Kusnandar | Engineering | Research Excellence Award

Published on by Top Teachers

Research Excellence Award

Kusnandar
National Research and Innovation Agency, Indonesia

Kusnandar
Affiliation National Research and Innovation Agency
Country Indonesia
Scopus ID 57217677745
Documents 7
Citations 55
h-index 4
Subject Area Engineering
Event Top Teachers Awards
Google Scholar kbNqvhgAAAAJ

Kusnandar is an Indonesian engineering researcher and academic specialist recognized for contributions to thermal systems, refrigeration engineering, HVAC technologies, energy modeling, and sustainable thermal management. His interdisciplinary research integrates numerical simulations, machine learning methods, experimental validation, and energy-efficient system design for manufacturing environments and building applications. His scholarly works have addressed thermal compensation techniques, cooling optimization, machine tool thermal behavior, and sustainable energy management systems in industrial and educational infrastructure.[1][2]

Abstract

This article documents the academic and research achievements of Kusnandar in the field of engineering, with emphasis on thermal systems, refrigeration technologies, computational modeling, and energy-efficient building applications. His work combines experimental methods, machine learning approaches, CFD simulations, and energy optimization techniques for industrial and institutional environments. Through collaborative research in Indonesia and Taiwan, he has contributed to sustainable cooling systems, thermal compensation in machine tools, and HVAC performance enhancement. His scholarly publications and technical engagements demonstrate interdisciplinary integration between manufacturing systems, thermal sciences, and energy engineering.[3][4]

Keywords

Thermal Systems, HVAC Engineering, Refrigeration, Heat Transfer, Machine Learning, CFD Simulation, Sustainable Cooling, Energy Modeling, Experimental Validation, Manufacturing Systems, Energy Efficiency, Thermal Compensation.

Introduction

Engineering research related to energy conservation and thermal management has become increasingly important in industrial manufacturing, educational infrastructure, and sustainable urban systems. Kusnandar has contributed to this field through investigations involving refrigeration systems, HVAC optimization, thermal behavior in machine tools, and predictive modeling using data-driven methods. His academic profile reflects a combination of engineering practice, industrial collaboration, and applied computational analysis.[5]

He obtained a Ph.D. from the Graduate Institute of Precision Manufacturing at National Chin-Yi University of Technology (NCUT), Taiwan, after completing graduate and undergraduate studies in mechanical engineering in Indonesia. His research trajectory integrates thermal engineering with computational and machine learning techniques, particularly in relation to energy efficiency and sustainable manufacturing systems.[6]

Research Profile

Kusnandar has developed expertise across multiple engineering domains involving heat transfer, thermal systems, and energy-efficient infrastructure. His research profile demonstrates the integration of experimental investigations with computational modeling and industrial applications. The majority of his research focuses on thermal management systems, energy conversion, and predictive analysis for manufacturing and building environments.[7]

  • Thermal Systems, Energy Conversion, Refrigeration, HVAC, and Heat Transfer.
  • Numerical Modeling using CFD, FEM, and hybrid thermal simulation techniques.
  • Machine learning applications for predictive thermal behavior analysis in machine tools.
  • Sustainable cooling technologies and renewable energy integration.
  • Experimental validation, sensor integration, and thermal monitoring systems.

In addition to academic research, he has participated in commissioning systems and energy audit projects in Taiwan involving hotels, hospitals, biotechnology facilities, and cleanroom environments. These collaborative activities expanded his expertise in HVAC balancing, energy performance testing, and industrial thermal optimization.[8]

Research Contributions

Kusnandar’s research contributions are primarily associated with sustainable thermal management, building energy optimization, refrigeration engineering, and machine tool thermal analysis. His studies frequently combine field measurements, simulation frameworks, and machine learning prediction models to improve engineering efficiency and operational stability.[9]

  • Development of predictive thermal compensation models for machine tool systems using machine learning techniques.
  • Research on coupling air conditioning systems with refrigeration showcase equipment for energy-efficient retail environments.
  • Energy-efficient retrofitting approaches for institutional hot water heating systems.
  • Investigation of industrial enclosure cooling performance and thermal stability enhancement.
  • Energy modeling and field measurement analysis for university and manufacturing buildings.

