Lei Shi | Cosmology | Young Researcher Award

Mr. Lei Shi | Cosmology | Young Researcher Award

Associate Professor | Universite Le Havre Normandie | France

Mr. Lei Shi is an accomplished theoretical chemist whose work centers on quantum dynamics, molecular simulations, and the development of advanced computational frameworks for understanding fundamental processes in molecular physics. His research spans high-dimensional quantum simulations, atom surface scattering, hydrogen-bond dynamics in water clusters, and electron cation interactions relevant to cold plasma environments. He has contributed significantly to pushing the limits of realistic quantum simulations, notably achieving a landmark full-dimensional quantum dynamics study using an ab initio neural-network potential energy surface, enabling direct comparison with cutting-edge experimental data. His work integrates time-dependent quantum mechanical methods, machine-learning potential energy surfaces, and tensor decomposition techniques to explore complex dynamical behavior with exceptional accuracy. He has collaborated widely with leading groups in quantum dynamics, contributing to the refinement of potential energy surfaces, the implementation of multilayer multiconfiguration approaches, and the interpretation of spectroscopic signatures in molecular clusters. His publications highlight advances in canonical polyadic finite-basis representation, quantum and classical scattering dynamics, and quantum mechanical transport properties, earning recognition such as editor selections and journal cover features. His professional experience includes conducting and guiding computational research, mentoring doctoral researchers, and contributing to the molecular simulation community through active collaboration networks. Through his combined expertise in quantum theory, numerical modeling, and interdisciplinary scientific exchange, he has established himself as a rising scientist contributing impactful insights into molecular motion, energy transfer, and the quantum nature of matter.

Profiles: Orcid | Google Scholar

Featured Publications

Shi, L., Schröder, M., Meyer, H.-D., Peláez, D., Wodtke, A. M., Golibrzuch, K., Schönemann, A.-M., Kandratsenka, A., & Gatti, F. (2025). Full quantum dynamics study for H atom scattering from graphene. The Journal of Physical Chemistry A.

Bindech, O., Gatti, F., Mandal, S., Marquardt, R., Shi, L., & Tremblay, J. C. (2024). The mean square displacement of a ballistic quantum particle. Molecular Physics.

Shi, L., Schröder, M., Meyer, H.-D., Peláez, D., Wodtke, A. M., Golibrzuch, K., Schönemann, A.-M., Kandratsenka, A., & Gatti, F. (2024). Erratum: “Quantum and classical molecular dynamics for H atom scattering from graphene” [J. Chem. Phys. 159, 194102 (2023)]. The Journal of Chemical Physics.

Shi, L., Schröder, M., Meyer, H.-D., Peláez, D., Wodtke, A. M., Golibrzuch, K., Schönemann, A.-M., Kandratsenka, A., & Gatti, F. (2023). Quantum and classical molecular dynamics for H atom scattering from graphene. The Journal of Chemical Physics, 159, 194102.

Nadoveza, N., Panadés-Barrueta, R. L., Shi, L., Gatti, F., & Peláez, D. (2023). Analytical high-dimensional operators in canonical polyadic finite basis representation (CP-FBR). The Journal of Chemical Physics, 158, (publication date: 2023-03-21).

Jinglin Li | Computer Science | Best Researcher Award

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.

Boulais YOVOGAN | Biology and Life Sciences | Best Researcher Award

Dr. Boulais YOVOGAN | Biology and Life Sciences | Best Researcher Award

Medical entomologist and Geneticist Centre de Recherche Entomologique de Cotonou (CREC) Benin

Dr. Boulais Yovogan is a passionate medical entomologist and geneticist based at the Centre de Recherche Entomologique de Cotonou (CREC), Benin. With extensive field and academic experience, Dr. Yovogan has contributed significantly to vector surveillance, malaria control, and genetic resource management. His dynamic role includes research, field coordination, and mentoring students at the University of Abomey-Calavi (UAC). He is also actively involved in international collaborations with institutions like the London School of Hygiene & Tropical Medicine (LSHTM).

Profile

Google Scholar

🎓 Education

Dr. Yovogan earned his PhD in Life Sciences (2024) from the University of Abomey-Calavi (UAC), Benin, specializing in medical entomology and genetic resources. His academic journey blends biological sciences with practical research experience, making him a key contributor to tropical disease research in West Africa.

🧪 Experience

With hands-on expertise in entomological surveillance and vector control, Dr. Yovogan led the transmission component of the New Net Project (2019–2024), under UNITAID and LSHTM. At CREC, he contributes to national and international research initiatives. Simultaneously, he supports teaching at the Zoology Department of UAC, guiding students in technical laboratory handling and tool usage.

