Riasat Ali | Particle physics and cosmology | Editorial Board Member | 3098

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Editorial Board Member

Riasat Ali
Riasat Ali
Affiliation Shanghai University
Country China
Scopus ID 57212863194
Documents 76
Citations 1,159
h-index 20
Subject Area Particle Physics and Cosmology
Event Global Particle Physics Excellence Awards
ORCID Connected via Scopus

Riasat Ali is a researcher affiliated with Shanghai University, China, whose academic work focuses on particle physics, cosmology, black hole physics, gravitation, and related theoretical investigations. His research profile demonstrates continuous scholarly engagement in contemporary astrophysical and gravitational studies, particularly in modified gravity models, plasma effects on black hole shadows, and quantum gravity-inspired thermodynamics.[1]

Abstract

This article presents an overview of the academic profile and scholarly contributions of Riasat Ali in the fields of particle physics and cosmology. His research portfolio includes investigations into black hole thermodynamics, plasma-induced gravitational lensing, Hawking radiation, and modified gravity theories. Through publications in recognized international journals, his work contributes to ongoing discussions in theoretical astrophysics and gravitational physics.[1][2]

Keywords

Particle physics, cosmology, black hole physics, Horndeski gravity, Hawking radiation, plasma physics, gravitational lensing, modified gravity, astrophysics, quantum gravity.

Introduction

Theoretical particle physics and cosmology continue to provide important frameworks for understanding gravitational phenomena, spacetime geometry, and high-energy astrophysical systems. Researchers working in these areas frequently examine black hole behavior, quantum corrections, and observational signatures associated with relativistic environments. Riasat Ali has contributed to these themes through studies involving black hole shadows, photon deflection, and thermodynamic properties within alternative gravity frameworks.[2]

Research Profile

According to Scopus author records, Riasat Ali has authored or co-authored 76 indexed documents and accumulated more than 1,159 citations with an h-index of 20. His publications primarily focus on gravitational physics, black hole thermodynamics, plasma effects in astrophysical systems, and modified theories of gravity.[1]

His recent works examine topics such as charged hairy black holes in Horndeski gravity, photon deflection in dispersive media, and generalized uncertainty principle corrections in black hole systems. These investigations contribute to the broader understanding of relativistic astrophysical environments and quantum-inspired gravitational models.[2]

Research Contributions

  • Investigated unstable equilibrium and chaos-bound violations in charged hairy black holes within Horndeski gravity frameworks.
  • Studied photon deflection and black hole shadow formation under the influence of plasma and dispersive media.
  • Explored Hawking temperature corrections and thermodynamic properties associated with generalized uncertainty principles.
  • Published research associated with modified gravity theories including Rastall gravity and f(Q,BQ) gravity models.

Publications

  1. “Unstable equilibrium and chaos-bound violation for a charged hairy black hole in Horndeski gravity,” New Astronomy, 2026.
  2. “Deflection of photon and shadow cast for black hole spacetime under the impact of a dispersive medium,” Indian Journal of Physics, 2026.
  3. “Greybody Factor and Hawking Temperature of ModMax-AdS Black Holes Surrounded by Perfect Fluid Dark Matter,” Fortschritte Der Physik, 2025.
  4. “Exploring plasma and dark matter on photon deflection by Reissner–Nordström black hole with scalar hair and its shadow,” Annals of Physics, 2025.

Research Impact

The research contributions of Riasat Ali demonstrate interdisciplinary engagement between cosmology, astrophysics, and gravitational theory. His publication metrics and citation record indicate continued academic visibility within theoretical physics communities. The integration of plasma physics, dark matter models, and quantum corrections into black hole studies reflects current directions in modern gravitational research.[1]

Award Suitability

Riasat Ali’s research profile aligns with the objectives of the Global Particle Physics Excellence Awards, particularly in the recognition of emerging contributions to theoretical particle physics and cosmology. His sustained publication activity, citation impact, and involvement in advanced gravitational studies support his suitability for editorial and scholarly recognition within the international academic community.

Conclusion

Riasat Ali has established a notable academic presence in the domains of particle physics and cosmology through research on black hole dynamics, modified gravity, and relativistic astrophysics. His scholarly activities, publication output, and citation performance indicate active participation in contemporary theoretical physics research and continued contribution to advancing cosmological understanding.

