Rahman Ullah Khan | Applied Mathematics | Best Researcher Award

Posted on by Math Scientist

Dr. Rahman Ullah Khan | Applied Mathematics | Best Researcher Award

Ph.D at Quaid e Azam University Islamabad, Pakistan

Dr. Rahman Ullah Khan is an accomplished mathematician specializing in fractional differential equations and fixed point theory. 🎓 Currently pursuing his Ph.D. at Quaid-i-Azam University, Islamabad, his research focuses on the existence, uniqueness, and stability of solutions to complex fractional systems. His work combines rigorous mathematical theory with computational techniques, utilizing tools like MATLAB and Mathematica for numerical solutions. 💻 Dr. Khan has published several notable papers in high-impact journals, including Boundary Value Problems and Physica Scripta, showcasing his expertise in advanced mathematical analysis. 📚 He actively contributes to the academic community by presenting his findings at international conferences and engaging in teaching roles, mentoring future mathematicians. 🌍 Beyond his research, Dr. Khan has demonstrated leadership in organizing seminars and events to promote mathematical education and global collaborations. His dedication to advancing the field of pure mathematics, combined with his passion for knowledge-sharing, makes him a standout researcher. 🌟

Professional Profile 

Google Scholar
Scopus Profile

Education 📖🎓

Dr. Rahman Ullah Khan holds a Ph.D. in Mathematics from Quaid-i-Azam University, Islamabad, Pakistan, where he is currently conducting research on fractional differential equations and fixed point theory. 🎓 He completed his M.Phil. in Mathematics at the same institution, with an exceptional GPA, demonstrating his strong foundation in applied and pure mathematics. 📚 Throughout his academic journey, Dr. Khan’s thesis focused on solving fractional differential equations using advanced mathematical techniques, which showcased his commitment to solving complex mathematical problems.

Professional Experience ✨

Dr. Khan’s professional journey includes serving as a teaching assistant at Quaid-i-Azam University, where he taught fractional differential equations and applied mathematics. 📘 He has also worked as a private math tutor, helping students grasp complex mathematical concepts. Additionally, he has held leadership roles, including Vice President of the Quaidian Mathematical Society, and has organized seminars to enhance the academic community’s knowledge of mathematics. 🌐

Research Interests 🧮

Dr. Khan’s research interests are primarily centered on fractional differential equations and fixed point theory. 🧠 He focuses on solving fractional systems using fixed point theorems to establish solution existence, uniqueness, and stability. His work applies these concepts to real-world problems, using computational methods such as MATLAB to simulate and analyze results. 💡 His research aims to bridge the gap between theoretical mathematics and its applications in areas like engineering and physics, with an emphasis on making mathematical models more efficient and practical. 🔍

Awards and Honors 🏆

Dr. Khan has received recognition for his exceptional academic performance, including high GPAs in both his M.Phil. and Ph.D. programs. 🌟 His contributions to mathematics are widely respected, and his research articles have been published in reputable journals like Boundary Value Problems and Physica Scripta. 🏆 He has also been invited to present his findings at several international conferences, where his work on fractional differential equations has been well-received. 🌍

Conclusion🌍📚

Dr. Rahman Ullah Khan is a promising and passionate mathematician with a strong academic background and significant research contributions in the field of fractional differential equations and fixed point theory. 📈 His deep knowledge, combined with computational skills and leadership in the academic community, makes him an asset to the field of mathematics. With a commitment to advancing mathematical solutions for real-world problems, Dr. Khan is poised for further success in both research and teaching. 🌟 His dedication to knowledge-sharing and solving complex mathematical problems continues to inspire future generations of mathematicians.

