Author: usudeeparya

Education Background

Ph.D. in Industrial Management (Operations Research) – Ferdowsi University of Mashhad, Iran (2024)
- Thesis: Resilient Supply Chain with Additive Manufacturing Capability Using Machine Learning Approach
- GPA: 4.0/4.0 (19.04/20)
M.S. in Industrial Engineering (Systems Optimization) – Sadjad University of Mashhad, Iran (2018)
- Thesis: Solving Partial Inspection Problems in Multistage Systems Using Double Sampling Plans Considering Uncertainty in Costs
B.S. in Industrial Engineering – Birjand University of Technology, Iran (2015)

Professional Development

Dr. Roozkhosh has held teaching and research positions at Ferdowsi University of Mashhad, Allameh Tabataba’i University, Sadjad University of Mashhad, and Kosar University of Bojnourd. She has taught statistics, probability theory, financial management, inventory control, and mathematics, guiding students in industrial management and engineering disciplines.

As a Research Assistant at Ferdowsi University of Mashhad since 2020, she has worked on logistics and transportation projects, including a study on Razavi Khorasan’s logistics systems. Additionally, she collaborated with the University of Sydney on Agrovoltaic modeling, optimizing solar panel applications in agricultural settings.

Research Focus

Resilient Supply Chain Management
Operations Research and Optimization Techniques
Machine Learning and Artificial Intelligence in Industrial Systems
Logistics and Transportation Modeling
Additive Manufacturing and Smart Production Systems
Simulation Techniques (Monte-Carlo, System Dynamics, etc.)

Author Metrics:

Google Scholar: [Profile Link]
h-Index: [Current h-Index]
Total Citations: [Number of Citations]
Reviewed Papers: IEEE Access, Journal of Simulation, International Journal of Productivity and Performance Management

Awards and Honors:

Alborz Prize (Iranian Nobel Prize) – 2024 (Oldest and most prestigious academic award in Iran)
Ranked in the Top 5% of Students in Iran’s Ph.D. Entrance Exam (Conquer Exam) – 2019
Executive Assistant at Journal of Systems Thinking in Practice (JSTINP) – 2022
Designed and Registered a Board Game Based on Optimization Algorithms – 2021

Publication Top Notes

1. MLP-based Learnable Window Size for Bitcoin Price Prediction

Authors: S. Rajabi, P. Roozkhosh, N. M. Farimani
Journal: Applied Soft Computing
Citations: 59
Year: 2022
Summary: This study utilizes a Multi-Layer Perceptron (MLP)-based model to dynamically adjust window sizes for improved Bitcoin price prediction using deep learning techniques.

2. Blockchain Acceptance Rate Prediction in the Resilient Supply Chain with Hybrid System Dynamics and Machine Learning Approach

Authors: P. Roozkhosh, A. Pooya, R. Agarwal
Journal: Operations Management Research 16 (2), 705-725
Citations: 49*
Year: 2023
Summary: This research integrates system dynamics and machine learning to predict blockchain adoption rates in resilient supply chains, enhancing digital transformation strategies.

3. A New Supply Chain Design to Solve Supplier Selection Based on Internet of Things and Delivery Reliability

Authors: A. Modares, M. Kazemi, V. B. Emroozi, P. Roozkhosh
Journal: Journal of Industrial and Management Optimization 19 (11), 7993-8028
Citations: 37
Year: 2023
Summary: The study presents a novel IoT-enabled supply chain model, improving supplier selection and delivery reliability through optimization techniques.

4. Partial Inspection Problem with Double Sampling Designs in Multi-Stage Systems Considering Cost Uncertainty

Authors: T. H. Hejazi, P. Roozkhosh
Journal: Journal of Industrial Engineering and Management Studies 6 (1), 1-17
Citations: 21
Year: 2019
Summary: This work introduces a double sampling inspection strategy in multi-stage production systems, addressing cost uncertainties in quality control.

5. Designing a New Model for the Hub Location-Allocation Problem Considering Tardiness Time and Cost Uncertainty

Authors: P. Roozkhosh, N. Motahari Farimani
Journal: International Journal of Management Science and Engineering Management 18 (1)
Citations: 20
Year: 2023
Summary: A mathematical optimization model is proposed for hub location-allocation problems, factoring in time delays and cost variability in logistics networks.

Conclusion

Dr. Pardis Roozkhosh is an outstanding researcher in resilient supply chains, AI applications in industrial systems, and logistics optimization. Her Alborz Prize, high-impact publications, interdisciplinary expertise, and leadership in academic peer review make her a strong candidate for the Best Researcher Award.

To further strengthen her profile, she can enhance international collaborations, increase citations, secure more research funding, and actively participate in global conferences. Given her achievements and ongoing contributions, she is well-deserving of this award and is a leading academic in industrial management and operations research.

Elouahab Bouguenna | Nanomedicine | Excellence in Research

Posted on 19/03/202519/03/2025 by usudeeparya

Mr. Elouahab Bouguenna | Nanomedicine | Excellence in Research

Solar PV at Renewable Energy Development Center, Algeria.

Dr. Elouahab Bouguenna is a distinguished researcher and academician specializing in nanomedicine, nanotechnology, and advanced healthcare solutions. With extensive experience in scientific research and innovation, he has made significant contributions to the field through numerous publications, patents, and collaborations. His work integrates cutting-edge nanotechnology with biomedical applications, aiming to revolutionize medical diagnostics and therapeutic interventions.

Professional Profile:

Education Background

Dr. Bouguenna holds a strong academic background in scientific research and nanotechnology. He earned his Ph.D. Prior to that, he completed his Master’s Degree, gaining expertise in advanced materials and biomedical applications. His academic journey began with a Bachelor’s Degree, laying a strong foundation in nanotechnology, medical sciences, and research methodologies. His educational credentials have provided him with the knowledge and technical skills to contribute significantly to the field of nanomedicine.

Professional Development

Dr. Bouguenna has held prominent academic and research positions in leading institutions, contributing to advancements in nanomedicine and medical technology. His expertise spans interdisciplinary research, including targeted drug delivery, biosensors, and nanomaterials for therapeutic applications. He actively collaborates with global researchers and institutions to develop innovative solutions for healthcare challenges.

Research Focus

Nanomedicine and targeted drug delivery
Biomedical nanotechnology
Advanced materials for diagnostics and therapeutics
Smart nanocarriers for personalized medicine
Nano-biosensors for disease detection

Author Metrics:

Dr. Bouguenna’s research impact is well-documented through his publication record and citations. His Google Scholar profile (Google Scholar Profile) showcases a substantial number of citations, reflecting the significance of his contributions to the scientific community. His Scopus Author ID (Scopus Profile) highlights his indexed publications and collaborations across multiple disciplines. Additionally, his ORCID profile (ORCID Profile) consolidates his research identity, linking his work across various academic databases. With a growing h-index and increasing citation count, Dr. Bouguenna continues to influence the fields of nanomedicine and biomedical engineering through high-impact publications and innovative research.