References

  1. Elsevier. (2026). Scopus author details: Riasat Ali, Author ID 57212863194. Scopus.
    http://scopus.com/authid/detail.uri?authorId=57212863194
  2. Ali, R. H. (2026). Unstable equilibrium and chaos-bound violation for a charged hairy black hole in Horndeski gravity. New Astronomy.
    10.1016/j.newast.2026.102564
  3. Ali, R. H. (2025). Exploring plasma and dark matter on photon deflection by Reissner–Nordström black hole with scalar hair and its shadow. Annals of Physics.
    https://doi.org/10.1016/j.aop.2025.170201
  4. Google Scholar. (2026). Riasat Ali citation profile.
    https://scholar.google.com/citations?user=Stp2lpMAAAAJ&hl=en

Haranath Ghosh | Computational Methods | Research Excellence Award

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Prof. Dr. Haranath Ghosh | Computational Methods | Research Excellence Award

Professor | Raja Ramanna Centre for Advanced Technology | India

Prof. Dr. Haranath Ghosh is a leading researcher at the Raja Ramanna Centre for Advanced Technology, specializing in condensed matter physics and material science. His interests include superconductivity, electron correlation, and optical properties of advanced materials. He demonstrates expertise in theoretical modeling, computational analysis, and spectroscopy. He has received recognition for impactful scientific contributions. With over 1,593 citations, an h-index of 20, and 43 i10-index (Google Scholar), his work significantly advances understanding of quantum materials and supports innovations in modern physics and technology.

 

Citation Metrics (Google Scholar)

1600
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400
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1593

h-index

20

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43

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i10-index

View Google Scholar Profile

Featured Publications

 

Ulugbek Kurbanov | Theoretical Advances | Research Excellence Award

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Dr. Ulugbek Kurbanov | Theoretical Advances | Research Excellence Award

Head of laboratory | Institute of Nuclear Physics of Uzbekistan Academy of Sciences | Uzbekistan

Dr. Ulugbek Kurbanov is a researcher at the Institute of Nuclear Physics, Uzbekistan Academy of Sciences, focusing on condensed matter physics, particularly high-temperature superconductivity and charge transport in cuprates. His research interests include superconductor–insulator transitions and impurity effects. He demonstrates expertise in experimental and theoretical analysis of low-temperature phenomena. He has authored 22 documents with 54 citations and an h-index of 4 (Scopus). His contributions enhance understanding of superconducting materials, supporting future advancements in electronic and quantum technologies.

 

Citation Metrics (Scopus)

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54

Documents

22

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4

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View Scopus Profile View ORCID Profile View Google Scholar Profile

Featured Publications


The new metal-insulator transitions and nanoscale phase separation in doped cuprates

– Superlattices and Microstructures, 2015 (Citations: 6)

 

Abdisalam Hassan Muse | Computational Methods | Best Researcher Award

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Assoc Prof Dr. Abdisalam Hassan Muse | Computational Methods | Best Researcher Award 

Assoc Prof Dr. Abdisalam Hassan Muse, Amoud University, Somalia

Assoc. Prof. Dr. Abdisalam Hassan Muse is an accomplished educator and researcher, with a Ph.D. in Statistics from PAUSTI-JKUAT, Kenya. He has over 13 years of experience teaching mathematics, statistics, and data science at both secondary and university levels. Dr. Muse has a strong research focus on Bayesian statistics, econometrics, and data science, with expertise in statistical modeling, machine learning, and time series analysis. His academic work is complemented by active participation in international workshops and trainings in advanced statistical methods.

Orcid Profile

Educational Details

Assoc. Prof. Dr. Abdisalam Hassan Muse earned his Ph.D. in Statistics from the Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), hosted at Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya (May 2019 – Oct 2022). He also holds two Master’s degrees: an MSc in Climate Change and Environmental Sustainability from Amoud University, Borama, Somalia (September 2018 – Incomplete), and an MSc in Mathematics and Statistics from the same institution (September 2015 – July 2017). His diverse academic foundation also includes a BA in Islamic Studies from Beder International University, Borama, Somalia (September 2013 – July 2016), a BSc in Mathematics and Physics from Amoud University (September 2010 – July 2012), and Diplomas in Islamic Studies (2008–2010) and Education for Mathematics and Physics (2005–2007) from Amoud University and Zaylac Institute of Islamic Studies, respectively. He completed his secondary education at Sheikh Ali Jawhar Secondary School in Borama, Somaliland, earning the Somaliland Certificate of Secondary Education (September 2001 – July 2005).

