Ioana Monica Sas-Boca | Engineering | Best Researcher Award

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Ioana Monica Sas-Boca | Engineering | Best Researcher Award

Mrs. Ioana Monica Sas-Boca, Technical University of Cluj-Napoca Materials Science and Engineering Department, Romania

Dr. Ioana Monica Sas-Boca is a Romanian academic and researcher affiliated with the Technical University of Cluj-Napoca, where she serves as a Lecturer in the Department of Materials Science and Engineering. With over two decades of experience in higher education, she has built a strong foundation in materials engineering and technical education. Known for her active role in didactic and research activities, Dr. Sas-Boca combines expertise in mechanical engineering with innovation in teaching methodologies. She has also contributed significantly to vocational training for adults, especially in IT and data processing. Her international exposure includes research internships in France and contributions to multiple European projects. Passionate about academic development, she has authored scientific books, peer-reviewed publications, and participated in several national and international conferences. She is recognized for her strong team spirit, communication abilities, and proficiency in multiple educational and design platforms.

Publication Profile

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🎓 Education

Dr. Sas-Boca holds a PhD in Engineering (2012) from the Technical University of Cluj-Napoca, with a thesis on using friction force in compaction processes. She earned a Master’s degree in Solid State Physics (2006–2008) from Babeș-Bolyai University, where she specialized in magnetic and superconducting materials and conducted research in France. Her educational path also includes a postgraduate specialization in Energy Audit–Construction (2010), a certificate in Innovation Management (2012), and Advanced Studies in Special Procedures in Manufacturing Engineering (2002–2003). Earlier, she graduated with a degree in Materials Processing Engineering (1996–2001) and also completed a teacher training program in 2000. Her secondary education was at George Coșbuc Năsăud National College in mathematics and physics. She also completed the DIDATEC training for engineering educators, emphasizing modern ICT-based education. Dr. Sas-Boca consistently expanded her qualifications, aligning her technical education with pedagogical expertise.

💼 Experience

Dr. Sas-Boca began her academic journey in 2001 as a full-time PhD student involved in didactic and research activities at the Technical University of Cluj-Napoca. She later served as Assistant Lecturer (2004–2016) in the Department of Materials Processing Engineering before becoming a Lecturer in 2016. Her work involves teaching and research in material science, with a focus on engineering and higher education. Additionally, she contributed significantly to professional retraining through her role as a Lecturer-Trainer at SC Profag SRL (2004–2008), where she taught unemployed individuals in IT-based skills, such as data entry and processing. She played an instrumental role in curriculum development, training evaluation, and quality assurance. Her leadership as a specialization coordinator and involvement in continuous education and blended-learning projects showcase her commitment to innovative pedagogy and mentorship. Dr. Sas-Boca is recognized for adaptability, team coordination, and effective communication in academic and industrial contexts.

🏆 Honors and Awards

Dr. Ioana Monica Sas-Boca has been recognized for her excellence in research and academic contributions. She has authored three books, including two as the sole author, and published 26 scientific papers indexed in Web of Science—five of which are in top-tier Q1 and Q2 journals. She has presented 24 papers at national and international conferences and published 11 more in other globally recognized databases. Her scholarly impact includes 110 citations in Web of Science, 105 in Scopus, and over 225 citations overall, with 85 recommendations from other indexing platforms as of July 2025. She has been awarded three scientific research support grants in 2022 and 2023, reflecting her ongoing contribution to innovative research. Additionally, she participated in six national and international research contracts and one industrial project, and served as a member of the ROSE teaching project, further highlighting her academic leadership and service to the research community.

🔬 Research Focus

Dr. Sas-Boca’s research focuses on materials science and engineering, with a particular emphasis on friction-based compaction processes, mechanical properties of advanced materials, and energy-efficient construction practices. Her PhD research pioneered the use of friction force as an active deformation mechanism, contributing to more sustainable and efficient material processing methods. She also explores solid-state physics topics like magnetic and superconducting materials, aligning physics with real-world industrial applications. Her interdisciplinary interests extend to data processing, innovation management, and energy audits for construction—indicating a holistic approach that blends materials engineering with environmental and sustainability concerns. Through her involvement in blended-learning educational platforms, she also contributes to pedagogical research, especially in integrating ICT and modern technologies into engineering education. Her work bridges theoretical modeling, practical design, and experimental validation, and she continuously contributes to both academic research and industry-focused solutions in Romania and across Europe.

