Aaid Algahtani | Innovative Research Award | Statistics

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Innovative Research Award

Aaid Algahtani
King Saud University, Saudi Arabia

Aaid Algahtani
Affiliation King Saud University
Country Saudi Arabia
Scholar ID 57226338713
Documents 2
Subject Area Statistics
Event International Academic Achievements & Awards
ORCID 0009-0003-2151-0085

Aaid Algahtani of King Saud University, Saudi Arabia, in the context of the Elite Academic Visionary Award. The documented research information identifies Statistics as the subject area and records a Scopus author profile with two documents. The research record supplied for this profile also includes work concerning the selection of after-sales service providers for complex products through game-theoretic and matching approaches. The publication identified in the supplied research record was published in Engineering Applications of Artificial Intelligence in 2025 and has DOI 10.1016/j.engappai.2025.112524. [1]

Abstract

The Elite Academic Visionary Award profile recognizes documented academic and research activity associated with Aaid Algahtani, King Saud University, with Statistics identified as the principal subject area in the supplied researcher information. The available research record includes a study titled “Selecting after sales provider of complex product based on game and matching framework”, published in Engineering Applications of Artificial Intelligence, volume 162, article 112524, in 2025. [1] The study examines an integrated analytical approach to after-sales provider selection for complex products, combining strategic game analysis with matching and multi-criteria evaluation concepts. Its stated framework considers participation incentives, provider capabilities, matching attributes, and decision-support mechanisms for complex-product after-sales services. [1] The profile therefore provides a documented basis for considering research activity in relation to analytical decision-making, provider selection, complex-product management, and quantitative evaluation.

Keywords

Elite Academic Visionary Award; Aaid Algahtani; Statistics; King Saud University; after-sales service; complex products; provider selection; game theory; matching framework; decision analysis; supply chain collaboration; multi-criteria evaluation.

Introduction

Academic research in statistics and quantitative decision sciences increasingly intersects with management engineering, operations research, supply-chain coordination, and artificial-intelligence-assisted decision support. Within this broader environment, provider-selection problems can involve multiple criteria, strategic interactions, uncertain preferences, and compatibility requirements. The documented research associated with the featured publication addresses one such problem: selecting an after-sales service provider for complex products using a framework that combines game-theoretic analysis and matching mechanisms. [1]

The publication describes complex products as systems requiring specialized development, maintenance, refurbishment, and long-term service arrangements. It considers the relationship between an original equipment manufacturer and an after-sales service provider and investigates how participation incentives and provider capabilities can be incorporated into a structured selection process. [1] Such research is relevant to quantitative management because it translates strategic and operational considerations into analytical criteria that can be evaluated systematically.

Research Profile

The supplied researcher information identifies Aaid Algahtani with King Saud University in Saudi Arabia and assigns Statistics as the principal subject area. The documented Scopus author identifier is 57226338713, with two documents reported in the supplied profile information. Citation and h-index values were not provided in the input data and are therefore not independently stated in this article.

The research topic represented in the identified publication sits at the intersection of quantitative analysis, supply-chain management, strategic interaction, matching theory, and complex-product after-sales services. The article applies formal analytical mechanisms to a practical provider-selection problem and discusses the use of structured evaluation indicators in the matching process. [1]

Research Contributions

The documented study contributes an integrated framework for analyzing after-sales provider selection for complex products. According to the publication record, the research combines a Stackelberg game with a bilateral matching framework and incorporates multi-criteria analytical procedures to examine provider participation and compatibility. [1]

  • It models strategic interaction between an original equipment manufacturer and an after-sales service provider through a sequential game framework. [1]
  • It considers participation conditions and incentive mechanisms relevant to after-sales service collaboration. [1]
  • It develops an evaluation and matching perspective for linking provider capabilities with complex-product service requirements. [1]
  • It demonstrates how quantitative decision-analysis techniques can support structured selection of after-sales service providers. [1]

Publications

The publication record supplied for this profile identifies the following research article as relevant to the featured research theme:

The article is listed as volume 162, article number 112524, and its DOI provides a persistent identifier for the publication. [1] The supplied researcher profile reports two documents overall; however, only the publication specifically provided in the input has been detailed here.

