Key courses of academic curriculum uncovered by data mining of students' grades

Learning is a complex cognitive process that depends not only on an individual capability of knowledge absorption but it can be also influenced by various group interactions and by the structure of an academic curriculum. We have applied methods of statistical analyses and data mining (Principal Component Analysis and Maximal Spanning Tree) for anonymized students' scores at Faculty of Physics, Warsaw University of Technology. A slight negative linear correlation exists between mean and variance of course grades, i.e. courses with higher mean scores tend to possess a lower scores variance. There are courses playing a central role, e.g. their scores are highly correlated to other scores and they are in the centre of corresponding Maximal Spanning Trees. Other courses contribute significantly to students' score variance as well to the first principal component and they are responsible for differentiation of students' scores. Correlations of the first principal component to courses' mean scores and scores variance suggest that this component can be used for assigning ECTS points to a given course. The analyse is independent from declared curricula of considered courses. The proposed methodology is universal and can be applied for analysis of student's scores and academic curriculum at any faculty

Redefining Sports: Esports, Environments, Signals and Functions

The sports landscape is constantly changing due to innovation and entrepreneurship. The availability of technology led to the emergence of esports and augmented sports. Biofeedback and sensing technologies can be used for athlete monitoring and training purposes. Research on motor control deals with planning and execution of bodily movements and provides some insights towards formal presentation of sports.Previous research provided many sports categorization models. On many occasions, published articles failed to distinguish recreational/leisure competitive gameplay activity (gaming) from athletic performance (esports). Our goal was to define esports by extending existing universal sport definitions and propose a novel modular computational framework for categorizing sports through environments and signals.We have fulfilled our goals by illustrating how signals flow within competitive (sports) environments. Our esports definition introduces esports as a group of sports similar to motorsports. Moreover, we have defined mathematical foundations for signal processing by various actors (athletes, referees, environments, intermediate processing steps). We have demonstrated that representing sports as a multidimensional signal can lead to the categorization of sports through computation. We claim that our approach could be applied to transfer training methods from similar sports, analysis of the training process, and referee error measurement.Our study was not without limitations. Further research is required to validate our theoretical model by embedding available variables in latent space to calculate similarity measures between sports

Virtuality Engineering in Esports

Traditional sports and esports benefit from the development of Information and Communications Technologies (ICT), including gaming, 4D image/video processing, augmented reality (AR), virtual reality (VR), machine learning (ML), artificial intelligence (AI), big data, high-performance computing (HPC), and cloud computing. On the fuzzy border between the areas of physical and modified reality, both types of sports can coexist.  The hardware layer of esports includes PC, consoles, smartphones, and peripherals used to interface with computers, including sensors and feedback devices. The IT layer of esports includes algorithms required in the development of games, online platforms, and virtual reality. The esports community includes amateur and professional players, spectators, esports organizers, sponsors, and other stakeholders. Esports and gaming research spans throughout law (intellectual rights, insurance, safety, and age restrictions), administration (teams, clubs, organizations, league regulations, and tournaments) biology (medicine, psychology, addiction, training and education) Olympic and non-Olympic disciplines, ethical issues, game producers, finance, gambling, data acquisition and analysis. Our article aims to presents selected research issues of esports in the ICT virtualization layer

THE INFLUENCE OF SELECTED BODY DIMENSIONAL PARAMETERS ON THE MECHANICAL PARAMETERS OF THE VERTICAL JUMP

INTRODUCTION: The purpose of the present study was to determine the influence of body fat, as well as selected limb lengths and proportions, on the maximal mechanical power and height of the countermovement jump. The effect of countermovement depth, body mass and jump height on maximal power, known from our previous study, was taken into consideration as well. METHODS: Untrained physical education students (56 female and 38 male) volunteered to take part in the CMJ jumping test, consisting of 3 jumps of different countermovement depths performed at one-minute intervals on a computerized Kistler force plate. Results of the highest jump were selected for each subject for further processing. The following variables were included in the statistical analysis: the maximal mechanical power (Pmax) developed during take-off, the height of the jump (h), the counter-movement depth (d), body mass (m) and height (H), lengths of the foot (F), shank (S), leg (L), trunk (T), shank to leg length ratio (s) and fat mass. The Shapiro-Wilk test was used to examine the distributions of the tested variables. Pearson’s correlation matrix and multiple regression analysis were employed to identify the relationships between the tested variables. RESULTS AND CONCLUSIONS: In both female and male groups the multiple regression procedure (the forward stepwise method) pointed the height of the jump, the countermovement depth and the body mass as variables having an effect on maximal power. Significant effects of the fat mass and the shank-to-leg length ratio on the height of jumps were found. REFERENCES: Aura, O., Viitasalo, J. T. (1989). Biomechanical Characteristics of Jumping. Intertional Journal of Sport Biomechanics 5, 89-98. Bobbert, M. F., Gerritsen, K. G. M., Litjens, M. C. A., Van Soest, A. J. (1995). Explanation of Differences in Jump Height Between Countermovement and Squat Jumps. Book of Abstracts. XV ISB Congress. Jyväskylä Dowling, J. J., Vamos, L. (1993). Identification of Kinetic and Temporal Factors Related to Vertical Jump Performance. J. Appl. Biomech. 9, 95-110. Harley, R. A., Doust, J. H. (1994). Effects of Different Degrees of Knee Flexion During Continuous Vertical Jumping on Power Output Using the Bosco Formula. Journal of Sports Sciences 12, 2, 139-140. Gajewski, J., Janiak, J., Eliasz, J., Wit, A. (1996). Determinants of the Maximal Mechanical Power Developed During the Countermovement Jump. In J. Abrantes (Ed.), XIV International Symposium on Biomechanics in Sports (pp.420-423). Lisbon

