Search Results (1,558)

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

Introduction: Elasmobranchs are an important component of deep-water and slope ecosystems, playing a key role in benthic and demersal food webs. Many species inhabiting offshore banks of the northeastern Atlantic are characterized by low productivity and high sensitivity to fishing pressure, which makes fishery-independent assessments particularly relevant. The Porcupine Bank supports a diverse assemblage of deep-water sharks and skates, yet quantitative information derived from standardized trawl surveys remains essential to characterize community structure and support ecosystem-based management. This study aims to provide an updated overview of the composition, relative abundance, biomass, and occurrence of elasmobranch species on the Porcupine Bank. Methodology: Data were collected during the Porcupine bottom trawl survey carried out in September–October 2023. The survey used a stratified random sampling design by depth and comprised a total of 88 valid demersal trawl hauls. Results: A total of 23 elasmobranch species belonging to four orders (Carcharhiniformes, Squaliformes, Rajiformes, and Hexanchiformes) were recorded. The assemblage was dominated by deep-water sharks, particularly squaliforms and carcharhiniforms. Galeus melastomus was the most dominant species, showing the highest stratified mean biomass and abundance and occurring in the majority of hauls. Other abundant and recurrent species included Etmopterus spinax, Scyliorhinus canicula, and Deania calceus. Skates of the genera Dipturus and Leucoraja were less abundant but showed consistent occurrences across depth strata. Several deep-water species, such as Apristurus spp. and Rajella fyllae, were recorded only sporadically, with very low abundances and limited occurrence. Conclusions: The results highlight the predominance of small- to medium-sized deep-water sharks on the Porcupine Bank and the comparatively lower contribution of rajid skates. This study provides a robust description of elasmobranch assemblage structure based on standardized sampling and constitutes a valuable baseline for future monitoring and comparative assessments in offshore Atlantic ecosystems. Full article

Accelerated carbonation of cement-based materials offers a promising route for CO₂ sequestration driven by waste heat co-emitted from cement and power plants; however, existing kinetic models typically describe the low-temperature gas–liquid–solid regime near 100 °C and the high-temperature gas–solid regime near 600 °C in isolation, limiting their applicability to plant-scale reactor design. This study proposes a unified dual-regime kinetic framework spanning 20–700 °C. The low-temperature branch couples Henry’s-law CO₂ solubility, a sigmoidal water-film stability function, and an Arrhenius ionic reaction term, whereas the high-temperature branch integrates shrinking-core surface reaction and product-layer diffusion with an attenuation term near the CaCO₃ decomposition onset. Seven parameters were calibrated by bounded least squares against a 51-point temperature dataset compiled from the author’s previously published carbonation experiments. The calibrated model reproduced the bimodal temperature dependence of the carbonation degree (R² = 0.62; RMSE = 0.083), with peaks near 100 °C and 640 °C, and predicted reactor volumes of order-of-magnitude 150–200 m³ for a 1 Mt/y cement plant under three waste-heat operating points. The framework bridges particle-scale kinetic and plant-scale design, and identifies mixing as the dominant operational sensitivity at the clinker-cooler condition. Full article

Studies aimed at identifying genotypes tolerant to water deficit are essential for the development of superior plant materials adapted to regions with limited water availability, such as the Brazilian Semi-Arid. This study evaluated the physiological, biochemical, and enzymatic responses of Stylosanthes spp. subjected to different levels of water availability (60%, 40%, and 20% of pot capacity). The experiment was conducted using a completely randomized design using a 3 × 2 factorial scheme, comparing the accession BGF 11-001 and the cultivar BRS-Bela (cv. Bela). Physiological traits, biochemical variables, and antioxidant enzyme activity were analyzed. The accession BGF 11-001 showed resilience under water deficit, maintaining high chlorophyll content even under severe stress. This response was associated with increased accumulation of amino acids such as proline, as well as enhanced antioxidant activity, indicating a tolerance mechanism based on osmotic adjustment and cellular protection. In contrast, cv. Bela exhibited higher sensitivity to water stress, with a pronounced reduction in photosynthetic pigments and greater accumulation of compatible solutes, including total soluble proteins, reducing sugars, amino acids, and proline, without significant activation of antioxidant enzymes. Overall, the results demonstrate that the genotypes adopt distinct strategies to cope with water stress, with BGF 11-001 being more efficient in activating defense mechanisms. Therefore, BGF 11-001 has agronomic potential for cultivation in drought-prone regions and is a promising genetic resource for forage breeding programs aimed at improving drought tolerance. Full article

