Lijia Fang | Ammonia Combustion | Best Researcher Award

Mr. Lijia Fang | Ammonia Combustion | Best Researcher Award

Mr. Lijia Fang | Sophia University | Japan

Mr. Lijia Fang is a Ph.D. student in the Department of Science and Engineering at Sophia University, Japan, specializing in AI-driven combustion control and sustainable fuel systems. His research integrates artificial intelligence with combustion engineering to optimize fuel efficiency and minimize emissions. Currently, his work focuses on exploring the synergies between ammonia and ethanol fuels to enhance clean combustion performance and significantly reduce nitrogen oxide emissions. He has completed a major research project titled “Influence of Pre-Chamber Nozzle and Main Chamber Geometry on Ammonia Combustion: A Combined Experimental and Predictive Study” and has published two research papers, including one under review in Applied Thermal Engineering and another published in Energies (2024). His studies employ machine learning algorithms to predict in-cylinder combustion pressure and validate ammonia–oxygen combustion models in constant-volume chambers. Mr. Fang has contributed to seven patents, demonstrating his strong involvement in practical innovation, particularly in electronic systems and control circuits. His research aims to accelerate the transition toward low-carbon, high-efficiency combustion systems by integrating AI-based optimization methods with experimental validation. With 2 publications indexed and cited by 6 documents, he currently holds an h-index of 2, reflecting his emerging impact in the field of sustainable combustion and energy technologies. Through his interdisciplinary expertise in artificial intelligence, mechanical design, and environmental sustainability, Mr. Fang continues to advance cutting-edge research that supports the global pursuit of cleaner and more efficient energy solutions.

Profiles: Scopus | Orcid

Featured Publications

Fang, L., Singh, H., Ohashi, T., Sanno, M., Lin, G., Yilmaz, E., Ichiyanagi, M., & Suzuki, T. (2024). Effect of machine learning algorithms on prediction of in-cylinder combustion pressure of ammonia–oxygen in a constant-volume combustion chamber. Energies, 17(3), 746. https://doi.org/10.3390/en17030746

 

Hanin Othman | Environmental Science and Smart Technology | Young Researcher Award 

Ms. Hanin Othman | Environmental Science and Smart Technology | Young Researcher Award 

Ms. Hanin Othman | Pennsylvania State University | United States

Author Profiles

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Ms. Hanin Othman began her academic journey with a strong foundation in architecture and design. She earned her Bachelor of Science in Architectural Engineering from The Hashemite University, Jordan in 2014, where she graduated among the top students in her class and received multiple honors, including the Best Graduation Project Award. Her academic excellence continued through her Master of Science in Architectural Engineering from The University of Jordan in 2019, where her thesis publication earned the Best Master Thesis Published Paper Award. Building upon these achievements, she is currently pursuing a Ph.D. in Architecture (Sustainability) at Pennsylvania State University, USA. Her educational path reflects a consistent dedication to research and innovation at the intersection of environmental science and smart technology.

Professional Endeavors

Ms. Othman’s professional journey is marked by her deep involvement in academia, research, and creative technology. At Penn State University, she serves as a Graduate Teaching Assistant, instructing courses such as Basic Design and Research and Sustainable Architecture I & II. She is also the Artist and Maker in Residence at the Learning Factory, where she designs and leads workshops on low-cost IoT-based environmental monitoring systems using Arduino. Her earlier professional experience includes working as an Architect and Design Studio Supervisor at The Hashemite University, and as an Architect at Ruba Hosuah for Engineering. These roles allowed her to integrate sustainability principles with modern design methods, reinforcing her commitment to environmental science and smart technology.

Contributions and Research Focus

Ms. Hanin Othman’s research lies at the dynamic intersection of environmental science and smart technology, with a particular focus on sustainability, digital fabrication, and sensing technologies in architecture. Her doctoral research emphasizes the development of IoT-based indoor air quality monitoring systems, integrating mobile platforms, robotic sensing, and data-driven optimization techniques. She also explores urban microclimates, vegetation impacts, and thermal comfort using simulation tools such as ENVI-met, Design Builder, and CFD modeling. Additionally, her work in robotic fabrication bridges computational design with traditional craftsmanship, pushing the boundaries of sustainable architectural innovation.

Impact and Influence

Ms. Othman has made a remarkable impact both as a researcher and an educator. Her innovative approach to integrating environmental science and smart technology into architecture has influenced sustainable design practices and academic pedagogy. She has been recognized through numerous awards, including the ICDS Rising Researcher Award (2025–2026), Fox Scholar Award (2025), Sustainability Graduate Student Award Nomination (2024–2025), and the Artists and Makers in Residence Program (2024–2025). Her teaching contributions at Penn State University have inspired many students to embrace interdisciplinary and environmentally responsive design approaches.

Academic Cites

Ms. Othman’s research contributions have been featured in conferences, workshops, and institutional publications, reflecting the growing academic acknowledgment of her work. Her studies in environmental science and smart technology have attracted attention for their real-world applicability and technological innovation. Through her participation in Penn State’s Institute for Computational and Data Sciences (ICDS) and Hamer Center for Community Design, she has collaborated on projects that integrate digital twins, IoT networks, and data-driven environmental monitoring   all contributing to the broader academic discourse on sustainable and smart architectural systems.

Legacy and Future Contributions

Looking forward, Ms. Hanin Othman’s legacy will be defined by her contributions to sustainable architectural research and the advancement of environmental science and smart technology. Her ongoing projects, such as TwinSight: A Data-Driven Digital Twin Framework for Human-Centric Health Monitoring, highlight her vision for human-centered, data-driven, and equitable environmental design. As she continues to mentor students and collaborate across disciplines, her influence will extend to shaping the next generation of architects and environmental technologists. Her long-term goal is to pioneer scalable, low-cost sensing and smart systems that redefine sustainability in architecture.

Environmental Science and Smart Technology

Ms. Hanin Othman’s research integrates Environmental Science and Smart Technology through IoT-based sensing systems, robotic fabrication, and data-driven environmental design. Her interdisciplinary approach to Environmental Science and Smart Technology bridges architecture, engineering, and sustainability, redefining how design responds to ecological and technological challenges. The future of Environmental Science and Smart Technology continues to evolve through her innovative contributions, academic leadership, and commitment to global sustainable development.

Featured Publications

Othman, H., & Alshboul, A. A. (2020). The role of urban morphology on outdoor thermal comfort: The case of Al-Sharq City–Az Zarqa. Urban Climate, 34, 100706.

Othman, H., Azari, R., & Guimarães, T. (2024). Low-cost IoT-based indoor air quality monitoring. Technology|Architecture + Design, 8(2: Coding), 250–270.

Othman, H., Sieves, G., Guimarães, T., & Azari, R. (2025). A calibration chamber framework for low-cost indoor air quality sensor validation. Building and Environment, 113856.

Othman, H., Sieves, G., Guimaraes, T., & Azari, R. (2024). Development of a calibration chamber to evaluate the performance of a low-cost IAQ sensing device. In SIGraDi 2024 - Biodigital Intelligent Systems Conference 1.

Othman, H., & Azari, R. (2023). Exploring low-cost sensors for indoor air quality (IAQ) monitoring: A review of stationary and mobile sensing systems. In Proceedings of the 11th International Conference of the Arab Society 

Imam, C. A., Othman, H. A. S., & Çapunaman, Ö. B. (2023). Robotic plaster carving: Formalizing subtractive detailing of plaster surfaces for construction and crafts. In 41st Conference on Education and Research in Computer Aided Architectural