His applied engineering research demonstrates practical relevance to industrial sustainability and energy conservation initiatives, particularly in manufacturing systems and educational facilities. The interdisciplinary nature of his work supports broader engineering objectives involving environmental performance and operational reliability.[10]

Publications

Kusnandar has authored and co-authored research publications in internationally recognized engineering and energy journals. His publication record demonstrates continuing engagement with thermal engineering, machine tool analysis, and energy efficiency research.[11]

  1. Kusnandar, Nasril, Danny M Gandana, Agus Widodo, and Galang I Islami. “Thermal environment effect on machine tool ball screw based on experimental investigation and numerical simulation via machine learning prediction.” Journal of Engineering, 2026. DOI: https://doi.org/10.1155/je/6435980
  2. Kusnandar, Nasril, Danny M Gandana, Agus Widodo, and Galang I Islami. “A review of thermal effect and compensation techniques in machine tools.” Scientia Iranica, 2025 (Under Review).
  3. Kusnandar, Luo W. J., Permana I., Wang F. J., and Bayarkhuu G. “Energy Efficient for a Machine Tool Building in a University through Field Measurement and Energy Modelling.” Energy Engineering, 2023, Vol. 120(6), pp. 1387–1399. DOI: https://doi.org/10.32604/ee.2023.027459
  4. Kusnandar, Permana I., Chiang W. M., Wang F. J., and Liou C. “Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store.” Energies, 2022, 15(13), 4857. DOI: https://doi.org/10.3390/en15134857
  5. Chiang W. M., Wang F. J., and Kusnandar. “Performance improvement of an industrial control enclosure cooling system.” Thermal Science, 2022, Vol. 26(3A), pp. 2043–2052. DOI: https://doi.org/10.2298/TSCI201205177C
  6. Wang F. J., Kusnandar, Lin H., and Tsai M. “Energy Efficient Approaches by Retrofitting Heat Pumps Water Heating System for a University Dormitory.” Buildings, 2021, Vol. 11, 356. DOI: https://doi.org/10.3390/buildings11080356

Research Impact

The research impact associated with Kusnandar’s academic work is reflected in the integration of energy-efficient engineering methods with sustainable manufacturing and building operation systems. His publications address practical industrial challenges related to thermal instability, cooling efficiency, and energy consumption reduction.[12]

His studies involving machine tool thermal behavior contribute to manufacturing precision and operational reliability, while his building energy modeling research supports improved environmental performance and energy conservation strategies. The application of machine learning within thermal engineering also demonstrates the growing role of intelligent predictive systems in engineering analysis.[13]

Award Suitability

Kusnandar’s academic background, international research collaborations, engineering publications, and contributions to sustainable thermal management support his suitability for recognition through the Top Teachers Awards. His work demonstrates a combination of research productivity, educational engagement, and applied engineering innovation within the broader field of energy and thermal systems engineering.[14]

His professional experience includes teaching, institutional leadership, postdoctoral research, and industrial collaboration across Indonesia and Taiwan. The integration of academic scholarship with real-world engineering applications reflects a sustained contribution to engineering education and technological development.[15]

Conclusion

Kusnandar represents an engineering academic whose research activities contribute to advancements in thermal systems, energy-efficient technologies, refrigeration engineering, and computational thermal analysis. Through scholarly publications, interdisciplinary methodologies, and international collaborative activities, he has participated in the development of sustainable engineering solutions relevant to manufacturing and building environments. His academic profile aligns with contemporary engineering priorities emphasizing sustainability, efficiency, and intelligent thermal management systems.[16]

References

  1. Elsevier. (n.d.). Scopus author details: Kusnandar, Author ID 57217677745. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57217677745
  2. Google Scholar. (n.d.). Kusnandar citation profile and scholarly metrics. https://scholar.google.com/citations?hl=id&user=kbNqvhgAAAAJ
  3. Kusnandar et al. (2026). Thermal environment effect on machine tool ball screw based on experimental investigation and numerical simulation via machine learning prediction. https://doi.org/10.1155/je/6435980
  4. Kusnandar et al. (2023). Energy Efficient for a Machine Tool Building in a University through Field Measurement and Energy Modelling. https://doi.org/10.32604/ee.2023.027459
  5. Energies Journal. (2022). Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store. https://doi.org/10.3390/en15134857
  6. National Chin-Yi University of Technology. (n.d.). Graduate Institute of Precision Manufacturing academic records.
  7. Research profile documentation relating to HVAC engineering, thermal systems, CFD simulations, and machine learning applications in engineering systems.
  8. Industry collaborative project records involving commissioning systems, energy audits, and HVAC balancing activities in Taiwan from 2019–2023.
  9. Thermal Science. (2022). Performance improvement of an industrial control enclosure cooling system. https://doi.org/10.2298/TSCI201205177C
  10. Buildings Journal. (2021). Energy Efficient Approaches by Retrofitting Heat Pumps Water Heating System for a University Dormitory. https://doi.org/10.3390/buildings11080356
  11. Publication data compiled from Scopus indexing and Google Scholar author records.
  12. Engineering research concerning sustainable thermal management and energy optimization systems in manufacturing environments.
  13. Research applications involving machine learning integration in predictive thermal engineering systems.
  14. Top Teachers Awards. (n.d.). Academic recognition and global teaching excellence platform. https://topteachers.net/
  15. Professional records relating to teaching, academic administration, and postdoctoral research appointments in Indonesia and Taiwan.
  16. Comprehensive academic summary compiled from publication records, institutional affiliations, and engineering research activities.

