Optimizing technical skills and physical loading in small-sided basketball games.

Abstract Differences in physiological, physical, and technical demands of small-sided basketball games related to the number of players, court size, and work-to-rest ratios are not well characterised. A controlled trial was conducted to compare the influence of number of players (2v2/4v4), court size (half/full court) and work-to-rest ratios (4x2.5 min/2x5 min) on the demands of small-sided games. Sixteen elite male and female junior players (aged 15-19 years) completed eight variations of a small-sided game in randomised order over a six-week period. Heart rate responses and rating of perceived exertion (RPE) were measured to assess the physiological load. Movement patterns and technical elements were assessed by video analysis. There were *60% more technical elements in 2v2 and *20% more in half court games. Heart rate (86 + 4% & 83 + 5% of maximum; mean + SD) and RPE (8 + 2 & 6+ 2; scale 1-10) were moderately higher in 2v2 than 4v4 small-sided games, respectively. The 2v2 format elicited substantially more sprints (36 +12%; mean +90% confidence limits) and high intensity shuffling (75 +17%) than 4v4. Full court games required substantially more jogging (9 +6%) compared to half court games. Fewer players in small-sided basketball games substantially increases the technical, physiological and physical demands

Numerical study of rolling process on the plastic strain distribution in wire + arc additive manufactured Ti-6Al-4V

Wire+arc additive manufacturing (WAAM) is an additive manufacturing (AM) process that employs wire as the feedstock and an arc as energy source, to construct near net-shape components at high build rates. Ti-6Al-4V deposits typically form large columnar prior β grains that can grow through the entire component height, leading to anisotropy and lower mechanical properties, compared to the equivalent wrought alloy. Cold-working techniques such as rolling can be used to promote grain refinement in Ti-6Al-4V WAAM parts, thus increasing strength and eliminating anisotropy concomitantly. Additionally, rolling can be beneficial in terms of reduction of residual stress and distortion. The aim of this study is to illustrate the effect of rolling process parameters on the plastic deformation characteristics in Ti-6Al-4V WAAM structures. To produce a certain refinement of the microstructure, a certain amount of strain is typically required; thus suitable design guidelines for practical applications are needed. The effect of different rolling process parameters, in particular, rolling load and roller profile radius on the plastic strain distribution is investigated based on the finite element method. From a numerical point of view, the effect of the stiffness of the roller is investigated, e.g. deformable vs. rigid roller. Results indicate that for an identical rolling load, the deformable roller produces lower equivalent plastic strains due to its own elastic deformation. Additionally, a lower friction coefficient produces higher equivalent plastic strains near the top surface but, it has an insignificant effect on the plastic deformation further away from the top surface. However, numerically the computation time significantly increased for a higher friction coefficient. Larger roller profile radii lead to lower plastic strain near the top surface, but simultaneously had nearly no noticeable effect on plastic strains at deeper depth. In addition, the effect of interspace between rollers on the uniformity of the plastic strain during multi-pass rolling was investigated for a selected example. The results show that a higher uniform plastic strain distribution is obtained when the interspace between two rollers is equal to the residual width of the groove produced by a single rolling pas

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out

Numerical investigation of the effect of rolling on the localized stress and strain induction for wire + arc additive manufactured structures

Cold rolling can be used in-process or post-process to improve microstructure, mechanical properties and residual stress in directed-energy-deposition techniques, such as the high deposition rate wire + arc additive manufacturing (WAAM) process. Finite element simulations of the rolling process are employed to investigate the effect of rolling parameters, in particular rolling load and roller profile radius on the residual stress field as well as plastic strain distribution for the profiled roller. The results show the response to rolling of commonly used structural metals in WAAM, i.e., AA2319, S335JR steel and Ti-6Al-4V, taking into account the presence of residual stresses. The rolling load leads to changes in the location and the maximum value of the compressive residual stresses, as well as the depth of the compressive residual stresses. However, the roller profile radius only changes the maximum value of these compressive residual stresses. Changing the rolling load influences the equivalent plastic strain close to the top surface of the wall as well as in deeper areas, whereas the influence of the roller profile radius is negligible. The plastic strain distribution is virtually unaffected by the initial residual stresses prior to rolling. Finally, design curves were generated from the simulations for different materials, suggesting ideal rolling load and roller profile combinations for microstructural improvement requiring certain plastic strains at a specific depth of the additive structure

