Search Results (5,511)

Real-time precipitation monitoring through satellite remote sensing represents a critical technological frontier for operational hydrology in data-scarce mountainous regions. Following a comprehensive evaluation of reanalysis precipitation products in the downstream Yarlung Zangpo watershed, this investigation advances understanding by systematically assessing eight satellite-based precipitation retrieval algorithms against ground truth observations from 18 meteorological stations (2014–2022). Multi-temporal performance analysis employed statistical metrics including correlation analysis, root mean square error, mean absolute error, and bias assessment to characterize algorithm reliability across annual, monthly, and seasonal scales. Representative monthly spatial analysis (January, April, July) and comprehensive 12 month × 18 station heatmap visualization revealed pronounced seasonal performance variations and elevation-dependent error patterns. Satellite retrieval algorithms demonstrated systematic underestimation tendencies, with observational precipitation averaging 2358 mm/yr, substantially exceeding remote sensing estimates across six of eight products. IMERG_EarlyRun and IMERG_LateRun achieved optimal performance with annual correlation coefficients of 0.41/0.37 and minimal bias (relative bias: −3.0%/1.4%), substantially outperforming other products. Unexpectedly, IMERG_FinalRun exhibited severe deterioration (correlation: 0.37, relative bias: −73.8%) compared to Early/Late Run products despite comprehensive gauge adjustment, indicating critical limitations of statistical correction procedures in data-sparse mountainous environments. Temporal analysis revealed substantial year-to-year performance variability across all products, with algorithm accuracy strongly modulated by annual precipitation characteristics and underlying meteorological conditions. Station-level assessment demonstrated that 100% of stations showed underestimation for IMERG_FinalRun versus balanced patterns for IMERG_EarlyRun/LateRun (53% underestimation, 47% overestimation), confirming systematic gauge-adjustment failures. Supplementary terrain–precipitation analysis indicated GSMaP_MVK_G shows superior spatial pattern representation, while IMERG_LateRun excels in capturing temporal variations, suggesting multi-product integration strategies for comprehensive monitoring. Comparative assessment with previous reanalysis evaluation establishes that satellite products offer superior real-time availability but exhibit greater temporal variability compared to model-based approaches’ consistent performance. IMERG_EarlyRun and IMERG_LateRun are recommended for operational real-time applications, GSMaP_MVK_G for terrain-sensitive spatial analysis, and reanalysis products for seasonal assessment, while IMERG_FinalRun and FY2 require substantial improvement before deployment in high-altitude watershed management systems. Full article

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

The Semipalatinsk Test Site (STS), one of the most heavily contaminated nuclear test sites globally, presents critical challenges for ecological monitoring and restoration due to long-term radioactive pollution and soil degradation. This study applied the Remote Sensing Ecological Index (RSEI) model to systematically evaluate the spatiotemporal changes in ecological quality at STS from 2003 to 2023. The RSEI model integrated multi-indicator data, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), WET (Wetness), and NDBSI (Normalized Difference Built-up and Soil Index), enabling a comprehensive assessment of ecological dynamics. Results demonstrated a significant improvement in ecological quality, with the RSEI increasing by 29.59% (from 0.345 in 2003 to 0.447 in 2023). PCA results indicated that ecological recovery was primarily influenced by surface temperature, vegetation cover, and soil moisture, with radioactive residues further hindering recovery in severely contaminated zones. The proportion of “Poor” areas declined from 14.99% to 0.61%, while “Moderate” and “Good” areas expanded to 55.76% and 8.87%, respectively. Peripheral regions showed faster recovery due to effective natural and management interventions, while core high-contamination zones (Sary-Uzen) exhibited slower recovery due to persistent radioactive residues. This study highlights the applicability of RSEI for assessing ecological recovery in nuclear test sites and emphasizes the need for targeted remediation strategies. These findings provide valuable insights for global ecological management of nuclear test sites, supporting sustainable restoration efforts. Full article

Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment. Full article

Strawberry (Fragaria × ananassa (Weston) Rozier) is a high-value crop whose market success depends on fruit quality traits such as sweetness, firmness, and pigmentation. In sustainable agriculture, wood distillates are gaining interest as natural biostimulants. This study evaluated the effects of foliar application of two commercial wood distillates (WD1 and WD2) and one produced in a pilot plant at the Institute for Bioeconomy of the National Research Council of Italy (IBE-CNR) on strawberry physiology, fruit yield, and fruit quality under greenhouse conditions. Non-destructive ecophysiological measurements were integrated using optical sensors for proximal phenotyping, enabling continuous monitoring of plant physiology and fruit ripening. Leaf gas exchange and chlorophyll fluorescence were measured with a portable photosynthesis system, while vegetation indices and pigment-related parameters were obtained using spectroradiometric sensors and fluorescence devices. To assess the functional relevance of vegetation indices, a linear regression analysis was performed between net photosynthetic rate (A) and the Photochemical Reflectance Index (PRI), confirming a significant positive correlation and supporting PRI as a proxy for photosynthetic efficiency. All treatments improved photosynthetic efficiency during fruiting, with significant increases in net photosynthetic rate, quantum yield of photosystem II, and electron transport rate compared to control plants. IBE-CNR and WD2 enhanced fruit yield, while all treatments increased fruit soluble solids content. Non-invasive monitoring enabled real-time assessment of physiological responses and pigment accumulation, confirming the potential of wood distillates as biostimulants and the value of advanced sensing technologies for sustainable, data-driven crop management. Full article

Multi-material Directed Energy Deposition (DED) Additive Manufacturing (AM) processes enable the integration of different material properties into a single structure, addressing the requirements of various applications and working environments. Laser-based Directed Energy Deposition (DED-LB) has been employed in the past for surface coatings as well as for the repair and repurposing of high-value industrial components, with the goal of extending product lifetime without relying on expensive and time-consuming manufacturing from scratch. While powder DED-LB has traditionally been used for multi-material AM, the more resource-efficient and cost-effective wire DED-LB process is now being explored as a solution for creating hybrid materials. This work focuses on the critical aspects of implementing multi-material DED-LB, specifically defining an optimal operating process window that ensures the best quality and performance of the final parts. By investigating the possibility of combining stainless steel and nickel-based alloys, this study seeks to unlock new possibilities for the repair and optimization of naval components, ultimately improving operational efficiency and reducing downtime for critical naval equipment. The analysis of the experimental results has revealed strong compatibility of stainless steel 316 with Inconel 718 and stainless steel 17-4PH, while the gray cast iron forms acceptable fusion only with stainless steel 17-4PH. Finally, during the experimental phase, substrate pre-heating and process monitoring with thermocouples will be employed to manage and assess heat distribution in the working area, ensuring defect-free material joining. Full article

Accurate assessment of water quality is crucial for protecting public health and promoting environmental sustainability. Conventional laboratory-based methods for evaluating microbial contaminants are often time-consuming, resource-intensive, and reactive in nature, limiting their effectiveness for real-time water quality monitoring and management. This study examines the application of data-driven machine learning models to predict E. coli concentrations in Midmar Dam, utilizing readily available physicochemical parameters. A comparative analysis was conducted using five classical standalone ML algorithms: Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost). These models were assessed based on their predictive performance using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the models evaluated, the kNN algorithm demonstrated superior performance, achieving the lowest MSE and RMSE values, thereby highlighting its effectiveness in capturing the complex relationships between physicochemical indicators and microbial contamination levels. The findings demonstrate the potential of ML-based approaches to serve as efficient, scalable, and proactive tools for sustainable water-quality monitoring and management in dams. Full article

This work presents an automated method for detecting and monitoring volcanic islands in the Tonga archipelago using Sentinel-2 satellite imagery. The method is able to detect newly created islands, as well as an increase in island size, a possible precursor to an explosion due to magma chamber inflation. At its core, the method combines a U-Net-type convolutional neural network (CNN) for semantic segmentation with a custom change detection algorithm, enabling the identification of land–water boundaries and the tracking of volcanic island dynamics. The algorithm analyzes morphological changes through image comparison and Intersection over Union (IoU), capturing the emergence, disappearance, and evolution of volcanic islands. The segmentation model, trained on a custom dataset of Pacific Ocean imagery, achieved an IoU score of 97.36% on the primary test dataset and 83.54% on a subset of challenging cases involving small, recently formed volcanic islands. Generalization capability was validated using the SNOWED dataset, where the segmentation model attained an IoU of 81.02%. Applied to recent volcanic events, the workflow successfully detected changes in island morphology and provided time-series analyses. Practical feasibility of the methodology was assessed by testing it on a large region in Tonga, using an HPC cluster. This system offers potential applications for geophysical studies and navigation safety in volcanically active regions. Full article