Research Impact

The documented research addresses a practical decision problem in complex-product after-sales management. Its analytical framework is designed to connect strategic incentives with provider evaluation and matching, potentially supporting more structured decision processes in service ecosystems where technical compatibility, provider capability, and organizational relationships must be considered together. [1]

From a methodological perspective, the research illustrates how game theory and matching approaches can be combined with quantitative evaluation techniques. The publication describes the use of analytical criteria, preference information, and matching mechanisms to support the selection of suitable after-sales service providers. [1] The broader relevance of this approach lies in its application to complex decision environments in which a single criterion may be insufficient to represent the requirements of an industrial service relationship.

Award Suitability

For the purposes of an academic recognition profile, the Elite Academic Visionary Award can be assessed against documented evidence such as research subject area, publication activity, methodological contribution, institutional affiliation, and relevance of scholarly work. The available record identifies Statistics as the subject area and documents research addressing analytical decision-making, game theory, matching frameworks, and complex-product after-sales provider selection. [1]

The documented publication provides evidence of research activity involving formal analytical modeling and quantitative evaluation. Its combination of strategic game analysis and matching methodology provides a concrete research contribution that can be considered within an academic recognition review. Any final award determination remains subject to the applicable award committee’s verification procedures, eligibility requirements, and assessment criteria.

  • Documented academic affiliation: King Saud University.
  • Documented subject area: Statistics.
  • Documented Scopus author identifier: 57226338713.
  • Reported documents in the supplied profile: 2.
  • Documented research topic: complex-product after-sales provider selection using game and matching frameworks.
  • Documented publication DOI: 10.1016/j.engappai.2025.112524.

Conclusion

Aaid Algahtani’s supplied academic profile is associated with King Saud University and the subject area of Statistics. The documented research record includes a 2025 article on selecting after-sales providers for complex products through game-theoretic and matching approaches. [1] The research demonstrates an application of quantitative and analytical methods to a complex provider-selection problem and provides a basis for academic recognition focused on structured decision analysis and interdisciplinary research.

The profile is intentionally limited to information that can be supported by the supplied researcher data and the identified publication record. Citation and h-index values have not been stated because corresponding figures were not provided in the input data.

References

  1. Test for Mean Projective Shape Change and 3D Object Identification from its Digital Camera Images
    https://www.scopus.com/authid/detail.uri?authorId=57226338713
  2. DBLP. (2025). Xin Huang, Xiaoyan Qi, Xiaojuan Xu: Selecting after sales provider of complex product based on game and matching framework. DBLP Computer Science Bibliography.
    https://dblp.org/rec/journals/eaai/HuangQX25

John Rowen Miano | Statistics | Best Researcher Award

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Mr. John Rowen Miano | Statistics | Best Researcher Award

Mr. John Rowen Miano | Statistics – Student at Cebu Technological University, Philippines

John Rowen Miano is an aspiring early-career researcher whose work sits at the intersection of computational biology and agricultural science. Based at Cebu Technological University, he is known for applying mathematical and computational tools to explore natural product chemistry, particularly in the field of agrochemical development. His independent research using molecular docking techniques to investigate plant-derived inhibitors has drawn academic interest and showcases his potential as a young innovator in sustainable agriculture. His initiative, curiosity, and analytical mindset distinguish him among his peers, making him a promising candidate for future academic and scientific excellence.

Profile Verified:

ORCID

Education:

John is currently pursuing his studies at Cebu Technological University under the Department of Mathematics and Statistics. His academic focus blends quantitative analysis with biological research, giving him a unique edge in computational studies. Through coursework and project-based learning, he has developed strong foundations in mathematics, statistics, and bioinformatics—skills that are critical for in silico research and predictive modeling. His education emphasizes both theoretical understanding and practical application, which is evident in his recent research outputs.

Experience:

John’s primary experience comes from his involvement as a student researcher at his university. During this time, he has conducted independent and guided research focused on plant-based antimicrobial agents. He has experience in molecular docking, virtual screening, database preparation, and software tools such as AutoDock and PyRx. His work has been presented at conferences and shared on academic platforms like Zenodo. He has collaborated with faculty for project feedback and scientific validation, and he is gradually building a network of fellow researchers within his institution.