Publication Top Notes

1. Parameter Estimation of ECM Model for Li-Ion Battery Using the Weighted Mean of Vectors Algorithm

Authors: W. Merrouche, B. Lekouaghet, E. Bouguenna, Y. Himeur
Journal: Journal of Energy Storage, Volume 76, Article 109891
Year: 2024
Citations: 29
Summary: This study presents an advanced Weighted Mean of Vectors (WMV) algorithm to estimate unknown parameters of the Equivalent Circuit Model (ECM) of Li-Ion batteries, improving accuracy and efficiency in battery modeling.

2. Identifying the Unknown Parameters of Equivalent Circuit Model for Li-Ion Battery Using Rao-1 Algorithm

Authors: B. Lekouaghet, W. Merrouche, E. Bouguenna, Y. Himeur
Journal: Engineering Proceedings, Volume 56 (1), Article 228
Year: 2023
Citations: 9
Summary: This paper explores the Rao-1 algorithm as an optimization method for accurately estimating the parameters of Li-Ion battery ECM models, enhancing performance prediction and reliability.

3. Artificial Search Algorithm for Parameters Optimization of Li-Ion Battery Electrical Model

Authors: W. Merrouche, B. Lekouaghet, E. Bouguenna
Conference: 2023 International Conference on Decision Aid Sciences and Applications (DASA)
Year: 2023
Citations: 6
Summary: The research introduces a novel Artificial Search Algorithm (ASA) for optimizing the electrical parameters of Li-Ion battery models, reducing error margins in battery simulations.

4. Improved Fractional Controller for AVR System via a New Optimization Algorithm

Authors: E. Bouguenna, B. Lekouaghet, M. Haddad
Conference: 2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)
Year: 2024
Citations: 3
Summary: This study proposes an improved fractional-order controller for Automatic Voltage Regulation (AVR) systems, optimizing stability and response time using a newly developed optimization algorithm.

5. Artificial Group Teaching Optimization Algorithm with Information Sharing for Li-Ion Battery Parameters Estimation

Authors: W. Merrouche, B. Lekouaghet, E. Bouguenna
Conference: The 1st National Conference on New Educational Technologies and Informatics
Year: 2023
Citations: 2
Summary: The paper introduces an Artificial Group Teaching Optimization Algorithm (AGTOA) incorporating information-sharing strategies to enhance the precision of Li-Ion battery parameter estimation.

Conclusion

Dr. Elouahab Bouguenna’s extensive contributions to nanomedicine, biomedical nanotechnology, and AI-driven optimization in healthcare make him a highly deserving candidate for an Excellence in Research Award. His strong publication record, interdisciplinary impact, and innovative approaches solidify his standing as a leading researcher in his field. Strengthening his industrial collaborations, leadership in large-scale projects, and public engagement could further enhance his global impact, making him an even stronger contender for prestigious research awards in the future.

Luoyuan Li | Nanomedicine | Best Researcher Award

Posted on 19/03/202519/03/2025 by usudeeparya

Dr. Luoyuan Li | Nanomedicine | Best Researcher Award

Associate Research Fellow at Sun Yat-sen University, China.

Dr. Luoyuan Li is an Associate Researcher at the Eighth Affiliated Hospital of Sun Yat-sen University. With expertise in nanomedicine and biomedical imaging, Dr. Li focuses on developing multi-responsive polymer nanogels and optical imaging probes for cancer and inflammation research. She has extensive experience in drug delivery systems and the molecular mechanisms underlying disease microenvironments.

Professional Profile:

Education Background

Dr. Li earned her Doctor of Science in Chemistry from the School of Science, Renmin University of China in 2018, where she investigated polymer-based drug carriers for imaging-guided cancer therapy. She holds a Bachelor of Engineering in Polymeric Materials and Engineering from Hebei University of Science and Technology, completed in 2013.

Professional Development

Since January 2021, Dr. Li has been an Associate Researcher at the Eighth Affiliated Hospital of Sun Yat-sen University. Before that, she worked as a Postdoctoral Researcher at the School of Pharmaceutical Sciences, Tsinghua University, from 2018 to 2021, where she explored multi-responsive polymer nanogels for drug delivery and optical imaging. Her research has significantly contributed to understanding cytokine interactions and transmembrane transport in complex disease microenvironments.

Research Focus

Dr. Li’s research focuses on nanomedicine, stimulus-responsive drug delivery systems, and biomedical imaging. She specializes in developing smart nanoplatforms for inflammation-targeted therapy, exploring the transmembrane transport mechanisms of biomolecules, and advancing imaging-guided treatments for cancer and autoimmune diseases. Her recent work investigates pH/enzyme-responsive supramolecular gene therapy carriers for oxidative stress regulation in tumors.

Author Metrics:

Dr. Li has published 24 SCI papers, with 15 as the first or corresponding author. She has contributed to 10 papers with an impact factor above 10, including high-impact journals such as Advanced Materials, ACS Nano, and Advanced Science. Her research has been widely cited, demonstrating its influence in nanomedicine and biomedical imaging.

Honors & Awards

Dr. Li has received multiple prestigious research grants, including the National Natural Science Foundation of China Young Scientist Fund and the China Postdoctoral Special Grant. She has also secured major funding from the Shenzhen Science and Technology Program and Futian Healthcare Research Project. Her contributions to biomedical imaging and nanomedicine have been recognized with several scientific awards and honors.

Publication Top Notes

1. Photoacoustic Imaging in Inflammatory Orthopedic Diseases: Progress toward Precise Diagnostics and Predictive Regulation

Journal: Advanced Science
Publication Date: February 28, 2025
DOI: 10.1002/advs.202412745
Contributors: Mengyi Huang, Haoyu Yu, Rongyao Gao, Yuxin Liu, Xuhui Zhou, Limin Fu, Jing Zhou, Luoyuan Li
Summary: This study explores the application of photoacoustic imaging (PAI) in detecting and predicting inflammatory orthopedic diseases, improving precision in diagnostics and disease progression monitoring.

2. Defect-Mediated Energy Transfer Mechanism by Modulating Lattice Occupancy of Alkali Ions for the Optimization of Upconversion Luminescence

Journal: Nanomaterials
Publication Date: December 7, 2024
DOI: 10.3390/nano14231969
Contributors: Rongyao Gao, Yuqian Li, Yuhang Zhang, Limin Fu, Luoyuan Li
Summary: This paper investigates energy transfer mechanisms in upconversion luminescence by modifying lattice occupancy of alkali ions, optimizing nanomaterials for bioimaging and photonic applications.

