David Santiago Pellicer Zubeldía | Transportation Engineering | Best Researcher Award

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Mr. David Santiago Pellicer Zubeldía | Transportation Engineering | Best Researcher Award

Worker | University of Zaragoza | Spain

Short Bio ✨

David Santiago Pellicer Zubeldía is an Industrial Engineer with a specialization in Mechanical and Electrical Engineering from the University of Zaragoza. He has focused his expertise on Machine & Vehicle (Mechanical) Design, particularly within the railway sector. David is passionate about research and development in this field, and he is actively searching for a PhD project to further his academic and professional goals.

Profile👤

Orcid

Education 🎓

David completed his Master’s in Industrial Engineering with a major in Machine & Vehicle (Mechanical) Design in 2021 at the University of Zaragoza. Prior to that, he earned a Degree in Industrial Technologies Engineering (Mechanical + Electrical Engineering) in 2019 from the same institution. Additionally, he pursued a Master’s in Railway Systems at TECH Technological University in 2023. David has also undertaken various specialized courses, including Python programming, SolidWorks, and ANSYS.

Experience 💼

David has accumulated significant experience in railway project development and mechanical design through roles in companies such as INECO, FutureLift S.L., and SEGULA Technologies. At INECO, he worked on I+D+i railway projects, performing technical analyses and writing technical documentation. At FutureLift S.L., he improved hydraulic lifting machines through R+D+i techniques and oversaw design updates. At SEGULA Technologies, he contributed to R+D+i tasks and railway component design, managing 3D modeling and creating technical documents in English.

Research Interest 🔬

David’s primary research interest lies in railway engineering, with a focus on improving vehicle design, wheel wear analysis, and the implementation of the Hyperloop concept. His research revolves around finding innovative solutions for enhancing transportation systems, including studies on vacuum tube transport and wheelbase sensitivity analysis.

Awards 🏆

David has earned honors for his academic achievements, including his Master’s Thesis on the rolling phenomenon of reduced-diameter railway wheels in freight wagons, completed in 2021. He was also recognized for his work on Safe and Sustainable Mobility, where he designed an e-bike and contributed to bikeway safety design in 2021.

Publications 📚

  1. Pellicer, David S. 2024. “Demonstration of the theoretical superelevation formulae for railway lines.” Read article.
    Cited by articles on railway line design.
  2. Pellicer, David S., & Emilio Larrodé. 2024. “Sensitivity Analysis of Bogie Wheelbase and Axle Load for Low-Floor Freight Wagons, Based on Wheel Wear.” Machines, 12(8), 515. Read article.
    Cited by articles on freight wagon dynamics.
  3. Pellicer, David S., & Emilio Larrodé. 2024. “Analysis of the Effectiveness of a Freight Transport Vehicle at High Speed in a Vacuum Tube (Hyperloop Transport System).” Algorithms, 17(1), 17. Read article.
    Cited by articles on futuristic transportation.
  4. Pellicer, David S. 2023. “The Problem of Ballast Pick-Up.” Trenvista, 1(1), 48. Read article.
    Cited by studies on railway ballast management.
  5. Pellicer, David S. 2020. “Conceptual Development, Analysis, and Simulation of the Transport Capacity of a Freight Transport Vehicle in Vacuum Tubes at High Speed (Hyperloop Concept).” SSRN. Read article.
    Cited by articles on Hyperloop systems.

Conclusion 🚀

David Santiago Pellicer Zubeldía’s blend of industrial engineering, railway expertise, and research capabilities positions him as a forward-thinking professional eager to push the boundaries of transportation technologies. His journey from mechanical design to Hyperloop research highlights his commitment to innovation and his readiness to contribute to cutting-edge engineering projects.

Nimrah Saeed – Environmental Sustainability – Best Researcher Award

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Nimrah Saeed - Environmental Sustainability - Best Researcher Award

Zhejiang University - China

AUTHOR PROFILE

SCOPUS

PROFESSIONAL SUMMARY 🧩

Nimrah Saeed is a distinguished researcher and engineer specializing in innovative energy systems and advanced power technologies. Her expertise spans from blockchain-enabled microgrid management to novel converter topologies, showcasing her commitment to advancing sustainable and efficient energy solutions.

