Belkacem Bekhiti | Engineering | Best Researcher Award

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Prof. Belkacem Bekhiti | Engineering | Best Researcher Award

Prof. Belkacem Bekhiti | Institute of Aeronautics and Space Studies, University of Blida | Algeria

Dr. Bekhiti Belkacem is a distinguished academic and researcher in control theory, robotics, and aerospace engineering, currently serving as a Lecturer at the Institute of Aeronautics and Space Studies, Blida University 1, Algeria. His expertise spans guidance, navigation, and control systems, integrating theoretical modeling with real-world aerospace applications. He holds a Doctorate in Electrical Engineering with a specialization in Automatic Control from the University of Boumerdes, a Magister in Advanced Control of Complex Systems from the National Polytechnic School, Oran, a Master’s in Automatic Control from the University of Djelfa, and an Engineering degree in Electrical Engineering from Boumerdes. His career includes teaching positions at Blida and Djelfa Universities, collaboration with the Algerian Air Agency, and supervision of advanced student projects in UAVs, satellite control, and robotics. His research focuses on MIMO control, matrix polynomial theory, robotic modeling, nonlinear adaptive control, and intelligent aerospace system design, merging classical automation with artificial intelligence and fractional-order control. He has authored several books and numerous international publications, presented his work at major conferences, and earned recognition for his contributions to intelligent control and aerospace systems. His influence extends across the Algerian and international research communities, where he continues to inspire innovation and academic excellence in modern control and aeronautical engineering.

Profile : Google Scholar 

Featured Publications 

  • Bekhiti, B. (2015). On the theory of λ-matrices based MIMO control system design. Control and Cybernetics.

  • Bekhiti, B. (2017). Intelligent block spectral factors relocation in a quadrotor UAV. International Journal of Scientific Computing (IJSCC).

  • Bekhiti, B. (2018). On λ-matrices and their applications in MIMO control systems design. International Journal of Mathematical and Computational Intelligence (IJMIC).

  • Bekhiti, B. (2020). On the block decomposition and spectral factors of λ-matrices. Control and Cybernetics.

  • Bekhiti, B. (2020). Internal stability improvement of a natural gas centrifugal compressor. Journal of Natural Gas Science and Engineering.

Jingyi Gao | Engineering | Best Researcher Award

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Ms. Jingyi Gao | University of Virginia | United States

Ms. Jingyi Gao | University of Virginia | United States

Jingyi Gao is a Ph.D. candidate in Systems and Information Engineering at the University of Virginia with a 3.75 GPA, focusing on time series prediction, Bayesian probabilistic modeling, and federated learning. She holds an M.S. in Applied Mathematics and Statistics from the Johns Hopkins University (GPA 3.9) and dual bachelor’s degrees in Mathematics–Computer Science and Economics from the University of California, San Diego. Jingyi has extensive teaching experience, serving as a teaching assistant at UVA where she has instructed over 1,000 students across multiple courses in statistical modeling, data mining, AI, and big data systems, and previously supported courses at Johns Hopkins and UC San Diego. She has mentored underrepresented students through the Data Justice Academy and completed research internships at the University of Pittsburgh and Tencent, developing machine learning models for stress detection, healthcare data analysis, and cloud resource forecasting. Jingyi has authored several publications, including work accepted by Pattern Recognition and under review at AAAI and IISE Transactions. Her recent projects involve designing deep latent variable models for ergonomic risk assessment, developing real-time adaptive prediction frameworks for occupational health monitoring, creating federated learning approaches for multi-output Gaussian processes, and modeling behavioral regularity and predictability from multidimensional sensing signals. Combining expertise in machine learning, statistical modeling, and data-driven decision systems, Jingyi aims to advance human-centered intelligent systems through interpretable and privacy-preserving predictive modeling.

Profile: Scopus | Google Scholar

Featured Publications 

Gao, J., Rahman, A., Lim, S., & Chung, S. TimeSets: A real-time adaptive prediction framework for multivariate time series (Manuscript under review at the Association for the Advancement of Artificial Intelligence).

Gao, J., Lim, S., & Chung, S. Gait-based hand load estimation via deep latent variable models with auxiliary information (Manuscript under review at IISE Transactions).