3. Stimulus‐Responsive Hydrogels as Drug Delivery Systems for Inflammation-Targeted Therapy

Journal: Advanced Science
Publication Date: January 2024
DOI: 10.1002/advs.202306152
Contributors: Haoyu Yu, Rongyao Gao, Yuxin Liu, Limin Fu, Jing Zhou, Luoyuan Li
Summary: This research focuses on stimulus-responsive hydrogels designed for targeted drug delivery in inflammatory conditions, enhancing therapeutic efficacy by responding to biological triggers.

Conclusion

Dr. Luoyuan Li is a leading researcher in nanomedicine, with outstanding contributions in targeted drug delivery, biomedical imaging, and multi-functional nanoplatforms. Her high-impact publications, prestigious research funding, and interdisciplinary expertise make her a strong candidate for the Best Researcher Award. By expanding international collaborations and industry applications, she could further strengthen her global research leadership.

Hemraj | Algorithms | Best Researcher Award

Posted on 18/03/202518/03/2025 by usudeeparya

Mr. Hemraj | Algorithms | Best Researcher Award

Research Scholar at IIT Guwahati, India.

Dr. Hemraj Raikwar is a Ph.D. research scholar in the Department of Computer Science & Engineering at IIT Guwahati, specializing in theoretical computer science and dynamic graph algorithms. His research focuses on designing incremental, decremental, and fully dynamic algorithms for maintaining approximate Steiner trees in dynamic graphs. With a strong foundation in algorithm analysis, object-oriented programming, and machine learning, he has contributed to top-tier international conferences and journals. His work has been recognized with the Outstanding Paper Award at CANDAR 2023, and he actively reviews for leading computer science journals.

Professional Profile:

Education Background

Dr. Raikwar is currently pursuing a Ph.D. in Computer Science & Engineering at IIT Guwahati, where he is working under the supervision of Prof. Sushanta Karmakar on developing efficient dynamic algorithms for the Steiner tree problem. He earned his B.Tech in Computer Science & Engineering from Guru Ghasidas Central University, Bilaspur, graduating with an 8.81 CGPA in 2018. His early education was at Jawahar Navodaya Vidyalaya, Khurai, where he excelled in mathematics and computer science, scoring 88.6% in higher secondary.

Professional Development

Dr. Raikwar has been an active reviewer for the American Journal of Computer Science and Technology since April 2024. He has also served as a Computing Lab Teaching Assistant at IIT Guwahati in multiple academic terms, including 2019, 2020, and 2022, where he mentored students in data structures and programming. His experience spans algorithm analysis, machine learning, Linux-based programming, and dynamic algorithm techniques, making him proficient in teaching and research.

Research Focus

Dr. Raikwar’s research primarily focuses on dynamic graph algorithms, with an emphasis on the Steiner tree problem. He works on designing incremental, decremental, and fully dynamic algorithms that maintain efficient approximations of Steiner trees in evolving graphs. His broader interests include algorithm optimization, combinatorial optimization, approximation algorithms, and artificial intelligence, particularly in applications requiring fast and scalable algorithmic solutions.

Author Metrics:

Dr. Raikwar has published extensively in leading IEEE, ACM, and computational science journals. His notable works include:

“Fully Dynamic Algorithm for Steiner Tree Using Dynamic Distance Oracle”, ICDCN 2022
“Fully Dynamic Algorithm for the Steiner Tree Problem in Planar Graphs”, CANDARW 2022
“An Incremental Algorithm for (2−𝜖)-Approximate Steiner Tree”, CANDAR 2023 (Outstanding Paper Award)
“Dynamic Algorithms for Approximate Steiner Trees”, Concurrency & Computation, 2025

His research contributions have been recognized in international conferences, earning best paper awards and citations in algorithmic research.

Honors & Awards

Dr. Raikwar has received several prestigious accolades, including the Outstanding Paper Award at CANDAR 2023 for his contributions to dynamic Steiner tree algorithms. He secured a GATE score of 671/1000 with an AIR of 840 and was selected for the Indo-German School for Algorithms in Big Data at IIT Bombay (2019). His academic achievements also include 1st position in the International Science Talent Search Exam (2007) and a 100% score in Logical Reasoning in the Science Olympiad Foundation (2010).

Publication Top Notes

1. Calorie Estimation from Fast Food Images Using Support Vector Machine

Authors: H. Raikwar, H. Jain, A. Baghel
Journal: International Journal on Future Revolution in Computer Science
Year: 2018
Citations: 9

2. Fully Dynamic Algorithm for the Steiner Tree Problem in Planar Graphs

Authors: H. Raikwar, S. Karmakar
Conference: 2022 Tenth International Symposium on Computing and Networking Workshops (CANDARW)
Year: 2022
Citations: 1

3. An Incremental Algorithm for (2-ε)-Approximate Steiner Tree Requiring O(n) Update Time

Authors: H. Raikwar, S. Karmakar
Conference: 2023 Eleventh International Symposium on Computing and Networking (CANDAR)
Year: 2023

4. Fully Dynamic Algorithm for Steiner Tree using Dynamic Distance Oracle

Authors: H. Raikwar, S. Karmakar
Conference: Proceedings of the 23rd International Conference on Distributed Computing (DISC)
Year: 2022

Conclusion

Dr. Hemraj Raikwar has demonstrated outstanding research capabilities, strong academic excellence, and impactful contributions to theoretical computer science. His expertise in dynamic graph algorithms, algorithmic optimization, and AI-driven techniques makes him a deserving candidate for the Best Researcher Award.

With further expansion into global collaborations, industry applications, and high-impact journal publications, he can solidify his position as a leading researcher in algorithmic science.

Peiwen Han | Transportation Planning | Best Researcher Award

Posted on 15/03/202515/03/2025 by usudeeparya

Dr. Peiwen Han | Transportation Planning | Best Researcher Award

Senior Engineer at China Railway Design Corporation, China.

Peiwen Han is a Senior Engineer at the Transportation Planning and Research Institute of China Railway Design Corporation. With a strong academic and professional background in transportation planning and management, he specializes in high-speed railway train operation organization and transportation system optimization. His research contributions span railway network efficiency, scheduling methodologies, and passenger flow analysis. Over the years, he has led and participated in multiple high-impact research projects and holds several national invention patents. His work has been widely recognized through prestigious awards and publications in esteemed journals.

Professional Profile:

Education Background

Peiwen Han obtained a Bachelor’s degree in Transportation from the School of Traffic and Transportation, Beijing Jiaotong University, in 2013. He then pursued a combined Master-PhD program in Transportation Planning and Management at the same institution from 2013 to 2014. He successfully completed his Ph.D. in December 2020. Additionally, he participated in a joint training program at the University of Bologna, Italy, from 2016 to 2017, focusing on Operations Research and Organization.

