Piezoelectric vibration energy harvesting from airflow in HVAC (Heating Ventilation and Air Conditioning) systems

This study focuses on the design and wind tunnel testing of a high efficiency Energy Harvesting device, based on piezoelectric materials, with possible applications for the sustainability of smart buildings, structures and infrastructures. The development of the device was supported by ESA (the European Space Agency) under a program for the space technology transfer in the period 2014-2016. The EH device harvests the airflow inside Heating, Ventilation and Air Conditioning (HVAC) systems, using a piezoelectric component and an appropriate customizable aerodynamic appendix or fin that takes advantage of specific airflow phenomena (vortex shedding and galloping), and can be implemented for optimizing the energy consumption inside buildings. Focus is given on several relevant aspects of wind tunnel testing: different configurations for the piezoelectric bender (rectangular, cylindrical and T-shaped) are tested and compared, and the effective energy harvesting potential of a working prototype device is assessed

OpenTED Browser: Insights into European Public Spendings

We present the OpenTED browser, a Web application allowing to interactively browse public spending data related to public procurements in the European Union. The application relies on Open Data recently published by the European Commission and the Publications Office of the European Union, from which we imported a curated dataset of 4.2 million contract award notices spanning the period 2006-2015. The application is designed to easily filter notices and visualise relationships between public contracting authorities and private contractors. The simple design allows for example to quickly find information about who the biggest suppliers of local governments are, and the nature of the contracted goods and services. We believe the tool, which we make Open Source, is a valuable source of information for journalists, NGOs, analysts and citizens for getting information on public procurement data, from large scale trends to local municipal developments.Comment: ECML, PKDD, SoGood workshop 201

Feature selection in high-dimensional dataset using MapReduce

This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features

Why demand uncertainty curbs investment: Evidence froma a panel of Italian manufacturing firms

From a theoretical point of view, uncertainty over the demand for a firmÂ’s product may not have clear effects on investments, because of the influence of a number of factors, such as the production technology and the amount of competition in the product market.Until now, a deeper investigation of the interplay of different factors in the temporal dimension has not been possible because the empirical research has been based on cross-section analysis. This omission makes biased estimates of the investment-uncertainty relationship likely.The aim of this paper is to extend the findings of the empirical literature by using a panel of Italian firms over the period 1996-2004, covering a complete business cycle. The availability of a panel of survey data on companiesÂ’ investment plans, expected future sales and demand uncertainty allows us to account for unobservable individual firm differences, macroeconomic shocks and the temporal evolution of the investment-uncertainty relationship. A key finding of our paper concerns the role of the competition faced by Italian firms in 1996-2004. The gradual loss of market power experienced by Italian manufacturing firms along with the increasing flexibility of labour input may have weakened the negative effect of uncertainty on investment decisions. We show that, in repeated cross-section estimates, the omission of firm-specific effects together with the dynamic interplay described above, would have lead to misleading conclusions about the relevance of demand uncertainty in explaining investment decisions.planned investments, demand uncertainty, survey data, panel estimation.