Gao, J., & Chung, S. Federated automatic latent variable selection in multi-output Gaussian processes (Accepted for publication in Pattern Recognition)*.

Gao, J., Yan, R., & Doryab, A. Modeling regularity and predictability in human behavior from multidimensional sensing signals and personal characteristics. Proceedings of the International Conference on Machine Learning and Applications (ICMLA). Institute of Electrical and Electronics Engineers.

Chen, T., Chen, Y., Gao, J., Gao, P., Moon, J. H., Ren, J., … & Woolf, T. B. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv.

Zeng Meng | Engineering | Best Researcher Award

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Prof. Zeng Meng | Engineering | Best Researcher Award

Professor, at Hefei university of technology, China.

Professor Meng Zeng is a leading academic at Hefei University of Technology, specializing in the optimization of uncertain structures, aerospace and civil structural design, and structural topology optimization. With a sharp focus on engineering innovation, Prof. Zeng has guided over 20 funded projects, including prestigious grants from the National Natural Science Foundation of China. Recognized as an Outstanding Youth of Anhui Province, he is celebrated for his dedication to scientific progress. His excellence is marked by receiving two first-class Science & Technology Progress Awards from the Anhui Society of Mechanics. From 2021 to 2024, he has been consistently listed among the world’s top 2% scientists and is a highly cited author in the journal Computers & Structures. 📚 He has authored 90+ SCI papers, including 50+ as the first or corresponding author. His work has received over 4,200 citations, with 8 ESI highly cited papers and 3 hot papers. 🌟

Professional Profile

Scopus

ORCID

🎓 Education

Professor Meng Zeng earned his academic credentials with distinction in civil and structural engineering. Though detailed records of his academic institutions are not publicly specified, his educational background reflects a strong foundation in mechanics and structural design, which paved the way for his current leadership in aerospace and civil engineering innovation. Throughout his education, Prof. Zeng focused on uncertainty modeling, computational mechanics, and optimization techniques, equipping him with the analytical expertise required for cutting-edge structural analysis. His academic training has fostered a mindset geared toward solving real-world engineering problems using theoretical rigor and computational sophistication. 🎓 As an educator and mentor, he now imparts this rich knowledge base to his students and research collaborators at Hefei University of Technology. His journey from a dedicated student to a globally recognized professor exemplifies the impact of solid academic preparation in shaping research excellence. 💼

💼 Experience

Professor Meng Zeng currently serves as a professor at Hefei University of Technology, where he leads research in structural optimization and engineering mechanics. With over 20 funded projects, his contributions span across the National Natural Science Foundation of China (NSFC), including two general and two youth projects. 🏗️ He has applied his research expertise to both aerospace and civil structure applications, combining theory with practical innovations. His role encompasses research leadership, postgraduate supervision, and national-level project management. His accolades from Anhui Society of Mechanics, including two first prizes in scientific and technological progress, affirm his high impact on the engineering community. 🏆 Prof. Zeng also represents China in international research through his involvement in peer-reviewed journals and collaborations. A consistent presence in the top 2% of global scientists (2021–2024), his work shapes modern methodologies in topology optimization and structural resilience under uncertain conditions. 🧠

🔬 Research Interest 

Prof. Meng Zeng’s research interests lie at the intersection of engineering mechanics and computational optimization. His primary focus is on the optimization of uncertain structures, where he develops methods to enhance structural performance despite variations in material properties or loading conditions. 🚀 He is also deeply involved in aerospace and civil structure analysis, contributing to safer and more efficient designs. Prof. Zeng is renowned for his work in structural topology optimization, an area that determines the optimal material layout within a given design space, a key element in lightweight and high-performance structural engineering. 🔧 His research integrates probabilistic methods, finite element analysis, and machine learning algorithms to solve complex, real-world problems. As a thought leader, Prof. Zeng not only advances theoretical mechanics but also offers transformative insights for engineering design under uncertainty, positioning him at the forefront of innovation in applied structural optimization. 📈

