Search Results (31,962)

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

Background: Endothelial dysfunction and renal injury are emerging as a common feature of long COVID, especially in those with hypertension. It is not yet well characterised whether SARS-CoV-2 infection exacerbates podocyte dysfunction, fibrotic signalling and renal hemodynamic remodelling, over and above the effects of hypertension alone and there are no reliable early biomarkers in this population. Methods: We conducted a comparative cross-sectional study with prospective 6-month treatment response follow-up in 120 adult patients (aged 30–60 years) with essential hypertension (Stage I, II or III; n = 40 per stage), at Bukhara Regional Multidisciplinary Hospital. Each stage subgroup was further divided into post-COVID (3–6 months after recovery; n = 20) and non-COVID (n = 20) strata. Patients with diabetes, known chronic kidney disease, previous myocardial infarction or stroke and other major comorbidities were excluded. Serum cystatin-C, creatinine, aldosterone, TGF-β1 and VEGF-A; urinary nephrin and microalbumin; cystatin-C-derived eGFR (CKD-EPI) and oral protein-loaded renal functional reserve (RFR); and renal Doppler indices (Vps, Ved, RI, PI) of the main, segmental and interlobar arteries were assessed before and after 6 months of guideline-based renin–angiotensin–aldosterone system (RAAS) blockade (enalapril 5–10 mg or azilsartan 40–80 mg, ±eplerenone). Comparisons were made by Student’s t-test—associations by Pearson correlation. Results: At baseline, post-COVID hypertensive patients exhibited consistently higher endothelial–podocyte injury markers than non-COVID counterparts. Urinary nephrin was elevated across all stages (Stage I: 126.5 ± 9.1 vs. 91.9 ± 8.3 pg/mL, p < 0.01; Stage III: 203.3 ± 11.2 vs. 164.5 ± 9.7 pg/mL, p < 0.05), as were VEGF-A (Stage III: 286.1 ± 16.4 vs. 223.2 ± 12.6 pg/mL, p < 0.01) and TGF-β1 (Stage III: 186.4 ± 10.1 pg/mL, 1.3-fold higher; p < 0.01). The detection of microalbuminuria was 100% in Stage III post-COVID patients and 85% in non-COVID controls. The post-COVID groups had selective loss of renal functional reserve (7.8 ± 1.1% in Stage III compared to 12.5 ± 1.6% in non-COVID controls, p < 0.001). Nephrinuria correlated strongly with RFR (r = −0.824, p < 0.001), eGFR (r = −0.797, p < 0.001) and aldosterone (r = 0.613, p < 0.001). Six months of RAAS blockade reduced nephrinuria, microalbuminuria and TGF-β1 in both arms but the magnitude of biomarker reduction appeared smaller in the post-COVID group, particularly in Stage III. Conclusions: Long COVID appears to be associated with persistent endothelial dysfunction and podocyte injury in hypertensive patients. These results indicate that nephrinuria, VEGF-A, TGF-β1 and renal functional reserve are potential exploratory markers of endothelial and renal abnormalities in hypertensive patients following COVID-19. Before clinical utility can be determined, larger studies with multivariable modelling, diagnostic-performance analyses and correction for multiple testing are needed. The differences in biomarker response between groups observed in this study need to be confirmed in larger prospective studies with multivariable modelling and formal interaction analyses. Full article

Background/Objectives: COVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological and cognitive complications, even in the absence of significant structural brain abnormalities. Understanding the evolving health concerns in the context of viral infections is critical to service member readiness, fitness, and mission completion. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Methods: Using a cross-sectional, non-human primate model (female cynomolgus macaques), we employed magnetoencephalography (MEG) to assess resting-state brain activity following vaccination with escalating doses of a novel psoralen-inactivated SARS-CoV-2 vaccine (PsIV) or a combination of PsIV and a DNA vaccine (prime boost), and subsequent challenge with the Delta variant (SARS-CoV-2 B.1.617.2). MEG scans were acquired 41 days after inoculation. Source series were constructed for 42 regions of interest for each subject, and band power was computed. Results: Band power demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. Significantly lower power was observed across the brain at all bandwidths in the unvaccinated group relative to the prime boost group. As PsIV concentration increased, spectral power increased, with the prime boost group having the greatest power. Conclusions: This approach not only underscores the role of vaccination in mitigating neuropathology but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections. Full article

