A5059145238- Traffic Prediction and Management Techniques
- Human Mobility and Location-Based Analysis
- Urban Transport and Accessibility
- Physical Education and Pedagogy
- Moringa oleifera research and applications
- Education Practices and Challenges
- Time Series Analysis and Forecasting
- Inclusion and Disability in Education and Sport
- Biofield Effects and Biophysics
- Green IT and Sustainability
- Gait Recognition and Analysis
- Transportation Planning and Optimization
- Proteins in Food Systems
- Environmental Impact and Sustainability
- Curcumin's Biomedical Applications
- Human Pose and Action Recognition
- Data Quality and Management
- Digital Mental Health Interventions
- Vehicle emissions and performance
- Data Management and Algorithms
- Child Therapy and Development
- Health Systems, Economic Evaluations, Quality of Life
- Anomaly Detection Techniques and Applications
Zhejiang University of Technology
2025
Loughborough University
2021-2024
Zhejiang University
2023
Hunan University
2021
Otsuka Pharmaceutical (Spain)
2020
Hunan University of Traditional Chinese Medicine
2008
Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management urban planning. However, this work challenging, especially on weekends, due inefficient representations spatiotemporal features for such and considerable randomness mobility weekends. In article, we propose deep learning framework attention network (STANet) with neural algorithm logic unit (NALU), so-called STANet-NALU, understand dynamic cars...
With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, novel emission relationship model that aims improve efficiency of monitoring estimation is proposed in this paper. Deeply exploring intrinsic link between production data emissions, paves new path “measuring through electricity”, contrast factor method commonly...
With the fast development of urbanization and motorization, an increasing number people choose to buy private cars fulfill their daily travel needs. In particular, many from various positions city drive specified areas, then they will stop stay for a certain period time, leading spatiotemporal evolution urban area attractiveness (AA). this paper, we aim at understanding AA based on analyses car trajectory datasets. Specifically, by extracting point-of-stop (PoS) data trajectories, design...
Next destination recommendation is a crucial research area for understanding human travel behavior. However, existing studies often overlook the problem of underfitting, which arises due to limited regularity in users' patterns. To tackle this issue, we leverage diverse co-occurrence patterns (CoPs) discover potential user preferences. These capture intersections with similar spatial and temporal characteristics travels. traditional graph neural network (GNN)-based approaches struggle...
Human action recognition (HAR) from RGB videos is essential and challenging in the computer vision field due to its wide range of real-world applications fields human behaviour analysis, human-computer interactions, robotics surveillance etc. Since breakthrough fast development deep learning technology, performance HAR based on neural networks has been significantly improved this decade. In survey, we discuss growing use for HAR, such as representative two-stream 3D CNNs, particularly...
Abstract Background This was a multicentre, 8-week, single-arm, open-label, pragmatic trial to explore the acceptance and performance of using Digital Medicine System (DMS) with health care professionals (HCPs) adult subjects schizophrenia, schizoaffective disorder (SAD), or first episode psychosis (FEP) on an oral atypical antipsychotic (aripiprazole, olanzapine, quetiapine, risperidone). Methods Subjects received initial introduction DMS, had HCP visits at screening/baseline, Week 4,...