Coronavirus pandemic, remote learning and emerging education inequalities

Recent studies predict that the school closures and distance learning of the 2020 pandemic will lead to lower average education levels, but they may also result into greater and new education inequalities. Using PISA 2018 data from France, Germany, Italy, Spain and the United Kingdom, we find that, even before the pandemic, students lacking the resources needed to learn remotely – ICT resources at home, at school or a quiet place to study – experience strong and significant cognitive gaps with respect to their peers that, in mathematics, range from 70 percent of a school year in the United Kingdom, Germany and France to 25 percent in Spain. Gaps in reading are similar. With school closures and remote learning, these cognitive losses are predicted to increase. We find similar results by considering days of absence from school. In the longer run, students in Spain, Germany and Italy who cannot learn remotely are more likely to repeat grades and end their education early. Overall, cognitive gaps and school dropouts driven by a lack of ICT resources vary with countries’ educational systems and digital divides. Policies should aim to enhance the use of digital resources in education, and must be designed according to countries’ characteristics

Adams and Eves: The Gender Gap in Economics Majors

We investigate the gender gap in Economics among bachelor's and master's graduates in Italy between 2010 and 2019. First we establish that being female exerts a negative impact on the choice to major in Economics: at the bachelor level, only 73 women graduate in Economics for every 100 men, with the mathematical content of high school curricula as the key driver of the eect and a persistence of the gap at the master level. Second, within a full menu of major choices, Economics displays the largest gap, followed by STEM and then Business Economics. Third, decomposition analyses expose a unique role for the math background in driving the Economics gender gap relative to other elds. Fourth, a triple difference analysis of a high school reform shows that an increase in the math content of traditionally low math curricula caused an increase in the Economics gender gap among treated students

Adams and Eves: The Gender Gap in Economics Majors

We investigate the gender gap in Economics among bachelor’s and master’s graduates in Italy between 2010 and 2019. First we establish that being female exerts a negative impact on the choice to major in Economics: at the bachelor level, only 73 women graduate in Economics for every 100 men, with the mathematical content of high school curricula as the key driver of the effect and a persistence of the gap at the master level. Second, within a full menu of major choices, Economics displays the largest gap, followed by STEM and then Business Economics. Third, decomposition analyses expose a unique role for the math background in driving the Economics gender gap relative to other fields. Fourth, a triple difference analysis of a high school reform shows that an increase in the math content of traditionally low math curricula caused an increase in the Economics gender gap among treated students

Inter-municipal cooperation as a solution for public services delivery? The case of Unioni di Comuni in Emilia-Romagna Region

Inter municipal cooperation (IMC) represents a solution adopted all around the world in order to jointly provide services considering the complexity of contemporary socio-economic contexts. However, empirical evidence on IMC solutions is still week. The purpose of this paper is to analyse associations of municipalities (Unioni di Comuni, UC), the prevalent kind of IMC established in Italy, as a possible solution for sustainable public services delivery. Our research questions refer to the main features of Unioni di Comuni as an IMC for public services delivery in EmiliaRomagna Region (Italy), to the explanation of those characteristics, and to the evaluation of UCs and their features in terms of autonomy, resilience and sustainability. In order to meet our objectives, we accomplished a cluster analysis, considering administrative and socio-economic data; in addition, we examined specific characteristics within each cluster to proceed with a comparison between clusters in terms of revenues from transfers from other governments layers, own revenues, current expenses and financial autonomy index in the last years. Our results suggest a general tendency: to provide services through UC in E-R; and to enhance their development in terms of public service specialization. But at the same time, UCs generally decreased their own financial autonomy, relying on transfers from other public institutions. In line with Resource-Dependence Theory (RDT), our empirical analysis finds different clusters of UCs which act as new centres for public service delivery in Emilia-Romagna Region in order to reduce uncertainty over resources through the creation of new inter-institutional balances. However, the statement that strong UCs compensate weak starting territorial features of municipalities is not self-evident

