Rosa Alba Pugliesi | Radiology | Innovative Research Award

Innovative Research Award

Rosa Alba Pugliesi
University of Palermo, Italy
Rosa Alba Pugliesi
Affiliation University of Palermo
Country Italy
Scopus ID 57811418200
Documents 24
Citations 39
h-index 3
Subject Area Radiology
ORCID 0000-0001-5108-2104
Website International Research Scientist Awards

Rosa Alba Pugliesi is a researcher affiliated with the University of Palermo, Italy, whose academic work primarily focuses on radiology and diagnostic imaging. Her scholarly contributions encompass clinical imaging, interventional radiology, and emerging medical imaging technologies that support evidence-based healthcare. Through peer-reviewed publications and multidisciplinary collaborations, she continues to contribute to the advancement of imaging sciences and medical research.[1]

Abstract

This article presents an academic overview of Rosa Alba Pugliesi in recognition of the Innovative Research Award at the International Research Scientist Awards. It summarizes her institutional affiliation, research interests, scholarly productivity, and contributions to radiology while emphasizing evidence-based scientific practice. The profile follows a neutral encyclopedic style supported by recognized academic sources and professional research identifiers.Particular attention is given to research quality, interdisciplinary collaboration, and continued participation in medical imaging research that contributes to diagnostic innovation and improved clinical practice.[1]

Keywords

Radiology, Diagnostic Imaging, Interventional Radiology, Medical Imaging, Computed Tomography, Clinical Research, Artificial Intelligence, Image Analysis, Healthcare Innovation, University of Palermo, Scopus, ORCID, Research Excellence, Scientific Publications, Innovative Research Award.

Introduction

Radiology remains one of the most rapidly evolving areas of medical science, integrating advanced imaging technologies with data-driven clinical decision making. Researchers in this discipline contribute to earlier disease detection, treatment planning, and patient safety through continuous scientific investigation. Academic recognition highlights the value of these contributions while encouraging responsible innovation and international collaboration.[3]

Research Profile

Rosa Alba Pugliesi is affiliated with the University of Palermo, Italy, where her research activities focus primarily on radiology and advanced medical imaging. Her academic profile reflects continuous participation in diagnostic imaging research, emphasizing clinical applications that improve disease detection and patient management. Through collaborative investigations, she contributes to multidisciplinary projects integrating imaging technologies with evidence-based medical practice.[1]

Research Contributions

The research contributions of Rosa Alba Pugliesi encompass diagnostic radiology, computed tomography, image-guided interventions, and the clinical evaluation of imaging techniques. Her investigations frequently examine procedural optimization, diagnostic accuracy, and patient safety while supporting improved healthcare outcomes through scientifically validated methodologies. These studies contribute to the continuous refinement of radiological practice across diverse clinical settings.[3]

Publications

The published work of Rosa Alba Pugliesi includes peer-reviewed journal articles addressing interventional radiology, CT-guided procedures, renal imaging outcomes, total-body PET/CT, and artificial intelligence in abdominal imaging. These publications have appeared in internationally recognized scientific journals and collectively illustrate an expanding research portfolio within diagnostic imaging.bHer publication record also includes research datasets associated with published studies, demonstrating a commitment to scientific transparency and data accessibility. The combination of journal articles and openly available research outputs supports reproducibility while encouraging future investigations by the broader academic community. This publication strategy aligns with current international standards for responsible scientific communication.[3]

Research Impact

According to the available Scopus profile, Rosa Alba Pugliesi has authored twenty-four indexed publications that have received scholarly citations within the international research community. These bibliometric indicators reflect growing academic visibility and demonstrate that her work contributes to ongoing discussions in radiology and diagnostic imaging research. Citation activity also indicates continued relevance among researchers working in related medical specialties. integration of clinical expertise with innovative imaging research supports continued academic development while contributing to advancements that may improve diagnostic quality and patient-centered healthcare practices worldwide.[2]

Award Suitability

Rosa Alba Pugliesi demonstrates a research profile that aligns with the objectives of the Innovative Research Award presented by the International Research Scientist Awards. Her scholarly activities in radiology, medical imaging, and diagnostic innovation are supported by peer-reviewed publications, internationally recognized research identifiers, and active scientific collaboration. These accomplishments reflect a sustained commitment to advancing healthcare through evidence-based research.[1]

