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Yayın Discovering cis-regulatory modules by optimizing barbecues(Elsevier Science Bv, 2009-05-28) Mosig, Axel; Bıyıkoğlu, Türker; Prohaska, Sonja J.; Stadler, Peter F.Gene expression in eukaryotic cells is regulated by a complex network of interactions, in which transcription factors and their binding sites on the genomic DNA play a determining role. As transcription factors rarely, if ever, act in isolation, binding sites of interacting factors are typically arranged in close proximity forming so-called cis-regulatory modules. Even when the individual binding sites are known, module discovery remains a hard combinatorial problem, which we formalize here as the Best Barbecue Problem. It asks for simultaneously stabbing a maximum number of differently colored intervals from K arrangements of colored intervals. This geometric problem turns out to be an elementary, yet previously unstudied combinatorial optimization problem of detecting common edges in a family of hypergraphs, a decision version of which we show here to be NP-complete. Due to its relevance in biological applications, we propose algorithmic variations that are suitable for the analysis of real data sets comprising either many sequences or many binding sites. Being based on set systems induced by interval arrangements, our problem setting generalizes to discovering patterns of co-localized itemsets in non-sequential objects that consist of corresponding arrangements or induce set systems of co-localized items. In fact, our optimization problem is a generalization of the popular concept of frequent itemset mining.Yayın Progressive damage analyses of masonry buildings by dynamic analyses(Springer International Publishing AG, 2020-08) Aras, Fuat; Akbaş, Tolga; Ekşi, Hızır; Çeribaşı, SeyitThis study investigates the effects of prescribed damage on the walls of masonry buildings by experimental and numerical methods. Ambient vibration survey method was applied to an existing, two-story, unreinforced masonry building to determine its dynamic characteristics, such as mode shapes and natural frequencies. Then, the walls on two exterior sides of the building were demolished, and dynamic testing was repeated for the damaged building. As the next step, the amount of damage on the building was increased by more impacts, and the dynamic characteristics of the heavily damaged building were identified. The results obtained from the undamaged, damaged and heavily damaged building were compared, and the damage effect on the natural frequencies of the building was noted. Besides, finite element analyses of the undamaged, damaged and heavily damaged buildings were performed. It was found that, the numerical models, constructed with code-based material properties, do not sufficiently represent the dynamic behavior of masonry buildings. Secondly, as the result of the sustained damage, while the experimental and the numerical modal analyses revealed the decrease in the dominant frequencies of the building, the difference between them increases with the severity of the damage. With the framework presented in this study, the behavior of masonry buildings can better be determined and used for analysis purposes.Yayın A cooperative neural network control structure and its application for systems having dead-zone nonlinearities(Springer International Publishing Ag, 2022-03) Dinçmen, ErkinAn adaptive control structure utilizing two feed-forward neural networks (NN) is proposed to deal with systems having unknown nonlinearities. One of the networks is trained to mimic the nonlinear system dynamics. Its training will be repeated with periods in order to keep it an updated valid model of the system all the times since the parameters and/or nonlinearities of the system may change during time. The other network, which is the Controller NN, adapts itself continuously by collaborating with the Model NN. The stability-convergence analysis of both networks is performed via Lyapunov method. An example system is chosen to show the applicability of the control algorithm. This example system is created by combining a linear dynamics model with a dead-zone function to represent a nonlinear system to be controlled. It should be noted that the proposed control structure can be used in any nonlinear system without knowing the system dynamics. The only information required by Model NN is the training set consisting input-output data pairs of the system. The Model NN is trained offline with this training set, and afterward the Controller NN adapts its weights online continuously during the control task with the help of Model NN. The performances of PD and PID controllers are also given for comparison purposes.
