Mr. Wenpei Lu | Thermodynamics | Research Excellence Award

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Mr. Wenpei Lu | Thermodynamics | Research Excellence Award

University of Paris-Saclay | France

Mr. Wenpei Lu is an emerging researcher in thermal physics and energy engineering, with a strong interdisciplinary background spanning microfluidics, heat transfer, multiphase flow, and enhanced oil recovery systems. His work focuses on experimental and numerical investigations that advance the understanding of droplet-based microfluidics and convective heat transfer, contributing to high-efficiency energy utilization and improved subsurface engineering processes. He has carried out influential research on Steam-Assisted Gravity Drainage (SAGD), foam-assisted recovery systems, steam flow control technologies, and the behavior of nanoparticle-stabilized foams in porous media. Across his projects, he integrates advanced experimentation with computational simulation tools such as ANSYS Fluent, MATLAB, and CMG-STARS to evaluate flow behaviors, optimize thermal mechanisms, and develop innovative solutions for oilfield challenges. His scientific output includes studies on reservoir stimulation, stress-sensitive pressure behavior, steam distribution optimization, and capillary-driven flow mechanisms. According to Scopus, his research has received citations from 32 documents, demonstrating growing recognition in the field, and he holds an h-index of 1. His Google Scholar profile similarly reflects active citation growth aligned with his early career stage. With a research foundation that connects thermal sciences, micro-scale flow physics, and applied petroleum engineering, Mr. Lu is well-positioned for impactful contributions to the scientific community. His innovative methodologies, interdisciplinary approach, and commitment to advancing energy-efficient recovery technologies align strongly with the objectives of the Research Excellence Award, making him a suitable candidate whose work demonstrates both scientific rigor and real-world relevance.

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Scopus

Featured Publications

Cao, C., Zhou, F., Cheng, L., Liu, S., Lu, W., & Wang, Q. (2021). A comprehensive method for acid diversion performance evaluation in strongly heterogeneous carbonate reservoirs stimulation using CT. Journal of Petroleum Science and Engineering, 203, 108614.

Cao, C., Cheng, L., Zhang, X., Jia, P., & Lu, W. (2021). A comprehensive model integrating stress sensitivity for pressure transient behavior study on the two-zone for offshore loose sandstone reservoirs. SPE Middle East Oil and Gas Show and Conference, Bahrain.

Prof. Dr. Ahmed Ghezal | Difference Equations | Research Excellence Award

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Prof. Dr. Ahmed Ghezal | Difference Equations | Research Excellence Award

Researcher | Abdelhafid Boussouf University of Mila | Algeria

Prof. Dr. Ahmed Ghezal is a highly accomplished researcher in mathematical statistics, stochastic processes, and difference equations, widely recognized for his meaningful contributions to nonlinear time series modeling and Markov-switching frameworks. His work bridges theoretical rigor with applied mathematical modeling, focusing on the probabilistic properties, asymptotic inference, and stability analysis of advanced stochastic systems. With a research output that spans influential studies on bilinear, threshold, GARCH-type, and fuzzy difference equations, he has significantly advanced the understanding of periodicity, regime switching, and nonlinear dynamics in statistical models. His scholarly impact is evidenced through strong citation metrics across global indexing platforms, including Scopus with 342 citations from 93 documents and an h-index of 13, and Google Scholar with 304 citations, an h-index of 10, and an i10-index of 10. His Web of Science metrics further highlight his influence, with 214 citations, 41 publications, and an h-index of 10, reflecting consistent contributions to high-quality mathematical and statistical journals. His research excellence is marked by rigorous analytical methods, strong theoretical proofs, and innovative extensions to existing models, making his work foundational for scholars exploring nonlinear dynamics and complex stochastic structures. Given his impactful publication record, strong citation profile, and substantial contributions to statistical theory and difference equations, Prof. Ghezal demonstrates outstanding suitability for the Research Excellence Award, representing a researcher whose contributions continue to shape contemporary developments in mathematical modeling and stochastic analysis.

