Kusnandar | Engineering | Research Excellence Award

Research Excellence Award

Kusnandar
National Research and Innovation Agency, Indonesia

Kusnandar
Affiliation National Research and Innovation Agency
Country Indonesia
Scopus ID 57217677745
Documents 7
Citations 55
h-index 4
Subject Area Engineering
Event Top Teachers Awards
Google Scholar kbNqvhgAAAAJ

Kusnandar is an Indonesian engineering researcher and academic specialist recognized for contributions to thermal systems, refrigeration engineering, HVAC technologies, energy modeling, and sustainable thermal management. His interdisciplinary research integrates numerical simulations, machine learning methods, experimental validation, and energy-efficient system design for manufacturing environments and building applications. His scholarly works have addressed thermal compensation techniques, cooling optimization, machine tool thermal behavior, and sustainable energy management systems in industrial and educational infrastructure.[1][2]

Abstract

This article documents the academic and research achievements of Kusnandar in the field of engineering, with emphasis on thermal systems, refrigeration technologies, computational modeling, and energy-efficient building applications. His work combines experimental methods, machine learning approaches, CFD simulations, and energy optimization techniques for industrial and institutional environments. Through collaborative research in Indonesia and Taiwan, he has contributed to sustainable cooling systems, thermal compensation in machine tools, and HVAC performance enhancement. His scholarly publications and technical engagements demonstrate interdisciplinary integration between manufacturing systems, thermal sciences, and energy engineering.[3][4]

Keywords

Thermal Systems, HVAC Engineering, Refrigeration, Heat Transfer, Machine Learning, CFD Simulation, Sustainable Cooling, Energy Modeling, Experimental Validation, Manufacturing Systems, Energy Efficiency, Thermal Compensation.

Introduction

Engineering research related to energy conservation and thermal management has become increasingly important in industrial manufacturing, educational infrastructure, and sustainable urban systems. Kusnandar has contributed to this field through investigations involving refrigeration systems, HVAC optimization, thermal behavior in machine tools, and predictive modeling using data-driven methods. His academic profile reflects a combination of engineering practice, industrial collaboration, and applied computational analysis.[5]

He obtained a Ph.D. from the Graduate Institute of Precision Manufacturing at National Chin-Yi University of Technology (NCUT), Taiwan, after completing graduate and undergraduate studies in mechanical engineering in Indonesia. His research trajectory integrates thermal engineering with computational and machine learning techniques, particularly in relation to energy efficiency and sustainable manufacturing systems.[6]

Research Profile

Kusnandar has developed expertise across multiple engineering domains involving heat transfer, thermal systems, and energy-efficient infrastructure. His research profile demonstrates the integration of experimental investigations with computational modeling and industrial applications. The majority of his research focuses on thermal management systems, energy conversion, and predictive analysis for manufacturing and building environments.[7]

  • Thermal Systems, Energy Conversion, Refrigeration, HVAC, and Heat Transfer.
  • Numerical Modeling using CFD, FEM, and hybrid thermal simulation techniques.
  • Machine learning applications for predictive thermal behavior analysis in machine tools.
  • Sustainable cooling technologies and renewable energy integration.
  • Experimental validation, sensor integration, and thermal monitoring systems.

In addition to academic research, he has participated in commissioning systems and energy audit projects in Taiwan involving hotels, hospitals, biotechnology facilities, and cleanroom environments. These collaborative activities expanded his expertise in HVAC balancing, energy performance testing, and industrial thermal optimization.[8]

Research Contributions

Kusnandar’s research contributions are primarily associated with sustainable thermal management, building energy optimization, refrigeration engineering, and machine tool thermal analysis. His studies frequently combine field measurements, simulation frameworks, and machine learning prediction models to improve engineering efficiency and operational stability.[9]

  • Development of predictive thermal compensation models for machine tool systems using machine learning techniques.
  • Research on coupling air conditioning systems with refrigeration showcase equipment for energy-efficient retail environments.
  • Energy-efficient retrofitting approaches for institutional hot water heating systems.
  • Investigation of industrial enclosure cooling performance and thermal stability enhancement.
  • Energy modeling and field measurement analysis for university and manufacturing buildings.

