Simon Yishak | Manufacturing Engineering | Academic Excellence in Mechanics Award

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Mr. Simon Yishak | Manufacturing Engineering | Academic Excellence in Mechanics Award

Lecturer at Arba Minch University, Ethiopia

🌟 Simon Yishak Kolebaye is a passionate academic leader serving as a lecturer and Head of the Automotive Engineering Department at Arba Minch University, Ethiopia, since 2016. 🎓 He earned his BSc in Mechanical Engineering from Mizan Tepi University and an MSc in Manufacturing Engineering and Automation from Arba Minch University. 🛠️ With nine years of professional experience, Simon focuses on bridging academia and industry through innovative research, community engagement, and industry-technology transfer. 🚀 His expertise in advanced manufacturing and process optimization reflects his commitment to Ethiopia’s technological growth. 🌍

Publication Profile

scopus

Education🎓

MSc in Manufacturing Engineering and Automation (2021) – Arba Minch University BSc in Mechanical Engineering, Manufacturing Stream (2015) – Mizan Tepi University Specialized in advanced manufacturing, CNC technology, additive manufacturing, process planning, welding machines, and automation. 🤖 His academic training integrates engineering principles with cutting-edge technologies to enhance manufacturing systems. 🚀

Experience 📌

Head of Automotive Engineering Department at Arba Minch University (2016–present)  Led department operations, curriculum development, and student mentorship. Coordinated research projects bridging academic solutions with industry needs. Actively engaged in teaching advanced manufacturing technologies, workshop technology, and process optimization. Contributed to community-focused projects, enhancing education and safety in Ethiopia.

Awards and Honors 🏆

Recognized for exceptional leadership in academic program management. Received grants for innovative research projects funded by Arba Minch University.  Honored for community service initiatives improving local education and infrastructure.  Acknowledged for excellence in publishing impactful research in advanced manufacturing.

Research Focus 🔬

Focused on additive manufacturing and process optimization for energy storage, graphene composites, and pipeline applications. Specialized in thermoplastic infill patterns, laser scanning for nickel alloys, and biocomposites. Worked on sustainability, utilizing waste-derived materials for manufacturing innovations.  Published studies on CNC automation, rapid prototyping, and advanced manufacturing systems. Dedicated to developing scalable, eco-friendly, and cost-effective manufacturing solutions.

Publications 📖

1. Additive Manufacturing (3D Printing)

Graphene Enhanced PETG Optimization:

Title: Fused deposition modeling process parameter optimization on the development of graphene enhanced polyethylene terephthalate glycol

Journal: Scientific Reports (2024, 14(1), 30744)

Focus: Optimizing parameters for FDM using graphene-reinforced PETG.

Citations: 0

Graphene-Reinforced PETG Impeller Production:

Title: Optimizing additive manufacturing parameters for graphene-reinforced PETG impeller production: A fuzzy AHP-TOPSIS approach

Journal: Results in Engineering (2024, 24, 103018)

Focus: Application of multi-criteria decision-making tools for PETG optimization.

Citations: 4

Thermoplastic Polyurethane for Pipeline Applications:

Title: Analysis and Optimization of Thermoplastic Polyurethane Infill Patterns for Additive Manufacturing in Pipeline Applications

Journal: Advances in Polymer Technology (2024)

Focus: Infill pattern optimization in AM applications.

Citations: 0

2. Laser Manufacturing

Nickel-Based Superalloys:

Title: Role of laser power and scan speed combination on the surface quality of additive manufactured nickel-based superalloy

Journal: Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications (2024, 238(6), pp. 1142–1154)

Focus: Investigates laser parameters on the surface quality of nickel alloys.

Citations: 13

3. Composites and Biocomposites

Biocomposites of Jute/Bagasse/Coir/Nano TiO2:

Title: An Investigation on the Activation Energy and Thermal Degradation of Biocomposites of Jute/Bagasse/Coir/Nano TiO2/Epoxy-Reinforced Polyaramid Fibers

Journal: Journal of Nanomaterials (2022)

Focus: Studied thermal degradation of sustainable biocomposites.

Citations: 33

Conclusion

Mr. Simon Yishak demonstrates exceptional qualifications and expertise that align closely with the goals of the Research for Academic Excellence in Mechanics Award. His academic rigor, innovative research, and practical contributions to manufacturing engineering position him as a strong candidate for this prestigious recognition. By focusing on international collaborations, patent development, and expanding his research into emerging fields, Simon could further solidify his candidacy and amplify his contributions to the discipline.

