About

Dr. Xin Wu is an MD with a background in medicine from Nantong Medical College and a Master of Science in Medical Physiology from Suzhou Medical College. He serves as an Adjunct Assistant Professor and Research Assistant Professor at Texas A&M University Naresh K. Vashisht College of Medicine, specifically within the Department of Neuroscience & Experimental Therapeutics. His research focuses on the role of mechanical forces in stimulating cell signaling pathways, particularly in cardiovascular and neuronal systems. His recent work investigates how ion channels are activated by mechanical stress, how they are modulated by integrins, and how integrin-mediated signaling pathways influence ion channel function and mechanotransduction under physiological and pathological conditions. Additionally, he studies epilepsy, neurosteroids, and new drug development. Dr. Wu employs a variety of technical approaches, including atomic force microscopy, electrophysiology, optical imaging, histocytochemistry, PCR, gene transfection, and LC-MS/MS, to explore the connection between extracellular matrix components and tissue regulation in injury and remodeling.

Research topics

• Engineering
• Geography
• Business
• Computer Science
• Economics
• Environmental economics
• Industrial organization
• Electrical engineering
• Operations research
• Economy
• Reliability engineering

Selected publications

• Economic Contribution Measurement of Energy Sector Development During Late Industrialization Period in Guangdong Province

  DOAJ (DOAJ: Directory of Open Access Journals) · 2026-04-01

  articleOpen access1st authorCorresponding

  [Objective] Energy is widely considered the fuel of industry and the lifeline of the national economy. The impressive economic and development achievements of Guangdong after reform and opening up relied heavily on the support and logistical backing from the development of its energy industry. Being a major energy consumer with limited resources and thus featuring low self-sufficiency in energy, Guangdong has always faced the threat of energy scarcity. After decades of development and transition, its energy sector is gradually evolving into a diversified new energy system composed of traditional thermal power, nuclear power, offshore wind power, and photovoltaic power generation. It has shifted from being a limiting factor in economic production to becoming an integral component of the province's high-tech manufacturing industry chain. Analyzing the economic contribution of the energy sector from a macroeconomic perspective holds practical significance for formulating scientific energy industry development plans and promoting high-quality, coordinated development of energy and economy in Guangdong. [Method] Firstly, a research dataset was established by integrating the indicator data that represent the development of the energy sector and economy in Guangdong. Subsequently, both the vector autoregression model and the Feder two-sector production function model were employed to conduct a quantitative analysis of the overall economic contribution and spillover effects of Guangdong's energy sector. [Result] The analysis indicates that, during the late industrialization phase, a mutually reinforcing relationship existed between the energy sector and economic development in Guangdong. The production of the energy sector makes a significant overall contribution to economic growth, with notable spillover effects. However, the economic stimulus effect of energy investments is comparatively low. [Conclusion] The study empirically estimates the economic contribution of the energy sector in Guangdong and based on the findings, suggests recommendations for high-quality development of Guangdong's energy sector. These can serve as references for the development planning and policy-making of Guangdong's energy development.

  PublisherDOI

• A Dynamic Particle Swarm Optimization Approach for Identifying Control Parameters of Virtual Synchronous Generators

  2025-07-17

  article

  To address the challenges of low identification accuracy and slow convergence in Virtual Synchronous Generator (VSG) parameter tuning under distributed energy integration scenarios, this paper proposes a dynamic Particle Swarm Optimization (PSO) approach with adaptive inertia weight and learning factors. A tailored control model is constructed for the VSG inverter, incorporating active and reactive power control loops and targeting key parameters such as damping factor, inertia constant, voltage gain, and droop coefficient. The proposed method enhances global search and avoids local optima through adaptive parameter adjustment and improved initialization strategies. Simulation results, conducted on a MATLAB-based platform, demonstrate that the proposed approach reduces the relative error of key parameters (e.g., damping factor to 3.69%, inertia constant to 5.3%) and significantly lowers the RMSE compared to traditional PSO methods. The algorithm exhibits robust performance under nonlinear and dynamic load disturbances, confirming its engineering applicability and superiority in complex operational environments.

  PublisherDOI

• Control parameter identification method for virtual synchronous generator considering electric vehicles based on dynamic particle swarm optimization

  Frontiers in Energy Research · 2025-04-07

  articleOpen access1st authorCorresponding

  To address the issues of insufficient control parameter identification accuracy and convergence speed during the grid connection of distributed power sources, a control parameter identification method for the Virtual Synchronous Generator (VSG) converter model considering the integration of electric vehicles (EVs) based on the dynamic particle swarm optimization algorithm is proposed. By constructing a VSG inverter control model suitable for distributed power sources and EV charging systems, analyzing the interactions between active and reactive power control loops under EV integration scenarios, selecting parameters and observations to be identified, and improving the Particle Swarm Optimization (PSO) algorithm based on actual conditions, the method ensures enhanced system adaptability. Simulation results demonstrate that the proposed method exhibits higher dynamic response capabilities, system stability, and adaptability under varying load conditions and uncertainties introduced by EV charging behaviors, highlighting its significant engineering application value.

