Random Lie-point symmetries of stochastic differential equations

We study the invariance of stochastic differential equations under random diffeomorphisms, and establish the determining equations for random Lie-point symmetries of stochastic differential equations, both in Ito and in Stratonovich form. We also discuss relations with previous results in the literature.Comment: In new version (November 2017) we have added an important ERRATUM. Due to a trivial mistake in a formula, several examples should be revised; more relevant, the qualitatove results of Section VIII turn out to be wrong, as discussed in the erratum. See also arXiv:1711.0199

The Effect of Job–Education Vertical Mismatch on Wages Among Recent PhD Graduates: Evidence From an Instrumental Variable Analysis

Existing studies suggest that recent PhD graduates with a job vertically mismatched with their education tend to earn lower wages than their matched counterparts. However, by being based on cross-sectional ordinary least squares (OLS) estimates, these studies raise endogeneity concerns and can only be considered evidence of a correlation between vertical mismatch and wages. This paper improves this literature by applying a heteroskedasticity-based instrumental variable estimation approach to analyzing Italian PhD holders’ cross-sectional micro-data. Our analysis suggests that previous empirical studies have provided slightly upward estimates of the impact of vertical mismatch on wages. Nevertheless, our results show that the effect of overeducation on wages is sizeable. However, no wage effect is found for overskilling. The heterogeneity of these findings by field of study and gender are also inspected

An AmI-Based Software Architecture Enabling Evolutionary Computation in Blended Commerce: The Shopping Plan Application

This work describes an approach to synergistically exploit ambient intelligence technologies, mobile devices, and evolutionary computation in order to support blended commerce or ubiquitous commerce scenarios. The work proposes a software architecture consisting of three main components: linked data for e-commerce, cloud-based services, and mobile apps. The three components implement a scenario where a shopping mall is presented as an intelligent environment in which customers use NFC capabilities of their smartphones in order to handle e-coupons produced, suggested, and consumed by the abovesaid environment. The main function of the intelligent environment is to help customers define shopping plans, which minimize the overall shopping cost by looking for best prices, discounts, and coupons. The paper proposes a genetic algorithm to find suboptimal solutions for the shopping plan problem in a highly dynamic context, where the final cost of a product for an individual customer is dependent on his previous purchases. In particular, the work provides details on the Shopping Plan software prototype and some experimentation results showing the overall performance of the genetic algorithm

Economics research and climate change. A Scopus-based bibliometric investigation

This paper investigates the evolution over time of the economics literature devoted to climate change. The analysis is based on 1974-2021 data extracted from the Scopus database and focuses on the publication outlets included in the first quartile of the “Economics, Econometric, and Finance” SCImago Ranking. We inspect the size and the impact of this economics literature, the geographical pattern of its production, and we provide a content analysis based on the keywords associated with the documents analysed. This study provides a detailed overview of the (still limited) interest that economists demonstrate for climate change

Enabling technologies for future learning scenarios: the semantic grid for human learning

In this paper, starting from the limitations and constrains of traditional human learning approaches, we outline new suitable approaches to education and training in future knowledge based society. In our vision, learning and teaching are no longer standalone activities but complex, conversational and experiential-based processes implying collaboration, direct experience, mutual trust and shared interests. We identify characteristics of the environments suitable for these processes, and we compare different enabling technology infrastructures in order to justify why the Semantic Grid for Human Learning, that is a particular enhanced instance of the traditional Semantic Grid, is the most appropriate infrastructure to build our vision on. Finally, we present a realistic learning scenario as a case study, proving the effectiveness of our innovative learning approachesforfuture Education and Training

Innovation in the Solid Waste Management industry: integrating Neoclassical and Complexity Theory perspectives

Often considered a traditional labour intensive activity, in recent years, the solid waste management (SWM) industry has been largely interested in innovation. Nonetheless, the analysis of innovations in the SW industry is frequently confined to process innovation in the disposal segment, neglecting other kinds of innovation – such as product innovation and organizational innovation – in other segments. While several economic theoretical frameworks have been developed for interpreting eco-innovation in general, a specific analysis of innovation in each segment of SWM is still missing, despite the specificities of this sector. To fill this gap, this paper shows how complexity theory can be profitably used to integrate the more traditional neoclassical approach, offering a comprehensive theoretical framework to analyse innovation in the SWM industry from both a market and firm perspective (the neoclassical approach) and from a social perspective (the complexity theory framework). Four main typologies of the SW market system, exhibiting different kinds of innovation, are outlined: (i) a “traditional” landfill-oriented system; (ii) a modern “waste-to-energy” incinerator-oriented system; (iii) a “light recycling” system with integrated solutions and a selection performance that is lower than 50%; and (iv) a “hard recycling” system

The Effect of Job–Education Vertical Mismatch on Wages Among Recent PhD Graduates: Evidence From an Instrumental Variable Analysis

Existing studies suggest that recent PhD graduates with a job vertically mismatched with their education tend to earn lower wages than their matched counterparts. However, by being based on cross-sectional ordinary least squares (OLS) estimates, these studies raise endogeneity concerns and can only be considered evidence of a correlation between vertical mismatch and wages. This paper improves this literature by applying a heteroskedasticity-based instrumental variable estimation approach to analyzing Italian PhD holders’ cross-sectional micro-data. Our analysis suggests that previous empirical studies have provided slightly upward estimates of the impact of vertical mismatch on wages. Nevertheless, our results show that the efect of overeducation on wages is sizeable. However, no wage efect is found for overskilling. The heterogeneity of these fndings by feld of study and gender are also inspecte

Exploring Networking of Third Sector Organizations: A Case Study Based on the Quartieri Spagnoli Neighbourhood in Naples (Italy)

In the last decades, European Third Sector Organizations have been increasingly affected by marketization and the reduction of public resources for social services, hence pushed to develop new strategies to accomplish their social mission while remaining economically efficient. The existing literature suggests that the activation of networking activities can be a strategy to overcome these problems. By using a partially mixed-methods approach, this study investigates factors that stimulate the TSOs’ networking and attitudes toward different types of informal and formal networks. Our results suggest that TSOs try to cope with contextual challenges by activating both formal and informal networks, depending on financial and structural opportunities, but also suggest that the rationalities moving TSOs are highly heterogeneous