Sabastine Emmanuel | Computational Mathematics | Best Researcher Award

Dr. Sabastine Emmanuel | Computational Mathematics | Best Researcher Award

Lecturer at Universiti Sains Malaysia/Federal University Lokoja, Malaysia.

Dr. Sabastine Emmanuel is a Nigerian mathematician and lecturer at Federal University Lokoja with strong academic and pedagogical foundations in applied mathematics. He specializes in numerical methods and has contributed to undergraduate research in the fields of differential equations and computational methods. With over a decade of teaching experience, he is committed to nurturing mathematical skills and problem-solving capabilities in his students.

Publication Profile 

Scopus

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Educational Background 🎓

  • M.Sc. Mathematics, University of Ilorin, Ilorin, Nigeria (2015)

  • B.Sc. Mathematics, Nasarawa State University, Keffi, Nigeria (2010)

Professional Experience 💼

Dr. Sabastine Emmanuel is a dedicated academic professional currently serving as a Lecturer II in the Department of Mathematics at Federal University Lokoja, Kogi State, Nigeria, a position he has held since October 2018. He began his academic career at the same university as a Graduate Assistant in December 2012, and was later promoted to Assistant Lecturer in March 2015. Before joining academia, he gained teaching experience during his NYSC program at Victory Comprehensive College, Ondo, and also served as a classroom teacher at World Islamic College, Akwanga. Over the years, Dr. Emmanuel has taught a broad range of undergraduate mathematics courses, including Numerical Analysis, Discrete Mathematics, Differential Equations, Operations Research, and FORTRAN Programming. He has supervised several undergraduate research projects, focusing on numerical methods and mathematical modeling.

Research Interests 🔬

Dr. Emmanuel’s research interests lie primarily in Numerical Analysis, Differential Equations, Computational Mathematics, and Mathematical Modeling. He is particularly focused on the development and analysis of numerical techniques for solving ordinary and partial differential equations, integro-differential equations, and nonlinear mathematical problems. He is also interested in optimization methods including swarm intelligence and evolutionary algorithms.

Publications 📚

1. Multi-derivative Hybrid Block Methods for Singular Initial Value Problems with Application

Authors: S. Emmanuel, S. Sathasivam, M. O. Ogunniran

Journal: Scientific African

Volume: 24

Article ID: e02141

Year: 2024

Citations: 5

Summary: This study proposes and applies new multi-derivative hybrid block numerical methods to solve singular initial value problems (SIVPs), which are challenging due to their singular nature at the initial point. The paper demonstrates the method’s robustness and accuracy through theoretical analysis and computational experiments.

2. Estimating Nonlinear Regression Parameters Using Particle Swarm Optimization and Genetic Algorithm

Authors: S. Emmanuel, I. Okoye, C. Ezenweke, D. Shobanke, I. Adeniyi

Journal: FUDMA Journal of Sciences

Volume: 6, Issue: 6

Pages: 202–213

Year: 2022

Citations: 5

Summary: This paper compares the performance of two population-based optimization techniques—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)—in estimating parameters of nonlinear regression models. It provides insight into their convergence behavior, computational efficiency, and estimation accuracy.

3. Uniform Order Legendre Approach for Continuous Hybrid Block Methods for the Solution of First Order Ordinary Differential Equations

Authors: N. S. Yakusak, S. Emmanuel, M. O. Ogunniran

Journal: IOSR Journal of Mathematics

Volume: 11, Issue: 1

Pages: 09–14

Year: 2015

Citations: 5

Summary: The study presents a uniform order Legendre polynomial-based hybrid block method for solving first-order ODEs. The approach enhances solution accuracy and stability through continuous integration strategies and uniform convergence analysis.

4. Mathematical Modeling of Corruption Dynamics: Examining the Reintegration of Formerly Corrupt Individuals

Authors: R. S. Isa, S. Emmanuel, N. T. Danat, S. H. Tsok

Journal: FUDMA Journal of Sciences

Volume: 7, Issue: 4

Pages: 1–13

Year: 2023

Citations: 4

Summary: This paper develops a compartmental mathematical model to analyze the dynamics of corruption in society, with particular attention to the reintegration of previously corrupt individuals. It evaluates control strategies and potential reintegration policies using system stability analysis and simulations.

