Mahendra Gaikwad | Machine Learning | Best Researcher Award

Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.

Zihan Deng | Artificial Intelligence | Best Researcher Award

Dr. Zihan Deng | Artificial Intelligence | Best Researcher Award

Harbin Institute of Technology, China

Zihan Deng is a young and accomplished researcher in the field of imaging technology and computational tomography, with a strong foundation in deep learning and artificial intelligence. With a robust academic background and an array of interdisciplinary experiences, Deng has made significant contributions through high-impact publications, competitive grants, and patents. His expertise lies at the intersection of optical instrumentation and medical image analysis, and he continues to actively engage in scientific exploration with promising results.

Profile

Orcid

Education

Deng completed his undergraduate studies in Computer Science and Technology at Harbin Engineering University (2019–2023), ranking in the top 5% of his class. His academic curriculum included rigorous coursework in mathematics and computer science, scoring consistently above 90 in core subjects. He was subsequently recommended for direct admission into the graduate program at Harbin Institute of Technology, where he is currently pursuing his Master’s degree at the Institute of Ultra-Precision Optical Instrument Engineering under the mentorship of Professor Junning Cui and Academician Jiubin Tan. His research spans CT reconstruction, deep learning-based image enhancement, and X-ray detection technologies.

Experience

Deng has accumulated diverse experience through internships and collaborative projects. He served in leadership roles within student organizations and academic competitions, including receiving awards in national-level modeling and software contests. He undertook summer research at Tsinghua University’s IDG/McGovern Brain Research Institute and was later selected to join Germany’s PTB “Chief Engineer Class” as a visiting scholar. Professionally, he interned with Chengdu Shuzhilian Technology and Guangzhou CVTE, where he contributed to image processing and video enhancement projects. He has also played key roles in multimillion-yuan research collaborations with institutions like CGN Research Institute and GF High-End Semiconductor Imaging Systems.

Research Interest

Deng’s research interests revolve around imaging technology, deep learning, and CT reconstruction methods. He focuses on developing advanced algorithms for sparse-angle computed tomography, artifact reduction, and multi-view image correction using neural networks. His work integrates domain-specific knowledge from instrumentation science with state-of-the-art machine learning frameworks to improve image quality in both medical diagnostics and industrial inspection. He also investigates beam hardening correction and reconstruction under large field-of-view (FOV) conditions, addressing challenges in high-precision imaging systems.

Award

Over the course of his academic journey, Deng has received 11 scholarships and numerous accolades. These include five first-class and two second-class academic scholarships from Harbin Engineering University, the prestigious Xiaomi Scholarship, and the Outstanding Youth League Member Award. His undergraduate thesis on sparse-angle CT reconstruction was selected as an Excellent Graduation Project (top 2%). He has also won national-level awards in competitions such as the Mathematical Modeling Contest and the English Proficiency Championship.

Publication

Deng has authored or co-authored several influential papers in prestigious journals and conferences. His representative publications include:

  1. Deng Z., Wang Z., et al. (2024). “COO-DuDo: Computation Overhead Optimization Methods for Dual-Domain Sparse-View CT Reconstruction”, Expert Systems with Applications (JCR Q1, IF=7.5, in press) – cited in advanced CT algorithm research.

  2. Deng Z., Wang Z., Lin L., Wang S., Cui J. (2024). “Research on the Effectiveness of Multi-View Slice Correction Technology Based on Deep Learning in High-Pitch Spiral Scanning Reconstruction”, Journal of X-Ray Science and Technology (JCR Q2, IF=3.0) – applied in spiral CT systems.

  3. Wang Z.#, Deng Z.#, Liu F., et al. (2023). “OSNet & MNetO for Linear Computed Tomography in Multi-Scenarios”, IEEE Transactions on Instrumentation and Measurement (JCR Q1, IF=5.6) – widely cited in instrumentation imaging.

  4. Deng Z., Deng K., Wang Z., et al.. “Small Class Discussion-Based Teaching in Instrumentation Education”, The International Journal of Education – cited in engineering education reform discussions.

