Mr. Xiaogang Liu | Molecular Biology | Best Researcher Award

Mr. Xiaogang Liu | Molecular Biology | Best Researcher Award

Beijing Institute of Technology, China.

Dr. Xiaogang Liu is a dedicated Lecturer at the Zhuhai Campus of Beijing Institute of Technology. With a strong foundation in Biochemistry and Molecular Biology, he specializes in Chemical Biology. His research primarily focuses on Computational Chemistry to study pH-Responsive Fluorescent Probes and develop related software. In addition to his research contributions, he is passionate about teaching and mentoring students in the areas of Molecular Biology and Immunology.

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Education 🎓

Xiaogang Liu earned his Bachelor’s Degree in Biochemistry from Shanxi University, where he laid the groundwork for his extensive studies in the fields of Biochemistry and Molecular Biology. During his time at Shanxi University, he gained a solid understanding of the core principles of biochemical processes and molecular interactions, which set the stage for his later academic pursuits. His education at Shanxi University sparked his passion for Chemical Biology, ultimately shaping his career in research and teaching.

Experience 💼

Dr. Liu has taken on multiple roles in research and academia. Currently, as a Lecturer at Beijing Institute of Technology, he actively teaches and supervises students while also conducting research in Bioinformatics and Chemical Biology. His work focuses on exploring computational methodologies to understand complex chemical systems and biological molecules.

Research Interests 🔬

Chemical Biology: Investigating pH-Responsive Fluorescent Probes

Computational Chemistry: Developing software to support chemical biology research

Bioinformatics: Exploring the intersection of biology and computing to enhance molecular biology applications
Dr. Liu's research also delves into the integration of Computational Chemistry and Chemical Biology to advance understanding in fields like fluorescence sensing, probe design, and software development.

Selected Publications 📚

Computational Chemistry Study of pH-Responsive Fluorescent Probes and Development of Supporting Software
Journal: Molecules (2025), Volume: 30(2), Article 273
Contributors: Xiaogang Liu

Large Scale Virtual Screening for Finding Inhibitor against the RNA-dependent RNA Polymerase from Herbal Medicine for SARS-Cov-2 Therapy
Proceedings of the 11th International Conference on Biomedical Engineering and Bioinformatics (2022)
Contributors: Xiaogang Liu, Zirong Liang, Shiye Wu, Ying Wang, Binquan Gou

Virtual Screening for Finding Inhibitor Against the Main Protease of SARS-Cov-2 from the FDA-Approved Drugs Database
International Conference on Biomedical and Intelligent Systems (IC-BIS 2022) (2022-12-06)
Contributors: Xiaogang Liu, Shiye Wu, Zitong Zhu, Ying Wang, Binquan Gou

Teaching Reform and Application of Cell Biology in Bioengineering Specialty
Science & Technology Information (2021-12-13)
Contributors: Xiaogang Liu

 

 

Prof. Junyu Zhou | Medical and Prevention | Young Scientist Award

Prof. Junyu Zhou | Medical and Prevention | Young Scientist Award

Peking University, China.

Prof. Junyu Zhou is a pioneering researcher in nutritional genomics, metabolic disorders, and personalized medicine. He leverages bioinformatics, computational biology, and experimental approaches to explore gene-diet interactions, focusing on Asian populations. His innovative work includes developing AI models for bioactive compound prediction and uncovering gut microbiota's role in metabolic health. Junyu's recent research extends to natural compound discovery for neurodegenerative diseases, emphasizing computational screening with advanced AI techniques.

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Education 🎓

Junyu Zhou holds advanced degrees in fields related to nutritional genomics and computational biology. His academic training provides a strong foundation for his cutting-edge research in metabolic disorders and personalized medicine.

Experience 💼

As an Assistant Researcher at Peking University, Junyu Zhou has led and collaborated on multiple groundbreaking projects. His experience spans computational modeling, experimental validations, and interdisciplinary collaborations with global research teams in metabolic diseases and nutritional genomics.

Research Interests 🔬

Nutritional Genomics 🧬

Junyu Zhou investigates gene-diet interactions to understand how genetic variations influence nutritional responses, particularly in Asian populations. His work aims to develop personalized dietary recommendations to improve health outcomes.

Metabolic Diseases 🩺

Focusing on conditions such as diabetes and obesity, Junyu studies the underlying mechanisms of metabolic disorders. His research integrates genetics and gut microbiota to unveil new therapeutic targets.

Computational Biology & Bioinformatics 💻

Junyu applies advanced computational tools to analyze genetic data and predict drug-target interactions. His expertise in bioinformatics helps bridge data science and biology for innovative discoveries.

Gut Microbiota 🌱

Exploring the role of gut microbiota in metabolic health, Junyu's research uncovers microbial contributions to diseases and identifies probiotic strategies for improved metabolic functions.

Natural Product Research 🌿

Junyu's work includes computational screening of natural compounds, focusing on their potential to treat diseases such as Alzheimer's and neurodegenerative disorders.

Personalized Medicine 🩹

By integrating genomics and computational biology, Junyu develops precision healthcare approaches, tailoring interventions to individual genetic and metabolic profiles.

Machine Learning in Drug Discovery 🤖

Junyu employs AI-driven techniques to streamline drug discovery processes. His work includes predictive models for bioactive compounds, enhancing efficiency in identifying new therapeutic agents.

Publications Top Notes 📚

Microbial Dysbiosis Linked to Metabolic Dysfunction-Associated Fatty Liver Disease in Asians: Prevotella copri Promotes Lipopolysaccharide Biosynthesis and Network Instability in the Prevotella Enterotype, Published in: International Journal of Molecular Sciences, 2024, Contributors: Yuan, H.; Wu, X.; Wang, X.; Zhou, J.-Y.; Park, S. Link

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis, Published in: Food & Function, 2024, Contributors: Junyu Zhou; Heng Yuan; Sunmin Park. Link

Association of Metabolic Diseases and Moderate Fat Intake with Myocardial Infarction Risk, Published in: Nutrients, December 11, 2024, Contributors: Junyu Zhou; Meiling Liu; Sunmin Park. Link

 

 

 

 

 

 

 

 

 

 

 

 

Panjit Musik | Computing science | Best Researcher Award

🌟Assoc Prof Dr. Panjit Musik. Computing science, Best Researcher Award🏆

Associate Professor at Panjit Musik walailak university, Thailand

Assoc. Prof. Dr. Panjit Musik, born on July 4, 1961, is a distinguished academic in the fields of Physics, Computational Science, and Smart Farming. He currently teaches at the School of Science, Walailak University in Thailand. His academic journey and professional accomplishments reflect a commitment to advancing education and research in scientific and technological innovations.

Author Metrics

Scopus Profile

Dr. Musik has authored numerous research papers published in international and national journals, contributing significantly to the fields of Physics, Computational Science, and Smart Farming. His works are frequently cited, reflecting his influence in these research areas.

Panjit Musik is associated with Walailak University in Tha Sala, Thailand. His academic profile on Scopus shows a modest yet emerging research output, with 4 documents and 5 citations, resulting in an h-index of 1.

Education

Dr. Musik earned his Doctor of Philosophy in Computational Science from Walailak University in 2005. He holds a Master of Science in Teaching Physics from Chiang Mai University, obtained in 1990, and a Bachelor of Education in Physics from Thaksin University, completed in 1983. This strong educational foundation underpins his extensive research and teaching career.

Research Focus

Dr. Musik’s research interests are diverse and interdisciplinary, encompassing Physics Teaching, Real-Time Physics Labs, Computational Modeling and Simulation, and Smart Farming. His work aims to integrate technological advancements with educational practices to enhance learning outcomes and develop innovative solutions for agricultural challenges.

