Shivank Mittal | Structural Health Monitoring | Best Researcher Award

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Mr. Shivank Mittal | Structural Health Monitoring | Best Researcher Award

Ph.D. candidate, Western University, Canada

Shivank Mittal is a Ph.D. candidate in Structural Engineering at the University of Western Ontario, specializing in non-contact structural health monitoring using vision-based methodologies. With a Master of Technology in Civil Engineering from IIT Guwahati and a Bachelor of Technology from Jamia Millia Islamia, he has demonstrated academic excellence and a strong foundation in civil engineering principles. His research focuses on leveraging high-speed camera systems and advanced signal processing techniques for real-time assessment of structural integrity, aiming to enhance sustainability and reduce monitoring costs. Beyond his research, Shivank has contributed to the field through industry experience at COWI India and active involvement in academic teaching and student governance. His work bridges the gap between theoretical research and practical application, positioning him as a promising contributor to the future of civil infrastructure monitoring.

Profile

Education

Ph.D. in Structural Engineering
University of Western Ontario, London, Canada
May 2022 – Present
Thesis Advisor: Dr. Ayan Sadhu​

M.Tech. in Civil Engineering
Indian Institute of Technology Guwahati, Assam, India
June 2019 – July 2021
CPI: 9.29/10
Thesis Advisors: Dr. Arunasis Chakraborty​ScienceDirect

B.Tech. in Civil Engineering
Jamia Millia Islamia, New Delhi, India
June 2012 – July 2016
CPI: 8.5/10​

Shivank’s academic journey reflects a commitment to excellence and a deep understanding of civil engineering principles, providing a solid foundation for his innovative research in structural health monitoring.​

Experience

Research Scholar
Smart Cities and Communities Laboratory, University of Western Ontario
May 2022 – Present
Advisor: Dr. Ayan Sadhu
Focused on developing vision-based methodologies using high-speed cameras for non-contact structural health monitoring.​

Associate Bridge Engineer
COWI India, Gurugram
August 2021 – April 2022
Worked on the design of pile caps and pier caps for the Jurong Region Line in Singapore, employing Strut and Tie methods.​

Research Scholar
Indian Institute of Technology Guwahati
June 2019 – July 2021
Developed SPoTMAn, a MATLAB-based GUI for signal processing in structural health monitoring.​

Content Development Expert
IES Master Publication, New Delhi
December 2017 – August 2018
Developed study materials and question papers for competitive exams.​

Summer Industrial Intern
Delhi Metro Rail Corporation, New Delhi
June 2015 – July 2015
Gained hands-on experience in construction and concrete mix design.​

Shivank’s diverse experiences have equipped him with a unique blend of research acumen and practical engineering skills, enhancing his contributions to the field of structural health monitoring.​

Research Focus

Shivank Mittal’s research centers on advancing vision-based structural health monitoring (SHM) methodologies. His work aims to develop cost-effective, non-contact techniques for real-time assessment of structural integrity. By utilizing high-speed camera systems, his research seeks to capture dynamic responses of structures, enabling the identification of potential issues without the need for direct sensor installation. This approach not only reduces maintenance costs but also enhances the safety and longevity of infrastructure. His innovative use of signal processing techniques, including wavelet transforms and non-parametric regression, further refines the accuracy and reliability of SHM systems. Through these advancements, Shivank contributes to the evolution of smart infrastructure monitoring, aligning with the growing emphasis on sustainability and efficiency in civil engineering practices.​arXiv+8ResearchGate+8arXiv+8

Publication Top Notes

1. Towards Vision-Based Structural Modal Identification at Low Frame Rate Using Blind Source Separation**

Journal of Infrastructure Intelligence and Resilience, 2024
Co-authored with Ayan Sadhu
This paper presents a novel approach for structural modal identification using low-frame-rate video data, employing blind source separation techniques to enhance the accuracy of modal parameter extraction.​ASCE Library+1ScienceDirect+1

2. Recent Advancements and Future Trends in Indirect Bridge Health Monitoring**

Practice Periodical on Structural Design and Construction, 2023
Co-authored with Premjeet Singh and Ayan Sadhu
The article reviews the latest developments in indirect methods for bridge health monitoring, discussing emerging technologies and methodologies in the field.

Conclusion

Shivank Mittal is highly deserving of consideration for the Best Researcher Award, particularly due to:

  • His research innovation in non-contact structural monitoring and signal processing.
  • His strong academic record and real-world engineering experience.
  • His consistent involvement in academia, leadership, and community engagement.

With stronger emphasis on peer-reviewed publications and impact metrics, he would not only be a nominee but a likely winner.

​

tuğçe oral | Environmental and Public Health | Innovative Research Award

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Assist. Prof. Dr. tuğçe oral | Environmental and Public Health | Innovative Research Award

Phd, Usküdar University, Turkey

Dr. Tuğçe Oral is an Assistant Professor at Üsküdar University, Turkey, specializing in Occupational Health and Safety. Born on April 27, 1990, she has built a career combining academic excellence with practical experience in the field of workplace safety. She earned her undergraduate degree from Istanbul University in 2012, followed by a master’s and PhD in Occupational Health and Safety from Üsküdar University. Dr. Oral has contributed extensively to the academic and professional discourse through her research on risk assessment, workplace ergonomics, and health policies. Her interdisciplinary approach integrates engineering, social sciences, and public health, making her work pivotal in advancing safer work environments. In addition to publishing multiple high-impact articles, she also mentors graduate students and has guided several master’s theses. Dr. Oral remains committed to improving worker safety standards and promoting sustainable occupational health practices both nationally and globally.

Profile

Google Scholar

Education

Dr. Tuğçe Oral’s academic journey began with a bachelor’s degree from Istanbul University in 2012. Driven by her growing interest in workplace safety and public health, she pursued a master’s degree in Occupational Health and Safety at Üsküdar University, completing it in 2020. Her dedication to deepening her expertise led her to continue her studies at the doctoral level at the same institution, culminating in a PhD in 2024. Throughout her academic career, Dr. Oral focused on the intersection of industrial operations and human health, particularly in high-risk environments such as manufacturing and energy sectors. Her research explores innovative methodologies in risk analysis, such as Fine Kinney and FMEA models, and applies them to real-world contexts, including food packaging and tourism. Her solid academic foundation has been instrumental in developing robust educational and safety programs in her field.

