Getahun Mekuria | Engineering | Research Excellence Award

Mr. Getahun Mekuria | Engineering | Research Excellence Award

Lecturer | Debre Berhan University | Ethiopia

Mr. Getahun Mekuria is an industrial engineering researcher and lecturer with expertise spanning operations research and optimization, manufacturing systems and automation including additive manufacturing, human factors and ergonomics, supply chain and logistics, quality control, continuous improvement, and sustainability. His research approach integrates empirical investigations, digital twin simulations, lean manufacturing, and hybrid technologies such as IoT, big data, and blockchain to optimize productivity, quality, and environmental performance in manufacturing and industrial systems. He has contributed book chapters and numerous peer-reviewed journal articles, exploring topics from service quality and maintenance practices to air pollution assessment and wastewater treatment. Actively engaged in editorial and peer-review roles, he also mentors students in computational methods, system modeling, and industrial design. His work emphasizes practical and sustainable solutions, fostering innovation, operational excellence, and integration of human and technological factors in industrial and manufacturing environments.

Citation Metrics (Google Scholar)

15

10

5

0

 

 

Citations
7

h-index
2

🟦 Citations    🟩 h-index


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Featured Publications

Isye Nurhasanah | Engineering | Research Excellence Award

Mrs. Isye Nurhasanah | Engineering | Research Excellence Award

Lecturer | Institut Teknologi Sumatera | Indonesia

Mrs. Isye Nurhasanah is an urban and regional planning scholar whose work explores spatial and aspatial planning, tourism development, governance, social innovation, participatory planning, and island studies. Her research integrates community-based approaches with sustainability perspectives to understand capacity building, local empowerment, and alternative governance arrangements in small island contexts. She has contributed to studies on ecotourism, landscape change, urban political ecology, and social resilience, producing interdisciplinary insights that bridge planning theory and real-world community needs. Her professional experience spans academic teaching, applied research, and collaborative development projects that support inclusive, knowledge-driven, and sustainable regional growth.

Citation Metrics (Google Scholar)

300

200

100

0

Citations
251

i10-index
8

h-index
8

🟦 Citations    🟄 i10-index    🟩 h-index


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Tawakalitu Odubiyi | Engineering | Best Researcher Award

Dr. Tawakalitu Odubiyi | Engineering | Best Researcher Award

Research Associate at INTI International University | Malaysia

Dr. Tawakalitu Odubiyi is a Teaching and Research Associate with over six years of international experience spanning Germany, Malaysia, the UK, Finland, South Africa, and Nigeria. She holds a PhD in Civil Engineering from Technische UniversitƤt Berlin and a Master’s in Construction Management from the University of Johannesburg. Her expertise lies in Quantity Surveying, Civil Engineering, and the application of Digital Twin and BIM technologies in the built environment.

Profile:

Academic Background

Dr. Odubiyi holds a PhD in Civil Engineering from Technische UniversitƤt Berlin, Germany, where her research focused on digital transformation in construction. She also earned a Master of Technology in Construction Management from the University of Johannesburg, South Africa. Her academic background reflects a strong foundation in both technical and managerial aspects of the built environment.

Professional Experience:

Dr. Odubiyi has gained diverse research and teaching experience through various academic and industrial positions. She is currently serving as an Independent Researcher at INTI International University in Malaysia, where she conducts empirical research and leads seminars on sustainable technologies. Previously, she worked as a Research Scientist at Technische UniversitƤt Berlin, contributing to projects on business models for Cloud BIM and Digital Twin solutions. Her industry experience includes roles as a BIM Consultant at Bentley Systems in London and a BIM Researcher at Trimble Inc. in Finland, where she developed strategic digital roadmaps and conducted case studies on cost estimation and contract administration. She also served as a Teaching Assistant at the University of Johannesburg and as a Mentor and Lecturer at TU Berlin, demonstrating a strong commitment to student support and academic leadership.

Research and Publications:

Dr. Odubiyi has authored 16 scholarly articles using a mix of qualitative, quantitative, and grounded theory approaches. She has actively presented her findings at international conferences and workshops. Her research focuses on digital transformation in construction, cost management innovation, and the role of BIM in contract and project administration.

Areas of Expertise:

Her core areas of expertise include Quantity Surveying, Construction Management, BIM integration, contract administration (FIDIC, NEC), and curriculum design. She is proficient in BIM tools such as Autodesk Revit, Navisworks, CostX, and Bentley iTwin. She is also experienced in research methodology, grant writing, and academic mentoring across cross-cultural and distance learning environments.

