Wilhelm Stork | Experimental Methods | Excellence in Research Award

Published on by Physicist Particle

Excellence in Research Award

Wilhelm Stork
Karlsruhe Institute of Technology, Germany
Wilhelm Stork
Affiliation Karlsruhe Institute of Technology
Country Germany
Scopus ID 7003711649
Documents 296
Citations 2,779
h-index 22
Subject Area Computational Methods
Event Global Particle Physics Excellence Awards

Wilhelm Stork is a German academic researcher and professor affiliated with the Karlsruhe Institute of Technology, with recognized contributions in computational methods, optics, sensor systems, medical engineering, automation technologies, and artificial intelligence applications. His scholarly portfolio includes interdisciplinary research in holographic displays, digital health systems, reinforcement learning, optical engineering, robotics, and intelligent sensing technologies.[1] The researcher has established a notable academic presence through publications indexed in Scopus and Google Scholar databases, demonstrating sustained contributions to computational and applied scientific research.[2]

Abstract

This academic recognition article documents the professional achievements and scholarly contributions of Wilhelm Stork in the field of computational methods and interdisciplinary engineering sciences. His research activities encompass optical systems, machine learning applications, holographic display technologies, digital health systems, medical engineering, and intelligent automation. The researcher has contributed to international conferences, peer-reviewed journals, and collaborative scientific projects that bridge theoretical computation with practical engineering applications.[1] The body of work attributed to Wilhelm Stork reflects continuing engagement in emerging computational technologies and multidisciplinary scientific innovation.[2]

Keywords

Computational Methods, Artificial Intelligence, Optical Engineering, Digital Health, Holographic Displays, Reinforcement Learning, Medical Technology, Sensor Systems, Automation Engineering, Physics-Constrained Analysis.

Introduction

Computational methods continue to play an essential role in modern scientific and engineering research through the integration of algorithmic intelligence, automation systems, and advanced data processing techniques. Researchers operating within this interdisciplinary framework contribute to innovations across healthcare technologies, optical sciences, robotics, artificial intelligence, and engineering analytics. Wilhelm Stork has been associated with several scholarly initiatives addressing these evolving scientific domains through applied computational research and engineering methodologies.[2]

His academic activities at the Karlsruhe Institute of Technology demonstrate sustained involvement in international research collaborations and publication efforts. Areas represented within his scholarly portfolio include reinforcement learning for spectral analysis, holographic waveguide systems, medical decision support systems, digital health software engineering, and sensor-based imaging technologies.[3] These contributions align with ongoing developments in computational science and intelligent systems engineering.

Research Profile

According to Scopus author records, Wilhelm Stork has authored or co-authored 296 indexed documents with a citation count exceeding 2,700 and an h-index of 22.[1] Additional citation metrics available through Google Scholar indicate broader scholarly influence across engineering and computational research communities.[2]

The research profile demonstrates interdisciplinary engagement involving optics, medical technology, automation engineering, sensor systems, computational intelligence, and digital healthcare innovation. Publications associated with the researcher include conference proceedings, journal articles, open-access engineering studies, and collaborative investigations into intelligent systems.[2]

  • Optical and holographic display technologies
  • Artificial intelligence and reinforcement learning
  • Digital health systems and healthcare informatics
  • Sensor technologies and automation engineering
  • Vehicle vision systems and computational imaging
  • Human-robot interaction and intelligent automation

Research Contributions

Wilhelm Stork has contributed to multiple contemporary research themes integrating computational analysis with practical engineering systems. His work on holographic waveguide displays and automated optical recording technologies represents ongoing advancements in display engineering and optics research.[4]

Research publications in artificial intelligence include investigations into explainable AI systems, reinforcement learning for spectral analysis, and machine learning methods for treatment plan generation in healthcare environments.[5] These studies reflect the growing relevance of computational methods within medical and analytical sciences.

Additional collaborative contributions involve sensor analysis, vehicle camera contamination modeling, optical coherence tomography enhancement, and human-robot interaction frameworks.[6] The multidisciplinary nature of these investigations demonstrates the integration of engineering design, algorithmic modeling, and intelligent automation technologies.

