Search Results (15,973)

The Dead Sea, the Earth’s lowest major surface water body, serves as the terminal basin for surface and groundwater flow in its surrounding region. However, anthropogenic activities and natural processes contribute to significant alterations in the lake’s area. The scope and implications of these changes remain insufficiently documented, necessitating further investigation. The CA-Markov model was used to project the Dead Sea’s surface area for 2034 and 2050. Time series of observed and future climate data, especially temperature data, under Representative Concentration Pathways (RCPs) 4.5 and 8.5, were analyzed to track climate change. Statistical analyses of the Kendall correlation matrix were performed on the observed and predicted surface areas, water levels, and temperatures. This study revealed that the Dead Sea decreased by 41.8% from 1971 to 2022, and the sea level is expected to decrease by 12.63 m and 33 m by 2034 and 2050, respectively. In addition, there were significant inverse relationships between surface area, water level, and temperature, with correlations of r = −0.79 (p = 0.001) and r = −0.82 (p = 0.001), respectively. Notably, from 2022 to 2050, the mean annual temperature is expected to increase by at least 1 °C. The long-term strategic vision for stabilizing Dead Sea water levels involves a twofold approach: (1) augmenting natural inflow by introducing 300–400 million cubic meters (MCM) from manufactured sources and channeling them into the Jordan River, and (2) reducing water extraction by Dead Sea industries by a maximum of 330 MCM. Full article

This work aims to determine whether pre-seismic ground dislocations extracted from interferometric satellite synthetic aperture radar (InSAR) imagery contain predictive information for distinguishing between two magnitude classes of an upcoming earthquake, assuming that an earthquake’s occurrence is already imminent. For this reason, twenty-three earthquakes of various magnitudes that occurred in Greece during the year 2020 were analyzed using SAR data to construct a time-series of five six-day InSAR images for each earthquake, spanning a total 24-day period before the earthquake. For each earthquake, four ground dislocation images covering the area around each earthquake were derived from the interferograms, each showing the dislocation during a six-day time interval. Images showing the total ground dislocation during the entire 24-day period before the earthquake were also produced by fusing the four images. Three machine learning classifiers were used to relate the earthquake magnitude class to pre-seismic ground dislocations. High accuracies were obtained with both support vector machine (SVM) and random forest (RF), yet they were highly dependent on the type of images used. In a subsequent analysis, five regression models were applied to estimate the earthquakes’ epicenters from dislocation images. The results reveal that the proposed approach is able to achieve well-localized epicentral area prediction, indicating the potential predictive value of this tool for seismic hazard assessment and emergency planning. Full article

Background/Objectives: Preoperative differentiation among benign thyroid nodules, follicular variant papillary thyroid carcinoma (FV-PTC), and classical papillary thyroid carcinoma (C-PTC) remains clinically challenging. FV-PTC is particularly difficult to identify due to its substantial sonographic and cytological overlap with both benign nodules and other malignant subtypes, frequently resulting in overtreatment or delayed diagnosis. This study aimed to develop and validate an interpretable multimodal model for accurate three-class discrimination using routine ultrasound images, with a specific focus on improving the preoperative identification of FV-PTC. Methods: This retrospective study included 479 pathologically confirmed thyroid nodules from 462 patients. Conventional ultrasound features and radiomics features extracted from grayscale ultrasound and color Doppler flow imaging were used to construct three predictive models: a Conventional Ultrasound model (conventional ultrasound features only), a Radiomics model (radiomics features only), and an Integrated model (combined features). Each model was trained using four machine learning classifiers. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis, and clinical usefulness was evaluated using decision curve analysis (DCA). Results: The support vector machine (SVM)-based Integrated Model achieved the best overall performance. In the independent testing cohort, the AUCs were 0.853 for FV-PTC, 0.882 for C-PTC and 0.928 for benign nodules. The Integrated Model showed the greatest improvement for FV-PTC, with a ΔAUC of 0.141 compared with the Conventional Ultrasound Model. SHAP (SHapley Additive exPlanations) analysis identified wavelet-HL_gldm_Dependence and wavelet-HH_glcm_InverseVariance as the two most important radiomics predictors in both the Radiomics Model and the Integrated Model, demonstrating robust cross-model stability and high discriminative power. Conclusions: The SVM-based Integrated Model demonstrated promising performance for three-class classification of thyroid nodules and enhanced the preoperative identification of FV-PTC. This approach may provide an interpretable and noninvasive decision-support tool for refining subtype-specific risk stratification and supporting individualized clinical management. Full article