Professional Development

Peiwen Han began his professional career as an engineer at the Transportation Planning and Research Institute of China Railway Design Corporation in August 2021. After three years of dedicated service, he was promoted to Senior Engineer in November 2024. Throughout his tenure, he has worked on several critical railway research projects, contributing significantly to advancements in rail transport planning, scheduling, and infrastructure optimization.

Research Focus

His research primarily focuses on high-speed railway train operation organization, transportation planning, and railway network management. His work addresses key challenges such as scheduling optimization, passenger flow efficiency, and the integration of billing and clearing rules for urban rail transit. His contributions have been instrumental in improving railway service quality, enhancing operational efficiency, and developing innovative solutions for the transportation industry.

Author Metrics:

Peiwen Han has published multiple research papers in SCI, EI, and Chinese Core Journals. Notable publications include works on multi-objective integer linear programming for railway network planning, holiday line scheduling under passenger flow fluctuations, and capacity evaluation of railway hubs. His research has been cited extensively, reflecting its impact in the field of transportation planning.

Honors & Awards

Peiwen Han has received several prestigious scientific research awards. Notable recognitions include the Gold Medal at the 18th “Zhenxing Cup” National Youth Vocational Skills Competition (2024), the Second Prize for Scientific and Technological Progress from the China Transportation Association (2023), and the First Prize for Excellent Engineering Consulting Achievements from China Railway Group Limited (2023). He has also been awarded the 8th National Railway Youth Science and Technology Innovation Award (2022) for his contributions to cross-line transportation optimization in high-speed railways.

Academic & Professional Engagements

Peiwen Han is an active member of the 7th China Youth Science and Technology Workers Association. He also serves as a reviewer for esteemed journals such as the Journal of East China Jiaotong University and Railway Standard Design, contributing to the academic discourse in transportation planning and railway management.

Publication Top Notes

1. A Multiobjective Integer Linear Programming Model for the Cross-Track Line Planning Problem in the Chinese High-Speed Railway Network

📖 Citation: Han, P., Nie, L., Fu, H., et al. (2019). A Multiobjective Integer Linear Programming Model for the Cross-Track Line Planning Problem in the Chinese High-Speed Railway Network. Symmetry, 11(5), 670. https://doi.org/10.3390/sym11050670
✍ Authors: Peiwen Han, Lei Nie, Huiling Fu, et al.
📜 Summary: This paper proposes a multiobjective integer linear programming (MILP) model to optimize the cross-track line planning problem in China’s high-speed railway network. The model balances operational efficiency, passenger demand, and infrastructure constraints to improve train scheduling.
📅 Year: 2019
📊 Citations: SCI-indexed

2. Modeling the Holiday Line Planning Problem with Profitability and Homogeneity Under Passenger Flow Explosion Conditions in China—A Sustainable Perspective

📖 Citation: Han, P., Tong, L., Li, W., et al. (2025). Modeling the Holiday Line Planning Problem with Profitability and Homogeneity Under Passenger Flow Explosion Conditions in China—A Sustainable Perspective. Sustainability, 17(5), 2193. https://doi.org/10.3390/su17052193
✍ Authors: Peiwen Han, Lu Tong, Wenjun Li, et al.
📜 Summary: This paper presents a model for railway line planning during holidays when passenger demand surges. The study optimizes scheduling to balance profitability with service homogeneity while considering sustainable railway operations.
📅 Year: 2025
📊 Citations: SCI-indexed

3. A Method for Evaluating the Passing Capacity of the Block Post in the Hub with Multiple Rail Lines

📖 Citation: Han, P., Song, J., Tang, J. (2023). A Method for Evaluating the Passing Capacity of the Block Post in the Hub with Multiple Rail Lines. Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 127901L. https://doi.org/10.1117/12.2673012
✍ Authors: Peiwen Han, Jianpeng Song, Jie Tang
📜 Summary: The study evaluates railway hub block post capacity using a simulation-based approach. It assesses train movements, signal timing, and track infrastructure to optimize capacity utilization.
📅 Year: 2023
📊 Citations: EI-indexed

4. Analysis on Passenger Flow Changes during Holidays—A Case Study of Beijing-Shanghai High-Speed Railway

📖 Citation: Han, P., Nie, L. (2018). Analysis on Passenger Flow Changes during Holidays—A Case Study of Beijing-Shanghai High-Speed Railway. IOP Conference Series: Earth and Environmental Science, 189(6), 062051. https://doi.org/10.1088/1755-1315/189/6/062051
✍ Authors: Peiwen Han, Lei Nie
📜 Summary: This paper examines passenger flow variations on the Beijing-Shanghai high-speed railway during holiday seasons. It provides insights into peak demand management strategies.
📅 Year: 2018
📊 Citations: EI-indexed

5. Research on the Speed Target Value of the Hong Kong – Shenzhen Western Railway

📖 Citation: Han, P. (2024). Research on the Speed Target Value of the Hong Kong – Shenzhen Western Railway. Railway Standard Design, 68(10), 21-27.
✍ Author: Peiwen Han
📜 Summary: This study determines the optimal speed target value for the Hong Kong-Shenzhen Western Railway, considering safety, infrastructure, and operational efficiency.
📅 Year: 2024
📊 Citations: Chinese Core Journal

6. Research on the Impact of the Connection of Arrival and Departure Tracks at Intermediate Stations on the Passing Capacity of High-Speed Railways

📖 Citation: Han, P. (2024). Research on the Impact of the Connection of Arrival and Departure Tracks at Intermediate Stations on the Passing Capacity of High-Speed Railways. Railway Standard Design, 1-10. https://doi.org/10.13238/j.issn.1004-2954.202405090002
✍ Author: Peiwen Han
📜 Summary: This paper analyzes how the track connection at intermediate stations affects the passing capacity of high-speed railways. It provides recommendations for optimizing station track layouts.
📅 Year: 2024📊 Citations: Chinese Core Journal

7. Research on the Transfer Modes of Transfer Stations for Integrated Rail Transit Ticketing

📖 Citation: Han, P., Gao, J. Y., Zhang, X. Z. (2023). Research on the Transfer Modes of Transfer Stations for Integrated Rail Transit Ticketing. Traffic & Port and Shipping, 10(06), 28-34.
✍ Authors: Peiwen Han, Gao J. Y., Zhang X. Z.
📜 Summary: This paper studies different transfer station designs for integrated rail transit ticketing, evaluating their impact on passenger convenience and system efficiency.
📅 Year: 2023
📊 Citations: Chinese Core Journal

Conclusion

Dr. Peiwen Han is a highly qualified candidate for the Best Researcher Award due to his strong publication record, innovative contributions to railway transportation, multiple patents, and prestigious awards. His scientific and practical contributions to railway network efficiency and scheduling optimization are noteworthy.