Yurong Wang | Additive manufacturing | Best Researcher Award

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Mr. Yurong Wang | Additive manufacturing | Best Researcher Award

Mr at  Tsinghua University, China

A PhD candidate in Mechanical Engineering at Sichuan University, this researcher specializes in additive manufacturing, powder bed fusion, and advanced material processes. With a passion for material characterization and innovation, they strive to advance mechanical engineering technologies.

Professional Profiles:

orcid

🎓 Education

PhD Student (Mechanical Engineering) – Sichuan UniversityMaster’s (Mechanical Engineering) – Tsinghua University & Guangxi UniversityBachelor’s (Mechanical and Vehicle Engineering) – Hunan University

💼 Experience

Research assistant in additive manufacturing projects at Sichuan UniversityIntern at advanced materials lab, Tsinghua UniversityUndergraduate researcher in mechanical design at Hunan University

🏆 Awards and Honors

Best Graduate Research Award – Sichuan UniversityOutstanding Master’s Thesis Award – Tsinghua UniversityInnovation Excellence Award – Guangxi University

🔍 Research Focus

Additive Manufacturing 🛠️Powder Bed Fusion ⚙️Advanced Material Processes 🔩Material Characterization 🧪

✍️Publications Top Note 

Strengthened Microstructure and Mechanical Properties of Austenitic 316L Stainless Steels by Grain Refinement and Solute Segregation

Journal of Materials Research and Technology (2025)
DOI: 10.1016/j.jmrt.2024.12.086
Authors: Yurong Wang, Buwei Xiao, Xiaoyu Liang, Huabei Peng, Jun Zhou, Feng Lin

This study explores how refining grain structure and promoting solute segregation enhances the mechanical properties of 316L stainless steel. The findings reveal improved strength and toughness, making it a promising material for advanced engineering applications.

2. Effect of Laser Energy on Anisotropic Material Properties of a Novel Austenitic Stainless Steel with a Fine-Grained Microstructure
Journal of Manufacturing and Materials Processing

This paper investigates the influence of laser energy on the anisotropic properties of fine-grained austenitic stainless steel. The research highlights how laser processing parameters can optimize material performance, contributing to advancements in additive manufacturing.

Conclusion

This individual is highly suitable for the Best Researcher Award, as they have a strong educational background, expertise in cutting-edge research areas, and the potential for impactful contributions to additive manufacturing and advanced materials science. They demonstrate the qualities of a forward-thinking, innovative researcher poised to make significant strides in their field. With continued focus on publishing high-quality research and fostering industry partnerships, their potential to achieve even greater success and recognition is substantial.

 

Jinxia Zhang | Defect detection | Best Researcher Award

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Assoc Prof Dr. Jinxia Zhang | Defect detection | Best Researcher Award

 Associate Professor at Southeast University, China

Assoc Prof Dr. Jinxia Zhang is an Associate Professor at Southeast University, Nanjing, China, specializing in saliency detection, visual attention, computer vision, and deep learning. With a Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology, he has extensive experience in artificial intelligence research. His journey includes time as a visiting scholar at Harvard Medical School and numerous prestigious research projects funded by national foundations. Assoc Prof Dr. Jinxia Zhang leads key AI initiatives, driving innovations in multimodal understanding, defect analysis, and object detection. His academic and professional contributions have positioned him as a prominent researcher in visual computing and AI.

Publication Profile

scholar

Education 🎓

Assoc Prof Dr. Jinxia Zhang  earned his M.Sc. and Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology in 2015. His doctoral research laid a foundation for his interest in artificial intelligence, particularly in areas like visual attention and computer vision. Prior to his postgraduate work, he completed his B.Sc. in Computer Science and Technology at the same institution in 2009, where he developed a solid understanding of computational theories and applications. His education has provided him with both theoretical knowledge and practical skills that are central to his current research on AI and deep learning.Assoc Prof Dr. Jinxia Zhang  is currently an Associate Professor at Southeast University, Nanjing, a role he has held since 2019. From 2016 to 2019, he served as a Lecturer at the same university, where he significantly contributed to AI teaching and research. His early career included a prestigious stint as a Visiting Scholar at Harvard Medical School, USA, between 2012 and 2014, where he collaborated on cutting-edge AI-driven healthcare projects. His international exposure and academic roles have enriched his teaching and research, particularly in computer vision and AI, making him a key figure in the field.

Awards and Honors  🏆

Assoc Prof Dr. Jinxia Zhang  has received numerous accolades for his research excellence and contributions to the field of AI. He was awarded the National Natural Science Foundation of China grant in 2018 for his project on salient object detection. In 2017, he secured the Jiangsu Natural Science Foundation Grant for his innovative research on visual cognitive characteristics. Additionally, his work in defect diagnosis for photovoltaic modules was recognized as part of the National Key Research and Development Plan. These prestigious awards underscore his pioneering contributions in artificial intelligence and computer vision research.

