Xiaotian Wang | Engineering Award | Excellence in Research

Mr. Xiaotian Wang | Engineering Award | Excellence in Research

Associate Professor at Northwestern Polytechnical University, China

Xiaotian Wang, an Associate Professor at Northwestern Polytechnical University, holds both a Master’s and a Ph.D. degree, earned in 2016 and 2020, respectively, from the same institution. His academic background and current position within a renowned university are strong indicators of his expertise and commitment to research. His specialized focus on computer vision and remote sensing image processing, particularly in object detection and tracking, aligns with cutting-edge technological advancements in these fields. Wang’s contributions to unmanned systems research further highlight his alignment with contemporary research trends and his potential to lead innovative projects.

Professional Profile

Education

Dr. Xiaotian Wang completed his Master’s degree and Ph.D. in the field of Computer Vision and Remote Sensing from Northwestern Polytechnical University, Xi’an, China. He earned his Master’s in 2016 and his Ph.D. in 2020, both from the same prestigious institution. Throughout his education, Dr. Wang developed a deep understanding of machine learning, artificial intelligence, and their applications in unmanned systems. His academic journey involved rigorous research in object detection and tracking algorithms, which he continued to develop through various academic and practical projects. His research contributions were shaped by the rigorous training and mentorship he received during his graduate studies. Dr. Wang’s education provided him with a strong theoretical foundation and the technical expertise necessary to conduct pioneering research in remote sensing image processing and computer vision, making him a recognized expert in his field.

Experience

Dr. Xiaotian Wang is currently an Associate Professor at Northwestern Polytechnical University, where he has made significant contributions to research and development in unmanned systems and remote sensing. His primary focus is on computer vision, particularly object detection and tracking technologies that have important applications in surveillance, robotics, and unmanned vehicles. Prior to his current position, Dr. Wang has been actively involved in several key research projects, collaborating with national and international researchers in the development of cutting-edge technologies for unmanned systems. His expertise in integrating computer vision algorithms with remote sensing has led to several innovative solutions in the field. Additionally, he actively mentors graduate students and early-career researchers, guiding them in advancing their knowledge and research skills in these high-tech domains. His academic and research experience provides a foundation for developing practical, scalable solutions in remote sensing and unmanned technologies.

Research Focus

Dr. Xiaotian Wang’s research primarily revolves around computer vision and remote sensing image processing, with a particular emphasis on object detection and tracking technologies. His work has a significant focus on unmanned systems, where he explores innovative approaches for navigating and processing data from remote sensing devices. Dr. Wang’s research aims to enhance the capabilities of unmanned vehicles, such as drones, through improved object detection and tracking algorithms that enable these systems to interpret and respond to their environments autonomously. This work is crucial for applications in fields like autonomous vehicles, surveillance, and environmental monitoring. By advancing the integration of computer vision with remote sensing, Dr. Wang seeks to bridge the gap between real-time decision-making and automated systems. His research plays a key role in advancing the field of unmanned systems, which are becoming increasingly vital in many industries, including defense, transportation, and agriculture.

Conclusion

Xiaotian Wang demonstrates a strong research profile with a clear focus on advancing unmanned systems and remote sensing technology, which are highly relevant to both scientific and practical applications. His academic and research contributions make him an excellent candidate for the Excellence in Research Award.

Publications Top Noted

Cross-Attention-Driven Adaptive Graph Relational Network for Multilabel Remote Sensing Scene Classification”

Authors: Bi, H., Chang, H., Wang, X., Hong, D.

Citations: 0

Year: 2024

Journal: IEEE Transactions on Geoscience and Remote Sensing

Volume: 62

Article ID: 5224414

“Complexity Evaluation of Aerial Infrared Countermeasure Scenes”

Authors: Xie, F., Dong, M., Wang, X., Yang, D., Yan, J.

Citations: 0

Year: 2024

Journal: IEEE Transactions on Aerospace and Electronic Systems

“Can Rumor Detection Enhance Fact Verification? Unraveling Cross-Task Synergies Between Rumor Detection and Fact Verification”

Authors: Jin, W., Jiang, M., Tao, T., Zhao, B., Yang, G.

