Keynote Speakers


Dr. Zakiyeva Nazgul obtained her Ph.D. degree in Statistics at the National University of Singapore in 2020, and received a bachelor's degree in Mathematics from Nazarbayev University in 2016. Currently, she is a postdoctoral researcher at Technical University in Berlin. She was awarded with Marie Skłodowska-Curie European Postdoctoral Fellowship in 2022.

"Article’s scientific prestige: Measuring the impact of individual articles in the web of science" .

Our research presents a comprehensive analysis of more than 63 million articles and over a billion citations from Web of Science, spanning 254 subjects over the years 1981 to 2020. Within this vast and multi-disciplinary citation network, we introduce the innovative Article's Scientific Prestige (ASP) metric. This novel metric, calculated through eigenvector centrality, goes beyond traditional measures like the number of citations (#Cit) and journal grades to provide a more robust evaluation of the scientific impact of individual articles.

Our findings reveal a significant disparity between ASP and #Cit, particularly pronounced for less-cited articles. While both metrics effectively assess the prestige of well-recognized articles, such as Nobel Prize-winning works, ASP shines in ranking articles with fewer citations, offering a more persuasive evaluation. We challenge the conventional belief that journal grades, often influenced by a few highly cited articles, accurately reflect an article's scientific impact.

Furthermore, we underscore the limited relevance of factors like the number of references and coauthors, emphasizing the pivotal role of subject matter in shaping scientific impact. Our research contributes valuable insights into redefining the landscape of research impact assessment, ultimately reshaping the way we evaluate scientific excellence."mber of references and coauthors are less relevant to scientific impact, but subjects do make a difference." 

Dr. Kazim Erdogdu After completing his BSc. degree in the Computer Sciences division of Ege University's Department of Mathematics, he went on to get his MSc. degree in Geometry at the Department of Mathematics of Ege University's Graduate Faculty of Natural and Applied Science. He earned his PhD from Yaşar University's Graduate School's Department of Computer Engineering. After six years of working as an adjunct professor at Ege University's vocational schools and Yaşar University's undergraduate programs, he was hired as a full-time faculty member in the Department of Software Engineer in 2019. In 2021, he was appointed Assistant Professor (Ph.D.) at Yaşar University, where he continues to teach both undergraduate and graduate students. He has authored publications for national and international journals that are included in the SCI-E, Scopus, and TR-Dizin indices. He worked on TÜBİTAK and BAP projects. Combinatorial optimization, multi-objective optimization, evolutionary and memetic algorithms, and routing problems are the topics of his research.

"Determining the Electric Vehicle's Battery Capacity and Optimizing the Energy Consumption in Electric Travelling Salesman Problem with Time Windows" .

There is an increasing demand for cleaner energy as transportation-related global pollution rises. Each day, more and more vehicles using petroleum emit hazardous pollutants into the atmosphere that are harmful for human health. Because of this, the development and integration of electric cars into the transportation system is one of the worldwide technological and scientific trends. Creating an effective electric motor and battery for an electric vehicle is a significant component of its manufacturing process. The total cost of electric vehicles may be decreased by lowering the production and operating costs of these parts. Environmental preservation will also be aided by reducing the overall distance traveled and the overall energy used by the electric vehicles.

This research considered two different kinds of Electric Traveling Salesman Problem with Time Windows (E-TSPTW). The trade-offs between the goals of limiting distance and energy consumption were evaluated in the first one. The goal of the second one was to determine the optimum battery capacity for the electiric vehicles in the given circumstances. A hybrid Simulated Anealing meta-heuristic was used to solve these problems, and the results were compared to those of a mixed integer linear programming model. According to the experimental results for the second type of E-TSPTW, it is possible to achieve a 35% reduction in the initial battery capacity under the specified conditions. That, in turn, highly contributes to the prevention of the pollution caused by vehicles run on fuel.

Alex Karagrigoriou 

University of the Aegean & Hellenic Open University

I have studied at the University of Patras, Greece (BSc, 1984) and the University of Maryland, USA (MA, 1988, Ph.D, 1992). I have worked at the University of Maryland, the United States Department of Agriculture (USDA) and the Institute of Statistical Sciences, Taiwan and taught at the Universities of Maryland, Athens, Cyprus, Aegean and the Hellenic Open University.

My research activities cover various areas of Statistics such as Statistical Modeling, Model Selection Criteria, Biostatistics, Medical Statistics, Information Theory, Divergence Measures, Time Series Analysis, Goodness of Fit Tests, Applied Probability, Markov and Semi-Markov Processes, Economic Demography, Finance, Reliability Theory etc. I have published more than 100 articles and given more than 80 invited presentations in international conferences, symposia, and universities all over the world. I have published 2 textbooks in Greece, edited jointly 9 collective volumes (Wiley, ISAST, Springer & iSTE Wiley and co-edited special issues of journals such as J. of Mathematics and Statistics, J. of Reliability and Statistical Studies, Mathematics in Engineering, Science and Aerospace, Mathematics & Entropy. I have supervised 3 Postdocs, 6 PhD and over 50 Master Theses. I have great experience in the design and execution of research projects which involve statistical analysis of medical, biomedical, socioeconomic and economic data and have been involved in a number of research programs with external funding of 1.5 million euros, over the last 20 years (Central Banks, Government Units, National Research Foundations and the European Union).

"An Exponentiality Test of Fit Based on a Tail Characterization against Heavy and Light-Tailed Alternatives"

Log-concavity and log-convexity play a key role in various scientific fields, especially in those where the distinction between exponential and non-exponential distributions is necessary for inferential purposes. In the present study, we introduce a testing procedure for the tail part of a distribution which can be used for the distinction between exponential and non-exponential distributions. The conspiracy and catastrophe principles are initially used to establish a characteriza-tion of (the tail part of) the exponential distribution, which is one of the main contributions of the present work, leading the way for the construction of the new test of fit. The proposed test and its implementation are thoroughly discussed, and an extended simulation study has been undertaken to clarify issues related to its implementation and explore the extent of its capabilities. A real data case is also investigated.

Keywords: exponentiality test; tail characterization; log-concavity; log-convexity; extreme events; heavy-tailed distributions; light-tailed distributions