Professional Experience

Starting her academic career as a Research Assistant, Dr. Tuğçe Oral has steadily advanced to her current role as an Assistant Professor at Üsküdar University’s Faculty of Health Sciences. She has been deeply involved in the academic development of Occupational Health and Safety as a discipline, contributing both as an educator and researcher. Dr. Oral has supervised graduate theses focused on safety models and legal frameworks, such as her recent work on safety culture in solar power plants. Her professional portfolio includes teaching, curriculum development, and academic publishing. In addition to her university responsibilities, she engages with the wider professional community through collaborative research and peer-reviewed publications. She is known for her applied approach, bridging theoretical knowledge with field-based risk assessments and solutions. Dr. Oral’s leadership in the education of future safety professionals continues to shape standards in occupational health across Turkey and internationally.

Research Focus

Dr. Tuğçe Oral’s research is centered on occupational health, industrial safety, and ergonomic risk analysis. She focuses on evaluating workplace hazards using quantitative and qualitative methods to improve safety protocols in high-risk sectors such as energy, construction, and manufacturing. Her work employs advanced models like Fine Kinney, FMEA, DEMATEL, CODAS, and HRNS to assess and mitigate operational risks. Dr. Oral has also explored the psychosocial aspects of workplace safety, including the effects of social dynamics on treatment outcomes in substance use disorders. Her multi-disciplinary approach integrates technology, policy analysis, and public health to formulate comprehensive risk management strategies. Through her research, she aims to contribute to safer work environments by promoting preventive measures and informed regulatory practices. Dr. Oral is committed to fostering sustainability and worker well-being through innovative and evidence-based health and safety interventions.

Publication Top Notes

  1. 2016-2018 YILLARI ARASINDA SEYAHAT SEKTÖRÜNE AİT İŞ KAZASI VERİLERİNİN DEĞERLENDİRİLMESİ
    Published in Ohs Academy (2020), Cited by 14
    Summary: Evaluates travel industry work accident data from 2016–2018. Proposes improved safety interventions.

  2. MOBİLYA ATÖLYELERİNDE FINE KINNEY YÖNTEMİ İLE RİSK DEĞERLENDİRMESİ UYGULAMASI
    OHS Academy (2019), Cited by 10
    Summary: Applies Fine Kinney method for risk analysis in furniture workshops to identify critical safety hazards.

  3. İnsan sağlığı hizmetleri çalışanlarının maruz kaldığı meslek hastalığı etkenlerinin iş sağlığı ve güvenliği kapsamında incelenmesi
    Genel Sağlık Bilimleri Dergisi (2021), Cited by 9
    Summary: Analyzes occupational diseases among health service workers, recommending preventive strategies.

🧾 Conclusion

Dr. Tuğçe Oral is a highly suitable candidate for the Research for Innovative Research Award. Her combination of academic rigor, methodological innovation, and real-world relevance embodies the award’s mission to recognize forward-thinking, applicable research that contributes to societal well-being and environmental/public health. With further expansion into international networks and practical applications, she has the potential to be a transformative figure in occupational health and safety on a global scale.

Dr. Sami Ullah | Solar Cell | Best Researcher Award

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Dr. Sami Ullah | Solar Cell | Best Researcher Award

Postdoc fellow, Institute of Physics, Slovak academy of sciences, Slovakia

Dr. Sami Ullah is a physicist specializing in photovoltaics, with over five years of research experience in perovskite solar cells. His expertise encompasses laser patterning, thin film preparation, materials characterization, device fabrication, and testing. His research focuses on scaling up perovskite technology, optimizing self-assembled monolayers (SAMs) deposition, vacuum deposition, charge transport layer engineering, and integrating 2D materials into optoelectronics, sensors, and energy harvesting applications.

Profile

Education

  • Ph.D. in Physics (Photovoltaics and Advanced Materials)
    University of Balochistan, Quetta, Pakistan (2019–2022)
    Thesis: Transport Layer Engineering of Efficient Perovskite Solar Cells​

  • M.S. in Physics (Nanotechnology and Nanosciences)
    University of Balochistan, Quetta, Pakistan (2014–2016)
    Thesis: ZnO Nanostructure-Based Dye-Sensitized Solar Cells​

Work Experience

  • Postdoctoral Researcher
    Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia (May 2024–Present)
    Focus: Co-deposition of SAMs for stable inverted perovskite solar cells and fabrication of crystalline FAPbI₃-based p-i-n perovskite solar cells.​

  • Lecturer
    Department of Physics, University of Balochistan, Quetta, Pakistan (2012–2024)
    Responsibilities: Teaching and conducting laboratory experiments.​

  • Visiting Researcher
    Chimie ParisTech – PSL Research University, Paris, France (2021–2022)
    Achievements: Fabricated n-i-p perovskite solar cells with over 21% power conversion efficiency in a glove box environment.​

  • Guest Researcher
    Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia (2019–2020)
    Contributions: Engineered transport layers (SnO₂ and NiOₓ), fabricated n-i-p/p-i-n perovskite solar cells, and utilized 2D MXene (Ti₃C₂Tx) as an additive and interlayer.​

Awards and Honors

  • Guest Researcher Scholarship
    Higher Education Commission (HEC) of Pakistan, 2021
    Awarded a one-year research stay at Chimie ParisTech, PSL-CNRS, Paris, France.​

Research Focus

Dr. Ullah’s research aims to enhance the efficiency and stability of perovskite solar cells. His work on SAMs deposition techniques addresses interfacial charge recombination, improving energy band alignment and surface morphology. Additionally, his exploration of 2D materials like MXene aims to optimize charge transport layers, contributing to the development of high-performance, stable photovoltaic devices.​ResearchGate

Publication Top Notes

  1. “Simulation-based optimization of CdS/CdTe solar cells incorporating MXene-enhanced SnO₂ buffer layer: insights from experimentally validated material properties”
    Journal: Solar Energy (2025)
    This study investigates the incorporation of MXene-enhanced SnO₂ buffer layers in CdS/CdTe solar cells, offering insights into material properties and optimization strategies.

  2. “Self-powered TENG probe for scanning surface charge distribution”
    Journal: Nanotechnology (2024)
    The paper presents a self-powered triboelectric nanogenerator (TENG) probe designed for scanning surface charge distributions, highlighting its potential applications in nanoscale measurements.

  3. “Tailoring the electronic properties of the SnO₂ nanoparticle layer for n-i-p perovskite solar cells by Ti₃C₂Tx MXene”
    Journal: Materials Today Communications (2023)
    This research explores the modification of SnO₂ nanoparticle layers with Ti₃C₂Tx MXene to enhance the electronic properties of n-i-p perovskite solar cells.

  4. “Mesoporous SnO₂ Nanoparticle-Based Electron Transport Layer for Perovskite Solar Cells”
    Journal: ACS Applied Nano Materials (2022)
    The article discusses the development of mesoporous SnO₂ nanoparticle-based electron transport layers, aiming to improve the performance of perovskite solar cells.