Background/Objectives: Treatment for laryngeal cancer often impacts voice, swallowing, communication, and quality of life. Patient-reported outcome measures (PROMs) are increasingly used to evaluate survivorship, but their application and connection with objective functional measures vary widely. The objective was to explore how PROMs are used in laryngeal cancer research, identify the functional areas they assess, analyze their link with objective clinical outcomes, and identify methodological gaps in current studies. Methods: This scoping review followed PRISMA-ScR guidelines. Searches were conducted in PubMed/MEDLINE, Scopus, and Web of Science from their start until April 2026. Included studies involved adults with laryngeal cancer reporting PROMs and/or objective functional outcomes. Data on study features, PROM tools, evaluated domains, and how PROMs relate to objective outcomes were extracted and summarized descriptively. Results: Ninety-five studies with 10,807 participants were included. Most were observational (84.2%) and conducted at a single center (72.6%). Voice-related outcomes were the most common (86.3%), followed by psychological (72.6%) and swallowing outcomes (65.3%). Less frequently assessed were nutritional (22.1%) and supportive care domains (41.1%). The Voice Handicap Index family was the most used PROM group (30.5%). Over half the studies reported PROMs and objective measures separately without statistical integration (51.6%), while only 13.7% performed analytical integration, and none used predictive multivariable models. Significant variation existed in PROM choices, assessed domains, and integration approaches. Conclusions: PROM use in laryngeal cancer survivorship research is heterogeneous and predominantly focused on voice-related outcomes. Limited analytical integration with objective measures hampers a comprehensive understanding of recovery. There is a need for standardized, multidimensional assessment frameworks that include functional, nutritional, psychosocial, and objective outcomes to effectively support patient-centered survivorship care and rehabilitation planning. Full article

The aqueous seed extract of Ammodaucus leucotrichus Cosson & Durieu (AL-AE), a Saharan annual herb of the family Apiaceae, was evaluated for the first time as a green corrosion inhibitor for mild steel in 1.0 M hydrochloric acid. GC-MS analysis after acetylation derivatization identified ten constituents representing 99.22% of the total detected area, with 17-pentatriacontene (47.69%), 2,4-di-tert-butylphenol (13.24%), and myo-inositol (8.62%) as the dominant species. Inhibition performance was assessed by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) over 25–100 ppm at 298–328 K. At 100 ppm and 298 K, AL-AE achieved 96.17% by EIS and 97.10% by PDP. Adsorption obeyed the Langmuir model with a standard free energy of adsorption of −38.2 kJ mol⁻¹, consistent with a mixed physisorption–chemisorption mechanism. SEM/EDS confirmed protective film formation, with surface oxygen dropping from 34.9 to 4.1 wt%. Density functional theory (DFT) calculations at the B97-3c/CPCM (water) level in ORCA 6.1 identified 2,4-di-tert-butylphenol as the most reactive constituent, while Fukui index analysis based on Mulliken population analysis located the preferential adsorption sites on each molecule. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Pegmatite-related rare-metal exploration in high-altitude mountainous regions is commonly limited by rugged terrain, complex structural frameworks, and uneven bedrock exposure. This study presents a multi-source remote-sensing workflow for regional-scale rare-metal prospectivity assessment in the Pusharong area of western Sichuan, China, by integrating GF-5A Advanced Hyperspectral Imager (AHSI) data, ASTER thermal infrared (TIR) data, and structural interpretation. GF-5A hyperspectral data were used as the primary source for extracting mineral-related anomaly responses associated with muscovite, tourmaline, cookeite, and spodumene. Mixture Tuned Matched Filtering (MTMF) was applied to enhance weak target-related spectral responses, whereas Spectral Angle Mapper (SAM) provided an independent spectral-consistency constraint to reduce potential over-identification. ASTER TIR-derived Quartz Index (QI) and Feldspar Ratio Index (FRI) responses were used as supplementary lithological and differentiation-related background constraints rather than as continuous quartz–feldspar mineral-distribution maps. Structural interpretation was further integrated to evaluate the spatial relationship between mineral-related anomalies and favourable fault settings. Preliminary point-based validation shows a high degree of consistency between the mapped anomaly zones and available field or geochemical observations, with an overall consistency of 92.86% and a Kappa coefficient of 0.91. The integrated workflow delineated four prospective target zones for follow-up verification, with T1 showing the strongest multi-source support, followed by T2 and T3, whereas T4 is regarded as a lower-priority verification target. These results demonstrate the usefulness of the workflow for first-pass regional target prioritisation in complex mountainous terrain, but the delineated targets require further field, mineralogical, geochemical, and drilling verification before any deposit-scale interpretation can be made. Full article