Profile

Scopus | Google Scholar

Featured Publications

Bibi, A., & Ghezal, A. (2018). Markov-switching bilinear−GARCH models: Structure and estimation. Communications in Statistics – Theory and Methods, 47(2), 307–323. (Cited 21)

Bibi, A., & Ghezal, A. (2015). On the Markov-switching bilinear processes: Stationarity, higher-order moments and beta-mixing. Stochastics, 87(6), 889–912. (Cited 21)

Ghezal, A. (2023). Note on a rational system of 4k+4-order difference equations: Periodic solution and convergence. Journal of Applied Mathematics and Computing, 69(2), 2207–2215. (Cited 20)

Ghezal, A. (2021). QMLE for periodic time-varying asymmetric GARCH models. Communications in Mathematics and Statistics, 9(3), 273–297. (Cited 16)

Ghezal, A. (2024). Spectral representation of Markov-switching bilinear processes. São Paulo Journal of Mathematical Sciences, 18(1), 459–479. (Cited 15)

Prof. Qingna Li | Numerical Optimization | Best Researcher Award

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Prof. Qingna Li | Numerical Optimization | Best Researcher Award

Professor in Numerical Optimization | Beijing Institute of Technology | China

Prof. Qingna Li is a leading scholar in computational mathematics and optimization, widely recognized for her influential contributions to numerical algorithms, matrix optimization, support vector machines, hyperparameter tuning through bilevel optimization, large-scale MIMO detection, and correlation matrix modeling. Her research bridges theoretical rigor with practical impact, with applications spanning signal processing, machine learning, classification systems, hypergraph matching, molecular conformation, and radar surveillance analysis. She has produced a strong body of high-impact work, reflected in a growing citation footprint: Google Scholar reports 489 citations, an h-index of 10, and 11 i10-index publications, while Scopus documents also reflect sustained citation growth and scholarly impact across optimization, numerical analysis, and applied machine learning. Professor Li has developed several widely used MATLAB packages for SVM models, correlation matrix estimation, and MIMO detection, each grounded in her peer-reviewed publications and enabling significant uptake by researchers and practitioners. Her work on derivative-free methods, semismooth Newton frameworks, quadratic programming relaxations, and data-driven wavelet approaches has strengthened modern optimization theory and advanced computational tools for high-dimensional problems. She is an active researcher with a documented record of collaborative publications in top journals such as SIAM Journal on Optimization, IMA Journal of Numerical Analysis, Computational Optimization and Applications, and Applied and Computational Harmonic Analysis. With a demonstrated ability to generate impactful methods, lead research groups, and contribute meaningfully to the global optimization community, Professor Qingna Li is exceptionally well suited for a Best Researcher Award, given her innovation, citation influence, research leadership, and sustained contributions to computational optimization.

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Google Scholar

Featured Publications

Li, Q., & Li, D. H. (2011). A class of derivative-free methods for large-scale nonlinear monotone equations. IMA Journal of Numerical Analysis, 31(4), 1625–1635. Citations: 192.

Li, Q., & Qi, H. (2011). A sequential semismooth Newton method for the nearest low-rank correlation matrix problem. SIAM Journal on Optimization, 21(4), 1641–1666. Citations: 50.

Yin, J., & Li, Q. (2019). A semismooth Newton method for support vector classification and regression. Computational Optimization and Applications, 73(2), 477–508. Citations: 33.

Zhao, P. F., Li, Q., Chen, W. K., & Liu, Y. F. (2021). An efficient quadratic programming relaxation-based algorithm for large-scale MIMO detection. SIAM Journal on Optimization, 31(2), 1519–1545. Citations: 9.

Pang, T., Li, Q., Wen, Z., & Shen, Z. (2020). Phase retrieval: A data-driven wavelet frame-based approach. Applied and Computational Harmonic Analysis, 49(3), 971–1000. Citations: 11.

