We demonstrate that soliton perturbation theory, though widely used, predicts an incorrect phase distribution for solitons of stochastically driven nonlinear Schrödinger equations in physically relevant parameter regimes. We propose a simple variational model that accounts for the effect of radiation on phase evolution and correctly predicts its distribution.
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http://dx.doi.org/10.1364/OL.36.001659 | DOI Listing |
Nutr Res
December 2024
Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. Electronic address:
The potential impact of one-carbon metabolism (OCM)-related B vitamins (vitamin B, B, B, and folate) on colorectal cancer survival warrants investigation but research is sparse. This cohort study examined the association between the prediagnostic dietary intakes of OCM-related B vitamins and colorectal cancer survival. A total of 2799 colorectal cancer patients from the Guangdong Colorectal Cancer Cohort, enrolled at baseline in 2010, were followed for mortality outcomes through 2023.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China.
Polymer-based dielectric films are increasingly demanded for devices under high electric fields used in new energy vehicles, photovoltaic grid connections, oil and gas exploration, and aerospace. However, leakage current is one of the significant factors limiting the improvement of the insulation performance. This paper tested the leakage current and condensed state structure characteristics of biaxially oriented polypropylene (BOPP) films and obtained the nonlinear characteristics of leakage current of BOPP films in the range of 40-440 V/μm and 40-110 °C.
View Article and Find Full Text PDFBiochemistry
January 2025
Department of Chemistry, Washington University in St. Louis, One Brookings Drive, St. Louis, Missouri 63130, United States.
Branch-point syntheses in nonribosomal peptide assembly are rare but useful strategies to generate tripodal peptides with advantageous hexadentate iron-chelating capabilities, as seen in siderophores. However, the chemical logic underlying the peptide branching by nonribosomal peptide synthetase (NRPS) often remains complex and elusive. Here, we review the common strategies for the biosynthesis of branched nonribosomal peptides (NRPs) and present our biochemical investigation on the NRPS-catalyzed assembly of fimsbactin A, a branched mixed-ligand siderophore produced by the human pathogenic strain .
View Article and Find Full Text PDFEnviron Health Perspect
January 2025
Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK.
Background: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance.
Objectives: The Cholera and Other Illness Surveillance (COVIS) system database has reported infections in the United States since 1988, offering a unique opportunity to both explore the forecasting capabilities machine learning could provide and to characterize complex environmental drivers of infections.
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
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