Fazal e Wahab | Engineering | Innovative Research Award

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Innovative Research Award

Fazal e Wahab
Hubei Polytechnic University
Fazal e Wahab
Affiliation Hubei Polytechnic University
Country China
Scopus ID 57216410031
Documents 14
Citations 111
h-index 7
Subject Area Engineering
Event Top Teachers Awards
ORCID 0000-0003-4827-170X
Google Scholar 8t4Pxo8AAAAJ

Fazal e Wahab is an academic researcher and engineering educator affiliated with Hubei Polytechnic University, China. His scholarly work primarily focuses on speech enhancement, signal processing, machine learning applications, and low-latency intelligent systems for embedded and edge computing environments. Over the course of his academic and professional career, he has contributed to research in audio-visual speech enhancement, real-time denoising systems, neural network optimization, and applied engineering technologies. His publications in internationally indexed journals and conferences demonstrate sustained engagement with contemporary developments in communication engineering and intelligent multimedia systems.[1]

Abstract

This academic article documents the scholarly profile, research achievements, and educational contributions of Fazal e Wahab in the field of engineering and intelligent signal processing. His work addresses challenges associated with speech enhancement, audiovisual communication systems, and machine learning implementation for resource-constrained edge devices. Through interdisciplinary research involving signal processing, neural networks, embedded systems, and audio enhancement technologies, he has contributed to practical and computationally efficient methods for real-time communication systems. His publication record includes SCI-indexed journal articles, conference proceedings, funded engineering projects, and collaborative international research activities.[2]

Keywords

Speech Enhancement, Signal Processing, Edge Computing, Deep Learning, Audio-Visual Systems, Engineering Education, Machine Learning, Embedded Systems, Real-Time Denoising, Communication Engineering.

Introduction

The development of intelligent speech processing systems has become increasingly important in modern communication engineering, particularly in environments requiring low-latency and computationally efficient solutions. Researchers working in this field address technical challenges associated with noise suppression, speech intelligibility, audio enhancement, and multimodal communication systems. Fazal e Wahab has participated in this evolving research area through studies focused on lightweight neural architectures, edge-device optimization, and robust audiovisual speech enhancement frameworks.[3]

In addition to research activities, he has contributed extensively to university-level engineering education through undergraduate teaching, curriculum development, laboratory instruction, and supervision of student innovation projects. His academic trajectory includes higher education and research engagement in Pakistan and China, reflecting international academic collaboration and interdisciplinary engineering practice.[4]

Research Profile

Fazal e Wahab completed a Ph.D. in Information and Communication Engineering at the University of Science and Technology of China (USTC) in 2025. His doctoral research focused on optimized lightweight deep learning models for real-time single-channel speech enhancement systems. His investigations emphasized computational efficiency, streaming denoising, echo cancellation, and dereverberation systems applicable to edge and embedded hardware environments.[5]

His academic experience also includes an M.S. in Electrical Engineering from CECOS University and a B.S. in Electronic Engineering from Dawood University of Engineering and Technology. Professionally, he has served as a lecturer, researcher, engineering instructor, and instrumentation engineer, contributing both to industrial engineering operations and university-level technical education.[6]

  • Research specialization in speech enhancement and audio signal processing.
  • Experience in machine learning for edge and embedded systems.
  • Academic supervision of funded engineering projects and applied research.
  • Participation in international scientific collaboration and peer review activities.