📚 Publications

📘 Friction Force as an Active Deformation Mechanism in Compaction Processes
📗 Innovative Methods in Materials Engineering Education
📙 Practical Guide to Material Processing Technologies
📝 Investigation of Friction-Based Compaction Mechanisms in Engineering Alloys
📄 Magnetic Properties of Superconducting Thin Films: An Experimental Study
📄 Energy Audit Methods Applied in Construction Sector
📄 Use of ICT Platforms in Technical Education: A DIDATEC Project Review
📄 Solid-State Phenomena in Metallic Systems: A Simulation-Based Approach
📄 Advanced Characterization of Friction-Induced Compaction in Powders
📄 Blended Learning in Engineering: Implementation and Challenges
📄 Thermomechanical Behavior of Compacted Metallic Powders
📄 Materials Engineering Approaches to Energy Efficiency in Buildings
📄 Evaluation of Stress-Strain Distributions during Powder Compaction
📄 Microstructural Changes in Friction-Compacted Powder Materials
📄 A Review on Superconducting Ceramics for Energy Applications
📄 Finite Element Analysis of Powder Consolidation under Friction Forces
📄 Digital Literacy for Engineering Students through Blended Platforms
📄 Thermal Behavior of Engineered Composite Powders
📄 ICT Training for Engineering Educators: A National Perspective
📄 Design and Optimization of Compaction Tools for Powder Metallurgy
📄 Material Behavior under Uniaxial vs. Friction-Based Compression
📄 Teaching Engineering Concepts Using Simulation and Modeling Software
📄 Comparative Study of Magnetic Properties in Soft and Hard Materials
📄 Building Energy Efficiency: Tools, Methods, and Implementation

Yufan Song | Engineering | Best Paper Award

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Yufan Song | Engineering | Best Paper Award

Dr. Yufan Song, Nanjing University of Aeronautics and Astronautics, China

Yufan Song, born in 1999 in Hebei, China, is a Ph.D. student specializing in Information and Communication Engineering at Nanjing University of Aeronautics and Astronautics (NUAA). With a strong academic foundation from the University of Electronic Science and Technology of China (UESTC), she has swiftly become a rising researcher in the field of synthetic aperture radar (SAR) and remote sensing image processing. Her work is driven by the ambition to push the boundaries of microwave imaging techniques and data interpretation from SAR platforms. Yufan’s research is marked by innovation and technical depth, leading to the publication of eight SCI-indexed journal articles and 14 patents. She holds memberships in prestigious professional organizations such as IEEE and CSIG. Through rigorous academic training and a passion for solving complex imaging challenges, Yufan continues to contribute significantly to advancements in SAR-based Earth observation technologies.

Publication Profile

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🎓 Education

Yufan Song commenced her academic journey at the University of Electronic Science and Technology of China (UESTC), Chengdu, where she earned her Bachelor’s degree from the College of Information and Communication Engineering in 2020. During her undergraduate studies, she developed a keen interest in signal processing and microwave technologies. Building on that foundation, she pursued doctoral studies at Nanjing University of Aeronautics and Astronautics (NUAA), where she is currently enrolled in the Ph.D. program in Information and Communication Engineering. Her education is marked by a consistent focus on research and development, particularly in advanced remote sensing technologies and synthetic aperture radar (SAR) systems. Throughout her academic path, Yufan has cultivated in-depth technical knowledge, hands-on experience with SAR data analysis, and expertise in image reconstruction, ambiguity suppression, and sparse signal processing. Her education reflects both strong theoretical grounding and applied research excellence.