His applied engineering research demonstrates practical relevance to industrial sustainability and energy conservation initiatives, particularly in manufacturing systems and educational facilities. The interdisciplinary nature of his work supports broader engineering objectives involving environmental performance and operational reliability.[10]

Publications

Kusnandar has authored and co-authored research publications in internationally recognized engineering and energy journals. His publication record demonstrates continuing engagement with thermal engineering, machine tool analysis, and energy efficiency research.[11]

  1. Kusnandar, Nasril, Danny M Gandana, Agus Widodo, and Galang I Islami. “Thermal environment effect on machine tool ball screw based on experimental investigation and numerical simulation via machine learning prediction.” Journal of Engineering, 2026. DOI: https://doi.org/10.1155/je/6435980
  2. Kusnandar, Nasril, Danny M Gandana, Agus Widodo, and Galang I Islami. “A review of thermal effect and compensation techniques in machine tools.” Scientia Iranica, 2025 (Under Review).
  3. Kusnandar, Luo W. J., Permana I., Wang F. J., and Bayarkhuu G. “Energy Efficient for a Machine Tool Building in a University through Field Measurement and Energy Modelling.” Energy Engineering, 2023, Vol. 120(6), pp. 1387–1399. DOI: https://doi.org/10.32604/ee.2023.027459
  4. Kusnandar, Permana I., Chiang W. M., Wang F. J., and Liou C. “Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store.” Energies, 2022, 15(13), 4857. DOI: https://doi.org/10.3390/en15134857
  5. Chiang W. M., Wang F. J., and Kusnandar. “Performance improvement of an industrial control enclosure cooling system.” Thermal Science, 2022, Vol. 26(3A), pp. 2043–2052. DOI: https://doi.org/10.2298/TSCI201205177C
  6. Wang F. J., Kusnandar, Lin H., and Tsai M. “Energy Efficient Approaches by Retrofitting Heat Pumps Water Heating System for a University Dormitory.” Buildings, 2021, Vol. 11, 356. DOI: https://doi.org/10.3390/buildings11080356

Research Impact

The research impact associated with Kusnandar’s academic work is reflected in the integration of energy-efficient engineering methods with sustainable manufacturing and building operation systems. His publications address practical industrial challenges related to thermal instability, cooling efficiency, and energy consumption reduction.[12]

His studies involving machine tool thermal behavior contribute to manufacturing precision and operational reliability, while his building energy modeling research supports improved environmental performance and energy conservation strategies. The application of machine learning within thermal engineering also demonstrates the growing role of intelligent predictive systems in engineering analysis.[13]

Award Suitability

Kusnandar’s academic background, international research collaborations, engineering publications, and contributions to sustainable thermal management support his suitability for recognition through the Top Teachers Awards. His work demonstrates a combination of research productivity, educational engagement, and applied engineering innovation within the broader field of energy and thermal systems engineering.[14]

His professional experience includes teaching, institutional leadership, postdoctoral research, and industrial collaboration across Indonesia and Taiwan. The integration of academic scholarship with real-world engineering applications reflects a sustained contribution to engineering education and technological development.[15]

Conclusion

Kusnandar represents an engineering academic whose research activities contribute to advancements in thermal systems, energy-efficient technologies, refrigeration engineering, and computational thermal analysis. Through scholarly publications, interdisciplinary methodologies, and international collaborative activities, he has participated in the development of sustainable engineering solutions relevant to manufacturing and building environments. His academic profile aligns with contemporary engineering priorities emphasizing sustainability, efficiency, and intelligent thermal management systems.[16]