Introduction: Skates (Rajidae) are cornerstone elasmobranchs, yet their intrinsic biological constraints, like slow growth, late maturation, and low fecundity, render them exceptionally susceptible to anthropogenic pressure. Despite their ecological and economic importance, tracking their population trajectories is historically hindered by “taxonomic blurring” and aggregated reporting in commercial fisheries. Objective: This study evaluates long-term conservation trends and exploitation dynamics of Rajidae species in the Northeast Atlantic and the Mediterranean Sea. Methodology: We analyzed 31 Rajidae species across the Northeast Atlantic and the Mediterranean Sea (FAO Areas 27 and 37) by integrating IUCN Red List assessments, species-specific life-history traits (maximum body size and depth distribution), and FAO fisheries landing data from 1992 to 2023. Descriptive analyses and Spearman correlations were used to assess temporal trends in conservation status and exploitation patterns. Results: Our synthesis reveals that some species show improvements in IUCN Red List category assessments, likely driven by recent management interventions such as species-specific reporting, catch quotas, and targeted retention bans. However, we also identify a critical mismatch between policy and biology: current Total Allowable Catches (TACs) and minimum landing sizes often do not explicitly incorporate species-specific life-history traits, inadvertently favoring smaller, less-marketable taxa while leaving larger, vulnerable species at risk. While FAO landings offer a valuable broad-scale overview of exploitation, the results highlight the limitations of aggregated fisheries statistics for species-level conservation assessments. Conclusions: These findings underline the need to adopt more precise and species-specific fisheries management approaches for Rajidae, including expanded regional monitoring programs, the use of data collected by on-board observers or electronic monitoring tools, and improved control of data reporting procedures, to prevent continued aggregation of species-level data. Full article

Accurate assessment of carbon storage dynamics and their driving factors is important for ecological sustainability and land management on the Loess Plateau under China’s dual carbon goals. In this study, the InVEST and PLUS models were integrated to evaluate carbon storage changes from 2000 to 2020 and simulate future carbon storage patterns for 2030 under four development scenarios, including natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP). In addition, the XGBoost-SHAP framework was employed to identify the dominant drivers and nonlinear response relationships controlling spatial variation in carbon storage. During 2000–2020, ecosystem carbon storage across the Loess Plateau generally increased, rising from 5.780 Pg to 5.893 Pg. Spatially, carbon storage displayed a pronounced pattern characterized by higher levels in the southeast and lower levels in the northwest, aligning with forest–grassland restoration belts. Scenario simulations showed that EP produced the largest carbon storage gain, with total carbon storage projected to reach 5.962 Pg in 2030. In contrast, RD reduced carbon storage to 5.858 Pg because of intensive construction land expansion. XGBoost-SHAP results identified net primary productivity (NPP) as the most influential factor controlling spatial variation in carbon storage, accounting for 57.3% of the total explanatory importance, whereas soil erosion (SE) exhibited a strong negative effect on carbon storage. Population density (POPD) also exerted a negative effect, whereas gross domestic product (GDP) showed positive contributions in economically developed counties. These findings enhance understanding of the spatial response characteristics of carbon storage under environmental gradients and human disturbance across the Loess Plateau. They further provide scientific support for differentiated ecological management and regionally adapted carbon mitigation planning. Full article

Trust in embodied intelligence is dynamic, contextual, and interaction-dependent, but many existing computational approaches still model trust using static similarity structures. This study proposes and evaluates a compositional trust modeling approach based on quantum natural language processing (QNLP). Using open-ended survey responses about human trust in embodied agents, we compared classical NLP clustering and QNLP-based clustering in terms of dimension coverage, semantic coherence, contextual sensitivity, and robustness. The QNLP pipeline captured richer latent structure, producing ten clusters and identifying eight trust dimensions, including two emergent dimensions: calibrated trust and predictive reliability. Compared with classical approaches, QNLP clusters showed improved semantic separation and stronger context retention under preprocessing variation. These findings support a temporally structured view of trust in embodied AI and demonstrate that compositional quantum-inspired representations can reveal nuanced trust dynamics that are difficult to detect with conventional methods. This study contributes both a methodological framework for trust-sensitive text modeling and a theoretical account linking trust formation to retrospective calibration and prospective expectation in human–agent interaction. Full article

Introduction: Vaccination against COVID-19 has been crucial in controlling the pandemic. While side effects are typically mild, rare neurological complications have been reported. This is a case series of ten patients who reported of persistent fasciculations after COVID-19 vaccination. Methods: We describe the clinical presentation and diagnostic work-up of ten patients with new-onset fasciculations in temporal proximity to COVID-19 vaccination. Patients with prior SARS-CoV-2 infection or known alternative causes of fasciculations were excluded. Routine clinical data, including neurological examination, laboratory results, and electrophysiology (electromyography and nerve conduction studies), were analyzed. Results: Ten patients (5 male, 5 female; mean age 42.4 years) reported fasciculations beginning within 6 h to 13 days post-vaccination and persisting for 2–12 months at the time of presentation. Fasciculations were accompanied by additional symptoms such as paresthesia and fatigue. Laboratory results were mostly unremarkable; two patients had positive myositis antibodies without clinical correlates. Electrophysiology was unremarkable in six patients, while fasciculation potentials were detected in four patients. Nine were diagnosed with probable benign fasciculation syndrome (BFS), and one met diagnostic criteria for amyotrophic lateral sclerosis (ALS). Discussion: In this small, retrospective case series, most cases of post-vaccination fasciculations were benign and compatible with BFS. Whether BFS onset was causally linked to vaccination or due to a nocebo effect remains unclear. One patient was diagnosed with ALS, though a causal link remains speculative given the study’s limitations and rarity of similar reports. Larger, prospective studies are needed to validate these observations and explore underlying pathophysiological mechanisms. Full article