Mr. Muheki Jonas | Computational Physics | Research Excellence Award

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Mr. Muheki Jonas | Computational Physics | Research Excellence Award

University of Houston | United States

Mr. Jonas Muheki is a rising researcher in physics specializing in biological, medical, and photonic sciences, with a focus on developing opto-electromagnetic modalities for cancer therapy and advanced biosensing technologies. His work spans computational modeling, metamaterial design, plasmonics, and machine learning-enhanced diagnostics, contributing to innovations in solar absorbers, terahertz sensors, and high-sensitivity biomedical detection platforms. He has produced impactful research recognized through citations on major indexing platforms, including Scopus and Google Scholar, reflecting strong scientific visibility. His interdisciplinary expertise positions him as a strong candidate for innovation-focused awards in physics, biomedical engineering, and advanced sensing technologies.

Citation Metrics (Google Scholar)

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Featured Publications

Prof. Xin Tang | Bioengineering | Best Researcher Award

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Prof. Xin Tang | Bioengineering | Best Researcher Award

Associate Professor | University of Florida | United States

Prof. Xin Tang is a leading researcher in biomechanics, mechanobiology, biophysics, and cancer mechanomedicine, focusing on how mechanical forces, calcium signaling, YAP pathways, and microenvironmental cues shape cancer progression. His work integrates advanced imaging, nano and microfabrication, genome editing, and protein engineering to develop all-optical mechanobiology platforms and functional interrogation systems for tumor biology. He also contributes to theoretical modeling that links molecular mechano-sensitivity with tissue-scale behavior. His research bridges fundamental biophysics with translational cancer science, providing new insights into drug resistance, mechanotransduction, and therapeutic targeting, while shaping interdisciplinary advances in soft matter and cellular mechanics.

Citation Metrics (Google Scholar)

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Featured Publications

Mr. Sumit Hassan Eshan | Smart Antenna | Young Researcher Award

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Mr. Sumit Hassan Eshan | Smart Antenna | Young Researcher Award

Lead Technical Engineer | Contessa Solutions and Consultants Ltd | Bangladesh

Mr. Sumit Hassan Eshan is a Bangladeshi researcher and engineering professional whose scholarly contributions span smart antennas, nanomaterial-based biomedical sensing, wireless power transfer, terahertz communication, and advanced materials for next-generation wireless systems. His research integrates materials science with electromagnetic design, focusing on nano-engineered antennas using graphene, carbon nanotubes, and transition-metal dichalcogenides for medical diagnostics, on-body sensing, and 6G terahertz applications. Eshan has authored 12 peer-reviewed publications, including four journal papers and eight conference papers across respected SCI and Scopus-indexed venues. One of his works was highlighted on the front cover of a Q1 journal, showcasing the novelty of his contributions to nanomaterial-enabled antennas for cancer detection. His citation record demonstrates his growing academic influence, with 76 citations, an h-index of 6, and an i10-index of 4 on Google Scholar, and 54 citations with an h-index of 5 on Scopus. Eshan’s research covers interdisciplinary domains such as wireless power amplification, nanomaterial spin-coating techniques, biomedical on-body antenna systems, and efficient THz structures for future communication technologies. His continuous engagement as a peer reviewer for prominent engineering journals further reflects his expertise in antenna design, wireless communication, applied electromagnetics, and emerging materials. With a strong foundation in experimental and simulation-based design using CST Studio Suite, Eshan aims to advance innovative antenna technologies that bridge healthcare diagnostics and next-generation wireless systems. His scholarly record positions him as a promising early-career researcher contributing impactful solutions at the intersection of engineering, materials science, and biomedical sensing.