🏅 Awards

Professor Meng Zeng’s academic excellence and scientific innovation have earned him numerous accolades. Notably, he was selected as an Outstanding Youth of Anhui Province, a recognition of his early-career contributions to engineering science. 🏆 He has received two First-Class Science and Technology Progress Awards from the Anhui Society of Mechanics, underscoring the high societal and technological value of his work. Between 2021 and 2024, Prof. Zeng was consecutively listed among the world’s top 2% scientists, a global benchmark of research excellence. Additionally, he is a highly cited author in the prestigious journal Computers & Structures, highlighting the global reach and influence of his research. 🌟 These awards are a testament to his impact on both fundamental research and practical engineering applications, positioning him as a top-tier scientist and thought leader in the field of structural mechanics and optimization. 🎖️

📚 Top Noted Publications

Professor Meng Zeng has published over 90 peer-reviewed SCI papers, with more than 50 as first or corresponding author. His work is widely cited, having accumulated over 4,200 Google Scholar citations. He has contributed 8 ESI Highly Cited Papers and 3 Hot Papers, affirming his global academic influence. 🔍 His research often appears in top journals such as:

  1. Reliability-Based Topology Optimization (RBTO):

    • Articles:

      • Data-driven RBTO using extended multiscale FEM and neural networks

      • RBTO for continuum structures with nonlinear dynamics

      • Stress-constrained RBTO with fidelity transformation method

    • Highlights: Use of machine learning (e.g., neural networks), multiscale finite element modeling, and probabilistic analysis to optimize structural performance under uncertainty.

  2. Dynamic and Transient Response in Optimization:

    • Articles:

      • Transient dynamic topology optimization using equivalent static loads

      • Uncertainty-oriented topology optimization of dynamic structures

    • Highlights: Focus on efficient dynamic response prediction and hybrid uncertainties (probabilistic + spatial/random field modeling).

  3. Metaheuristic and Bio-Inspired Algorithms:

    • Article:

      • Starfish Optimization Algorithm (SFOA) – Compared with 100 optimizers.

    • Highlights: Development of novel optimization algorithms inspired by biological behaviors; applied to structural or global optimization problems.

  4. Concurrent Topology and Material Design:

    • Article:

      • Concurrent optimization of topology and fiber orientation under stress constraints

    • Highlights: Combines material orientation (like composite fibers) with shape optimization for optimal mechanical performance.

  5. Uncertainty Quantification Techniques:

    • Article:

      • Weight index-based uniform partitioning of multi-dimensional probability space

    • Highlights: Proposes a novel method to efficiently sample and compute in high-dimensional uncertainty spaces.

  6. Materials & Structural Systems Innovation:

    • Articles:

      • Porous functionally graded composite plates with graphene reinforcements

      • Non-uniform rectangular honeycomb sandwich panel

    • Highlights: Focus on lightweight, high-strength materials and sandwich structures for performance and efficiency.

Conclusion

Professor Meng Zeng is a highly suitable candidate for the Best Researcher Award. His extensive research output, impactful publications, strong citation record, and recognition at national and international levels highlight a career marked by innovation, consistency, and academic leadership. With minor improvements in global engagement and interdisciplinary expansion, he stands out as a role model for excellence in engineering research.

Karla Filian | Engineering | Best Researcher Award

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Mrs Karla Filian |  Engineering |  Best Researcher Award

Graduate student in the Master’s program in Earth Sciences,  at Faculty of Engineering in Earth Sciences, ESPOL Polytechnic University,  Ecuador

Karla Filian Haz is a graduate student pursuing a Master’s in Earth Sciences at ESPOL Polytechnic University. With a background in Mining Engineering, she works as a Project Analyst, contributing to research and academic initiatives in Earth Sciences. Her research focuses on environmental pollution mitigation, water treatment technologies, and sustainable engineering solutions. She has co-authored two indexed journal articles and two conference papers, collaborating with international institutions such as Ghent University and the Mexican Geological Survey. Her work aims to develop innovative solutions for environmental management in mining and water treatment.

Profile:

Academic & Professional Background:

Mining Engineer pursuing a Master’s in Earth Sciences at ESPOL. Currently a Project Analyst, contributing to research, academic initiatives, and program coordination in Earth Sciences. Expertise in event organization, documentation management, and compliance.