Research Contributions

The research contributions of Fazal e Wahab are associated with efficient speech enhancement systems using lightweight neural network architectures. His studies investigate methods for reducing computational complexity while maintaining speech intelligibility and enhancement quality in real-time applications. This area of research is particularly relevant for embedded systems, mobile communication technologies, and assistive audio interfaces.[7]

His published work includes investigations into gated convolutional recurrent neural networks, dual-transformer architectures, multimodal audiovisual processing systems, and adaptive deep learning techniques for speech enhancement. Several publications focus on resource-constrained devices and edge deployment scenarios, demonstrating applied relevance in consumer electronics and intelligent communication technologies.[8]

  • Development of lightweight deep learning models for speech enhancement.
  • Research on audio-visual speech enhancement frameworks using transformer architectures.
  • Optimization of neural systems for edge and embedded devices.
  • Contribution to intelligent signal processing and real-time communication systems.
  • Supervision of funded engineering innovation and assistive technology projects.

Publications

The publication record of Fazal e Wahab includes journal articles and conference papers indexed in SCI, EI, and Scopus databases. His publications span topics related to speech enhancement, multimedia systems, signal processing, energy systems, and intelligent engineering applications.[9]

  1. “Lightweight Adaptive Deep Learning for Efficient Real-Time Speech Enhancement on Edge Devices,” IEEE Transactions on Consumer Electronics, 2025.
  2. “Compact Deep Neural Networks for Real-Time Speech Enhancement on Resource-Limited Devices,” Speech Communication, 2024.
  3. “Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement,” International Journal of Interactive Multimedia and Artificial Intelligence, 2023.
  4. “Multi-Model Dual-Transformer Network for Audio-Visual Speech Enhancement,” AVSEC 2024.
  5. “Integrating Graph Neural Networks and Visual Encoding for Robust Audiovisual Speech Enhancement,” IEEC 2026.
  6. “Frequency-Aware Selective State-Space Modeling for Audio-Visual Speech Enhancement,” Digital Signal Processing, 2026.
  7. “Dynamic Multi-Kernel Convolutional Network With Noise Injected Features for Audio-Only Speech Enhancement,” Neurocomputing, 2025.
  8. “Multimodal Learning-Based Speech Enhancement and Separation,” Computers in Biology and Medicine, 2025.

Research Impact

The research activities of Fazal e Wahab demonstrate measurable academic visibility through Scopus-indexed publications, citation performance, and interdisciplinary engineering collaborations. His studies contribute to ongoing advancements in speech enhancement technologies and intelligent multimedia processing systems. The citation profile associated with his publications indicates scholarly engagement within signal processing and communication engineering communities.[10]

Beyond scholarly publication, his mentorship of funded engineering projects has supported prototype development, applied innovation, and student-centered engineering education. Several supervised projects addressed healthcare technologies, smart home systems, assistive devices, and IoT-enabled monitoring systems, demonstrating practical societal relevance and engineering application.[11]

Award Suitability

The academic and professional profile of Fazal e Wahab reflects several characteristics associated with scholarly recognition in engineering and higher education. His combination of research productivity, international academic engagement, peer-reviewed publication activity, student mentorship, and interdisciplinary engineering expertise demonstrates sustained contribution to communication engineering and intelligent systems research.[12]

His involvement in advanced research related to speech enhancement and machine learning for edge computing environments aligns with emerging global priorities in intelligent communication technologies. Additionally, his experience in teaching, curriculum support, and applied project supervision reflects commitment to engineering education and knowledge dissemination within academic institutions.[13]

Conclusion

Fazal e Wahab has established a multidisciplinary academic profile combining research, teaching, engineering practice, and international scholarly collaboration. His contributions to speech enhancement, signal processing, and machine learning applications for embedded systems represent ongoing engagement with technically relevant and practically applicable research domains. Through journal publications, conference participation, funded project supervision, and academic service, he continues to contribute to the broader development of communication engineering and intelligent multimedia technologies.[13]

References

  1. Elsevier. (n.d.). Scopus author details: Fazal e Wahab, Author ID 57216410031. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57216410031
  2. ORCID. (n.d.). ORCID profile record for Fazal e Wahab. https://orcid.org/0000-0003-4827-170X
  3. IEEE. (2025). Lightweight Adaptive Deep Learning for Efficient Real-Time Speech Enhancement on Edge Devices. https://doi.org/10.1109/TCE.2025.3598007
  4. University of Science and Technology of China. (2025). Doctoral dissertation and academic research profile.
  5. Speech Communication. (2024). Compact Deep Neural Networks for Real-Time Speech Enhancement on Resource-Limited Devices.https://doi.org/10.1016/j.specom.2023.103008
  6. CECOS University. (2015). Master of Science in Electrical Engineering academic record.
  7. International Journal of Interactive Multimedia and Artificial Intelligence. (2023). Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement.
  8. AVSEC Proceedings. (2024). Multi-Model Dual-Transformer Network for Audio-Visual Speech Enhancement.
  9. Computers in Biology and Medicine. (2025). Multimodal Learning-Based Speech Enhancement and Separation. https://doi.org/10.1016/j.compbiomed.2025.110082
  10. Digital Signal Processing. (2026). Frequency-Aware Selective State-Space Modeling for Audio-Visual Speech Enhancement.
  11. National ICT R&D Fund. (n.d.). Applied engineering and IoT-based funded student projects.
  12. Top Teachers Awards. (n.d.). International academic recognition and award platform.https://topteachers.net/
  13. Google Scholar. (n.d.). Academic citation profile of Fazal e Wahab. https://scholar.google.com/citations?hl=en&authuser=1&user=8t4Pxo8AAAAJ