PAPER REVIEWER AT JOURNAL OF POWER AND ENERGY ENGINEERING 📝

Since 2020, Nimrah has contributed her expertise as a Paper Reviewer for the Journal of Power and Energy Engineering, Scientific Research Publishing, China. In this role, she evaluates research manuscripts, ensuring the quality and integrity of published studies within the field of power and energy engineering.

PAPER REVIEWER AT AMERICAN JOURNAL OF ELECTRIC POWER AND ENERGY SYSTEMS 🔍

Nimrah has also served as a Paper Reviewer for the American Journal of Electric Power and Energy Systems, Science Publishing Group, New York, USA, since 2018. Her critical evaluation of research papers helps maintain high standards and promotes advancements in electric power and energy systems.

DOCTORATE RESEARCH ON SMART MICROGRID ENERGY MANAGEMENT 💡

Nimrah's PhD research focuses on the development of a Blockchain-enabled Smart Microgrid Energy Management and Trading System. This innovative framework integrates RSA-based blockchain technology for secure transactions and employs an AI-based fuzzy logic controller for optimized energy trading and management. The system’s advanced features include smart contracts for automating trading processes and carbon credit trading to promote sustainability.

POSTGRADUATE RESEARCH ON ISOLATED DC-DC CONVERTERS 🔋

During her postgraduate studies, Nimrah investigated impedance source isolated DC-DC converters for renewable generation systems. She proposed novel topologies such as the Cascaded Z-source Isolated DC-DC Converter (CZIDC) and the Quasi Z-source Multilevel DC-DC step-up isolated Converter (ML-qZDC). Her research, verified through SIMULINK MATLAB, highlighted these converters’ efficiency and versatility in energy applications.

UNDERGRADUATE RESEARCH ON MULTILEVEL CACHE DESIGN 🖥️

In her undergraduate research, Nimrah utilized design space exploration (DSE) techniques to evaluate multilevel cache design alternatives for multi-core systems. She employed the MARSSx86 simulation tool to achieve high-performance simulations and detailed cache modeling, contributing to advancements in multi-core architecture and system simulation.

ENGINEERING EXPERIENCE AT HEAVY INDUSTRIES TAXILA ⚙️

In July 2011, Nimrah worked as an Electrical Engineer at Heavy Industries Taxila (HIT) in Pakistan. Her role involved hands-on engineering tasks, providing her with valuable experience in industrial applications and practical engineering solutions.

NOTABLE PUBLICATION

Optimal State-of-Charge Management for Electric Vehicle Batteries Using Eagle Particle Swarm Optimization-Based Hybrid Deep Reinforcement Learning
Authors: M.Z. Afzal, F. Wen, M. Aurangzeb, N. Saeed
Year: 2023
Conference: 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023

Design of Adaptive Training Control in Dispatcher Training Simulators
Authors: X. Lai, H. Chen, A. Dong, N. Saeed, Z. Han
Year: 2023
Conference: Proceedings - 2023 8th Asia Conference on Power and Electrical Engineering, ACPEE 2023

Suranjan Goswami – Transportation – Best Researcher Award

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Suranjan Goswami - Transportation - Best Researcher Award

Indian Institute of Information Technology - India

AUTHOR PROFILE

SCOPUS

PROFESSIONAL SUMMARY 🧠

Suranjan Goswami is a dedicated AI and Computer Vision Engineer known for his expertise in developing and deploying advanced AI and machine learning models. With a strong background in Python, feature engineering, and data analysis, Suranjan focuses on enhancing project performance and efficiency through innovative AI solutions.

CURRENT POSITION AT OLA ELECTRIC 🚗

As a Senior Research Engineer at Ola Electric since November 2023, Suranjan is pivotal in developing a vision pipeline for automating mechanical tasks at the Ola Future Factory. His work includes creating an AI-based path planning system for optimal storage and route planning in dark warehouses, implementing camera-based pick and place systems using robots, and enhancing 3D point cloud registration and stitching for precise vehicle frame alignment.

PREVIOUS ROLE AT TRIMBLE 🌐

From September 2022 to September 2023, Suranjan served as a Computer Vision Engineer at Trimble. There, he developed and deployed sophisticated AI models that significantly improved performance and efficiency. His role involved spearheading projects in multi-spectral odometry and point cloud registration, demonstrating his proficiency in deep learning technologies like GAN and ResNet for image analysis.