Monitoring the activities of athletes with lower-limb amputations who use running-specific prostheses is essential for evaluating their training regimes, as well as the effectiveness and mechanical fatigue wear of their prostheses over time. Recent advancements in Inertial Measurement Units (IMUs) and activity detection algorithms offer new opportunities for objective assessment, but their application in Paralympic sports remains unexplored. The aims of this work were to design and implement an innovative protocol and analytical software for short-term and long-term activity detection of athletes with transtibial and transfemoral amputation and then test its validity on a sample of elite Paralympic runners and triathletes. Overall, the ability of the model to detect activities presented an accuracy of 98%, and the error in the stride counting for all activities fell within a 1% margin. Full article

The development of highly sensitive approaches for detecting tumor cells in biological samples remains a critical challenge in laboratory and clinical oncology. In this study, we investigated the structural and magnetic properties of iron oxide nanoparticles incorporated into cellulose microspheres of two size ranges (~100 and ~700 μm) and evaluated their potential for targeted tumor cell isolation. In the smaller microspheres, magnetite-based magnetic nanoparticles (MNPs) were synthesized in situ via co-precipitation, whereas pre-synthesized MNPs were embedded into the larger microspheres. The geometrical characteristics of the resulting magnetic cellulose microspheres (MSCMNs) were assessed by confocal microscopy. Transmission electron microscopy and X-ray diffraction analyses revealed an average magnetic core size of approximately 17 nm. Magnetic properties of the MNPs within MSCMNs were characterized using a highly sensitive nonlinear magnetic response technique, and their dynamic parameters were derived using a formalism based on the stochastic Hilbert–Landau–Lifshitz equation. To evaluate their applicability in cancer diagnostics and treatment monitoring, the MSCMNs were functionalized with a TKD peptide that selectively binds membrane-associated Hsp70 (mHsp70), yielding TKD@MSCMNs. Magnetic separation enabled the isolation of tumor cells from biological fluids. The specificity of TKD-mediated binding was confirmed using Flamma648-labeled Hsp70 and compared with control alloferone-conjugated microspheres (All@MSCMNs). The ability of TKD@MSCMNs to selectively extract mHsp70-positive tumor cells was validated using C6 glioma cells and mHsp70-negative FetMSCs controls. Following co-incubation, the extraction efficiency for C6 cells was 28 ± 14%, significantly higher than that for FetMSC (7 ± 7%, p < 0.05). These findings highlight the potential of TKD-functionalized magnetic cellulose microspheres as a sensitive platform for tumor cell detection and isolation. Full article

Background and Objectives: Osteoporosis is a prevalent skeletal disorder characterized by decreased bone mass and compromised bone microarchitecture, leading to an elevated risk of fractures and significant morbidity, particularly among aging populations. Early diagnosis and personalized management are critical to reducing fracture incidence and associated healthcare burdens. Recent advances in artificial intelligence (AI) and machine learning (ML) have led to potential improvements in enhancing osteoporosis care by enabling accurate diagnostic imaging analysis, robust fracture risk prediction, and personalized therapeutic strategies. Materials and Methods: We performed a narrative review to summarize and critically evaluate the current literature on AI and ML applications in osteoporosis diagnosis and management. We searched relevant literature from inception to January 2025 to provide a comprehensive perspective, focusing on key themes, methodological approaches, and clinical implications. Results: Deep learning models, especially convolutional neural networks, facilitate rapid and accurate bone mineral density assessment from routine radiographs, expanding screening capabilities beyond conventional dual-energy X-ray absorptiometry (DXA). Machine learning algorithms harness clinical and demographic data to generate fracture risk models that often outperform traditional tools, enabling timely identification of high-risk individuals. Furthermore, AI-driven analyses of historical treatment responses coupled with real-time monitoring through wearable technologies and mobile applications allow for personalized therapeutic optimization and enhance patient engagement. Despite these promising advances, challenges remain regarding ethical considerations, data privacy, legal liability, incomplete model validation, lack of standardization, and the need for critical appraisal of real-world clinical efficacy for widespread clinical adoption. Conclusions: This narrative review indicates that AI and ML hold significant promise to revolutionize osteoporosis management by enabling early detection, precise risk stratification, and tailored interventions. However, the current evidence is heterogeneous, often lacking robust external validation and quantitative synthesis. Critical gaps include insufficient evaluation of model robustness across diverse populations, discussion of negative or conflicting results, and a comprehensive assessment of the limitations inherent in current AI evidence. Strategic efforts to validate, regulate, and critically integrate these technologies into routine clinical workflows are essential to realize their full potential and address the growing burden of osteoporosis worldwide. Full article