Research Interests:

John’s research interests include molecular docking, phytochemistry, plant pathology, and the computational screening of bioactive compounds. He is particularly focused on identifying eco-friendly alternatives to synthetic agrochemicals by analyzing the inhibitory effects of natural phytochemicals against plant pathogens. His current study involves the evaluation of Euphorbia tirucalli compounds against Xanthomonas oryzae, the causative agent of bacterial leaf blight in rice. His broader interests also include artificial intelligence applications in drug discovery, sustainable agriculture, and the use of statistical models to predict pathogen resistance.

Awards:

As an emerging researcher, John has not yet received formal awards; however, he has been recognized at the university level for research presentation and participation. His poster presentation at a recent academic conference has gained early citations, demonstrating the relevance and growing academic attention toward his work. His nomination for the “Best Researcher Award” reflects both his existing achievements and the future potential that he holds as a developing scientific contributor.

Publications 📚:

  1. 🧪 Phytochemicals of Euphorbia tirucalli and their Inhibitory Potential against Xanthomonas oryzae Ddl Enzyme: An In silico Evaluation for Potential Agrochemical
    📅 Published: 2024 | Platform: Zenodo
    🔗 DOI: 10.5281/ZENODO.12183931
    📌 Cited by 2 articles

Conclusion:

John Rowen Miano is a highly motivated and intellectually capable young researcher. His contributions—although still at the early stage—exemplify innovation, relevance, and commitment to solving real-world agricultural problems. With a strong foundation in mathematical sciences and a growing body of work in computational biology, he is poised to become a key contributor to sustainable agrochemical discovery. His single-author research, proactive approach, and dedication to scientific exploration make him a strong nominee for the “Best Researcher Award” under an early-career or emerging talent category. He represents the next generation of researchers who merge computational power with natural science to address urgent agricultural and environmental challenges.

 

 

Sarra Leulmi | Probability and Statistics | Best Researcher Award

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Dr. Sarra Leulmi | Probability and Statistics | Best Researcher Award

Class A lecturer | Université frères Mentouri, Constantine-1, Algeria | Algeria

Based on the detailed curriculum vitae provided for Mme Sarra Leulmi, here is an analysis of her strengths, areas for improvement, and a conclusion regarding her suitability for the Best Researcher Award:

Strengths

  1. Extensive Research Experience: Mme Leulmi has an impressive track record of research in the field of mathematics, particularly in nonparametric estimation and functional data. Her work is published in reputable journals such as Communications in Statistics-Theory and Methods and Journal of Siberian Federal University. This indicates a solid reputation in her field and substantial contribution to the academic community.
  2. Diverse Publications: Her extensive list of publications, including peer-reviewed journal articles and conference proceedings, highlights her active engagement in research and knowledge dissemination. This breadth of work showcases her commitment to advancing the field of applied mathematics and statistics.
  3. International and National Recognition: Mme Leulmi has participated in numerous international and national conferences, reflecting her recognition and involvement in the global research community. Her presentations cover a wide range of topics within her field, demonstrating her versatility and broad expertise.
  4. Supervision and Teaching Experience: She has supervised multiple master’s and doctoral theses, contributing to the development of future researchers. Her teaching roles span various levels, from high school to doctoral supervision, indicating her strong pedagogical skills and commitment to education.
  5. Research Projects: Mme Leulmi is involved in significant research projects, such as the PRFU project at the University of Constantine 1, which emphasizes her role in leading and contributing to impactful research initiatives.

Areas for Improvement

  1. Broader Impact Metrics: While Mme Leulmi’s publications and conference presentations are extensive, it would be beneficial to include metrics such as citation indices or impact factors of her published work. These metrics can provide a clearer picture of the impact and influence of her research.
  2. Interdisciplinary Research: Expanding her research to include interdisciplinary approaches or collaborations with other fields might enhance the applicability and relevance of her work. This could open new avenues for research and increase the broader impact of her contributions.
  3. Research Innovation: Emphasizing novel and cutting-edge research methods or applications could strengthen her profile. While her work is thorough and valuable, showcasing innovative approaches or breakthroughs might bolster her candidacy for prestigious awards.
  4. Public Engagement and Outreach: Increasing efforts in public outreach or engaging with broader audiences outside of academia could further highlight the societal impact of her research. This might include public lectures, science communication, or involvement in community-based projects.