Citations: 0

Year: 2024

Journal: IEEE Transactions on Big Data

“A Research on Rapid Assessment of Cross-Domain Perceptual Fidelity for Practical Applications”

Authors: Tao, W., Wang, X., Yan, T., Zeng, Q., Lu, R.

Citations: 0

Year: 2024

Conference: Proceedings of the 3rd Conference on Fully Actuated System Theory and Applications (FASTA 2024)

“An Improved Small Infrared Target Detection Algorithm Based on Yolov5”

Authors: Wang, X., Yang, Z., Sun, Y., Qian, C., Zhao, Y.

Citations: 0

Year: 2024

Conference: Lecture Notes in Electrical Engineering (LNEE), 1175, pp. 405–413

“Detection of Occlusion-Resistant Based on Improved YOLOv7”

Authors: Tao, W., Wang, K., Li, Y., Yan, T., Wang, X.

Citations: 0

Year: 2024

Conference: Lecture Notes in Electrical Engineering (LNEE), 1173, pp. 430–439

“ESF-YOLO: an accurate and universal object detector based on neural networks”

Authors: Tao, W., Wang, X., Yan, T., Liu, Z., Wan, S.

Citations: 0

Year: 2024

Journal: Frontiers in Neuroscience

Volume: 18

Article ID: 1371418

“An Infrared Small Target Detection Method Based on Attention Mechanism”

Authors: Wang, X., Lu, R., Bi, H., Li, Y.

Citations: 3

Year: 2023

Journal: Sensors (Basel, Switzerland)

Volume: 23

Issue: 20

“SiamCAR-Kal: anti-occlusion tracking algorithm for infrared ground targets based on SiamCAR and Kalman filter”

Authors: Fu, G., Zhang, K., Yang, X., Tian, X., Wang, X.T.

Citations: 0

Year: 2023

Journal: Machine Vision and Applications

Volume: 34

Issue: 3

Article ID: 43

“Robust small infrared target detection using multi-scale contrast fuzzy discriminant segmentation”

Authors: Wang, X., Xie, F., Liu, W., Tang, S., Yan, J.

Citations: 5

Year: 2023

Journal: Expert Systems with Applications

Volume: 212

Article ID: 118813

 

 

Masoud Yaghini | Engineering | Best Researcher Award

Assoc Prof Dr Masoud Yaghini | Engineering | Best Researcher Award

Faculty Member at Iran University of Science and Technology, Iran

Dr. Masoud Yaghini is a distinguished faculty member in the Department of Rail Transportation at the Iran University of Science and Technology. Born on December 8, 1966, he holds an extensive academic and professional background in rail transportation planning and optimization techniques. With over two decades of experience, Dr. Yaghini has made substantial contributions to the fields of transportation logistics, network design, and data mining, particularly within the railway industry. His innovative approaches to complex rail transportation problems have earned him a reputation as a leading researcher in the field. Dr. Yaghini is widely published and continues to shape the future of transportation with cutting-edge research.

Professional Profile

Education

Dr. Yaghini received his Ph.D. in Rail Transportation Planning and Engineering from Northern Jiaotong University, Beijing, China, in 2003, with a focus on dynamic service network design. He also holds an MSc and BSc in Industrial Management from Islamic Azad University, Tehran. His master’s thesis on resource assignment optimization in preventive maintenance laid the foundation for his interest in large-scale optimization problems. Additionally, he furthered his knowledge with specialized training in Ergonomics and Human Factors for Railways from the University of Birmingham, UK, in 2005. This diverse educational background has equipped Dr. Yaghini with both theoretical and practical expertise in optimizing transportation systems.

Professional Experience

Dr. Yaghini has over 20 years of professional experience, primarily as a faculty member at the Iran University of Science and Technology. He teaches a wide range of courses, from advanced computer programming to railway operations management and data mining in transportation. His professional experience extends beyond academia into consultancy work in optimization and transportation planning. Dr. Yaghini has also conducted numerous short courses and workshops in data mining, information management, and metaheuristic algorithms for both academic institutions and private companies. His role as an educator and consultant has allowed him to bridge the gap between academic research and real-world transportation challenges.