💼 Experience

Yufan Song’s experience is anchored in academic research with a strong focus on microwave imaging and SAR technologies. As a Ph.D. student at NUAA, she has undertaken six significant research projects related to sparse imaging, SAR signal processing, and ambiguity reduction in sliding spotlight SAR systems. Her practical contributions include developing innovative algorithms for moving and stationary target separation, squint-mode SAR phase correction, and compressive sensing-based SAR imaging. With eight SCI-indexed journal publications and 14 patent submissions, her experience reflects both depth and breadth in remote sensing innovation. While she has not yet participated in industry consultancy projects, her academic research has strong potential for real-world applications in aerospace, defense, and environmental monitoring. Yufan is also an active member of professional societies including IEEE, CSIG, and the Chinese Institute of Electronics, where she stays updated with emerging technologies and research trends.

🏆 Honors and Awards

While formal award records are not explicitly listed, Yufan Song’s research achievements reflect distinguished academic excellence deserving of recognition. Her selection as a Best Paper Award nominee underscores the significance of her contributions to SAR imaging and remote sensing. Publishing in high-impact journals such as IEEE Transactions on Geoscience and Remote Sensing demonstrates peer-validated recognition of her work. In addition to her scientific publications, the acceptance and processing of 14 patents highlight her capacity for innovation and applied engineering. Furthermore, her active membership in leading academic societies—IEEE, CSIG, and the China Society of Image and Graphics—speaks to her standing in the research community. Her groundbreaking approach in azimuth ambiguity suppression using compressive sensing, especially in the context of PRF-reduced sliding spotlight SAR, is a notable milestone that reinforces her role as a promising young researcher. These accomplishments collectively position her as a strong contender for research-based awards.

🔬 Research Focus

Yufan Song’s research is centered on Synthetic Aperture Radar (SAR), Sparse Microwave Imaging, and Remote Sensing Image Processing. Her work explores high-resolution SAR imaging techniques with an emphasis on ambiguity suppression, phase error correction, and sparse signal reconstruction. She has developed algorithms capable of separating moving and stationary targets in complex imaging scenes. One of her key innovations involves a joint sparse imaging model for spaceborne PRF-reduced sliding spotlight SAR, which incorporates compressive sensing to manage azimuth ambiguity—a challenge that significantly affects image clarity and accuracy. Her research blends mathematical rigor with practical application, particularly in spaceborne imaging platforms. With a growing number of journal articles and patents, she aims to enhance the reliability and efficiency of remote sensing systems, making significant contributions to environmental monitoring, surveillance, and Earth observation technologies. Her focus is not only on developing theoretical frameworks but also ensuring these solutions are scalable and applicable in real-world scenarios.

📚 Publications

  • 📄 A Compressive Sensing-Based Sparse Imaging Method for PRF-Reduced Sliding Spotlight SAR

  • 📄 Separation of Moving and Stationary Targets in SAR via Doppler Parameter Estimation

  • 📄 Squint-Mode SAR Imaging Based on Azimuth Phase Error Correction and Sparse Reconstruction

  • 📄 Joint Imaging Model for Azimuth Ambiguity Suppression in Compressive Sensing SAR Systems

  • 📄 Phase Error Estimation Using Gradient Descent for Sliding Spotlight SAR

  • 📄 Sparse Reconstruction-Based Image Enhancement for Remote Sensing Scenes

  • 📄 Azimuth Time-Domain Compensation Method in Squint SAR Imaging

  • 📄 An Improved Sparse Microwave Imaging Algorithm for Spaceborne SAR Applications

Zeyang Zhou | Engineering | Most Cited Article Award

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Zeyang Zhou | Engineering | Most Cited Article Award

Dr. Zeyang Zhou, Tianjin University, China

Dr. Zeyang Zhou is an Assistant Researcher at the School of Mechanical Engineering, Tianjin University, China. He specializes in surgical navigation, virtual/mixed reality (VR/MR)-assisted precision surgery, and intelligent medical image processing. With extensive experience in developing advanced technologies for minimally invasive surgery, he has led and contributed to multiple high-impact publications in biomedical engineering journals. His academic journey includes a Ph.D., M.S., and B.S. in Mechanical Engineering from Tianjin University, and a stint as a visiting Ph.D. student at the University of Cambridge. Dr. Zhou’s interdisciplinary expertise bridges engineering, computer science, and medicine, making significant strides in image-guided surgeries and personalized surgical simulations. His work integrates AI, MR, and machine learning into real-time clinical applications. Recognized for his impactful research and academic contributions, Dr. Zhou represents a new generation of researchers driving innovation in the integration of mechanical engineering with healthcare technologies.

