Search Results (894)

Stomoxys calcitrans (Linnaeus, 1758) is a hematophagous fly species of veterinary importance, known for its negative effects on animal health and productivity. The stress caused by their painful bites results in losses in milk and meat production. Despite its impact, data on its ecology and activity in Algeria are lacking. Such knowledge is needed to evaluate its potential effects on livestock production and rural health, and to support surveillance, outbreak prediction, and control strategies. This study aimed to investigate the monthly and daily dynamics of S. calcitrans in livestock farms in the Batna region and evaluate the influence of climatic factors on its abundance. From July 2022 to July 2023, Vavoua traps were placed monthly from 7 a.m. to 6 p.m. on four farms in the Batna region, representing different livestock types. Captured flies were identified, sexed, and counted every two hours. Climatic data were collected both in situ and from NASA POWER datasets. Fly abundance was analyzed using non-parametric statistics, Spearman’s correlation, and multiple regression analysis. A total of 1244 S. calcitrans were captured, mainly from cattle farms. Activity occurred from August to December, with a peak in September. Males were more abundant and exhibited a bimodal activity in September. Fly abundance was positively correlated with temperature and precipitation and negatively correlated with wind speed and humidity. This study presents the first ecological data on S. calcitrans in northeastern Algeria, highlighting its seasonal dynamics and the climatic drivers that influence it. The results highlight the species’ preference for cattle and indicate that temperature and rainfall are key factors influencing its abundance. These findings lay the groundwork for targeted control strategies against this neglected pest in Algeria. Full article

Circulating tumor cells (CTCs) are crucial biomarkers for lung cancer metastasis and recurrence, garnering significant clinical attention. Despite this, efficient and cost-effective detection methods remain scarce. Consequently, there is an urgent demand for the development of highly sensitive CTC detection technologies to enhance lung cancer diagnosis and treatment. This study utilized microspheres and A549 cells to model CTCs, assessing the impact of acoustic field forces on cell viability and proliferation and confirming capture efficiency. Subsequently, CTCs from the peripheral blood of patients with lung cancer were captured and identified using fluorescence in situ hybridization, and the results were compared to the immunomagnetic bead method to evaluate the differences between the techniques. Finally, epidermal growth factor receptor (EGFR) mutation analysis was conducted on CTC-positive samples. The findings showed that acoustic microfluidic technology effectively captures microspheres, A549 cells, and CTCs without compromising cell viability or proliferation. Moreover, EGFR mutation analysis successfully identified mutation types in four samples, establishing a basis for personalized targeted therapy. In conclusion, acoustic microfluidic technology preserves cell viability while efficiently capturing CTCs. When integrated with EGFR mutation analysis, it provides robust support for the precise diagnosis and treatment of lung cancer as well as personalized drug therapy. Full article

This research evaluates three wind data sources, the Sentinel-1 wind product, the global reanalysis ERA5, and the regional reanalysis CARRA, across Iceland’s North, South, West, and East coastal regions. The analysis mainly focuses on validating Sentinel-1 high-resolution capabilities for capturing fine-scale wind patterns in coastal zones, where traditional reanalyses may have tangible limitations. Performance is evaluated through intercomparison of datasets and analysis of regional wind speed variability, with in situ coastal meteorological observations providing ground-truth validation. The results highlight the relative strengths and limitations of each source, offering guidance for improving wind-driven and wind-dependent applications in Iceland’s coastal regions, such as hazard assessment, marine operations, and renewable energy planning. Full article

In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is a valuable tool for providing critical data. This study investigates whether node-level WSE data from the SWOT satellite can effectively function as virtual hydrological stations under such extreme conditions. The study was applied in all of RS state considering 100 in situ gauges and was subdivided into three sections: (i) an evaluation of the variation in SWOTʹs WSE data compared to the variation in in situ levels from telemetric gauges, considering subsequent cycles of passes between July 2023 and April 2025, yielding an MAE = 35 cm and an RMSE = 73 cm after outlier removal; (ii) an evaluation of the variation in SWOTʹs WSE data compared to the variation in telemetric level data, considering one window prior to and another during the extreme event, resulting an MAE = 26 cm and an RMSE = 34 cm; (iii) an analysis of SWOTʹs data availability during the extreme event, when in situ telemetric data were unavailable. The results demonstrate an agreement between the variation observed in SWOT data and that in telemetric gauges in RS, even during extreme events. Moreover, in the absence of in situ data, SWOT was still able to capture WSE data. Full article

