Mona AbouEleaz | Engineering and Technology | Best Researcher Award

Mona AbouEleaz | Engineering and Technology | Best Researcher Award

Assoc. Prof. Dr Mona AbouEleaz, Mansoura University, Egypt

Assoc. Prof. Dr. Mona AbouEleaz 🌍⚙️ is a distinguished academic in Production Engineering at Mansoura University, Egypt. With a Ph.D. in Production Engineering 🎓, her expertise spans Smart Manufacturing, Industry 4.0, Lean Six Sigma, and Quality Improvement approaches 📊. She has held roles as a postdoctoral researcher in Canada 🇨🇦 and visiting lecturer in the UK 🇬🇧. As the Executive Director of the University Center for Career Development, she fosters student growth 🚀. Dr. AbouEleaz has published extensively 📚, supervised over 35 projects 🛠️, and actively contributes to academic accreditation and international collaborations 🌐. She enjoys reading, drawing, sports, and traveling ✈️🎨.

Publication Profile

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Education

Assoc. Prof. Dr. Mona AbouEleaz holds a Ph.D. in Production Engineering from Mansoura University, Egypt (2010–2015), with a thesis titled “Lean Six Sigma Practices into an Organizational Quality Management System” 🎯📊. She earned her M.Sc. in Industrial Engineering from Alexandria University (2005–2008), focusing on “Hierarchic Framework for Quality Improvement Approaches” ⚙️📈. Dr. AbouEleaz also holds a B.Sc. in Production Engineering & Mechanical Design from Mansoura University, graduating with honors and ranking first in her class 🏅🎓. Her academic journey reflects a strong commitment to quality improvement, engineering excellence, and continuous innovation 🔍💡.

Experience

Assoc. Prof. Dr. Mona AbouEleaz is a distinguished academic with extensive experience in mechanical design and production engineering. She’s a lecturer at Mansoura University, Egypt 🇪🇬, and has served as a visiting lecturer at the University of Central Lancashire, UK 🇬🇧, and a postdoctoral researcher at the University of Calgary, Canada 🇨🇦. Dr. AbouEleaz excels in leadership roles, serving as Executive Director of the University Career Center and coordinating ERASMUS programs 🌍. Her contributions to strategic planning, quality assurance ✅, and international collaboration 🤝 have significantly advanced Mansoura University’s academic landscape.

Research Focus

Assoc. Prof. Dr. Mona AbouEleaz’s research focuses on advanced manufacturing processes, particularly in Wire Electrical Discharge Machining (WEDM) and material performance analysis. Her work explores surface roughness, material removal rates, and optimization of machining parameters for alloys like AISI304 and AISI316. She also delves into roundness measurement techniques, finite element analysis, and the impact of ERP systems on production efficiency. Additionally, her studies address supply chain performance and process improvement strategies in manufacturing. ⚙️🔩📊✨🔍

Research Interest

Assoc. Prof. Dr. Mona AbouEleaz’s research focuses on Smart Manufacturing and Industry 4.0 🚀🏭. She specializes in enhancing the performance of production and service systems through Quality Improvement approaches, including Lean Six Sigma 📊, optimization methods ⚙️, and Response Surface Methodology. Her expertise extends to Uncertainty Analysis, MCDM (Multi-Criteria Decision Making), and Decision Making Under Uncertainty 🤔. Dr. Mona also works on Measurement Uncertainty, Mechanical Testing 🧪, and utilizes tools like Minitab and ANSYS for statistical and engineering analysis. Her research aims to boost efficiency, precision, and decision-making in advanced manufacturing environments 🌟.

Publication Top Notes

Experimental investigation of surface roughness and material removal rate in wire EDM of stainless steel 304

Methods of roundness measurement: An experimental Comparative study

Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization

The Impact of Enterprise Resource Planning (ERP) Implementation on Performance of Firms: A Case to Support Production Process Improvement.