Antimicrobial resistance represents a major global health challenge, highlighting the urgent need for alternative bioactive compounds from natural sources. This study investigated the phytochemical composition and antimicrobial potential of saponin-enriched extracts from the trunk bark of Argania spinosa (L.) Skeels, collected from two contrasting Algerian regions: the coastal area of Stidia (ES) and the Saharan region of Tindouf (ET). Extraction yields were comparable (approximately 12.6%). UHPLC-MS analysis revealed distinct phytochemical profiles, with ES enriched in oleanane-type saponins and flavonoids, whereas ET showed a higher abundance of bayogenin-type derivatives. Key compounds included arganine C, E, and J, as well as catechin and quercetin. Antimicrobial activity was evaluated using agar well diffusion and broth microdilution assays against clinically relevant microorganisms, including the reference strains Staphylococcus aureus and Listeria innocua, together with Staphylococcus epidermidis, Escherichia coli, Klebsiella pneumoniae, Serratia marcescens, Proteus mirabilis, and Candida albicans. Both extracts exhibited broad-spectrum antimicrobial activity, although ES consistently showed lower Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal, Fungicidal Concentration (MBC)/(MFC) values than ET. MIC values ranged from 25 to 50 mg/mL for ES and from 50 to 100 mg/mL for ET. Synergistic interactions were observed between ES and gentamicin against S. aureus and between both extracts and kanamycin against K. pneumoniae. Membrane permeability assays demonstrated that both extracts increased bacterial membrane permeability, with ET producing a stronger permeabilizing effect. Atomic force microscopy of ES-treated cells revealed marked alterations in bacterial surface morphology, while molecular docking supported strong interactions of mi-saponin B and arganine derivatives with key bacterial targets. Collectively, these findings highlight the potential of A. spinosa bark saponins as natural antimicrobial agents and promising antibiotic adjuvants against multidrug-resistant pathogens. Full article

Accurately mapping surface freshwater bodies (e.g., ponds, reservoirs, and small lakes) is vital for managing insular ecosystems and communities. However, satellite-based extraction in coastal settings is challenged by seawater intrusion, complex topography, and cloud cover. Focusing on bedrock islands outside China’s Pearl River Estuary, this study developed a robust method to address these issues. We used both Gaofen-1 (GF-1) optical and Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) imagery, supported by field-collected water quality samples from surface freshwater body shorelines for model training and validation. The performance of two index-based methods (the Normalized Difference Water Index, NDWI, and the Normalized Difference Vegetation Index, NDVI), two machine learning algorithms (Random Forest, RF, and Support Vector Machine, SVM), and a U-Net convolutional neural network (U-Net) deep learning model was compared. The U-Net model achieved the highest accuracy, with Area Under the Curve (AUC) values of 0.881 (GF-1) and 0.840 (GF-3). It effectively discriminated freshwater from seawater and mitigated cloud interference, demonstrating superior precision and robustness over traditional methods. This work establishes a high-precision framework for monitoring island freshwater resources, supporting sustainable water management. The proposed framework provides a practical tool for tracking freshwater availability under climate variability and anthropogenic pressures, contributing to the monitoring of Sustainable Development Goal (SDG) indicator 6.3.2 on ambient water quality. Full article

Search and rescue (SAR) dog–handler teams work under challenging conditions that may influence long-term physiological stress and arousal. Hair steroid analysis provides a reliable measure of chronic endocrine activity in SAR teams. Hair cortisol (HCL) and hair testosterone (HTL) offer non-invasive markers of chronic hypothalamic–pituitary–adrenal (HPA) and (the hypothalamic–pituitary–gonadal) HPG axis activity. This study examined long-term endocrine patterns in SAR dogs and their handlers and explored correlations within and between species. Hair samples were collected from 60 SAR dogs and their handlers. Dog hair was taken from the interscapular region, and human hair from the occipital area. Cortisol and testosterone were extracted using established methanol-based protocols and quantified via ELISA. Dogs showed a mean HCL of 10.974 pg/mg and a mean HTL of 3.008 pg/mg. Female dogs had significantly higher cortisol levels than males, and cortisol tended to increase with age. Testosterone did not differ by sex, breed, or castration status. Handlers showed a mean HCL of 10.874 pg/mg and a mean HTL of 2.925 pg/mg, with no sex differences. However, handler cortisol levels varied significantly by dog breed. Additionally, HCL levels of dogs and their handlers were negatively correlated. Full article