RESEARCH EXPERIENCE AT DRDO 🔬

Between January 2014 and October 2015, Suranjan worked as a Junior Research Fellow at the Defence Research and Development Organization (DRDO). His responsibilities included developing and deploying performance evaluation metrics for the DRONA network, collaborating with statisticians and psychologists on evaluation scales, and managing R&D tasks at the Defence Institute of Psychological Research (DIPR) in Delhi.

EXPERTISE IN COMPUTER VISION AND AI 🖼️

Suranjan's expertise spans across computer vision, AI, and machine learning, with a strong focus on generative AI and multi-spectral imaging. He has a deep understanding of point cloud registration and statistical analysis, and his proficiency in Python has been crucial for data ETL and analysis, contributing to the successful execution of various high-impact projects.

DEEP LEARNING AND DATA ANALYSIS 📊

Utilizing deep learning technologies such as GAN and ResNet, Suranjan has driven advancements in image analysis. His skills in data analysis and statistical methods have enabled him to deliver valuable insights and recommendations through collaborative efforts with cross-functional teams, further enhancing the efficacy of AI models.

ONLINE PRESENCE AND PROFESSIONAL NETWORK 🌐

Suranjan maintains a strong professional presence through various online platforms, including LinkedIn, Google Scholar, IEEE DataPort, and GitHub. These profiles showcase his extensive contributions to the field, including published research and project portfolios, reinforcing his role as a leading expert in AI and computer vision.

NOTABLE PUBLICATION

A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images
Authors: Goswami, S., Singh, S.K., Chaudhuri, B.B.
Year: 2023
Conference: Communications in Computer and Information Science

A Simple Mutual Information Based Registration Method for Thermal-Optical Image Pairs Applied on a Novel Dataset
Authors: Goswami, S., Singh, S.K.
Year: 2022
Conference: 2022 3rd International Conference for Emerging Technology, INCET 2022

A Simple Deep Learning Based Image Illumination Correction Method for Paintings
Authors: Goswami, S., Singh, S.K.
Year: 2020
Journal: Pattern Recognition Letters

Fang Yang – Transportation Engineering – Best Researcher Award

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Fang Yang - Transportation Engineering - Best Researcher Award

Kunming University of Science and Technology - China

AUTHOR PROFILE

SCOPUS

EXPERT IN ELECTRIC VEHICLE CHARGING SAFETY

Fang Yang is a leading researcher in the field of electric vehicle technology, with a focus on enhancing the safety and efficiency of electric bike charging systems. His work explores innovative methods for detecting charging anomalies and promoting safe charging practices through advanced data analysis and machine learning techniques.

PROLIFIC AUTHOR IN ENGINEERING AND TRANSPORTATION

Fang has contributed significantly to academic literature with several high-impact publications. Notably, his paper on electric bike charging anomaly detection was published in Engineering Applications of Artificial Intelligence, highlighting his expertise in big data applications for transportation systems.

MAJOR PROJECT CONTRIBUTOR

Fang has played a pivotal role in various major projects, including evaluating traffic impacts and organizing traffic during the construction of Guiyang Rail Transit Line S2. His contributions extend to optimizing safety operations for new energy vehicle charging piles and researching big data public services for Kunming mobile signaling.

ADVANCING MACHINE LEARNING IN TRANSPORTATION

His research also includes leveraging machine learning to enhance the safety of electric bicycle charging systems. His work in this area has been featured in iScience, reflecting his commitment to applying cutting-edge technology to real-world transportation challenges.

RESEARCH IN URBAN RAIL TRANSIT DEMANDS

Fang's research extends to the predictability of passenger demands in urban rail transit. His study, published in Transportation, delves into short-term predictions for passenger origins and destinations, showcasing his expertise in optimizing urban transit systems.

FOCUS ON DATA-DRIVEN FORECASTING

His paper on battery swapping demands for electric bicycles, published in the Journal of Transportation Systems Engineering and Information Technology, underscores his proficiency in data-driven forecasting and its applications in improving transportation infrastructure.

DIVERSE RESEARCH EXPERIENCE

With extensive experience across multiple research projects, Fang Yang's work spans from safety analysis of new energy vehicle infrastructure to public service optimization using big data. His diverse expertise reflects a broad commitment to advancing transportation systems through innovative research.