Giovanni Maria Ferraris | Engineering | Research Excellence Award

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Dr. Giovanni Maria Ferraris | Engineering | Research Excellence Award

University of Genoa | Italy

Dr. Giovanni Maria Ferraris is an interdisciplinary engineering researcher specializing in occupational health and safety, fire prevention, risk analysis, and industrial project management, with contributions spanning energy systems, environmental protection, and critical infrastructure. His research integrates applied engineering solutions with safety, sustainability, and innovation in complex industrial and public systems. He has authored 6 Scopus-indexed documents with 3 citations and an h-index of 1, reflecting emerging scholarly impact. His profile is further strengthened by academic engagement in engineering, security, and decision-making systems. Ferraris’s work bridges research, policy, and practice in high-risk and technologically advanced environments.

Citation Metrics (Scopus)

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Featured Publications

Chen Yang | Engineering | Research Excellence Award

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Prof. Chen Yang | Engineering | Research Excellence Award

School of Energy and Power, Chongqing University  |  China

Prof. Chen Yang  research centers on advanced energy systems, renewable energy utilization, and thermal power engineering, with strong emphasis on modeling, optimization, and dynamic control of complex thermo-energy systems, supported by a research record of 1,004 citations across 868 documents, 98 publications, and an h-index of 18. His contributions span ultra-supercritical circulating fluidized bed boilers, nuclear power reactor secondary systems, compressed air energy storage, and hybrid solid oxide fuel cell–gas turbine systems, advancing the efficiency, reliability, and safety of large-scale power generation. He has developed multi-physics and multi-scale reduced-order modeling techniques to address nonlinear dynamics, uncertainty, cooperative simulation, and system stability challenges, enabling enhanced operational performance under transient and abnormal working conditions. His work integrates mechanistic models with artificial intelligence, including neural networks and time-series methods, to achieve online simulation, intelligent prediction, fault early warning, and predictive control in energy systems. He has also contributed to thermodynamic coupling analysis, waste heat utilization strategies, and multi-objective optimization frameworks for green energy systems. Through these innovations, his research significantly supports sustainable power technology development, promotes intelligent and resilient energy infrastructures, and contributes to low-carbon energy transformation and modern energy system advancement.

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Featured Publications

Konstantinos Azis | Engineering | Research Excellence Award

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Dr. Konstantinos Azis | Engineering | Research Excellence Award

Democritus University of Thrace | Greece

Dr. Konstantinos Azis is an accomplished environmental engineer and postdoctoral researcher whose work focuses on advanced wastewater treatment technologies, membrane bioreactor systems, and intelligent process control for sustainable water management. His research integrates biological, physicochemical, and automated monitoring approaches to optimize the performance of aerobic, anoxic, and anaerobic treatment processes, particularly in membrane systems and intermittently aerated bioreactors. He specializes in the design and operation of high-efficiency treatment units, development of real-time control strategies using programmable logic controllers, simulation-driven optimization with STOAT, and monitoring of key environmental parameters through continuous online sensors. His contributions extend to biological degradation studies of micropollutants, pharmaceuticals, and agrochemical contaminants, as well as post-treatment polishing processes such as activated carbon adsorption, sand filtration, ultrafiltration, and advanced oxidation. His research output demonstrates strong international visibility, with publications addressing membrane fouling mitigation, nutrient removal enhancement, biofouling dynamics, and energy-efficient aeration strategies. Dr. Azis has contributed significantly to environmental biotechnology by combining laboratory experimentation, field-scale evaluation, and computational modeling, offering practical solutions for water reuse and circular economy applications. His work has earned recognition through contributions to high-impact journals, service as a reviewer for numerous international scientific journals, and involvement as a Guest Editor in thematic issues focusing on sustainable wastewater treatment technologies. His scholarly influence is reflected in Scopus metrics: 118 citations and h-index 7, and Google Scholar metrics: 163 citations, h-index 8, i10-index 8, demonstrating the growing impact and relevance of his research across the wastewater engineering and environmental science communities. His scientific record and active research engagement position him as a strong candidate for the Research Excellence Award.