Profiles

Scopus | ORCID | Google Scholar | LinkedIn | ResearchGate

Featured Publications

Hasan, R. R., Jasmine, J., Saleque, A. M., Eshan, S. H., Tusher, R. T. H., Zabin, S., Nowshin, N., Rahman, M. A., & Tsang, Y. H. (2023). Spin coated multi-walled carbon nanotube patch antenna for breast cancer detection. Advanced Materials Technologies, 8(20), 1–13. (Q1, cited)

Anowar, T. I., Hasan, R. R., Eshan, S. H., & Foysal, M. (2025). Enhanced wireless power transfer system using integrated RF amplification. Results in Engineering. (Q1, cited)

Hasan, R. R., Saha, S., Eshan, S. H., Basak, R., Ivan, M. N. A. S., Saleque, A. M., Tusher, R. T. H., Zabin, S., Rahman, M. A., & Tsang, Y. H. (2024). A compact spin-coated graphene UWB antenna for breast tumor detection. Advanced Engineering Materials. (Q1, cited)

Roy, A., Bhuiyan, M. R., Islam, M. A., Saha, P., Eshan, S. H., Hasan, R. R., & Basak, R. (2024). Tungsten disulfide based wearable antenna in terahertz band for sixth generation applications. Telecommunication Computing Electronics and Control, 22(2), 545–555. (Scopus, cited)

Lia, L., Zishan, M. S. R., Eshan, S. H., & Hasan, R. R. (2024). Graphene based terahertz patch antenna for breast tumor detection. Telecommunication Computing Electronics and Control, 22(5), 1073–1082. (Scopus, cited)

Prof. Na Guo | Computational Chemistry | Research Excellence Award

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Prof. Na Guo | Computational Chemistry | Research Excellence Award

Professor | Guang’an Institute of Technology | China

Prof. Guo Na is a computational and physical chemist whose research bridges quantum chemistry, materials science, and nanomaterials for next-generation catalysis and energy technologies. Her work focuses on unraveling atomic-scale mechanisms that govern chemical reactivity, electrocatalytic conversion, and low-dimensional material behavior, with strong emphasis on first-principles modeling, computational simulations, and structure–property relationships in two-dimensional systems. She has made influential contributions to the design of atomically precise catalysts, including graphene-based platforms, single-atom catalysts, metal–carbon monolayers, and photo-responsive nanomaterials. Her studies have advanced understanding of electrochemical hydrogenation, CO2/CO reduction, nitrogen reduction, and molecular reactivity at metal–surface interfaces, providing pathways for sustainable chemical transformations. Across her career, she has collaborated extensively on multidisciplinary investigations published in leading journals such as Nature, Nature Communications, JACS, Angewandte Chemie, and Advanced Materials. Her research has also shed light on the physics of low-dimensional materials, optoelectronic modulation, and nanoscale device behavior, supported by advanced computational modeling of charge transfer, catalytic activation, and surface dynamics. Her publication record is reflected in Scopus with 107 citations from 107 documents and an h-index of 2, alongside additional visibility on Google Scholar, highlighting both the breadth and collaborative nature of her work. Overall, her research portfolio integrates theoretical rigor with practical applications in catalysis, clean energy, electronic materials, and molecular engineering, contributing significantly to material innovation and sustainable chemistry.

Publication Profile

Scopus

Featured Publications

Liu, X., Hu, C., Guo, N., Li, X., & Liu, B. (2025). Ruthenium-catalyzed electrochemical ketone hydrogenation to secondary alcohols under ambient conditions. Angewandte Chemie International Edition.

Tang, S., Guo, N., Chen, C., Yao, B., Liu, X., Ma, C., Liu, Q., Ren, S., He, C., & Liu, B. (2025). Electrochemical alkyne semi-hydrogenation via proton-coupled electron transfer on Cu(111) surface. Angewandte Chemie.

Yang, H., Guo, N., Zhang, C., Wang, L., et al. (2025). Scalable H2O2 production via O2 reduction using immobilized vanadyl phthalocyanine. Angewandte Chemie International Edition.

Su, J., Guo, N., Lu, J., Zhang, C., et al. (2024). Intelligent synthesis of magnetic nanographenes via chemist-intuited atomic robotic probe. Nature Synthesis.

Lyv, P., Guo, N., Lu, J., Zhang, C., et al. (2024). Air-stable wafer-scale ferromagnetic metallo-carbon nitride monolayer. Journal of the American Chemical Society.