Professional Experience

Dr. Abdisalam Hassan Muse has over 13 years of experience in education, including two years of postgraduate teaching and supervision and six years of undergraduate university teaching. His teaching expertise spans mathematics, statistics, data science, and the application of technology in statistical experiments. He also has 11 years of secondary teaching experience. Throughout his career, he has demonstrated a strong ability to communicate complex concepts, both in academic and classroom settings. Dr. Muse has actively participated in several international workshops and training programs, focusing on Bayesian statistics, data science, official statistics, disease modeling, and statistical software like R and Python. Additionally, he has experience in research and training related to fragile environments and post-distribution monitoring for aid programs.

Research Interest

Dr. Muse’s research is focused on a variety of statistical fields, including Bayesian statistics, econometrics, survival analysis, official statistics, and demography. His expertise extends to data science, statistical modeling, machine learning, mathematical statistics, and stochastic processes. He has a passion for applying advanced statistical techniques to real-world problems, with a keen interest in environmental statistics, computational statistics, probability distributions, regression modeling, and time series analysis. His skills also encompass areas like Ito calculus, education statistics, and population analysis.

Top Notable Publications

Prevalence and determinants of home delivery among pregnant women in Somaliland: Insights from SLDHS 2020 data
Atención Primaria
2025-02 | Journal Article
DOI: 10.1016/j.aprim.2024.103082
ISSN: 0212-6567
Source: Abdisalam Hassan Muse

Cardiovascular disease prevalence and associated factors in a low-resource setting: A multilevel analysis from Somalia’s first demographic health survey
Current Problems in Cardiology
2024-12 | Journal Article
DOI: 10.1016/j.cpcardiol.2024.102861
Source: Crossref

Prevalence and determinants of hypertension among adults in Somalia using Somalia demographic health survey data, SDHS 2020
Current Problems in Cardiology
2024-11 | Journal Article
DOI: 10.1016/j.cpcardiol.2024.102783
ISSN: 0146-2806
Source: Abdisalam Hassan Muse

Analyzing Unimproved Drinking Water Sources and Their Determinants Using Supervised Machine Learning: Evidence from the Somaliland Demographic Health Survey 2020
Water
2024-10 | Journal Article
DOI: 10.3390/w16202986
Source: Multidisciplinary Digital Publishing Institute

Conclusion

Assoc. Prof. Dr. Abdisalam Hassan Muse is a highly qualified candidate for the Best Researcher Award. His strong educational background, extensive professional experience, and commitment to impactful research make him a standout in the field of statistics and data science. Dr. Muse’s innovative approach, coupled with his dedication to community engagement, positions him as a leading figure in advancing computational methodologies for the betterment of society.

 

Shahid Akbar | Computer Science | Best Researcher Award

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Dr. Shahid Akbar | Computer Science | Best Researcher Award

Orcid Profile 

Scopus Profile

Educational Details

Postdoctoral Fellow
IFFS, University of Electronic Science and Technology of China
June 2023 – Present

Ph.D. in Computer Science
Abdul Wali Khan University Mardan, Pakistan
2017 – 2021
Dissertation Title: An Intelligent Computational Model for Identification of Anticancer Peptides

M.S. in Computer Science
Abdul Wali Khan University Mardan, Pakistan
2012 – 2016

Bachelor’s in Computer and Information Technology
Islamic University of Technology, Dhaka, Bangladesh
2008 – 2011

Professional Experience

Dr. Shahid Akbar currently serves as a Postdoctoral Fellow at the IFFS, University of Electronic Science and Technology of China, where he is engaged in advanced research in bioinformatics and artificial intelligence. Prior to this, he was a Lecturer in the Department of Computer Science at Abdul Wali Khan University Mardan, Pakistan, from August 2015 to June 2023, where he taught various undergraduate courses and contributed to the academic development of students in the field. Before that, he held a similar position at the Government College of Management Sciences in Swabi, Pakistan, from January 2012 to August 2015, where he began his academic career. Through these roles, Dr. Akbar has built a solid foundation in teaching and research, fostering a strong interest in the application of computational methods to solve complex scientific problems.