The beneficial utilization of small and medium floods requires a clear flood-control safety boundary before floodwater can be moderately stored and regulated as a water resource. For the Three Gorges Reservoir, a large river-type reservoir with long-distance backwater effects and tributary blocking, this boundary cannot be determined solely from the dam-front water level. This study developed a one-dimensional unsteady hydrodynamic model with dynamic roughness calibration to investigate the risk-constrained flood-season operating water level of the Three Gorges Reservoir. Typical flood events and the 20-year return period design flood were used to examine the responses of the maximum dam-front flood-regulation water level, excess flood volume, longitudinal water levels, and exceedance risk at key reservoir-area sections under different initial regulation water levels and release-discharge conditions. The results show that the Changshou reach is the main control section for high-water-level inundation risk under the study scenarios. When the initial regulation water level is at or below 155 m, the dam-front flood-regulation water level, the peak water level at Changshou, and the exceedance duration generally vary only slightly. When the initial regulation water level exceeds 155 m, these risk indicators increase markedly, indicating a reduced flood-control safety margin. Perturbation analysis further shows that the dam-front flood-regulation indicators are relatively insensitive to small roughness and dam-front boundary perturbations, whereas the Changshou water level and exceedance duration are more sensitive to roughness and flood-volume perturbations. Therefore, 155 m should be interpreted as a conservative operational reference boundary under the current design-flood framework, existing operation rules, and the assumption of no forecast-based pre-release, rather than as an absolute safety threshold. Increasing release discharge can reduce high-water-level risk in the reservoir area under preset release limits, but its practical application must remain conditional on downstream flood-control constraints and real-time flood-conveyance capacity. The results provide a hydrodynamic basis for risk-constrained flood-season operation of large river-type reservoirs. Full article

Borate-crosslinked hydroxypropyl guar (HPG) gels are widely used as water-based fracturing fluids in oilfield stimulation. During hydraulic fracturing, their effectiveness depends on the rapid formation of a low-permeability filter cake on fracture walls, which helps reduce fluid invasion, maintain fracture pressure, and support fracture propagation. In high- and ultra-high-permeability reservoirs, however, rapid matrix invasion may occur faster than effective filter-cake formation, causing severe pre-cake spurt loss or even uncontrolled leakoff. Conventional filter-paper tests tend to emphasize stabilized wall-building behavior and may therefore fail to represent the early-time spurt loss in porous reservoir media. In this study, the leakoff behavior of borate-crosslinked HPG fracturing fluids was investigated using a modified static fluid-loss apparatus. Experiments were conducted at differential pressures of 0.5–6.0 MPa through filter paper and artificial sandstone disks with permeabilities from 0.120 to more than 4.0 μm². The filter-paper tests showed typical wall-building behavior, with limited spurt loss and stable late-time leakoff. In contrast, the sandstone-disk tests revealed a transition from cake-controlled leakoff to early-time spurt-loss-dominated leakoff as permeability and differential pressure increased. When permeability exceeded approximately 1.55–2.42 μm², spurt loss (V_sp) became the main contributor to total leakoff, whereas the late-time wall-building coefficient (C_w) was much less sensitive to permeability. This indicates that permeability mainly controls the pre-cake invasion stage rather than the stabilized leakoff stage. Based on these results, an empirical spurt-loss model considering permeability and pressure differential was developed, and spurt-loss zoning maps were constructed for engineering evaluation. Limited ultra-high-permeability tests further showed that quartz particles promoted early bridging and reduced leakoff under moderate pressure differentials, but the particle-assisted barrier lost effectiveness under higher pressure differentials. These findings demonstrate that filter-paper-based criteria are insufficient for evaluating HPG gel performance in extreme-permeability formations and that a spurt-loss-based framework is needed for fluid-loss-control design and fracturing-fluid selection in high-permeability reservoirs. Full article