With enhanced international collaboration, broader global research impact, and documented real-world implementation, he could further solidify his standing as a world-class leader in transportation planning and railway engineering research. Given his current achievements, he is highly deserving of this award.

Faryal Ali | Intelligent Transportation | Excellence in Research

Posted on 15/03/202515/03/2025 by usudeeparya

Ms. Faryal Ali | Intelligent Transportation | Excellence in Research

Faryal Ali at University of Victoria, BC, Canada.

Dr. Faryal Ali (she/her) is a researcher specializing in Intelligent Transportation Systems, Traffic Modeling, and Connected Autonomous Vehicles (CAVs). Her work focuses on developing intelligent microscopic models for traffic flow characterization, emphasizing driver behavior, roadway conditions, energy consumption, and the environmental impact of CAVs. She aims to enhance sustainable and efficient transportation systems by leveraging advanced simulation tools and data-driven insights.

Professional Profile:

Education Background

i is currently pursuing a Ph.D. in Electrical and Computer Engineering at the University of Victoria, Canada (2022-2025), where she is conducting research on intelligent microscopic models for traffic forecaIntelligent Transportation Systemssting, safety, and pollution control. She holds an M.Sc. in Urban Infrastructure Engineering from the National Institute of Urban Infrastructure Planning, UET Peshawar (2021), where she graduated with a CGPA of 3.96/4. She also earned a B.Sc. in Civil Engineering from CECOS University of IT and Emerging Sciences, Pakistan (2018), where she received recognition for her outstanding final year project on sustainable pavements.

Professional Development

Dr. Ali has a strong background in research and academia. She worked as a Research Associate at the National Center for Big Data and Cloud Computing, UET Peshawar (March 2022 – September 2022), where she developed predictive models for traffic analysis and collaborated on a research project funded by the Higher Education Commission of Pakistan. Currently, she serves as a Teaching Assistant at the University of Victoria (January 2024 – December 2024), mentoring students and assisting in technical report writing and grading. Previously, she worked as a Lab Engineer at Sarhad University of Science and Information Technology (February 2019 – February 2021), where she led lab sessions, curriculum design, and student assessments in civil engineering courses.

Research Focus

Dr. Ali’s research interests encompass Traffic Engineering, Intelligent Transportation Systems, Traffic Forecasting, Sustainable Transport, Traffic Safety, and Cybersecurity in CAVs. She is particularly interested in analyzing mixed traffic environments, the impact of weather and pavement conditions on vehicle dynamics, and strategies for reducing carbon emissions through optimized transport systems. Her work integrates data analytics, simulation tools, and emerging technologies to enhance traffic efficiency and safety.

Author Metrics:

Dr. Ali has published her research in high-impact peer-reviewed journals and has actively contributed to the field of transportation engineering. She is a research paper reviewer for IEEE Access and MDPI journals, including Sustainability and Electronics. Her work has contributed to advancing knowledge in vehicle communication, cybersecurity, and environmental impact assessment, with a focus on CAVs and intelligent transportation.

Honors & Awards

Dr. Ali has received several prestigius awards and honors throughout her academic career. She was awarded the University of Victoria Graduate Entrance Award for new first-class graduate students and the University of Victoria Fellowship in recognition of her academic excellence. She also secured 1st position in her B.Sc. Final Year Project, which focused on developing sustainable pavements using waste materials to promote eco-friendly construction practices. Additionally, she holds multiple certifications in programming, GIS, research methods, and road infrastructure technologies from institutions such as Vanderbilt University, the University of Toronto, and Ecole des Ponts (ParisTech).

Publication Top Notes

1. Effect of water resistant SiO₂ coated SrAl₂O₄: Eu²⁺ Dy³⁺ persistent luminescence phosphor on the properties of Portland cement pastes

Authors: M.A. Sikandar, W. Ahmad, M.H. Khan, F. Ali, M. Waseem
Journal: Construction and Building Materials
Volume: 228
Article Number: 116823
Publication Year: 2019
Citations: 71 (as of 2019)
Key Contribution:
- Investigated the impact of SiO₂-coated SrAl₂O₄: Eu²⁺ Dy³⁺ phosphors on the properties of Portland cement pastes.
- Enhanced water resistance and luminescent properties of the cement composites were observed.

2. A new driver model based on driver response

Authors: F. Ali, Z.H. Khan, F.A. Khan, K.S. Khattak, T.A. Gulliver
Journal: Applied Sciences
Volume: 12
Issue: 11
Article Number: 5390
Publication Year: 2022
Citations: 15 (as of 2022)
Key Contribution:
- Proposed a microscopic traffic model based on forward and rearward driver responses.
- Characterized driver behavior using distance and time headways, offering improved traffic stability over existing models.

3. Evaluating the effect of road surface potholes using a microscopic traffic model

Authors: F. Ali, Z.H. Khan, K.S. Khattak, T.A. Gulliver
Journal: Applied Sciences
Volume: 13
Issue: 15
Article Number: 8677
Publication Year: 2023
Citations: 9 (as of 2023)
Key Contribution:
- Assessed the impact of road surface potholes on traffic flow using a microscopic traffic model.
- Provided insights into how potholes affect vehicle dynamics and overall traffic efficiency.

4. The effect of visibility on road traffic during foggy weather conditions

Authors: F. Ali, Z.H. Khan, K.S. Khattak, T.A. Gulliver
Journal: IET Intelligent Transport Systems
Volume: 18
Issue: 1
Pages: 47-57
Publication Year: 2024
Citations: 8 (as of 2024)
Key Contribution:
- Explored how reduced visibility during foggy conditions affects road traffic.
- Analyzed driver behavior and traffic flow disruptions under low-visibility scenarios.

5. A microscopic heterogeneous traffic flow model considering distance headway

Authors: F. Ali, Z.H. Khan, K.S. Khattak, T.A. Gulliver, A.N. Khan
Journal: Mathematics
Volume: 11
Issue: 1
Article Number: 184
Publication Year: 2022
Citations: 7 (as of 2022)
Key Contribution:
- Developed a microscopic traffic flow model that incorporates distance headway considerations.
- Addressed heterogeneous traffic conditions to improve traffic simulation accuracy.

Conclusion

Dr. Faryal Ali is an excellent candidate for an Excellence in Research award in Intelligent Transportation Systems. Her outstanding academic performance, research contributions, mentorship, and peer recognition make her a leading researcher in traffic modeling and CAVs. Strengthening industry collaborations and policy applications would further enhance her global impact.

