Search Results (540)

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union ($IoU$) of 0.57, with performance rising to an actual $IoU$ of 0.89 on the subset of predictions with high confidence (predicted $IoU$ > 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

The Jiangsu Coastal Thermal Front (JCF), a persistent feature in Chinese marginal seas, plays a significant role in modulating phytoplankton dynamics and carbon cycling. However, the multi-scale spatiotemporal variability of the persistent JCF and the underlying mechanisms driving its ecological effects remain limited. Using satellite observations and reanalysis data, this study systematically investigates the JCF’s distribution and its regulatory impact on phytoplankton chlorophyll-a (Chla) and particulate organic carbon (POC). Results show the persistent JCF is most active in summer and winter, primarily in Haizhou Bay and the Jiangsu Shoal. In summer, stratification-induced nutrient limitation within the Haizhou Bay thermal front decreases Chla and POC (by ~−20% and ~−40%, respectively), whereas nutrient-replete non-frontal waters support higher biomass. In the Jiangsu Shoal, the thermal front blocks the southward transport of POC, helping to maintain stable POC levels in the nearshore non-frontal region; meanwhile, the shift from southward to northward transport leaves the offshore non-frontal area without sufficient replenishment, resulting in a ~35% decrease in POC. In winter, the Haizhou Bay thermal frontal barrier effect restricts suspended particulate matter, alleviating light limitation inside the front and enhancing Chla (up to 15%) while reducing POC due to diminished resuspension. We elucidate that the JCF shapes ecological patterns through two primary pathways: by directly acting as a barrier to material transport and by interacting with ancillary processes like upwelling. These findings advance the mechanistic understanding of frontal impacts on coastal ecosystems and provide a mechanistic basis for understanding synergistic coastal carbon sinks. Full article

Behavioral and educational researchers increasingly rely on rich datasets that capture how students respond to technology-enhanced instruction, yet few open resources document the full pipeline from experimental design to data curation in authentic classroom settings. This data descriptor presents a clustered quasi-experimental dataset on the impact of an instructional architecture that combines virtual reality (VR) simulations with artificial intelligence (AI)-driven formative feedback to enhance undergraduate students’ communication and problem-solving performance. The study was conducted at a large private university in Mexico during the 2024–2025 academic year and involved six intact classes (three intervention, three comparison; n = 180). Exposure to AI and VR was operationalized as a session-level “dose” (minutes of use, number of feedback events, number of scenarios, perceived presence), while performance was assessed with analytic rubrics (six criteria for communication and seven for problem solving) scored independently by two raters, with interrater reliability estimated via ICC (2, k). Additional Likert-type scales measured presence, perceived usefulness of feedback and self-efficacy. The curated dataset includes raw and cleaned tabular files, a detailed codebook, scoring guides and replication scripts for multilevel models and ancillary analyses. By releasing this dataset, we seek to enable reanalysis, methodological replication and cross-study comparisons in technology-enhanced education, and to provide an authentic resource for teaching statistics, econometrics and research methods in the behavioral sciences. Full article

Clear cell renal cell carcinoma is the most common histological type of renal cancer, which is a common cancer type usually associated with a long clinical course. During this course, various metastatic sites can be observed. In this review, we have focused on metastases to the thyroid gland. We conducted research in three medical databases, including PubMed, Scopus, and Web of Science, using the same search algorithm. Our inclusion criteria focused on case reports and case series studies since 2011, covering therapeutic strategies for the primary and/or metastatic disease, as well as subsequent follow-up data. Studies with insufficient or uncertain data, or written in a language other than English, were excluded. An analysis of 510 articles from PubMed, 1729 from Scopus, and 649 from Web of Science, after application of inclusion and exclusion criteria, resulted in 77 reports, analyzing 189 patients. A description of the clinical, pathological, ancillary, and follow-up data, in the light of recent therapeutic schemes, was attempted. Our results suggest that metastases’ imaging features comprised more commonly a solitary nodule with a median size of 3.5 cm and worrisome features in ultrasonography, such as heterogeneity, hypoechogenicity, partially solid configuration, and variable internal vascularization. Histological and immunohistochemical examination of the lesion is necessary because these findings are not specific. Common non-thyroid metastases are seen in the urogenital system, lungs, and pancreas. We calculated the restricted mean survival from primary diagnosis at 274.6 months (95% CI: 264.3–285.0 months) and the restricted mean survival from thyroid metastases treatment at 93.9 months (95% CI: 65.3–122.4 months). Results regarding how patient characteristics affect these survival numbers were statistically nonsignificant (p > 0.05). Full article