Sensors and biosensors designed for biomarker detection in biological samples often suffer from performance loss caused by surface fouling, interface blocking, and matrix interference. Although these effects are frequently discussed separately, in real sensing systems they are strongly interconnected and they determine analytical reliability, especially in body fluids like serum, plasma, whole blood, sweat, and other complex media. This review provides a practical and mechanism-oriented overview of how these processes originate, how they differ, and how they ultimately lead to signal drift, reduced sensitivity, false-positive responses, and shortened sensor lifetime. We first discuss the molecular origins of interface failure, including protein adsorption, conditioning film formation, nonspecific binding, ionic strength effects, pH fluctuations, viscosity-related diffusion changes, and electroactive interferents. The impact of these phenomena is then compared across major sensing platforms, including electrochemical, potentiometric, optical, capacitive sensors, field-effect transistors and wearable biosensors. A central part of this review focuses on practical prevention strategies already employed in real biomarker sensing platforms. These include hydration-driven antifouling coatings, zwitterionic and hydrogel interfaces, post-immobilization blocking with bovine serum albumin, mercaptohexanol and ethanolamine, ionophore and membrane engineering in ion-selective electrodes, hydrophobic solid-contact layers for water-layer suppression, regeneration workflows, membrane and microfluidic pre-treatment, and AI-assisted drift correction. By combining advances in materials engineering, surface chemistry, sample handling, and algorithmic correction, this review highlights strategies to improve sensor stability in complex biological fluids. Overall, it offers a practical guide for developing next-generation low-fouling, drift-resistant, and self-correcting sensing systems for reliable biomarker analysis at the point of care. Full article

Rapid urbanisation in the Global South has accelerated the expansion of informal settlements. This has increased exposure to water-, fire-, and health-related risks and undermined pathways toward sustainable urban development. Although such risks are often framed as environmental outcomes, growing evidence suggests that they are fundamentally shaped by spatial planning conditions. This study investigates the spatial planning drivers of disaster risks in informal settlements in Mopani District, South Africa. An exploratory mixed-methods design was adopted, combining data from 605 households and 87 key informants. Quantitative data were analysed using exploratory factor analysis and multinomial logistic regression, while qualitative data were analysed thematically. The results show that water-related risks were the most prevalent, affecting 50.7% of households, followed by health risks (26.3%) and fire risks (14.7%). Activity patterns emerged as the strongest and most consistent predictor of disaster risk outcomes. The findings demonstrate that disaster risk is systematically shaped by the spatial organisation of settlements, activity concentration, built-environment conditions, and institutional limitations. These dynamics have direct implications for urban sustainability. The study contributes to the literature by advancing a systems-based spatial planning perspective on disaster risk in informal settlements and by providing empirical evidence from South Africa on the persistent gap between the policy intentions of SPLUMA and its implementation. It further highlights that achieving sustainable and resilient cities requires a shift from reactive disaster management towards proactive, risk-sensitive spatial planning approaches that integrate informal settlements into formal planning systems. Full article