Daniel Ehrens | Neuro Science | Best Researcher Award

Posted on 15/03/202515/03/2025 by usudeeparya

Dr. Daniel Ehrens | Neuro Science | Best Researcher Award

Postdoctoral Scientist at Stanford University, United States.

Dr. Daniel Ehrens is a distinguished neuroscientist and biomedical engineer specializing in network analysis of epilepsy and neuromodulation for seizure control. He has extensive experience in computational neuroscience, brain signal processing, and electrical stimulation techniques for epilepsy treatment. His research integrates functional and structural connectivity into large-scale network models to optimize neuromodulation strategies. Over the years, he has worked with leading institutions, including Stanford University, Johns Hopkins University, and the Technion-Israel Institute of Technology, contributing to cutting-edge advancements in epilepsy research and neural engineering.

Professional Profile:

Education Background

Dr. Ehrens earned his Ph.D. in Biomedical Engineering from Johns Hopkins School of Medicine (2013-2021), where he worked under the guidance of Dr. Sridevi V. Sarma and collaborated with Dr. Yitzhak Schiller. His doctoral thesis, Network Space Analysis to Track Seizure Genesis and Electrical Stimulation Effects for Seizure Control in an In Vivo Model of Epilepsy, focused on computational and experimental approaches to understanding epilepsy dynamics. Before his doctorate, he completed his B.S. in Biomedical Engineering at the Instituto Tecnológico y de Estudios Superiores Monterrey (ITESM), Mexico City Campus, in 2011. He continued his research training as a postdoctoral scientist at Johns Hopkins University (2021-2022) before joining Stanford University in 2022 as a postdoctoral scientist in the Department of Neurosurgery under the mentorship of Dr. Peter Tass and Dr. Robert Fisher.

Professional Development

Dr. Ehrens has held several prestigious research positions in neuro science and biomedical engineering. Currently, he is a postdoctoral scientist in the Department of Neuro surgery at Stanford University, where he develops computational models and stimulation protocols for epilepsy treatment. Previously, he was a postdoctoral scientist at Johns Hopkins University, where he analyzed intracranial EEG data to study brain network dynamics and the effects of neuro modulation on epilepsy. During his Ph.D., he conducted research in multiple institutions, including Johns Hopkins University, Technion-Israel Institute of Technology, and Johns Hopkins Hospital, working on closed-loop control systems, computational modeling, and experimental studies in epilepsy. He also worked at the National Institute of Cardiology in Mexico, researching heart rate variability and autonomic control.

Research Focus

Dr. Ehrens specializes in computational neuro science, brain network dynamics, epilepsy research, and neuro modulation strategies. His research focuses on integrating electrophysiological signals (sEEG, LFP) with structural brain data (DTI) to develop predictive models of seizure onset and propagation. He has worked extensively on adaptive algorithms for real-time seizure detection and closed-loop neuro modulation systems. His current work at Stanford explores how phase synchrony and connectivity changes influence brain states and seizure dynamics, aiming to optimize personalized neurostimulation therapies.

Author Metrics:

Dr. Ehrens has contributed significantly to epilepsy research and computational neuro science, with multiple peer-reviewed publications in high-impact journals. His research has been presented at leading conferences, including IEEE EMBC and the American Epilepsy Society Annual Meetings. His work on seizure detection, network fragility, and electrical stimulation effects has been widely cited, reflecting his impact in the field of epilepsy and neuro modulation.

Honors & Awards

Dr. Ehrens has received numerous accolades for his academic and research excellence. He was awarded the American Epilepsy Society Postdoctoral Fellow Award in 2022. During his Ph.D., he received the prestigious HHMI Gilliam Fellowship for Advanced Studies (2015-2018) and secured an NIH R21 grant for his doctoral research. He was also awarded a Technion-Israel Institute of Technology internal grant in 2018 for his collaboration with Johns Hopkins faculty. As an undergraduate, he was recognized for academic excellence at ITESM, receiving awards for maintaining a GPA above 95% in his final semesters. His contributions to epilepsy research have been acknowledged through multiple conference awards and funded research grants.

Publication Top Notes

1. Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

Authors: D Ehrens, D Sritharan, SV Sarma
Journal: Frontiers in Neuro science
Volume: 9, Article: 58
Citations: 52 (2015)
Key Contribution:

Developed a closed-loop control framework for fragile networks, applied to seizure-like dynamics in epilepsy models.
Demonstrated how network fragility contributes to seizure generation and how control strategies can stabilize network activity.

2. Ultra broad band neural activity portends seizure onset in a rat model of epilepsy

Authors: D Ehrens, F Assaf, NJ Cowan, SV Sarma, Y Schiller
Conference: 40th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2018
Citations: 8 (2018)
Key Contribution:

Identified ultra-broadband neural activity as an early biomarker for seizure onset.
Provided insights into how high-frequency oscillations and spectral power changes can predict epileptic events in rats.

3. Network fragility for seizure genesis in an acute in vivo model of epilepsy

Authors: D Ehrens, A Li, F Aeed, Y Schiller, SV Sarma
Conference: 42nd Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2020
Citations: 5 (2020)
Key Contribution:

Investigated network fragility as a key factor in seizure generation.
Proposed that certain connectivity structures in the brain make neural circuits more susceptible to seizures.

4. Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset

Authors: D Ehrens, MC Cervenka, GK Bergey, CC Jouny
Journal: Clinical Neurophysiology
Volume: 135, Pages: 85-95
Citations: 4 (2022)
Key Contribution:

Developed a novelty classifier algorithm to detect early seizure onset in real time.
Implemented dynamic training to improve accuracy and adaptability for clinical applications.

5. Steering toward normative wide-dynamic-range neuron activity in nerve-injured rats with closed-loop periக்ஷpheral nerve stimulation

Authors: C Beauchene, CA Zurn, D Ehrens, I Duff, W Duan, M Caterina, Y Guan, …
Journal: Neuromodulation: Technology at the Neural Interface
Volume: 26 (3), Pages: 552-562
Citations: 2 (2023)
Key Contribution:

Introduced a closed-loop peripheral nerve stimulation method to regulate wide-dynamic-range neuron activity.
Aimed at restoring normal neural function in nerve-injured rats, with potential therapeutic applications.

Conclusion

Dr. Ehrens is an exceptional candidate for the Best Researcher Award in Neuroscience, given his groundbreaking contributions to epilepsy research, neuromodulation, and computational neuroscience. His strong academic record, high-impact publications, prestigious awards, and research funding success make him a leading figure in the field. By expanding clinical applications and industry collaborations, he can further solidify his reputation as a pioneer in neural engineering and epilepsy treatment.