A critical review of supply chain performance evaluation

A study of drilling parameter optimization of functionally graded material steel–aluminum alloy using 3D finite element analysis

Automated Classification of Marble Types Using Texture Features and Neural Networks: A Robust Approach for Enhanced Accuracy and Reproducibility

Types Using Texture Features and Neural Networks: A Robust Approach

Investigating the Impact of Cutting Parameters on Roundness and Circular Runout Using Signal-to-noise Ratio Analysis

 

Xiaolan Qiu | Engineering and Technology | Best Researcher Award

Xiaolan Qiu | Engineering and Technology | Best Researcher Award

Prof Xiaolan Qiu, Aerospace Information Research Institute, Chinese Academy of Sciences, China

Based on the information provided, Prof. Xiaolan Qiu appears to be an outstanding candidate for the Best Researcher Award. Here are key reasons for this recommendation, formatted with paragraph headings for clarity:

Publication profile

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Academic Qualifications

Prof. Qiu holds a Ph.D. in Signal and Information Processing from the Graduate School of the Chinese Academy of Sciences. Her doctoral research focused on advanced imaging algorithms for bistatic Synthetic Aperture Radar (SAR), demonstrating her deep understanding of complex signal processing.

Research Experience

With extensive experience in spaceborne SAR data processing, Prof. Qiu has been involved in significant projects since 2006. She played a critical role in developing data processing systems for China’s first SAR satellite, CRS-1, and has led teams responsible for various SAR satellites, including Gaofen-3. Her current leadership in managing data processors for upcoming SAR satellites underscores her pivotal role in advancing SAR technology.

Publications and Contributions

Prof. Qiu has a robust publication record, contributing to various journals such as IEEE Transactions on Geoscience and Remote Sensing. Her works on bistatic SAR algorithms, SAR image analysis, and polarimetric data processing are highly cited and reflect her contributions to the field of remote sensing. Notable papers include “An improved NLCS algorithm with capability analysis for one-stationary BiSAR” and “Focusing of medium-earth-orbit SAR with advanced nonlinear chirp scaling algorithm.”

Awards and Recognitions

Prof. Qiu has received several prestigious awards, including the Outstanding Scientific and Technological Achievement Award from the Chinese Academy of Sciences in 2016 and the Wang Kuancheng Education Foundation award for Outstanding Woman Scientists in 2010. These honors signify her influence and recognition in the scientific community.

Leadership and Collaboration

As a deputy director of the Key Laboratory of GIPAS at the Institute of Electronics, Chinese Academy of Sciences, Prof. Qiu demonstrates strong leadership in guiding research initiatives and fostering collaboration within the academic and scientific communities. Her role as a project leader for SAR data processing and analysis showcases her ability to manage complex research projects effectively.

Conclusion

Prof. Xiaolan Qiu’s impressive educational background, extensive research experience, high-impact publications, numerous accolades, and leadership in significant research projects make her a highly deserving candidate for the Best Researcher Award. Her contributions not only advance the field of signal and information processing but also enhance the capabilities of SAR technology in various applications.

Publication top notes

An improved NLCS algorithm with capability analysis for one-stationary BiSAR

Some reflections on bistatic SAR of forward-looking configuration

Focusing of medium-earth-orbit SAR with advanced nonlinear chirp scaling algorithm

Synthetic aperture radar three-dimensional imaging——from TomoSAR and array InSAR to microwave vision

Preliminary exploration of systematic geolocation accuracy of GF-3 SAR satellite system

An Omega-K algorithm with phase error compensation for bistatic SAR of a translational invariant case

Urban 3D imaging using airborne TomoSAR: Contextual information-based approach in the statistical way

SRSDD-v1. 0: A high-resolution SAR rotation ship detection dataset

Projection shape template-based ship target recognition in TerraSAR-X images

A novel motion parameter estimation algorithm of fast moving targets via single-antenna airborne SAR system