References

  1. Elsevier. (n.d.). Scopus author details: Kusnandar, Author ID 57217677745. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57217677745
  2. Google Scholar. (n.d.). Kusnandar citation profile and scholarly metrics. https://scholar.google.com/citations?hl=id&user=kbNqvhgAAAAJ
  3. Kusnandar et al. (2026). Thermal environment effect on machine tool ball screw based on experimental investigation and numerical simulation via machine learning prediction. https://doi.org/10.1155/je/6435980
  4. Kusnandar et al. (2023). Energy Efficient for a Machine Tool Building in a University through Field Measurement and Energy Modelling. https://doi.org/10.32604/ee.2023.027459
  5. Energies Journal. (2022). Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store. https://doi.org/10.3390/en15134857
  6. National Chin-Yi University of Technology. (n.d.). Graduate Institute of Precision Manufacturing academic records.
  7. Research profile documentation relating to HVAC engineering, thermal systems, CFD simulations, and machine learning applications in engineering systems.
  8. Industry collaborative project records involving commissioning systems, energy audits, and HVAC balancing activities in Taiwan from 2019–2023.
  9. Thermal Science. (2022). Performance improvement of an industrial control enclosure cooling system. https://doi.org/10.2298/TSCI201205177C
  10. Buildings Journal. (2021). Energy Efficient Approaches by Retrofitting Heat Pumps Water Heating System for a University Dormitory. https://doi.org/10.3390/buildings11080356
  11. Publication data compiled from Scopus indexing and Google Scholar author records.
  12. Engineering research concerning sustainable thermal management and energy optimization systems in manufacturing environments.
  13. Research applications involving machine learning integration in predictive thermal engineering systems.
  14. Top Teachers Awards. (n.d.). Academic recognition and global teaching excellence platform. https://topteachers.net/
  15. Professional records relating to teaching, academic administration, and postdoctoral research appointments in Indonesia and Taiwan.
  16. Comprehensive academic summary compiled from publication records, institutional affiliations, and engineering research activities.

Fazal e Wahab | Engineering | Innovative Research Award

Innovative Research Award

Fazal e Wahab
Hubei Polytechnic University
Fazal e Wahab
Affiliation Hubei Polytechnic University
Country China
Scopus ID 57216410031
Documents 14
Citations 111
h-index 7
Subject Area Engineering
Event Top Teachers Awards
ORCID 0000-0003-4827-170X
Google Scholar 8t4Pxo8AAAAJ

Fazal e Wahab is an academic researcher and engineering educator affiliated with Hubei Polytechnic University, China. His scholarly work primarily focuses on speech enhancement, signal processing, machine learning applications, and low-latency intelligent systems for embedded and edge computing environments. Over the course of his academic and professional career, he has contributed to research in audio-visual speech enhancement, real-time denoising systems, neural network optimization, and applied engineering technologies. His publications in internationally indexed journals and conferences demonstrate sustained engagement with contemporary developments in communication engineering and intelligent multimedia systems.[1]

Abstract

This academic article documents the scholarly profile, research achievements, and educational contributions of Fazal e Wahab in the field of engineering and intelligent signal processing. His work addresses challenges associated with speech enhancement, audiovisual communication systems, and machine learning implementation for resource-constrained edge devices. Through interdisciplinary research involving signal processing, neural networks, embedded systems, and audio enhancement technologies, he has contributed to practical and computationally efficient methods for real-time communication systems. His publication record includes SCI-indexed journal articles, conference proceedings, funded engineering projects, and collaborative international research activities.[2]

Keywords

Speech Enhancement, Signal Processing, Edge Computing, Deep Learning, Audio-Visual Systems, Engineering Education, Machine Learning, Embedded Systems, Real-Time Denoising, Communication Engineering.

Introduction

The development of intelligent speech processing systems has become increasingly important in modern communication engineering, particularly in environments requiring low-latency and computationally efficient solutions. Researchers working in this field address technical challenges associated with noise suppression, speech intelligibility, audio enhancement, and multimodal communication systems. Fazal e Wahab has participated in this evolving research area through studies focused on lightweight neural architectures, edge-device optimization, and robust audiovisual speech enhancement frameworks.[3]

In addition to research activities, he has contributed extensively to university-level engineering education through undergraduate teaching, curriculum development, laboratory instruction, and supervision of student innovation projects. His academic trajectory includes higher education and research engagement in Pakistan and China, reflecting international academic collaboration and interdisciplinary engineering practice.[4]

Research Profile

Fazal e Wahab completed a Ph.D. in Information and Communication Engineering at the University of Science and Technology of China (USTC) in 2025. His doctoral research focused on optimized lightweight deep learning models for real-time single-channel speech enhancement systems. His investigations emphasized computational efficiency, streaming denoising, echo cancellation, and dereverberation systems applicable to edge and embedded hardware environments.[5]

His academic experience also includes an M.S. in Electrical Engineering from CECOS University and a B.S. in Electronic Engineering from Dawood University of Engineering and Technology. Professionally, he has served as a lecturer, researcher, engineering instructor, and instrumentation engineer, contributing both to industrial engineering operations and university-level technical education.[6]

  • Research specialization in speech enhancement and audio signal processing.
  • Experience in machine learning for edge and embedded systems.
  • Academic supervision of funded engineering projects and applied research.
  • Participation in international scientific collaboration and peer review activities.