  5. “A synergistic effect of the ion beam sputtered NiOₓ hole transport layer and MXene doping on inverted perovskite solar cells”
    Journal: Nanotechnology (2022)
    This publication examines the combined effect of ion beam sputtered NiOₓ hole transport layers and MXene doping on the performance of inverted perovskite solar cells.

Conclusion

Dr. Sami Ullah demonstrates a robust and impactful research career characterized by:

  • Innovative contributions to perovskite and thin-film photovoltaics,
  • Cross-disciplinary work involving nanomaterials and sensors,
  • A consistent publication record in prestigious, peer-reviewed journals,
  • Valuable international research collaborations and national academic service.

 

 

ALEXIS KORDOLEMIS | Buckling of thin shells | Best Researcher Award

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Assist. Prof. Dr. ALEXIS KORDOLEMIS | Buckling of thin shells | Best Researcher Award

Assistant Professor, University of Greenwich, United Kingdom

Dr. Alexis Kordolemis is a Lecturer in Mechanical Engineering at the University of Greenwich, UK. Born in Megali Vrisi, Greece, in 1982, he earned his BSc in Civil Engineering from the University of Thessaly (2006), MSc in Computational Mechanics from the National Technical University of Athens (2008), and PhD in Structural Mechanics from the University of Thessaly (2014) under Prof. Antonios E. Giannakopoulos. His doctoral research focused on smart textiles. Dr. Kordolemis has held positions as a Structural Engineer at Guardian Industrial (UK) Ltd. and as a Postdoctoral Research Associate at the University of Bristol’s Bristol Composites Institute and the University of Thessaly. He joined the University of Greenwich in 2020. His research interests include multi-scale modelling, architectured materials, and generalized continuum theories. He is a member of the Technical Chamber of Greece and the Greek Society of Civil Engineers. ​

Profile

Google Scholar

Orcid

Education 

  • PhD in Structural Mechanics (Smart Textiles): University of Thessaly, Greece (2014).

  • MSc in Computational Mechanics: National Technical University of Athens, Greece (2008).

  • BSc in Civil Engineering: University of Thessaly, Greece (2006).

  • Fellow of Higher Education Academy (FHEA): University of Greenwich, UK (2022).​

Experience

  • Lecturer in Mechanical Engineering: University of Greenwich, UK (2020–present).

  • Structural Engineer: Guardian Industrial (UK) Ltd. (2019–2020).

  • Postdoctoral Research Associate: University of Bristol, UK (2015–2019).

  • Postdoctoral Research Associate: University of Thessaly, Greece (2014–2015).

  • Civil Engineer (Construction Site): Highway of Central Greece-E65 (2010–2011).

  • Project Manager: Development Management Company for Central Greece and Thessaly (2013–2015).​

Research Focus

Dr. Kordolemis’s research delves into the mechanical behavior of composite materials across multiple scales, from micro to macro. He employs advanced constitutive models derived from generalized continuum theories, such as Cosserat, strain gradient, and couple stress theories, to enhance the understanding of material behavior. His work aims to design more effective structural composite components resistant to failure mechanisms like buckling, delaminations, and cracks. Key areas of interest include structural mechanics, multi-scale modeling, architectured materials, micro-mechanics, and smart materials.

Publication Top Notes

  1. Kordolemis, A., Giannakopoulos, A. E., & Aravas, N. (2017). “Pretwisted beam subjected to thermal loads: a gradient thermoelastic analogue.” Journal of Thermal Stresses, 40(10), 1231–1253. [DOI: 10.1080/01495739.2017.1308810]

    • Summary: This paper presents a theoretical model for the behavior of pretwisted beams under thermal loads, utilizing gradient thermoelastic theory to predict deformation and stress distributions.​

  2. Kordolemis, A., & Weaver, P. M. (2017). “Geometric–material analogy for multiscale modelling of twisted plates.” International Journal of Solids and Structures, 110, 24–35. [DOI: 10.1016/j.ijsolstr.2017.02.006]

    • Summary: The authors introduce a multiscale modeling approach that combines geometric and material analogies to analyze the behavior of twisted plates, enhancing the understanding of their mechanical properties.

  3. Kordolemis, A., & Giannakopoulos, A. E. (2014). “Micropolar 2D elastic cables with applications to smart cables and textiles.” Journal of Engineering Mechanics, 140(10), 04014079. [DOI: 10.1061/(ASCE)EM.1943-7889.0000780]

    • Summary: This study develops a micropolar theory for two-dimensional elastic cables, exploring their applications in smart cables and textiles, and providing insights into their mechanical behavior.​

  4. Zisis, T., & Kordolemis, A. (2010). “Development of strong surfaces using functionally graded composites inspired by natural teeth—a theoretical approach.” Journal of Engineering Materials and Technology, 132(3), 031004. [DOI: 10.1115/1.4000808]

    • Summary: The paper proposes a theoretical model for creating strong surfaces using functionally graded composites, drawing inspiration from the natural structure of teeth to enhance material strength.

       

Conclusion

Dr. Alexis Kordolemis is highly suitable for consideration for a Best Researcher Award, especially within domains involving composite materials, mechanics of materials, and advanced structural modeling. His academic rigor, international exposure, and applied research expertise make him a standout candidate.

​

Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

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Mr. Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

PhD Student, Islamic Azad University, Iran

Shayan Ghazimoghadam is a Ph.D. student in Structural Engineering at Islamic Azad University of Shahrood, Iran, specializing in data-driven structural health monitoring. His research integrates artificial intelligence with civil engineering to develop unsupervised deep learning methods for real-time damage detection in structures. Shayan’s work focuses on creating digital twins for infrastructure assessment, aiming to enhance predictive maintenance and safety. He has authored several publications, including a notable paper on vibration-based damage diagnosis using multi-head self-attention LSTM autoencoders, published in Measurement journal. Additionally, he has presented his research at national conferences and served as a keynote speaker, demonstrating his commitment to advancing the field of structural health monitoring.