Conclusion

Rosa Alba Pugliesi has established an emerging academic profile through research contributions in diagnostic radiology, computed tomography, and medical imaging. Her scholarly publications, collaborative research activities, and commitment to evidence-based clinical investigation demonstrate an ongoing dedication to scientific advancement. These achievements support her recognition within the international research community and highlight the significance of her contributions to modern healthcare.[1]

References

  1. Pugliesi, R. A., Muna, S. F., Mahnken, A. H., Maalouf, N., Chatzis, G., & Apitzsch, J. (2026). Exploratory Analysis of Early Renal Function Changes After Transcatheter Aortic Valve Implantation (TAVI): Limited Predictive Value Beyond Baseline Renal Function. Journal of Clinical Medicine.ย 
    https://doi.org/10.3390/jcm15103726
  2. Pugliesi, R. A., Muna, S. F., Mahnken, A. H., Maalouf, N., Chatzis, G., & Apitzsch, J. (2026). Procedural and Device Neutrality of Post-TAVI Renal Outcomes: A Multivariable Analysis of Valve Type, Size, and Anatomy. Journal of Clinical Medicine.ย 
    https://doi.org/10.3390/jcm15062175
  3. Pugliesi, R. A., Maalouf, N., Gullo, G., Mahnken, A. H., & Apitzsch, J. (2026). Pulmonary Hemorrhage and Pneumothorax Risk During CT-Guided Lung Biopsy for Suspected Lung Cancer. Cancers, 18(5), 743.
    https://doi.org/10.3390/cancers18050743
  4. Pugliesi, R. A., Ben Mansour, K., Apitzsch, J., Papachristodoulou, A., Rafailidis, V., & Katz, D. S. (2025). Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact. Journal of Clinical Medicine.ย 
    https://doi.org/10.3390/jcm14238466
  5. Pugliesi, R. A., Mahnken, A. H., Maalouf, N., & Apitzsch, J. (2025). Predicting Pneumothorax and Hemorrhage After CT-Guided Lung Biopsy: Role of Lesion Size, Depth and Their Interaction. Journal of Clinical Medicine.
    https://doi.org/10.3390/jcm14238269

Ms. Somaye Mohammadi | Vibration Analysis | Best Researcher Award

Ms. Somaye Mohammadi | Vibration Analysis | Best Researcher Award

Assistant Professor , Sharif University of Technology, Best Researcher Award

Dr. S. Mohammadi is an accomplished mechanical engineer with a strong focus on vibration analysis, acoustics, and machine condition monitoring ๐Ÿ› ๏ธ๐Ÿ”. He earned his Ph.D. from Amirkabir University of Technology, where he specialized in tire/road noise prediction and reduction ๐Ÿ”Š๐Ÿ›ฃ๏ธ. His research spans intelligent fault diagnosis, dynamic balancing, and advanced signal processing ๐Ÿ“Š๐Ÿค–. With a deep commitment to industrial problem-solving and academic excellence, Dr. Mohammadi has published extensively in top-tier journals and conferences ๐Ÿง ๐Ÿ“š. His collaborative work with leading automotive and petrochemical industries demonstrates his practical impact in engineering innovation ๐Ÿš—๐Ÿญ.

Professional Profile

ORCID

Education and Experience

Dr. Mohammadi holds a Ph.D. (2016โ€“2021), M.Sc. (2014โ€“2016), and B.Sc. (2010โ€“2014) in Mechanical Engineering from Amirkabir University of Technology ๐ŸŽ“๐Ÿ‡ฎ๐Ÿ‡ท. His doctoral research focused on modeling and predicting tire/road noise using semi-analytical and statistical methods ๐Ÿ”Š๐Ÿ“ˆ. He has extensive experience in academia and industry, collaborating with IPCO and other companies on dynamic balancing, machine reliability, and condition monitoring โš™๏ธ๐Ÿ—๏ธ. He has published over 25 journal and conference papers and actively participates in technical events and applied engineering research, bridging theory and practice effectively ๐Ÿ“š๐Ÿ› ๏ธ.

