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 s channel single top quark production in pp collisions at √s = 7 and 8 TeV

Abstract: A search is presented for single top quark production in the s channel in proton-proton collisions with the CMS detector at the CERN LHC in decay modes of the top quark containing a muon or an electron in the final state. The signal is extracted through a maximum-likelihood fit to the distribution of a multivariate discriminant defined using boosted decision trees to separate the expected signal contribution from background processes. Data collected at centre-of-mass energies of 7 and 8 TeV yield cross sections of 7.1 +/- 8.1 pb and 13.4 +/- 7.3 pb, respectively, and a best fit value of 2.0 +/- 0.9 for the combined ratio of the measured and expected values. The signal significance is 2.5 standard deviations, and the upper limit on the rate relative to the standard model expectation is 4.7 at 95% confidence level.

Engineered Chimeric Peptides as Antimicrobial Surface Coating Agents toward Infection-Free Implants

Abstract: Prevention of bacterial colonization and consequent biofilm formation remains a major challenge in implantable medical devices. Implant-associated infections are not only a major cause of implant failures but also their conventional treatment with antibiotics brings further complications due to the escalation in multidrug resistance to a variety of bacterial species. Owing to their unique properties, antimicrobial peptides (AMPs) have gained significant attention as effective agents to combat colonization of microorganisms. These peptides have been shown to exhibit a wide spectrum of activities with specificity to a target cell while having a low tendency for developing bacterial resistance. Engineering biomaterial surfaces that feature AMP properties, therefore, offer a promising approach to prevent implant infections. Here, we engineered a chimeric peptide with bifunctionality that both forms a robust solid-surface coating while presenting antimicrobial property. The individual domains of the chimeric p...

Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms

Abstract: Energy consumption of a rail transit system depends on many parameters. One of the most effective methods of reducing energy consumption in a rail transit system is optimising the speed profile of the trains along the route. A new efficient method will be presented for the optimisation of the coasting points for trains in a global manner. The proposed approach includes realistic system modelling using multi-train, multi-line simulation software and application of artificial neural networks (ANN) and genetic algorithms (GA). The simulation software used can model regenerative braking and train performance at low voltages. Using ANN and GA together, optimal coasting points for long line sections covering five stations and two lines are achieved. Simulation software is used for creating training and test data for the ANN. These data are used for training of the ANN. Trained ANNs are then used for estimating energy consumption and travel time for new sets of coasting points. Finally, the outputs of the ANN are optimised to find optimal train coasting points. For this purpose, a fitness function with target travel time, energy consumption and weighting factors is proposed. An interesting observation is that the use of ANN increases the speed of optimisation. The proposed method is used for optimising coasting points for minimum energy consumption for a given travel time on the first 5 km section of Istanbul Aksaray-Airport metro line, where trains operate every 150 s. The section covers five passenger stations, which means four coasting points for each line. It has been demonstrated that an eight input ANNs can be trained with acceptable error margins for such a system.

Search for high-mass resonances in dilepton final states in proton-proton collisions at √s = 13 TeV

Abstract: A search is presented for new high-mass resonances decaying into electron or muon pairs. The search uses proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the CMS experiment at the LHC in 2016, corresponding to an integrated luminosity of 36 fb$^{−1}$. Observations are in agreement with standard model expectations. Upper limits on the product of a new resonance production cross section and branching fraction to dileptons are calculated in a model-independent manner. This permits the interpretation of the limits in models predicting a narrow dielectron or dimuon resonance. A scan of different intrinsic width hypotheses is performed. Limits are set on the masses of various hypothetical particles. For the $ {Z}_{\mathrm{SSM}}^{\prime}\left({Z}_{{}^{\psi}}^{\prime}\right) $ particle, which arises in the sequential standard model (superstring-inspired model), a lower mass limit of 4.50 (3.90) TeV is set at 95% confidence level. The lightest Kaluza-Klein graviton arising in the Randall-Sundrum model of extra dimensions, with coupling parameters k/M$_{Pl}$ of 0.01, 0.05, and 0.10, is excluded at 95% confidence level below 2.10, 3.65, and 4.25 TeV, respectively. In a simplified model of dark matter production via a vector or axial vector mediator, limits at 95% confidence level are obtained on the masses of the dark matter particle and its mediator.