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Background Implementation of high-quality national audits for perinatal mortality are needed to improve the registration of all perinatal deaths and the identification of the causes of death. This study aims to evaluate the implementation of a Regional Audit System for Stillbirth in Emilia-Romagna Region, Italy. Methods For each stillbirth (≥ 22 weeks of gestation, ≥ 500 g) occurred between January 1, 2014 to December 1, 2016 (n = 332), the same diagnostic workup was performed and a clinical record with data about mother and stillborn was completed. Every case was discussed in a multidisciplinary local audit to assess both the cause of death (ReCoDe classification) and the quality of care. Data were reviewed by the Regional Audit Group. Stillbirth rates, causes of death and the quality of care were established for each case. Results Total stillbirth rate was 3.09 per 1000 births (332/107,528). Late stillbirth rate was 2.3 per 1000 (251/107,087). Sixteen stillbirths were not registered by the Regional Birth Register. The most prevalent cause of death was placental disorder (33.3%), followed by fetal (17.6%), cord (14.2%) and maternal disorders (7.6%). Unexplained cases were 14%. Compared to local audits, the regional group attributed different causes of death in 17% of cases. At multivariate analysis, infections were associated with early stillbirths (OR 3.38, CI95% 1.62–7.03) and intrapartum cases (OR 6.64, CI95% 2.61–17.02). Placental disorders were related to growth restriction (OR 1.89, CI95% 1.06–3.36) and were more frequent before term (OR 1.86, CI95% 1.11–3.15). Stillbirths judged possibly/probably preventable with a different management (10.9%) occurred more frequently in non-Italian women and were mainly related to maternal disorders (OR 6.64, CI95% 2.61–17.02). Conclusions Regional Audit System for Stillbirth improves the registration of stillbirth and allows to define the causes of death. Moreover, sub-optimal care was recognized, allowing to identify populations which could benefit from preventive measures.

The permeability of nation-state borders determines the flow of people and commodities between countries and therefore greatly influences many aspects of human development from trade and economic inequality to migration and the ethnic composition of societies worldwide. While past research on the topic has focused on border fortification (walls, fences, etc.) or the legal dimension of border controls, we take a different approach by arguing that transport infrastructure (paths, roads, railroads, ferries) together with political checkpoints can be used as valuable indicators for the permeability of borders worldwide. More and better transport infrastructure increases permeability, whereas checkpoints create the political capacity for reducing entries. Using automatized computational methods combined with extensive manual checks, we parse data from OpenStreetMap and the World Food Programme to detect cross-border transport infrastructure and checkpoints. Based on this information, we define an index of border permeability for 312 land borders globally. Subsequent analyses show that regardless of the degree of closure enforcement at checkpoints, Europe and Africa have the most, and the Americas the least, permeable borders worldwide. Regression models reveal that border permeability is higher in densely populated areas and that economic development, by far the most relevant explanatory factor, has a curvilinear relationship with border permeability: Borders of very rich and very poor countries are highly permeable, whereas those of moderately prosperous nation-states are significantly harder to cross. Implications of this remarkably clear pattern are discussed.

The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.

Introduction: Few data on the diagnostic performance of serological tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are currently available. We evaluated sensitivity and specificity of five different widely used commercial serological assays for the detection of SARS-CoV-2–specific IgG, IgM, and IgA antibodies using reverse transcriptase-PCR assay in nasopharyngeal swab as reference standard test.Methods: A total of 337 plasma samples collected in the period April–June 2020 from SARS-CoV-2 RT-PCR positive (n = 207) and negative (n = 130) subjects were investigated by one point-of-care lateral flow immunochromatographic assay (LFIA IgG and IgM, Technogenetics) and four fully automated assays: two chemiluminescence immunoassays (CLIA-iFlash IgG and IgM, Shenzhen YHLO Biotech and CLIA-LIAISON® XL IgG, DiaSorin), one electrochemiluminescence immunoassay (ECLIA-Elecsys® total predominant IgG, Roche), and one enzyme-linked immunosorbent assay (ELISA IgA, Euroimmune).Results: The overall sensitivity of all IgG serological assays was >80% and the specificity was >97%. The sensitivity of IgG assays was lower within 2 weeks from the onset of symptoms ranging from 70.8 to 80%. The LFIA and CLIA-iFlash IgM showed an overall low sensitivity of 47.6 and 54.6%, while the specificity was 98.5 and 96.2%, respectively. The ELISA IgA yielded a sensitivity of 84.3% and specificity of 81.7%. However, the ELISA IgA result was indeterminate in 11.7% of cases.Conclusions: IgG serological assays seem to be a reliable tool for the retrospective diagnosis of SARS-CoV-2 infection. IgM assays seem to have a low sensitivity and IgA assay is limited by a substantial rate of indeterminate results.

Currently, no consensus has been reached on the optimal blood compartment to be used for surveillance of cytomegalovirus (CMV) and Epstein-Barr virus (EBV) DNAemia. Although several comparative studies have been performed correlating CMV and EBV DNA loads in whole blood (WB) versus plasma, to our knowledge, no studies to date have analyzed the kinetics of both viruses in the 2 blood compartments. In this retrospective noninterventional multicenter cohort study, the kinetics of CMV and EBV DNA in 121 hematopoietic stem cell transplantation (HSCT) recipients were investigated by analyzing in parallel 569 and 351 paired samples from 80 and 58 sequential episodes of CMV and EBV DNAemia, respectively. Unlike previous studies, this study used a single automated molecular method that was CE-marked and Food and Drug Administration-approved for use in quantifying CMV and EBV DNA in both plasma and WB. Furthermore, the complete viral replication kinetics of all episodes (including both the ascending and the descending phases of the active infection) was examined in each patient. The previously observed overall correlation between CMV DNA levels in WB and plasma was confirmed (Spearman's ρ =.85; P

The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems

Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits--at a higher level of granularity than previous sources--to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed.

Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.

Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.