AZIONAMENTO CON CHOPPER AD ALTA DINAMICA CON CONTROLLO DIGITALE: PROGETTAZIONE E REALIZZAZIONE

L'oggetto della tesi è la progettazione e la realizzazione di un azionamento per motore a corrente continua a magneti permanenti ed eccitazione serie. Il convertitore è un chopper a legame diretto, con ramo di frenatura. E' controllato digitalmente, tramite PIC 8bit, con regolazione di velocità e corrente in cascata

Disentangling the Innovation - Internalization Process Through a Structural Equation Model

Innovation virtuously impacts on the degree of international growth, which in turn positively influences innovation activities and then firms�™ performance (Filipescu et al., 2009). Many authors have tried to identify and explain the relationship between these two phenomena at firm level. Only recently, few empirical studies investigate them at a more aggregate level (see e.g. Mariotti et al., 2008). Moreover the literature focuses only on one direction of causality, while scant attention has been paid to inspect empirically innovation and internationalization together (Kafouros et al., 2008; Filippetti et al., 2009; Frenz and Ietto-Gillies, 2007). This paper provides an empirical analysis of the mutual relationship of these two phenomena, taking into account various features of the regions themselves. The empirical study is conducted on data concerning 20 Italian regions covering the period 2000-2008. To better understand the complex relationship between internationalization and innovation, we refer to the Structural Equation Models (SEM). These are multivariate regression type models, in which response variables could in turn act as dependent and predictor within a system of equations, and all variables are assumed to influence one-another reciprocally, either directly or through other variables as intermediaries (Bollen, 1989; McAdam et al., 2010). Through the SEM the relationships are expressed by a set of parameters which explain the magnitude of the effect (direct or indirect) between independent (either observed or latent) and dependent variables. Indeed, internationalization and innovation could act as both dependent and predictor which measurement could be difficult then suggesting the use of latent variables, and where the system of indicators is complex enough to lead at a model specified through two-way relations intrinsically connected. Using SEM approach we are able to specify flexible models dealing with non-standard relations stylized along panel data structure, in which spatial and temporal dimensions do matter

Incentives, job satisfaction and performance: empirical evidence in italian social enterprises

The paper offers a contribution to the understanding of the relations between incentives, satisfaction and performance of employees in social enterprises. It starts by criticizing the general hypotheses of the principal-agent theory and especially that employee satisfaction is determined exclusively by the level of salary received. These criticisms are explained both by looking to the organizational definition of job satisfaction by Locke and by taking a behavioural economics perspective. Job satisfaction is thus assumed to derive from a composed mix of incentives received on the job, equity perceived and employee motivations. It is no longer possible to assume that the wage is the sole (not even the most important) variable influencing worker performance. This claim is especially valid in social enterprises, where worker performance is difficult to monitor and evaluate, while high intrinsic motivations can better explain job satisfaction. The empirical analysis helps to shed light on the determinants of job satisfaction and individual performance. Data was collected on 4,134 employees working in 320 Italian social cooperatives. The paper introduces the methodologies of categorical principal components analysis, factor analysis, and Rasch models to group the items of intrinsic and extrinsic satisfaction, motivations and fairness. The data was then analysed by means of linear regression where the dependent variables are not only the stated degree of job satisfaction, but also satisfaction with extrinsic and intrinsic aspects of the job. The models come to demonstrate the particular relevance of employee motivations and fairness perceived in explaining job satisfaction and its sub-dimensions. Furthermore, organizational perceptions and the work environment are found to be significant as are individual perceptions and motivations.

FOOTBALL ANALYTICS: PERFORMANCE ANALYSIS DIFFERENTIATE BY ROLE

Nowadays, data science covers many areas of our life, and also sport applications. In this context, we focusing on football, and propose an overview about a project in the field of performance analysis (Carpita et al., 2019; Carpita and Golia, 2020) and prediction of football match results (Carpita et al., 2015). The idea is to adopt a non-supervised approach, thanks some clustering around variables techniques (i.e. KPI: Key Performance Indicator), in order to create some composite index for each area of performance (e.g. technical-mental-physical), differentiate by role (Carpita et al., 2020). The final goal is to help coaches and scouting to take decisions and to evaluate impartially players performance. In our presentation, we will submit an overview about the results of a preliminary analysis of our technical-dataset: data visualization and comparison between players KPI’s performance, differentiate by role

An innovative xG Model for football analytics

In the field of football analytics, we want to improve (in terms of prediction performance) one of the emerging tool: the expected goal (xG) model. With this final goal, we merged match event data with some players’ performance composite indicators obtained using a Partial Least Squares - Structural Equation Model (PLS-SEM). Using a sample of match tracking data relying to season 2019/2020 of the Italian Serie A, composed by 660 shots and 25 features, a logistic regression model was applied on different scenarios for sample balanced techniques. Results seem to be interesting in terms of sensitivity, F1 and AUC indices, compared with a benchmark

Chapter Prediction of wine sensorial quality: a classification problem

When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, context) terms (Jackson, 2017). In this paper, the wine quality was evaluated by experienced judges who scored the wine on the base of a 0-10 scale, with 0 meaning very bad and 10 excellent, so, the resulting variable was categorical. The models applied to predict this variable provide the prediction of the occurrence probabilities of each of its categories. Nevertheless, jointly with this probabilities’ record, the practitioners need the predicted value (category) of the variable, so the statistical problem to be covered refers to the way in which this probabilities’ record is transformed into a single value. In this paper we compare the predictive performances of the default method (Bayes Classifier - BC), which assigns a unit to the most likely category, and other two methods (Maximum Difference Classifier and Maximum Ratio Classifier). The BC is the optimal criterion if one is interested in the accuracy of the classification, but, given that it favors the prevalent category most, when there is not a category of interest, it cannot be the best choice. The data under study concern the quality of the red variant of the Portuguese "Vinho Verde" wine (Cortez et al., 2009), measured on a 0-10 scale. Nevertheless, only 6 scores were used, with 2 scores with a very few number of observations, so this is the right context for predictive performance comparisons. In the study, we investigated different merging of categories and we used 11 explanatory variables to estimate the probabilities’ record of the wine quality variable

Categorical classifiers in multiclass classification with imbalanced datasets

This paper discusses, in a multiclass classification setting, the issue of the choice of the so-called categorical classifier, which is the procedure or criterion that transforms the probabilities produced by a probabilistic classifier into a single category or class. The standard choice is the Bayes Classifier (BC), but it has some limits with rare classes. This paper studies the classification performance of the BC versus two alternatives, that are the Max Difference Classifier (MDC) and Max Ratio Classifier (MRC), through an extensive simulation and some case studies. The results show that both MDC and MRC are preferable to BC in a multiclass setting with imbalanced data

Football analytics: a Higher-Order PLS-SEM approach to evaluate players’ performance

Nowdays, data science is applied in several area of our life, and also many applications in sports fields are increasing. In this context, we are focusing on football (e.g. soccer); thanks to this work we have the aim to give a new approach in the evaluation of football players’ performance given from the EA Sports experts and available on Kaggle in the KES dataset. For this purpose, we adopt a Higher-Order PLS-SEM approach to the sofifa KPIs (e.g. Key Performance Indicators) in order to compute a composite indicator and compare it with the well-known overall index from EA Sports. The final goal is to suggest a new performance index for helping coaches and scouting staff of professional teams to take strategic decisions, in order to evaluate impartially players’ performance