Nanoplastics pose significant environmental and public health risks, prompting the need for sensitive, cost-effective, and rapid assays for ecotoxicity assessment. The present work proposes the use of a portable smartphone-based platform to enhance traditional Daphnia magna acute toxicity assays by integrating behavior analysis and heart rate measurements. The aim is to improve sensitivity in detecting toxic effects of nanoplastics. In particular, the study focused on nano-sized carboxylated polystyrene (PS) nanoparticles. Two variability factors that could influence biological effects of nanoplastics, the particle size and the age of the organisms, were considered. Results demonstrated that the application of the proposed integrated approach allowed the detection of early subtle effects such as a significant impact on the heart rate and behavior of Daphnia magna under short-term exposure to PS carboxylated nanoparticles. In particular, a stimulation of heart rate was observed for both neonates and adults either for 40 nm or 200 nm particles after 48 h exposure, presumably attributable to an interference of carboxylated PS NPs with adrenergic-type receptors. Behavioral alterations were detectable for 40 nm particles but not for 200 nm ones consisting of a decrease in velocity and alterations of trajectories. Obtained results demonstrated the suitability of the proposed smartphone platform for friendly and real-time integration of behavioral analysis with physiological outcome measurements during acute exposure of Daphnia magna to nano-sized carboxylated PS NPs, expanding the sensitivity of the traditional acute toxicity tests. It offers a novel, cost-effective, and field-applicable method for environmental monitoring of nanoparticle toxicity and impact. Full article

Drought is a natural phenomenon that has significant environmental and socioeconomic impacts. Drought indices are fundamental tools for quantifying and monitoring this hazard. In regions where ground data are scarce, hydrological modeling offers an alternative for drought monitoring and developing early warning systems. This study conducted a systematic literature review, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, to analyze the integrated application of the SWAT (Soil and Water Assessment Tool) model and the use of drought indices. A total of 803 articles published between 2011 and 2025 were identified in the Scopus and Web of Science databases, of which 115 met the eligibility criteria and were included in the review. The analysis revealed significant advances in the use of SWAT for drought monitoring and prediction, including the development of indices and forecasting systems. However, notable gaps remain, particularly the limited use of advanced statistical methodologies (e.g., machine learning and non-stationarity analyses) and the lack of harmonization and standardization across indices. Overall, this review establishes SWAT as a robust tool to support drought management strategies, while highlighting substantial untapped potential. Future research addressing these gaps is essential to strengthen drought indices and improve operational warning systems. Full article

Accurate environmental monitoring in outdoor and indoor settings is critical for exposure assessment in environmental and public health research. Conventional methods, predominantly relying on high-end instruments or laboratory analyses, face limitations in real-world applications due to their high cost and inflexibility. Recent advances in low-cost sensor technologies have enabled more adaptable monitoring. This study systematically reviews research utilizing low-cost sensors for environmental monitoring in real-world settings across China. A literature search was performed using the Web of Science database, resulting in the inclusion of 43 eligible studies out of 31,003 initially identified records. These studies primarily investigated air pollution (17 studies), noise (14), light (7), and water pollution (5). Results reveal that air and noise pollution were the most extensively examined factors. Nevertheless, the reviewed studies exhibited notable shortcomings, including limited geographical/thematic coverage, inadequate reliability validation, small sample sizes (typically under 100 participants), and short durations (often under one month). This review discusses these challenges and suggests future research directions. By synthesizing current practices and identifying gaps, this work offers valuable insights to guide the design of future sensor-based environmental monitoring projects and inform the selection of suitable sensors. Full article