Microplastic contamination in wetland ecosystems is an escalating environmental threat, compromising ecosystem services, biogeochemical cycling and biodiversity conservation. This study assessed the occurrence, distribution and physicochemical characteristics of microplastics in the Ramsar-designated Pallikaranai wetland, southern India. Six representative subsamples were collected from spatially distinct locations and analyzed using density separation, followed by polymer identification via Raman spectroscopy and energy-dispersive X-ray spectroscopy (EDS). Microplastics were ubiquitously detected across both sediment and water matrices, with significantly higher abundances in sediments, indicating their role as a major sink. The dominant polymer types, polyethylene (PE), polypropylene (PP) and polystyrene (PS), along with prevalent morphotypes such as fragments, fibers, beads and foams, reflect diverse and persistent anthropogenic inputs. The compositional profile strongly implicates mismanaged domestic and urban waste as the primary source. The widespread presence and accumulation of microplastics in this ecologically sensitive wetland raise concerns over potential impacts on trophic interactions, habitat quality and long-term ecosystem resilience. These findings underscore the urgent need for targeted waste management strategies, pollution mitigation frameworks and continuous monitoring to safeguard the ecological integrity of the Pallikaranai wetland and similar Ramsar-listed ecosystems. Full article

In this study, a functional surface coating composed of Fe-Mn binary oxide (FM) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS, PP) was applied to a PVDF membrane (PP-FM-PVDF) for efficient arsenate (As(V)) removal. PP acts as a dispersant and hydrophilic modifier, ensuring uniform FM distribution and reducing the water contact angle to 50.1°. The PP-FM-PVDF membrane achieves a maximum As(V) adsorption capacity of 30.43 mg/g, outperforming pristine and singly modified membranes. The batch adsorption data fit the Langmuir isotherm (R² = 0.999) and pseudo-second-order kinetic model (R² = 0.99), indicating monolayer chemisorption. The coating increases the specific surface area to 27.33 m²/g and the tensile strength to 6.41 MPa. Dynamic filtration shows that 2.70 L (2149.7 L/m²) of 100 μg/L As(V) solution can be treated before the permeate concentration exceeds the WHO guideline of 10 μg/L. After alkaline regeneration (pH 11), 62.9% of the initial capacity is retained. Complementary surface-sensitive analyses (zeta potential, XPS, and EXAFS) reveal that arsenate adsorption occurs primarily through ligand exchange between arsenate oxyanions and Fe/Mn surface hydroxyl groups on the coating, forming inner-sphere bidentate complexes (Fe–O–As and Mn–O–As), while electrostatic interactions play a secondary, pH-dependent role. This surface engineering strategy—synergistically integrating a conductive hydrophilic polymer with a metal oxide as a functional coating on PVDF—offers a reusable, high-performance platform for arsenate remediation, underscoring the critical role of interface design in environmental membrane applications. Full article

Background/Objectives: Drug administration by microneedle patch (MNP) offers advantages over conventional dosage forms as a painless, self-administered skin patch for parenteral delivery. Dissolvable MNPs are typically manufactured by casting an aqueous formulation containing dissolved active pharmaceutical ingredient (API) and excipients into a mold and allowing it to dry. This process can be detrimental to APIs that are sensitive to dissolution and drying during the casting process. Methods: This study presents a MNP fabrication process in which drug particles are suspended in an organic solvent carrier without being dissolved in the solvent. Results: We started with drug particles either as pure API or formulated with excipients to stabilize them. We then screened nine organic solvents, ranging from high (methanol) to low (toluene) polarity, to identify those that suspend the drug particles without dissolution or damage to the API. To guide formulation of stabilized drug particles, we generated a companion database of 16 common stabilizing excipients and measured their solubility in our panel of organic solvents to identify excipient–solvent combinations that did not lead to excipient dissolution. We generated a second database of 14 water-soluble polymers to serve as the microneedle matrix material and determined their solubility in our panel of solvents to identify solvents that enabled polymer dissolution. Using these data, we designed casting solutions that suspended particles of API (and excipients) in an organic solvent that dissolved a matrix polymer. Casting and drying these solutions on molds produced MNPs for delivery of three model compounds: lyophilized tetanus toxoid (i.e., a vaccine), methotrexate (i.e., a small molecule drug), and insulin (i.e., a biologic). Conclusions: We conclude that this fabrication method, guided by the excipient and polymer solubility databases, offers a novel method to produce MNPs by suspension casting of drugs in organic solvents. Full article