  5. Li Z., Li K., Deng Z., et al. (2024). “Assessment of Sheetlet Thickness in Human Left Ventricular Free Wall Using X-ray Phase-Contrast Microtomography”, Medical Image Analysis (JCR Q1, IF=10.9, accepted) – applied in cardiovascular research.

  6. Deng Z., Wang Z., Lin L., et al. (2025). “Computation Overhead Optimization Dual-Domain Network for Sparse-View CT Reconstruction”, ICASSP 2025 (CCF-B Conference) – in review, expected to support efficient CT image pipelines.

  7. Deng Z., Wang Z., Lin L., Wang S. “Hel-MUNet: Mamba-Unet with Helical Encoding for Clinical High Pitch Helical CT Reconstruction”, MICCAI 2025 (under review) – aligned with cutting-edge clinical imaging methods.

Conclusion

Zihan Deng exemplifies the next generation of research professionals driving innovation in imaging and artificial intelligence. Through a blend of strong theoretical foundation, hands-on project experience, and impactful publications, he has demonstrated exceptional capability in solving complex technical problems. With continued guidance under leading scholars and global exposure, Deng is well-positioned to become a prominent figure in the advancement of smart medical imaging and intelligent instrumentation.

Jia Kaiewei | Artificial Intelligence | Best Scholar Award

Dr. Jia Kaiewei | Artificial Intelligence | Best Scholar Award

Professor at Liaoning Technical University, Huludao, China

Kaiwei Jia is an accomplished academician and researcher currently serving as a Professor and Doctoral Supervisor in the field of Management Science and Engineering. He also holds the role of Vice Dean at the School of Business Administration, Liaoning Technical University. His academic journey is marked by extensive contributions to teaching, research, and institutional development. As a core member of the Liaoning Provincial Teaching Guidance Committee for Finance, he plays a significant role in shaping the financial education framework in the region. With a background in Economics and Statistics, Professor Jia has emerged as a thought leader in financial econometrics and policy research. His career is defined by a blend of theoretical insight and empirical rigor, and he has guided numerous graduate and doctoral students in their academic endeavors. Through his sustained commitment to academic excellence and administrative leadership, he has made substantial contributions to the academic community and the broader field of finance and economics.

Profile

Scopus

Education

Kaiwei Jia’s educational background is deeply rooted in economics and statistics. He earned his Ph.D. in Economics after completing a rigorous postgraduate program that emphasized macroeconomic policy, financial analysis, and quantitative methods. Subsequently, he undertook postdoctoral research in Statistics, where he refined his understanding of data interpretation, econometric modeling, and the application of statistical methodologies to economic problems. This interdisciplinary training has provided him with a comprehensive toolkit for analyzing complex economic phenomena. His academic progression reflects a strong emphasis on research-driven education, equipping him with both theoretical and practical skills. His transition from postgraduate studies to postdoctoral research marked a significant shift in his academic career, allowing him to delve deeper into areas such as financial econometrics, risk modeling, and empirical policy analysis.

Experience

Throughout his career, Professor Jia has maintained an unwavering commitment to teaching and mentoring. He has designed and delivered undergraduate, master’s, and doctoral-level courses in Econometrics, Financial Risk Management, Financial Econometrics, and Financial Data Analysis. His lectures are known for their analytical depth and emphasis on real-world application, which have earned him the respect of both peers and students. Beyond the classroom, he has played a pivotal role in curriculum development and academic governance at Liaoning Technical University. As Vice Dean, he has led several institutional initiatives aimed at enhancing academic quality and fostering innovation in finance education. Additionally, his membership in the Liaoning Provincial Teaching Guidance Committee for Finance has enabled him to influence regional academic standards, ensuring that finance education remains aligned with contemporary global developments.

Research Interest

Professor Jia’s research interests span a diverse array of topics within economics and finance. He focuses on financial stability and risk management, particularly the dynamics of financial contagion and systemic risk. His work explores the governance and risk prevention mechanisms in financial institutions, combining institutional theory with quantitative modeling. Additionally, he is deeply engaged in the study of monetary policy theory and methodology, emphasizing both the rules-based and discretionary approaches to macroeconomic regulation. His research extends to econometric methods, where he utilizes advanced statistical techniques to analyze financial and economic data. More recently, he has contributed to emerging areas such as green finance and climate finance, investigating how environmental factors intersect with financial risk and investment decisions. His multidisciplinary research approach integrates macroeconomic theory, quantitative analysis, and policy insights.