Profile

Google Scholar​

Education

Shayan Ghazimoghadam completed his Bachelor of Science in Civil Engineering at Islamic Azad University of Gorgan, Iran, in 2018, where he excelled in design courses, achieving a GPA of 19/20 in Steel Structures and a perfect 20/20 in Concrete Structures. He then pursued a Master of Science in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, graduating in 2022 with a GPA of 19.07/20. His master’s dissertation focused on structural damage identification under ambient vibration using an unsupervised deep learning method, supervised by Dr. Seyed Ali Asghar Hosseinzadeh. Currently, Shayan is a Ph.D. student at Islamic Azad University of Shahrood, Iran, where he continues to explore innovative approaches in structural health monitoring and artificial intelligence applications in civil engineering.​

Experience 

Between 2022 and 2023, Shayan Ghazimoghadam served as a Research Assistant at Golestan University, Gorgan, Iran, under the guidance of Dr. Seyed Ali Asghar Hosseinzadeh. During this period, he conducted research on real-time structural health monitoring utilizing AI-powered techniques. His work involved developing and testing unsupervised deep learning algorithms for damage detection in structures based on vibration data. Shayan’s contributions led to the presentation of his findings at national conferences, showcasing his ability to communicate complex research outcomes effectively. This experience has significantly enhanced his expertise in integrating artificial intelligence with structural engineering, positioning him as a promising researcher in the field of structural health monitoring.​

Awards and Honors 

Shayan Ghazimoghadam has been recognized for his academic excellence and research contributions. He ranked first among M.Sc. students in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, Iran, in 2022, achieving a GPA of 19.07/20. His innovative research on structural damage identification using unsupervised deep learning methods has been published in reputable journals, including the Measurement journal, where his paper has garnered 26 citations as of 2024. Additionally, Shayan was invited as a keynote speaker at the 3rd National Conference on Civil Engineering, Intelligent Development, and Sustainable Systems in 2023, where he presented on AI-powered structural damage identification and localization through accelerometer data. These accolades underscore his commitment to advancing the field of structural health monitoring and his potential for future contributions to civil engineering research.​

Research Focus

Shayan Ghazimoghadam’s research focuses on the integration of artificial intelligence with structural health monitoring (SHM) to develop innovative solutions for infrastructure maintenance. His primary interests include data-driven SHM, unsupervised structural damage identification, and the application of digital twins for condition assessment. Shayan aims to enhance the accuracy and efficiency of damage detection in structures by employing unsupervised deep learning techniques, particularly multi-head self-attention LSTM autoencoders. His work contributes to the development of digital twins, virtual representations of physical assets, to monitor and assess the condition of infrastructure in real time. By leveraging AI and machine learning, Shayan seeks to revolutionize traditional SHM practices, offering more proactive and predictive maintenance strategies that can lead to safer and more sustainable infrastructure systems.

Publication Top Notes​

📘 1. A Novel Unsupervised Deep Learning Approach for Vibration-Based Damage Diagnosis Using a Multi-Head Self-Attention LSTM Autoencoder

Authors: S. Ghazimoghadam, S.A.A. Hosseinzadeh
Journal: Measurement, Volume 229, Article 114410
Year: 2024
Citations (as of 2025): 26
DOI: Measurement 229, 114410 (sample link, please verify)

🔍 Summary:

This publication introduces a novel unsupervised deep learning method for real-time structural damage detection using only ambient vibration data. The approach combines Long Short-Term Memory (LSTM) autoencoders with multi-head self-attention mechanisms, enabling the system to effectively learn temporal features and focus on critical data patterns without the need for labeled damage data.

By leveraging unsupervised learning, the model is highly adaptable and scalable, making it suitable for practical deployment in real-world structural health monitoring (SHM) scenarios. The method was validated using benchmark datasets, showing superior performance in damage localization and diagnosis accuracy compared to traditional approaches.

📗 2. Transformer-Based Time-Series GAN for Data Augmentation in Bridge Monitoring Digital Twins

Authors: V. Mousavi, M. Rashidi, S. Ghazimoghadam, M. Mohammadi, B. Samali
Journal: Automation in Construction, Volume 175, Article 106208
Year: 2025 (Under Review)
DOI: Automation in Construction 175, 106208 (check for final link when published)

🔍 Summary:

This paper presents a Transformer-based Generative Adversarial Network (GAN) for augmenting time-series sensor data in bridge monitoring systems. The technique is particularly geared towards Digital Twin models, which require large, diverse, and high-quality datasets to simulate and predict structural behavior accurately.

The GAN architecture uses a Transformer encoder to better capture temporal dependencies in structural response data, generating realistic synthetic datasets for training SHM models. By augmenting scarce or incomplete datasets, this method improves predictive performance, anomaly detection, and damage assessment capabilities of digital twins used in civil infrastructure.

Masoud Khajenoor | Chemical Engineering | Engineering Development Award

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Dr Masoud Khajenoor | Chemical Engineering | Engineering Development Award

Dr, Masoud Khajenoori, University of Kashan, Iran

Dr. Masoud Khajenoori is an Assistant Professor in the Department of Chemical Engineering at the Faculty of Engineering. With extensive experience in heterogeneous catalysis, gas separation technologies, and simulation of molecular dynamics, he has established himself as a dedicated researcher in the field. His scientific interests include dry reforming of methane over nano-catalysts, modeling of gas centrifuge systems, and investigation of mass transfer in human airways. Dr. Khajenoori has co-authored multiple peer-reviewed journal articles, collaborating with researchers across fields such as nanotechnology, chemical engineering, and nuclear science. His work addresses both fundamental and applied aspects of energy-efficient gas separation and reaction mechanisms. Through his research, Dr. Khajenoori aims to contribute to sustainable energy solutions and advanced separation systems. He actively mentors students and participates in collaborative research projects, enhancing interdisciplinary academic activities and bridging theoretical research with industrial application.

Profile

Google Scholar

🔹 Education 

Dr. Masoud Khajenoori holds a Ph.D. in Chemical Engineering, specializing in molecular simulation and heterogeneous catalysis. His academic training provided a solid foundation in the principles of reaction engineering, mass transfer, and nanomaterials. He pursued both his undergraduate and graduate studies in top-ranked institutions, where he focused on advanced simulation techniques including Direct Simulation Monte Carlo (DSMC) and computational modeling of gas-solid systems. His doctoral research emphasized the development and application of nano-catalysts for dry reforming reactions, with a specific interest in CeO₂-promoted Ni/MgO catalysts. Throughout his academic journey, Dr. Khajenoori was recognized for his analytical skills, academic excellence, and interdisciplinary approach to solving complex engineering problems. His strong background in physics, thermodynamics, and numerical methods enables him to carry out pioneering research in gas centrifugation and nanoparticle behavior under various flow conditions. He continues to apply this expertise in both teaching and research activities.

🔹 Employment

Dr. Masoud Khajenoori is currently employed as an Assistant Professor in the Department of Chemical Engineering, Faculty of Engineering. He holds a full-time, on-contract position, where he actively teaches undergraduate and graduate courses in reaction engineering, process simulation, and heat and mass transfer. As a faculty member, he has contributed significantly to curriculum development and academic planning, ensuring alignment with global research and industry trends. Beyond teaching, Dr. Khajenoori leads several research projects focusing on gas centrifuge modeling, nano-catalysis, and chemical process optimization. He plays a vital role in mentoring students, supervising thesis projects, and fostering interdisciplinary collaborations with national and international partners. He frequently engages in publishing high-impact journal articles and contributes to peer reviews for scientific journals. His employment reflects a commitment to advancing both academic excellence and technological innovation in chemical engineering.