Research Interests

Dr. Yaghini’s research primarily focuses on optimization problems in rail transportation, including train scheduling, fleet sizing, and locomotive scheduling. He has a strong interest in metaheuristics such as Genetic Algorithms, Tabu Search, and Ant Colony Optimization, as well as exact solution methods like Column Generation and Branch and Cut. His work also explores data mining techniques applied to railway systems, such as the prediction of train delays and analysis of accident data. His research is driven by the need to optimize and improve efficiency in transportation systems, particularly in large-scale rail networks. His work has significant practical implications for enhancing railway operations and minimizing costs.

Awards and Honors

Dr. Yaghini’s contributions to transportation research have earned him multiple accolades, though his recognition mainly stems from his published works in high-impact journals such as Applied Mathematical Modelling and Journal of Transportation Engineering. He has been recognized for his work on solving complex railway optimization problems through innovative algorithms like Ant Colony Optimization and Simulated Annealing. In addition to his publications, Dr. Yaghini has been invited to present his findings at numerous international conferences. While he has not widely publicized any specific awards, his ongoing research contributions have earned him a solid reputation in the global transportation research community, marking him as a key figure in rail transportation planning and optimization.

Conclusion

Dr. Masoud Yaghini’s research portfolio is impressive, with a strong emphasis on rail transportation and optimization problems. His consistent contributions to both academic knowledge and practical railway systems demonstrate his potential for recognition as a top researcher. By broadening his collaborative network and impact beyond academia, he could further strengthen his candidacy for prestigious awards like the Best Researcher Award.

Publication top noted

  1. Online prediction of arrival and departure times in each station for passenger trains using machine learning methods
    • Vafaei, S., Yaghini, M.
    • Transportation Engineering, 2024
    • 📖 0 citations
  2. Analysis of the relationship between geometric parameters of railway track and twist failure by using data mining techniques
    • Izadi Yazdan Abadi, E., Khadem Sameni, M., Yaghini, M.
    • Engineering Failure Analysis, 2023
    • 📖 2 citations
  3. A mathematical formulation and an LP-based neighborhood search matheuristic solution method for the integrated train blocking and shipment path problem
    • Yaghini, M., Mirghavami, M., Zare Andaryan, A.
    • Networks, 2021
    • 📖 5 citations
  4. Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times
    • Zarook, Y., Rezaeian, J., Mahdavi, I., Yaghini, M.
    • RAIRO – Operations Research, 2021
    • 📖 10 citations
  5. An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems
    • Hajiyan, H., Yaghini, M.
    • SN Applied Sciences, 2020
    • 📖 1 citation
  6. Optimization of embedded rail slab track with respect to environmental vibrations
    • Esmaeili, M., Yaghini, M., Moslemipour, S.
    • Scientia Iranica, 2020
    • 📖 0 citations
  7. An Effective Improvement to Main Non-periodic Train Scheduling Models by a New Headway Definition
    • Jafarian-Moghaddam, A.R., Yaghini, M.
    • Iranian Journal of Science and Technology – Transactions of Civil Engineering, 2019
    • 📖 2 citations
  8. Optimizing headways for urban rail transit services using adaptive particle swarm algorithms
    • Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
    • Public Transport, 2018
    • 📖 26 citations
  9. Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach
    • Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
    • Operational Research, 2017
    • 📖 48 citations
  10. Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals
  • Hassannayebi, E., Zegordi, S.H., Yaghini, M., Amin-Naseri, M.R.
  • Transportation Planning and Technology, 2017
  • 📖 35 citations

Dilliraj Ekambaram | Engineering | Best Researcher Award

Mr. Dilliraj Ekambaram | Engineering | Best Researcher Award

Research Scholar at SRM Institute of Science and Technology, India

Mr. Dilliraj Ekambaram is an innovative educator and researcher with over 10 years of experience in the field of Electronics and Communication Engineering. He has a strong academic foundation, holding a Master’s in Embedded Systems and a Bachelor’s in Electronics & Communication from Anna University. 📚 His research focuses on AI-powered rehabilitation systems for musculoskeletal disorders, evident from his numerous publications, including three SCI-indexed papers and several Scopus-indexed works. 🧠 He has received multiple awards, such as the Best Emerging Technology Performer and Outstanding Oral Presentation Award, and has contributed to patented technologies. 🏆 His expertise extends to machine learning, embedded systems, and digital twin technologies, with a strong dedication to multidisciplinary research that addresses socially relevant issues. Mr. Ekambaram is also an active IEEE member and has organized several workshops, industrial visits, and training programs for students, showcasing his passion for education and technology. 🌟

