Albatool Alnojeidi | Rehabilitation Science | Best Researcher Award

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Dr. Albatool Alnojeidi | Rehabilitation Science | Best Researcher Award

πŸ“Œ Albatool Humod Alnojeidi is a board-licensed physical therapist and an Assistant Professor at Al Imam Mohammad Ibn Saud Islamic University in Riyadh, Saudi Arabia. She holds an M.S. in Health (MSH) with a concentration in Chronic Disease and a Ph.D. in Rehabilitation Science. Her research focuses on chronic low back pain (CLBP), exploring sociocultural and psychological factors affecting pain perception and treatment outcomes. Dr. Alnojeidi has contributed significantly to the field through research, teaching, and mentoring future healthcare professionals. Her work highlights the unique challenges of Arab-Americans with CLBP, addressing gaps in clinical practice and healthcare interventions. She has published multiple research papers in indexed journals and actively participates in professional organizations such as the International Association for the Study of Pain and the Saudi Physical Therapy Association. Her dedication to advancing rehabilitation science has earned her recognition in the academic and research community.

Profile

Education πŸŽ“

πŸŽ“ Dr. Albatool Alnojeidi holds a Ph.D. in Rehabilitation Science, where her research focused on chronic low back pain and its sociocultural determinants. She earned a Master of Science in Health (MSH) with a concentration in Chronic Disease, equipping her with expertise in pain management, rehabilitation strategies, and public health. Her academic journey provided a strong foundation in clinical research, biomechanics, and patient-centered care approaches. Dr. Alnojeidi’s education blends clinical expertise with advanced research methodologies, allowing her to contribute effectively to the field of rehabilitation science. Through her studies, she developed a keen interest in understanding the interplay between physical activity, chronic pain, and sociocultural factors, shaping her research and academic career. She continues to leverage her education to improve healthcare practices and influence policy changes related to chronic pain management.

Experience πŸ‘¨β€πŸ«

πŸ’Ό Dr. Albatool Alnojeidi is currently an Assistant Professor at Al Imam Mohammad Ibn Saud Islamic University, where she integrates clinical expertise and research to educate future healthcare professionals. She has extensive experience in rehabilitation science, chronic pain research, and patient care. Her work focuses on chronic low back pain (CLBP) in Arab and Arab-American populations, addressing the impact of sociocultural, psychological, and healthcare disparities. She has led multiple research projects exploring pain-related injustice appraisal, physical activity, and chronic pain experiences. In addition to her teaching and research responsibilities, Dr. Alnojeidi actively participates in academic collaborations, professional organizations, and mentoring students. She has presented her findings at international conferences and published her research in indexed journals. Her experience extends to working on policy recommendations for pain management and advancing rehabilitation interventions, making a significant impact in her field.

Research Interests πŸ”¬

πŸ”¬ Dr. Albatool Alnojeidi specializes in chronic low back pain (CLBP) research, with a particular emphasis on sociocultural, psychological, and physical activity-related factors. Her work investigates the experiences of Arab-Americans with CLBP, addressing healthcare disparities, pain-related injustice, and cultural influences on pain perception. She has published studies on the effects of discrimination, ethnic identity, and pain-related injustice on chronic pain outcomes. Her ongoing projects aim to characterize CLBP in Arab-American and Saudi populations, identifying key demographic, psychological, and healthcare determinants. Dr. Alnojeidi’s research is transforming clinical pain management, offering insights into tailored interventions for underrepresented groups. She has contributed to indexed journals, participated in global research collaborations, and received recognition for her innovative approaches. Her ultimate goal is to bridge the gap between research and clinical practice, improving pain management strategies for diverse patient populations through evidence-based rehabilitation.

Awards & Recognitions πŸ…

πŸ† Dr. Albatool Alnojeidi has been recognized for her outstanding contributions to rehabilitation science and chronic pain research. She has received multiple nominations and awards, including the Best Researcher Award and Young Scientist Award, acknowledging her groundbreaking studies on chronic low back pain (CLBP) among Arab-American populations. Her research on pain-related injustice appraisal and sociocultural determinants of CLBP has been widely cited and has influenced clinical practices. Dr. Alnojeidi’s contributions to evidence-based rehabilitation and patient-centered care have earned her recognition from professional organizations, including the International Association for the Study of Pain and the Saudi Physical Therapy Association. Her scientific publications, mentorship, and advocacy for culturally informed pain management have positioned her as a leader in the field. Through her dedication to research, education, and clinical practice, she continues to receive accolades for her impact on healthcare and rehabilitation sciences.

Publications πŸ“š

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