Conclusion

Mme Sarra Leulmi appears to be a highly qualified candidate for the Best Researcher Award. Her extensive research background, significant publications, active participation in conferences, and supervisory roles illustrate a deep commitment to her field. Her work on nonparametric estimation and functional data has clearly made a substantial contribution to mathematics.

However, for an award of this nature, enhancing the visibility of her research impact and exploring interdisciplinary or innovative research opportunities could further strengthen her application. Overall, her strong academic credentials and substantial contributions to her field make her a strong contender for the award.

Short Biography 📚

Dr. Sarra Leulmi is a prominent mathematician specializing in nonparametric statistics and functional data analysis. Born on December 17, 1987, in Skikda, Algeria, Dr. Leulmi has made significant contributions to the field of statistical estimation, particularly in the context of censored and functional data. Her academic career is distinguished by her extensive research, numerous publications, and her role in advancing mathematical education.

Profile

SCOPUS

Education 🎓

Dr. Leulmi completed her Baccalauréat in Exact Sciences with a focus on Mathematics in 2005. She earned her Diplôme d’Études Supérieures (D.E.S.) in Mathematics with high honors in 2009 from Université Frères Mentouri, Constantine. She pursued further studies in Applied Mathematics, completing her Magistère with distinction in 2012. Dr. Leulmi achieved her Doctorate in Mathematics, specializing in Probability and Statistics, in 2018, with the thesis titled “Nonparametric Estimation for Functional Data”. She was awarded Habilitation Universitaire in Mathematics in 2021.

Experience 🏫

Dr. Leulmi has held various academic positions, starting as a Mathematics Teacher at Lycée Mustafa Ben Boulaid (2010-2012). She then served as a Maître Assistante in Bioinformatics and Sciences and Techniques Departments at Université Frères Mentouri. From 2012 to 2021, she progressed from Maître Assistante to Maître-Conférence classe ‘B’. Since 2021, she has been a Maître-Conférence classe ‘A’ at the same institution, where she teaches a range of courses in statistics and mathematics.

Research Interests 🔬

Dr. Leulmi’s research focuses on nonparametric estimation methods for functional and censored data, local linear regression, and statistical modeling of heterogeneous data. Her work aims to advance the understanding of statistical estimation techniques in complex data environments, including functional data and models with truncation and censoring.

Awards 🏆

Dr. Leulmi has been recognized for her contributions to mathematics and statistics through various academic accolades. Her research has been featured in numerous prestigious journals, highlighting her impactful work in the field.

Publications 📑

Leulmi, S., & Messaci, F. (2018). Local linear estimation of a generalized regression function with functional dependent data. Communications in Statistics-Theory and Methods, 47(23), 5795-5811. Link

Leulmi, S., & Messaci, F. (2019). A Class of Local Linear Estimators with Functional Data. Journal of Siberian Federal University. Mathematics & Physics, 12(3), 379-391. Link

Leulmi, S. (2019). Local linear estimation of the conditional quantile for censored data and functional regressors. Communications in Statistics-Theory and Methods, 1-15. Link

Leulmi, S. (2020). Nonparametric local linear regression estimation for censored data and functional regressors. Journal of the Korean Statistical Society, 49(1), 1-22. Link

Boudada, H., & Leulmi, S., Kharfouch, S. (2020). Rate of the Almost Sure Convergence of a Generalized Regression Estimate Based on Truncated and Functional Data. Journal of Siberian Federal University. Mathematics & Physics, 13(4), 1-12. Link

Leulmi, F., & Leulmi, S., Kharfouch, S. (2022). On the nonparametric estimation of the functional regression based on censored data under strong mixing condition. Journal of Siberian Federal University. Mathematics & Physics, 15(4), 523-536. Link

Boudada, H., & Leulmi, S. (2023). Local linear estimation of the conditional mode under left truncation for functional regressors. Kybernetika, 59(4), 548-574. Link

Leulmi, S. (2024). Asymptotic normality of local linear functional regression estimator based upon censored data. Communications in Statistics – Theory and Methods. DOI: 10.1080/03610926.2024.2378376