Ozgun Kara

The High Luminosity phase of the Large Hadron Collider will deliver 10 times more integrated luminosity than the existing collider, posing significant challenges for radiation tolerance and event pileup on detectors, especially for forward calorimetry. As part of its upgrade program, the Compact Muon Solenoid collaboration is designing a high-granularity calorimeter (HGCAL) to replace the existing endcap calorimeters. It will feature unprecedented transverse and longitudinal readout and triggering segmentation for both electromagnetic and hadronic sections. The electromagnetic section and a large fraction of the hadronic section will be based on hexagonal silicon sensors of 0.5–1 cm2 cell size, with the remainder of the hadronic section being based on highly-segmented scintillators with silicon photomultiplier readout. The intrinsic high-precision timing capabilities of the silicon sensors will add an extra dimension to event reconstruction, especially in terms of pileup rejection. First hexagonal silicon modules, using the existing Skiroc2 front-end ASIC developed for CALICE, have been tested in beams at Fermilab and CERN in 2016. We present results from these tests, in terms of system stability, calibration with minimum-ionizing particles and resolution (energy, position and timing) for electrons, and the comparisons of these quantities with GEANT4-based simulation.

Graph Neural Networks (GNNs) have established themselves as the preferred methodology in a multitude of domains, ranging from computer vision to computational biology, especially in contexts where data inherently conform to graph structures. While many existing methods have endeavored to model GNNs using various techniques, a prevalent challenge they grapple with is the issue of over-smoothing. This paper presents new Graph Neural Network models that incorporate two first-order Partial Differential Equations (PDEs). These models do not increase complexity but effectively mitigate the over-smoothing problem. Our experimental findings highlight the capacity of our new PDE model to achieve comparable results with higher-order PDE models and fix the over-smoothing problem up to 64 layers. These results underscore the adaptability and versatility of GNNs, indicating that unconventional approaches can yield outcomes on par with established techniques.

Стаття присвячена аналізу сучасного стану та основних трендів у галузі смарт-туризму з акцентом на вплив цих технологій на розвиток міського туризму. Метою статті є аналіз світового досвіду впровадження смарт-технологій у туризмі та визначення перспектив їхнього застосування для українських туристичних дестинацій, зокрема міста Києва. Методологія дослідження базується на аналізі літературних джерел, статистичних даних та емпіричних спостережень. Результати розкривають потенціал смарт-технологій у покращенні якості туристичних послуг та збільшенні привабливості міста Києва для туристів. У дослідженні висвітлено сутність концепцій «смарт-сіті» та «смарт-туризм». Проаналізовано динаміку показників розвитку туристичної сфери столиці України за 2020-2022 роки, позиції міста в світових рейтингах смарт-міст та документи, що визначають впровадження концепцій «смарт-сіті» та «смарт-туризму» в столиці України. Авторами запропоновано перелік заходів задля посилення позиції міста Києва у цих рейтингах та зокрема розвитку напрямку смарт-туризму в столиці України як туристичній дестинації. Автори досліджують глобальні інновації та технологічні рішення, що сприяють ефективному туристичному менеджменту, і адаптації цих підходів у контексті України, з особливим фокусом на Київ. Розглядаються потенціал і виклики, пов'язані з імплементацією смарт-технологій у сфері туризму, які можуть підвищити привабливість Києва як туристичної дестинації. Стаття надає стратегічні рекомендації для розвитку смарт-туризму в Україні, виходячи з аналізу світових кращих практик та місцевих специфік. Дослідження вказує на необхідність подальших наукових досліджень у сфері смарт-туризму для вдосконалення інфраструктури та залучення більшої кількості туристів до міста. Розробка стратегій, які включають смарт-технології в туризмі, може стати ключовим фактором залучення туристів та підвищення конкурентоспроможності туристичних дестинацій на міжнародному рівні.