Research Contributions

The research contributions of Fazal e Wahab are associated with efficient speech enhancement systems using lightweight neural network architectures. His studies investigate methods for reducing computational complexity while maintaining speech intelligibility and enhancement quality in real-time applications. This area of research is particularly relevant for embedded systems, mobile communication technologies, and assistive audio interfaces.[7]

His published work includes investigations into gated convolutional recurrent neural networks, dual-transformer architectures, multimodal audiovisual processing systems, and adaptive deep learning techniques for speech enhancement. Several publications focus on resource-constrained devices and edge deployment scenarios, demonstrating applied relevance in consumer electronics and intelligent communication technologies.[8]

  • Development of lightweight deep learning models for speech enhancement.
  • Research on audio-visual speech enhancement frameworks using transformer architectures.
  • Optimization of neural systems for edge and embedded devices.
  • Contribution to intelligent signal processing and real-time communication systems.
  • Supervision of funded engineering innovation and assistive technology projects.

Publications

The publication record of Fazal e Wahab includes journal articles and conference papers indexed in SCI, EI, and Scopus databases. His publications span topics related to speech enhancement, multimedia systems, signal processing, energy systems, and intelligent engineering applications.[9]

  1. “Lightweight Adaptive Deep Learning for Efficient Real-Time Speech Enhancement on Edge Devices,” IEEE Transactions on Consumer Electronics, 2025.
  2. “Compact Deep Neural Networks for Real-Time Speech Enhancement on Resource-Limited Devices,” Speech Communication, 2024.
  3. “Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement,” International Journal of Interactive Multimedia and Artificial Intelligence, 2023.
  4. “Multi-Model Dual-Transformer Network for Audio-Visual Speech Enhancement,” AVSEC 2024.
  5. “Integrating Graph Neural Networks and Visual Encoding for Robust Audiovisual Speech Enhancement,” IEEC 2026.
  6. “Frequency-Aware Selective State-Space Modeling for Audio-Visual Speech Enhancement,” Digital Signal Processing, 2026.
  7. “Dynamic Multi-Kernel Convolutional Network With Noise Injected Features for Audio-Only Speech Enhancement,” Neurocomputing, 2025.
  8. “Multimodal Learning-Based Speech Enhancement and Separation,” Computers in Biology and Medicine, 2025.

Research Impact

The research activities of Fazal e Wahab demonstrate measurable academic visibility through Scopus-indexed publications, citation performance, and interdisciplinary engineering collaborations. His studies contribute to ongoing advancements in speech enhancement technologies and intelligent multimedia processing systems. The citation profile associated with his publications indicates scholarly engagement within signal processing and communication engineering communities.[10]

Beyond scholarly publication, his mentorship of funded engineering projects has supported prototype development, applied innovation, and student-centered engineering education. Several supervised projects addressed healthcare technologies, smart home systems, assistive devices, and IoT-enabled monitoring systems, demonstrating practical societal relevance and engineering application.[11]

Award Suitability

The academic and professional profile of Fazal e Wahab reflects several characteristics associated with scholarly recognition in engineering and higher education. His combination of research productivity, international academic engagement, peer-reviewed publication activity, student mentorship, and interdisciplinary engineering expertise demonstrates sustained contribution to communication engineering and intelligent systems research.[12]

His involvement in advanced research related to speech enhancement and machine learning for edge computing environments aligns with emerging global priorities in intelligent communication technologies. Additionally, his experience in teaching, curriculum support, and applied project supervision reflects commitment to engineering education and knowledge dissemination within academic institutions.[13]

Conclusion

Fazal e Wahab has established a multidisciplinary academic profile combining research, teaching, engineering practice, and international scholarly collaboration. His contributions to speech enhancement, signal processing, and machine learning applications for embedded systems represent ongoing engagement with technically relevant and practically applicable research domains. Through journal publications, conference participation, funded project supervision, and academic service, he continues to contribute to the broader development of communication engineering and intelligent multimedia technologies.[13]