Professional Development

Dr. Mohammadi has significantly contributed to professional development in mechanical engineering through active involvement in research, publications, and conferences ๐ŸŽค๐Ÿ“„. He has attended numerous national and international events such as CMFD, ISAV, and IRNDT, presenting cutting-edge research on condition monitoring, acoustic diagnostics, and vibration analysis ๐Ÿ”๐Ÿง . He continuously updates his skills in AI, machine learning, and signal processing for predictive maintenance and fault detection ๐Ÿค–๐Ÿ“Š. His multidisciplinary approach enables practical solutions for complex industrial problems, making him a valuable contributor to academic and engineering communities ๐ŸŒ๐Ÿ”ง.

Research Focus

Dr. Mohammadi’sย  research centers on mechanical vibrations, acoustics, and intelligent fault detection using AI and signal processing ๐Ÿง ๐Ÿ”Š. His work addresses real-world engineering challenges like tire noise reduction, gearbox diagnostics, and turbine reliability โš™๏ธ๐Ÿญ. He combines statistical methods with machine learning to predict failures and optimize performance in rotating machinery, engines, and industrial systems ๐Ÿค–๐Ÿ”ง. His interdisciplinary expertise bridges mechanical design, acoustics, and data analytics to improve machinery health monitoring and performance efficiency ๐Ÿ“‰๐Ÿ“ˆ. His research supports sustainable and cost-effective engineering operations ๐Ÿ”„๐Ÿ’ก.

Awards and Honors

Dr. Mohammadi has received multiple recognitions for his research excellence and technical contributions ๐ŸŽ–๏ธ๐Ÿ“š. He has been invited to present at prestigious conferences like CMFD, ISAV, and IRNDT and collaborated with top engineers and institutions on vibration and fault diagnosis projects ๐Ÿค๐Ÿ”. His publications in high-impact journals such as Applied Acoustics, Journal of Vibration and Control, and Machines have earned critical acclaim from the academic community ๐ŸŒŸ๐Ÿ“ฐ. He was also involved in award-supported industrial collaborations, including projects with IPCO and petrochemical companies, showcasing practical impact and innovation ๐Ÿญ๐Ÿ….

Publication Top Notes

1.๐Ÿ” Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning
๐Ÿ“… Published: April 2025 โ€“ Machines

๐Ÿ“Œ DOI: 10.3390/machines13050351

๐Ÿ‘ฅ Co-authors: Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Len Gelman

๐Ÿง  Key Contribution: Developed an innovative technique combining advanced signal processing (enhanced envelope detection) with transfer learning, significantly improving fault diagnosis accuracy across variable operating conditions in rolling bearings. This paper bridges AI and mechanical reliability โ€“ a cutting-edge intersection in engineering diagnostics.

2.๐Ÿ“Š A Comprehensive Study on Statistical Prediction and Reduction of Tire/Road Noise
๐Ÿ“… Published: October 2022 โ€“ Journal of Vibration and Control

๐Ÿ“Œ DOI: 10.1177/10775463211013184

๐Ÿ‘ฅ Co-authors: Abdolreza Ohadi, Mostafa Irannejad-Parizi

๐Ÿง  Key Contribution: Offers a data-driven, statistical framework for predicting and mitigating tire/road interaction noise, addressing environmental and comfort challenges in vehicle design. The study integrates modeling, statistical methods, and experimental validation, making it valuable for the automotive industry.

3.๐Ÿ”‰ Effect of Modeling Sidewalls on Tire Vibration and Noise

๐Ÿ“… Published: September 2022 โ€“ Journal of Automobile Engineering (IMechE Part D)

๐Ÿ“Œ DOI: 10.1177/09544070211052368

๐Ÿ‘ฅ Co-author: Abdolreza Ohad

๐Ÿง  Key Contribution: Investigated how sidewall modeling precision influences vibrational behavior and noise in tires. The research advanced numerical tire modeling techniques, which are essential for designing quieter, more stable vehicles.

Conclusion

Dr. Mohammadi’s blend of deep theoretical knowledge, strong publication output, practical industrial applications, and multidisciplinary research makes him a standout researcher. His work addresses real-world engineering challenges with smart solutions, reinforcing his eligibility for the Best Researcher Award. He not only contributes to advancing scientific understanding but also to improving industrial reliability and performance โ€” hallmarks of a truly impactful researcher ๐Ÿ…๐Ÿš€.