Research Interest

Dr. Shahid Akbar’s research interests lie at the intersection of bioinformatics and artificial intelligence. He specializes in machine learning, deep learning, pattern recognition, and neural networks, with a particular focus on developing intelligent computational models for identifying anticancer peptides.

Technical Skills

Dr. Akbar is proficient in various programming languages and tools, including Python, Keras, TensorFlow, SQL Server, R, MATLAB, Spark, and Hadoop. His expertise extends to data warehousing and advanced Java programming.

Awards and Distinctions

OIC Scholarship (3 years)

AWKUM Talented PhD Students Scholarship

Courses Taught

Dr. Akbar has taught a range of undergraduate courses, including:

Machine Learning

Data Structures and Algorithms

Introduction to Programming

Pattern Recognition

Object-Oriented Programming

Data Mining and Warehousing

Artificial Intelligence

Junior Project/Graduate Project

Top Notable Publications

Hybrid Residue Based Sequential Encoding Mechanism with XGBoost Improved Ensemble Model for Identifying 5-Hydroxymethylcytosine Modifications

Authors: Uddin, I., Awan, H.H., Khalid, M., Abdolrasol, M.G.M., Alghamdi, T.A.H.

Journal: Scientific Reports

Year: 2024

Volume: 14

Issue: 1

Article Number: 20819

Citations: 0

StackedEnC-AOP: Prediction of Antioxidant Proteins Using Transform Evolutionary and Sequential Features Based Multi-Scale Vector with Stacked Ensemble Learning

Authors: Rukh, G., Akbar, S., Rehman, G., Alarfaj, F.K., Zou, Q.

Journal: BMC Bioinformatics

Year: 2024

Volume: 25

Issue: 1

Article Number: 256

Citations: 0

Deepstacked-AVPs: Predicting Antiviral Peptides Using Tri-Segment Evolutionary Profile and Word Embedding Based Multi-Perspective Features with Deep Stacking Model

Authors: Akbar, S., Raza, A., Zou, Q.

Journal: BMC Bioinformatics

Year: 2024

Volume: 25

Issue: 1

Article Number: 102

Citations: 17

AIPs-DeepEnC-GA: Predicting Anti-Inflammatory Peptides Using Embedded Evolutionary and Sequential Feature Integration with Genetic Algorithm Based Deep Ensemble Model

Authors: Raza, A., Uddin, J., Zou, Q., Alghamdi, W., Liu, R.

Journal: Chemometrics and Intelligent Laboratory Systems

Year: 2024

Volume: 254

Article Number: 105239

Citations: 0

Comprehensive Analysis of Computational Methods for Predicting Anti-Inflammatory Peptides

Authors: Raza, A., Uddin, J., Akbar, S., Zou, Q., Ahmad, A.

Journal: Archives of Computational Methods in Engineering

Year: 2024

Volume: 31

Issue: 6

Pages: 3211–3229

Citations: 3

DeepAVP-TPPred: Identification of Antiviral Peptides Using Transformed Image-Based Localized Descriptors and Binary Tree Growth Algorithm

Authors: Ullah, M., Akbar, S., Raza, A., Zou, Q.

Journal: Bioinformatics

Year: 2024

Volume: 40

Issue: 5

Article Number: btae305

Citations: 9

iAFPs-Mv-BiTCN: Predicting Antifungal Peptides Using Self-Attention Transformer Embedding and Transform Evolutionary Based Multi-View Features with Bidirectional Temporal Convolutional Networks

Authors: Akbar, S., Zou, Q., Raza, A., Alarfaj, F.K.

Journal: Artificial Intelligence in Medicine

Year: 2024

Volume: 151

Article Number: 102860

Citations: 18

Blockchain-Based Logging to Defeat Malicious Insiders: The Case of Remote Health Monitoring Systems

Authors: Javed, H., Abaid, Z., Akbar, S., Alkahtani, H.K., Raza, A.