Balloon pulmonary angioplasty (BPA) has become an established treatment modality for patients with inoperable chronic thromboembolic pulmonary hypertension (CTEPH), particularly in those with distal pulmonary artery lesions or significant comorbidities precluding pulmonary endarterectomy. BPA provides significant improvement in pulmonary hemodynamics, right ventricular function, exercise tolerance, and quality of life. Recent randomized controlled trials, including the RACE and MR-BPA trials, have demonstrated that BPA results in greater reduction in pulmonary vascular resistance and mean pulmonary arterial pressure compared to riociguat, although with a higher incidence of procedure-related complications. Ancillary follow-up data further suggest that a sequential strategy combining medical therapy and BPA may optimize outcomes and reduce adverse events. Advances in procedural techniques, imaging guidance, and patient selection have substantially improved the safety profile of BPA. International registries and expert consensus guidelines now support its incorporation into the multimodal management of CTEPH. This review synthesizes current evidence on the efficacy, safety, and practical aspects of BPA, while highlighting ongoing challenges, including long-term outcome data, standardization of treatment endpoints, and the role of combination therapy. BPA is poised to play an increasingly central role in personalized care strategies for CTEPH. Full article

Virtual power plants (VPPs) provide essential regulation capabilities by aggregating diverse distributed energy resources (DERs). Accurately assessing the value of VPPs from the grid’s side is essential for improving market mechanism design and, in turn, encouraging participation of VPPs. However, existing assessment methods neglect the refined evaluations integrating Automatic Generation Control (AGC)-based operational simulations derived from economic dispatch results, thereby failing to comprehensively capture the multi-dimensional benefits VPPs contribute to the grid. To bridge this gap, this study proposes a multi-dimensional benefit assessment method of VPPs and a simulation method from the grid’s perspective. First, the environmental, security, and economic benefits of VPPs are characterized. A decoupled quantitative assessment framework based on the Vickrey-Clarke-Groves (VCG) mechanism is then established to evaluate these benefits by analyzing system cost variations induced by VPP aggregation. Next, the method of actual operation simulation based on scheduling outcomes is discussed. The corresponding system operation costs are obtained under various scenarios. Case studies utilizing real-world data from a provincial power grid in China analyzed the benefits of VPPs across multiple scenarios defined by varying renewable energy penetration rates, aggregation sizes, and output stability. Notably, the value of the VPP differs significantly across renewable energy penetration levels. Under high penetration, its value increases by 18.5% compared with the low-penetration case, and the value of security and ancillary services accounts for the largest share (50.3%), a component frequently overlooked in existing literature. These findings offer valuable insights for optimizing electricity market mechanisms. Full article

Background: Starvation represents a specific pathological entity characterized by severe nutritional deprivation leading to multi-organ failure. Despite its forensic relevance, a comprehensive synthesis of autopsy findings remains lacking. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Scopus, and Google Scholar were searched from inception to June 2025 using a pre-specified Boolean query. Eligible studies included case reports, case series and cohort investigations reporting post-mortem evidence of starvation or starvation-related malnutrition. Data extracted encompassed demographic, contextual, macroscopic, histological, and ancillary findings. Results: Fourteen studies were included, comprising 20 individual cases and two population-based cohorts (totaling 1647 deaths). Most cases (75%) involved children, predominantly victims of domestic neglect; adults accounted for 25%, mainly due to anorexia nervosa or voluntary fasting. Six cadavers were severely decomposed or mummified. Across studies, consistent autopsy findings included extreme emaciation, near-total loss of subcutaneous and visceral fat, empty gastrointestinal tract, and diffuse organ atrophy, especially of the liver, heart, thymus, and pancreas. Histology revealed hepatic steatosis, myocardial fibrosis, thymic involution and gelatinous transformation of adipose tissue. Ancillary methods (dual-energy X-ray absorptiometry, stable isotope and anthropological analyses) confirmed malnutrition in decomposed or skeletonized remains. Conclusions: This review delineates the morphological and histopathological hallmarks of starvation and suggests the possible diagnostic value of ancillary techniques in advanced decomposition. The predominance of neglect-related pediatric cases underscores starvation as a forensic indicator of social and caregiving failure. Establishing reproducible morphological and histological indicators may improve the consistency of forensic diagnosis and strengthen the evidentiary basis for determining starvation as a cause of death. Full article