Shallow lunar subsurface characterization is a key requirement for future exploration activities, particularly for in situ resource utilization and the identification of protected environments for human and robotic operations. This work presents the preliminary design and performance assessment of an orbital very high frequency (VHF) radar sounder tailored to the detection of subsurface water ice deposits and lava tubes at depths relevant to exploration. The analysis combines physically based modeling of acquisition geometry, electromagnetic properties, and surface roughness with quantitative evaluation of signal-to-noise and signal-to-clutter ratios. Results indicate that surface clutter constitutes the primary limitation for subsurface detectability in orbital sounding, thereby driving both instrument design and mission geometry. Quantitative performance bounds are derived for penetration depth and spatial resolution, providing guidance for identifying regions where subsurface access may be achieved with reduced operational risk. One-dimensional electromagnetic simulations further demonstrate the advantages of operating in the VHF regime. While lower-frequency systems retain sensitivity to some subsurface interfaces, their limited vertical resolution prevents reliable separation of closely spaced structures, such as the roof and floor of lava tubes. In contrast, the proposed VHF sounder enables clear separation of multiple subsurface interfaces, allowing geometric characterization of cavities and improved discrimination of ice-bearing layers. These results establish the feasibility and relevance of a VHF orbital radar sounder as a dedicated tool for shallow lunar subsurface investigations in support of future exploration missions. Full article

Satellite-derived bathymetry (SDB) provides a cost-effective means for mapping shallow-water depths, yet its scalability and cross-regional generalizability remain challenging in optically complex coastal environments. This study systematically evaluates machine learning (ML) and deep learning (DL) approaches for transferable SDB over the 0–20 m depth range using multispectral Sentinel-2 imagery. A Random Forest model and four deep learning architectures–ResNet-50, ResNet-101, EfficientNet-B4, and ConvNeXt-Large–are developed and trained using data from Pratas Island (South China Sea) and selected reef regions of the Great Barrier Reef (GBR), and subsequently evaluated on spatially independent intra-regional and cross-regional test areas to assess generalization performance. Model sensitivity is investigated with respect to key training configurations, including loss-function design and data-splitting strategy. To enhance shallow-water learning, we introduce a Smooth Weight Function (SWF)-weighted RMSE loss that emphasizes near-surface depths and compare it with conventional RMSE and relative percentage error (RPE) objectives. In terms of training data, preserving spatial continuity during training substantially improves both numerical accuracy and structural consistency of predictions compared with random patch splitting. While the Random Forest model performs competitively in intra-regional tests, its accuracy degrades under cross-regional transfer (RMSE increasing from 1.53 m to 2.99–3.78 m). Deep learning models, although not always outperforming Random Forest in intra-regional settings, exhibit greater robustness to geographic shift. Using the spatially continuous training strategy, intra-regional RMSE ranges from 1.15 to 1.92 m over the full 0–20 m range, with shallow-water RMSE as low as 0.26 m for depths ≤ 3 m. Cross-regional transfer to geographically independent reefs yields moderate RMSE values of approximately 2.46–2.98 m (0–20 m range), indicating that geographic transfer remains challenging despite meaningful improvements over Random Forest. We further benchmark the proposed architectures against a task-specific bathymetry network using the public MagicBathyNet dataset. Under a unified 0–16 m shallow-water configuration using aerial RGB imagery, the proposed models achieve RMSE values between 0.19 and 0.22 m, outperforming both the baseline U-Net and the transformer-based bathymetry architecture while using substantially fewer parameters. In addition, we exploit multi-temporal repeat imagery for both training and inference, which increases training diversity and improves robustness to temporal variability arising from changing sun angles, atmospheric conditions, water properties, and tides. During inference, predictions from multiple repeat images are aggregated using the median to reduce noise and improve stability. Finally, we release optimized network architectures and pretrained weights to facilitate scalable application to new sites. This work demonstrates a practical pathway toward transferable, large-area SDB from multispectral satellite imagery using deep learning. Full article

