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Yayın The effect of academic inbreeding on scientific effectiveness(Springer, 2011-09) İnanç Tunçer, Özlem; Tunçer, OnurIn academia, the term "inbreeding'' refers to a situation wherein PhDs are employed in the very same institution that trained them during their doctoral studies. Academic inbreeding has a negative perception on the account that it damages both scientific effectiveness and productivity. In this article, the effect of inbreeding on scientific effectiveness is investigated through a case study. This problem is addressed by utilizing Hirsch index as a reliable metric of an academic's scientific productivity. Utilizing the dataset, constructed with academic performance indicators of individuals from the Mechanical and Aeronautical Engineering Departments, of the Turkish Technical Universities, we demonstrate that academic inbreeding has a negative impact on apparent scientific effectiveness through a negative binomial model. This model appears to be the most suitable one for the dataset which is a type of count data. We report chi-square statistics and likelihood ratio test for the parameter alpha. According to the chi-square statistics the model is significant as a whole. The incidence rate ratio for the variable "inbreeding'' is estimated to be 0.11 and this ratio tells that, holding all the other factors constant, for the inbred faculty, the h-index is about 89% lower when compared to the non-inbred faculty. Furthermore, there exists negative and statistically significant correlation with an individual's productivity and the percentage of inbred faculty members at the very same department. Excessive practice of inbreeding adversely affects the overall productivity. Decision makers are urged to limit this practice to a minimum in order to foster a vibrant research environment. Furthermore, it is also found that scientific productivity of an individual decreases towards the end of his scientific career.Yayın Dendrimers are the unique chemical trees with maximum spectral radius(Univ Kragujevac, 2012) Bıyıkoğlu, Türker; Leydold, JosefIt is shown that dendrimers have maximum spectral radius and maximum Collatz-Sinogowitz index among all chemical trees of given size. The result is also generalized for the class of chemical trees with prescribed number of pendant vertices.Yayın Graphs with given degree sequence and maximal spectral radius(Electronic Journal of Combinatorics, 2008-09-15) Bıyıkoğlu, Türker; Leydold, JosefWe describe the structure of those graphs that have largest spectral radius in the class of all connected graphs with a given degree sequence. We show that in such a graph the degree sequence is non-increasing with respect to an ordering of the vertices induced by breadth-first search. For trees the resulting structure is uniquely determined up to isomorphism. We also show that the largest spectral radius in such classes of trees is strictly monotone with respect to majorization.Yayın Semiregular trees with minimal Laplacian spectral radius(Elsevier Inc, 2010-04-15) Bıyıkoğlu, Türker; Leydold, JosefA semiregular tree is a tree where all non-pendant vertices have the same degree. Among all semiregular trees with fixed order and degree, a graph with minimal (adjacency/Laplacian) spectral radius is a caterpillar. Counter examples show that the result cannot be generalized to the class of trees with a given (non-constant) degree sequence.Yayın Neighborhoods in development: Human development index and self-organizing maps(Springer, 2013-01) Rende, Sevinç; Donduran, MuratThe Human Development Index (HDI) has been instrumental in broadening the discussion of economic development beyond money-metric progress, in particular, by ranking a country against other countries in terms of the well being of their citizens. We propose self-organizing maps to explore similarities among countries using the components of the HDI rather than rankings. The similarities approach using the HDI components reveals information which is not available from ranking or bilateral comparisons. By illustrating clusters of countries, which we call "neighborhoods in development", self-organizing maps draw out the potential for mutual policy learning among countries and shift the focus to discovering what kind of policies might have led countries change their position in the rankings.
