Amsha Alsegiani | Design and Analysis of FRP Components | Innovative Research Award

Innovative Research Award

Amsha Alsegiani — King Saud University, Saudi Arabia

Amsha Alsegiani
Affiliation King Saud University
Country Saudi Arabia
Scopus ID 57188854043
Documents 28
Citations 261
h-index 8
Subject Area Design and Analysis of FRP Components
Event International Research Awards on Fiberreinforced Polymer
ORCID 0000-0003-2463-3606

Amsha Alsegiani is an assistant professor at King Saud University whose academic work spans pharmaceutical chemistry, neuroinflammation, cognitive decline, pharmacological research, and analytical method development. Her education and research experience include medicinal and biological chemistry, with doctoral research examining cofilin signaling in neuroinflammation and cognitive deficits. Her publications address inflammatory pathways, oxidative stress, neurodegenerative disorders, and drug evaluation. This profile summarizes her documented academic background and selected research contributions in the context of the Innovative Research Award. The supplied award category, Design and Analysis of FRP Components, is distinct from her documented principal research areas and would require additional evidence of direct FRP-related contributions.[1]

Abstract

Amsha Alsegiani’s research spans pharmaceutical chemistry, pharmacology, neuroscience, and analytical chemistry, focusing on neuroinflammation, cognitive decline, oxidative stress, and inflammatory diseases. Her academic qualifications and publications demonstrate interdisciplinary biomedical research experience. Evaluating her suitability for an FRP-specific award requires additional evidence of direct contributions to fiber-reinforced polymer science and related applications. [1] [2] [3][4]

Keywords

Pharmaceutical chemistry, neuroinflammation, cognitive decline, cofilin signaling, oxidative stress, pharmacology, inflammatory pathways, analytical chemistry, drug development, neuroscience, research innovation, interdisciplinary research.

Introduction

Innovative research connects scientific questions with systematic experimentation and evidence-based interpretation. Alsegiani’s academic record reflects work across medicinal chemistry and biomedical science, including investigations into mechanisms associated with neurological conditions and inflammatory disease. Her career also includes teaching, academic advising, postgraduate education, and committee responsibilities at King Saud University. These combined activities provide context for reviewing her research development, scholarly output, and potential interdisciplinary value. [1] [3]

Research Profile

Amsha Alsegiani has been an assistant professor at King Saud University since March 2023, following teaching appointments at King Saud University and the University of Toledo. Her doctoral research examined neuroinflammation, cognitive deficits, and cofilin signaling. Her reported Scopus metrics include 28 documents, 261 citations, and an h-index of 8. [1]

Research Contributions

Amsha Alsegiani’s research spans neuroinflammation, neurodegeneration, pharmacological investigations, analytical chemistry, and medicinal chemistry. Her studies examine cofilin signaling, inflammatory pathways, drug evaluation, and pharmaceutical compound characterization. These contributions demonstrate interdisciplinary expertise across experimental and analytical methods. However, direct contributions to FRP component design, composite mechanics, or polymer manufacturing remain undocumented.[2] [3][4]

Publications

Amsha Alsegiani’s selected publications cover analytical chemistry, neuroprotection, cofilin signaling, neuroinflammation, and gut microbiota. Her research from 2024 to 2026 further explores inflammatory pathways, ulcerative colitis, pharmacokinetics, and oxidative stress, demonstrating her interdisciplinary contributions to biomedical research. [1] [2][3][4][5]

Research Impact

Amsha Alsegiani’s citation metrics indicate scholarly engagement with her research on cofilin signaling, neuroinflammation, gut microbiota, and pharmaceutical analysis. Her work contributes to understanding neurological aging and drug evaluation. Research impact should be assessed through methodological rigor, reproducibility, scientific relevance, and independent scholarly uptake, alongside citation counts and publication quality.[1]

Award Suitability

Amsha Alsegiani’s publications, academic responsibilities, and research funding demonstrate contributions to pharmaceutical chemistry and biomedical science. Award assessment should consider originality, methodological rigor, publication quality, and research outcomes. However, her documented research does not directly align with fiber-reinforced polymer science. Relevant composite-material studies, patents, or collaborations are needed to establish FRP-specific award suitability.

Conclusion

Amsha Alsegiani’s academic profile demonstrates contributions to pharmaceutical chemistry, neuroscience, pharmacology, and analytical research. Her publications, teaching experience, and academic service reflect interdisciplinary engagement and scholarly development. Further assessment of her suitability for the Innovative Research Award should consider research originality, measurable impact, and documented contributions to fiber-reinforced polymer science.

References

  1. Scopus. Amsha Alsegiani. https://www.scopus.com/authid/detail.uri?authorId=57188854043
  2. Alsegiani, A. S., & Shah, Z. A. (2022). The influence of gut microbiota alteration on age-related neuroinflammation and cognitive decline. Neural Regeneration Research, 17(11), 2407. https://doi.org/10.4103/1673-5374.335837.
  3. Alsegiani, A. S., & Shah, Z. A. (2020). The role of cofilin in age-related neuroinflammation. Neural Regeneration Research, 15(8), 1451. https://doi.org/10.4103/1673-5374.274330.
  4. Abdel-Hay, K. M., De Ruiter, J., Smith, F., Alsegiani, A. S., et al. (2016). GC–MS differentiation of the six regioisomeric dimethoxybenzoyl-1-pentylindoles: Isomeric cannabinoid substances. Journal of Pharmaceutical and Biomedical Analysis, 125, 360–368. https://doi.org/10.1016/j.jpba.2016.04.012.
  5. Landgraf, A. D., Alsegiani, A. S., Alaqel, S., Thanna, S., Shah, Z. A., & Sucheck, S. J. (2020). Neuroprotective and anti-neuroinflammatory properties of ebselen derivatives and their potential to inhibit neurodegeneration. ACS Chemical Neuroscience, 11(19), 3008. https://doi.org/10.1021/acschemneuro.0c00328.