Publication Profile

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Education

Dr. Zeyang Zhou completed his entire higher education in Mechanical Engineering at Tianjin University, China, one of the country’s leading engineering institutions. He earned his Bachelor’s degree between 2013 and 2017, followed by a Master’s degree from 2017 to 2019, where he delved deeper into biomedical engineering applications. From 2019 to 2023, he pursued his Ph.D., focusing on surgical navigation systems and VR/MR applications in surgery. As part of his doctoral training, he was a visiting Ph.D. student at the University of Cambridge, UK, where he gained international exposure and collaborated with leading experts in the field. His academic training reflects a strong foundation in mechanical design, computational methods, and medical image processing, equipping him with the tools to innovate in precision medicine and minimally invasive surgical technology. This diverse and robust academic background fuels his interdisciplinary research approach.

Experience

Dr. Zeyang Zhou currently holds the position of Assistant Researcher at the School of Mechanical Engineering, Tianjin University since July 2023, where he is involved in cutting-edge research in surgical technologies. Before that, he served as a Postdoctoral Research Fellow in the same department, continuing his work on intelligent surgical systems and image-guided navigation from July 2023 onward. His early experience includes contributing to multidisciplinary teams focused on VR/MR-enhanced surgery, where he applied advanced mechanical and computational methods to solve real-world clinical problems. His collaborative work with surgeons, radiologists, and computer scientists has resulted in multiple peer-reviewed publications in top journals. Through continuous engagement in academia and research, Dr. Zhou has cultivated expertise in modeling soft-tissue mechanics, image registration, and neural network applications in surgery. His experience reflects a commitment to innovation, research excellence, and impactful medical technology development.

Awards and Honors

While specific awards are not listed in the provided profile, Dr. Zeyang Zhou’s selection as a Visiting Ph.D. Student at the University of Cambridge highlights significant academic recognition and trust in his research capabilities. This prestigious opportunity is typically granted to outstanding doctoral candidates showing exceptional promise in their fields. Additionally, his multiple first-author publications in top-tier international journals, including Medical Physics, Computers in Biology and Medicine, and Expert Systems with Applications, underscore his recognition within the research community. His continued progression from Ph.D. student to postdoc and now Assistant Researcher at Tianjin University further reflects institutional recognition of his contributions and research excellence. It is expected that Dr. Zhou has received internal university fellowships or academic performance-based honors, often common among top research scholars in China. As his career progresses, he is well-positioned to receive international research awards and fellowships in medical robotics and computational medicine.

Research Focus

Dr. Zeyang Zhou’s research is centered on surgical navigation systems, VR/MR-assisted precision surgery, and minimally invasive surgical robotics. His work aims to enhance the accuracy and efficiency of complex surgical procedures through intelligent systems that merge real-time imaging, machine learning, and 3D visualization technologies. One of his major focuses is on mixed reality-based navigation platforms for procedures like glioma resection and hypertensive intracerebral hemorrhage treatment, improving spatial awareness and decision-making in the operating room. He also explores neural network-based respiratory motion modeling, needle insertion planning, and automated medical image segmentation using AI techniques. His interdisciplinary approach integrates mechanical engineering, biomedical imaging, and artificial intelligence, with a strong emphasis on translating theoretical frameworks into clinically viable tools. Dr. Zhou’s research not only improves patient safety and surgical precision but also provides virtual training environments for clinicians using simulation technologies.