 

Mr. Yixiang Xu | Computational fluid mechanics | Research Excellence Award

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Mr. Yixiang Xu | Computational fluid mechanics | Research Excellence Award

Lecturer | School of Mechanical Engineering, Suzhou University of Science and Technology | China

Mr. Xu Yixiang is a computational fluid dynamics researcher specializing in multiphase flow simulation, interfacial dynamics, and coupled numerical algorithms. His work centers on advancing the ISPH-FVM coupling framework, a hybrid method that integrates the Lagrangian strengths of incompressible smoothed particle hydrodynamics with the efficiency and stability of Eulerian finite volume solvers. Through this unified approach, he has developed improved surface-tension discretization schemes, enhanced mapping techniques, and robust interface-tracking models capable of handling large density ratios and complex topological evolutions. His contributions significantly advance the simulation accuracy of bubble rising, coalescence, droplet deformation, free-surface interaction, and thermo-magnetohydrodynamic phenomena. Xu’s research outputs demonstrate strong recognition in the field, reflected in his Scopus citation metrics of 78 citations, 9 indexed documents, and an h-index of 5, alongside additional citations recorded in Google Scholar. Supported by national research funding, his studies provide computational tools that deepen understanding of fluid behavior in engineering processes such as heat transfer, magnetic-field-driven flows, and advanced multiphase systems. His collaborative works with leading laboratories further reinforce the scientific impact of his ISPH-FVM advancements, which have been adopted to model complex flow behaviors in viscous liquids, conductive fluids, and ferrofluids. Xu’s continued innovations contribute to bridging meshless and grid-based computational paradigms, offering scalable and accurate methodologies for challenging fluid-mechanics applications.

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Scopus 

Featured Publications 

Xu, Y., Yang, G., et al. (2023). A three-dimensional ISPH-FVM coupling method for simulation of bubble rising in viscous stagnant liquid. Ocean Engineering, 278, 114497. (Citations: 18)

Xu, Y., Yang, G., et al. (2023). Improvement of surface tension discrete model in the ISPH-FVM coupling method. International Journal of Multiphase Flow, 160, 104347. (Citations: 14)

Xu, Y., Yang, G., et al. (2021). A coupled SPH–FVM method for simulating incompressible interfacial flows with large density difference. Engineering Analysis with Boundary Elements, 128, 227–243. (Citations: 22)

Xu, Y. (2026). Numerical investigation of bubble-induced heat transfer under external electric field based on ISPH-FVM coupling method. International Journal of Heat and Fluid Flow, 117, 110132. (Citations: 6)

Xu, Y., Yang, G., et al. (2024). Comparison of surface tension models for the simulation of two-phase flow in an ISPH-FVM coupling method. European Journal of Mechanics – B/Fluids, 105, 57–96. (Citations: 10)

Prof. Xuejuan Chen | Differential Equations | Research Excellence Award

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Prof. Xuejuan Chen | Differential Equations | Research Excellence Award

Associate Professor | Jimei University | China

Prof. Xuejuan Chen is a distinguished computational mathematician whose research focuses on high-order numerical algorithms, fractional differential equations, nonlocal models, and advanced simulation methods for complex physical systems. Her work bridges theoretical numerical analysis with real-world applications, particularly in anomalous diffusion, optimal control, groundwater pollution modeling, and fractional dynamical systems. She has made significant contributions to the development of accelerated spectral deferred correction methods, high-precision finite difference schemes, distributed-order fractional models, and efficient Crank–Nicolson–based algorithms for nonsmooth data. Prof. Chen’s research stands out for its emphasis on accuracy, stability, and computational efficiency, offering scalable techniques for solving challenging nonlocal and fractional PDEs. With a strong publication record in leading journals such as Computers & Mathematics with Applications, Computer Methods in Applied Mechanics and Engineering, and Numerical Mathematics: Theory, Methods and Applications, she continues to advance the frontier of computational mathematics. Her scholarly influence is reflected in Scopus metrics, including 197 citations, 14 documents, and an h-index of 6, supported by contributions cited across fractional calculus, scientific computing, and applied mathematics. She also maintains a strong research presence on Google Scholar, where her citation count and impact continue to grow. Prof. Chen’s work not only advances numerical theory but also provides practical, high-accuracy computational tools for scientists and engineers working on nonlocal and fractional modeling problems in physics, engineering, and environmental science.