Conventional hydroponic systems, although resource-efficient, face significant sustainability challenges due to the discharge of nutrient-rich effluents, resulting in severe environmental pressures. In alignment with the European Union’s “Farm to Fork” strategy, innovative circular economy approaches are required to decouple crop production from environmental degradation. This study evaluates a novel Cascade Hydroponic System (CHS), designed to maximize resource utility by recovering and reusing the drainage from a primary salt-sensitive crop (cucumber) to a secondary, more salt-tolerant cultivation (melon). A comparative Life Cycle Assessment (LCA) is performed in accordance with the Product Environmental Footprint Category Rules (PEFCRs), utilizing primary operational data and direct monitoring of nutrient concentrations in the system’s effluent. The convergence of these elements establishes the novelty of this study. The CHS is benchmarked against a conventional Separated Hydroponic System (SHS) for a functional unit (FU) defined as “the simultaneous production of 1.0 kg of cucumber and 1.0 kg of melon”. The CHS demonstrated lower characterized impacts compared to SHS across all 16 assessed Environmental Footprint categories under the examined pilot-scale conditions. The key findings include reductions of 65.7%, 41.8%, and 30% in Water Use, Climate Change, and Freshwater Eutrophication scores, respectively. Based on the normalization results, the CHS revealed a 58% lower total environmental footprint score compared to SHS. Process contribution analysis indicates that the marked decrease in the environmental burden is associated with the use of fertilizers. While these inputs represent a significant share of the conventional system’s impact scores, their contribution was substantially lower in the CHS. Although based on pilot-scale operational data from a single crop cycle, the results highlight the considerable environmental potential of cascading nutrient reuse configurations, thus enhancing resource use efficiency and mitigating the associated environmental impacts while also contributing novel empirical knowledge to a field that has been limitedly studied. Full article

Drought is a critical constraint for legume production in semi-arid regions, yet breeding for drought tolerance in faba bean through induced mutagenesis remains largely unexplored. To our knowledge, this is the first EMS-derived mutant population in faba bean specifically developed for drought tolerance, comprising 45 M₂/M₃ lines derived from small-seeded cv. Zina and large-seeded cv. Aguadulce Superlonga), evaluated under two irrigation regimes—100% field capacity (well-watered control) and 40% field capacity (severe stress)—over two consecutive growing seasons in a randomized complete block design with three replications. Drought stress caused severe yield losses, reducing mean seed number per plant by 42.2% and mean seed weight per plant by 47.1%. Analysis of variance revealed highly significant effects of genotype, irrigation, and generation/year on both yield components. The non-significant genotype × irrigation interaction indicated similar proportional drought response across genotypes, while the non-significant three-way interaction suggested relatively consistent genotype rankings across generations/growing seasons. Among the ten drought tolerance indices evaluated, seed-number-based mean productivity (MPn) and stress tolerance index (STIn) were the most discriminating, whereas weight-based indices failed to differentiate genotypes due to the inherent seed-size contrast between botanical backgrounds. Dunnett’s comparisons identified genotype 23 (Zina-derived) as the top performer, significantly exceeding its parent for both MPn and STIn; genotypes 22, 24, 12, 3, and 15 similarly outperformed controls. Cluster analysis broadly distinguished three groups: a tolerant cluster dominated by Zina-derived lines, a moderately tolerant cluster (Zina wild-type), and a sensitive cluster of Aguadulce Superlonga-derived lines. These findings suggest that EMS mutagenesis generated potentially heritable and exploitable variation for drought tolerance, with selected lines representing promising candidates for further multi-environment validation. Full article