3D #DigitalInvasions: a crowdsourcing project for mobile user generated content

This paper introduces the #InvasioniDigitali project which is an online crowdsourcing initiative started in Italy in 2013 with the aim to promote the value of and engagement with local heritage. The paper focuses on two case studies of pilot ‘invasions’ using 3D data capture by students at museums and heritage sites in Sicily

A network landscape model: stability analysis and numerical tests

Versão dos autores para este artigo.A Network Landscape Model (NLM) for the evaluation of the ecological trend of an environmental system is here presented and investigated. The model consists in a network of dynamical systems, here each node represents a single Landscape Unit (LU), endowed by a system of ODEs for two variables relevant to the production of bio-energy and to the percentage of green areas, respectively. The main goal of the paper consists in testing the relevance of connectivity between the LUs. For this purpose we consider rst the Single LU Model (SLM) and investigate its equilibria and their stability, in terms of two bifurcation parameters. Then the network dynamics is theoretically investigated by means of a bifurcation analysis of a proper simpli ed di erential system, that allows to understand how the coupling between di erent LUs modi es the asymptotic scenarios for the single LU model. Numerical simulations of NLM are performed, with reference to an environmental system in Northern Italy, and results are discussed in connection with SLM.GNFM - INdAM; FC

Fusing NIR and Process Sensors Data for Polymer Production Monitoring

Process analytical technology and multivariate process monitoring are nowadays the most effective approaches to achieve real-time quality monitoring/control in production. However, their use is not yet a common practice, and industries benefit much less than they could from the outcome of the hundreds of sensors that constantly monitor production in industrial plants. The huge amount of sensor data collected are still mostly used to produce univariate control charts, monitoring one compartment at a time, and the product quality variables are generally used to monitor production, despite their low frequency (offline measurements at analytical laboratory), which is not suitable for real-time monitoring. On the contrary, it would be extremely advantageous to benefit from predictive models that, based on online sensors, will be able to return quality parameters in real time. As a matter of fact, the plant setup influences the product quality, and process sensors (flow meters, thermocouples, etc.) implicitly register process variability, correlation trends, drift, etc. When the available spectroscopic sensors, reflecting chemical composition and structure, consent to monitor the intermediate products, coupling process, and spectroscopic sensor and extracting/fusing information by multivariate analysis from this data would enhance the evaluation of the produced material features allowing production quality to be estimated at a very early stage. The present work, at a pilot plant scale, applied multivariate statistical process control (MSPC) charts, obtained by data fusion of process sensor data and near-infrared (NIR) probes, on a continuous styrene-acrylonitrile (SAN) production process. Furthermore, PLS regression was used for real-time prediction of the Melt Flow Index and percentage of bounded acrylonitrile (%AN). The results show that the MSPC model was able to detect deviations from normal operative conditions, indicating the variables responsible for the deviation, be they spectral or process. Moreover, predictive regression models obtained using the fused data showed better results than models computed using single datasets in terms of both errors of prediction and R2. Thus, the fusion of spectra and process data improved the real-time monitoring, allowing an easier visualization of the process ongoing, a faster understanding of possible faults, and real-time assessment of the final product quality

Use of nano gold obtained by laser ablation for SEIRA analyses of colorants

The analysis of dyes in cultural heritage samples is a well-known challenging task, due to their inherent high tinting strength and consequent low concentration in the carrying matrix a fact that severely limits the number of analytical techniques that can be efficiently and micro-destructively employed for their detection and unambiguous identification. In the present study, an advanced and alternative SEIRA based analytical protocol for the analysis of small quantities of synthetic colorants has been proposed. The method has been set up for the identification of Acid Orange 7 (AO7) using Au nanoparticles obtained by laser ablation in solution (LASiS). Analyses have been performed applying a drop containing a mixture between the colorant and the Au colloidal solution in its unaggregated state on a gold coated glass slide for RAS (Reflection Absorption Spectroscopy) analysis. The first results showed that, thanks to the enhancement produced by the nanoparticles, it is possible to analyze small amount of diluted solutions containing the colorant. Thus, the method has been successfully applied for the analysis of few pieces of dyed wool, after the development of a suitable micro extraction procedure

Exploiting within-breed variability in the autochthonous Reggiana breed identified several candidate genes affecting pigmentation-related traits, stature and udder defects in cattle

