Barriers to Entry, Deregulation and Workplace Training: A Theoretical Model with Evidence from Europe

We study the impact of barriers to entry on workplace training. Our theoretical model indicates that there are two contrasting effects of deregulation on training. With a given number of firms, deregulation reduces the size of rents per unit of output that firms can reap by training their employees. Yet, the number of firms increases, thereby raising output and profit gains from training and improving investment incentives. The latter effect always prevails. Our empirical analysis, based on repeated cross-section data from 15 European countries and 12 industries, confirms the predictions of the model and shows that deregulation increases training incidence.training, product market competition, Europe

Is Training More Frequent When the Wage Premium Is Smaller ?

According to Becker [1964], when labour markets are perfectly competitive, general training is paid by the worker, who reaps all the benefits from the investment. Therefore, ceteris paribus, the greater the training wage premium, the greater the investment in general training. Using data from the European Community Household Panel, we compute a proxy of the training wage premium in clusters of homogeneous workers and find that smaller premia induce greater incidence of off-site training, which is likely to impart general skills. Our findings suggest that the Becker model provides insufficient guidance to understand empirical training patterns. Conversely, they are not inconsistent with theories of training in imperfectly competitive labour markets, in which firms may be willing to finance general training if the wage structure is compressed, that is, if the increase in productivity after training is greater than the increase in pay.general training; off-site training; training wage premia; wage compression; ECHP

Is training more frequent when the wage premium is smaller? Evidence from the European Community Household Panel

According to Becker [1964], when labour markets are perfectly competitive, general training is paid by the worker, who reaps all the benefits from the investment. Therefore, ceteris paribus, the greater the training wage premium, the greater the investment in general training. Using data from the European Community Household Panel, we compute a proxy of the training wage premium in clusters of homogeneous workers and find that smaller premia induce greater incidence of off-site training, which is likely to impart general skills. Our findings suggest that the Becker model provides insufficient guidance to understand empirical training patterns. Conversely, they are not inconsistent with theories of training in imperfectly competitive labour markets, in which firms may be willing to finance general training if the wage structure is compressed, that is, if the increase in productivity after training is greater than the increase in pay.General training; Off-site training; Training wage premia; Wage compression; ECHP

Temporal Information in Data Science: An Integrated Framework and its Applications

Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems.Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems

GEODATABASE TO STORE NON HOMOGENEOUS CARTOGRAPHY: AN APPLICATORY EXAMPLE

L'enorme massa di dati cartografici disomogenei in formato digitale, attualmente disponibile ad un'utenza diffusa tramite la rete, ha determinato la pressante necessità di meccanismi automatici ed efficienti per assicurarne la conservazione e nel contempo l'accesso in modo semplice e veloce. La metodologia Database relazionale può fornire una risposta a tale esigenza. Nel presente contributo viene presentata una struttura Database relazionale per l’immagazzinamento di cartografia non omogenea. Questa è infatti in grado di archiviare cartografia in formato vettoriale e raster, corredata di informazioni di tipo attributo collegate alle carte. Si possono memorizzare e gestire: immagini satellitari di formati e scale diverse, cartografia tradizionale in formato raster, cartografia tematica in formato raster e vettoriale e tutte le informazioni collegate ai supporti cartografici.The nowadays huge amount of digital mapping data, also distributed by Internet, have caused the need of efficient methods in order to store and to access them rapidly. Database methodology can be an answer to this problem. In the present paper a relational database structure is introduced. The database can store different format and spatial resolution satellite imagery, raster format of historical paper maps, vector maps and attribute data connected to the cartography

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only

Space tethers: parameters reconstructions and tests

In the last several years, the need for an alternative to chemical propulsive systems for low-orbit satellite deorbiting has become increasingly evident; a Tethered System can provide adequate thrust or drag without the complications of combustions and with a minimal impact on the environment. In this context, the authors are part of a team that is studying various tether applications and building a prototype of an electrodynamic tether system. The goal of this paper is to characterize tether materials in order to find valid solutions for future space tether missions. Mission requirements (e.g., the survivability to hypervelocity impacts and the capability to damp oscillations in electrodynamic tethers) influence the choice of tether parameters such as cross section geometry (round wires or tapes), materials, length, and cross section sizes. The determination of the elastic characteristics and damping coefficients is carried out through a campaign of experiments conducted with both direct stress/strain measurements and the laboratory facility SPAcecRraft Testbed for Autonomous proximity operatioNs experimentS (SPARTANS) on a low friction table at the University of Padova. In the latter case, the stiffness and damping of a flexible line were verified by applying different tensile load profiles and then measuring the tether-line dynamic response in terms of tension spike amplitude, oscillation decay, and estimation of the damping coefficient

An updated inventory of the vascular flora of the Cerbaie hills (Tuscany, Italy)

We present an updated list of the vascular flora occurring in the Cerbaie hills (Tuscany), a site of high naturalistic interest. The list is based on a literature survey and on field studies carried out in the years 2010–2022. The Cerbaie hills host a flora of 1,107 specific and subspecific taxa (including 100 naturalized aliens), 32 casual aliens and 10 hybrid taxa. Two taxa are new for Tuscany: Carex oedipostyla and Thalictrum simplex subsp. galioides; 330 taxa are new for the study area. Concerning old records, 344 have been confirmed, while 47 were not confirmed, albeit considered reliable. Moreover, we considered three taxa as locally extinct, 19 as doubtfully occurring, and three as wrongly reported. Despite the low elevation of the study area, life forms and chorotypes show marked Eurosiberian affinities, in agreement with the temperate and continental climate