Urban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution gap between high-resolution optical data and low-resolution satellite thermal imagery. This study introduces a novel deep learning approach—named Dilated Spatio-Temporal U-Net (DST-UNet)—to bridge this gap. DST-UNET is a modified U-Net architecture which incorporates dilated convolutions to address the multiscale nature of urban thermal patterns. The model is trained to generate high-resolution, airborne-like thermal maps from available, low-resolution satellite imagery and ancillary data. Our results demonstrate that the DST-UNet can effectively generalise across different urban environments, enabling municipalities to generate detailed thermal maps with a frequency far exceeding that of traditional airborne campaigns. This framework leverages open-source data from missions like Landsat to provide a cost-effective and scalable solution for continuous, high-resolution urban thermal monitoring, empowering more effective climate resilience and public health initiatives. Full article

Background/Objectives: In the present study, the Metabolic dysfunction-associated fatty liver disease (MAFLD) and Metabolic dysfunction-associated steatotic liver disease (MASLD) diagnostic criteria were applied to evaluate the relative performance in predicting short-term advanced fibrosis (AF) progression (AFpr) and hepatocellular carcinoma (HCC), as well as an ancillary outcome, i.e., the occurrence of acute cardiovascular events (ACEs) in steatotic liver disease (SLD) patients. Methods: We retrospectively analyzed the data stored in the University Hospital (UH)’s Official Health Documents Digitization Archive of 931 SLD patients, with a follow-up of 3 years. Based on the Body Mass Index (BMI), patients were subdivided into lean “L” (BMI < 25 kg/m²) (n = 134) and not-lean “NL” (n = 797), and, subsequently, into NL-MASLD (n = 206), NL-MASLD/MAFLD (n = 481), NL-MAFLD (n = 110), L-MASLD (n = 39), L-MASLD/MAFLD (n = 68), and L-MAFLD (n = 27). All study outcomes (AFpr, HCC, and ACE) were primarily evaluated in NL-SLD and by conducting a sub-analysis of L-SLD individuals. Results: MASLD and MAFLD criteria similarly estimated [p = 0.076] the overall 3-year risk of AF progression in NL-SLD. In the L-SLD sub-analysis, MAFLD criteria better estimated the overall 3-year risk of AF progression [p = 0.006]. Multivariate competing risk analysis (adjusted for sex, age, diabetes, steatosis, and fibrosis severity) revealed diabetes [adjusted Hazard Ratio (aHR) = 2.113, p = 0.001], high-sensitivity C-reactive protein (aHR = 1.441; p = 0.02), and Homeostatic Model Assessment for Insulin Resistance (aHR = 1.228; p = 0.03) as being associated with AF progression in L-MAFLD. Compared to MAFLD, MASLD diagnostic criteria similarly estimated the 3-year risk of HCC occurrence both in NL [HR = 1.104, C.I. 95%: 0.824–1.593, p = 0.741] and L [HR = 1.260, C.I. 95%: 0.768–2.104, p = 0.701] patients. Finally, no significant differences were reported between the MAFLD or MASLD criteria for ACE risk occurrence in all study groups. Conclusions: The MAFLD criteria better estimate the AF progression risk, limited to L-SLD patients. Full article