References

  1. Elsevier. (n.d.). Scopus author details: Fazal e Wahab, Author ID 57216410031. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57216410031
  2. ORCID. (n.d.). ORCID profile record for Fazal e Wahab. https://orcid.org/0000-0003-4827-170X
  3. IEEE. (2025). Lightweight Adaptive Deep Learning for Efficient Real-Time Speech Enhancement on Edge Devices. https://doi.org/10.1109/TCE.2025.3598007
  4. University of Science and Technology of China. (2025). Doctoral dissertation and academic research profile.
  5. Speech Communication. (2024). Compact Deep Neural Networks for Real-Time Speech Enhancement on Resource-Limited Devices.https://doi.org/10.1016/j.specom.2023.103008
  6. CECOS University. (2015). Master of Science in Electrical Engineering academic record.
  7. International Journal of Interactive Multimedia and Artificial Intelligence. (2023). Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement.
  8. AVSEC Proceedings. (2024). Multi-Model Dual-Transformer Network for Audio-Visual Speech Enhancement.
  9. Computers in Biology and Medicine. (2025). Multimodal Learning-Based Speech Enhancement and Separation. https://doi.org/10.1016/j.compbiomed.2025.110082
  10. Digital Signal Processing. (2026). Frequency-Aware Selective State-Space Modeling for Audio-Visual Speech Enhancement.
  11. National ICT R&D Fund. (n.d.). Applied engineering and IoT-based funded student projects.
  12. Top Teachers Awards. (n.d.). International academic recognition and award platform.https://topteachers.net/
  13. Google Scholar. (n.d.). Academic citation profile of Fazal e Wahab. https://scholar.google.com/citations?hl=en&authuser=1&user=8t4Pxo8AAAAJ

Kishore Debnath | Manufacturing | Best Researcher Award

Dr. Kishore Debnath | Manufacturing | Best Researcher Award

Associate Professor at China Academy of Building Research, China

Dr. Kishore Debnath is a distinguished academic and researcher in the field of mechanical engineering, specializing in advanced manufacturing and materials science. With a strong foundation in engineering research, he has significantly contributed to machining processes, sustainable manufacturing, and industrial automation. He earned his Ph.D. in Mechanical Engineering from the Indian Institute of Technology (IIT) Roorkee and has since been actively engaged in research and teaching. Currently serving as an Associate Professor at the National Institute of Technology (NIT) Meghalaya, Dr. Debnath has played a key role in academic development, research advancement, and administrative leadership. His work focuses on optimizing machining performance, eco-friendly manufacturing solutions, and computational modeling. Over the years, he has received numerous accolades, including the Excellent Research Contribution Award and multiple Best Paper Awards at prestigious conferences. Through his expertise and leadership, Dr. Debnath continues to shape the future of mechanical engineering research and education.

Professional profiles📖

Google Scholar

Scopus Profile

ORCID

Education 🎓

Dr. Kishore Debnath pursued his Ph.D. in Mechanical Engineering from the Indian Institute of Technology (IIT) Roorkee between 2011 and 2015, where he focused on advanced machining processes and material optimization techniques. Prior to this, he completed his M.Tech. in Mechanical Engineering at the National Institute of Technology (NIT) Rourkela (2009-2011), specializing in manufacturing engineering and process optimization. His research during this period was centered on improving machining efficiency and material sustainability. His undergraduate studies were at the National Institute of Technology (NIT) Agartala, where he earned a B.E. in Mechanical Engineering (2004-2008). His education has provided him with a robust technical foundation and a deep understanding of mechanical and industrial processes, allowing him to make significant contributions to academia and the engineering industry.

work Experience💼

Dr. Kishore Debnath has an extensive teaching and research career, having served in multiple academic positions. He is currently an Associate Professor at NIT Meghalaya (2023–Present), where he leads various research initiatives and academic programs. Previously, he held the role of Assistant Professor (Grade-I) from 2019 to 2023 and Assistant Professor (Grade-II) from 2015 to 2019 at the same institution. His initial role at NIT Meghalaya was as an Assistant Professor (Consolidated Pay) from July to November 2015. In addition to teaching, he has been deeply involved in academic and administrative responsibilities, including serving as the Professor In-Charge of the Centre for International Relations (2024–Present) and Co-ordinator for the Quality Improvement Programme (2024–Present). He has also held key leadership positions such as Head of the Mechanical Engineering Department (2022–2024) and Member of the Senate (2022–2024), playing a significant role in institutional development.