  • Development of automated mastering systems for holographic waveguide displays
  • Research in reinforcement learning for physics-constrained spectral optimization
  • Applications of explainable artificial intelligence in engineering analysis
  • Medical decision support systems using machine learning algorithms
  • Computational approaches to vehicle camera lens contamination analysis
  • Digital health software engineering methodologies

Publications

Selected publications associated with Wilhelm Stork include journal articles, conference proceedings, and collaborative research contributions in optics, computational intelligence, medical technology, and engineering systems.[2]

  1. Patient Perceptions of Blockchain-Based Health Information Exchange: User-Centered Design Study, Journal of Medical Internet Research, 2026.
  2. Hybrid Reinforcement Learning to Optimize for Physics-Constrained Spectral Analysis, IEEE International Conference on Big Data, 2025.
  3. Limits of Immersion-Free Recording of Holographic Waveguide Displays, Optics Letters, 2025.
  4. C-Scrum: Agile and Automated Software Development for Digital Health, Intelligent Health Systems Proceedings, 2025.
  5. A Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms, Intelligent Health Systems Proceedings, 2025.
  6. Human-Robot Interaction with Everyday Robots: A Taxonomy, International Conference on Robotics and Automation Sciences, 2025.
  7. AI-Based Detection and Correction of Motion Artifacts in Optical Coherence Tomography Scans of the Retina, International Conference in Electronic Engineering & Information Technology, 2025.

Research Impact

The citation metrics associated with Wilhelm Stork indicate sustained scholarly engagement within computational engineering and interdisciplinary scientific research communities. The Scopus profile records 2,779 citations and an h-index of 22, reflecting measurable academic influence across indexed scientific literature.[1]

Research themes represented in the publication portfolio correspond to rapidly advancing technological domains such as artificial intelligence, computational imaging, medical informatics, automation systems, and intelligent sensing technologies. These areas remain increasingly relevant within global scientific and industrial research initiatives.[5]

Collaborative publications involving healthcare technologies, optics engineering, robotics, and data-driven analysis further demonstrate the interdisciplinary applicability of computational methods in solving complex engineering and medical challenges.[6]

Award Suitability

The research portfolio of Wilhelm Stork aligns with the objectives of the Global Particle Physics Excellence Awards through demonstrated contributions to computational methodologies, interdisciplinary engineering systems, and intelligent technological innovation. His academic output reflects consistent participation in scientific research involving machine learning, optical systems, computational analysis, and digital healthcare technologies.[2]

The breadth of collaborative and applied scientific work, combined with indexed scholarly impact metrics and ongoing publication activity, supports recognition within academic award frameworks emphasizing computational advancement and multidisciplinary research excellence.[1]

Conclusion

Wilhelm Stork has established a multidisciplinary academic profile characterized by contributions to computational methods, optical engineering, artificial intelligence, medical technology, and intelligent automation systems. His publication record and citation performance indicate continuing scholarly engagement in applied scientific research and engineering innovation.[1] The researcher’s involvement in emerging computational technologies and interdisciplinary collaborations positions his work within contemporary developments in advanced engineering and digital scientific systems.[2]

References

    1. Elsevier. (n.d.). Scopus author details: Wilhelm Stork, Author ID 7003711649. Scopus.
      https://www.scopus.com/authid/detail.uri?authorId=7003711649&source=sd-apx
    2. Google Scholar. (2026). Wilhelm Stork – Google Scholar Citations.
      https://scholar.google.com/citations?hl=de&user=Ygptk90AAAAJ&view_op=list_works&sortby=pubdate
    3. Feil, M., Wilm, T., Esslinger, M., Fiess, R., & Stork, W. (2025). Limits of immersion-free recording of holographic waveguide displays. Optics Letters, 50(2), 606–609.
      https://opg.optica.org/ol/abstract.cfm?uri=ol-50-2-606
    4. Gerdes, M., Mazura, F., Petzold, R., Weimar, S.N., Schinle, M., Stork, W., & Stock, S. (2025). C-Scrum: Agile and automated software development for digital health. Intelligent Health Systems–From Technology to Data and Knowledge, 1453–1454.
      https://ebooks.iospress.nl/doi/10.3233/SHTI250646
    5. Gerdes, M., Weimar, S.N., Mazura, F., Schinle, M., Stock, S., & Stork, W. (2025). Effective Requirements Engineering in Early-Stage Digital Health Startups. Intelligent Health Systems–From Technology to Data and Knowledge, 1378–1382.
      DOI: 10.3233/SHTI250628
    6. Mazura, F., Gerdes, M., Petzold, R., Stork, W., Schinle, M., & Stock, S. (2025). A Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms. Intelligent Health Systems–From Technology to Data and Knowledge, 113–117.
      https://ebooks.iospress.nl/doi/10.3233/SHTI250284

Waheeba Al-Amrani | Particle Experiments | Women Researcher Award

Published on by Physicist Particle

Prof. Waheeba Al-Amrani | Particle Experiments | Women Researcher Award 

Prof. Waheeba Al-Amrani, Ibb University, Yemen

Prof. Waheeba Al-Amrani is a distinguished scholar and academic at Ibb University, Yemen. She holds a Ph.D. in Environmental Chemistry from Universiti Sains Malaysia, where her groundbreaking work focused on bioregeneration of modified adsorbents for wastewater treatment. With a Master’s degree in Physical Chemistry from Menoufia University and a Bachelor’s degree in General Chemistry from Ibb University, she has consistently demonstrated academic excellence.