🔹 Research Focus

Dr. Masoud Khajenoori’s research centers on gas separation technologies, catalytic processes, and computational modeling. His primary focus lies in the dry reforming of methane using nano-engineered catalysts such as CeO₂-promoted Ni/MgO, addressing both energy efficiency and CO₂ utilization. He has developed comprehensive models for gas centrifuge systems using DSMC (Direct Simulation Monte Carlo) and Sickafus analytical methods, enabling precise simulations of multi-component gas separation. Another area of his research involves the prediction and modeling of physical properties like thermal conductivity and viscosity in rare gases and radioactive compounds. Additionally, he has worked on simulations of nanoparticle deposition in human airways, bridging chemical engineering and biomedical applications. His recent projects extend into molecular pump optimization using metaheuristic algorithms, reflecting a strong commitment to computational chemical engineering. Dr. Khajenoori’s work provides novel insights into improving separation power, catalyst performance, and sustainable gas processing technologies.

🔹 Publication Top Notes

1. Dry reforming over CeO₂-promoted Ni/MgO nano-catalyst: effect of Ni loading and CH₄/CO₂ molar ratio

  • Authors: M. Khajenoori, M. Rezaei, F. Meshkani

  • Journal: Journal of Industrial and Engineering Chemistry, Vol. 21, Pages 717–722, 2015

  • Citations: 116

  • Summary:
    This study investigates the catalytic performance of CeO₂-promoted Ni/MgO nano-catalysts in the dry reforming of methane (DRM). The researchers evaluated how varying nickel loadings and CH₄/CO₂ ratios affect conversion efficiency and catalyst stability. Results showed that an optimal Ni content improves dispersion, reduces sintering, and enhances resistance to carbon deposition. CeO₂ acts as a structural promoter, increasing oxygen storage and supporting CO₂ activation. This research contributes to the development of sustainable reforming processes using greenhouse gases as feedstocks.

2. Simulation of Gas Centrifuge Separation Process for Binary and Ternary Isotope Mixtures Using Direct Simulation Monte Carlo (DSMC) Method

  • Authors: M. Khajenoori, A. R. Alaei

  • Journal: Progress in Nuclear Energy, Vol. 85, Pages 506–516, 2015

  • Citations: 41

  • Summary:
    This paper presents a DSMC-based simulation for analyzing gas centrifuge separation efficiency in binary and ternary isotope mixtures, particularly uranium enrichment. The study compares simulation results with analytical models and experimental benchmarks, showing excellent agreement and improved understanding of separation mechanisms at molecular levels. The findings support the optimization of gas centrifuge designs in nuclear fuel cycles.

3. Thermal Conductivity and Viscosity Prediction of Rare Gases and Radioactive Gas Mixtures Using Artificial Neural Networks

  • Authors: M. Khajenoori, H. Khorsand, M. Rezaei

  • Journal: Applied Thermal Engineering, Vol. 60, Issues 1–2, Pages 129–136, 2013

  • Citations: 36

  • Summary:
    This research applies artificial neural network (ANN) models to predict the thermal conductivity and viscosity of rare gases and radioactive gas mixtures, often used in nuclear and space applications. The ANN model achieved high accuracy compared to traditional equations, offering a fast and reliable predictive tool for complex gas behavior under varied temperature and pressure conditions.

4. Study of Nanoparticles’ Deposition in Human Airways Using a Two-phase Eulerian–Lagrangian Model

  • Authors: M. Khajenoori, A. Ebrahimnia-Bajestan, M. B. Shafii

  • Journal: Journal of Aerosol Science, Vol. 103, Pages 32–43, 2016

  • Citations: 29

  • Summary:
    This interdisciplinary study models how inhaled nanoparticles deposit in the respiratory tract using a two-phase flow simulation approach. The research is significant in evaluating health risks of nano-sized particles from environmental and industrial exposure. Findings highlight the impact of particle size, breathing rate, and flow dynamics on deposition efficiency in various airway regions.

5. CFD Simulation and Optimization of Molecular Drag Pump Using Genetic Algorithm and Response Surface Method

  • Authors: M. Khajenoori, M. Aminyavari, M. T. Ahmadi

  • Journal: Vacuum, Vol. 119, Pages 173–182, 2015

  • Citations: 22

  • Summary:
    The paper combines computational fluid dynamics (CFD), genetic algorithms (GA), and response surface methodology (RSM) to optimize the performance of molecular drag pumps (MDPs). By adjusting geometrical parameters, the team significantly enhanced throughput and compression ratios. The integrated approach serves as a blueprint for designing high-performance vacuum systems used in electronics and semiconductors.

6. Experimental and Theoretical Study on CeO₂-modified Ni Catalysts Supported on Mesoporous MgO for CO₂ Reforming of Methane

  • Authors: M. Khajenoori, F. Meshkani, A. A. Mirzaei

  • Journal: International Journal of Hydrogen Energy, Vol. 38, Issue 4, Pages 1905–1916, 2013

  • Citations: 61

  • Summary:
    This article investigates the effect of CeO₂ addition on Ni/MgO catalysts prepared via sol–gel and co-precipitation methods for CO₂ reforming of methane. The CeO₂-modified catalysts displayed superior catalytic stability, higher activity, and resistance to carbon formation. Experimental results were validated using kinetic modeling and characterization techniques like XRD and BET analysis.

Conclusion

Dr. Masoud Khajenoori demonstrates strong potential and current achievements in engineering research and development. His work on process modeling, clean energy, and advanced simulations contributes meaningfully to engineering knowledge and innovation. While he would benefit from increased industry collaboration and wider dissemination of his work, his solid research foundation, technical sophistication, and contribution to education make him a strong contender for the Research for Engineering Development Award.

Francisco Sierra Lopez | Medicine and Health Sciences | Best Researcher Award

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Dr. Francisco Sierra Lopez| Medicine and Health Sciences | Best Researcher Award

Guest researcher, High Specialty Regional Hospital of Ixtapaluca (HRAEI), Mexico

Francisco Sierra López is a guest researcher at the High Specialty Regional Hospital of Ixtapaluca (HRAEI), Mexico. A molecular biologist and biomedical innovator, he specializes in the study of extracellular vesicles and their role in infections, cancer, and immunological processes. He received his Bachelor’s in Biology and completed postdoctoral research at the National Autonomous University of Mexico (UNAM), followed by a Master’s and Ph.D. in Science from the Center for Research and Advanced Studies (CINVESTAV-IPN). His interdisciplinary approach led to the discovery and patenting of immunogenic giant extracellular vesicles (VEGs) derived from protozoan parasites. Francisco has authored four internationally recognized publications and is credited with both a national and a WIPO-registered patent. His research continues to explore novel diagnostic and therapeutic avenues in oncology and infectious diseases.

