A5050422997- Stability and Control of Uncertain Systems
- Control Systems and Identification
- Adaptive Control of Nonlinear Systems
- Neural Networks Stability and Synchronization
- Bioinformatics and Genomic Networks
- Fault Detection and Control Systems
- Neural Networks and Applications
- Advanced Control Systems Optimization
- Gene expression and cancer classification
- Advanced Algorithms and Applications
- Advanced Graph Theory Research
- Adaptive Dynamic Programming Control
- Stability and Controllability of Differential Equations
- Advanced Sensor and Control Systems
- Graph Labeling and Dimension Problems
- Nonlinear Dynamics and Pattern Formation
- Stochastic processes and financial applications
- Optimization and Search Problems
- Vehicle Routing Optimization Methods
- Industrial Technology and Control Systems
- Facility Location and Emergency Management
- Machine Learning in Bioinformatics
- Simulation and Modeling Applications
- Mathematical and Theoretical Epidemiology and Ecology Models
- AI-based Problem Solving and Planning
Shandong University of Science and Technology
2016-2025
Huazhong Agricultural University
2011-2025
Northwestern Polytechnical University
2019-2025
First Affiliated Hospital of Chengdu Medical College
2025
Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche
2025
Hefei University of Technology
2023-2024
Civil Aviation University of China
2008-2024
Ministry of Natural Resources
2024
Chongqing University of Posts and Telecommunications
2024
Second Xiangya Hospital of Central South University
2024
Abstract Integrated network analysis pipeline (iNAP) is an online for generating and analyzing comprehensive ecological networks in microbiome studies. It implemented two sections, that is, construction analysis, integrates many open‐access tools. Network contains multiple feasible alternatives, including correlation‐based approaches (Pearson's correlation Spearman's rank along with random matrix theory, sparse correlations compositional data) conditional dependence‐based methods (extended...
Abstract We show that institutional investors are more likely to invest in firms from regions which they have stronger social ties but find no evidence these investments earn a differential return. Firms with locations many higher valuations and liquidity. These effects largest for small little analyst coverage, suggesting the investors’ behavior is explained by their increased awareness of socially proximate locations. Our results highlight structure affects firms’ access capital...
In this paper, we propose a novel fourth-order memristive chaotic system (MCS), in which both its dynamical behaviors and the preassigned-time stabilization problem are analyzed. First, of proposed MCS studied detail, such as infinite unstable equilibrium points, attractor, Lyapunov exponents, Kaplan–Yorke dimension, bifurcation. Then, T–S fuzzy method is employed to characterize MCS, simpler model built deal with nonlinearity caused by memristor MCS. addition, two intermittent controllers...
Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter system driven by neural network (DNN). It designed to find the optimal values tunable parameters in computer systems, from simple client-server large data center, where human can be costly often cannot achieve...
This paper presents a novel adaptive finite-time tracking control scheme for nonlinear systems. During the design process of scheme, unmodeled dynamics in systems are taken into account. The radial basis function neural networks (RBFNNs) adopted to approximate unknown functions. Meanwhile, based on RBFNNs, assumptions with respect also relaxed. provides new stability criterion, making more suitable practice than traditional methods. Combining RBFNNs and backstepping technique, controller is...
Abstract Identifying cancer driver genes plays a curial role in the development of precision oncology and therapeutics. Although plethora methods have been developed to tackle this problem, complex mechanisms intricate interactions between still make identification challenging. In work, we propose novel machine learning method heterophilic graph diffusion convolutional networks (called HGDCs) boost cancer-driver gene identification. Specifically, HGDC first introduces generate an auxiliary...
This paper studies the stability for nonlinear stochastic discrete-time systems. First of all, several definitions on are introduced, such as stability, asymptotical and p th moment exponential stability. Moreover, using method Lyapunov functionals, some efficient criteria obtained. Some examples presented to illustrate effectiveness proposed theoretical results.
Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information construct drug-cancer cell line features, but there is still a need explore combine topological protein interaction network (PPI). Therefore, we propose network-embedding-based prediction model, NEXGB, which integrates corresponding modules lines with PPI information. NEXGB extracts features each node by struc2vec. Then,...
Background: Correctional officers face widespread workplace violence and the resulting overwork that can profoundly damage their physical mental health. Purpose: This study aims to investigate mediating role of in relationship between manifestation health issues among correctional officers. Methods: enlisted 472 eligible participants. Cross-sectional data were obtained using Chinese version Workplace Violence Scale (WVS), while was evaluated through relevant scales. Analysis involved...
Identification of cancer driver genes is crucial for understanding the molecular mechanisms cancer. To address limitations graph convolutional networks-based gene identification methods, including biased prediction results caused by layer structures that focus more on either structural characteristics (e.g., degree) or biological features mutation frequency) nodes in network, as well sparse supervisory information, we propose a method called Multi-Task Graph Contrastive Learning (MTGCL)...
With the increasing utilization of anticoagulants, selection appropriate anticoagulants has emerged as a significant quandary. The objective this study was to evaluate recent trend in and expenditure within specific region, aiming provide valuable insights into optimal choice across other healthcare facilities. retrospectively analyzed. data on anticoagulant utilizations tertiary-care hospitals district were collected from January 2019 December 2023. expenditure, defined daily doses (DDDs),...
Resolving the ecological drivers mediating diversity patterns of microbial communities across space and through time is a central issue in ecology. Both regional species pools local community assembly contribute to spatial turnover biodiversity. In this study, we extended concept pool temporal, investigated seasonal dynamics intertidal microbiomes four domains/kingdoms (bacteria, archaea, fungi, protists). The results showed that variations β-diversity were primarily governed by processes...
A e ight control scheme in which a radial basis function network (RBFN)aids conventional controller has been developed. The RBFN controller, consisting of variable Gaussian functions, uses only online learning to represent the local inverse dynamics aircraft system. With Lyapunov synthesis approach, tuning rule for updating all parameters (including centers, widths, as well weights output layer ) is derived, extends Gomi and Kawato’ s strategy, where were adaptable. (Gomi, H., Kawato, M., “...