NOTABLE PUBLICATION

Predictability of Short-Term Passengers’ Origin and Destination Demands in Urban Rail Transit.
Authors: F. Yang, C. Shuai, Q. Qian, M. He, J. Lee
Year: 2023
Journal: Transportation, 50(6), pp. 2375–2401

Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network.
Authors: C. Shuai, X. Zhang, Y. Wang, F. Yang, G. Xu
Year: 2023
Journal: IEEE Intelligent Transportation Systems Magazine, 15(4), pp. 121–136

Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping.
Authors: C. Shuai, Y. Sun, X. Zhang, X. Ouyang, Z. Chen
Year: 2023
Journal: IEEE Transactions on Industrial Electronics, 70(7), pp. 7280–7289

Promoting Charging Safety of Electric Bicycles via Machine Learning.
Authors: C. Shuai, F. Yang, W. Wang, Z. Chen, X. Ouyang
Year: 2023
Journal: iScience, 26(1), 105786

Battery Swapping Demands Forecast for Electric Bicycles Based on Data-Driven.
Authors: C.-Y. Shuai, F. Yang, X. Ouyang, G. Xu
Year: 2021
Journal: Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 21(2), pp. 173–179

Ferry Jie – Transportation – Excellence in Research

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Ferry Jie - Transportation - Excellence in Research

Edith Cowan University - Australia

AUTHOR PROFILE

ORCID
Scopus

EARLY ACADEMIC PURSUITS:

Ferry Jie commenced his academic journey with a dedication to research, consistently upholding a high standard of excellence. His educational background and early academic pursuits laid the foundation for a prolific career in supply chain management and logistics.

PROFESSIONAL ENDEAVORS:

Ferry Jie currently serves as the Secretary to the Chartered Institute Logistics and Transport (CILTA) Western Australia (WA) Committee, showcasing his commitment to professional development and industry engagement. Additionally, he is an active member of the Supply Chain & Logistics Association of Australia (SCLAA) WA Committee.

CONTRIBUTIONS AND RESEARCH FOCUS:

Dr. Ferry Jie has significantly contributed to the fields of supply chain management and logistics, demonstrating international scholarly leadership. His impactful research has earned him invitations as a keynote speaker and lecturer at symposiums and conferences across the globe, including Indonesia, Malaysia, Vietnam, China, the UK, and Australia. Notably, his research output includes over 55 refereed journal articles and 10 conference papers, reflecting a commitment to advancing knowledge in his field.

IMPACT AND INFLUENCE:

Ferry Jie's influence extends beyond academia, with a substantial impact on industry practices. His research has been published in top-tier journals, with 31 articles ranked in Q1 and 23 articles in Q2 according to Scimago. Moreover, his research grants and awards, totaling over $1.85 million, underscore the practical applications and relevance of his work.

ACADEMIC CITES:

Dr. Jie's extensive publication record in reputable journals highlights his academic influence. His work has contributed to the scholarly discourse in supply chain management, logistics, and related fields, earning recognition from peers and researchers globally.

LEGACY AND FUTURE CONTRIBUTIONS:

As a recognized figure in supply chain management, Ferry Jie's legacy is marked by a commitment to excellence, impactful research, and a dedication to industry engagement. His future contributions are anticipated to further shape and advance the fields of logistics and supply chain management, leaving an enduring impact on academia and industry alike.

SERVICE/OTHER RELEVANT PROJECTS/ENGAGEMENTS:

Ferry Jie's involvement in diverse projects, including the Pfizer Project and Small Business Development Corporation's Supply Chain Management Workshop, underscores his commitment to practical applications and industry collaboration. His engagement with various stakeholders demonstrates a proactive approach to addressing real-world challenges and fostering meaningful connections between academia and industry.

MEMBERSHIP OF PROFESSIONAL SOCIETIES:

As a member of esteemed professional societies such as the Supply Chain and Logistics Association of Australia (SCLAA), CIPSA, Chartered Institute Logistics and Transport, Australia (CILTA), and others, Ferry Jie actively contributes to the broader professional community. His memberships reflect a commitment to ongoing learning, collaboration, and knowledge exchange within the field.

NOTABLE PUBLICATION

Monetary policy, macroprudential policy, and bank risk-taking behaviour in the Indonesian banking industry.  

An experimental study for the decision-making support of shared transport options in Western Australia.