Profile

Google Scholar

Education

Francisco Sierra López holds a Bachelor of Science in Biology and pursued his postdoctoral training at the National Autonomous University of Mexico (UNAM), one of Latin America’s most prestigious research institutions. He earned his Master’s and Doctorate degrees in Science from the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), specializing in cell and molecular biology. During his academic tenure, he engaged in groundbreaking studies on protozoan parasites and molecular signaling, which later expanded into the study of extracellular vesicles across species. His educational journey was characterized by a consistent focus on translational research, integrating bench-side discovery with real-world applications. Francisco’s advanced training in biomedical sciences forms the cornerstone of his research career, laying a foundation for his subsequent innovations in immunogenic vesicle technologies.

Experience

Francisco Sierra López currently serves as a guest researcher at the High Specialty Regional Hospital of Ixtapaluca (HRAEI), where he investigates biomedical applications of extracellular vesicles (EVs). His earlier career includes roles as a doctoral and postdoctoral researcher at CINVESTAV-IPN and UNAM, respectively. Throughout his scientific path, Francisco has designed and led interdisciplinary research projects—particularly those examining extracellular vesicle secretion in pathogenic protozoa, cancerous tissues, and immune cell lines. He has served as principal investigator in four funded research projects and collaborated with various specialists in parasitology, oncology, and cellular biology. Despite having no direct industry collaborations to date, his research findings hold strong potential for clinical and pharmaceutical innovation. Francisco’s work is marked by technical precision and creative experimentation, leading to peer-reviewed publications and internationally recognized patents.

Research Focus

Francisco Sierra López’s research centers on the secretion and application of extracellular vesicles (EVs) across a spectrum of biological contexts, from protozoan parasitology to immunology and oncology. He has conducted experimental studies on EVs secreted by protozoan parasites, such as Entamoeba histolytica and Acanthamoeba culbertsoni, elucidating their roles in host-pathogen interactions. His work also extends to understanding EVs in cancer cells (including ovarian cancer and leukemia) and immune cells (monocytes, macrophages), particularly under co-infection conditions such as COVID-19. Francisco’s innovative methodologies have led to two patents concerning the purification and immunogenic potential of EVs. His cross-disciplinary approach bridges cellular biology with biomedical application, aiming to transform vesicle biology into actionable health interventions. This research contributes not only to fundamental biology but also to diagnostics, vaccine development, and therapeutic targeting.

Publication Top Notes

1. Influence of Micropatterned Grill Lines on Entamoeba histolytica Trophozoites Morphology and Migration

Authors: F. Sierra-López, L. Baylón-Pacheco, et al.
Journal: Frontiers in Cellular and Infection Microbiology, Vol 8, 295, 2018
Cited by: 8
Summary: This study reveals how surface topography influences the morphology and motility of E. histolytica, highlighting the interaction between physical microenvironments and protozoan behavior.

2. Characterization of Low Molecular Weight Protein Tyrosine Phosphatases of Entamoeba histolytica

Authors: F. Sierra-López, L. Baylón-Pacheco, SC Vanegas-Villa, JL Rosales-Encina
Journal: Biochimie, Vol 180, pp. 43–53, 2021
Cited by: 5
Summary: This biochemical investigation characterizes phosphatases in E. histolytica, offering insight into potential enzymatic targets for therapeutic intervention in amoebiasis.

3. Extracellular Vesicles Secreted by Acanthamoeba culbertsoni Have COX and Proteolytic Activity and Induce Hemolysis

Authors: F. Sierra-López, I. Castelan-Ramírez, D. Hernández-Martínez, et al.
Journal: Microorganisms, Vol 11(11), Article 2762, 2023
Cited by: 3
Summary: The paper explores enzymatic activities within EVs secreted by A. culbertsoni, showing their role in red blood cell lysis and pathogenicity mechanisms.

4. A Fraction of Escherichia coli Bacteria Induces an Increase in the Secretion of Extracellular Vesicle Polydispersity in Macrophages

Authors: SMM Sierra-López F, Iglesias-Vázquez V, et al.
Journal: International Journal of Molecular Sciences, Vol 26(8), 2025
Summary: This upcoming article discusses how bacterial co-infection alters EV secretion profiles in immune cells, with implications for inflammation and viral pathogenesis.

Patents

1. Immunogenic Giant Extracellular Vesicles of Parasitic Protozoa

Patent No.: MX/a/2016/014875 | IMPI | Issued: March 22, 2023
Folio: MX/E/2016/081517
Inventors: Francisco Sierra López, Luis A. Carreño Sánchez, José L. Rosales Encina
Patent Link (IMPI)

2. Same Patent Filed at WIPO

Folio: PCT/IB2017/056917 | Published: June 17, 2018
Patent Link (WIPO)

Conclusion

Dr. Francisco Sierra López stands out as a high-potential early-career researcher whose innovative work on extracellular vesicles spans a rare combination of protozoan pathogenesis, cancer biology, and immunology. His cross-disciplinary research, patented technologies, and early scholarly impact indicate a trajectory toward excellence in biomedical science.

Junjie Zhang | Electrocatalysis | Best Researcher Award

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Dr Junjie Zhang | Electrocatalysis | Best Researcher Award

University Instructor, Civil Aviation Flight University of China, Malaysia

Dr. JunJie Zhang is a Lecturer at the Civil Aviation Flight University of China, specializing in electrocatalysis and fuel cell technology. With a background in engineering, Dr. Zhang has made significant contributions to the development of non-precious metal-doped carbon-based electrocatalysts for fuel cells. He holds a Ph.D. in Engineering from Dalian Maritime University, where his research focused on the preparation and optimization of these advanced electrocatalysts. Dr. Zhang has authored several high-impact publications in prestigious journals such as Journal of Materials Science and ChemistrySelect. His research aims to improve the efficiency of energy conversion and storage technologies, with a particular emphasis on the oxygen reduction reaction (ORR). He is proficient in advanced techniques such as density functional theory simulations, material characterization, and experimental design. Dr. Zhang continues to drive innovation in electrocatalysis, focusing on sustainable energy technologies and green chemistry.

Profile

Orcid

Education

Dr. JunJie Zhang completed his Ph.D. in Engineering at Dalian Maritime University, a leading institution in China. His doctoral research focused on the preparation and optimization of non-precious metal-doped carbon-based electrocatalysts for fuel cells. This work involved extensive use of advanced techniques such as material property analysis, electrocatalytic characterization, and density functional theory (DFT) simulation calculations. His educational journey has equipped him with a solid foundation in both theoretical and practical aspects of electrochemistry and material science. Before his Ph.D., Dr. Zhang completed his undergraduate studies in engineering, laying the groundwork for his expertise in nanomaterials, energy systems, and electrochemical processes. Through his educational pursuits, he has acquired a robust understanding of the scientific principles governing catalysis, electrochemical reactions, and the application of computational methods to enhance material design. His academic training continues to influence his current work and his approach to innovative research in electrocatalysis.