🔬 Research Interest

Dr. Yovogan’s core interests lie in vector-borne diseases, especially malaria transmission, insecticide resistance, and vector surveillance methods. He evaluates innovative vector control tools and contributes to improving entomological data collection techniques for enhanced disease control strategies.

🏆 Awards

Although early in his decorated career, Dr. Yovogan is a strong contender for recognition due to his impactful research. His nomination for the Best Researcher Award under the International Popular Scientist Awards underscores his scientific leadership and contributions to public health and vector control.

📚 Publications Top Notes: 

Dr. Boulais Yovogan has authored 33 peer-reviewed articles in reputed journals including Malaria Journal, Plos One, Parasites & Vectors, and BMC Public Health. His research has been cited 313 times, with an h-index of 8 and i10-index of 7. A recent publication includes:

“Evaluation of a dual-active ingredient LLIN against malaria vectors in Benin”
📖 Malaria Journal (2025)Cited by 5 articles

Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidal nets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a …

Assessing the efficacy of two dual-active ingredients long-lasting insecticidal nets for the control of malaria transmitted by pyrethroid-resistant vectors in Benin: study …

Pre-intervention characteristics of the mosquito species in Benin in preparation for a randomized controlled trial assessing the efficacy of dual active-ingredient long-lasting …

WHO cone bio-assays of classical and new-generation long-lasting insecticidal nets call for innovative insecticides targeting the knock-down resistance mechanism in Benin

Malaria burden and associated risk factors in an area of pyrethroid-resistant vectors in Southern Benin

Pyrethroid Resistance Intensity in Anopheles gambiae s.l. from Different Agricultural Production Zones in Benin, West Africa

Effectiveness of pyriproxyfen-pyrethroid and chlorfenapyr-pyrethroid long-lasting insecticidal nets (LLINs) compared with pyrethroid-only LLINs for malaria control in the third …

What can be learned from the residual efficacy of three formulations of insecticides (pirimiphos-methyl, clothianidin and deltamethrin mixture, and clothianidin alone) in large …

 

Leading Edge in Academic Excellence Award

Leading Edge in Academic Excellence Award

Introduction:

Step onto the path of unparalleled academic distinction with the 'Leading Edge in Academic Excellence Award.' This prestigious accolade honors those who redefine the standards of excellence in academia, pushing the boundaries of knowledge and inspiring the next generation of thinkers.

Award Eligibility:

Open to accomplished individuals of any age, the Leading Edge in Academic Excellence Award seeks candidates with exceptional qualifications and a proven track record of transformative publications that contribute significantly to their field.

Qualification and Publications:

Candidates must showcase a distinguished academic background, demonstrating excellence in research and a commitment to advancing knowledge through impactful publications. This award recognizes the leaders who are shaping the future of their disciplines.

Recurrence and Evaluation Criteria:

An annual recognition, this award focuses on sustained excellence in academia. Evaluation criteria include the impact of research, contributions to the academic community, and the overall influence on the advancement of knowledge.

Submission Guidelines:

Nominees are invited to submit a comprehensive biography, an abstract summarizing their groundbreaking research, and supporting files that highlight the depth and impact of their academic contributions. The submission process is designed to be accessible and straightforward.

Recognition and Community Impact:

Beyond the honor of receiving the Leading Edge in Academic Excellence Award, recipients gain visibility for their outstanding contributions. The award emphasizes not only academic prowess but also the positive impact on the broader community and the next generation of scholars.

Biography and Abstract:

Craft a compelling biography that captures your academic journey and motivations. The abstract should provide a concise yet powerful overview of your groundbreaking research, showcasing its significance and innovation.

Outstanding Academic Achievement Award

Outstanding Academic Achievement Award

Introduction:

Welcome to a celebration of excellence! The Outstanding Academic Achievement Award honors those whose dedication to academic pursuit has set them apart as true scholars. Join us in recognizing and applauding the brilliance that shapes the future of academia.

Eligibility:

  • Age Limit: None
  • Qualifications: Open to all academic disciplines
  • Publications: Not mandatory
  • Requirements: Demonstrate exceptional academic achievement and commitment

Evaluation Criteria:

Candidates will be evaluated based on academic performance, research contributions, and their impact on the academic community.

Submission Guidelines:

  • Submit a detailed biography
  • Include an abstract of academic achievements
  • Attach supporting files highlighting significant contributions
  • Submission deadline: [Specify Date]

Recognition:

Recipients will be honored at a prestigious ceremony, gaining visibility in the academic community.

Community Impact:

This Outstanding Academic Achievement Award aims to inspire and motivate academic excellence, creating a positive impact on the educational landscape.