Publication Profile

Scopus | ORCID

Featured Publications

Chen, A., Chen, X., Yan, Y., & Guo, W. (2026). A corrected Crank–Nicolson scheme for the time fractional parabolic integro-differential equation with nonsmooth data. Mathematics and Computers in Simulation, 242, 279–296.

Wang, J., Chen, X., & Chen, J. (2025). A high-precision numerical method based on spectral deferred correction for solving the time-fractional Allen–Cahn equation. Computers & Mathematics with Applications, 180, 1–27.

Yang, Z., Chen, X., Chen, Y., & Wang, J. (2024). Accurate numerical simulations for fractional diffusion equations using spectral deferred correction methods. Computers & Mathematics with Applications, 153, 123–129.

Chen, X., Mao, Z., & Karniadakis, G. E. (2022). Efficient and accurate numerical methods using the accelerated spectral deferred correction for solving fractional differential equations. Numerical Mathematics: Theory, Methods and Applications, 15(4), 876–902.

Chen, X., Zeng, F., & Karniadakis, G. E. (2017). A tunable finite difference method for fractional differential equations with non-smooth solutions. Computer Methods in Applied Mechanics and Engineering, 318, 193–214.

Prof. Younghun Kwon | Quantum Computer | Research Excellence Award

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Prof. Younghun Kwon | Quantum Computer | Research Excellence Award

Professor | Hanyang University | South Korea

Prof. Younghun Kwon is a distinguished quantum physicist whose research has significantly advanced the foundations and applications of quantum information science, quantum computation, and artificial intelligence–driven quantum technologies. As the head of the Mathematical Science Lab at Hanyang University, he has made pioneering contributions to quantum state discrimination, quantum error correction, sequential state discrimination, quantum communication, quantum correlations, coherence theory, and the hardware implementation of superconducting quantum computing systems. His work spans both fundamental theoretical insights and practical architectures that form the building blocks of future quantum computers. Notably, he has proposed new hardware structures for superconducting quantum processors, introduced innovative quantum error-correcting strategies for biased-noise systems, and demonstrated groundbreaking results revealing how classical prior probabilities can induce nonlocal quantum effects. His achievements also include foundational solutions to multi-qubit and multi-party state discrimination problems, which have remained open for decades. His research integrates advanced mathematical modeling, experimental implementation frameworks, and AI-augmented quantum processing methods. With an extensive publication record covering high-impact journals, international conferences, and patented technologies, his work continues to influence quantum information theory worldwide. According to Scopus, his research output includes 23 documents with 119 citations and an h-index of 6, while Google Scholar reflects a broader research impact with significantly higher citation counts across quantum information science and hybrid quantum systems. His scholarly trajectory demonstrates sustained leadership in merging quantum mechanics, computation, and intelligent systems to accelerate the realization of practical large-scale quantum technologies.

Profile

Scopus | ORCID

Featured Publications

Ha, D., & Kwon, Y. (2023). Complete analysis to minimum-error discrimination of four mixed qubit states with arbitrary prior probabilities. Quantum Information Processing, 22, 67. (Citations: 3)

Kim, Y., Kang, J., & Kwon, Y. (2023). Design of quantum error correcting code for biased error on heavy-hexagon structure. Quantum Information Processing, 22, 230. (Citations: 5)

Namkung, M., & Kwon, Y. (2020). Understanding various types of unambiguous discrimination in view of coherence distribution. Entropy, 22, 1422. (Citations: 5)

Namkung, M., & Kwon, Y. (2019). Almost minimum-error discrimination of N-ary weak coherent states by Jaynes-Cummings Hamiltonian dynamics. Scientific Reports, 9, 19664. (Citations: 13)

Ha, D., & Kwon, Y. (2013). Complete analysis for three-qubit mixed-state discrimination. Physical Review A, 87, 062302. (Citations: 52)