Seagrass meadows are important blue carbon habitats, but their patchy distribution in intertidal zones makes accurate UAV mapping difficult under shallow water cover and complex sediment backgrounds. This study developed a fine-grained semantic segmentation framework with explainability analysis to improve intertidal seagrass extraction from high-resolution UAV visible and multispectral imagery. Exposed seagrass (ESG) and shallow-submerged seagrass (SSG) were mapped separately to represent two observable intertidal states. Visible bands, multispectral bands, and vegetation indices were used as model inputs. U-Net and DeepLabV3+ served as baseline models, while UPerNet-ConvNeXtV2-Tiny was tested under the same settings. Kernel SHAP and permutation importance were used to assess feature contributions. UPerNet-ConvNeXtV2-Tiny achieved the best performance, with an overall accuracy (ACC), mean Intersection over Union (mIoU), and F1 score of 97.45%, 94.63%, and 97.23%, respectively. It outperformed the baseline models in suppressing background interference, preserving patch morphology, and reducing omission errors in weak response and boundary areas, while demonstrating better cross-scenario applicability in independent test areas. Explainability analysis showed that model discrimination was mainly associated with red and green-related features, especially RGB-R, MS-R, MS-G, RGB-G, and NGRDI. ESG and SSG showed different feature dependence patterns, indicating that high-resolution UAV imagery can support accurate seagrass mapping and reveal spectral differences between intertidal seagrass states. These findings provide a practical framework for UAV-based intertidal seagrass mapping and monitoring and offer guidance for feature selection and model explainability analysis. Full article

Background/Objectives: The “small-n-large-p” dilemma in mammography artificial intelligence (AI)—where the number of candidate imaging features far exceeds the number of labeled cases—commonly results in model overfitting, unstable feature selection, and poor generalization across clinical settings. This study aims to reassess the internal performance of the HiTopology-GOA-CSA (Grasshopper Optimization Algorithm–Crow Search Algorithm) feature-selection framework for mammography using a larger real Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) cohort and a stricter leakage-aware evaluation strategy. Methods: In this retrospective computational study using public anonymized datasets, an expanded internal cohort of 98 CBIS-DDSM mass cases (49 benign, 49 malignant) was assembled from digital imaging and communications in medicine (DICOM) region of interest (ROI) series. A total of 1074 features were extracted per case, including 88 handcrafted radiomic descriptors and 986 EfficientNet-B5 deep features. HiTopology-GOA-CSA selected 102 features, corresponding to 91% feature reduction. Two internal evaluation modes were compared: Mode A, which matched the original pilot methodology by performing feature selection once on the full cohort before cross-validation, and Mode B, which used strict nested feature selection within training folds. Performance was assessed with 5-fold stratified cross-validation using a multilayer perceptron (MLP) classifier. Results: On the expanded cohort, Mode A achieved an area under the receiver operating characteristic curve (AUC) of 0.726 (95% CI: 0.594–0.858), sensitivity of 0.658, specificity of 0.651, and F1-score of 0.644. Under the stricter nested evaluation, Mode B achieved AUC of 0.683 (95% CI: 0.549–0.817), sensitivity of 0.598, specificity of 0.631, and F1-score of 0.595. Mean pairwise Jaccard similarity across nested folds was 0.604, indicating moderate feature stability. Benchmark comparisons showed that the proposed method was competitive but did not outperform standard baselines; LASSO logistic regression achieved the highest AUC of 0.739, while the proposed HiTopology-GOA-CSA + MLP achieved an AUC of 0.683. Real external validation on the locked VinDr-Mammo subset (n = 25) remained near-random (AUC of 0.500 [95% CI: 0.304–0.696]), with complete prediction collapse (sensitivity of 1.000, specificity of 0.000). Conclusions: The framework demonstrated feasibility for structured feature selection and stress testing in a small-cohort mammography AI setting; however, external validation revealed near-random discrimination and prediction collapse, indicating limited generalizability. These findings emphasize the need for benchmark comparisons, transparent uncertainty reporting, patient-level validation, and larger multicenter datasets before clinical translation. Full article