Publication Profile

Scopus | Orcid | Google Scholar

Featured Publications

Azis, K., Mavriou, Z., Karpouzas, D. G., Ntougias, S., & Melidis, P. (2021). Evaluation of sand filtration and activated carbon adsorption for the post-treatment of a secondary biologically-treated fungicide-containing wastewater. Processes, 9(7), 1223.

Azis, K., Zerva, I., Melidis, P., Caceres, C., Bourtzis, K., & Ntougias, S. (2019). Biochemical and nutritional characterization of the medfly gut symbiont Enterobacter sp. AA26 for its use as probiotics in sterile insect technique applications. BMC Biotechnology, 19(Suppl 2), 90.

Azis, K., Ntougias, S., & Melidis, P. (2021). NH4+-N versus pH and ORP versus NO3−-N sensors during online monitoring of an intermittently aerated and fed membrane bioreactor. Environmental Science and Pollution Research, 28(26), 33837–33843.

Azis, K., Ntougias, S., & Melidis, P. (2019). Fouling control, using various cleaning methods, applied on an MBR system through continuous TMP monitoring. Desalination and Water Treatment, 167, 343–350.

Papazlatani, C. V., Kolovou, M., Gkounou, E. E., Azis, K., Mavriou, Z., & others. (2022). Isolation, characterization and industrial application of a Cladosporium herbarum fungal strain able to degrade the fungicide imazalil. Environmental Pollution, 301, 119030.

Vasso Papadimitriou | Engineering | Best Researcher Award

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Ms. Vasso Papadimitriou | Engineering | Best Researcher Award

Aristotle University of Thessaloniki | Greece

Ms. Vasso Papadimitriou is an accomplished researcher and academic affiliated with the Aristotle University of Thessaloniki and the Region of Central Macedonia, Greece. Her research primarily focuses on construction project management, cost estimation models, and the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly Artificial Neural Networks (ANNs), in the field of building renovation and project planning. She has contributed significantly to the development of predictive and hybrid models that enhance accuracy in early-stage and final cost estimation for construction and renovation projects. Dr. Papadimitriou’s innovative work combines ANN methodologies, including Radial Basis Function (RBF) and Multilayer Perceptron (MLP) networks, with Multi-Criteria Decision-Making (MCDM) approaches such as the TOPSIS Methodology to create efficient, data-driven tools for project assessment and optimization. Her research also aligns with Sustainable Development Goals (SDG 9 and SDG 17), focusing on promoting innovation, infrastructure, and partnerships for sustainable growth. She has published in international peer-reviewed journals indexed in Scopus, Web of Science (SCI-Expanded, ESCI), and other scientific databases. According to Scopus, she has 6 publications, 3 citations, and an h-index of 1. On Google Scholar, she holds 14 total citations, an h-index of 3, and an i10-index of 1, while ResearchGate records 6 publications, 11 citations, and an h-index of 2. Her interdisciplinary approach bridges civil engineering, computer science, and digital construction, contributing to advancements in cost modeling and sustainable infrastructure management. Through her publications and research collaborations, Dr. Papadimitriou continues to make impactful contributions to the field of engineering innovation and AI-driven construction technology. Her outstanding achievements and innovative contributions to predictive modeling and sustainable construction management make her a deserving nominee for the Best Researcher Award.

Publication Profile

Scopus | Google Scholar

Featured Publications

Papadimitriou, V. E., & Aretoulis, G. N. (2024). A final cost estimating model for building renovation projects. Buildings, 14(4), 1072.

Papadimitriou, V. E., Aretoulis, G. N., & Papathanasiou, J. (2024). Radial Basis Function (RBF) and Multilayer Perceptron (MLP) comparative analysis on building renovation cost estimation: The case of Greece. Algorithms, 17(9), 390.

Papadimitriou, V., & Aretoulis, G. (2023). Neural network models as a cost prediction tool to prevent building construction projects from a failure—A literature review. Proceedings of the Erasmus+ PROSPER Project International Scientific Conference, 1–10.

Papadimitriou, V. E., & Aretoulis, G. N. (2025). An innovative approach regarding efficient and expedited early building renovation cost estimation utilizing ANNs and the TOPSIS methodology. Algorithms, 18(11), 696.