Research Focus  🔬

Assoc Prof Dr. Jinxia Zhang ‘s research focuses on the intersection of visual attention, saliency detection, and deep learning within artificial intelligence. He leads projects on multimodal understanding and e-commerce applications, and is a Principal Investigator for research into AI-based fruit and vegetable recognition. His earlier work in defect diagnosis for photovoltaic modules and salient object detection in complex scenes has been supported by prominent grants. His innovative approach combines perceptual grouping and visual attention to develop cutting-edge solutions in computer vision, making significant advancements in how machines perceive and interact with visual data.

Conclusion

The candidate demonstrates an impressive body of work across several domains of artificial intelligence, particularly in salient object detection, visual cognition, and multimodal learning. Their academic achievements, project leadership, and dedication to advancing AI make them a strong contender for the Best Researcher Award. By continuing to broaden their industry collaborations and expanding the scope of their research impact, they can become a globally recognized leader in AI and computer vision.

Publication  Top Notes

  • Towards the Quantitative Evaluation of Visual Attention Models (2015)
    • Citation: 75
    • Journal: Vision Research
    • Key Contributors: Z. Bylinskii, E.M. DeGennaro, R. Rajalingham, H. Ruda, J. Zhang, J.K. Tsotsos
    • Highlights: Focuses on quantitative approaches to evaluate visual attention models, essential for improving saliency detection.
  • A Novel Graph-Based Optimization Framework for Salient Object Detection (2017)
    • Citation: 63
    • Journal: Pattern Recognition
    • Key Contributors: J. Zhang, K.A. Ehinger, H. Wei, K. Zhang, J. Yang
    • Highlights: Presents a new graph-based optimization method for enhancing the accuracy of salient object detection.
  • Salient Object Detection by Fusing Local and Global Contexts (2020)
    • Citation: 60
    • Journal: IEEE Transactions on Multimedia
    • Key Contributors: Q. Ren, S. Lu, J. Zhang, R. Hu
    • Highlights: This paper integrates both local and global visual contexts to refine salient object detection in multimedia applications.
  • Inter-Hour Direct Normal Irradiance Forecast with Multiple Data Types and Time-Series (2019)
    • Citation: 36
    • Journal: Journal of Modern Power Systems and Clean Energy
    • Key Contributors: T. Zhu, H. Zhou, H. Wei, X. Zhao, K. Zhang, J. Zhang
    • Highlights: Introduces a time-series forecasting model for direct normal irradiance, benefiting renewable energy systems.
  • Winter is Coming: How Humans Forage in a Temporally Structured Environment (2015)
    • Citation: 35
    • Journal: Journal of Vision
    • Key Contributors: D. Fougnie, S.M. Cormiea, J. Zhang, G.A. Alvarez, J.M. Wolfe
    • Highlights: Examines human visual foraging behavior in dynamically changing environments.
  • Domain Adaptation for Epileptic EEG Classification Using Adversarial Learning and Riemannian Manifold (2022)
    • Citation: 25
    • Journal: Biomedical Signal Processing and Control
    • Key Contributors: P. Peng, L. Xie, K. Zhang, J. Zhang, L. Yang, H. Wei
    • Highlights: This paper explores domain adaptation techniques to improve epileptic EEG classification through adversarial learning.
  • A Lightweight Network for Photovoltaic Cell Defect Detection in Electroluminescence Images (2024)
    • Citation: 23
    • Journal: Applied Energy
    • Key Contributors: J. Zhang, X. Chen, H. Wei, K. Zhang
    • Highlights: Develops a lightweight neural network for detecting defects in photovoltaic cells using knowledge distillation.
  • Salient Object Detection via Deformed Smoothness Constraint (2018)
    • Citation: 21
    • Journal: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: X. Wu, X. Ma, J. Zhang, A. Wang, Z. Jin
    • Highlights: Proposes a deformed smoothness constraint approach for improving salient object detection.
  • Character Recognition via a Compact Convolutional Neural Network (2017)
    • Citation: 20
    • Conference: International Conference on Digital Image Computing
    • Key Contributors: H. Zhao, Y. Hu, J. Zhang
    • Highlights: Develops a compact CNN for robust character recognition in natural scene images.
  • A Prior-Based Graph for Salient Object Detection (2014)
    • Citation: 23
    • Conference: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: J. Zhang, K.A. Ehinger, J. Ding, J. Yang
    • Highlights: Uses a prior-based graph model to enhance the performance of salient object detection algorithms.