5. Estimating the Transmission Dynamics of Dengue Fever in Subtropical Malaysia Using SEIR Model

Authors: S. Emmanuel, S. Sathasivam, M. K. M. Ali, T. J. Kee, Y. S. Ling

Journal: Journal of Quality Measurement and Analysis (JQMA)

Volume: 19, Issue: 2

Pages: 45–56

Year: 2023

Citations: 4

Summary: Using the SEIR (Susceptible-Exposed-Infectious-Recovered) epidemiological model, this study simulates the spread of dengue fever in Malaysia. The authors analyze transmission rates, recovery parameters, and control measures to better understand the disease dynamics and support public health interventions.

Conclusion

Dr. Sabastine Emmanuel stands out as a dedicated, innovative, and steadily emerging researcher in the field of Computational Mathematics. His expertise in numerical methods, optimization algorithms, and real-world mathematical modeling places him among the rising scholars contributing to impactful and interdisciplinary research.

His balanced academic profile, which includes strong pedagogical engagement, peer-reviewed publications, and applications in public health and socio-economic systems, aligns well with the goals of a “Best Researcher Award”—especially one focused on innovative applied mathematics.

 

 

Weiwei Song | Spatiotemporal Cloud Computing | Best Researcher Award

Dr. Weiwei Song | Spatiotemporal Cloud Computing | Best Researcher Award

Lecturer at Kunming University of Science and Technology, China

Dr. Weiwei Song is a Lecturer at the Faculty of Land Resources Engineering, Kunming University of Science and Technology. Born in 1976, he specializes in spatiotemporal cloud computing, large-scale spatial data analytics, spatiotemporal artificial intelligence, and ecological-environmental monitoring. His research has significantly contributed to geospatial technology and natural resource management, with multiple ongoing projects funded by national and provincial agencies.

Publication Profile 

Scopus

Educational Background 🎓

  • Ph.D. in Earth Exploration and Information Technology – Kunming University of Science and Technology (2015)

  • Postdoctoral Research – George Mason University, USA (2017)

Professional Experience 💼

  • Lecturer (2018–Present) – Faculty of Land Resources Engineering, Kunming University of Science and Technology

  • Led multiple research projects in geospatial technology and artificial intelligence applications for environmental monitoring and natural resource management

  • Guest Editor for Annals of GIS, Special Issue: “Advancements of GIS in the New IT Era”

Research Interests 🔬

  • Spatiotemporal Cloud Computing

  • Big Spatiotemporal Data Analytics

  • Artificial Intelligence in Geospatial Analysis

  • Ecological-Environmental Monitoring

Awards and Honors🏆✨

  • 2019 – Second Prize, Science & Technology Progress Award (Chinese Society for Surveying, Mapping and Geoinformation)

  • 2020 – Engineering Gold Medal (China Association for Geographic Information Industry)

  • 2021 – First Prize, Yunnan Provincial Geomatics Advancement Award

  • 2022 – Third Prize, Yunnan Science & Technology Progress Award

Conclusion🌟

Dr. Weiwei Song is an accomplished researcher in geospatial sciences, with expertise in cloud computing and artificial intelligence applications for environmental monitoring. His contributions to major government-funded projects and recognition in scientific communities highlight his impact in the field. Through his academic and research endeavors, he continues to advance spatiotemporal data analytics and geospatial technologies, enhancing decision-making processes in natural resource management.

Publications 📚

1️⃣ Wei, Q., et al. (2025). 🛰️ Spatiotemporal estimation of surface NO₂ concentrations in the Pearl River Delta region based on TROPOMI data and machine learning. Atmospheric Pollution Research, 16(3): 102353.


2️⃣ Li, J., et al. (2024). 🌍 Study on Spatio-Temporal Indexing Model of Geohazard Monitoring Data Based on DataStream Clustering Algorithm. ISPRS International Journal of Geo-Information, 13(3): 93.


3️⃣ Chen, J., et al. (2024). 🤖 1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 32(04): 593-623.


4️⃣ You, N., et al. (2023). 🖼️ Research on image denoising in edge detection based on wavelet transform. Applied Sciences, 13(3): 1837.


5️⃣ You, N., et al. (2023). 🏞️ Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection. Applied Sciences, 13(7): 4454.


6️⃣ Lu, J., et al. (2023). 🍏 A Study of Apple Orchards Extraction in the Zhaotong Region Based on Sentinel Images and Improved Spectral Angle Features. Applied Sciences, 13(20): 11194.


7️⃣ Li, Y., et al. (2023). ☁️ A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment. Applied Sciences, 13(3): 1869.


8️⃣ Song, W. and C. Wu (2021). 🗺️ Introduction to advancements of GIS in the new IT era. Taylor & Francis, 27: 1-4.


9️⃣ Hu, F., et al. (2020). 💾 A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data. International Journal of Digital Earth, 13(3): 410-428.


🔟 Jin, B., et al. (2019). 🛰️ Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images. Journal of the Indian Society of Remote Sensing, 47: 951-965.


Tianjun Sun | Big Data Analytics | Best Researcher Award

Assoc. Prof. Dr. Tianjun Sun | Big Data Analytics | Best Researcher Award

College of automotive Engineering at Jilin University, China

Professor Sun Tiejun is a distinguished researcher specializing in vehicle engineering, artificial intelligence, and automotive control systems. He is affiliated with Jilin University and has contributed significantly to the field of autonomous vehicle control, intelligent decision-making, and hybrid electric vehicle optimization.

Publication Profile 

Scopus

Educational Background 🎓

  • Harbin University of Science and Technology
    • Bachelor’s Degree in Electronic Information Science and Technology
    • Bachelor’s Degree in Control Engineering
  • Jilin University
    • Doctorate in Vehicle Engineering
  • Postdoctoral Research:
    • College of Automotive Engineering, Jilin University

Professional Experience 💼

  • State Key Laboratory of Automotive Simulation and Control, Jilin University
  • National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University
  • Equipment Industry Development Center, Ministry of Industry and Information Technology
  • Senior Member, SAE (Society of Automotive Engineers)
  • High-level D Talents of Jilin Province

Research Interests 🔬

  • Autonomous Vehicle Control
    • Human-like mechanism deep learning models for motion control
    • Brain-inspired decision-making for intelligent car-following
  • Artificial Intelligence in Automotive Engineering
    • Application of AI in vehicle motion prediction and decision-making
  • Hybrid Electric Vehicle Optimization
    • Data-driven control strategies for plug-in hybrid vehicles
  • Bionic Engineering & Robotics
    • Integration of basal ganglia-inspired neural networks into vehicle control

Awards and Honors🏆✨

  • SAE Senior Member – Recognized for contributions to the automotive engineering community.
  • High-level D Talent of Jilin Province – Acknowledged for outstanding expertise in automotive technology.
  • Key Researcher at National Laboratories – Integral to advancements in automotive simulation and chassis integration.

Conclusion🌟

Professor Sun Tiejun is a leading expert in vehicle engineering, particularly in AI-driven decision-making and motion control for autonomous and hybrid vehicles. His interdisciplinary research blends artificial intelligence, control engineering, and bionic mechanisms to enhance the efficiency and safety of next-generation transportation systems. His work has been recognized in prestigious journals, and he continues to make significant contributions to automotive engineering.

Publications 📚

📄 Article
🚗 Trajectory Tracking Control Method for Autonomous Vehicles Considering Time-Varying Reference and Steering Delay
👨‍🔬 Authors: Z. Yang, Zhengcai; H. Zhang, Huiquan; L. Ge, Linhe; T. Sun, Tianjun
📖 Journal: Qiche Gongcheng/Automotive Engineering, 2025
🔢 Citations: 0


📄 Article
🤖 Human-like Mechanism Deep Learning Model for Longitudinal Motion Control of Autonomous Vehicles
👨‍🔬 Authors: Z. Gao, Zhenhai; T. Yu, Tong; F. Gao, Fei; R. Zhao, Rui; T. Sun, Tianjun
📖 Journal: Engineering Applications of Artificial Intelligence, 2024
🔢 Citations: 2