Journal: IEEE Access

Year: 2024

Volume: 12

Pages: 12062–12079

Citations: 2

Conclusion

In summary, Dr. Shahid Akbar’s diverse research interests, significant contributions to bioinformatics, robust technical skills, extensive educational background, and active participation in academia and professional development make him an exceptional candidate for the Best Researcher Award. His work not only advances the field of computer science but also contributes to addressing critical challenges in healthcare, particularly in cancer research.

 

Zhengshan Liu | Experimental Methods | Best Researcher Award

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Dr. Zhengshan Liu | Experimental Methods | Best Researcher Award

Dr. Zhengshan Liu, THE UNIVERSITY OF TEXAS, United States

Dr. Zhengshan Liu is a resident doctor in the Psychiatry Residency Program at the University of Texas Southwestern Medical Center, where he has been training since July 2020. Starting in July 2024, Dr. Liu will transition to a new role as an attending physician at the Parkland Psychiatry Emergency Center in Dallas, TX. With a strong background in both clinical practice and research, Dr. Liu’s expertise lies in neuropsychiatric disorders, particularly schizophrenia, and the role of inflammation in psychosis. His upcoming position will allow him to further contribute to the mental health field through direct patient care in an emergency setting.

PROFILE

Scopus Profile

Educational Details

Dr. Zhengshan Liu holds a Ph.D. in Biology from the University of Rochester, USA, where he also earned a Master’s degree in Biology during his studies from 2011 to 2017. Before his time in the United States, Dr. Liu completed a Master’s degree in Biology at Sun Yat-sen University, China, from 2005 to 2008. His academic journey began at Anhui Medical University, China, where he earned his Doctor of Medicine degree between 2000 and 2005. This strong foundation in both medicine and biology has shaped his expertise in neuropsychiatric research and clinical practice.

Professional Experience

Since October 2021, Mr. Yew Hoe Wong has been serving as a Teaching Assistant at Imperial College London. In this capacity, he has managed and coordinated several final year and undergraduate design projects, ensuring they meet academic requirements and are executed effectively. He has played a pivotal role in assisting with lesson plan development and providing feedback to enhance student performance. Additionally, during his tenure as a Teaching Assistant from October 2021 to December 2021, Mr. Wong supported lesson development, facilitated classroom management, and provided objective feedback, thereby fostering an improved learning environment and supporting student comprehension and engagement.

Teaching  Experience

Dr. Zhengshan Liu has extensive teaching experience, having served as a Teaching Assistant in Molecular Biology at the University of Rochester, New York, during the Fall of 2011 and Spring of 2012, as well as in Aging Biology in the Fall of 2012. His earlier teaching roles include overseeing medical student rotations at The First Affiliated Hospital of Guangdong Pharmaceutical University in Guangzhou, China, during the Fall of 2009. More recently, from 2020 to 2023, Dr. Liu contributed to the education of medical students at the University of Texas Southwestern Medical Center in Dallas, Texas, by supervising clinical rotations, thereby enriching their practical training in medicine.

Physician Experience

Dr. Zhengshan Liu gained valuable clinical experience at The First Affiliated Hospital of Guangdong Pharmaceutical University in Guangzhou, China. During his tenure there, he completed a residency in Neurology from July 2008 to June 2010. His work at the hospital provided him with a strong foundation in neurological practice, enabling him to develop the clinical skills essential for his subsequent medical career. Additionally, he oversaw medical student rotations in the Fall of 2009, contributing to the training and development of future physicians.

Research Interest

Ethan’s research focuses on modeling and simulation within nuclear power plants, emphasizing the integration of scientific theories and innovative design. His interests also extend to the application of foundational engineering principles in real-world scenarios, inspired by landmark engineering feats like the SpaceX Rocket. He is dedicated to advancing nuclear power plant technology through precise modeling and simulation techniques, contributing to more efficient and sustainable energy solutions.