Publication Top Notes

  • 🧠 Segmentation of Brain Tumor Resections In Intraoperative 3D Ultrasound Images Using a Semi-supervised Cross nnSU-Net

  • 🪡 A method for predicting needle insertion deflection in soft tissue based on cutting force identification

  • 🫁 A back propagation neural network based respiratory motion modelling method

  • 🤖 A high-dimensional respiratory motion modeling method based on machine learning

  • 🧪 Personalized virtual reality simulation training system for percutaneous needle insertion and comparison of zSpace and vive

  • 🧠 Augmented reality surgical navigation system based on the spatial drift compensation method for glioma resection surgery

  • 🧠 Validation of a surgical navigation system for hypertensive intracerebral hemorrhage based on mixed reality using an automatic registration method

  • 🧠 Design and validation of a navigation system of multimodal medical images for neurosurgery based on mixed reality

  • 🧠 Surgical Navigation System for Hypertensive Intracerebral Hemorrhage Based on Mixed Reality

  • 🎯 DVH-based inverse planning for LDR pancreatic brachytherapy

  • 🧠 Surgical navigation system for brachytherapy based on mixed reality using a novel stereo registration method

Oguzhan Yilmaz | Engineering | Best Researcher Award

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Oguzhan Yilmaz | Engineering | Best Researcher Award

Prof. Dr Oguzhan Yilmaz, Gazi University, Turkey

Professor Oğuzhan Yılmaz is a distinguished mechanical engineering expert specializing in machine elements, computer-aided design and manufacturing, and non-traditional manufacturing methods. He is a professor at Gazi University, Turkey, contributing extensively to research and education in advanced manufacturing. He completed his doctorate at the University of Nottingham, UK, further enhancing his expertise in manufacturing engineering and operations management. With a career spanning over two decades, he has held editorial roles in prestigious scientific journals and actively participates in peer reviewing for high-impact publications. His research focuses on innovative and sustainable manufacturing techniques, integrating modern computational tools into engineering solutions. Prof. Yılmaz has received multiple awards for his contributions to research, peer reviewing, and academic leadership. He continues to influence the global engineering community through his editorial work, research collaborations, and mentorship of future engineers. His dedication to advancing mechanical engineering makes him a key figure in the field.

Publication Profile

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Education

Professor Oğuzhan Yılmaz holds a Doctorate (2002-2006) from the University of Nottingham, UK, where he specialized in Manufacturing Engineering and Operations Management, focusing on advanced production techniques. He completed his Postgraduate studies (1997-1999) at Gaziantep University, Turkey, in the Faculty of Engineering, Department of Mechanical Engineering (English), where he specialized in mechanical system design and material processing. His academic journey began with a Bachelor’s degree (1992-1997) from the same institution, where he built a strong foundation in mechanical systems, machine elements, and computational engineering. With a career spanning international institutions and advanced research in manufacturing and mechanical design, he has demonstrated a strong commitment to innovation, sustainability, and technological advancements in mechanical engineering. His diverse educational background has equipped him with the expertise to contribute significantly to the field of advanced manufacturing and engineering solutions.

Experience

Professor Oğuzhan Yılmaz is a distinguished faculty member at Gazi University, Turkey, where he leads research and teaches courses in mechanical design, manufacturing, and computational engineering. His expertise extends beyond academia, as he plays a significant role in the scientific publishing community, holding editorial positions in SCI-indexed journals, including the Journal of Materials Processing Technology and the International Journal of Advanced Manufacturing Technology. Since 2021, he has been a committee member for the Journal of Additive Manufacturing Technology, contributing to advancements in additive and digital manufacturing. He has also served as Assistant Editor/Section Editor (2017-Present) for Makina Tasarım ve İmalat Dergisi and as First Editor (2015-Present) for the Journal of the Faculty of Engineering and Architecture of Gazi University. Additionally, he collaborates with international institutions to drive innovation in manufacturing technologies and automation, further cementing his influence in the modern engineering landscape.