Award

In recognition of his scholarly achievements and academic leadership, Professor Jia has received several prestigious awards. He was honored with the First Prize in the 7th Liaoning Provincial Outstanding Achievement Award in Statistical Sciences, which acknowledges innovative contributions in statistical research. He also received the Second Prize in the Liaoning Provincial Philosophy and Social Science Achievement Award for his impactful work in economics and financial policy. These accolades reflect both the quality and societal relevance of his research, highlighting his role as a leading scholar in his field. His award-winning work has contributed to enhancing the understanding of financial risk, policy formulation, and statistical analysis at both regional and national levels.

Publication

Kaiwei Jia has published more than 30 academic papers in respected journals indexed by SSCI and CSSCI. His recent works reflect his ongoing dedication to cutting-edge research. In 2023, he co-authored “Did the ‘double carbon’ policy improve the green total factor productivity of iron and steel enterprises? A quasi-natural experiment based on carbon emission trading pilot,” published in Frontiers in Energy Research, exploring policy impact through econometric analysis. In the same year, he contributed to Frontiers in Psychology with “Digital financial and banking competition network: Evidence from China,” which examined competitive dynamics using network models. His 2022 publications include “Construction and empirical of investor sentiment evaluation system based on partial least squares” and “Empirical research of risk correlation based on Copula function method,” both appearing in the Journal of Liaoning Technical University (Natural Science Edition). These studies utilized advanced statistical tools to analyze investor behavior and risk correlation. Another 2022 work titled “Spatiotemporal Evolution of Provincial Carbon Emission Network in China,” published on SSRN, tackled environmental finance issues using spatial network methods. These publications not only reflect his diverse expertise but also have been cited by multiple articles, indicating his work’s influence within the academic community.

Conclusion

In summary, Professor Kaiwei Jia’s academic career is characterized by a strong dedication to education, a robust portfolio of interdisciplinary research, and impactful contributions to financial policy and risk management. His dual expertise in economics and statistics has allowed him to bridge theoretical frameworks with empirical application, making his research both rigorous and relevant. Through his teaching, he has nurtured the next generation of economists and financial analysts, while his administrative leadership has helped shape academic standards in finance education. His scholarly output and recognition through awards reflect a sustained contribution to the academic and policy-making community. Professor Jia continues to explore innovative themes in green finance and systemic risk, ensuring that his research remains at the forefront of addressing contemporary economic challenges.

Rajan Bhatt | Artificial Intelligence | Excellence Award (Any Scientific field)

Dr. Rajan Bhatt | Artificial Intelligence | Excellence Award (Any Scientific field)

Associate Professor| Punjab Agricultural University, Ludhiana | India

Dr. Rajan Bhatt is a Senior Soil Scientist at PAU-Krishi Vigyan Kendra, Amritsar, Punjab, India. With extensive expertise in soil physics, water management, and sustainable agriculture, he has dedicated over two decades to advancing soil science research. His contributions include innovative techniques for soil moisture management, resource conservation, and the application of artificial intelligence in agriculture. Recognized globally for his work, Dr. Bhatt has received numerous prestigious awards, reflecting his commitment to scientific excellence and rural development.

Profile

Scopus

Education

Dr. Bhatt holds a Ph.D. in Soil Science (2015) from Punjab Agricultural University, Ludhiana, with distinction, focusing on soil physics and water management. His academic journey began with a B.Sc. in Agriculture (2000) from Guru Nanak Dev University, followed by an M.Sc. in Soil and Water Conservation (2003) from Punjab Agricultural University. Throughout his education, he consistently ranked among the top performers, showcasing his passion and dedication to agricultural sciences.

Experience

Currently an Associate Professor in Soil Science, Dr. Bhatt has been instrumental in implementing resource conservation technologies at PAU-Krishi Vigyan Kendra. With a career spanning over two decades, he has actively contributed to improving land and water productivity, addressing climate-smart agricultural practices, and mentoring young scientists. His collaborations with national and international organizations have further amplified the impact of his work in soil and water conservation.