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according to the CFAR target–background separation margin. Specifically, CFAR is used as an interpretable statistical tool to construct an anomaly response map. The separation margin is then calculated by comparing the average CFAR anomaly responses of annotated target regions and their surrounding contextual backgrounds. Based on this indicator, a You Only Look Once version 8 (YOLOv8)-based mode-selective detector is constructed with three key components. First, a lightweight representation-enhanced backbone that integrates ResNet18 and a dilated convolutional spatial pyramid (DCSP) module is adopted to improve contextual representation while maintaining moderate model complexity. Second, a mode-selective neck (MSN) is designed with three predefined fusion modes, where the appropriate fusion depth is selected according to the CFAR-guided target–background separation margin of each dataset. Third, a complete intersection over the union modulated head (CMH) is developed to enhance classification-regression alignment and suppress clutter-induced responses. Experiments on SAR-Aircraft-1.0, High-Resolution SAR Images Dataset (HRSID), and SAR Ship Detection Dataset (SSDD) indicate that datasets with smaller CFAR target–background separation margins benefit from deeper fusion, while datasets with larger separation margins can adopt shallower fusion. Moreover, the proposed CGMSN achieves superior performance over representative detectors, demonstrating its effectiveness on the evaluated SAR datasets with diverse scene characteristics. Full article

Synthetic aperture radar (SAR) imagery provides all-weather, day-and-night observation capabilities that complement electro-optical (EO) imaging; however, the limited number of operational SAR satellites and the difficulty of acquiring expert-annotated SAR datasets constrain deep-learning-based SAR image analysis. In this paper, we propose EO2SAR-Diff, a conditional latent diffusion framework that translates EO aerial images into realistic synthetic SAR images. The framework comprises three core components: (1) domain-adaptive LoRA pre-training that anchors the Stable Diffusion backbone in the remote sensing domain, (2) a style extraction and injection network that captures SAR-specific visual characteristics via multi-scale feature encoding and parallel cross-attention, and (3) a multi-branch ControlNet with three parallel branches for complementary structural guidance. These components are coordinated by a dual-axis feature injection strategy that modulates conditioning strength along both spatial (per-block) and temporal (per-timestep) dimensions. Experiments on the DOTA 1.0 and SARDet-100K datasets demonstrate that EO2SAR-Diff ranks in the top tier among all compared methods in distributional alignment with real SAR imagery, in terms of FID and KID computed with two SAR-domain-adapted feature extractors. Augmenting the SAR training set with our synthetic images yields consistent improvements in downstream object detection performance, confirming the practical utility of the proposed framework. Full article

Ship detection in synthetic aperture radar (SAR) imagery, an indispensable all-weather technology for marine engineering and coastal safety, remains challenging in complex nearshore scenes due to coupled speckle noise, sea–land clutter, large scale variation, and extreme class imbalance. Existing decoupled pipelines fail to jointly mitigate these degradations, leading to high false alarm rates and poor generalization. We propose DN-AnchorNet, an end-to-end unified framework integrating a detection-oriented structure-preserving enhancement branch, a scale-adaptive anchor mechanism, and an adaptive weighted Smooth L1 loss. The detection-guided enhancement branch operates without paired clean data to preserve critical ship structures. The scale-adaptive anchor design enhances matching for small, elongated, and arbitrarily oriented ships, while the tailored loss improves regression robustness through dynamic threshold adjustment and valid positive-sample regression masking under class imbalance. Extensive experiments under the adopted fixed nearshore stress-test protocol of RSDD-SAR and SSDD+ show that DN-AnchorNet achieves the best overall performance among the compared representative oriented object detectors in this evaluation setting, with AP₅₀ values of 0.699 and 0.610, and F1-scores of 0.757 and 0.689, respectively. A strict zero-shot cross-dataset evaluation on HRSID provides supplementary evidence of DN-AnchorNet’s transferability to unseen marine SAR conditions. These results suggest that joint optimization can achieve a favorable accuracy–false-detection balance under challenging nearshore SAR detection conditions. Full article