Technical and Professional Skills:

Dr. Odubiyi is skilled in Agile methodologies and tools including Salesforce CRM, Confluence, Jira, Kanban, and BPMN. She has applied these in both academic project management and industry collaborations.

Awards and Recognition:

Dr. Odubiyi has received several prestigious awards, including the Marie Curie Horizon Fellowship from the European Union (2020), the Global Excellence Scholarship from the University of Johannesburg (2019), and the National Research Fellow Bursary (2018). In 2023, she received Community Impact Recognition from the Young Quantity Surveyors of Nigeria for her contributions to the profession.

Citation:

Cited by
All: 141
Since 2020: 132

h-index
All: 8
Since 2020: 8

i10-index
All: 7
Since 2020: 7

Publication Top Notes:

  • Using neural network model to estimate the rental price of residential properties
    2019
    21 citations

  • Strengths, weaknesses, opportunities and threats of virtual team in Nigerian construction industry
    2016
    21 citations

  • Information and communication technology application challenges in the construction industry: A narrative review
    2019
    14 citations

  • A concise review of the evolution of information and communication technologies for engineering innovations
    2021
    13 citations

  • Impact of security on rental price of residential properties: evidence from South Africa
    2019
    13 citations

  • Barriers to implementing quality management system in the Industry 4.0 era
    2019
    12 citations

  • Strategies for building information modelling adoption in the South African construction industry
    2019
    12 citations

  • Impact of Green features on rental value of residential properties: evidence from South Africa
    2024
    8 citations

  • Assessing South African construction worker’s knowledge of modern technologies for effective material management
    2021
    6 citations

  • Evaluation of the use of modern technologies for effective material management in South African construction industry
    2019
    5 citations

  • Bridging the gap between academic and practice Quantity Surveying in Nigerian construction industry
    2019
    5 citations

  • Roles and effects of utilizing recent technologies on material management for construction work in South Africa
    2019
    4 citations

  • Forecasting rental values of residential properties: a neural network model approach
    2020
    3 citations

  • A model validation and predicting the rental values of residential properties using logistic regression model
    2020
    2 citations

  • Information and Communication Technologies for construction services in South Africa: A PROCSA Scope
    2021
    1 citation

  • An Evaluation of Information and Communication Technology Application in South African Construction Industry
    2019
    1 citation

Jingyang Mao | Engineering | Best Researcher Award

Dr. Jingyang Mao | Engineering | Best Researcher Award

Lecturer Shanghai Institute of Technology China

šŸ§‘ā€šŸ« Dr. Jingyang Mao is a dedicated lecturer at the School of Electrical and Electronic Engineering, Faculty of Intelligence Technology, Shanghai Institute of Technology. With a Ph.D. in Control Science and Engineering from the University of Shanghai for Science and Technology (2022), he specializes in cutting-edge research on networked control systems and cyber-physical systems. His academic journey also includes a visiting scholar tenure at Louisiana State University, USA (2019–2021). Dr. Mao’s work bridges theoretical innovations with practical applications in modern engineering systems.

Profile

Orcid

Education

šŸŽ“ Ph.D. in Control Science and Engineering (2022)

  • University of Shanghai for Science and Technology, Shanghai, China

āœˆļø Visiting Scholar (2019–2021)

  • Department of Electrical and Computer Engineering, Louisiana State University, USA

Experience

šŸ‘Øā€šŸ’» Lecturer (2022–Present)

  • School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Focus: Cyber-physical systems, networked control, and adaptive filtering

Research Interests

šŸ” Dr. Mao’s research interests lie in the fields of:

  • Cyber-physical systems 🌐
  • Multi-rate systems ā±ļø
  • Joint recursive filtering šŸ”„
  • Unknown input estimation ā“
  • Adaptive event-triggered mechanisms āš™ļø

Awards

šŸ† Award Nomination: Best Researcher Award
Recognized for groundbreaking contributions to the theory and application of cyber-physical systems.

Publications Top Notes:

šŸ“„ “Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms”

Event‐based reduced‐order Hāˆž$H_{\infty }$ estimation for switched complex networks based on T‐S fuzzy model

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms

Event-Based Distributed Adaptive Kalman Filtering With Unknown Covariance of Process Noises