Research Interests

Dr. Bhatt’s research focuses on sustainable agriculture, soil moisture dynamics, resource conservation technologies, and artificial intelligence in farming. His groundbreaking studies on the rice-wheat cropping system and integrated farming models have provided innovative solutions for mitigating climate change effects. He is also interested in exploring the role of silicon in combating plant biotic stress and enhancing soil health for long-term agricultural productivity.

Awards

Dr. Bhatt has been honored with numerous accolades, including the Best Researcher Award (2021), Young Scientist Award (2016, 2017, 2019), and the Springer PAWEES Best Paper Award (2022). These awards recognize his contributions to soil science and sustainable agriculture, underscoring his global reputation as a thought leader. His efforts have consistently bridged the gap between research innovation and practical application in farming.

Publications

Prospects of Artificial Intelligence for the Sustainability of Sugarcane Production in the Modern Era of Climate Change: An Overview of Related Global Findings

  • Authors: Bhatt, R.; Hossain, A.; Majumder, D.; Brestic, M.; Maitra, S.
  • Publication Year: 2024
  • Citations: 0

Management of Yield Losses in Vigna radiata (L.) R. Wilczek Crop Caused by Charcoal-Rot Disease Through Synergistic Application of Biochar and Zinc Oxide Nanoparticles as Boosting Fertilizers and Nanofungicides

  • Authors: Mazhar, M.W.; Ishtiaq, M.; Maqbool, M.; Siddiqui, M.H.; Bhatt, R.
  • Publication Year: 2024
  • Citations: 1

Designing a Productive, Profitable Integrated Farming System Model With Low Water Footprints for Small and Marginal Farmers of Telangana

  • Authors: Karthik, R.; Ramana, M.V.; Kumari, C.P.; Elhindi, K.M.; Mattar, M.A.
  • Publication Year: 2024
  • Citations: 0

Long-Term Application of Agronomic Management Strategies Effects on Soil Organic Carbon, Energy Budgeting, and Carbon Footprint Under Rice–Wheat Cropping System

  • Authors: Naresh, R.K.; Singh, P.K.; Bhatt, R.; Al-Ansari, N.; Mattar, M.A.
  • Publication Year: 2024
  • Citations: 2

Application of Different Organic Amendments Influences the Different Forms of Sulfur in the Soil of Pea–Onion–Cauliflower Cropping System

  • Authors: Paul, S.C.; Bharti, R.; Lata, S.; Bhatt, R.; Siddiqui, M.H.
  • Publication Year: 2024
  • Citations: 0

Revealing the Hidden World of Soil Microbes: Metagenomic Insights Into Plant, Bacteria, and Fungi Interactions for Sustainable Agriculture and Ecosystem Restoration

  • Authors: Jagadesh, M.; Dash, M.; Kumari, A.; Bhatt, R.; Sharma, S.K.
  • Publication Year: 2024
  • Citations: 7

Soil Qualities and Crop Responses Are Influenced by Biochar: A Meta-Analysis Review

  • Authors: Bhatt, R.; Rajput, V.D.; Chandra, M.S.; Garg, A.K.; Verma, K.K.
  • Publication Year: 2024
  • Citations: 0

Optimizing Nutrient and Energy Efficiency in a Direct-Seeded Rice Production System: A Northwestern Punjab Case Study

  • Authors: Kaur, R.; Chhina, G.S.; Kaur, M.; Elhindi, K.M.; Mattar, M.A.
  • Publication Year: 2024
  • Citations: 1

Potassium and Jasmonic Acid—Induced Nitrogen and Sulfur Metabolisms Improve Resilience Against Arsenate Toxicity in Tomato Seedlings

  • Authors: Siddiqui, M.H.; Mukherjee, S.; Gupta, R.K.; Bhatt, R.; Kesawat, M.S.
  • Publication Year: 2024
  • Citations: 3

Conclusion

Dr. Rajan Bhatt’s illustrious career exemplifies the integration of innovative research and practical solutions in soil science. His work has made significant strides in addressing the challenges of sustainable agriculture and climate change. As a mentor, researcher, and leader, Dr. Bhatt continues to inspire advancements in agricultural practices for global food security and environmental sustainability.