Since the early 1990s, satellite altimetry has significantly improved our understanding of coastal and estuarine dynamics. The Casamance estuary in Senegal exemplifies a tropical microtidal system with limited instrumentation despite pressing environmental, social, and navigational concerns. This study explores the potential of SWOT satellite data to support the calibration and validation of high-resolution hydrodynamic models. Multi-source dataset of in situ measurements and altimetry observations has been combined with numerical modelling to investigate the hydrodynamics in response to physical drivers. Statistical metrics were used to quantify model performance. Results show that SWOT accurately captures water level variations in the main channel (width 800 m to 5 km), including both tidal and non-tidal contributions, with high correlation (R = 0.90) and low error (RMSE < 0.25 m). Performance decreases in tributaries (R = 0.42, RMSE up to 0.34 m), due to interpolated bathymetry and complex local dynamics. Notably, Delft3D achieves R = 0.877 at Diogué (RMSE = 0.204 m) and R = 0.843 at Carabane (RMSE = 0.225 m). These findings highlight the strategic value of SWOT for improving hydrodynamic modelling in data-scarce estuarine environments. Full article

Despite increasing concerns over recurrent phosphorus (P) pollution, the Ju River—a small tributary of the Yangtze River—has received limited scientific attention. To correct this, the present study integrates field-based observations with the Hydrological Simulation Program—FORTRAN (HSPF) model to comprehensively assess the conjunct effects of urban expansion and changing precipitation patterns on watershed hydrology and phosphorus dynamics at the small-catchment scale. A total of five urban expansion scenarios and three precipitation enhancement scenarios were simulated to capture both seasonal and event-driven variations in daily discharge and total phosphorus (TP) concentrations. The model was calibrated and validated using in situ water quality data, ensuring high reliability of the simulations. The results indicate that agricultural non-point sources are the primary contributor to total phosphorus (TP) loads. During the overlapping period of intensive farming and heavy rainfall (June–July), TP concentrations more than doubled compared to other months, with these two months accounting for over 70% of the annual TP load. Urban expansion significantly amplified hydrological extremes, increasing peak discharge by up to 224% under extreme rainfall, thereby intensifying flood risks. Although increased precipitation diluted TP concentrations, it simultaneously accelerated overall phosphorus export. This study offers a novel modeling–monitoring framework tailored for small watersheds and provides critical insights into how land use transitions and climate change jointly reshape nutrient cycling. The findings support the development of targeted, scenario-based strategies to mitigate eutrophication risks in vulnerable river systems. Full article

Background: Emerging pandemics can rapidly overwhelm hospital capacity, leading to increased mortality and healthcare costs. Objective: We develop an optimization-based framework that dynamically schedules hospital beds across multiple facilities to minimize total healthcare costs, including patient rejections and logistical expenses, under resource constraints. Methods: The model integrates several real-world flexibilities: standard hospital beds, buffer capacity from non-pandemic wards, in situ field hospitals, and inter-hospital patient transfers. To capture demand uncertainty, we link the model with an SEIRD epidemic forecasting approach and further extend it with a robust optimization variant that safeguards against worst-case surges. Recognizing computational challenges, we reformulate the problem to significantly reduce solution times and derive structural properties that provide guidance on when to open field hospitals, allocate buffer beds, and prioritize patients across facilities. Results: A case study based on COVID-19 data from Northern Virginia shows that the proposed framework reduces healthcare costs by more than 50% compared with current practice, mainly by lowering patient rejection rates. Conclusions: These results highlight the value of combining epidemic forecasting with prescriptive optimization to improve resilience and inform healthcare policy during crises. Full article

Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability. Full article

Streamflow prediction in ungauged watersheds remains a critical challenge in hydrological science due to the absence of in situ measurements, particularly in remote, data-scarce, and developing regions. This review synthesizes recent advancements in artificial intelligence (AI) for streamflow modeling, focusing on machine learning (ML), deep learning (DL), and hybrid modeling frameworks. Three core methodological domains are examined: regionalization techniques that transfer models from gauged to ungauged basins using physiographic similarity and transfer learning; synthetic data generation through proxy variables such as NDVI, soil moisture, and digital elevation models; and model performance evaluation using both deterministic and probabilistic metrics. Findings from recent literature consistently demonstrate that AI-based models, especially Long Short-Term Memory (LSTM) networks and hybrid attention-based architectures, outperform traditional conceptual and physically based models in capturing nonlinear hydrological responses across diverse climatic and physiographic settings. The integration of AI with remote sensing enhances generalizability, particularly in ungauged and human-impacted basins. This review also addresses several persistent research gaps, including inconsistencies in model evaluation protocols, limited transferability across heterogeneous regions, a lack of reproducibility and open-source tools, and insufficient integration of physical hydrological knowledge into AI models. To bridge these gaps, future research should prioritize the development of physics-informed AI frameworks, standardized benchmarking datasets, uncertainty quantification methods, and interpretable modeling tools to support robust, scalable, and operational streamflow forecasting in ungauged watersheds. Full article

Nearshore bathymetry is critical for coastal management and ecology. While airborne hyperspectral remote sensing provides high-resolution image data, obtaining rapid and accurate bathymetric inversion in coastal areas lacking in situ data remains challenging. The widely used Hyperspectral Optimization Process Exemplar (HOPE) achieves high accuracy but suffers from computational inefficiency, making it impractical for large-scale, high-resolution datasets. By contrast, HOPE-Pure Water (HOPE-PW) offers computational efficiency but exhibits limitations in capturing fine-scale spatial patterns of bottom reflectance (ρ), and its applicability in transitional waters between Case I and II types requires further validation. Against this background, we employed machine learning-based substrate classification (support vector machine, random forest, maximum likelihood) in Wenchang coastal waters, China, to constrain ρ estimation in HOPE-PW, with validation using ICESat-2 data that extends its conventional application scenarios. Results demonstrate that when constrained by the optimal classifier (random forest), HOPE-PW achieves comparable accuracy to HOPE in shallow water while reducing runtime by 56% and memory usage by 68%. However, HOPE-PW exhibits slight underestimation in deeper areas, likely because simplification reduces sensitivity to water optical properties. Future research will focus on this issue. This study proposes an efficient and reliable framework for monitoring and evaluating water depth in areas lacking in situ data, offering a practical solution for integrated coastal zone management. Full article

Cold Spray Additive Manufacturing (CSAM) is a solid-state process that is being increasingly used for structural repairs in aerospace and energy sectors. It enables the deposition of dense material at low temperatures by accelerating metal particles to supersonic velocities, thereby reducing thermal distortion. However, the structural integrity of CSAM repairs—particularly at the interface between the deposited layer and the substrate—remains a critical concern. Various post-treatments and characterization methods have been explored to optimize performance. While X-ray Computed Tomography (XCT) is effective for sub-surface inspection, it cannot be applied in situ during mechanical testing. Digital Image Correlation (DIC), a surface-based method, also lacks sub-surface sensitivity. To address this, Infrared Thermography (IRT) was employed alongside DIC during the tensile and fatigue testing of aluminum CSAM-repaired specimens. A cooled IRT camera operating at 200 FPS captured thermal data, with lock-in processing subsequently applied in post-processing. IRT successfully detected early interfacial damage and enabled the tracking of crack propagation, which was later confirmed through fracture surface analysis. This extended abstract presents findings from fatigue tests using IRT. Full article

Environmental contamination is a critical global concern, primarily due to detrimental greenhouse gas (GHG) emissions, especially carbon dioxide (CO₂), which significantly contribute to climate change. Moreover, the presence of harmful heavy metals like Ni, Cd, Cu, Hg, and Pb in soil and water ecosystems has led to poor water quality. Noble metal nanoparticles (MNPs), for instance, Pd, Ag, Pt, and Au, have emerged as promising solutions for addressing environmental pollution. However, the practical utilization of MNPs faces challenges as they tend to aggregate and lose stability. To overcome this issue, the reverse double-solvent method (RDSM) was utilized to synthesis melamine-based porous polyaminals (POPs) as a supportive material for the in situ growing of silver nanoparticles (Ag NPs). The porous structure of melamine-based porous polyaminals, featuring aminal-linked (-HN-C-NH-) and triazine groups, provides excellent binding sites for capturing Ag⁺ ions, thereby improving the dispersion and stability of the nanoparticles. The resulting material exhibited ultrafine particle sizes for Ag NPs, and the incorporation of Ag NPs within the porous polyaminals demonstrated a high surface area (~279 m²/g) and total pore volume (1.21 cm³/g), encompassing micropores and mesopores. Additionally, the Ag NPs@POPs showcased significant capacity for CO₂ capture (2.99 mmol/g at 273 K and 1 bar) and effectively removed Cu (II), with a remarkable removal efficiency of 99.04%. The nitrogen-rich porous polyaminals offer promising prospects for immobilizing and encapsulating Ag nanoparticles, making them outstanding adsorbents for selectively capturing carbon dioxide and removing metal ions. Pursuing this approach holds immense potential for various environmental applications. Full article

