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An NLP-based method to mine gene and function relationships from published articles
China research on next-generation computer chips is double the US output
Electron correlation and relativistic effects in the excited states of radium monofluoride
Abstract Highly accurate and precise electronic structure calculations of heavy radioactive atoms and their molecules are important for several research areas, including chemical, nuclear, and particle physics. Ab initio quantum chemistry can elucidate structural details in these systems that emerge from the interplay of relativistic and electron correlation effects, but the large number of electrons complicates the calculations, and the scarcity of experiments prevents insightful theory-experiment comparisons. Here we report the spectroscopy of the 14 lowest excited electronic states in the radioactive molecule radium monofluoride (RaF), which is proposed as a sensitive probe for searches of new physics. The observed excitation energies are compared with state-of-the-art relativistic Fock-space coupled cluster calculations, which achieve an agreement of ≥99.64% (within ~12 meV) with experiment for all states. Guided by theory, a firm assignment of the angular momentum and term symbol is made for 10 states and a tentative assignment for 4 states. The role of high-order electron correlation and quantum electrodynamics effects in the excitation energies is studied and found to be important for all states.
Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling
Abstract Accurate rainfall-runoff modeling is crucial for effective watershed management, hydraulic infrastructure safety, and flood mitigation. However, predicting rainfall-runoff remains challenging due to the nonlinear interplay between hydro-meteorological and topographical variables. This study introduces a hybrid Gaussian process regression (GPR) model integrated with K-means clustering (GPR-K-means) for short-term rainfall-runoff forecasting. The Orgeval watershed in France serves as the study area, providing hourly precipitation and streamflow data spanning 1970–2012. The performance of the GPR-K-means model is compared with standalone GPR and principal component regression (PCR) models across four forecasting horizons: 1-hour, 6-hour, 12-hour, and 24-hour ahead. The results reveal that the GPR-K-means model significantly improves forecasting accuracy across all lead times, with a Nash-Sutcliffe Efficiency (NSE) of approximately 0.999, 0.942, 0.891, and 0.859 for 1-hour, 6-hour, 12-hour, and 24-hour forecasts, respectively. These results outperform other ML models, such as Long Short-Term Memory, Support Vector Machines, and Random Forest, reported in the literature. The GPR-K-means model demonstrates enhanced reliability and robustness in hourly streamflow forecasting, emphasizing its potential for broader application in hydrological modeling. Furthermore, this study provides a novel methodology for combining clustering and Bayesian regression techniques in surface hydrology, contributing to more accurate and timely flood prediction.
Author Correction: Probing spin-electric transitions in a molecular exchange qubit
Superhydrophobic magnetic melamine sponge modified by flowerlike ZnO and stearic acid using dip coating method for oil and water separation
Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography
Hybrid attention structure preserving network for reconstruction of under-sampled OCT images
The evolution of vestibular function and health-related quality of life in bilateral vestibulopathy
Identification of an extracellular matrix signature for predicting prognosis and sensitivity to therapy of patients with gastric cancer
Abstract Extracellular matrix (ECM) is a vital component of the tumor microenvironment and plays a crucial role in the development and progression of gastric cancer (GC). Co-expression networks were established by means of the “WGCNA” package, the optimal model for extracellular matrix scores (ECMs) was developed and validated, with its accuracy in predicting the prognosis and treatment sensitivity of GC patients assessed. We performed univariate cox regression analysis [HR = 6.8 ( 3.3–14 ), p < 0.001] which demonstrated that ECMs was an independent risk character and perceptibly superior to other factors with further analysis of multivariate Cox regression [HR = 8.68 ( 4.16–18.08 ), p < 0.001]. The nomogram, presenting the clinical prognosis model for GC patients, demonstrated accuracy through KM analysis [HR = 3.97 (2.56–6.16), p < 0.001] and ROC curves with AUC values of 0.70, 0.72, and 0.72 at 1, 3, and 5 years, respectively. Using the ECMs model, we stratified GC patients into high- and low-risk groups, enabling precise predictions of prognosis and drug sensitivity. This stratification provides a new strategic direction for the personalized treatment of GC.