Publications Top Notes

📘 On qualitative analysis of a fractional hybrid Langevin differential equation with novel boundary conditions
Authors: G Ali, RU Khan, Kamran, A Aloqaily, N Mlaiki
Year: 2024
Citation: Boundary Value Problems 2024 (1), 62
Source: Boundary Value Problems

🔍 The study of nonlinear fractional boundary value problems involving the p-Laplacian operator
Authors: AU Khan, RU Khan, G Ali, S Aljawi
Year: 2024
Citation: Physica Scripta 99 (8), 085221
Source: Physica Scripta

🌐 The Existence and Stability of Integral Fractional Differential Equations
Authors: RU Khan, IL Popa
Year: 2025
Citation: Fractal and Fractional 9 (5), 295
Source: Fractal and Fractional

📝 Some novel existence and stability results for a nonlinear implicit fractional differential equation with non-local boundary conditions
Authors: RU Khan, IL Popa
Year: 2025
Citation: Partial Differential Equations in Applied Mathematics 13, 101132
Source: Partial Differential Equations in Applied Mathematics

💡 New Results on the Stability and Existence of Langevin Fractional Differential Equations with Boundary Conditions
Authors: RU Khan, M Samreen, G Ali, IL Popa
Year: 2025
Citation: Fractal and Fractional 9 (2), 127
Source: Fractal and Fractional

🔬 Existence and Stability of Implicit Fractional Differential Equations Involving the p-Laplacian Operator and Their Applications
Authors: RU Khan, M Samreen, G Ali, IL Popa
Year: 2024
Citation: Physica Scripta
Source: Physica Scripta

🧮 On the qualitative analysis of the boundary value problem of the Ψ-Caputo implicit fractional pantograph differential equation
Authors: RU Khan, M Samreen, G Ali, I Argyros
Year: 2024
Citation: Journal of Applied Math 2 (6), 1977-1977
Source: Journal of Applied Math

Shunli Wang | Mathematical Modeling | Lifetime Achievement Award

Posted on by Math Scientist

Prof. Dr. Shunli Wang | Mathematical Modeling | Lifetime Achievement Award

Academic Dean at Inner Mongolia University of Technology, China

Professor Dr. Shunli Wang is an internationally esteemed leader in New Energy and Energy Storage Systems 🔋, serving as Executive Vice President of the Smart Energy Storage Research Institute and Academic Dean at Inner Mongolia University of Technology 🎓. A Fellow of the Institution of Engineering and Technology (IET) and an academician of the Russian Academy of Natural Sciences 🌍, he ranks among the world’s top 2% scientists according to Stanford University 📊. Dr. Wang has authored over 258 SCI-indexed papers 📚, secured 63 patents and standards ⚙️, and directed 56 significant national and international research projects 🧪. His pioneering work in battery modeling, fault diagnosis, and intelligent control has shaped the future of smart grid applications and energy storage technologies 🚀. Renowned for bridging academia and industry, he has led transformative collaborations and cultivated top-tier talent 👨‍🏫. Dr. Wang’s contributions are driving global progress toward sustainable, intelligent energy solutions 🌱🌐.

Professional Profile

Google Scholar
Scopus Profile
ORCID Profile

Education 🎓📘

Professor Dr. Shunli Wang laid a strong academic foundation with a Ph.D. in Control Theory and Control Engineering from Northeastern University, China, where he honed his expertise in systems modeling and intelligent energy control. His early education was marked by academic excellence, leading to prestigious scholarships and research opportunities 🌟. Dr. Wang’s academic journey included postdoctoral research and collaborative projects with globally renowned institutions, further enriching his interdisciplinary knowledge base 🧠. His education not only equipped him with advanced technical skills but also ignited his passion for sustainable energy systems and automation ⚙️. With a commitment to lifelong learning, he continues to evolve through global academic exchanges and cutting-edge workshops, fostering innovative solutions in new energy technologies 🔬. Dr. Wang’s educational pathway is a model of intellectual rigor, strategic focus, and forward-thinking vision, laying the groundwork for his profound contributions to academia and industry alike 🌐.