Research Focus

Dr. Kishore Debnath’s research primarily revolves around advanced machining, manufacturing processes, and material science applications. His work in precision machining and surface engineering has led to improved cutting efficiency and wear resistance in industrial applications. He is actively involved in sustainable manufacturing, where he develops eco-friendly and energy-efficient machining techniques. His expertise in computational modeling and AI-driven process optimization allows for better prediction and control of machining operations. Additionally, he has contributed to industrial automation and the integration of smart manufacturing technologies under Industry 4.0. His research on composite and high-performance materials has applications in the aerospace and automotive industries. Through his innovative contributions, he continues to push the boundaries of manufacturing efficiency and sustainability, helping industries transition to more advanced and environmentally friendly practices.

Awards & Honors🏆 

Dr. Kishore Debnath has received numerous awards and honors for his contributions to research and academia. He was honored with the Excellent Research Contribution Award (Young Faculty) for 2022-23 and 2018-19 at NIT Meghalaya. He has also won Best Paper Awards at prestigious conferences, including the 2nd International Conference on Recent Innovations & Developments in Mechanical Engineering (ICRIDME 2024) and the International Conference on Industrial and Manufacturing Systems (CIMS-2020). Additionally, he has been awarded international travel grants, including CSIR-funded financial assistance to attend IMMT-2019 in Dubai, UAE, and SERB-DST-funded support for ISGMA-2014 in South Korea. His research has been recognized with multiple Best Poster Presentation Awards, further solidifying his reputation as a leading researcher in his field. He also secured the Graduate Aptitude Test in Engineering (GATE) Scholarship during his M.Tech. and Ph.D. studies, demonstrating his academic excellence from an early stage.

Conclusion✅

Dr. Kishore Debnath is a strong contender for the Best Researcher Award, given his proven research excellence, international recognition, and academic leadership. His contributions to mechanical and manufacturing engineering have been recognized with multiple awards, and his mentorship has led to successful research outputs. Strengthening his high-impact journal publications, industry collaborations, and research funding would further elevate his profile for prestigious global research accolades.

📚Publications to Noted

 

Drilling characteristics of sisal fiber-reinforced epoxy and polypropylene composites

📖 Materials and Manufacturing Processes 29 (11-12), 1401-1409

👥 Authors: K. Debnath, I. Singh, A. Dvivedi

🔢 Citations: 136

📅 Year: 2014

Hole making in natural fiber-reinforced polylactic acid laminates: an experimental investigation

📖 Journal of Thermoplastic Composite Materials 30 (1), 30-46

👥 Authors: P.K. Bajpai, K. Debnath, I. Singh

🔢 Citations: 123

📅 Year: 2017

Rotary mode ultrasonic drilling of glass fiber-reinforced epoxy laminates

📖 Journal of Composite Materials 49 (8), 949-963

👥 Authors: K. Debnath, I. Singh, A. Dvivedi

🔢 Citations: 82

📅 Year: 2015

Experimental investigations on drilling of lignocellulosic fiber-reinforced composite laminates

📖 Journal of Manufacturing Processes 34, 51-61

👥 Authors: M.R. Choudhury, M.S. Srinivas, K. Debnath

🔢 Citations: 76

📅 Year: 2018

Low-frequency modulation-assisted drilling of carbon-epoxy composite laminates

📖 Journal of Manufacturing Processes 25, 262-273

👥 Authors: K. Debnath, I. Singh

🔢 Citations: 58

📅 Year: 2017

On the analysis of force during secondary processing of natural fiber-reinforced composite laminates

📖 Polymer Composites 38 (1), 164-174

👥 Authors: K. Debnath, I. Singh, A. Dvivedi

🔢 Citations: 47

📅 Year: 2017

A new approach to control the delamination and thrust force during drilling of polymer nanocomposites reinforced by graphene oxide/carbon fiber

📖 Composite Structures 253, 112786

👥 Authors: J. Kumar, R.K. Verma, K. Debnath

🔢 Citations: 45

📅 Year: 2020

A review of the research and advances in electromagnetic joining of fiber-reinforced thermoplastic composites