Her research interests lie in developing innovative, low-cost solutions for pollutant removal, including advanced adsorption and bioremediation techniques. An accomplished educator and mentor, she has published 26 research papers and actively contributes to the academic and research communities through seminars, conferences, and teaching.

PROFILE

Scopus Profile

Educational Detail

Ph.D. in Environmental Chemistry: Universiti Sains Malaysia (USM), Pulau Penang, Malaysia, 2014
Dissertation: “Bioregeneration of mono amine modified silica and granular activated carbon loaded with mono-azo dyes in batch system.”

M.Sc. in Physical Chemistry: Menoufia University, Sheibin Alkoum, Menoufia, Egypt, 2009
Graduated with Excellence and Honors.
Thesis: “Removal of azo dyes using modified silica.”

B.Sc. in General Chemistry: Ibb University, Yemen, 2001
Graduated First Class with Honors.

Professional Experience

Prof. Waheeba Al-Amrani has extensive experience as a researcher and academic, contributing significantly to the fields of environmental and physical chemistry. She has been actively teaching both practical and theoretical chemistry courses at the undergraduate level at Ibb University, Yemen. Additionally, she supervises final-year research projects, mentoring students in innovative approaches to wastewater treatment and pollutant removal.

As a researcher, Prof. Al-Amrani has gained expertise in adsorption processes, employing various materials such as activated carbon and silica gel. Her work involves cultivating usable biomass, studying bioregeneration of loaded adsorbents, and applying these methodologies in advanced wastewater treatment technologies. She is proficient in using analytical techniques, including XRD, SEM, BET, EDX, HPLC, FTIR, and spectrophotometric analysis.

Prof. Al-Amrani has presented her research findings at numerous national and international conferences and seminars and has authored 26 publications in peer-reviewed journals.

Research Interests

Development of low-cost adsorbents for the removal of organic and inorganic pollutants, including mercury and anionic azo dyes, from aqueous solutions.

Bioregeneration of adsorbents, particularly granular activated carbon and modified silica, loaded with phenolic and azo dye pollutants.

Bacteria acclimation for bioremediation of wastewater, with a focus on phenolic and azo dye pollutants.

Employing advanced analytical techniques and quantum chemistry to evaluate adsorption and bioregeneration processes.

Top Notable Publications

Alkoshab, M.Q., Al-Amrani, W.A., Drmosh, Q.A., Onaizi, S.A. (2024). Zeolitic imidazolate framework-8/layered triple hydroxide composite for boosting the adsorptive removal of acid red 1 dye from wastewater. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 699, 134637.

Iddrisu, M., Al-Amrani, W.A., Merghani, A.A., Drmosh, Q.A., Onaizi, S.A. (2024). Effects of detergent on enzyme adsorption onto solid surfaces. Emergent Materials, 7(5), 2079–2086.

Al-Amrani, W.A., Onaizi, S.A. (2024). Adsorptive removal of heavy metals from wastewater using emerging nanostructured materials: A state-of-the-art review. Separation and Purification Technology, 343, 127018.

Bahadi, S.A., Iddrisu, M., Al-Sakkaf, M.K., Zahid, U., Onaizi, S.A. (2024). Optimization of methyl orange adsorption on MgFeAl-LTH through the manipulation of solution chemistry and synthesis conditions. Emergent Materials, 7(3), 959–971.

Bahadi, S.A., Iddrisu, M., Al-Sakkaf, M.K., Drmosh, Q.A., Onaizi, S.A. (2024). Chemically versus thermally reduced graphene oxide: Effects of reduction methods and reducing agents on the adsorption of phenolic compounds from wastewater. Emergent Materials, 7(2), 533–545.

Aziz, N.A.A., Hir, Z.A.M., Khalir, W.K.A.W.M., Al-Amrani, W.A., Hanafiah, M.A.K.M. (2024). Simultaneous adsorption of rare earth metal ions on chitosan-coated fumed silica – Characterization, kinetics, and isotherm studies. Ecological Engineering and Environmental Technology, 25(6), 172–187.

Hussin, S.M., Al-Amrani, W.A., Suah, F.B.M., Harimu, L., Hanafiah, M.A.K.M. (2024). Hydrogen peroxide treated desiccated coconut waste as a biosorbent in malachite green removal from aqueous solutions. Journal of Ecological Engineering, 25(3), 323–333.

Ganiyu, S.A., Suleiman, M.A., Al-Amrani, W.A., Usman, A.K., Onaizi, S.A. (2023). Adsorptive removal of organic pollutants from contaminated waters using zeolitic imidazolate framework composites: A comprehensive and up-to-date review. Separation and Purification Technology, 318, 123765.