Experience

Dr. JunJie Zhang has accumulated significant academic and research experience in the field of electrocatalysis, with a particular focus on fuel cell technology. As a Lecturer at the Civil Aviation Flight University of China, he is responsible for teaching courses related to materials science and energy systems, fostering the next generation of engineers and researchers. He has been involved in numerous research projects aimed at enhancing the performance of electrocatalysts, particularly for the oxygen reduction reaction (ORR), a key process in fuel cells and metal-air batteries. His expertise spans experimental design, data analysis, manuscript writing, and publication in peer-reviewed journals. Over the years, Dr. Zhang has worked with various research teams and collaborators on cutting-edge projects, developing novel catalysts derived from biomass materials and marine sources. His experience in computational simulations, coupled with his practical laboratory skills, has enabled him to make significant strides in improving the efficiency of electrochemical reactions in energy systems.

Research Focus

Dr. JunJie Zhang’s research focuses on the development of advanced electrocatalysts for energy conversion and storage technologies, particularly in fuel cells and metal-air batteries. His primary interest lies in the preparation and optimization of non-precious metal-doped carbon-based electrocatalysts, which are critical for enhancing the efficiency of the oxygen reduction reaction (ORR). Dr. Zhang has also explored the use of biomass-derived materials and marine resources as sustainable precursors for catalyst synthesis, aiming to reduce the cost and environmental impact of catalyst production. His research integrates experimental techniques with computational simulations, particularly density functional theory (DFT), to design and optimize catalysts at the molecular level. By focusing on green chemistry and sustainable energy solutions, Dr. Zhang’s work has the potential to drive innovations in clean energy technologies. His long-term goal is to contribute to the development of energy systems that are both cost-effective and environmentally friendly.

Publication Top Notes

1. Zhang, J., Wang, J., Fu, Y., Peng, X., Xia, M., Peng, W., Liang, Y., Wei, W. “Nanoscale Fe3O4 Electrocatalysts for Oxygen Reduction Reaction.” Molecules, 2025, 30: 1753.
Summary: This article investigates the development of nanoscale Fe3O4 electrocatalysts for oxygen reduction reaction (ORR). The study demonstrates the enhanced catalytic performance of Fe3O4-based catalysts, which are a promising alternative for fuel cell applications.

2. Zhang, J., Xing, P., Wei, W., et al. “DFT-guided design and synthesis of sea cucumber-derived N, S dual-doped porous carbon catalyst for enhanced oxygen reduction reaction and Zn-air battery performance.” Journal of Materials Science, 2023, 58: 11968–11981.
Summary: This paper explores the design and synthesis of N, S dual-doped porous carbon catalysts derived from sea cucumber for enhanced oxygen reduction reaction (ORR) and Zn-air battery performance. The study leverages density functional theory (DFT) to guide the synthesis and optimization process.

Conclusion:

JunJie Zhang is a highly deserving candidate for the Best Researcher Award due to his impactful contributions to the field of fuel cell technology and electrocatalysis. His research on improving oxygen reduction reactions using biomass-derived nanocarbon catalysts stands out for its innovation and sustainability. While there are areas for improvement, particularly in expanding his collaborative network and public outreach, Zhang’s achievements in materials science, energy systems, and his dedication to advancing green technologies make him an excellent candidate for this prestigious award.

Amir Abdollahi | Electrical Engineering | Best Researcher Award

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Prof. Dr. Amir Abdollahi | Electrical Engineering | Best Researcher Award

Professor, Shahid Bahonar University of Kerman, Iran

Professor Amir Abdollahi, born on September 3, 1985, is a distinguished researcher and educator in power systems engineering. He serves as a full professor and Head of the Energy and Environment Research Institute at Shahid Bahonar University of Kerman, Iran. Prof. Abdollahi earned his Ph.D. from Tarbiat Modares University, Tehran, focusing on dynamic demand response from the ISO perspective. His professional journey spans high-impact teaching, cutting-edge research in electricity markets, smart grids, and renewable energy systems. Recognized for his leadership and innovation, he is an active member of IEEE and a published expert across several energy domains. His contributions address national and global challenges in energy reliability, economics, and optimization.

Profiles

🎓 Education

Professor Abdollahi’s academic journey reflects excellence across Iran’s premier institutions. He completed his Ph.D. in Electrical Engineering (Power Systems) from Tarbiat Modares University, Tehran, in 2012 under the mentorship of Prof. Mohsen Parsa Moghaddam. His doctoral research explored Dynamic Demand Response Scheduling from the ISO perspective, laying the foundation for future work in energy systems optimization. He holds a Master’s degree (M.Sc., 2009) from Sharif University of Technology, where he worked with Prof. Mehdi Ehsan on Security-Constrained Unit Commitment and Generation Scheduling. He began his academic pursuit with a B.Sc. in Electrical Engineering from Shahid Bahonar University, where his undergraduate thesis focused on the Impact of Restructuring on Power System Operation. These milestones have shaped his versatile expertise in energy management, smart grids, and system reliability.

👨‍🏫 Experience

Prof. Abdollahi brings over a decade of academic and research experience. As a Professor at Shahid Bahonar University, he teaches undergraduate and graduate courses such as Power System Operation, Planning, Reliability, Restructuring, and Smart Grids. He has supervised numerous MSc and PhD theses in cutting-edge areas like energy market modeling, demand-side management, and renewable integration. He also leads the Energy and Environment Research Institute, where he spearheads interdisciplinary projects and national collaborations. His service as a mentor, administrator, and curriculum designer has significantly contributed to engineering education in Iran. He is also active in the IEEE community and often collaborates on international platforms involving smart electricity grids and optimization algorithms. His dynamic presence bridges research, teaching, and innovation.

🔬 Research Focus 

Prof. Abdollahi’s research encompasses power system flexibility, smart electricity grids, demand response, energy economics, and renewable integration. His doctoral and post-doctoral work on Dynamic Demand Response Scheduling laid a foundation for modern smart grid control mechanisms. He investigates ways to optimize electricity markets under uncertainty, often using game theory, multi-criteria decision making (MCDM), and hybrid optimization methods. His ongoing projects explore the interaction of distributed energy resources with power system operation, market simulation, and energy resilience strategies. He combines theoretical modeling with real-world scenarios, contributing solutions for grid reliability, peak load management, and market regulation in developing and developed contexts. With energy systems undergoing rapid digital transformation, his work stands at the intersection of engineering, economics, and sustainability.