Early-age temperature rise in mass concrete can generate substantial thermal gradients and increase the risk of cracking. In this study, a temperature-rise suppression concrete was developed by partially replacing conventional coarse aggregate with steel-encapsulated superabsorbent polymer (SAP)–water phase-change aggregates. Semi-adiabatic temperature-rise tests were conducted to characterize the early-age thermal response, and the corresponding adiabatic temperature-rise histories were reconstructed using a heat-loss compensation method. The results showed that the incorporation of steel-encapsulated SAP–water aggregates reduced the temperature rise and delayed the thermal peak under semi-adiabatic conditions. For SAP-15, the peak core temperature in the validated finite element simulation decreased from 51 °C to 44 °C, while the maximum adiabatic temperature rise decreased to 40.5 °C. Engineering-scale simulation of a bridge pile-cap foundation further showed reductions in internal peak temperature, temperature difference, and thermal stress. These findings demonstrate that steel-encapsulated SAP–water phase-change aggregates provide an effective material-based strategy for moderating early-age thermal accumulation and mitigating thermal cracking risk in mass concrete. Full article

Colorectal cancer (CRC) has been increasingly associated with gut microbiota dysbiosis and the presence of specific bacterial pathogens. This study evaluated the in vitro growth-inhibitory activity of 18 biologically active compounds, including phytochemicals, synthetic analogs, and clinically used antibiotics, against CRC-associated bacterial strains. Minimum inhibitory concentrations (MICs) were determined using the broth microdilution method and analyzed in relation to chemical structure. Conventional antibiotics, particularly tetracycline and ciprofloxacin, exhibited the strongest antibacterial activity. Among non-antibiotic compounds, nitroxoline and carbadox showed moderate activity, whereas quaternary benzylisoquinoline-derived alkaloids and polyphenols were less effective. Structure–activity relationship analysis suggested that aromatic heterocyclic scaffolds, electron-withdrawing substituents, and metal-chelating groups contribute to antibacterial potency. We obtained novel MIC data for several compounds, including ferron and oxyquinoline, against underexplored CRC-associated bacterial strains. These findings expand current knowledge of the antibacterial activity of structurally diverse compounds against CRC-associated bacteria and provide a basis for future studies on microbiota-targeted antimicrobial strategies. Full article

Passive reception of spaceborne synthetic aperture radar (SAR) signals is of great significance for acquiring target characteristics and identifying SAR operating states. With the rapidly growing demand for high-quality SAR signal reception, signal-receiving arrays are prone to beam performance deterioration and difficulty in beamforming under wide-angle scanning conditions. Traditional uniform arrays fail to meet practical engineering requirements and cannot balance multiple conflicting performance indicators. To address the above technical bottlenecks, this paper proposes a design method of a non-uniform planar receiving array based on the MOEA/D-HPSO algorithm. Taking maximum sidelobe level (MSL), array gain (G), and beamwidth (BW) as core performance indicators, a multi-objective optimization model of SAR signal-receiving array for wide-angle scanning is established. This method integrates the multi-objective decomposition strategy and hybrid genetic particle swarm optimization mechanism, decomposes complex multi-objective problems into several scalar subproblems, obtains uniformly distributed Pareto fronts, and effectively improves the diversity of solution sets. Simulation experimental results show that the proposed algorithm is superior to traditional mainstream algorithms such as NSGA-II and MOEA/D-DE in terms of convergence accuracy, solution set distribution, and various performance indicators. Typical array design examples verify that the proposed method can adapt to various engineering application scenarios and provide technical support for spaceborne SAR signal reception and spectrum management. Full article

Accurate characterization of mining-induced surface subsidence is essential for safety assessment in mining areas; however, single monitoring techniques have inherent limitations. Spaceborne interferometric synthetic aperture radar (InSAR) provides large-area coverage but suffers from low signal-to-noise ratio in the subsidence center, whereas terrestrial laser scanning offers high accuracy but limited spatial coverage. To achieve physically consistent quantitative fusion, a multi-source subsidence fusion framework based on variational data assimilation is proposed. By constructing an objective function that incorporates a background prior, D-InSAR-derived boundary constraints, TLS observations, spatial smoothness constraints, and gradient penalty terms, multi-source data are integrated into a unified optimization framework. The results show that, compared with RTK observations, the fused subsidence field achieves an RMSE of 0.12 m and an RRMSE of 2.4% approximately. Parameter sensitivity analysis indicates that the smoothing strength has the greatest influence on fusion accuracy, whereas the observation weight and gradient penalty coefficient exhibit relatively wide stable intervals, and the background constraint has a minor effect on the results. Parameter interaction analysis further demonstrates that the coupling between smoothing strength and observation weight is the most significant. The proposed method provides a physically consistent and parameter-controllable framework for multi-source deformation data fusion in mining subsidence monitoring. Full article