total phenolics (0.72 ± 0.12 mg GAE/g fw and 0.65 ± 0.15 mg GAE/g fw, respectively; p ≤ 0.05). ST broccoli also had the highest levels of soluble sugars (11.04 ± 2.45 mg SucE/g fw) and total tannins (0.46 ± 0.19 mg GAE/g fw). The water remaining after cooking broccoli (BOW) had the highest total flavonoids (2.72 ± 0.59 mg QE/g fw) and antioxidant capacity (ABTS and FRAP, 57.57 ± 18.22% and 79.34 ± 3.28%, respectively; p ≤ 0.05). The DPPH assay showed that AF (36.12 ± 15.71%) and ST (35.48 ± 2.28%) had the strongest antioxidant potential. DNA nicking assay showed that BOW and BLW were the most effective in preserving plasmid DNA supercoiled form (99.51% and 94.81%, respectively; p ≤ 0.05). These results demonstrate that thermal processing significantly affects the phytochemical composition and functional properties of broccoli, with steaming and air-frying generally preserving the highest nutritional quality. Additionally, cooking water, often discarded, retains high levels of bioactive compounds and exhibits strong antioxidant and DNA-protective effects. To the best of our knowledge, this is the first study to investigate how different thermal processing techniques of vegetables influence their ability to protect plasmid DNA structure. Furthermore, this is the first study to compare the DNA-protective effects of broccoli tissue extracts and the water remaining after cooking broccoli. Full article

Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the development of automatic classification systems for water potability based on physicochemical attributes. The models examined include the enhanced tabular transformer (ETT), feature tokenizer transformer (FTTransformer), self-attention and inter-sample network (SAINT), and tabular autoencoder pretraining enhancement (TAPE). The study utilized an open-access water quality dataset that includes nine key attributes such as pH, hardness, total dissolved solids (TDS), chloramines, sulphate, conductivity, organic carbon, trihalomethanes, and turbidity. The models were evaluated under a unified protocol involving 70–15–15 data partitioning, five-fold cross-validation, fixed random seed, and consistent hyperparameter settings. Among the evaluated models, the enhanced tabular transformer outperforms other models with an accuracy of 95.04% and an F1 score of 0.94. ETT is an advanced model because it can efficiently model high-order feature interactions through multi-head attention and deep hierarchical encoding. Feature importance analysis consistently highlighted chloramines, conductivity, and trihalomethanes as key predictive features across all models. SAINT demonstrated robust generalization through its dual-attention mechanism, while TAPE provided competitive results with reduced computational overhead due to unsupervised pretraining. Conversely, FTTransformer showed limitations, likely due to sensitivity to class imbalance and hyperparameter tuning. The results underscore the potential of transformer-based models, especially ETT, in enabling efficient, accurate, and scalable water quality monitoring. These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion. Full article

Drought stress severely limits agricultural productivity, with atmospheric and soil water deficits often occurring simultaneously in field conditions. While plant responses to individual drought factors are well-documented, recovery mechanisms following combined atmospheric–soil drought remain poorly understood, hindering drought resistance strategies and irrigation optimization. We set up two VPD treatments (low and high vapor pressure deficit) and two soil moisture treatments (CK: control soil moisture with sufficient irrigation, 85–95% field capacity; drought: soil moisture with deficit irrigation, 50–60% field capacity) in the pot experiment. We investigated wheat’s hydraulic transport (leaf hydraulic conductance, K_leaf) and gas exchange (stomatal conductance, g_s; photosynthetic rate, A_n) responses to combined drought stress from atmospheric and soil conditions at the heading stage, as well as rewatering 55 days after treatment initiation. The results revealed that: (1) high VPD and soil drought significantly reduced leaf hydraulic conductance (K_leaf), with a high VPD decreasing K_leaf by 31.6% and soil drought reducing K_leaf by 33.2%; The high VPD decreased stomatal conductance (g_s) by 43.6% but the photosynthetic rate (A_n) by only 12.3%; (2) After rewatering, g_s and A_n of atmospheric and soil drought recovered relatively rapidly, while K_leaf did not; (3) Atmospheric and soil drought stress led to adaptive changes in wheat’s stomatal regulation strategies, with an increasing severity of drought stress characterized by a shift from non-conservative to conservative water regulation behavior. These findings elucidate wheat’s hydraulic–stomatal coordination mechanisms under drought stress and their differential recovery patterns, providing theoretical foundation for improved irrigation management practices. Full article

This study presents a comprehensive numerical investigation into the influence of blade profile geometry on the internal flow dynamics and hydraulic performance of Cross-Flow Turbines (CFTs) under varying runner speeds. Four blade configurations, flat, round, sharp, and aerodynamic, were systematically evaluated using steady-state, two-dimensional Computational Fluid Dynamics (CFD) simulations. The Shear Stress Transport (SST) k–ω turbulence model was employed to resolve the flow separation, recirculation, and turbulence across both energy conversion stages of the turbine. The simulations were performed across runner speeds ranging from 270 to 940 rpm under a constant head of 10 m. The performance metrics, including the torque, hydraulic efficiency, water volume fraction, pressure distribution, and velocity field characteristics, were analyzed in detail. The aerodynamic blade consistently outperformed the other geometries, achieving a peak efficiency of 83.5% at 800 rpm, with improved flow attachment, reduced vortex shedding, and lower exit pressure. Sharp blades also demonstrated competitive efficiency within a narrower optimal speed range. In contrast, the flat and round blades exhibited higher turbulence and recirculation, particularly at off-optimal speeds. The results underscore the pivotal role of blade edge geometry in enhancing energy recovery, suppressing flow instabilities, and optimizing the stage-wise performance in CFTs. These findings offer valuable insights for the design of high-efficiency, site-adapted turbines suitable for micro-hydropower applications. Full article

Decentralized solar water disinfection systems (DSODIS) in continuous flow systems are alternatives for large-scale improved water access in rural contexts. However, DSODIS in rural Ghana are limited. An exploratory sequential mixed-methods design was used to explore the enablers of and barriers to, as well as reported barrier perceptions to, the effective implementation of DSODIS in the Sawla-Tuna-Kalba (STK) District of Ghana. The qualitative data (26 respondents) were analyzed thematically, and the quantitative data (1155 household heads) were subjected to Poisson regression analyses. Enablers were categorized into themes such as willingness to pay for DSODIS, household and community participation, and willingness to use water from DSODIS. Similarly, the barriers include environmental barriers, technological barriers, economic barriers, and political and legal barriers. Household characteristics such as main water source and income, age group, education, marital status, household size, being born in the community, and years living in the community are statistically associated with reported barrier perceptions. Households with unimproved water sources and high income (IRR = 1.432, p = 0.000) and improved water sources and high income (IRR = 1.295, p = 0.000) are 43% and 30% more likely, respectively, to report more barrier perceptions compared with households with unimproved water sources and low income. Females (IRR = 1.070, p = 0.032) are marginally more likely to report more barrier perceptions compared with males. The model output also indicates that household heads with higher educational attainment (IRR = 1.152, p = 0.001) are 15% more likely to report more barrier perceptions compared with those with no formal education. These findings provide valuable information for policymakers and stakeholders aiming to provide quality water in rural Ghana where centralized systems cannot be installed. Full article