Kritikos, P., Papadimitriou, V., & Aretoulis, G. N. (2021). Required project designers’ attributes as perceived by male and female engineers. International Journal of Decision Support System Technology (IJDSST), 13(4), 1–15.

 

 

Amr Ashmawi | Engineering | Best Researcher Award

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Mr. Amr Ashmawi | Engineering | Best Researcher Award

South Dakota State University | United States

Mr. Amr Ashmawi is a structural civil engineer and BIM specialist whose research integrates Building Information Modeling (BIM), Artificial Intelligence (AI), and automation technologies to advance construction engineering and management. His work focuses on enhancing construction site efficiency, digitalizing bridge inspection processes, and improving safety training through intelligent systems. With expertise in structural analysis, BIM development, and programming (C#, Python, Revit API, AutoCAD API), his research aims to transform traditional workflows into smart, data-driven solutions for sustainable and safe construction environments. He has contributed to advancing real-time data integration, predictive modeling for reinforced concrete (RC) structures, and automation in bridge assessment using machine learning. Amr’s scholarly impact is reflected in his early academic recognition, having 1 citation, 1 h-index, and 0 i10-index on Google Scholar, with research visibility growing rapidly. His publications are featured in Advances in Structural Engineering and Applied Sciences journals. He continues to explore the intersection of BIM and AI for next-generation construction technologies, positioning himself as an emerging researcher dedicated to automation, visualization, and intelligent infrastructure systems. His innovative contributions and growing influence in structural informatics and construction automation make him a deserving recipient of the Best Researcher Award.

Publication Profile

Featured Publications

Ashmawi, A., Nguyen, P., & Jawdhari, A. (2025). State-of-the-art review of machine learning applications for bridge inspections. Advances in Structural Engineering, 13694332251381220.

Nichols, L., Ashmawi, A., & Nguyen, P. (2025). Digitalizing bridge inspection processes using Building Information Modeling (BIM) and Business Intelligence (BI). Applied Sciences, 15(20), 10927.

Ashmawi, A., Nguyen, P., & Jawdhari, A. (2025). Enhancing construction safety training using artificial intelligence: Existing applications and future directions. Under publication.

Ashmawi, A., & Nguyen, P. (2025). Leveraging BIM for automated fire safety code compliance: A comprehensive review. Under review.

 

Mehran Pourgholi | Engineering | Best Researcher Award

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Assist. Prof. Dr. Mehran Pourgholi | Engineering | Best Researcher Award

Islamic Azad University | Iran

Assist. Prof. Dr. Mehran Pourgholi, an accomplished researcher in Civil and Structural Engineering, has made notable contributions to the fields of system identification, structural health monitoring, inverse problems, and stochastic subspace methods. His research focuses on enhancing the reliability of modal analysis and vibration-based damage detection in large-scale structures such as dams and steel buildings. Dr. Pourgholi integrates advanced computational approaches including entropy-based model selection, optimization algorithms, and error analysis frameworks to improve accuracy in structural system modeling. His collaborative work with experts from the University of Tabriz and Islamic Azad University has produced high-impact studies published in leading international journals such as the Journal of Vibration and Control, Mechanical Systems and Signal Processing, and Engineering Reports. According to Google Scholar, Dr. Pourgholi has 77 citations (68 since 2020), an h-index of 4, and an i10-index of 2, while Scopus records 51 citations across 46 documents with an h-index of 3. His influential publications on stochastic subspace identification and modal analysis have advanced understanding of dynamic behavior in civil structures. Recognized for his scholarly excellence, he has been honored with the Best Researcher Award for his significant impact in the field of structural system identification and vibration analysis.

Publication Profile

Scopus | Orcid | Google Scholar 

Featured Publications

Tarinejad, R., & Pourgholi, M. (2018). Modal identification of arch dams using balanced stochastic subspace identification. Journal of Vibration and Control, 24(10), 2030–2044.

Pourgholi, M., Mohammadzadeh Gilarlue, M. M., Vahdaini, T., & Azarbonyad, M. (2023). Influence of Hankel matrix dimension on system identification of structures using stochastic subspace algorithms. Mechanical Systems and Signal Processing, 186, 109893.

Pourgholi, M., Tarinejad, R., Khabir, M. E., & Mohammadzadeh Gilarlue, M. M. (2023). System identification of Karun IV Dam using balanced stochastic subspace algorithm considering the uncertainty of results. Journal of Vibration and Control, 29(23–24), 5342–5356.