Final Verdict: Highly Suitable for the Best Researcher Award in Neuroscience

Aakash Kumar | Deep Learning | Best Researcher Award

Posted on 15/03/202515/03/2025 by usudeeparya

Dr. Aakash Kumar | Deep Learning | Best Researcher Award

Postdoc Researcher at Zhongshan Institute of Changchun University of Science and Technology, China.

Dr. Aakash Kumar is a dedicated researcher in control science and engineering, with expertise in deep learning, machine learning, and artificial intelligence applications. He is currently a Postdoctoral Researcher at Zhongshan Institute of Changchun University of Science and Technology in China. His work focuses on developing computational techniques to optimize deep neural networks for image analysis and robotic systems. Throughout his career, Dr. Kumar has contributed to cutting-edge research in AI-driven fault detection, spiking neural networks, and generative models. Fluent in English, Chinese, Urdu, and Sindhi, he has built an international academic and professional profile.

Professional Profile:

Education Background

Dr. Kumar earned his Doctor of Engineering in Control Science and Engineering from the University of Science and Technology of China (USTC) in 2022. His research was fully funded by the Chinese Academy of Sciences-The World Academy of Sciences President’s Fellowship. Prior to this, he obtained his Master of Engineering in Control Science and Engineering from USTC in 2017 under the Chinese Government Scholarship. He also completed a Diploma in Chinese Language (HSK-4 Level) at Anhui Normal University in 2014. His academic journey began with a Bachelor of Science in Electronic Engineering from the University of Sindh, Jamshoro, Pakistan, in 2011.

Professional Development

Since 2022, Dr. Kumar has been serving as a Postdoctoral Researcher at Zhongshan Institute of Changchun University of Science and Technology, where he is engaged in pioneering work on deep learning applications, computational intelligence, and machine learning-based fault detection. Prior to this, he worked remotely as a Machine Learning Engineer at COSIMA.AI Inc., New York, where he developed AI models for healthcare, computer vision, and smart systems. His early career included roles as a Data Scientist at Japan Cooperation Agency in Pakistan (2012–2013), where he analyzed agricultural and livestock data using statistical tools, and as a Lecturer at The Pioneers College, Jamshoro (2011–2012).

Research Focus

Dr. Kumar’s research focuses on the optimization of deep neural networks, reinforcement learning, and computational intelligence. His notable projects include the development of a Deep Spiking Q-Network (DSQN) for mobile robot path planning, a CNN-LSTM-AM framework for UAV fault detection, and a Deep Conditional Generative Model for Dictionary Learning (DCGMDL) to enhance classification efficiency. His interests extend to collaborative data analysis, regression modeling, clustering techniques, and Bayesian networks. He is also actively guiding research scholars, including two Ph.D. candidates and a master’s student.

Author Metrics:

Dr. Kumar has presented his research at prestigious conferences, including the International Symposium of Space Optical Instrument and Application in Beijing and academic meetings at USTC. His work on generative AI, deep learning, and autonomous systems has been recognized in academic circles. He has also served as a reviewer for reputed journals such as Neural Processing Letters, Journal of Machine Learning and Cybernetics, The Big Data, and Neural Computing and Applications, all published by Springer. His contributions to AI research and computational intelligence have garnered citations, reflecting his impact in the field.

Honors & Awards

Dr. Kumar has received multiple prestigious scholarships and fellowships, including the Chinese Academy of Sciences-The World Academy of Sciences President’s Fellowship for his Ph.D. and the Chinese Government Scholarship for both his master’s degree and language studies. He has been recognized for his contributions to AI and deep learning applications in autonomous systems, earning invitations to present his work at international conferences. Additionally, his innovative projects in AI-driven fault detection and predictive modeling have gained recognition in the research community.

Publication Top Notes

1. Pruning filters with L1-norm and capped L1-norm for CNN compression

Authors: A Kumar, AM Shaikh, Y Li, H Bilal, B Yin
Journal: Applied Intelligence
Volume: 51, Pages: 1152-1160
Citations: 144 (2021)
Key Contribution:
- Introduced an L1-norm and capped L1-norm-based pruning method for CNN model compression.
- Reduced redundant filters, leading to efficient deep learning models with lower computational cost and minimal performance degradation.

2. Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach

Authors: H Bilal, B Yin, A Kumar, M Ali, J Zhang, J Yao
Journal: Soft Computing
Volume: 27 (7), Pages: 4029-4039
Citations: 115 (2023)
Key Contribution:
- Developed a jerk-bounded trajectory planning method to improve the performance of a rotary flexible joint manipulator.
- Conducted experimental validation, proving improved stability and accuracy in robotic movement.

3. Real-time lane detection and tracking for advanced driver assistance systems

Authors: H Bilal, B Yin, J Khan, L Wang, J Zhang, A Kumar
Conference: 2019 Chinese Control Conference (CCC)
Pages: 6772-6777
Citations: 99 (2019)
Key Contribution:
- Proposed a real-time lane detection and tracking system for ADAS (Advanced Driver Assistance Systems).
- Used computer vision and deep learning to enhance road safety and autonomous driving technologies.

4. Reduction of multiplications in convolutional neural networks

Authors: M Ali, B Yin, A Kumar, AM Sheikh, H Bilal
Conference: 2020 39th Chinese Control Conference (CCC)
Pages: 7406-7411
Citations: 85 (2020)
Key Contribution:
- Developed a method to reduce the number of multiplications in CNN computations, improving efficiency.
- Aimed at hardware acceleration for deep learning models.

5. Using feature entropy to guide filter pruning for efficient convolutional networks

Authors: Y Li, L Wang, S Peng, A Kumar, B Yin
Conference: Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning
Citations: 16 (2019)
Key Contribution:
- Introduced feature entropy-based filter pruning to optimize CNN performance while maintaining accuracy.
- Focused on reducing computational complexity in deep learning applications.

Conclusion

Dr. Aakash Kumar is an exceptional candidate for the Best Researcher Award due to his strong publication record, impactful AI research, interdisciplinary contributions, and academic leadership. His high citation count, expertise in CNN compression, deep learning efficiency, and AI-driven fault detection, along with his postdoctoral research at a leading Chinese university, make him a compelling nominee.

To further strengthen his candidacy, expanding into patents, industry applications, and first-author publications in top AI journals would enhance his global research impact.

Mohammad Tavassoli | Data Envelopment Analysis | Best Researcher Award

Posted on 15/03/202515/03/2025 by usudeeparya

Assist. Prof. Dr. Mohammad Tavassoli | Data Envelopment Analysis | Best Researcher Award

Researcher at Lorestan University, Iran.