Honors 

Dr. Zhengshan Liu has been recognized for his exceptional research and academic contributions through several prestigious awards. In 2013, he received the NYSTEM Training Grant, which supported his advanced studies in stem cell research. His work was further acknowledged in 2015 with the Society for Neuroscience (SfN) Travel Grant, enabling him to present his findings at international conferences. In 2017, Dr. Liu was awarded the University of Rochester Neuroscience Postdoctoral Fellowship, highlighting his significant contributions to the field of neuroscience. His dedication to understanding mental health disorders was further recognized in 2019 when he received the University of Rochester Schizophrenia Fellowship, supporting his research into the underlying mechanisms of schizophrenia.

Top Notable Publications

Shen, Z., Li, M., He, F., Chen, L., Liu, Z., Zheng, H., Xiong, F. (2023). Intravenous administration of an AAV9 vector ubiquitously expressing C1orf194 gene improved CMT-like neuropathy in C1orf194-/- mice. Neurotherapeutics. PMID: 37843769.

Liu, Z., Osipovitch, M., Benraiss, A., Huynh, N.P.T., Foti, R., Bates, J., Chandler-Militello, D., Findling, R.L., Tesar, P.J., Nedergaard, M., Windrem, M.S., Goldman, S.A. (2019). Dysregulated Glial Differentiation in Schizophrenia May Be Relieved by Suppression of SMAD4- and REST-Dependent Signaling. Cell Reports, 27(13), 3832-3843.

Windrem, M.S., Osipovitch, M., Liu, Z., Bates, J., Chandler-Militello, D., Miller, R.H., Nedergaard, M., Findling, R.L., Tesar, P.J., Goldman, S.A. (2017). Human iPSC Glial Mouse Chimeras Reveal Glial Contributions to Schizophrenia. Cell Stem Cell, 21(2), 195-208. PMID: 28736215.

Goldman, S.A., Liu, Z., Osipovitch, M. Methods of treating schizophrenia and other neuropsychiatric disorders. US Patent App. 17/254, 008.

Azpurua, J., Yang, J.N., Van Meter, M., Liu, Z., Kim, J., Gorbunova, V., Seluanov, A. (2013). IGF1R levels in the brain negatively correlate with longevity in 16 rodent species. Aging, 5(4), 304-314. PMID: 23651613.

Li, G., Xu, Y., Guan, D., Liu, Z., Liu, D.X. (2011). HSP70 protein promotes survival of C6 and U87 glioma cells by inhibition of ATF5 degradation. The Journal of Biological Chemistry, 286(23), 20251-20259. PMID: 21521685.

Liu, Z., Xu, Y.F., Feng, S.W., Li, Y., Yao, X.L., Lu, X.L., Zhang, C. (2009). Baculovirus-transduced mouse amniotic fluid-derived stem cells maintain differentiation potential. Annals of Hematology, 88(6), 565-572. PMID: 19066893.

Xu, Y., Liu, L., Li, Y., Zhou, C., Xiong, F., Liu, Z., Gu, R., Zhang, C. (2008). Myelin-forming ability of Schwann cell-like cells induced from rat adipose-derived stem cells in vitro. Brain Research, 1239(6), 49-55. PMID: 18804456.

Feng, S.W., Lu, X.L., Liu, Z., Zhang, Y.N., Liu, T.Y., Li, J.L., Yu, M.J., Zeng, Y., Zhang, C. (2008). Dynamic distribution of bone marrow-derived mesenchymal stromal cells and change of pathology after infusing into mdx mice. Cytotherapy, 10(3), 254-264. PMID: 18418771.

Li, Y., Zhang, C., Xiong, F., Yu, M.J., Peng, F.L., Shang, Y.C., Zhao, C.P., Xu, Y.F., Liu, Z., Zhou, C. (2008). Comparative study of mesenchymal stem cells from C57BL/10 and mdx mice. BMC Molecular and Cell Biology, 19(10), 9-24. PMID: 18489762.

Xu, Y., Liu, Z. (co-first author), Liu, L., Zhao, C., Xiong, F., Zhou, C., Li, Y., Zhang, C. (2008). Neurospheres from rat adipose-derived stem cells could be induced into functional Schwann cell-like cells in vitro. BMC Neuroscience, 12(10), 9-21. PMID: 18269732.