Magnetic flux leakage (MFL) is a non-destructive testing method used to detect railhead surface defects. For effective MFL testing, the homogeneity of the magnetic flux density distribution (MFDD) within the railhead is crucial. Inhomogeneous formation of the MFDD within the railhead reduces the efficiency of the MFL testing. The homogeneity of the MFDD depends on the distance between poles (DBP) of the MFL testing system. According to the literature, as the DBP parameter increases, the MFDD becomes more homogeneous. In this study, four different homogeneity levels of the MFDD are introduced based on the DBP parameter. 3D finite element method (FEM) modelling simulation is conducted to obtain MFL testing analyses. The analyses are performed on a rail that contains rectangular surface defects of varying depth and length. The results of this study are evaluated using characteristic features of the MFL signal B X component, namely the slope of the baseline, the bottom value and the peak-to-peak value. The results show that if the homogeneity level of the MFDD within the railhead is higher, the bottom value and slope of the baseline decrease and the peak-to-peak value increases. This indicates that higher homogeneity of the MFDD enhances the detection efficiency of the MFL testing. Eventually, it is found that with the formation of nearly 100% homogeneous MFDD in the railhead, the slope of the baseline, the bottom value and the peak-to-peak value are enhanced by up to 83%, 77% and 12%, respectively.

The bachelor assignment concerns the suitability of cellular manufacturing for the production environment of the sheet metal manufacturer. At the moment the company is functionally structured in terms of organizational structure and in terms of physical production layout. Being set up like this gives them flexibility to handle any routing. The downside is a lot of traffic between the departments, as the parts move from department to department through manufacturing. The company currently don't have any production cells, neither have they identified products which together have a 'common' routing. In this thesis we quantitatively analyzed if there's a business case for creating production cells in order to cut back on routing delays and costs. In order to evaluate a business case for this, we identified the common routings in the highly varied product mix based on historical production data. Thereafter, these routings are clustered into routing families by means of the discrete clustering method and the similarity coefficient algorithm. Finally, the results of the clustering methods are used to identify the routings that are suitable for cellular manufacturing.

The CREMA collaboration is pursuing a measurement of the ground-state hyperfine splitting (HFS) in muonic hydrogen (µp) with 1 ppm accuracy by means of pulsed laser spectroscopy to determine the two-photon-exchange contribution with 2 × 10 -4 relative accuracy.In the proposed experiment, the µp atom that undergoes a laser excitation from the singlet hyperfine state to the triplet hyperfine state, is quenched back to the singlet state by an inelastic collision with a H 2 molecule.The resulting increase of kinetic energy after the collisional deexcitation is used as a signature of a successful laser transition between hyperfine states.In this paper, we calculate the combined probability that a µp atom initially in the singlet hyperfine state undergoes a laser excitation to the triplet state followed by a collisional-induced deexcitation back to the singlet state.This combined probability has been computed using the optical Bloch equations including the inelastic and elastic collisions.Omitting the

The CREMA collaboration is pursuing a measurement of the ground-state hyperfine splitting (HFS) in muonic hydrogen (µp) with 1 ppm accuracy by means of pulsed laser spectroscopy to determine the two-photon-exchange contribution with 2 × 10 -4 relative accuracy.In the proposed experiment, the µp atom that undergoes a laser excitation from the singlet hyperfine state to the triplet hyperfine state, is quenched back to the singlet state by an inelastic collision with a H 2 molecule.The resulting increase of kinetic energy after the collisional deexcitation is used as a signature of a successful laser transition between hyperfine states.In this paper, we calculate the combined probability that a µp atom initially in the singlet hyperfine state undergoes a laser excitation to the triplet state followed by a collisional-induced deexcitation back to the singlet state.This combined probability has been computed using the optical Bloch equations including the inelastic and elastic collisions.Omitting the

The CREMA collaboration is pursuing a measurement of the ground-state hyperfine splitting (HFS) in muonic hydrogen (µp) with 1 ppm accuracy by means of pulsed laser spectroscopy to determine the two-photon-exchange contribution with 2 × 10 -4 relative accuracy.In the proposed experiment, the µp atom that undergoes a laser excitation from the singlet hyperfine state to the triplet hyperfine state, is quenched back to the singlet state by an inelastic collision with a H 2 molecule.The resulting increase of kinetic energy after the collisional deexcitation is used as a signature of a successful laser transition between hyperfine states.In this paper, we calculate the combined probability that a µp atom initially in the singlet hyperfine state undergoes a laser excitation to the triplet state followed by a collisional-induced deexcitation back to the singlet state.This combined probability has been computed using the optical Bloch equations including the inelastic and elastic collisions.Omitting the