The Effect of Virtual Reality Technology on the Imagery Skills and Performance of Target-Based Sports Athletes

The aim of this study is the examination of the effect of virtual reality based imagery (VRBI) training programs on the shot performance and imagery skills of athletes and, and to conduct a comparison with Visual Motor Behavior Rehearsal and Video Modeling (VMBR + VM). In the research, mixed research method and sequential explanatory design were used. In the quantitative dimension of the study the semi-experimental model was used, and in the qualitative dimension the case study design was adopted. The research participants were selected from athletes who were involved in our target sports: curling (n = 14), bowling (n = 13), and archery (n = 7). All participants were randomly assigned to VMBR + VM (n = 11), VRBI (n = 12), and Control (n = 11) groups through the “Research Randomizer” program. The quantitative data of the study was: the weekly shot performance scores of the athletes and the data obtained from the “Movement Imagery Questionnaire-Revised.” The qualitative data was obtained from the data collected from the semi-structured interview guide, which was developed by researchers and field experts. According to the results obtained from the study, there were statistically significant differences between the groups in terms of shot performance and imagery skills. VRBI training athletes showed more improvement in the 4-week period than the athletes in the VMBR + VM group, in terms of both shot performance and imagery skills. In addition, the VRBI group adapted to the imagery training earlier than the VMBR + VM group. As a result, it was seen that they showed faster development in shot performances. From these findings, it can be said that VRBI program is more efficient in terms of shot performance and imagery skills than VMBR + VM, which is the most used imaging training model. © Copyright © 2021 Bedir and Erhan.We thank the participants and students of Atat?rk University Faculty of Sport Sciences, Selim Can Da??stanl? and Irem Ba?adur

Inclusive production of protons, anti-protons and neutrons in p+p collisions at 158 GeV/c beam momentum

New data on the production of protons, anti-protons and neutrons in p+p interactions are presented. The data come from a sample of 4.8 million inelastic events obtained with the NA49 detector at the CERN SPS at 158 GeV/c beam momentum. The charged baryons are identified by energy loss measurement in a large TPC tracking system. Neutrons are detected in a forward hadronic calorimeter. Inclusive invariant cross sections are obtained in intervals from 0 to 1.9 GeV/c (0 to 1.5 GeV/c) in transverse momentum and from -0.05 to 0.95 (-0.05 to 0.4) in Feynman x for protons (anti-protons), respectively. pT integrated neutron cross sections are given in the interval from 0.1 to 0.9 in Feynman x. The data are compared to a wide sample of existing results in the SPS and ISR energy ranges as well as to proton and neutron measurements from HERA and RHIC.Comment: 69 pages, 72 figure

Deep and Modular Neural Networks

In this chapter, we focus on two important areas in neural computation, i.e., deep and modular neural networks, given the fact that both deep and modular neural networks have been among the most powerful machine learning and pattern recognition techniques for complex AI problem solving. We begin by providing a general overview of deep and modular neural networks to describe the general motivation behind such neural architectures and fundamental requirements imposed by complex AI problems. Next, we describe background and motivation, methodologies, major building blocks, and the state-of-the-art hybrid learning strategy in context of deep neural architectures. Then, we describe background and motivation, taxonomy and learning algorithms pertaining to various yet typical modular neural networks in a wide context. Furthermore, we also examine relevant issues and discuss open problems in deep and modular neural network research areas.

CMS Physics Technical Design Report: Addendum on High Density QCD with Heavy Ions

This report presents the capabilities of the CMS experiment to explore the rich heavy-ion physics programme offered by the CERN Large Hadron Collider (LHC). The collisions of lead nuclei at energies $\sqrt{s_{NN}}= 5.5\,{\rm TeV}$ , will probe quark and gluon matter at unprecedented values of energy density. The prime goal of this research is to study the fundamental theory of the strong interaction \u2014 Quantum Chromodynamics (QCD) \u2014 in extreme conditions of temperature, density and parton momentum fraction (low- x ). This report covers in detail the potential of CMS to carry out a series of representative Pb-Pb measurements. These include "bulk" observables, (charged hadron multiplicity, low p T inclusive hadron identified spectra and elliptic flow) which provide information on the collective properties of the system, as well as perturbative probes such as quarkonia, heavy-quarks, jets and high p T hadrons which yield "tomographic" information of the hottest and densest phases of the reaction