Automating labor: evidence from firm-level patent data

Do higher wages lead to more automation innovation? To answer this question, we first introduce a new measure of automation by using the frequency of certain keywords in patent text to identify automation innovations in machinery. We validate our measure by showing that it is correlated with a reduction in routine tasks in a cross-sectoral analysis in the US. Then we build a firm-level panel dataset on automation patents. We combine macroeconomic data from 41 countries and information on geographical patent history to build firm-specific measures of lowskill and high-skill wages. We find that an increase in low-skill wages leads to more automation innovation with an elasticity between 2 and 4. An increase in highskill wages tends to reduce automation innovation. Placebo regressions show that the effect is specific to automation innovations. Finally, we use the Hartz labor market reforms in Germany for an event study and find that they are associated with a relative reduction in automation innovations

An Unsupervised Approach for Automotive Driver Identification

The adoption of on-vehicle monitoring devices allows different entities to gather valuable data about driving styles, which can be further used to infer a variety of information for different purposes, such as fraud detection and driver profiling. In this paper, we focus on the identification of the number of people usually driving the same vehicle, proposing a data analytic work-flow specifically designed to address this problem. Our approach is based on unsupervised learning algorithms working on non-invasive data gathered from a specialized embedded device. In addition, we present a preliminary evaluation of our approach, showing promising driver identification capabilities and a limited computational effort

Hydrogen Purification and Odorization to Evaluate the Distribution of This Energy Carrier Through the Gas Pipelines

Due to hydrogen storage and transport problem, a concrete and immediate solution is the exploitation of the gas pipelines now used for natural gas. In this regard, this work aims to evaluate two main aspects that must be taken into account to make this approach possible: the separation of hydrogen from natural gas-hydrogen mixture and the odorization of the latter, in order to provide the safety of the pipelines. Therefore, the first part of this study is the evaluation of the efficiency of a purification system in presence of a variable quantity of methane in the inner stream. For these purposes, electrochemical hydrogen compression (EHC) system was selected, due to the great advantage of allowing both purification and compression in a single device. Different methane-hydrogen mixtures were taken into consideration, going to evaluate how an increasing amount of methane affects the efficiency of the system. The second part of this work is focused on a further development of a previous simulation study related to a possible process for natural gas-hydrogen mixtures odorization systems using AVEVA's PRO II software. As odorant, GASODOR S-FREE was taken into consideration, thanks to the fact that this is a common odorant used for methane with the great advantage of not containing sulfur, unlike THT and mercaptans

Study of Different H2/CO2 Ratios as Feed in Fischer-Tropsch Reactor with Iron-Based Nano-Hydrotalcite Catalysts

CO2-FTS is among the most viable methods for converting CO2 into useful chemicals and fuels in order to minimize CO2 emissions. Due to its chemical inertness, however, effective conversion continues to be a difficulty. The challenges in terms of yield and mechanism have attracted the interest of different research groups in the development of a new carbon dioxide hydrogenation catalysts, capable of reaching satisfactory results. In this work, a selection of nano ternary hydrotalcites (HTlc) were synthesized with and without ultrasound in order to develop active Fe-based catalysts for the Fischer–Tropsch synthesis. HTlc consists mostly of metal hydroxides in which different metal atoms are uniformly distributed at the atomic level. The reaction was carried in a lab scale plant in a fixed bed configuration. All fresh and used catalysts were examined and characterized using XRPD, ICP-OES, SEM, TEM, BET. Ternary HTlc composed of Mg, Cu, and Fe was synthesized using an ultrasound-assisted co-precipitation technique (MCF-US). HTlc was tested for carbon dioxide hydrogenation reaction with a study concerning different H2/CO2 ratios in order to evaluate the product distribution as well as the efficiency of the catalyst itself. The CO2 conversion resulted higher and more stable in feeds with higher H2 quantities. The selectivity towards higher chain hydrocarbons was higher for lower H2/CO2 ratios whereas methane and carbon monoxide selectivities were adequately low

IAU Office of Astronomy for Education, OAE Center Italy - Annual Report 2021

First annual report of the the IAU OAE Center Italy, an international office addressed to education and hosted and financed by Inaf. OAE Center Italy was established on the 3rd of March 2021, thanks to a Memorandum of Understanding signed by three parties: IAU, the Office of Astronomy for Education and INAF. OAE Center Italy is a joint project of a consortium of Italian partners, led and represented by INAF and of the IAU OAE, and is operated by INAF. The Italian partners are INAF, the Italian Astronomical Society (SAIt) and the University of Rome Tor Vergata (ToV)

Exploring manycore architectures for next-generation HPC systems through the MANGO approach

[EN] The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza-Alonso, D.; Brandolese, C.; Cappe, E.; Cilardo, A.... (2018). Exploring manycore architectures for next-generation HPC systems through the MANGO approach. Microprocessors and Microsystems. 61:154-170. https://doi.org/10.1016/j.micpro.2018.05.011S1541706

Trends in invasive bacterial diseases during the first 2 years of the COVID-19 pandemic: analyses of prospective surveillance data from 30 countries and territories in the IRIS Consortium.

BACKGROUND The Invasive Respiratory Infection Surveillance (IRIS) Consortium was established to assess the impact of the COVID-19 pandemic on invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis, and Streptococcus agalactiae. We aimed to analyse the incidence and distribution of these diseases during the first 2 years of the COVID-19 pandemic compared to the 2 years preceding the pandemic. METHODS For this prospective analysis, laboratories in 30 countries and territories representing five continents submitted surveillance data from Jan 1, 2018, to Jan 2, 2022, to private projects within databases in PubMLST. The impact of COVID-19 containment measures on the overall number of cases was analysed, and changes in disease distributions by patient age and serotype or group were examined. Interrupted time-series analyses were done to quantify the impact of pandemic response measures and their relaxation on disease rates, and autoregressive integrated moving average models were used to estimate effect sizes and forecast counterfactual trends by hemisphere. FINDINGS Overall, 116 841 cases were analysed: 76 481 in 2018-19, before the pandemic, and 40 360 in 2020-21, during the pandemic. During the pandemic there was a significant reduction in the risk of disease caused by S pneumoniae (risk ratio 0·47; 95% CI 0·40-0·55), H influenzae (0·51; 0·40-0·66) and N meningitidis (0·26; 0·21-0·31), while no significant changes were observed for S agalactiae (1·02; 0·75-1·40), which is not transmitted via the respiratory route. No major changes in the distribution of cases were observed when stratified by patient age or serotype or group. An estimated 36 289 (95% prediction interval 17 145-55 434) cases of invasive bacterial disease were averted during the first 2 years of the pandemic among IRIS-participating countries and territories. INTERPRETATION COVID-19 containment measures were associated with a sustained decrease in the incidence of invasive disease caused by S pneumoniae, H influenzae, and N meningitidis during the first 2 years of the pandemic, but cases began to increase in some countries towards the end of 2021 as pandemic restrictions were lifted. These IRIS data provide a better understanding of microbial transmission, will inform vaccine development and implementation, and can contribute to health-care service planning and provision of policies. FUNDING Wellcome Trust, NIHR Oxford Biomedical Research Centre, Spanish Ministry of Science and Innovation, Korea Disease Control and Prevention Agency, Torsten Söderberg Foundation, Stockholm County Council, Swedish Research Council, German Federal Ministry of Health, Robert Koch Institute, Pfizer, Merck, and the Greek National Public Health Organization