Autochthonous cattle breeds constitute important reservoirs of genetic diversity. Reggiana is an Italian local cattle breed reared in the north of Italy for the production of a mono-breed Parmigiano–Reggiano cheese. Reggiana cattle usually have a classical solid red coat colour and pale muzzle. As part of the strategies designed for the sustainable conservation of this genetic resource, we investigated at the genome-wise level the within-breed detected variability of three pigmentation-related traits (intensity of red coat colour, based on three classes – light/diluted, normal and dark; spotted patterns/piebaldism that sometime emerge in the breed; muzzle colour – pink/pale, grey and black), stature, presence/absence and number of supernumerary teats and teat length. A total of 1776 Reggiana cattle (about two-thirds of the extant breed population) were genotyped with the GeneSeek GGP Bovine 150k SNP array and single-marker and haplotype-based GWASs were carried out. The results indicated that two main groups of genetic factors affect the intensity of red coat colour: darkening genes (including EDN3 and a few other genes) and diluting genes (including PMEL and a few other genes). Muzzle colour was mainly determined by MC1R gene markers. Piebaldism was mainly associated with KIT gene markers. Stature was associated with BTA6 markers upstream of the NCAPG–LCORL genes. Teat defects were associated with TBX3/TBX5, MCC and LGR5 genes. Overall, the identified genomic regions not only can be directly used in selection plans in the Reggiana breed, but also contribute to clarifying the genetic mechanisms involved in determining exterior traits in cattle

Comparative analysis of inbreeding parameters and runs of homozygosity islands in 2 Italian autochthonous cattle breeds mainly raised in the Parmigiano-Reggiano cheese production region

Reggiana and Modenese are autochthonous cattle breeds, reared in the North of Italy, that can be mainly distinguished for their standard coat color (Reggiana is red, whereas Modenese is white with some pale gray shades). Almost all milk produced by these breeds is transformed into 2 mono-breed branded Parmigiano-Reggiano cheeses, from which farmers receive the economic incomes needed for the sustainable conservation of these animal genetic resources. After the setting up of their herd books in 1960s, these breeds experienced a strong reduction in the population size that was subsequently reverted starting in the 1990s (Reggiana) or more recently (Modenese) reaching at present a total of about 2,800 and 500 registered cows, respectively. Due to the small population size of these breeds, inbreeding is a very important cause of concern for their conservation programs. Inbreeding is traditionally estimated using pedigree data, which are summarized in an inbreeding coefficient calculated at the individual level (FPED). However, incompleteness of pedigree information and registration errors can affect the effectiveness of conservation strategies. High-throughput SNP genotyping platforms allow investigation of inbreeding using genome information that can overcome the limits of pedigree data. Several approaches have been proposed to estimate genomic inbreeding, with the use of runs of homozygosity (ROH) considered to be the more appropriate. In this study, several pedigree and genomic inbreeding parameters, calculated using the whole herd book populations or considering genotyping information (GeneSeek GGP Bovine 150K) from 1,684 Reggiana cattle and 323 Modenese cattle, were compared. Average inbreeding values per year were used to calculate effective population size. Reggiana breed had generally lower genomic inbreeding values than Modenese breed. The low correlation between pedigree-based and genomic-based parameters (ranging from 0.187 to 0.195 and 0.319 to 0.323 in the Reggiana and Modenese breeds, respectively) reflected the common problems of local populations in which pedigree records are not complete. The high proportion of short ROH over the total number of ROH indicates no major recent inbreeding events in both breeds. ROH islands spread over the genome of the 2 breeds (15 in Reggiana and 14 in Modenese) identified several signatures of selection. Some of these included genes affecting milk production traits, stature, body conformation traits (with a main ROH island in both breeds on BTA6 containing the ABCG2, NCAPG, and LCORL genes) and coat color (on BTA13 in Modenese containing the ASIP gene). In conclusion, this work provides an extensive comparative analysis of pedigree and genomic inbreeding parameters and relevant genomic information that will be useful in the conservation strategies of these 2 iconic local cattle breeds