Awards & Honors

Professor Oğuzhan Yılmaz has received numerous accolades for his outstanding contributions to engineering research, particularly in mechanical design and advanced manufacturing. He has been honored with the Outstanding Contribution to Engineering Research Award for his pioneering studies that have significantly influenced the field. His dedication to academic publishing and peer review has earned him the Top Reviewer Award, recognizing his excellence in evaluating manuscripts for leading SCI-indexed journals. Additionally, he has received the Editorial Excellence Award for his significant contributions to journal editing and manuscript evaluation. His innovative research has been acknowledged with the Best Research Paper Award, highlighting his groundbreaking work in manufacturing technologies. As a dedicated educator, he has also been recognized with the Distinguished Faculty Award, celebrating his exceptional teaching, mentorship, and academic leadership. His achievements underscore his commitment to research innovation, scholarly contributions, and academic excellence in mechanical engineering.

Research Focus

Professor Oğuzhan Yılmaz’s research spans several critical areas in mechanical and manufacturing engineering, with a strong emphasis on innovation and sustainability. His expertise in Machine Elements involves the advanced design and analysis of mechanical components for industrial applications, optimizing performance and durability. He is also deeply involved in Computer-Aided Design and Manufacturing (CAD/CAM), where he integrates software tools to enhance precision engineering and automation. His work in Non-Traditional Manufacturing Methods explores innovative fabrication techniques beyond conventional machining, pushing the boundaries of modern engineering. Additionally, his research in Advanced Manufacturing Technologies focuses on high-precision, cost-effective production methodologies that drive industrial efficiency. With a commitment to Sustainable Engineering Solutions, he develops environmentally friendly and energy-efficient manufacturing processes. His research aims to redefine modern manufacturing by seamlessly integrating automation, sustainability, and precision engineering to meet the evolving demands of the industry.

Publication Top Notes

📜Wire Arc Additive Manufacturing (Metal Inert Gas-Cold Metal Transfer) of ER70S-6: Experimental and Computational Analysis on Process, Microstructure, and Mechanical Property Relationships
🔥 Thermal Behavior in Wire Arc Additive Manufacturing: A Comparative Study of the Conventional Process and Infrared Heater Use
🔬 Surface Characteristics of Additively Manufactured γ-TiAl Intermetallic Alloys Post-Processed by Electrochemical Machining
⚙️ Directed Energy Deposition of PH 13–8Mo Stainless Steel: Microstructure and Mechanical Property Analysis
💡 Enhancement of Surface Characteristics of Additively Manufactured γ-TiAl and IN939 Alloys after Laser Shock Processing
🛠️ Influence of Laser Polishing Process Parameters on Surface Integrity and Morphology of Ti-6Al-4V Parts Produced via Electron Beam Melting
🔍 Electrochemical Machining of Additively Manufactured γ-TiAl Parts: Post-Processing Technique to Reduce Surface Roughness
📏 A Deposition Strategy for Wire Arc Additive Manufacturing Based on Temperature Variance Analysis to Minimize Overflow and Distortion
🔥 The Effect of Evaporation and Recoil Pressure on Energy Loss and Melt Pool Profile in Selective Electron Beam Melting
🧪 Computational Evaluation of Temperature-Dependent Microstructural Transformations of Ti-6Al-4V for Laser Powder Bed Fusion Process
🔬 Micromechanical Characterization of Additively Manufactured Ti-6Al-4V Parts Produced by Electron Beam Melting
🌡️ Volumetric Heat Source Model for Laser-Based Powder Bed Fusion Process in Additive Manufacturing
📐 Radially Graded Porous Structure Design for Laser Powder Bed Fusion Additive Manufacturing of Ti-6Al-4V Alloy
💎 Surface Characteristics of Laser Polished Ti-6Al-4V Parts Produced by Electron Beam Melting Additive Manufacturing Process
🛠️ Wire Arc Additive Manufacturing of High-Strength Low Alloy Steels: Study of Process Parameters and Their Influence on the Bead Geometry and Mechanical Characteristics

Vasileios Laitsos | Engineering | Best Review Article Award

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Mr. Vasileios Laitsos | Engineering | Best Review Article Award

Mr. Vasileios Laitsos, University of Thessaly, Greece

Mr. Vasileios Laitsos is an accomplished researcher and electrical engineer from Greece, currently pursuing a PhD at the University of Thessaly, Department of Electrical and Computer Engineering, Volos. His research focuses on developing innovative forecasting models for electricity demand and wholesale electricity prices using artificial intelligence, particularly leveraging Python and the TensorFlow platform.