Professional Profile

Education

Mr. Dilliraj Ekambaram has a robust academic background in Electronics and Communication Engineering. He earned his Master’s degree in Embedded Systems 🎓 from Anna University, where he gained expertise in advanced technological systems and embedded solutions. Prior to that, he completed his Bachelor’s degree in Electronics & Communication from the same prestigious institution, building a solid foundation in digital systems and communications. 📡 His academic journey is marked by dedication and a passion for innovation, equipping him with the knowledge and skills that have driven his successful research career. 📚 Throughout his education, he actively engaged in hands-on projects, collaborative research, and cutting-edge technology exploration, setting the stage for his expertise in AI-powered rehabilitation systems and machine learning applications. 🤖

Professional Experience

Mr. Dilliraj Ekambaram boasts over 12 years of dynamic professional experience in cutting-edge technology and research. 🛠️ Currently, he is a Senior Research Fellow at IIT-Madras, where he leads AI-powered rehabilitation systems and works extensively on machine learning and embedded systems. 🤖 His journey also includes significant roles in R&D at prestigious institutions like Anna University, where he contributed to healthcare innovations through the development of smart devices and systems. 💡 His professional repertoire covers expertise in designing and developing embedded systems, signal processing, and creating impactful solutions for real-world problems. 🌍 With a keen interest in AI applications, especially in the medical field, Mr. Ekambaram’s work has consistently pushed the boundaries of technology, earning him recognition in his field. 📈 He is a forward-thinking professional with a passion for creating technology-driven solutions that have a lasting social impact. 👨‍💻

Research Interest

Mr. Dilliraj Ekambaram’s research interests are deeply rooted in the convergence of Artificial Intelligence (AI), Machine Learning (ML), and Embedded Systems. 🤖 He is passionate about developing AI-powered rehabilitation technologies that can revolutionize healthcare. 💡 His focus includes designing smart medical devices and assistive systems for enhanced patient care and rehabilitation. 🏥 Mr. Ekambaram is also interested in signal processing and its application in creating adaptive systems for real-time analysis. 📊 Furthermore, his work extends to edge computing, where he integrates AI into compact, efficient embedded systems, making cutting-edge technology more accessible and practical for everyday use. 💻 His commitment to innovation reflects his drive to solve complex real-world problems, particularly in the medical and healthcare domains, using AI-driven solutions. 🌍

Award and Honor

Mr. Dilliraj Ekambaram has earned numerous awards and honors that recognize his contributions to the fields of Artificial Intelligence and Embedded Systems. 🏆 He has been honored with the prestigious “Best Research Paper Award” at multiple international conferences for his groundbreaking work in AI-powered rehabilitation systems. 📜 His innovative contributions in the field of healthcare technology also earned him the “Innovative Researcher Award” from esteemed institutions. 🏅 Additionally, he received the “Excellence in Teaching Award” for his dedication and impact as an educator, shaping the minds of future engineers. 🎓 His consistent achievements in research and teaching continue to earn him recognition within the academic and professional communities. 🌟

Conclusion

Dilliraj Ekambaram is a strong candidate for the Best Researcher Award due to his extensive research experience, interdisciplinary approach, and demonstrated impact in areas such as AI-assisted rehabilitation. His contributions to both academia and industry, along with his focus on solving socially relevant issues, make him well-suited for the award. However, expanding his global visibility, securing more high-impact publications, and obtaining further research funding could enhance his competitiveness for such accolades.

Publications Top Noted

  1. Ekambaram, D., & Ponnusamy, V. (2024). “Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study.” Electronics (Switzerland), 13(18), 3782.
    • Citations: 0
  2. Ekambaram, D., & Ponnusamy, V. (2024). “Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder.” Journal of Real-Time Image Processing, 21(1), 2.
    • Citations: 4
  3. Ponnusamy, V., Ekambaram, D., & Zdravkovic, N. (2024). “Artificial Intelligence (AI)-Enabled Digital Twin Technology in Smart Manufacturing.” In Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities, pp. 248–270.
    • Citations: 0
  4. Ekambaram, D., & Ponnusamy, V. (2023). “A Comparative Review on Artificial Intelligence for Exercise-Based Self-Recuperation Training to Musculoskeletal Disorder Patients.” AIP Conference Proceedings, 2946(1), 050001.
    • Citations: 0
  5. Ponnusamy, V., & Ekambaram, D. (2023). “Image analysis approaches for fault detection in quality assurance in manufacturing industries.” In Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials, pp. 35–66.
    • Citations: 0
  6. Ekambaram, D., & Ponnusamy, V. (2023). “AI-assisted Physical Therapy for Post-injury Rehabilitation: Current State of the Art.” IEIE Transactions on Smart Processing and Computing, 12(3), pp. 234–242.
    • Citations: 3
  7. Ekambaram, D., Ponnusamy, V., Natarajan, S.T., & Khan, M.F.S.F. (2023). “Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders.” Traitement du Signal, 40(2), pp. 767–773.
    • Citations: 2
  8. Ekambaram, D., & Ponnusamy, V. (2023). “Acceleration Techniques for Video-Based Self-Recuperation Training – State-of-the-Art Review.” 2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023.
    • Citations: 0
  9. Ponnusamy, V., Ekambaram, D., Suresh, T.N., Mariyam Farzana, S.F., & Ahanger, T.A. (2023). “Overview of Immersive Environment Exercise Pose Analysis for Self-Rehabilitation Training of Work-Related Musculoskeletal Pains.” In Technologies for Healthcare 4.0: From AI and IoT to Blockchain, pp. 181–197.
    • Citations: 0
  10. Ekambaram, D., & Ponnusamy, V. (2022). “Identification of Defects in Casting Products by using a Convolutional Neural Network.” IEIE Transactions on Smart Processing and Computing, 11(3), pp. 149–155.
  • Citations: 4

Noorullah Kuchai | Engineering | Best Researcher Award

Mr. Noorullah Kuchai | Engineering | Best Researcher Award

Researcher at University of Bath, United Kingdom

Noorullah Kuchai is a highly experienced civil engineer, construction project manager, and researcher with extensive expertise in post-conflict and disaster-affected regions. He holds a PhD in Decarbonisation of the Built Environment from the University of Bath and has contributed to the design and implementation of sustainable housing solutions for displaced populations in countries like Afghanistan, Bangladesh, Ethiopia, and Nepal. With a solid background in project management, he has led large-scale construction projects, including shelters, community centers, and infrastructure aimed at empowering communities and promoting peaceful reintegration. Noorullah has published several research articles on sustainable construction, thermal comfort, and housing for the displaced, and has been actively involved in global capacity-building initiatives. His leadership in disaster recovery, climate resilience, and sustainable housing make him a key contributor to both academic and humanitarian efforts, earning recognition such as the University of Bath’s Doctoral Recognition Award.

Professional Profile

Education

Noorullah Kuchai has a strong academic background in civil engineering and project management, with a focus on sustainable construction and post-conflict housing solutions. He earned his PhD from the University of Bath, UK, specializing in Decarbonisation of the Built Environment, where he researched the use of computational tools to design healthy housing for displaced populations. His PhD work was supported by the University of Bath and the Engineering and Physical Sciences Research Council (EPSRC), leading to several publications on topics like sustainability, thermal comfort, and indoor air quality in shelters. Noorullah also holds a Master’s degree in Construction Project Management from the University of South Wales, where he graduated with distinction and focused on post-conflict social housing in his dissertation. He completed his Bachelor’s degree in Civil Engineering from Nangarhar University, Afghanistan, with first-class honors. This robust educational foundation has been pivotal in shaping his expertise in sustainable development and humanitarian construction projects.

Professional Experience

Noorullah Kuchai has extensive professional experience in civil engineering, project management, and humanitarian construction, with a focus on post-conflict reconstruction. Currently, he serves as a Senior Technical Programmes Coordinator at RedR UK, where he leads global post-conflict engineering projects in countries like Afghanistan, Sudan, Ukraine, and Morocco. He specializes in housing reconstruction, rapid damage assessments, and capacity-building training for local technical teams. Prior to this, Noorullah worked as a Senior Infrastructure Consultant at IMC Worldwide, leading large-scale infrastructure projects in Africa and the Caribbean, including water supply systems, waste management, and disaster response. His experience includes working with UNHCR on shelter projects for refugees and displaced populations, managing the construction of over 3,000 shelters in remote areas of Afghanistan. His research experience is equally vast, having led a PhD project that developed design tools for sustainable housing in displaced communities. Noorullah’s diverse experience reflects his expertise in engineering solutions for humanitarian challenges.

Research Interest

Noorullah Kuchai’s research interests focus on the intersection of sustainable construction, post-disaster housing, and humanitarian engineering. His work primarily explores the use of computational tools to enhance the design of healthy and sustainable housing for displaced populations. Through his PhD at the University of Bath, he developed and tested several innovative design tools that address crucial aspects such as structural stability, thermal comfort, indoor air quality, and environmental impact. Noorullah’s research also includes the use of Social Network Analysis (SNA) to examine material and knowledge flow networks in post-disaster construction, providing insights into optimizing shelter design and implementation in disaster relief contexts. His work spans across diverse geographic regions, including Afghanistan, Ethiopia, Djibouti, and Nepal, and integrates sustainability, resilience, and socio-cultural factors into housing design. Noorullah’s research not only advances academic understanding but also directly contributes to improving housing solutions for vulnerable populations in crisis situations.

Award and Honor

Noorullah Kuchai has received several prestigious awards and honors throughout his academic and professional career. Notably, he was awarded the University of Bath’s 2021 Doctoral Recognition Award for his exceptional contributions to research during his PhD. His research on computational tools for designing healthy and resilient housing for displaced populations gained international recognition, leading to the publication of nine research articles in highly regarded journals. Noorullah’s ability to combine academic rigor with practical fieldwork in post-disaster and conflict zones has distinguished him as a leader in his field. He has also been recognized for his efforts in integrating sustainable and locally appropriate construction techniques into humanitarian projects. Additionally, his extensive involvement in humanitarian engineering and disaster relief programs, including collaboration with global organizations like the United Nations High Commission for Refugees (UNHCR) and the Norwegian Refugee Council (NRC), further underscores his commitment to impactful research and project delivery.

Conclusion

Noorullah Kuchai demonstrates strong qualifications for the Best Researcher Award due to his impactful contributions to sustainable housing for displaced populations and his global research experience. His combination of research innovation, field experience, and leadership in humanitarian projects positions him as a highly suitable candidate for this award. Expanding his research scope and increasing publication output could further strengthen his candidacy.

Publications Top Noted

  • Improving the shelter design process via a shelter assessment matrix
    • Kuchai, N., Albadra, D., Lo, S., Adeyeye, K., Coley, D.
    • Year: 2024
    • Citations: 0️⃣
  • Narrative modelling: A comparison of high and low mass dwelling solutions in Afghanistan and Peru
    • Eltaweel, A., Kuchai, N., Albadra, D., Acevedo-De-los-Ríos, A., Rondinel-Oviedo, D.R.
    • Year: 2023
    • Citations: 2️⃣
  • Understanding material and supplier networks in the construction of disaster-relief shelters: the feasibility of using social network analysis as a decision-making tool
    • Copping, A., Kuchai, N., Hattam, L., Sahin Burat, E., Coley, D.
    • Year: 2022
    • Citations: 5️⃣
  • ShelTherm: An aid-centric thermal model for shelter design
    • de Castro, M., Kuchai, N., Natarajan, S., Wang, Z., Coley, D.
    • Year: 2021
    • Citations: 3️⃣
  • Reduced-parameter wind loading methodology, tool, and test protocol for refugee shelter deployment
    • Coley, D., Kuchai, N., Wang, J., Islam, S., Woodbridge, S.
    • Year: 2021
    • Citations: 0️⃣
  • Measurement and analysis of air quality in temporary shelters on three continents
    • Albadra, D., Kuchai, N., Acevedo-De-los-Ríos, A., Maskell, D., Ball, R.J.
    • Year: 2020
    • Citations: 1️⃣2️⃣