Methodological developments in violence research

Über Jahrzehnte wurde Gewalt durch Interviews mit Betroffenen oder Tätern, durch teilnehmende Beobachtung oder Gewaltstatistiken untersucht, meist unter Verwendung entweder qualitativer oder quantitativer Analysemethoden. Seit der Jahrhundertwende stehen Forschenden eine Reihe neuer Ansätze zur Verfügung: Es gibt immer mehr Videoaufnahmen von gewaltsamen Ereignissen, Mixed Methods-Ansätze werden stetig weiterentwickelt und durch Computational Social Sciences finden Big Data-Ansätze Einzug in immer mehr Forschungsfelder. Diese drei Entwicklungen bieten großes Potenzial für die quantitative und qualitative Gewaltforschung. Der vorliegende Beitrag diskutiert Videodatenanalyse, Triangulation und Mixed Methods-Ansätze sowie Big Data und bespricht den gegenwärtigen und zukünftigen Einfluss der genannten Entwicklungen auf das Forschungsfeld. Das Augenmerk liegt besonders darauf, (1) wie neuere Videodaten genutzt werden können, um Gewalt zu untersuchen und wo ihre Vor- und Nachteile liegen, (2) wie Triangulation und Mixed Methods-Ansätze umfassendere Analysen und theoretische Verknüpfungen in der Gewaltforschung ermöglichen und (3) wo Anwendungen von Big Data und Computational Social Science in der Gewaltforschung liegen können.For decades violence research has relied on interviews with victims and perpetrators, on participant observation, and on survey methods, and most studies focused on either qualitative or quantitative analytic strategies. Since the turn of the millennium, researchers can draw on a range of new approaches: there are increasing amounts of video data of violent incidents, triangulation and mixed methods approaches become ever more sophisticated, and computational social sciences introduce big data analysis to more and more research fields. These three developments hold great potential for quantitative and qualitative violence research. This paper discusses video data analysis, mixed methods, and big data in the context of current and future violence research. Specific focus lies on (1) potentials and challenges of new video data for studying violence; (2) the role of triangulation and mixed methods in enabling more comprehensive violence research from multiple theoretical perspectives, and (3) what potential uses of big data and computational social science in violence research may look like

Exploring the effectiveness of immersive video for training decision-making capability in elite, youth basketball players

Decision-making is an essential capability for success in team sport athletes. Good decision-making is underpinned by perceptual-cognitive skills that allow athletes to assess the environment and choose the correct choice from a number of alternatives. Previous research has demonstrated that decision-making can be trained "off-line" by exposing athletes to gameplay scenarios and having them make decisions based on the information presented to them. These scenarios are typically presented on television monitors or using life-size projections but recent advances in immersive video capabilities provide opportunities to improve the fidelity of training by presenting a realistic, 360° view of the competition environment. The purpose of this study was to assess the effectiveness of immersive video training and whether training would improve decision-making performance in elite, youth basketball players (male and female). A training group completed 10 or 12 immersive video (360° video presented in a head-mounted display) training sessions in which they viewed and responded to gameplay scenarios across 3-weeks while the control group only participated in their usual training routine. Performance was assessed on an immersive video test and during small-sided games (SSG). The male training group had a large, non-significant improvement on immersive test score (+4.0 points) and in the SSG (+5.8 points) compared to the male control group (+0.3 points and +1.0 points, respectively). While both the female control group (+9.7 points) and training group (7.4 points) had large improvements in the immersive training test, only the female control improved their performance in the SSG (+6.9 points). Despite the mixed findings, there may be benefit for using immersive video for training decision-making skill in team sports. The implications of these findings (e.g., gender of the actors used to create stimuli, variety of scenarios presented) and the limitations of the experiment are discussed