Quantitative analysis of the effects of incorporating laser powder bed fusion manufactured conformal cooling inserts in steel moulds over four types of defects of a commercially produced injected part

The introduction of additively manufactured conformal cooling inserts in steel moulds for plastic injection is becoming a recommended standard. Fine adjustment of the temperatures in the mould has demonstrated potential to reduce cycle times and to increase production volumes. Within this context, the present article explores the historical production data of a commercially produced part, before and after the incorporation of an LPBF conformal cooling insert, to analyse what is the quantitative real effect on the efficiency of the production runs. The article analyses the change in the global rejection rates, and its effect over four different product defect types, i.e.: optical (surface), part integrity (bubbles, transparency, geometry), incomplete fill-in (interior), and breakages during extraction. The results demonstrate a specific decrease on the average appearance (from 20.53% to 13.48%; reduction of 7.05%) and variability (standard deviation from 14.16% to 6.81%; reduction of a 7.35%), of the global scrap rates, and a significant decrease in the scrap rates generated by optical defects and extraction part breakages. The article also characterises the former and the new processes by adjusting two distribution functions (Pareto Type-I and Weibull) and compares different estimates for the global expected scrap rates in past and future production runs.Peer ReviewedPostprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of tendencies. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 5-7 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. Next, we show a report with 8 graphs and a table with the short-term predictions for (1) European Union and its countries, (2) other countries, (3) Spain and its autonomous communities. We are currently adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold: Group A: countries that have reported more than 100 cumulated cases for 10 consecutive days or more ¿ 3-5 days prediction; Group B: countries that have reported more than 100 cumulated cases for 7 to 9 consecutive days ¿ 2 days prediction; Group C: countries that have reported more than 100 cumulated cases for 4 to 6 days ¿ 1 d ay prediction. We have introduced a change in fittings, that are now weighted at some points. The whole methodology employed in the inform is explained in the last pages of this document.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Empiric model for short-time prediction of COVID-19 spreading

Covid-19 appearance and fast spreading took by surprise the international community. Collaboration between researchers, public health workers and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both current state and short-term future trends can be carefully evaluated. Gompertz model has shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate that is able to show the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity. Moreover, it allows to carry out short-term predictions and long-term estimations that may facilitate policy decisions and the revision of in-place confinement measures and the development of new protocols. This model has been employed to fit the cumulative cases of Covid-19 from several Chinese provinces and from other countries with a successful containment of the disease. Results show that there are systematic differences in spreading velocity between countries. In countries that are in the initial stages of the Covid-19 outbreak, model predictions provide a reliable picture of its short-term evolution and may permit to unveil some characteristics of the long-term evolution. These predictions can also be generalized to short-term hospital and Intensive Care Units (ICU) requirements, which together with the equivalent predictions on mortality provide key information for health officials.CP, PJC and MC received funding from La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; PJC received funding from Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Grup Unitat de Tuberculosi Experimental, 2017-SGR-500; CP, DL, SA, MC received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00. This work has been also partially funded by the European Comission - DG Communications Networks, Content and Technology through the contract LC-01485746.Preprin

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of tendencies. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 5-7 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. Next, we show a report with 8 graphs and a table with the short-term predictions for (1) European Union and its countries, (2) other countries, (3) Spain and its autonomous communities. We are currently adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold: Group A: countries that have reported more than 100 cumulated cases for 10 consecutive days or more ¿ 3-5 days prediction; Group B: countries that have reported more than 100 cumulated cases for 7 to 9 consecutive days ¿ 2 days prediction; Group C: countries that have reported more than 100 cumulated cases for 4 to 6 days ¿ 1 d ay prediction. We have introduced a change in fittings, that are now weighted at some points. The whole methodology employed in the inform is explained in the last pages of this document.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

An efficient location-based forwarding strategy for named data networking and LEO satellite communications

Low Earth orbit (LEO) satellite constellations are increasingly gaining attention as future global Internet providers. At the same time, named data networking (NDN) is a new data-centric architecture that has been recently proposed to replace the classic TCP/IP architecture since it is particularly well suited to the most common usage of the Internet nowadays as a content delivery network. Certainly, the use of NDN is especially convenient in highly dynamic network environments, such as those of next LEO constellations incorporating inter-satellite links (ISL). Among other native facilities, such as inbuilt security, NDN readily supports the mobility of clients, thus helping to overcome one of the main problems raised in LEO satellite networks. Moreover, thanks to a stateful forwarding plane with support for multicast transmission and inbuilt data caches, NDN is also able to provide a more efficient usage of the installed transmission capacity. In this paper, we propose a new location-based forwarding strategy for LEO satellite networks that takes advantage of the knowledge of the relative position of the satellites and the grid structure formed by the ISLs to perform the forwarding of NDN packets. So, forwarding at each node is done using only local information (node and destination locations), without the need of interchanging information between nodes, as is the case with conventional routing protocols. Using simulation, we show that the proposed forwarding strategy is a good candidate to promote the efficient and effective future use of the NDN architecture in LEO satellite networks.Ministerio de Ciencia e Innovación | Ref. PID2020-113240RB-I0

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: ¿ a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. ¿ K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Peer ReviewedPostprin

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID‐19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (author's final draft