Suchergebnisse

Este artigo apresenta as séries de imposto inflacionário (II), transferências inflacionárias para os bancos comerciais (TI) e transferências inflacionárias totais (TIT=II+TI) para os países do Mercosul e para os Estados Unidos, no período que vai de 1989 a 2003.

RESUMO Este trabalho atualiza, para 2003, a série de taxas inflacionárias de 1947-1992, transferências inflacionárias para bancos comerciais e transferências inflacionárias totais anteriormente publicadas em Cysne (1994) e em Simonsen e Cysne (1995).

This work updates, to 2003, the 1947-1992 series of inflation tax, inflationary transfers to commercial banks and total inflationary transfers previously published in Cysne (1994) and in Simonsen and Cysne (1995). (Rev Econ Polit/DÜI)

Purpose
– The purpose of the research project was to examine the process of developing a data sharing framework between different public sector organisations.


Design/methodology/approach
– A two-year case study of a data sharing project between a UK fire and rescue service, local council, NHS primary care trust and a police force was undertaken.


Findings
– It is important to carefully determine the requirements for data sharing, to establish data sharing agreements, to have secure arrangements for data sharing, and to ensure compliance with data protection legislation.


Research limitations/implications
– Data sharing between public sector organisations can operate effectively if appropriate care is taken when creating data sharing agreements between partner organisations.


Practical implications
– Data sharing can assist in reducing duplication of effort between public sector organisations and can reduce costs and enable more co-ordinated provision of public services.


Originality/value
– The detailed analysis of a data sharing case study identified the need for a systematic data sharing framework. Such a framework is proposed and illustrated with practical examples of specification, implementation and evaluation.

Geographic information systems (GISs) provide map based spatial analyses of geo‐coded data. In this paper we examine a methodological framework for geographic information systems development that was developed and refined over a six year period based upon a fire prevention support geographic information system for a UK fire and rescue service. The methodological framework involves the use of a multi‐methodology approach that incorporates social and organizational analysis, spatial modelling, and functional design. Copyright © 2016 John Wiley & Sons, Ltd.

Development of health promoting policies requires an understanding not just of the interplay between different measures of health but also their relationship with broader education, criminal justice and other social issues. Methods to better utilise multi-sectoral data to inform policy are needed. Applying clustering techniques to 30 health and social metrics we identify 5 distinct local authority types, with poor outcomes for the majority of metrics concentrated in the same cluster. Clusters were distinguished especially by levels of: child poverty; breastfeeding initiation; children's tooth decay; teenage pregnancy; healthy eating; mental illness; tuberculosis and smoking deaths. Membership of the worst cluster (C5) was focused in Northern England which contains 15.7% of authorities analysed (n=324), but 63.0% of those in C5. The concentration of challenges in certain areas creates disproportionate pressures that may exceed the cumulative effects of individual challenges. Such distinct health clusters also raise issues of transferability of effective policies between areas with different cluster membership. © 2011 Elsevier Ltd.

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues. ; Peer Reviewed ; Postprint (published version)