Istanbul Technical University

About: Istanbul Technical University is a education organization based out in Istanbul, Turkey. It is known for research contribution in the topics: Fuzzy logic & Large Hadron Collider. The organization has 12889 authors who have published 25081 publications receiving 518242 citations. The organization is also known as: İstanbul Teknik Üniversitesi & Technical University of Istanbul.

Search for Dark Matter and Large Extra Dimensions in pp Collisions Yielding a Photon and Missing Transverse Energy

Abstract: Results are presented from a search for new physics in the final state containing a photon and missing transverse energy. The data correspond to an integrated luminosity of 5.0 inverse femtobarns collected in pp collisions at sqrt(s)=7 TeV by the CMS experiment. The observed event yield agrees with standard-model expectations for photon plus missing transverse energy events. Using models for production of dark-matter particles (chi), we set 90% confidence level (CL) upper limits of 13.6--15.4 femtobarns on chi production in the photon plus missing transverse energy state. These provide the most sensitive upper limits for spin-dependent chi-nucleon scattering for chi masses between 1 and 100 GeV. For spin-independent contributions, the present limits are extended to chi masses below 3.5 GeV. For models with 3--6 large extra dimensions, our data exclude extra-dimensional Planck scales between 1.65 and 1.71 TeV at 95% CL.

Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks

Abstract: Lake Van in eastern Turkey has been subject to water level rise during the last decade and, consequently, the low-lying areas along the shore are inundated, giving problems to local administrators, governmental officials, irrigation activities and to people's property. Therefore, forecasting water levels of the Lake has started to attract the attention of the researchers in the country. An attempt has been made to use artificial neural networks (ANN) for modeling the temporal change water levels of Lake Van. A back-propagation algorithm is used for training. The study indicated that neural networks can successfully model the complex relationship between the rainfall and consecutive water levels. Three different cases were considered with the network trained for different arrangements of input nodes, such as current and antecedent lake levels, rainfall amounts. All of the three models yields relatively close results to each other. The neural network model is simpler and more reliable than the conventional methods such as autoregressive (AR), moving average (MA), and autoregressive moving average with exogenous input (ARMAX) models. It is shown that the relative errors for these two different models, are below 10% which is acceptable for engineering studies. In this study, dynamic changes of the lake level are evaluated. In contrast to classical methods, ANNs do not require strict assumptions such as linearity, normality, homoscadacity etc.