📖 Polymer Engineering & Science 59 (10), 1965-1985

👥 Authors: M.R. Choudhury, K. Debnath

🔢 Citations: 43

📅 Year: 2019

Drilling of metal matrix composites: Experimental and finite element analysis

📖 Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

👥 Authors: V.K. Doomra, K. Debnath, I. Singh

🔢 Citations: 42

📅 Year: 2015

Fabrication methods of metal matrix composites (MMCs)

📖 Materials Today: Proceedings 46, 6840-6846

👥 Authors: A. Kumar, O. Vichare, K. Debnath, M. Paswan

🔢 Citations: 40

📅 Year: 2021

Detection of local defect resonance frequencies using bicoherence analysis

📖 Journal of Sound and Vibration 443, 703-716

👥 Authors: S. Roy, T. Bose, K. Debnath

🔢 Citations: 39

📅 Year: 2019

 

Fernando Marins | Production Engineering | Best Researcher Award

Prof. Dr.Fernando Marins | Production Engineering | Best Researcher Award

Professor at Universidade Estadual Paulista-UNESP,Brazil

Fernando Augusto Silva Marins is a Full Professor at UNESP – São Paulo State University, specializing in logistics, supply chain management, and operational research. With over four decades of academic and research experience, he has made significant contributions to the field through publications, patents, and industrial collaborations. His academic journey began with a Mechanical Engineering degree in 1976, followed by an MSc (1981) and PhD (1987), culminating in a postdoctoral internship at Brunel University in 1994. He has supervised numerous postgraduate students and participated in various international collaborations. A prolific researcher, he has published over 140 journal articles, authored two books, and contributed to 29 book chapters. He has also served as an editor and reviewer for multiple scientific journals. Recognized for his contributions, he has received 40 awards and honors, highlighting his impact on academia and industry. His research spans logistics, SCM, DEA, AHP, and simulation.

professional profiles📖

Scopus Profile

ORCID

Goolge scholar

Education 🎓

Fernando Augusto Silva Marins pursued his undergraduate degree in Mechanical Engineering in 1976. He later obtained a Master of Science (MSc) in 1981, where he deepened his understanding of engineering principles and logistics. In 1987, he earned his PhD, further refining his expertise in operational research and supply chain management. To expand his international academic exposure, he completed a postdoctoral internship at Brunel University, UK, in 1994. His educational background laid a strong foundation for his career in academia and research. With a focus on logistics, supply chain management, and decision-making methodologies, he has continuously integrated emerging technologies into his research. His extensive academic journey has enabled him to guide students at various levels, fostering new innovations and practical applications in his field. Through his diverse educational experiences, he has contributed significantly to research and academia, making a lasting impact on logistics and operational research disciplines.

work Experience💼

Fernando Augusto Silva Marins has been a Full Professor at UNESP – São Paulo State University, where he has dedicated his career to research, teaching, and industry collaborations. Over the years, he has mentored 26 PhD candidates, 37 MSc students, and 80 MBA researchers. His professional experience includes working as a researcher for CNPq, contributing extensively to operational research, logistics, and supply chain management. He has successfully led 34 research projects and actively participated in consultancy work, bridging the gap between academia and industry. His editorial contributions include serving as an editor and reviewer for numerous journals and scientific events. He has also held leadership roles within the Brazilian Operational Research Society (SOBRAPO), influencing policy and research directions. As a recognized academician, he continues to push the boundaries of research through advanced methodologies like DEA, AHP, and simulation, fostering innovations in logistics and supply chain management.

Research Focus

Fernando Augusto Silva Marins specializes in logistics, supply chain management (SCM), and operational research (OR), focusing on optimizing processes for efficiency and cost-effectiveness. His research integrates advanced methodologies such as Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), and simulation to solve complex decision-making problems. His work addresses critical challenges in transportation, inventory control, and production planning. By leveraging mathematical modeling and computational tools, he enhances decision-support systems for businesses and industries. His contributions have been widely published, with 140 journal papers and numerous conference presentations. His research extends to sustainability in logistics, emphasizing eco-friendly supply chain strategies. He collaborates with industries and academia to develop practical solutions, bridging theoretical research with real-world applications. With extensive experience in multi-criteria decision-making, his work influences operational strategies in various sectors. His ongoing projects focus on digital transformation in logistics, smart supply chains, and artificial intelligence applications in decision-making.