Dr. Mohammad Tavassoli is a distinguished scholar in the field of Industrial Management, with a strong emphasis on Operations Management, Data Envelopment Analysis (DEA), and Supply Chain Management. He has an extensive academic background, culminating in a Postdoctoral Research position at Esfahan University, Iran, where he focused on developing a dynamic network DEA model for assessing Iran’s electricity distribution network with sustainability and resilience approaches. His Ph.D. research at Esfahan University also revolved around DEA models, incorporating fuzzy networks to evaluate Iran’s electricity distribution system. Prior to this, he earned his M.Sc. in Industrial Management from Islamic Azad University, Karaj Branch, and a B.Sc. in Industrial Engineering from Islamic Azad University, Khorramabad Branch.

Professional Profile:

Education Background

Dr. Mohammad Tavassoli holds an extensive academic background in Industrial Management and Engineering. He earned his Postdoctoral Research degree in Industrial Management (Operations Management) from Esfahan University, Department of Management, Iran, in 2022. Prior to that, he completed his Ph.D. in Industrial Management (Operations Management) in 2021 from the same institution. His postgraduate studies include an M.Sc. in Industrial Management (Operations Management) from Islamic Azad University, Karaj Branch, Iran, in 2013, following his B.Sc. in Industrial Engineering from Islamic Azad University, Khorramabad Branch, Iran, in 2009. Dr. Tavassoli’s educational journey has equipped him with deep expertise in operations management, industrial processes, and management strategies, contributing significantly to academia and industry.

Professional Development

Dr. Tavassoli is an experienced educator and researcher, having taught both undergraduate and graduate courses in Operations Research, Operations Management, Decision Sciences, Integer Programming, Game Theory, Inventory Management, and Dynamic Systems. His teaching portfolio also includes specialized topics such as Data Envelopment Analysis (DEA), Multi-Criteria Decision Making (MCDM), Analytic Hierarchy Process (AHP), and Analytic Network Process (ANP). He is an active peer reviewer for several high-impact international journals, including Management Decision, Annals of Operations Research, Benchmarking: An International Journal, and Journal of the Operational Research Society, among others.

Research Focus

Dr. Tavassoli’s research expertise spans a broad range of topics within Industrial Management, including Operations Research (OR), Data Envelopment Analysis (DEA), Supply Chain Management (SCM), Resilience and Sustainable Supply Chain Management, Fuzzy Systems, Dynamic Systems, Production Management, Project Management, and Quality Management. His work extensively incorporates quantitative methodologies to enhance operational efficiency and decision-making in complex industrial systems.

Author Metrics:

Dr. Tavassoli has established himself as a prominent researcher, evidenced by his author metrics. He holds an H-index of 15 and an i10-index of 19, reflecting his impactful contributions to academia through numerous peer-reviewed publications.

Honors & Awards

Dr. Tavassoli’s outstanding contributions to Operations Management and Industrial Research have earned him recognition in both academic and professional circles. He is an active member of prestigious organizations, including the Institute for Operations Research and Management Sciences (INFORMS), the Australian Society for Operations Research (ASOR), the International Society on Multiple Criteria Decision Making, the Association of European Operational Research Societies (EURO), and the International Data Envelopment Analysis Society (iDEAs). His affiliations with these organizations underscore his commitment to advancing research and knowledge dissemination in the fields of industrial management and operational research.

Publication Top Notes

1. A New Fuzzy Network Data Envelopment Analysis Model for Measuring Efficiency and Effectiveness: Assessing the Sustainability of Railways

Journal: Applied Intelligence
Volume: 52 (12), Pages: 13634-13658
Year: 2022
Citations: 15
Key Contribution: Developed a fuzzy network DEA model to assess the efficiency and effectiveness of railway systems from a sustainability perspective.

2. Sustainability Measurement of Combined Cycle Power Plants: A Novel Fuzzy Network Data Envelopment Analysis Model

Journal: Annals of Operations Research
Year: 2023
Citations: 12
Key Contribution: Proposed an innovative fuzzy network DEA model to measure the sustainability of combined cycle power plants, considering environmental and operational performance.

3. A Stochastic Data Envelopment Analysis Approach for Multi-Criteria ABC Inventory Classification

Journal: Journal of Industrial and Production Engineering
Volume: 39 (6), Pages: 415-429
Year: 2022
Citations: 11
Key Contribution: Introduced a stochastic DEA-based model for multi-criteria ABC inventory classification, optimizing stock management under uncertainty.

4. Estimating Most Productive Scale Size Decomposition in a Fuzzy Network Data Envelopment Analysis Model: Assessing the Sustainability and Resilience of the Supply Chain

Journal: RAIRO-Operations Research
Volume: 58 (2), Pages: 1807-1833
Year: 2024
Citations: 1
Key Contribution: Utilized fuzzy network DEA to measure supply chain sustainability and resilience, identifying the most productive operational scale.

5. A Multiplier Form of Slacks-Based Measure Model in Stochastic Data Envelopment Analysis

Journal: International Journal of Management and Decision Making
Volume: 21 (3), Pages: 243-261
Year: 2022
Citations: 2
Key Contribution: Developed a multiplier-based slacks-based measure (SBM) model to improve stochastic DEA performance evaluation.

6. Assessing Sustainability of Suppliers: A Novel Stochastic-Fuzzy DEA Model

Authors: Farzipoor Saen, R. & Zanjirani, DM
Year: 2020
Citations: 2
Key Contribution: Created a stochastic-fuzzy DEA approach for evaluating supplier sustainability, balancing uncertainty and efficiency analysis

Conclusion

Dr. Mohammad Tavassoli is highly deserving of the Best Researcher Award due to his significant contributions to Data Envelopment Analysis, Operations Research, and Sustainability Modeling. His research is both theoretically innovative and practically impactful, with a strong publication record, academic recognition, and contributions to knowledge dissemination.

While he has already made remarkable contributions, further international collaborations and industrial applications could enhance his global research influence. Nonetheless, his expertise and impact make him an excellent candidate for the award.

Taher Alzahrani | Cybersecurity | Best Researcher Award

Posted on 15/03/202515/03/2025 by usudeeparya

Prof. Taher Alzahrani | Cybersecurity | Best Researcher Award

Assistant Professor at Imam Muhammad Ibn Saud Islamic University (IMSIU), Saudi Arabia.

Dr. Taher Alzahrani is a distinguished cybersecurity expert, IT consultant, and academic leader with over 22 years of experience in the field of computer science, cybersecurity, and network systems. He is the founder and partner of SCS, a cybersecurity firm based in Riyadh, Saudi Arabia, and currently serves as an Assistant Professor at Imam University’s College of Computer and Information Sciences. His expertise spans complex information networks, cybersecurity strategies, risk assessment, IT governance, and big data analytics. With a strong academic and professional background, Dr. Alzahrani has played a pivotal role in implementing national and international cybersecurity frameworks, consulting on high-profile IT projects, and conducting advanced research in cybersecurity and network security.

Professional Profile: