Lama AlKahlan | Dentistry – Finite element analysis | Innovative Research Award

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Dr. Lama AlKahlan | Dentistry – Finite element analysis | Innovative Research Award

Lama Ahmed Alkahlan is a dedicated PhD candidate in Orthodontics at King Saud University, Riyadh, Saudi Arabia. With a passion for advancing orthodontic care, she combines clinical expertise with innovative research to improve patient outcomes. Earning her Bachelor’s degree in General Dentistry with Excellence and First-Class Honors from King Saud University (2013-2019), Lama has consistently demonstrated academic excellence. She holds two U.S. patents for innovative dental devices and has published impactful research on orthodontic biomechanics and oral health-related quality of life. Her active participation in national and international conferences, including the Saudi Orthodontics Society Conference, AEEDC, and SIDC, reflects her commitment to lifelong learning and contribution to the global orthodontic community. In addition to her research, Lama serves as an invited speaker and actively engages in scientific exhibitions and hackathons, showcasing her leadership and vision for advancing dental sciences.

Professional Profile

Education

Lama Ahmed Alkahlan pursued her academic journey entirely at King Saud University, Riyadh. She earned her Bachelor Degree of General Dentistry (BDS) from 2013 to 2019, graduating with Excellence and First-Class Honors, showcasing her dedication to academic rigor and clinical proficiency. Building on this foundation, she embarked on a Doctorate of Science in Dentistry (DScD) in Orthodontics, which she is expected to complete between 2020 and 2025. Her education reflects a comprehensive blend of theoretical knowledge, clinical practice, and research innovation, aligning with global standards in orthodontics. Throughout her academic career, she has participated in specialized training, workshops, and seminars that enhance her clinical expertise and research capabilities, preparing her for a career at the forefront of orthodontic science. Her commitment to continuous education is further evidenced by her numerous contributions to scientific conferences and exhibitions.

Experience

Lama Ahmed Alkahlan’s professional experience is rooted in academic excellence, clinical practice, and research innovation. As a PhD candidate at the Department of Pediatric Dentistry and Orthodontics, King Saud University, she engages in advanced orthodontic clinical care while simultaneously conducting cutting-edge research. Her clinical practice is complemented by participation in various scientific conferences such as the Saudi Orthodontics Society Conference, AEEDC, and SIDC, where she both contributes knowledge and stays updated on global advancements. Lama has also actively participated in prominent exhibitions and hackathons, including the Saudi Hackathon for Health and MIT Saudi Hackathon for Health, reflecting her interdisciplinary approach and interest in healthcare innovation. Her invited speaker roles and contributions to university exhibitions underline her ability to translate complex scientific ideas to broader audiences, enhancing community engagement and educational outreach.

Research Interests

Lama Ahmed Alkahlan’s research focuses on clinical orthodontics, biomechanical innovations, and improving patient care outcomes. Her work includes extensive iterative finite element analysis of molar uprighting, offering novel methods for estimating clinical treatment time—critical for improving treatment predictability and efficiency. She is particularly interested in the intersection of clinical practice and engineering solutions, as reflected in her patented inventions. Her studies also explore the psychosocial impact of malocclusion on oral health-related quality of life among Saudi orthodontic patients, contributing valuable insights to patient-centered care models. Lama’s research bridges the gap between advanced computational modeling and practical clinical applications, aiming to optimize orthodontic interventions. Her multi-disciplinary approach includes collaboration with engineers, clinicians, and researchers, ensuring that her findings have both scientific rigor and direct clinical relevance.

Awards

Lama Ahmed Alkahlan’s career is marked by prestigious awards and significant innovations. She received the esteemed title of “The Ideal Student” from King Saud University’s College of Dentistry in May 2019, recognizing her outstanding academic and extracurricular contributions. She holds two U.S. patents: the Magnetic Dental Education Model (US 10096267 B1, 2018) and the Uprighting Dental Appliance (US 12274598 B1, 2025), demonstrating her commitment to advancing dental technology. Her participation in numerous national and international conferences has earned her recognition as a rising leader in orthodontics. Additionally, her research publications in respected journals, such as Applied Sciences (MDPI) and Bioscience Biotechnology Research Communications, highlight her scientific contributions. Lama’s blend of academic excellence, innovation, and leadership underscores her role as a promising future leader in the field of orthodontics.

Conclusion

Lama Ahmed Alkahlan exemplifies a new generation of orthodontic leaders, blending clinical expertise, academic excellence, innovative research, and technological advancement to elevate patient care standards and contribute meaningfully to global orthodontic science.

 Publications

  • Extensive Iterative Finite Element Analysis of Molar Uprighting with the Introduction of a Novel Method for Estimating Clinical Treatment Time

    Applied Sciences
    2025-06 | Journal article | Author
    DOI: 10.3390/app15126463
    CONTRIBUTORS: Lama AlKahlan; Naif A. Bindayel; Abdelhafid MALLEK; Mohamed Bendjaballah

  • Impacts of Malocclusion on Oral Health-Related Quality Among Saudi Orthodontic Patients

    Bioscience Biotechnology Research Communications
    2021-09-25 | Journal article
    DOI: 10.21786/bbrc/14.3.48
    Part of ISSN: 0974-6455
    Part of ISSN: 2321-4007
    CONTRIBUTORS: Huda Alkawari

Elyas Rostami | Mechanical Engineering | Best Researcher Award

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Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🤝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications 

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
    Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
    10.1109/access.2024.3368755.
  • 4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
    24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
  • [5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929