THE MAXIMAL MUSCLE TORQUES DISTRIBUTION AMONG MUSCLE GROUPS IN ELITE ATHLETES IN COMBAT SPORTS

INTRODUCTION: The purpose of the study was to point out typical asymmetries in strength distribution among main muscle groups in top athletes representing various kinds of combat sports. The differences in strength between tested groups were also considered and discussed. METHODS: Three groups of high-level sportsmen (11 fencers, 16 judokas and 16 boxers) took part in the experiment. Maximal muscle torques were measured in isometric conditions for flexion and extension in the elbow, shoulder, knee and hip joints for the left and right extremity. Special torquemeter devices (chair and bench) were utilized for measurement. The 2-way MACNOVA for repeated measures (8 variables) was employed to test differences in average strength between sports and sides. The logarithm of the body mass was included as a covariate. The Kolmogorov-Smirnov test was used to examine the distributions of the tested variables. RESULTS AND CONCLUSIONS: Statistical analysis revealed significant differences in average strength for both analyzed factors: sports (Rao's R=7.34,), sides (Rao's R=10.66, ) and their interaction (Rao's R=1.86, ). The asymmetrical strength distribution is thought to be the result of specific training methods applied in each tested sport discipline. The tested groups differed in strength in elbow, knee and hip for both flexion and extension. No matter how the body mass influence was controlled, the strength in the group of boxers was found lower, especially for knee and hip extensions. Fig.1. Mean values of maximal muscle torques for elbow (E), arm (A), knee (K), and hip (H) flexion (F) and extension (E) estimated for the athletes representing fencing, judo and boxing

Redefining Sports: Esports, Environments, Signals and Functions

The sports landscape is constantly changing due to innovation and entrepreneurship. The availability of technology led to the emergence of esports and augmented sports. Biofeedback and sensing technologies can be used for athlete monitoring and training purposes. Research on motor control deals with planning and execution of bodily movements and provides some insights towards formal presentation of sports.Previous research provided many sports categorization models. On many occasions, published articles failed to distinguish recreational/leisure competitive gameplay activity (gaming) from athletic performance (esports). Our goal was to define esports by extending existing universal sport definitions and propose a novel modular computational framework for categorizing sports through environments and signals.We have fulfilled our goals by illustrating how signals flow within competitive (sports) environments. Our esports definition introduces esports as a group of sports similar to motorsports. Moreover, we have defined mathematical foundations for signal processing by various actors (athletes, referees, environments, intermediate processing steps). We have demonstrated that representing sports as a multidimensional signal can lead to the categorization of sports through computation. We claim that our approach could be applied to transfer training methods from similar sports, analysis of the training process, and referee error measurement.Our study was not without limitations. Further research is required to validate our theoretical model by embedding available variables in latent space to calculate similarity measures between sports

Polityka Unii Europejskiej przeciwdziałająca szkodliwej międzynarodowej optymalizacji opodatkowania

The activity of international holding companies has become crucial for the European economy. In particular, attention should to paid to the tax-related issues, which arise out of the cross-border activity of holding companies. Increasingly, holding companies employ aggressive tax optimisation in their strategies. While the tax policies of individual E.U. Member States have turned out to be of little effectiveness, simultaneously, the lack of a common and harmonised tax policy designed to counteract tax optimisation has become a serious problem for the European Union. Therefore, the European Commission strives to develop a fiscal concept which will – on the one hand – allow to effectively combat international tax optimisation adopted by holding companies and – on the other hand – be integral with the internal tax systems of individual Member States.Działalność holdingów międzynarodowych stała się kluczowa dla gospodarki europejskiej. Szczególną uwagę należy zwrócić na aspekty podatkowe, które są związane z transgraniczną działalnością holdingów. Coraz częściej stosują one w swojej strategii agresywną optymalizację opodatkowania. Polityka podatkowa poszczególnych państw członkowskich okazała się mało skuteczna. Jednocześnie brak wspólnej i zharmonizowanej polityki podatkowej przeciwdziałającej optymalizacji opodatkowania stał się poważnym problemem dla Unii Europejskiej. Dlatego ideą Komisji Europejskiej jest – z jednej strony – wypracowanie koncepcji podatkowej, która będzie w stanie skutecznie przeciwdziałać międzynarodowej optymalizacji opodatkowania stosowanej przez holdingi, z drugiej zaś – będzie integralna z wewnętrznymi systemami podatkowymi państw członkowskich

A Statistical Calibration Method of Propagation Prediction Model Based on Measurement Results

Radio environment maps (REMs) are beginning to be an integral part of modern mobile radiocommunication systems and networks, especially for ad-hoc, cognitive, and dynamic spectrum access networks. The REMs will use emerging military systems of tactical communications. The REM is a kind of database used at the stage of planning and management of the radio resources and networks, which considers the geographical features of an area, environmental propagation properties, as well as the parameters of radio network elements and available services. At the REM, for spatial management of network nodes, various methods of propagation modeling for determining the attenuation and capacity of wireless links and radio ranges are used. One method of propagation prediction is based on a numerical solution of the wave equation in a parabolic form, which allows considering, i.a., atmospheric refraction, terrain shape, and soil electrical parameters. However, the determination of a current altitudinal profile of atmospheric refraction may be a problem. If the propagation-prediction model uses a fixed refraction profile, then the calibration of this model based on empirical measurements is required. We propose a methodology for calibrating the analyzed model based on an example empirical research scenario. The paper presents descriptions of the propagation model, test-bed and scenario used in measurements, and obtained signal attenuation results, which are used for the initial calibration of the model

Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework

Stiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R2 = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented