Università del Salento: ESE - Salento University Publishing
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Italiano e dialetto nell'insegnamento elementare della Puglia settentrionale: Zolle infocate di Filippo Maria Pugliese (1924)
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Comparing machine learning and conventional statistical approaches for injury prediction in young professional soccer players
Get PDFFrequent injuries pose a problem in professional soccer that is being tackledwith preventive measures. Consequently, injury prediction and preventionare also increasingly addressed from a statistical perspective. In a pilotstudy, several machine learning algorithms and conventional statistical approacheshave been compared regarding their potential to predict time-lossnon-contact lower-body injuries in professional youth soccer players, usingdata from a prospective cohort study with 56 players of which 22 were injured.The covariates considered here include basic soccer-related as wellas neuromuscular and biomechanical features derived from physical testing.Lasso regularized logistic regression, naive Bayes, linear discriminant analysis,k-nearest neighbors, classification trees, random forests, XGBoost, andsupport vector machines are considered for binary classification and predictionof an injury occurrence. The prediction results from a cross-validatedprocedure are compared regarding multiple quality measures. Post Lassologistic regression with a reduced penalty gives the best results with an accuracyof 0.625, a predictive likelihood of 0.593, and a Brier score of 0.228.The respective sensitivity and specificity are 0.773 and 0.529, with an AUC of0.672. Three features have been identified to be of particular relevance, theconcentric extensor peak torque of the knee, the transversal plane momentof the hip in a single-leg drop landing task, and the sway in postural controlunder static conditions. Moreover, an XGBoost model which primarily uses the two first-mentioned covariates slightly outperforms the Lasso model interms of accuracy (0.661), while for the other performance measures it isdominated by the Lasso
Topp-Leone Teissier Distribution-Neutrosophic Approach and Applications
Get PDFIn this paper, we introduce a new two-parameter extension of the Teisisier distribution using the Topp-Leone distribution as a generator, namely Topp- Leone Teissier distribution. The new model exhibits increasing, decreasing and bathtub shaped hazard rate functions. Several properties of the model are derived utilizing the Lambert W, the generalized integro-exponential and the incomplete generalized integro-exponential functions. Maximum like- lihood and Bayesian procedures are used to estimate the model parame- ters. Lindley’s approximation under squared error loss function is utilized for Bayesian computations. Moreover, a simulation study is carried out to analyze the performance of these estimators on the basis of mean squared er- ror. The applicability of the proposed model is evaluated using two real data sets. Also, we highlight the neutrosophic approach on Topp-Leone Teissier distribution as a pathway to address issues related to indeterminate, vague, or uncertain dataset.
Inclusion and demarcation. The corona-pandemic as a border-/boundary marker in North Macedonia
Get PDFThe corona-pandemic has reminded us of territorial borders, i.e., national borders throughout the world. Moreover, surprisingly, these very borders have emerged as successful tools for hindering the spread of the virus. In many cases, the coronavirus “stopped” at national borders due to the massive closures targeting, especially migrants, as they were seen as potential carriers and transmission risk factors. This article concerns this context intending to expose the border-/boundary-behavior in North Macedonia. Drawing upon the pandemic, it aims to challenge the frequent statements by politicians from Germany and the US on how the virus knows no borders. By paying attention to a southeastern european region, the article intends to show how the spread of the virus/disease reveals multiple borders and boundaries in the country, and the present-day world