Professional Experience 🏢🧑‍🏫

Dr. Shunli Wang brings over two decades of impactful leadership in academia and industry. Currently, he is the Executive Vice President of the Smart Energy Storage Research Institute and Academic Dean at the Inner Mongolia University of Technology 🏫. Previously, he held pivotal roles in major research institutions and high-tech enterprises, leading teams on energy storage, intelligent control, and fault diagnosis projects 🔋. His career highlights include directing 56 national and international research projects and mentoring hundreds of graduate students 👨‍🎓👩‍🎓. As a respected academician of the Russian Academy of Natural Sciences and IET Fellow 🌍, Dr. Wang bridges the gap between theoretical innovation and real-world application. He actively consults on policy and industrial strategy for smart grids and battery management systems ⚡. His professional journey exemplifies versatility, vision, and dedication, placing him at the forefront of global advancements in energy and automation technologies 🚀.

Research Interests 🔬⚡

Professor Dr. Shunli Wang’s research spans cutting-edge domains in new energy systems and advanced control engineering. His primary focus lies in battery modeling, intelligent energy storage systems, smart grid applications, fault diagnosis, and predictive control technologies 🔋🧠. He has developed innovative algorithms for real-time system optimization, helping to improve the reliability, safety, and efficiency of large-scale energy infrastructures 🌐. Dr. Wang’s work often integrates artificial intelligence, machine learning, and digital twin technologies, creating adaptive and intelligent control systems that meet the demands of future energy needs 🤖📡. With over 258 SCI-indexed publications and dozens of patents, his research has significantly influenced policy, industry standards, and academic curricula. Passionate about bridging fundamental science and applied technology, he continuously fosters interdisciplinary collaborations that advance energy sustainability, automation, and environmental resilience 🌱. His research is a powerful catalyst for a cleaner, smarter, and more connected energy future 💡.

Awards and Honors 🏅🎖

Dr. Shunli Wang’s outstanding contributions have earned him numerous prestigious awards and honors worldwide. He is ranked among the top 2% of global scientists by Stanford University, recognizing his influential research output and academic impact 📈. As a Fellow of the Institution of Engineering and Technology (IET) and a member of the Russian Academy of Natural Sciences, his work is celebrated for its global reach and transformative outcomes 🌍. Dr. Wang has received multiple national science and technology awards, innovation prizes, and academic leadership honors 🏆. His patents and publications have been widely cited, further solidifying his status as a thought leader in smart energy and control systems ⚙️📘. He is frequently invited to serve on editorial boards, keynote panels, and international think tanks, reinforcing his role as a visionary in sustainable innovation. These accolades underscore not only his academic excellence but also his enduring commitment to technological progress and societal betterment 🌐✨.

Conclusion 🌟📌

Professor Dr. Shunli Wang stands as a beacon of excellence in the realm of intelligent energy systems, blending deep academic insight with practical innovation. His multifaceted contributions—from education and groundbreaking research to international collaborations and mentorship—have profoundly shaped the global energy landscape 🔋🌍. With a visionary approach and a relentless pursuit of excellence, Dr. Wang continues to influence emerging trends in energy sustainability, smart grid design, and AI-powered control systems ⚡🤖. His legacy is built on innovation, impact, and integrity, serving as an inspiration to scholars, engineers, and policymakers worldwide 🧑‍🎓🌱. As the world navigates the complexities of energy transition and climate resilience, thought leaders like Dr. Wang are lighting the path forward—empowering new generations to innovate boldly and act wisely 🌟🚀. His story is not only one of personal achievement but also of global significance in shaping a smarter, greener future for all 💡🌐.

Publications Top Notes

Online state of charge estimation for lithium-ion batteries using improved fuzzy C-means sparrow backpropagation algorithm

  • Authors: Hai Nan, Wang Shunli, Cao Wen, Blaabjerg Frede, Fernandez Carlos

  • Year: 2025

  • Source: Journal of Energy Storage ⚡🔋

    • Innovative fuzzy-based methods for battery state estimation.

A high-speed recurrent state network with noise reduction for multi-temperature state of energy estimation of electric vehicles lithium-ion batteries

  • Authors: Zou Yuanru, Shi Haotian, Cao Wen, Wang Shunli, Nie Shiliang, Chen Dan

  • Year: 2025

  • Source: Energy 🚗🔋

    • Advancements in multi-temperature battery state of energy estimation.

Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction

  • Authors: Wang Shunli, Zhou Heng, Fernandez Carlos, Blaabjerg Frede

  • Year: 2025

  • Source: Energy 💡🔋

    • Optimized algorithms for accurate battery performance estimation.

Joint state of charge and state of energy estimation of special aircraft lithium-ion batteries by optimized genetic marginalization-extended particle filtering

  • Authors: Wang Shunli, Luo Tao, Hai Nan, Blaabjerg Frede, Fernandez Carlos

  • Year: 2025

  • Source: Journal of Energy Storage ✈️🔋

    • Enhancing battery estimation for aviation applications.

Improved volumetric noise-adaptive H-infinity filtering for accurate state of power estimation of lithium-ion batteries with multi-parameter constraint considering low-temperature influence

  • Authors: Wang Shunli, Hu Bohan, Zhou Lei, Fernandez Carlos, Blaabjerg Frede

  • Year: 2025

  • Source: Journal of Energy Storage ❄️🔋

    • State-of-power estimation under extreme conditions.

Battery pack capacity estimation based on improved cooperative co-evolutionary strategy and LightGBM hybrid models using indirect health features

  • Authors: Zhou Yifei, Wang Shunli, Li Zhehao, Feng Renjun, Fernandez Carlos

  • Year: 2025

  • Source: Journal of Energy Storage 🔋💡

    • Capacity estimation with advanced hybrid modeling techniques.

Enhanced transformer encoder long short-term memory hybrid neural network for multiple temperature state of charge estimation of lithium-ion batteries

  • Authors: Zou Yuanru, Wang Shunli, Cao Wen, Hai Nan, Fernandez Carlos

  • Year: 2025

  • Citations: 1

  • Source: Journal of Power Sources 🧠🔋

    • A hybrid approach for temperature-aware battery state estimation.

A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification

  • Authors: Chen Lei, Wang Shunli, Chen Lu, Fernandez Carlos, Blaabjerg Frede

  • Year: 2025

  • Source: Journal of Energy Storage 📊🔋

    • A multi-scale estimator for energy and battery performance.

Multiple measurement health factors extraction and transfer learning with convolutional-BiLSTM algorithm for state-of-health evaluation of energy storage batteries

  • Authors: Shi Zinan, Zhu Chenyu, Liang Huishi, Wang Shunli, Yu Chunmei

  • Year: 2025

  • Citations: 1

  • Source: Ionics 🔋💡

    • Health evaluation using advanced neural networks for energy storage.

Battery lumped fractional-order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time-varying core temperature conditions

  • Authors: Zeng Jiawei, Wang Shunli, Takyi-Aninakwa Paul, Fernandez Carlos, Guerrero Josep Manuel Ramos

  • Year: 2025

  • Citations: 1

  • Source: International Journal of Circuit Theory and Applications ⚡❄️

    • State-of-charge estimation with adaptive temperature modeling.

Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries

  • Authors: Wang Shunli, Fan Y., Jin S., Takyi-Aninakwa P., Fernandez C.

  • Year: 2023

  • Citations: 371

  • Source: Reliability Engineering & System Safety 🔋🔮

    • Improved life prediction with noise-adaptive neural networks.

An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage influence

  • Authors: Wang S., Takyi-Aninakwa P., Jin S., Yu C., Fernandez C., Stroe D.I.

  • Year: 2022

  • Citations: 309

  • Source: Energy ⚡🔋

    • A more accurate prediction of battery state-of-charge over its lifecycle.

Transforming knowledge systems for life on Earth: Visions of future systems and how to get there

  • Authors: Fazey I., Schäpke N., Caniglia G., Hodgson A., Kendrick I., Lyon C., Page G.

  • Year: 2020

  • Citations: 306

  • Source: Energy Research & Social Science 🌍🌱

    • Future sustainability through knowledge system transformations.

 

Aleksandar Senić | Mathematical Engineering | Best Researcher Award | 2039

Posted on by Math Scientist

Mr. Aleksandar Senić | Mathematical Engineering | Best Researcher Award

Assistant Lecturer at Faculty of Civil Engineering, Belgrade, Serbia

Aleksandar Senić is a researcher and expert in risk management for infrastructure projects, specializing in the application of fuzzy logic and machine learning to predict delays and cost overruns. As an Assistant Lecturer at the Faculty of Civil Engineering, University of Belgrade, he actively contributes to research on risk assessment and decision-making optimization. He has authored eight journal papers in indexed databases and a book on infrastructure project management. With over 30 industry projects, he plays a key role in translating research into practical solutions. As Director of the Construction Management Sector at Koridori Srbije, he oversees infrastructure projects worth €5 billion, ensuring efficient execution and risk mitigation. His work on hybrid risk assessment models and clustering risk factors has improved project planning and preventive strategies. Through collaborations with leading researchers, his contributions have gained international recognition, influencing best practices in construction risk management and decision-making in large-scale projects.

Professional Profile

Scopus Profile
ORCID Profile 

Education

Aleksandar Senić holds a strong academic background in civil engineering and risk management. He earned his Bachelor’s and Master’s degrees from the Faculty of Civil Engineering, University of Belgrade, specializing in construction management and infrastructure project risk assessment. His postgraduate research focused on applying fuzzy logic and machine learning techniques to predict delays and cost overruns in large-scale infrastructure projects. He pursued a Ph.D. in Civil Engineering, further advancing his expertise in hybrid risk assessment models integrating artificial intelligence and expert systems for optimizing decision-making in construction. Throughout his academic journey, he actively collaborated with leading researchers, contributing to cutting-edge methodologies in risk quantification and mitigation. His educational foundation, combined with extensive industry exposure, enables him to bridge the gap between theoretical research and practical application, making significant advancements in infrastructure project management. His academic credentials and research output have positioned him as a key contributor to the field of risk management in civil engineering.

Professional Experience

Aleksandar Senić is an Assistant Lecturer at the Faculty of Civil Engineering, University of Belgrade, and a leading expert in risk management for infrastructure projects. With extensive experience in applying fuzzy logic and machine learning, he specializes in predicting delays and cost overruns in large-scale road construction. As the Director of the Construction Management Sector at Koridori Srbije, he oversees the execution of €5 billion infrastructure projects, ensuring efficient delivery and risk mitigation. His expertise extends to industry consultancy, having contributed to over 30 projects focused on risk assessment and preventive strategies. He has authored a book on infrastructure project management and published 8 journal papers in indexed databases. His research, widely cited internationally, integrates hybrid risk assessment models to optimize decision-making. A member of the Serbian Chamber of Engineers, he actively collaborates with leading researchers, advancing best practices in construction risk management and preventive methodologies.

Research Interest

Aleksandar Senić’s research interests focus on advancing risk management methodologies in infrastructure projects through the integration of machine learning, fuzzy logic, and expert-driven models. His work aims to enhance predictive analytics for cost overruns and time extensions in road construction, enabling more efficient project planning and execution. He specializes in hybrid risk assessment models, combining statistical techniques with artificial intelligence to improve decision-making in large-scale infrastructure development. Additionally, his research explores preventive measures in project management, optimizing risk quantification frameworks to minimize uncertainties and enhance project resilience. His contributions also extend to construction project complexity analysis, where he clusters risk factors to prioritize interventions, reducing budget deviations and schedule delays. Through collaborations with leading researchers and industry professionals, he strives to develop innovative strategies for risk mitigation, ensuring sustainable and cost-effective infrastructure development while bridging the gap between academic research and practical engineering applications.

Award and Honor

Aleksandar Senić is a distinguished researcher and industry expert in risk management for infrastructure projects, recognized for his significant contributions to the field of construction engineering. As an Assistant Lecturer at the Faculty of Civil Engineering, University of Belgrade, and Director of the Construction Management Sector at Koridori Srbije, he has played a pivotal role in advancing methodologies for risk assessment, predictive modeling, and decision-making optimization. His research integrates machine learning, fuzzy logic, and expert surveys to enhance risk evaluation, leading to improved efficiency in large-scale infrastructure projects. With numerous publications in high-impact journals, his work has gained international recognition, influencing best practices in risk mitigation. His leadership in managing projects worth €5 billion underscores his practical expertise. Through active collaboration with leading researchers and contributions to industry projects, he continues to shape the future of infrastructure risk management, making him a deserving candidate for prestigious academic and professional honors.

Conclusion

Aleksandar Senić is a distinguished researcher in risk management for infrastructure projects, combining academic expertise with extensive industry experience. His contributions to integrating machine learning and fuzzy logic into risk assessment models have significantly advanced methodologies for predicting delays and cost overruns in large-scale construction projects. As a lecturer at the University of Belgrade and Director at Koridori Srbije, he bridges the gap between research and real-world applications, overseeing infrastructure projects worth €5 billion. With eight journal publications, a book, and collaborations with esteemed researchers, his work has gained international recognition. While his citation index and h-index indicate a growing impact, further engagement in high-impact research, patents, and editorial roles could enhance his profile. His expertise, leadership, and commitment to innovation in construction risk management make him a strong candidate for the Best Researcher Award, reflecting both his scholarly excellence and his contributions to improving infrastructure project outcomes.

Publications Top Noted

  • Prioritization of Preventive Measures: A Multi-Criteria Approach to Risk Mitigation in Road Infrastructure Projects
    • Authors: Aleksandar Senić, Marija Ivanović, Momčilo Dobrodolac, Zoran Stojadinović
    • Year: 2025
  • A Comprehensive Analysis of Road Crashes at Characteristic Infrastructural Locations: Integrating Data, Expert Assessments, and Artificial Intelligence
    • Authors: T. Ivanišević, M. Vujanić, A. Senić, A. Trifunović, S. Čičević
    • Year: 2024
  • Predicting Extension of Time and Increasing Contract Price in Road Infrastructure Projects Using a Sugeno Fuzzy Logic Model
    • Authors: Aleksandar Senić, Momčilo Dobrodolac, Zoran Stojadinović
    • Year: 2024
  • Development of Risk Quantification Models in Road Infrastructure Projects
    • Authors: Aleksandar Senić, Momčilo Dobrodolac, Zoran Stojadinović
    • Year: 2024
  • Evaluating the Road Environment Through the Lens of Professional Drivers: A Traffic Safety Perspective
    • Authors: Aleksandar Senić
    • Year: 2024
  • Early Highway Construction Cost Estimation: Selection of Key Cost Drivers
    • Authors: N. Simić, N. Ivanišević, Ð. Nedeljković, A. Senić, Z. Stojadinović, M. Ivanović
    • Year: 2023
  • Assessment of Concrete Compressive Strength Using Different Maturity Functions: Case Study
    • Authors: Dragan Bojović, Nevena Bašić, Ksenija Janković, Aleksandar Senić
    • Year: 2018
  • Determination of the In Situ Coefficient of Friction and Imperfection of Prestressing Cables
    • Authors: Dragan Bojović, Bojan Aranđelović, Ksenija Janković, Aleksandar Senić, Marko Stojanović
    • Year: 2017
  • Značaj Arhitektonskog Koncepta za Rentabilnost Projekata
    • Authors: Aleksandar Senić
    • Year: 2014