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The role of satisfaction with the way you look in 10 to 12-year-olds’ health and subjective well-being across genders and cultures
Tuning the f Band for Enhanced Surface Redox in Strained Rare Earth Oxides
Kriging to Kolmogorov-Arnold Network model accelerated discovery of oxygen control strategy in lead-based fast reactors
Development of the scientific and innovative potential of future teachers
Establishing Selectivity Trends with Reactions of Thioesterases and Semi-Synthetic Polyketides
Transcription factor ZNF263 primes human embryonic stem cells for pluripotency dissolution and lineage commitment
Analysis of risk factors and construction of nomogram model for nosocomial infection in patients with acute myocardial infarction after percutaneous coronary intervention
Molecular Engineering of Organic Prelithiation Agents via Frontier Orbital Regulation
Measuring the world’s rivers with videos from Space
Abstract The accurate measurement of volumes of water flowing in the world’s rivers is of critical importance for people, for nature and for industry. Our planet’s rapidly changing climate is increasing this need, as water becomes scarcer as a resource and more dangerous as a hazard. Additionally, many river monitoring networks globally are inadequate and declining. To date, satellite-based methods used observations of river width, water surface height, and water surface area, but did not include the critical parameter of water flow speed. Here, we present a significant advance by demonstrating a method for determining water flow speed with a high degree of accuracy using video imagery obtained by a constellation of low-earth-orbit satellites. The very high resolution of the video imagery also allows observations to be made in rivers as narrow as 70 m wide. Through a programme of ground-based validation measurements, we have demonstrated agreement in discharge measurements better than 5% at a range of river sites around the world. This development can herald a step change in capabilities for the measurement of rivers globally, allowing observations in remote locations, and during extreme events such as floods, with no need for people or equipment to be on site.
Emerging interest in science during early childhood period and evaluating its effectiveness
Poly(bis(guanidinium)-oxazoline)–Insulin Complex Exerting Long-Acting Glucose-Responsive Insulin Release in Mice and Minipigs
Mediating role of arterial stiffness in the association between physical activity and cardiovascular disease risk: a prospective cohort study
Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See
A network analysis of housing quality indicators and depression in women
Abstract Numerous studies have detected associations between poor housing quality and increased risk for mental illness. However, it currently remains unclear in associations between poor housing quality and increased risk for women’s mental illness which housing quality indicators drive this association and hence which specific indicators should be prioritised in housing quality assessments or improvements. In a sample of up to 9,669 pregnant women, we used a network analysis to investigate cross-sectional associations between poor housing quality indicators (e.g., house size, facilities, leaks or condensation/mould, decorations, and feelings towards the home) and depressive symptoms (assessed at age 28). All 36 edges showed non-zero associations, whereby when considering all poor housing quality indicators ‘feelings-towards-the-home’ had the strongest association with depressive symptoms, and ‘feelings-towards-the-home’, in turn, was most strongly associated with house problems, size, and facilities. Our findings highlight the importance of using multiple (or composite) person-centred measures of housing quality in the context of maternal mental health.
Y-Doped CuS Promotes Selective Electroreduction of CO <sub>2</sub> to Ethanol
Machine Learning-Based probabilistic prediction of glacial lake formation using erosional and topographic features
Abstract Glacial lake formation in high mountain regions, particularly the Himalayas, is accelerating due to climate-driven glacier retreat, increasing the risk of glacial lake outburst floods (GLOFs) that threaten downstream populations and infrastructure. While climate governs meltwater availability, the formation and evolution of glacial lakes are primarily controlled by geomorphological features such as cirques, valleys, flow channels, retreating glaciers, and neighbouring lakes. However, most predictive models overlook these controls, limiting hazard forecasting capabilities. This study develops a probabilistic framework to predict the probability of glacial lake formation (PGLF) in the Eastern Himalaya by integrating key erosional and topographic features. Using Google Earth imagery and digital elevation models within a 3 × 3 neighbourhood grid structure, we evaluated three predictive models: Logistic Regression (LR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN). BNN outperformed LR and ANN with an AUC of 0.878, while also estimating both aleatoric and epistemic uncertainties (10⁻³ to 10⁻⁴), enhancing prediction confidence. Neighbouring lakes, cirques, gentle slopes, and retreating glaciers emerged as the most influential predictors, demonstrating the importance of geomorphology, which is often omitted from prior models. The proposed approach offers a transferable framework for identifying high-risk glacial lake formation sites, supporting regional hazard mitigation, early warning systems, and sustainable water resource management in the Himalaya and other glaciated regions. Future improvements should integrate moraine development chronologies, automate data preparation, and incorporate field validation to further refine predictive accuracy and inform global mountain hazard management efforts.