A5104278275- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Black Holes and Theoretical Physics
- Computational Physics and Python Applications
- Dark Matter and Cosmic Phenomena
- Neutrino Physics Research
- Particle Accelerators and Free-Electron Lasers
- Atomic and Subatomic Physics Research
- Ship Hydrodynamics and Maneuverability
- Nuclear physics research studies
- Maritime Navigation and Safety
- Medical Imaging Techniques and Applications
- advanced mathematical theories
- Machine Fault Diagnosis Techniques
- Particle Detector Development and Performance
- Stochastic processes and statistical mechanics
- Scientific Research and Discoveries
- Atomic and Molecular Physics
- Algebraic and Geometric Analysis
- Advanced NMR Techniques and Applications
- Crystallography and Radiation Phenomena
- Diabetes Treatment and Management
- Markov Chains and Monte Carlo Methods
- Pharmaceutical Practices and Patient Outcomes
First Affiliated Hospital of Chengdu Medical College
2025
Sun Yat-sen University
2020-2024
Institute of High Energy Physics
2015-2024
Dalian Maritime University
2024
Liaoning Normal University
2020-2023
University of Turin
2017
Boğaziçi University
2017
Very short-term forecasting of ship motion involves future movements based on learned characteristics from historical data. However, exhibits not only temporal features but also in the frequency domain, which are often overlooked. This paper introduces a novel method called Fourier Transform and Multilayer Perceptron-net enhancement Deep Operator Network (DeepONet), abbreviated as FMD. approach effectively captures learns patterns both domains. Specifically, branch net DeepONet features,...
Due to the complexity of marine environment, ultra-short-term ship motion prediction has always been challenging. This study proposes a dual-channel network architecture, TF-Informer, which integrates temporal convolutional (TCN), frequency-enhanced channel attention mechanism (FECAM), and Informer improve accuracy, abbreviated as TF-Informer. TCN handles short-term dependencies extracts local features, while FECAM introduces frequency information enhance model's ability capture signals...
Background With the popularity of smart phones and development information technology, more patients are adopting diabetes APPs for self-management. However, at present, there few research reports on effect those coming from China. Objective The purpose this study was to evaluate effectiveness applicability an APP blood glucose control that is widely popular among Chinese with type 2 mellitus (T2DM). Methods This a 2-center, factorial design, equal proportional distribution, superiority...
Very short-term ship motion forecasting aims to predict future movements using historical data. While features both temporal and frequency characteristics, the latter is often neglected. This paper proposes a fully adaptive time–frequency coupling model self-attention mechanism based on Deep Operator Network (DeepONet), abbreviated as TFD. The multi-head attention layers enable trunk net adaptively learn relationships between different frequencies in domain assign varying weights...
Aim: Incretin therapies, including dipeptidyl peptidase-4 inhibitors (DPP-4is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs), are crucial for type 2 diabetes treatment. Evidence on their association with gallbladder, biliary diseases liver injury remains inconsistent. This study evaluated the between incretin therapies hepatobiliary adverse events using FDA's Adverse Event Reporting System (FAERS) data. Methods: Case reports involving from January 2006 to December 2023 were...