Coastal lagoons provide vital ecological functions, supporting diverse flora and fauna while being highly sensitive to environmental changes. In the southern Baltic Sea, the Puck Lagoon is a hydrologically distinct subregion of the Gulf of Gdańsk characterized by variable exchange of water with the outer bay and substantial freshwater inflows. Its benthic communities are particularly sensitive to salinity, yet the processes shaping this parameter remain insufficiently understood. In situ measurements in summer 2020 revealed relatively high salinity in the lagoon (up to 7.7 PSU) compared to the adjacent outer bay (7.2–7.4 PSU), with localized reductions near the Kuźnica Passage and the Reda River mouth. As a first step toward explaining the hydrodynamic processes responsible for these anomalies, we applied a high-resolution, two-dimensional model focused on three fundamental physical drivers: river inflows, open-boundary exchange, and wind forcing. These processes represent the primary controls on salinity in shallow lagoons and provide a basis for evaluating additional mechanisms. The model reproduced observed patterns with a mean absolute error of 0.15 PSU, confirming that this selective framework captures the key features of salinity variability and establishes a baseline for future three-dimensional modeling that will incorporate further processes such as vertical mixing, precipitation, and evaporation. Full article

A permeable reactive barrier (PRB) containing zero-valent iron (ZVI) is an in situ groundwater remediation technology that passively intercepts and treats contaminated groundwater plumes. Over time, secondary mineral precipitation within the PRB diminishes porosity and hydraulic conductivity, altering flow paths, residence times, and sometimes causing bypass of the reactive zone. This study utilizes the THMC software to simulate porosity reduction in a PRB, capturing the coupled effects of fluid flow and geochemical interactions. The simulation results indicate that porosity loss is most significant at the PRB entrance and stabilizes beyond 0.2 m. Porosity reduction is primarily caused by aragonite, siderite, and ferrous hydroxide precipitating in pore spaces. The model further elucidates the influence of groundwater chemistry, demonstrating that variations in bicarbonate concentrations significantly impact mineral precipitation processes, thereby leading to porosity reduction. Furthermore, the study highlights reaction kinetics, with anaerobic iron corrosion rates being critical in controlling porosity reduction via mineral precipitation. THMC software effectively simulates porosity reduction in PRBs, identifies key factors driving clogging, and informs design optimization for long-term remediation. Full article

While size effects in composite structures have been widely studied under quasi-static uniaxial loading, their influence under fatigue conditions, particularly in the presence of multiaxial stress states and elevated temperatures, remains insufficiently understood. This study investigates the fatigue behaviour of thick-walled $\pm {45}^{\circ }$ braided glass fibre-reinforced polyurethane composite box structures under varying temperature and loading conditions. A combined experimental approach is adopted, coupling quasi-static and fatigue tests on large-scale structures with reference data from standardised coupon specimens. The influence of temperature (23–80 °C) and multiaxial shear–compression loading is systematically evaluated. The results demonstrate a significant temperature-dependent decrease in compressive strength and fatigue life, with a linear degradation trend that aligns closely between the box structure and coupon data. Under moderate multiaxial conditions, the fatigue life of box structures is not significantly impaired compared to uniaxial test coupon specimens. Complementary non-destructive testing using air-coupled ultrasound confirms these trends, demonstrating that guided-wave phase-velocity measurements capture the evolution of anisotropic damage and are therefore suitable for in situ structural health monitoring applications. Furthermore, these findings highlight that (i) the temperature-dependent fatigue behaviour of thick-walled composites can be predicted using small-scale coupon data and (ii) small shear components have a limited impact on fatigue life within the studied loading regime. Full article