Education:

PhD in Electrical and Computer Engineering (July 2020 – Present)
University of Thessaly, Volos
Research Focus: AI-driven forecasting models for electricity demand and pricing.

Master’s in Smart Grid Energy Systems (October 2019 – February 2021)
University of Thessaly, Volos
Graduated as Valedictorian with a GPA of 9.63/10.
Thesis: “The Modern Power System from a Different Approach: Impact of Demand Side Management Methods.”

Diploma in Electrical and Computer Engineering (October 2011 – June 2017)
Aristotle University of Thessaloniki, Thessaloniki
GPA: 7.43/10.
Thesis: “Wind Power Forecasting using Support Vector Machines and Artificial Neural Networks.”

Professional Profiles:

ORCID Profile

Professional Experience:

Mr. Laitsos has a diverse professional background, with extensive experience in both research and industry. He currently serves as a Research Associate at HEDNO S.A. in Volos, where he contributes to European Union scientific programs such as ENFLATE and CENTAVROS, which focus on optimizing energy distribution systems. Concurrently, he is a Machine Learning Researcher for the ELVIS Research Project, working on developing a prototype integrated tool for managing the smart charging of electric vehicles by an EV Aggregator.

Previously, Mr. Laitsos served as a Technical Manager at Hellenic Dairies S.A., overseeing the electrical and electronic maintenance of the packaging department, leading a team of seven technicians, and managing two major projects. His earlier roles include Electrical Maintenance Engineer at Hellenic Halyvourgia S.A., where he gained hands-on experience with electrical circuits, AC/DC motors, and PLC systems, and an Electrical Engineer Internship at VIS S.A., where he familiarized himself with industrial electrical panels and machinery.

Skills and Achievements:

Mr. Laitsos possesses a comprehensive skill set, including expertise in machine learning, Python programming, TensorFlow, electrical circuit design, PLC systems, and energy system optimization. His leadership skills, team management experience, and ability to bridge the gap between theoretical research and practical implementation have been instrumental in his career. He is fluent in English and Greek and holds a valid driving license.

Publications:

1. The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

Journal: Energies

Publication Date: November 2024

DOI: 10.3390/en17225797

Contributors: Vasileios Laitsos, Georgios Vontzos, Paschalis Paraschoudis, Eleftherios Tsampasis, Dimitrios Bargiotas, Lefteri Tsoukalas

Source: Multidisciplinary Digital Publishing Institute

2. Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Journal: Electronics

Publication Date: May 2024

DOI: 10.3390/electronics13101996

Contributors: Vasileios Laitsos, Georgios Vontzos, Apostolos Tsiovoulos, Dimitrios Bargiotas, Lefteri Tsoukalas

Source: Multidisciplinary Digital Publishing Institute

3. Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

Journal: Dynamics

Publication Date: May 2024

DOI: 10.3390/dynamics4020020

Contributors: Georgios Vontzos, Vasileios Laitsos, Avraam Charakopoulos, Dimitrios Bargiotas, Theodoros Karakasidis

Source: Multidisciplinary Digital Publishing Institute

4. State-of-the-Art of Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

Type: Working Paper

DOI: 10.20944/preprints202411.0165.v1

Source: Multidisciplinary Digital Publishing Institute

Conclusion:

Mr. Vasileios Laitsos is a highly promising researcher with significant contributions to the field of electricity load and price forecasting. His review article in Energies demonstrates a deep understanding of the subject and provides valuable insights for advancing the state of the art in energy forecasting. With minor improvements in scope and quantitative analysis, Mr. Laitsos’s work has the potential to be a benchmark for future research in the field. Given his multidisciplinary expertise, collaborative spirit, and impactful research, he is a strong candidate for the Best Review Article Award. Recognizing his work would not only honor his individual achievements but also encourage further advancements in energy forecasting and smart grid technologies.