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes. Full article

Neurorehabilitation has become increasingly data-enabled, yet the conditions that most strongly modulate recovery, sleep consolidation, circadian alignment, medication ecology, and social–environmental context are rarely captured or acted upon. This opinion paper argues that an exposome perspective, defined as the cumulative pattern of external and internal exposures and their biological imprints across the life course, is not ancillary to rehabilitation but foundational to making therapy learnable, timely, and equitable. We propose a pragmatic model that centers on a minimal exposure dataset collected in minutes and interpreted at the point of care. Two clinical exemplars illustrate feasibility and utility. First, sleep and circadian rhythms: brief actigraphy and standardized reporting can make daily alertness windows visible, allowing teams to align high-intensity sessions to receptive states and to justify environmental adjustments as clinical interventions. Second, anticholinergic burden: a simple, trackable index can be integrated with functional goals to guide deprescribing and optimize cognitive availability for training. Implementation hinges less on new infrastructure than on workflow design: a short intake that surfaces high-yield exposures; embedding targets, e.g., sleep efficiency thresholds or anticholinergic load reductions, into plans of care; enabling secure import of device data; and training staff to interpret rhythm metrics and burden scores. We outline a parallel research agenda comprising pragmatic trials of bundled, exposure-informed care; longitudinal cohorts with time-stamped exposure streams; and causal methods suited to time-varying confounding, all under explicit equity and ethics safeguards. By measuring a few modifiable exposures and linking them to routine decisions, neurorehabilitation can convert context from a source of unexplained variance into actionable levers that improve outcomes and narrow unjust gaps in recovery. Full article

India’s aviation sector, crucial for connectivity, economic growth, and national integration, faces sustainability measurement challenges focused solely on carbon emissions. This study proposes the Aviation Sustainability Performance Index (ASPI-India), spanning four pillars: Environmental Stewardship, Social Responsibility, Governance Maturity, and Economic Resilience. Measurable indicators are derived from regulatory filings, commercial flight databases, geospatial tracking, and targeted surveys. Data sources include DGCA safety audits, AAI operational statistics, ADS-B flight path data, and passenger satisfaction surveys from 2010 to 2024. Fixed-effects panel models link ASPI-India to operational and financial outcomes like load factor stability, CASK, and credit rating resilience. Quasi-experimental designs exploit policy shocks through difference-in-differences estimation. Factor analysis validates the four-pillar structure, and robustness checks compare entropy, PCA, and equal weighting. Results show that a one-standard-deviation increase in ASPI-India improves load factor stability, ancillary revenue share, and credit terms, especially for carriers with diversified route networks. The framework provides actionable insights for airlines, regulators, and investors to embed sustainability in aviation management. Full article

Recently, telerehabilitation has taken on a significant role in rehabilitation programs, with benefits in improving balance. Many neurological diseases are associated with an increased fall risk and, considering the impact of falls on quality of life, the aim of this study is to evaluate the ability of the Silver Index (via the hunova^® robotic platform) to identify the fall risk and the effect of a telerehabilitation intervention (by ARC Intellicare) on fall risk in patients with neurological disorders. This is an ancillary study of a single-center, randomized controlled trial. Ninety patients with stroke, Multiple Sclerosis (MS), and Parkinson’s Disease (PD) participated, and were randomized into an ARC Intellicare group (experimental group) and a paper-based group (control group). Each group performed home treatment for 60 min a day, 3 days a week, for 8 weeks. Fall risk was assessed with clinical scales and hunova^®. Data analysis showed a correlation between clinical scales and the Silver Index. Furthermore, only the MS patients in the experimental group showed a significant decrease in fall risk (p = 0.015). This study suggested that the Silver Index is a valid tool for assessing fall risk in neurological disorders. It also confirmed that ARC Intellicare is a useful tool for remote rehabilitation at home. Full article

The transition toward sustainability in the electric power sector, driven by increasingly renewable integration, has amplified the need to understand complex market dynamics. This study addresses a critical gap in the existing literature by presenting a systematic and reproducible methodology for profiling generating-unit operators’ macro-behaviour in the Italian Ancillary Services market (MSD). Focusing on the Northern zone (NORD) during the pivotal period of 2022–2024, a stability-driven k-means clustering framework is applied to a dataset of capacity-normalized features from the day-ahead market (MGP), intraday market (MI), and MSD. The number of clusters is determined using the Gap Statistic with a 1-SE criterion and validated with bootstrap stability (Adjusted Rand Index), resulting in a robust and reproducible 13-group taxonomy. The use of up-to-date data (2022–2024) enabled a unique investigation into post-2021 market phenomena, including the effects of geopolitical events and extreme price volatility. The findings reveal clear operator-coherent archetypes ranging from units that mainly trade in the day-ahead market to specialists that monetize flexibility in the MSD. The analysis further highlights the dominance of thermoelectric and dispatchable hydro technologies in providing ancillary services, while illustrating varying degrees of responsiveness to price signals. The proposed taxonomy offers regulators and policymakers a practical tool to identify inefficiencies, monitor concentration risks, and inform future market design and policy decisions. Full article