Dora Pugliese | Civil and Architecture Engineering | Best Researcher Award

Dr. Dora Pugliese | Civil and Architecture Engineering | Best Researcher Award

Ph.D. Research & Teaching Assistant, University of Florence, Italy

Dora Pugliese is a driven and accomplished researcher, currently serving as a Ph.D. Research and Teaching Assistant at the University of Miami. Specializing in sustainable construction materials, Dora blends her expertise in civil and architectural engineering with a profound commitment to environmental sustainability. Her research into innovative, eco-friendly building materials, like cork-based mortars, aims to enhance both the resilience and environmental impact of construction practices.

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🎓 Education:

Dora graduated magna cum laude in 2019 with a Master’s degree in Architecture from the University of Florence, where she specialized in masonry analysis techniques. Licensed as an Italian Architect in 2020, she completed a Ph.D. summa cum laude in 2024 focusing on sustainable materials for historic masonry. She is now pursuing a second Ph.D. in Civil and Architectural Engineering at the University of Miami, where she applies her interdisciplinary knowledge to innovative construction techniques.

👩‍🔬 Experience:

Dora has contributed significantly to both academic and applied research settings. In her current role at the University of Miami, she investigates biodegradable and recyclable materials for sustainable construction. Her prior experience includes advanced research on masonry restoration using machine learning and Bayesian networks, leveraging her expertise in sustainable materials and historic architecture.

🔬 Research Interests:

Dora’s research centers on sustainable construction, material science, and environmental engineering. Her work particularly emphasizes the development of cork-modified composites for environmentally friendly building practices, aiming to reduce the ecological footprint of masonry construction and restoration.

🏆 Awards:

Dora has been recognized for her outstanding contributions to architectural and environmental engineering. Her commitment to advancing sustainable materials for historic preservation and structural engineering has earned her accolades within the academic and professional community.

📚 Publications and Citations:

Dora’s research is widely published in reputable journals. Below are some of her key publications:

“Development of Biodegradable and Recyclable FRLM Composites Incorporating Cork Aggregates for Sustainable Construction Practices,” Materials (2024).

“Exploring Sustainable Materials for Masonry Restoration,” Journal of Building Engineering (2023). Cited by 15 articles.

Assoc Prof. Dr. hacene mellah | Engineering | Best Researcher Award

Assoc Prof. Dr. hacene mellah | Engineering | Best Researcher Award

Assoc Prof. Dr. hacene mellah, bouira university, Algeria

Dr. Hacene Mellah is a dedicated researcher and esteemed lecturer in Electrical Engineering, specializing in electrical machines. Currently serving as an MCA at Akli Mohand Oulhadj University in Bouira, Algeria, he brings extensive experience in both academia and research. With a career marked by a strong commitment to advancing electrical engineering, Dr. Mellah contributes actively to various scholarly publications and committees. His insights and research have notably enhanced knowledge in electrical systems, control, and diagnostics.

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Education 🎓

Dr. Mellah’s academic journey began with a BAC in 2001, followed by an Engineering degree in Electrical Control from Ferhat Abbas University, Setif, in 2006. He obtained a Magister in Electrical Machines and Control in 2009 and a Doctorate in Electrical Machines in 2020, also from Ferhat Abbas University. His doctoral research focused on enhancing the intrinsic parameters of electrical machines, a pursuit that reflects his commitment to innovation in electrical engineering.

Professional Experience 👨‍🏫

Dr. Mellah has been actively teaching since 2010 across various institutions, including the University of Sétif and the University of Chlef. His teaching portfolio spans power electronics, control systems, and electrical systems design for both undergraduate and graduate levels. His expertise also extends to mentoring students in competitions and supervising theses, demonstrating his dedication to nurturing future engineers.

Research Interests 🔬

Dr. Mellah’s research primarily revolves around electric machine diagnostics, control strategies, and multi-physics modeling. His work explores innovative methods for machine learning diagnostics, thermal transfer, and parametric estimation. He applies advanced techniques such as artificial neural networks to improve the accuracy of diagnostic and control systems, particularly for electric machines like IM, DCM, and PMSG.

Awards 🏆

Throughout his career, Dr. Mellah has been recognized for his contributions to electrical engineering research and academia. His dedication to advancing sustainable and renewable energy systems has earned him respect among peers and accolades from professional organizations. His ongoing involvement with academic journals as an editor and reviewer further reflects his esteemed role in the research community.

Publication Top Notes 📚

Fuzzy Logic-Based Smart Control of Wind Energy Conversion System Using Cascaded Doubly Fed Induction Generator

Performance improvement of a DPC-FPID strategy with matrix converter using CDFIG in wind power system

Optimization of the Powers Exchanged between a Cascaded Doubly Fed Induction Generator and the Grid with a Matrix Converter

Point on Wave Energization Strategy and Sequential Phase Shifting for Sympathetic Inrush Current Mitigation in Three-Phase Transformer – Measurement

Comparative study of tolerant controls used for fault detection in dual-feed machines

Analysis and testing of internal combustion engine driven linear alternator

Improvement of Sliding Mode Control Strategy Founded on Cascaded Doubly Fed Induction Generator Powered by a Matrix Converter

A Fast-Intelligent Sensor Based on Cascade-Forward Neural Network Founded by Resilient Backpropagation for Simultaneous Parameters and State Space Estimation of Brushed DC Machines
Comparing performances of three CFNN used for DC machine combined parameter and states estimation.