Awards & Honors🏆 

Fernando Augusto Silva Marins has received over 40 awards and honors for his outstanding contributions to logistics, supply chain management, and operational research. His recognition spans academia, industry, and scientific organizations. He has been honored by the Brazilian Operational Research Society for his impactful research and leadership roles. His innovative work has earned him accolades in international conferences and scientific journals. He has received best paper awards, distinguished researcher titles, and excellence in teaching honors. His contributions to decision-making methodologies like DEA, AHP, and simulation have been widely recognized by peers and institutions. His research impact is reflected in his h-index of 18 in Scopus, showcasing the high citation count of his work. Additionally, he has been acknowledged for his role in mentoring and supervising students, further cementing his legacy in academia. His dedication to advancing research and education has earned him a reputation as a leading expert.

Skills 🛠️ 

Fernando Augusto Silva Marins possesses a diverse skill set, combining academic excellence, research innovation, and industry expertise. His technical skills include operational research methodologies, logistics optimization, supply chain management, and simulation modeling. He is proficient in decision-support tools like DEA and AHP, which enhance efficiency in complex systems. His expertise extends to programming and mathematical modeling, aiding in research and industrial applications. He has strong leadership and mentoring skills, having guided over 100 students across various academic levels. His editorial experience as a journal reviewer and editor showcases his ability to assess and refine high-quality research. With excellent project management skills, he has led multiple research initiatives, ensuring impactful results. His consultancy work demonstrates his ability to apply theoretical models to real-world business challenges. His communication and collaboration skills have facilitated global partnerships, fostering innovation in logistics and operational research. His ability to bridge theory with practice sets him apart.

Conclusion✅

Fernando Augusto Silva Marins is an exceptional candidate for the Best Researcher Award. His prolific research output, academic mentorship, and contributions to logistics and operations research make him a strong contender. With enhanced industry collaborations and a focus on increasing citation impact, his research influence can be further solidified. Overall, he meets and exceeds many of the criteria for this prestigious recognition

📚Publications to Noted

The ISO 31000 standard in supply chain risk management

Authors: U.R. De Oliveira, F.A.S. Marins, H.M. Rocha, V.A.P. Salomon

Journal: Journal of Cleaner Production

Citations: 298

Year: 2017

ERP systems: features, costs and trends

Authors: T.C.C. Padilha, F.A.S. Marins

Journal: Production

Citations: 179

Year: 2005

Introduction to Operational Research

Author: F.A.S. Marins

Publisher: São Paulo: Academic Culture, Paulista State University

Citations: 140

Year: 2011

Application of design of experiments on the simulation of a process in automotive industry

Authors: J.A.B. Montevechi, A.F. de Pinho, F. Leal, F.A.S. Marins

Conference: 2007 Winter Simulation Conference

Citations: 132

Year: 2007

Analytic hierarchy process and supply chain management: A bibliometric study

Authors: C.L. Tramarico, V.A.P. Salomon, F.A.S. Marins

Journal: Procedia Computer Science

Citations: 105

Year: 2015

Mitigation of the bullwhip effect considering trust and collaboration in supply chain management: a literature review

Authors: M.M.K. Almeida, F.A.S. Marins, A.M.P. Salgado, F.C.A. Santos, S.L. Silva

Journal: The International Journal of Advanced Manufacturing Technology

Citations: 104

Year: 2015

Reverse logistics management model

Authors: C.T. Hernández, F.A.S. Marins, R.C. Castro

Journal: Management & Production

Citations: 94

Year: 2012

A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill

Authors: A.F. da Silva, F.A.S. Marins

Journal: Energy Economics

Citations: 86

Year: 2014

Reverse logistics in a glass lamination company: a case study

Authors: M.E. Goncalves, F.A.S. Marins

Journal: Management & Production

Citations: 80

Year: 2006

Multi-criteria assessment of the benefits of a supply chain management training considering green issues

Authors: C.L. Tramarico, V.A.P. Salomon, F.A.S. Marins

Journal: Journal of Cleaner Production

Citations: 79

Year: 2017