  PublisherOA PDFDOI

• Adaptive protection scheme in modern distribution networks based on proximal policy optimization

  IET conference proceedings. · 2025-05-01 · 1 citations

  article1st authorCorresponding

  The worldwide trend of energy transition has thoroughly reshaped the structure of modern power distribution systems. With the proliferation of grid-edge elements such as distributed generation, energy storage and electric vehicle, the sensitivity and reliability of conventional distribution protection methods is greatly undermined. In this paper, we use the state-of-the-art machine learning algorithm to design a novel protection mechanism that can accurately detect and respond to faults under circumstances where conventional protection can be difficult to configure and function. The proposed approach is implemented and tested in a variety of fault simulations and is shown to achieve superior performance especially under complex operating conditions.

  PublisherDOI

• A Novel Intelligence Analysis Method Based on Graph Convolutional Neural Networks for Online Security of Power Distribution Networks

  2024-11-22 · 1 citations

  articleSenior author

  As the penetration of new energy power generation rises, various new energy systems increasingly impact the operation and management of distribution networks. Consequently, traditional methods for security analysis in distribution network systems are becoming inadequate for evaluating modern distribution networks. Based on this, this paper adopts a novel online safety intelligence analysis method for the distribution grid based on a graph convolutional neural network. This method takes the node voltage, current, power, and the location of distributed power generation as the node feature matrix, the connection relationship between nodes as the adjacency matrix, and the operation state of the distribution grid as the data label to establish a distribution grid operation graph database. It establishes a feature mining model of distribution grid operation data based on a graph convolutional neural network to complete the online intelligence analysis of the new-type distribution grid. It is proved that this method can realize faster, more accurate and more flexible online security intelligence analysis of the new distribution network.

  PublisherDOI

• Combined Operational Decision Making for Multi-microgrid System Based on Cloud-edge Collaborative Computing Framework

  2024-11-29 · 1 citations

  article

  With the utilization of digital intelligence technologies, flexible resources like distributed power generation and storage are being incorporated into modern smart distribution networks (DNs) in the form of microgrids (MGs). Additionally, the interaction between MGs and DN has shifted from a unidirectional power flow, where supply follows demand, to a bidirectional exchange of power and energy based on their operational and economic needs. In this context, this paper proposes a combined operational decision making method for multi-microgrid systems (MMGS) based on a cloud-edge collaborative computing framework. In this framework, the DN's computational resources are viewed as the cloud center, while the MGs act as edge nodes. The edge computing nodes calculate the individual MG optimization models and upload them to the cloud center, where the combined economic dispatch optimization model is computed, along with benefit distribution within the MMGS. Simulations were conducted on the MMGS consisting of a DN and three MGs in different regions. The results confirm the effectiveness of the proposed optimization model.

  PublisherDOI

• Mechanochemical treatment with red mud added for heavy metals solidification in municipal solid waste incineration fly ash

  Journal of Cleaner Production · 2023-02-28 · 22 citations

  article
  PublisherDOI

• Evaluation of water quality pollutants and eutrophication in the coastal waters of Shanghai

  2023-01-30

  book-chapterSenior author

  To explore the changing trend of water eutrophication in the coastal waters of Shanghai, this paper conducts an in-depth analysis of the physicochemical indicators of water quality in Shanghai waters from 2009 to 2019. We selected chemical oxygen demand (COD), inorganic nitrogen (DIN), active phosphate (DIP), petroleum, and heavy metals as research indicators to study water quality changes through the eutrophication index (E) and discuss the contribution rate of the overall water quality to the eutrophication of water bodies in the sea area. The research results show that the water quality of Shanghai's coastal waters is generally good from 2009 to 2019. DIP and DIN have a more significant impact on the pollution of the coastal waters of Shanghai, and oil and heavy metals have less contribution to pollution. The annual average concentrations of COD and petroleum meet the first-class seawater quality standards; lead (Pb) and zinc (Zn) in heavy metals meet the first-class seawater quality standards, while the concentrations of DIP and DIN seriously exceed the standard. According to the eutrophication index method results, the eutrophication levels of the coastal sea areas from 2009 to 2019 were all mesotrophic and showed a steady downward trend. To sum up, from 2009 to 2019, the overall water quality of the coastal waters of Shanghai has steadily improved, and the eutrophication indicators are normal, but there are still serious pollution risks, and marine ecological environment management measures need to be further improved.

  PublisherDOI

• Research of Static Voltage Stability Supported by the Distributed Generation

  Lecture notes in electrical engineering · 2023-01-01 · 1 citations

  book-chapter
  PublisherDOI

• Power System Reinforcement Considering Integrated and Complementary Operation of Wind-PV-Hydro

  Lecture notes in electrical engineering · 2023-01-01

  book-chapter
  PublisherDOI

Frequent coauthors

• Le Xie

  Texas A&M University

  37 shared

• Xiangtian Zheng

  Texas A&M University

  25 shared

• Tong Huang

  12 shared

• S. Sivaranjani

  M. Kumarasamy College of Engineering

  10 shared

• Nan Xu

  Lianyungang Oriental Hospital

  10 shared

• Dileep Kalathil

  Mitchell Institute

  10 shared

• Loc Trinh

  University of Southern California

  10 shared

• Yan Liu

  Fudan University

  9 shared

Labs

• Department of Neuroscience & Experimental TherapeuticsPI