Tarinejad, R., Pourgholi, M., & Yaghmaei-Sabegh, S. (2016). Signal processing of dynamic tests results using subspace identification based on orthogonal decomposition technique (SI-ORT). Modares Mechanical Engineering, 15(10), 104–116.

Pourgholi, M., Ghannadi, M., & Gavgani, S. S. (2024). Modal analysis of earthquake records for dams using stochastic subspace based on error analysis. Engineering Reports, 6(8), e12822

 

Manença Cristiano Nhanga| Materiais| Excellence in Research Award

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Mr.Manença Cristiano Nhanga |Materiais| Excellencein Research Award

Researcher staff at Púnguè University,Mozambique.
Manença Cristiano Nhanga is a researcher and staff member at Púnguè University, Mozambique. His academic and research interests focus on areas related to engineering, environmental sciences, and sustainable development. With a strong commitment to advancing knowledge and innovation, he actively contributes to research projects and academic initiatives at the university.
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Education 🎓

Púnguè University, Mozambique (2020 – 2024)Degree: Bachelor’s in Physics Teaching with a focus on Renewable Energy.Distinction: Best student in the Faculty of Exact and Technological Sciences with an average of 15/20.University of Minho, Portugal (Feb – June 2024)Conducted a research project on “Evaluation of Heavy Metals Content in Plastic Bags Used as Part of Food Cooking Containers by the Mozambican Community” under the guidance of Prof. Dr. Maria Dulce Geraldo, Prof. Dr. Manuel Filipe Costa, and Prof. Dr. Sérgio Nhapulo.Global Inclusive Society, Brazil (2023)Certified in International Relations, Cooperativism, Geopolitics, and Public Speaking.Mopeia Secondary School, Zambezia (2014-2018)Achieved the best academic performance and was exempted from the national examination in 10th grade.

work Experience💼

University of Minho, Portugal (Feb – June 2024)Poster Presentation at VII ETCAQ: Evaluation of Zn in Plastic Bags used as Food Containers in Mozambique.Book Chapter Author: Science Education and Sustainability in Hands-On Science 2024.Article Author: Evaluation of Heavy Metals in Plastic Bags used as Improvised Food Cooking Covers: A Case Study from Mozambique (Sustainability Journal 2025, 17, 964).Conference Participation:VII Meeting of Chemical Characterization and Analysis Techniques (June 7, 2024)Catalysis in Industry Lecture by Prof. Pham Thanh Huyen from Hanoi University of Science and Technology (June 20, 2024)Science Fair Jury Member at Colégio do Minho (May 23, 2024)Oral Presentation at the 21st Annual International Hands-On Science Conference 2024 in Goiás, Brazil.Electro Expresso, Lda [Assistant]Worked in service, maintenance, and logistics.Global Inclusive Society [Assistant Teacher] (2023)Taught English at intermediate and pre-advanced levels.EISA-JOINT Mozambique [Election Representative, Luabo] (2019)

Research Focus

Nhanga’s research primarily explores:Environmental Sustainability and Heavy Metal ContaminationRenewable Energy Applications in Physics EducationScience Education and Sustainable DevelopmentMaterial Safety in Food PackagingHis work addresses societal challenges in Mozambique, particularly regarding food safety, environmental impact, and education in underdeveloped regions.

Awards & Honors🏆 

Best Student in Physics Course – Púnguè University (2024)
🏆 Best Academic Performance – University of Minho (ERASMUS+ ICM, 2024)
🏆 Best Academic Performance in Secondary School – Mopeia Secondary School (2018)

Conclusion✅

Based on his academic excellence, research achievements, and international collaboration, Manença Cristiano Nhanga is a highly suitable candidate for the Excellence in Research Award. By enhancing his publication record, research funding, and interdisciplinary studies, he can further establish himself as a leading scholar in environmental and material sciences.

📚Publications to Noted

  • Nhanga, M. C., Geraldo, D., Nhapulo, S. L., João, A. F., Carneiro, J., & Costa, M. F. M. (2025). Evaluation of Heavy Metal Content in Plastic Bags Used as Improvised Food Cooking Covers: A Case Study from the Mozambican Community. Sustainability, 17, 964.

  • Nhanga, M. C. (2024). Evaluation of Heavy Metal Content in Plastic Bags Used as Part of Food Cooking Containers: A Socio-educational Sustainability Perspective in Mozambique. In Science Education and Sustainability (pp. XX-XX). ISBN 978-84-8158-971-9.