The detection and localization of large underwater targets are important for maritime security, marine resource exploration, and underwater situational awareness, while the increasing acoustic stealth of underwater vehicles has limited conventional acoustic methods. This review provides a systematic overview of non-acoustic detection and localization technologies for large underwater targets, with emphasis on their relevance to unmanned aerial, surface, and underwater platforms. Wake-based detection, magnetic anomaly detection (MAD), and gravity anomaly detection (GAD) are reviewed as three representative non-acoustic routes. A bibliometric analysis is first conducted to summarize research trends, major contributors, and emerging hotspots. Wake-based methods are discussed in terms of wake signatures, modeling approaches, sensing platforms, and localization potential. MAD is analyzed from the perspectives of magnetic dipole modeling, target-based detection, noise-based detection, artificial intelligence (AI)-based detection, and magnetic localization. GAD is discussed with respect to physical feasibility, gravity-gradient target modeling, inversion methods, and engineering constraints. The review shows that wake-based methods are suitable for wide-area search and trajectory inference, MAD is relatively mature for short-range confirmation and localization, and GAD remains promising but less mature. Future research should focus on onboard sensors, platform stability, weak-signal extraction, background suppression, quantitative evaluation metrics, multi-source fusion, autonomous mission planning, and multi-platform collaboration. Full article

Background: Mild Cognitive Impairment (MCI) is a clinical state between normal aging and dementia. It may involve impairment in one or several cognitive domains. MCI offers a key window for maintaining cognitive function and studying how deficits develop in the elderly, making it of great research value. Measurement tools for screening MCI are not yet standardized in China. The accuracy of diagnostic criteria and threshold values needs improvement. Previous studies on the neural mechanisms of MCI have examined various aspects, but the changes in the white matter microstructure in older adults with MCI remain unclear. Most past studies used Fractional Anisotropy (FA) analysis to examine changes in white matter fiber orientation, often ignoring fiber density. As a result, findings are often contradictory or difficult to interpret. Therefore, it is necessary to assess cognitive function in MCI populations using more comprehensive and standardized measurement tools. It is also important to explore the association between changes in white matter microstructure and cognitive function in MCI by analyzing FA and Mean Diffusivity (MD). Methods: First, we assessed cognitive function using the Cognitive Function Measurement Scale for the Elderly, developed by Beijing Normal University, with diagnoses based on the NIA-AA (National Institute on Aging—Alzheimer’s Association) criteria. Second, we employed Diffusion Tensor Imaging (DTI) combined with Tract-Based Spatial Statistics (TBSS) to investigate alterations in the white matter fiber tract integrity in individuals with MCI. Based on the metrics used, this study was divided into two analytical approaches: Analysis Mode 1 utilized FA to explore changes in white matter fiber orientation in the MCI group. Analysis Mode 2 utilized MD to examine changes in white matter fiber density in the MCI group. Third, we further explored the association between alterations in the white matter fiber tract integrity and cognitive function in individuals with MCI. Specifically, FA and MD values from brain regions showing significant differences between the MCI and normal control groups were extracted and correlated with cognitive test scores. Results: According to the results of the community measurement survey, the prevalence of MCI among the elderly in Shenzhen is approximately 21.54%. Individuals with MCI exhibited functional decline in memory, attention, language, executive function, and spatial processing. DTI results indicated that (1) FA values across the brain’s white matter fiber tracts showed a decreasing trend in the elderly with MCI, with no areas exhibiting significantly higher FA values. Specifically, FA values were significantly lower in the corpus callosum, internal capsule, corona radiata, thalamic radiation, external capsule, superior fronto-occipital fasciculus, and cingulum (cingulate gyrus). (2) White matter fiber tracts with significantly reduced FA values also demonstrated significantly increased MD values. Additionally, MD values in the cingulum (hippocampus), inferior cerebellar peduncle, and corticospinal tract were significantly reduced in the MCI group. (3) Correlation analysis revealed that the significant differences in FA and MD values within the white matter fiber tracts of older adults with MCI were correlated with scores on several cognitive tests. Conclusions: In the present study, older adults with MCI tended to exhibit functional decline across multiple cognitive domains and relatively extensive microstructural white matter damage. Observations suggested that white matter fiber density may be informative regarding these microstructural alterations, indicating that diffusion biomarkers in key regions such as the cingulum (hippocampus) warrant further investigation. Full article