Professional Journey

Dr. Musik’s professional journey began with a focus on physics education and has evolved to include computational modeling and smart farming technologies. He has developed numerous computer-based experimental sets and simulations, contributing to both academic and practical advancements in his fields of expertise.

Honors & Awards

Throughout his career, Dr. Musik has received several accolades for his contributions to science and education. His innovative work in developing experimental sets and integrating computational methods in education has been recognized by academic and professional institutions.

Publications Noted & Contributions

Dr. Musik has published extensively in international journals such as the Turkish Online Journal of Educational Technology and the International Journal on Smart Sensing and Intelligent Systems. His publications address key issues in computational physics, real-time experimental learning, and smart farming technologies, contributing to the academic discourse and practical applications in these areas.

Development of a Computer-Based Simple Pendulum Experiment Set for Teaching and Learning Physics

Authors: Sukmak, W., & Musik, P.
Journal: International Journal on Smart Sensing and Intelligent Systems, 2021, 14(1), pp. 1–8
Citations: 1

Abstract: This article presents the development of a computer-based experiment set designed to enhance the teaching and learning of physics through a simple pendulum experiment. The set aims to provide real-time data acquisition and analysis, making physics concepts more accessible and engaging for students. The development process, implementation, and educational benefits are discussed in detail.

Development of an Automated Water Management System in Orchards in Southern Thailand

Author: Musik, P.
Journal: International Journal on Smart Sensing and Intelligent Systems, 2020, 13(1), pp. 1–7
Citations: 2

Abstract: Dr. Musik explores the design and implementation of an automated water management system tailored for orchards in southern Thailand. This system leverages smart sensing technologies to optimize water usage, ensuring efficient irrigation and enhancing crop yields. The article details the system’s components, operational mechanisms, and the positive impact on orchard management.

Development of Computer-Based Experiment Set on Simple Harmonic Motion of Mass on Springs

Author: Musik, P.
Journal: Turkish Online Journal of Educational Technology, 2017, 16(4), pp. 1–11
Citations: 1

Abstract: This study describes the creation of an experimental set for investigating the simple harmonic motion of a mass on a spring. The set integrates computer-based tools to facilitate real-time data collection and visualization, aiming to improve students’ understanding of oscillatory motion through interactive and hands-on learning experiences.

Large-Scale Simulation Using Parallel Computing Toolkit and Server Message Block

Authors: Musik, P., & Jaroensutasinee, K.
Journal: WSEAS Transactions on Mathematics, 2007, 6(2), pp. 369–372
Citations: 1

Abstract: This paper discusses a large-scale simulation approach using a parallel computing toolkit and server message block. The simulation targets complex mathematical models, enhancing computational efficiency and accuracy. The authors highlight the methodology, computational framework, and potential applications in scientific research.

These articles reflect Dr. Panjit Musik’s extensive work in developing innovative educational tools and applying computational methods to solve practical problems in agriculture and physics education. His research contributes significantly to enhancing teaching methodologies and improving resource management in various domains.

Research Timeline

Dr. Musik’s research timeline spans over three decades, beginning with his master’s research in 1990 on computer control of humidity in experimental greenhouses. His doctoral research in 2005 focused on large-scale water flow simulation using Mathematica. In the years following, he has conducted numerous studies on integrating remote sensing data, developing computer-based experiments, and smart farming solutions.

Collaborations and Projects

Dr. Musik has collaborated with various researchers and institutions on projects aimed at developing innovative educational tools and smart farming technologies. His projects include the development of watershed and hydrologic process modeling for flood forecasting, automated water management systems in orchards, and GIS applications for agricultural water management.

Contributions to the Field

Dr. Musik’s contributions to the field include the development of computer-based experimental sets for physics education, large-scale simulations for environmental modeling, and smart farming technologies. His work has provided valuable insights and practical tools for educators, researchers, and farmers, advancing both academic knowledge and real-world applications.