Andrea Cynthia Santos | Operational research | Research Excellence Distinction Award

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Prof. Dr. Andrea Cynthia Santos | Operational research | Research Excellence Distinction Award

Le Havre Normandie University, Engineering Institute of Logistics (ISEL) | France

Prof. Dr. Andréa Cynthia Santos is a leading scholar in Computer Science whose research lies at the intersection of Operations Research, urban systems, and large-scale disaster management. Her work focuses on developing advanced optimization models, algorithms, and decision-support approaches that address complex sociotechnical challenges in industrial, natural, and health-related crisis scenarios. She is widely recognized for contributions to integrated routing, scheduling, robust optimization, network design, drone-based search strategies, and humanitarian logistics in post-disaster environments. Her research is characterized by strong interdisciplinary engagement, combining computational optimization, artificial intelligence, and systems engineering to improve resilience and sustainability in modern cities. She has produced a substantial body of scientific work, including numerous international journal articles, book chapters, and conference contributions, and has played a key role in organizing international scientific events. Beyond her research activity, she has demonstrated significant leadership in academic administration and scientific strategy, including directing major institutional programs, steering research initiatives, and contributing to national and international committees. She has led multiple research projects across national, regional, industrial, and international collaborations, supported by multidisciplinary teams. Her supervision of PhD candidates, postdoctoral researchers, and master’s students reflects her strong commitment to academic mentorship and capacity building. She has also served as an evaluator for global research organizations and participated in expert panels spanning science, technology, and innovation. Her professional experience includes roles in academic governance, digital transformation, international relations, curriculum development, and research program management, positioning her as an influential figure in the fields of operations research, logistics innovation, and sustainable urban systems.

Profiles: Scopus | Orcid

Featured Publications

Barbalho, T. J., Jiménez Laredo, J. L., & Santos, A. C. (2025). The resource-constrained project scheduling problem for risk reduction after industrial disasters involving dangerous substances. OR Spectrum. Advance online publication.

Coco, A. A., Duhamel, C., Santos, A. C., & Haddad, M. N. (2024). Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters. Networks, (July 2024).

Duhamel, C., & Santos, A. C. (2024). The strong network orientation problem. International Transactions in Operational Research.

Haddad, M. N., Santos, A. C., Duhamel, C., & Coco, A. A. (2023). Intelligent drone swarms to search for victims in post-disaster areas. Sensors, 23(23), 9540.

De Freitas, C. C., Aloise, D. J., Fontes, F. F. C., Santos, A. C., & Menezes, M. S. (2023). A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours. OR Spectrum, 45, 1–26.

Maham Mujahid | Applied Mathematics | Best Researcher Award

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Ms. Maham Mujahid | Applied Mathematics | Best Researcher Award

The Islamia University of Bahwalapur | Pakistan

Ms. Maham Mujahid is a mathematics researcher whose work centers on advanced fluid mechanics, with a particular emphasis on viscous and non-Newtonian fluid flows, nanofluid behavior, heat and mass transfer processes, and rheological analysis in complex geometries such as corrugated and curved channels. Her research integrates analytical and computational techniques, including perturbation methods and numerical simulations, to investigate pressure-driven flows, magnetized and hybrid nanofluids, nonlinear fluid models, slip and convective constraints, porous media effects, and entropy production in multiphase or biological flow environments. She has contributed significantly to the understanding of transport phenomena by publishing in high-impact international journals, covering themes such as Casson, Jeffrey, and Carreau-type fluids, thermal radiation, viscous dissipation, permeability, and metachronal wave motion. Her scholarly contributions also extend to hybrid nanofluid modeling based on Yamada–Ota and Xue frameworks, demonstrating her command of emerging areas in thermofluid systems. Alongside her research activities, she has gained substantial teaching experience at both undergraduate and graduate levels in courses related to calculus, linear algebra, integral equations, differential equations, numerical analysis, and fluid mechanics, consistently integrating theoretical knowledge with practical scientific applications. She is skilled in Mathematica, MATLAB, and various computational and productivity tools, enabling precise modeling, visualization, and academic communication. Her broader interests include computational fluid dynamics, non-Newtonian rheology, and the study of thermal and multiphase transport in engineered and natural systems, reflecting a strong commitment to advancing mathematical and physical sciences through research, teaching, and continuous professional development.

Profiles: Scopus | Google Scholar

Featured Publications

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2024). A study on the pressure‐driven flow of magnetized non‐Newtonian Casson fluid between two corrugated curved walls of an arbitrary phase difference. Heat Transfer, 53(8), 4510–4527.

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2024). Rheological study of water-based Cu nanofluid between two corrugated curved walls under constant pressure gradient. Alexandria Engineering Journal, 106, 691–703.

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2025). Flow of hybrid nanofluids between two permeable corrugated curved walls using Yamada–Ota and Xue models with variable viscosity. Physics of Fluids, 37(2).

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2024). Rheological analysis of pressure-driven Jeffrey fluid flow between corrugated porous curved walls with slip constraints. AIP Advances, 14(9).

Rafiq, M. Y., Abbas, Z., Munawar, F., Mujahid, M., & Durrani, A. (2025). Exploring entropy production in metachronal wave motion of Carreau fluid in a channel under lubrication hypothesis. International Journal of Thermofluids, 27, 101198.

Jinglin Li | Computer Science | Best Researcher Award

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Mr. Jinglin Li | Computer Science | Best Researcher Award

Engineer | China National Nuclear Corporation | China

Li Jinglin is a researcher specializing in intelligent systems, reinforcement learning, and energy-efficient technologies for industrial and service applications. He holds advanced degrees in Instrument Science and Technology, Electrical Engineering, and Vehicle Engineering with a focus on new energy systems. His research encompasses the development of intelligent interactive service technologies for elderly care, optimization of energy-harvesting wireless sensor networks, and multi-task scheduling for energy-secured unmanned vehicles. He has led projects on digital twin platform technologies and vertical displacement control of nuclear fusion plasma, applying deep reinforcement learning to enhance system performance and replace traditional control methods. Li has extensive experience in algorithm design, including MATLAB-based reinforcement learning, adaptive dynamic programming, and multi-level exploration deep Q-network scheduling, with applications in optimal microgrid transmission, mobile charging sequence scheduling, and network monitoring. His work has resulted in multiple first-author publications in high-impact journals covering reinforcement learning, wireless sensor networks, and energy management, as well as conference contributions in control and automation. Beyond his technical expertise, he demonstrates strong analytical, problem-solving, and team collaboration skills, with experience in summarizing complex research findings and implementing practical solutions. Li actively engages in academic presentations and has earned recognition for his research achievements. In addition to his research, he maintains leadership roles in university sports teams, reflecting his commitment to teamwork, discipline, and resilience. His professional approach combines a proactive mindset, logical thinking, and a dedication to advancing intelligent and sustainable technological solutions across both industrial and service domains.

Profile: Scopus

Featured Publications

Li, J. (2024). A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks. Ad Hoc Networks, 156, 103431.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks. Journal of Ambient Intelligence and Humanized Computing, 15(6), 2869–2881.

Li, J. (2023). Mobile charging sequence scheduling for optimal sensing coverage in wireless rechargeable sensor networks. Applied Sciences, 13(5), 2840.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. IEEE Transactions on Network and Service Management.

Li, J. (2024). A swarm deep reinforcement learning based on-demand mobile charging-scheduling and charging-time control joint algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. Expert Systems with Applications.