Research & Innovations:

  • Research Projects: 4
  • Publications: 2 indexed journal articles, 2 conference papers
  • Citations: h-index: 1, Citations: 2
  • Collaborations: Ghent University (Belgium), Catholic University of Santiago de Guayaquil, Universidad del Pacífico (Ecuador), Mexican Geological Survey (SGM)

Research Areas:

Environmental engineering, pollution mitigation in mining, water treatment technologies, sustainable engineering solutions.

Key Contributions:

Research on environmental pollution, tailing dam risks, and desalination optimization using advanced membranes. Findings contribute to sustainable solutions for water treatment and environmental management in the mining industry.

Publication Top  Notes:

Title: Assessment of Environmental Pollution and Risks Associated with Tailing Dams in a Historical Gold Mining Area of Ecuador
Authors: B. Salgado-Almeida, A. Briones-Escalante, D. Falquez-Torres, E. Peña-Carpio, S. Jiménez-Oyola
Journal: Resources (2024)
Citations: 1

 

Lin Chen | Mechanical Engineering | Best Researcher Award

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Prof. Dr Lin Chen |  Mechanical Engineering | Best Researcher Award

Member of Guangxi Mechanical Engineering Society, Director of Guangxi Computer Users Association, Young and Middle-aged Expert on Intellectual Property Rights in Chinese Universities and Research Institutions at  Guangxi university, China

Lin Chen is a dedicated researcher and educator in mechanical engineering, with expertise in signal processing, control theory, and engineering applications. His research focuses on electrified vehicles, data acquisition, and analysis. Over the past five years, he has secured 23 research grants, including two major projects funded by the National Natural Science Foundation of China (NSFC) on lithium-ion batteries for EVs. Lin has pioneered deep-learning-based control methods for multi-axial electromechanical systems, encompassing sensors, signal processing, optimization, robot control, and CNC systems. He has built and validated complex test rigs through experimental studies. His work has resulted in 41 patents and over 60 journal publications, contributing significantly to energy management and advanced control systems.

Profile:

Academic and Research Focus:

Lin Chen is actively engaged in research and education in mechanical engineering, signal processing, control theory, and engineering applications. His work spans electrified vehicles, data acquisition, and analysis, contributing to advancements in sustainable transportation and smart systems.

Research Grants and Funding:

Over the last five years, Lin Chen has secured 23 research grants as Principal Investigator (PI) and Co-Investigator (CI) in electromechanical systems and lithium-ion battery energy management. Notably, he has led two large-scale projects funded by the National Natural Science Foundation of China (NSFC) focused on lithium-ion batteries for electric vehicles (EVs).

Innovation and Technological Contributions:

Lin Chen has developed deep-learning-based control methods for multi-axial electromechanical systems, integrating sensors, digital signal processing, optimization, robot control, and CNC control. His research also includes building complex test rigs for experimental validation.

Publications and Intellectual Property:

To date, Lin Chen has produced over 60 journal papers and holds 41 patents, reflecting his significant contributions to the field of electromechanical systems and energy management technologies.

Publication Top Notes:

  • Integrative Multi-Omics Approaches for Identifying and Characterizing Biological Elements in Crop Traits: Current Progress and Future Prospects
    International Journal of Molecular Sciences, 2025-02-10
    DOI: 10.3390/ijms26041466

  • Integrative Multiomics Profiling of Passion Fruit Reveals the Genetic Basis for Fruit Color and Aroma
    Plant Physiology, 2024-03-29
    DOI: 10.1093/plphys/kiad640

  • Real-Time Detection of Viruses in Aerosols with Weak Light Imaging Based on Tesla Discharge
    Applied Physics Letters, 2022-08-08
    DOI: 10.1063/5.0104527

  • Biophoton Imaging Evaluation of the Process of Rheumatoid Arthritis in Rats
    Natural Science, 2021
    DOI: 10.4236/ns.2021.1310037

  • In Vivo Imaging of Biophoton Emission in the Whole Brain of Mice
    Natural Science, 2021
    DOI: 10.4236/ns.2021.139033

  • Spectral Blueshift of Biophotonic Activity and Transmission in the Ageing Mouse Brain
    Brain Research, 2020-12
    DOI: 10.1016/j.brainres.2020.147133