Dongwook Kim | Computational Modeling and Simulation of FRPs | Best Researcher Award

Best Researcher Award

Dongwook Kim
Daelim Industrial Co., Ltd, South Korea

Dongwook Kim
Affiliation Daelim Industrial Co., Ltd
Country South Korea
Scopus ID 57189694073
Documents 25
Citations 125
h-index 6
Subject Area Computational Modeling and Simulation of FRPs
Event International Research Awards on Fiberreinforced Polymer

Dongwook Kim is a South Korean researcher and engineering professional affiliated with Daelim Industrial Co., Ltd. His scholarly activities focus on computational modeling, building information modeling (BIM), structural engineering, digital construction technologies, and simulation methodologies applicable to fiber-reinforced polymer and infrastructure systems. Through contributions in automated compliance checking, quality control frameworks, and intelligent prediction models, his work demonstrates an interdisciplinary approach that combines engineering practice with digital transformation initiatives in construction research.[1]

Abstract

This article presents an overview of the research activities and scholarly achievements of Dongwook Kim. His publications emphasize the integration of BIM technologies, semantic interpretation systems, automated quality control procedures, and predictive analytics for advanced infrastructure applications. The research portfolio reflects growing contributions to smart construction methodologies and computational engineering practices relevant to modern structural systems.[2]

Keywords

Computational Modeling, Building Information Modeling, Fiber-Reinforced Polymer, Smart Construction, Structural Engineering, Simulation, Digital Twin Technology.

Introduction

The increasing adoption of digital technologies in civil engineering has created opportunities for integrating computational modeling with intelligent infrastructure management. Dongwook Kim’s research aligns with this transition by developing methodologies that improve regulatory compliance verification, shape quality control, and predictive assessment of structural performance. These investigations contribute to enhanced efficiency and reliability in engineering practice.[3]

Research Profile

According to available bibliometric indicators, the researcher has produced twenty-five scholarly documents and accumulated more than one hundred citations with an h-index of six. His studies address computational modeling and simulation techniques, particularly within the domains of structural systems and digital construction management.[1]

Research Contributions

  • Development of ontology-driven semantic rule interpretation for automated regulatory compliance checking.
  • Creation of BIM-integrated frameworks for production-scale quality control of precast concrete members.
  • Application of multitask learning techniques for fatigue prediction in composite deck slabs.
  • Research on innovative connection systems for composite Rahmen bridges and construction efficiency enhancement.

Publications

  • Automated BIM-Based Regulatory Compliance Checking for Bridge Infrastructure Using Ontology-Driven Semantic Rule Interpretation: A Multi-Model Validation Study.
  • Automated BIM-Integrated 3D Laser Scanning Framework for Shape Quality Control of Precast Concrete Members.
  • Automation of Shape Quality Control for Precast Concrete Members Using 3D Scanning and BIM.
  • Multitask Learning-Based Prediction of Fatigue Performance in Steel–Concrete Composite Deck Slabs.
  • Corrugated Inner Wall Connections for Composite Rahmen Bridges: Advancing Design and Construction Efficiency.

Research Impact

The research outputs of Dongwook Kim contribute to the advancement of intelligent infrastructure systems by enabling automated decision-making and improving the precision of engineering workflows. The integration of machine learning and BIM methodologies provides practical implications for construction productivity, quality assurance, and sustainable infrastructure management.[4]

Award Suitability

The combination of interdisciplinary scholarship, publication productivity, and measurable citation performance indicates that Dongwook Kim demonstrates qualifications consistent with the objectives of the International Research Awards on Fiberreinforced Polymer. His investigations into computational simulation and digital construction frameworks support the broader advancement of engineering research and innovation.[5]

Conclusion

Dongwook Kim’s body of work reflects a sustained commitment to computational engineering, intelligent infrastructure systems, and advanced construction technologies. His publications and research outcomes illustrate the growing role of digital methodologies in structural engineering and provide a foundation for future developments in smart and resilient infrastructure systems.[6]

References

  1. Elsevier. (n.d.). Scopus author details: Dongwook Kim, Author ID 57189694073. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57189694073
  2. Kim, D. (2026). Automated BIM-Integrated 3D Laser Scanning Framework for Shape Quality Control of Precast Concrete Members.
    https://doi.org/10.3390/buildings16122383
  3. Kim, D. (2026). Automation of Shape Quality Control for Precast Concrete Members Using 3D Scanning and BIM.
  4. Kim, D. (2026). Multitask Learning-Based Prediction of Fatigue Performance in Steel–Concrete Composite Deck Slabs.
    https://doi.org/10.12989/sss.2026.37.4.301
  5. Kim, D. (2025). Corrugated Inner Wall Connections for Composite Rahmen Bridges: Advancing Design and Construction Efficiency.
    https://doi.org/10.1016/j.kscej.2025.100195
  6. International Research Awards on Fiberreinforced Polymer. (n.d.). Award information and recognition criteria.
    fiberreinforcedpolymer.com