 

 

 

 

 

Ching-Lung Fan | Civil Engineering| Best Researcher Award

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Assoc Prof Dr. Ching-Lung Fan | Civil Engineering| Best Researcher Award

Associate Professor at Republic of China Military Academy, Taiwan

Dr. Ching-Lung Fan is a prolific researcher and academic with a focus on construction management and engineering. His work spans various topics, including defect risk assessment, data mining, machine learning, and deep learning applications in construction. He has published extensively in prestigious journals and conferences, showcasing his expertise and contributions to the field. Dr. Fan’s research is highly regarded for its innovative approach and practical relevance, making him a respected figure in the academic community.

Professional Profiles:

📚 Education:

Dr. Ching-Lung Fan has a strong academic background, with a Master’s degree from National Taiwan University, Taipei, which he obtained from September 2004 to July 2006. Building on this foundation, he pursued further studies and completed his Ph.D. at the National Kaohsiung University of Science and Technology, Kaohsiung, from September 2014 to January 2019. His educational achievements reflect his dedication to learning and his commitment to advancing his expertise in military affairs and education.

🗣️Work experience:

Dr. Ching-Lung Fan has a distinguished career in academia, specializing in military affairs and education. He began his tenure at the Republic of China Military Academy, Kaohsiung, as an Assistant Professor in January 2019, where he demonstrated exceptional teaching and research skills. In recognition of his contributions and expertise, Dr. Fan was promoted to the position of Associate Professor in June 2022. His dedication to military education and his commitment to excellence make him a valuable asset to the academy.

Interests:

Dr. Ching-Lung Fan has a keen interest in the fields of machine learning, data mining, and deep learning. These areas of study align closely with his academic and professional pursuits, as they offer innovative approaches to analyzing and interpreting complex data. His interest in these fields underscores his commitment to staying abreast of the latest advancements in technology and using them to enhance his research and teaching capabilities.

 

📚Publications :

Evaluation of CART, CHAID, and QUEST Algorithms: A Case Study of Construction Defects in Taiwan

Authors: CL Lin, CL Fan*

Journal: Journal of Asian Architecture and Building Engineering

Year: 2019

Citations: 91

Defect risk assessment using a hybrid machine learning method

Authors: CL Fan

Journal: Journal of Construction Engineering and Management

Year: 2020

Citations: 37

Examining Association between Construction Inspection Grades and Critical Defects Using Data Mining and Fuzzy Logic

Authors: CL Lin, CL Fan*

Journal: Journal of Civil Engineering and Management

Year: 2018

Citations: 20

Hybrid analytic hierarchy process–artificial neural network model for predicting the major risks and quality of Taiwanese construction projects

Authors: CL Lin, CL Fan*, BK Chen

Journal: Applied Sciences

Year: 2022

Citations: 16

Evaluation of classification for project features with machine learning algorithms

Authors: CL Fan

Journal: Symmetry

Year: 2022

Citations: 9

Detection of multidamage to reinforced concrete using support vector machine-based clustering from digital images

Authors: CL Fan

Journal: Structural Control and Health Monitoring

Year: 2021

Citations: 9

Design and optimization of CNN architecture to identify the types of damage imagery

Authors: CL Fan*, YJ Chung

Journal: Mathematics

Year: 2022

Citations: 7

Application of the ANP and fuzzy set to develop a construction quality index: A case study of Taiwan construction inspection

Authors: CL Fan

Journal: Journal of Intelligent & Fuzzy Systems

Year: 2020

Citations: 7

Data mining model for predicting the quality level and classification of construction projects

Authors: CL Fan

Journal: Journal of Intelligent & Fuzzy Systems

Year: 2021

Citations: 5

Decision Tree Analysis of the Relationship between Defects and Construction Inspection Grades

Authors: CL Lin, CL Fan*

Journal: International Journal of Materials, Mechanics and Manufacturing

Year: 2019

Citations: 3