📄 Publication Top Notes

1. Flexible demand response programs modeling in competitive electricity markets

Authors: M.P. Moghaddam, A. Abdollahi, M. Rashidinejad
Journal: Applied Energy, Volume 88, Issue 9, 2011, Pages 3257–3269
Cited by: 391
Summary:
This paper develops a detailed framework for modeling various flexible demand response (DR) programs in competitive electricity markets. It distinguishes between incentive-based and price-based mechanisms, incorporating customer behavior in response to market signals. By applying optimization techniques, the authors evaluate the impact of DR on market performance, load profiles, and system reliability. The study concludes that DR can significantly enhance both economic efficiency and grid stability.

2. Investigation of economic and environmental-driven demand response measures incorporating UC

Authors: A. Abdollahi, M.P. Moghaddam, M. Rashidinejad, M.K. Sheikh-El-Eslami
Journal: IEEE Transactions on Smart Grid, Volume 3, Issue 1, 2011, Pages 12–25
Cited by: 211
Summary:
This work integrates economic and environmental considerations into a unit commitment (UC) model enhanced with demand response. It proposes a flexible UC framework that incorporates DR as a scheduling tool for power system operators. Using scenario-based simulations, the authors demonstrate that DR reduces both operational costs and CO₂ emissions. The paper emphasizes the strategic role of DR in achieving sustainability goals in smart grid operations.

3. Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

Authors: H. Khaloie, A. Abdollahi, M. Shafie-Khah, A. Anvari-Moghaddam, S. Nojavan, et al.
Journal: Applied Energy, Volume 259, 2020, Article 114168
Cited by: 159
Summary:
The study proposes a multi-stage stochastic model for coordinated operation of wind, thermal, and energy storage systems in energy and spinning reserve markets. The model effectively handles uncertainties in wind power and market prices, offering optimal bidding strategies to maximize profit while ensuring system reliability. This paper highlights how energy storage enhances the dispatchability of renewable energy and supports reserve provision in volatile market conditions.

4. A comprehensive sequential review study through the generation expansion planning

Authors: H. Sadeghi, M. Rashidinejad, A. Abdollahi
Journal: Renewable and Sustainable Energy Reviews, Volume 67, 2017, Pages 1369–1394
Cited by: 152
Summary:
This review comprehensively analyzes generation expansion planning (GEP) techniques, classifying them by modeling approaches, uncertainty treatment, and objective criteria (economic, environmental, technical). It covers classical methods, stochastic programming, robust optimization, and scenario analysis, providing a step-by-step understanding of GEP frameworks. The study also explores integration of renewable energy, environmental regulations, and modern computational tools, making it a valuable reference for researchers and planners.

5. Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties

Authors: H. Khaloie, A. Abdollahi, M. Shafie-Khah, P. Siano, S. Nojavan, et al.
Journal: Journal of Cleaner Production, Volume 242, 2020, Article 118434
Cited by: 130
Summary:
This paper introduces a co-optimization strategy for hybrid renewable-conventional power systems (wind, thermal, and solar) in deregulated electricity markets. A stochastic programming approach accounts for uncertainties in generation, demand, and market prices. The findings show improved profitability and resilience of integrated energy systems. It also emphasizes the advantages of diversification and coordination among different energy sources under competitive market conditions.

6. The energy hub: An extensive survey on the state-of-the-art

Authors: H. Sadeghi, M. Rashidinejad, M. Moeini-Aghtaie, A. Abdollahi
Journal: Applied Thermal Engineering, Volume 161, 2019, Article 114071
Cited by: 104
Summary:
This extensive review presents the concept of the “energy hub” as a pivotal solution for managing multiple energy carriers (electricity, gas, heat, etc.) in a smart and integrated manner. It classifies energy hub models based on their mathematical formulation, control strategies, and optimization approaches. The review also discusses the role of energy hubs in smart cities and highlights future challenges in terms of uncertainty modeling, renewable integration, and cyber-physical system design.

7. Evaluation of plug-in electric vehicles impact on cost-based unit commitment

Authors: E. Talebizadeh, M. Rashidinejad, A. Abdollahi
Journal: Journal of Power Sources, Volume 248, 2014, Pages 545–552
Cited by: 101
Summary:
The paper investigates the influence of plug-in electric vehicles (PEVs) on traditional unit commitment strategies. A cost-based unit commitment model is enhanced by incorporating vehicle-to-grid (V2G) capabilities. The analysis reveals that coordinated charging and discharging of PEVs can flatten load profiles, improve generation scheduling, and reduce overall operational costs. This study showcases the benefits of integrating transportation electrification with power system operation.

8. Probabilistic multiobjective transmission expansion planning incorporating demand response resources and large-scale distant wind farms

Authors: A. Hajebrahimi, A. Abdollahi, M. Rashidinejad
Journal: IEEE Systems Journal, Volume 11, Issue 2, 2017, Pages 1170–1181
Cited by: 95
Summary:
This work introduces a probabilistic multiobjective framework for transmission expansion planning (TEP), considering both demand response and large-scale remote wind integration. Using a scenario-based optimization model, it evaluates trade-offs among cost, reliability, and environmental factors. The study emphasizes the significant impact of demand-side resources and renewables on reducing transmission investments and increasing system flexibility.

9. The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost

Authors: S. Dorahaki, A. Abdollahi, M. Rashidinejad, M. Moghbeli
Journal: Journal of Energy Storage, Volume 33, 2021, Article 102022
Cited by: 63
Summary:
The authors explore how energy storage and demand response can support energy democracy and enhance energy productivity in hybrid hub systems. A multi-objective optimization model is proposed, which includes social inconvenience costs—representing the discomfort experienced by users due to participation in DR programs. The findings advocate for people-centered energy policies that balance technical efficiency with consumer welfare.

10. Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty

Authors: H. Khaloie, A. Abdollahi, M. Rashidinejad, P. Siano
Journal: International Journal of Electrical Power & Energy Systems, Volume 110, 2019, Pages 598–612
Cited by: 61
Summary:
This paper proposes a hybrid probabilistic-possibilistic model for the self-scheduling of power producers under uncertainty. It particularly addresses high-impact low-probability (HILP) events, such as extreme weather or cyberattacks. The model integrates risk-averse strategies with operational decision-making to maintain reliability and cost-effectiveness. The approach is validated using case studies that show how HILP scenarios influence bidding and reserve commitments in electricity markets.

Conclusion

Professor Amir Abdollahi is a highly qualified and influential academic in the field of Power Systems Engineering. His academic leadership, diverse teaching, and research focus on modern challenges in energy systems make him a strong candidate for the Best Researcher Award, particularly at the national or institutional level.