Impact of city changes and weather on anthropogenic heat flux in Europe 1995–2015

How people live, work, move from place to place, consume and the technologies they use all affect heat emissions in a city which influences urban weather and climate. Here we document changes to a global anthropogenic heat flux (QF) model to enhance its spatial (30′′ × 30′′ to 0.5° × 0.5°) resolution and temporal coverage (historical, current and future). QF is estimated across Europe (1995–2015), considering changes in temperature, population and energy use. While on average QF is small (of the order 1.9–4.6 W m−2 across all the urban areas of Europe), significant spatial variability is documented (maximum 185 W m−2). Changes in energy consumption due to changes in climate are predicted to cause a 13% (11%) increase in QF on summer (winter) weekdays. The largest impact results from changes in temperature conditions which influences building energy use; for winter, with the coldest February on record, the mean flux for urban areas of Europe is 4.56 W m−2 and for summer (warmest July on record) is 2.23 W m−2. Detailed results from London highlight the spatial resolution used to model the QF is critical and must be appropriate for the application at hand, whether scientific understanding or decision making.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Commemoration of the tenth anniversary of the International Conference on Population and Development: actions undertaken to implement the programme of action of the conference in Latin America and the Caribbean

Includes bibliography

El papel de la Secretaría de la CEPAL en el seguimiento de la Conferencia de las Naciones Unidas sobre el Medio Ambiente y el Desarrollo = The role of the ECLAC Secretariat in following up the United Nations Conference on Environment and Development

Incluye Bibliografía

San Salvador commitment: adopted at the second ministerial conference on the information society in Latin America and the Caribbean

Includes bibliography

Report of the Third Ministerial Conference on the Information Society in Latin America and the Caribbean: Lima, 21-23 November, 2010

Incluye bibliografía