Growth divergence and income inequality in OECD countries: the role of trade and financial openness. LEQS Paper No. 148/2018 October 2019

This paper analyses trade and financial openness effects on growth and income inequality in 35 OECD countries. Our model takes into account both short run and long run effects of factors explaining income divergence between and within the countries. We estimate, for the period 1995-2016, an error correction model in which per capita GDP and inequality are driven by changes over time of selected factors and by the deviation from a long run relationship. Stylised facts suggest that trade and financial openness reduce the growth gaps across the countries but not income inequality, and the effects of finance are stronger in high income countries. Nevertheless, low and middle income countries benefit more from international trade. Our contribution to the existing literature is threefold: i) we study the short and long run effects of trade and financial openness on income level and distribution, ii) we focus on developed countries (OECD) rather than on developing and iii) we provide a sensitivity analysis including in our baseline equation an institutional indicator, a trade agreement proxy and a dummy of global financial crisis. Estimates results indicate that trade openness significantly improved the conditions of OECD low income countries both in short and long run mostly, consistently with the catching up theory. It also decreased inequality, but only in low and middle income countries. Differently financial openness had a positive and significant impact only in the short run on middle income countries and increased income disparities within countries in the short term in low income countries and in the long term in high income countries

Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

On Information Granulation via Data Filtering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this companion paper, we follow our previous paper (Martino et al. in Algorithms 15(5):148, 2022) in the context of comparing different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process and, conversely, to our previous work, we employ a filtering-based approach for the synthesis of information granules instead of a clustering-based one. Computational results on 6 open-access data sets corroborate the robustness of our filtering-based approach with respect to data stratification, if compared to a clustering-based granulation stage

Modelling and recognition of protein contact networks by multiple kernel learning and dissimilarity representations

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system

Calibration techniques for binary classification problems: A comparative analysis

Calibrating a classification system consists in transforming the output scores, which somehow state the confidence of the classifier regarding the predicted output, into proper probability estimates. Having a well-calibrated classifier has a non-negligible impact on many real-world applications, for example decision making systems synthesis for anomaly detection/fault prediction. In such industrial scenarios, risk assessment is certainly related to costs which must be covered. In this paper we review three state-of-the-art calibration techniques (Platt’s Scaling, Isotonic Regression and SplineCalib) and we propose three lightweight procedures based on a plain fitting of the reliability diagram. Computational results show that the three proposed techniques have comparable performances with respect to the three state-of-the-art approaches

Generational Relations, Technology and Digital Communication: a Comparison between Multicultural and Native Families

The paper presents some first results of an ongoing research on the transformational decision-making processes within the family dialogue in relation to the use of digital technologies by adults and adolescents for study and work. Are these processes perceived differently within multicultural families than within native families? Besides analysing these issues, the paper presents an interesting insight into the so-called “media diet” of adolescents, with a focus on the pandemic period. The research was carried out by administering a questionnaire to almost four hundred students at a high school in Reggio Emilia (Italy)

Microbiological challenge testing for Listeria monocytogenes in ready-to-eat food: a practical approach

Food business operators (FBOs) are the primary responsible for the safety of food they place on the market. The definition and validation of the product’s shelf-life is an essential part for ensuring microbiological safety of food and health of consumers. In the frame of the Regulation (EC) No 2073/2005 on microbiological criteria for foodstuffs, FBOs shall conduct shelf-life studies in order to assure that their food does not exceed the food safety criteria throughout the defined shelf-life. In particular this is required for ready-to-eat (RTE) food that supports the growth of Listeria monocytogenes. Among other studies, FBOs can rely on the conclusion drawn by microbiological challenge tests. A microbiological challenge test consists in the artificial contamination of a food with a pathogen microorganism and aims at simulating its behaviour during processing and distribution under the foreseen storage and handling conditions. A number of documents published by international health authorities and research institutions describes how to conduct challenge studies. The authors reviewed the existing literature and described the methodology for implementing such laboratory studies. All the main aspects for the conduction of L. monocytogenes microbiological challenge tests were considered, from the selection of the strains, preparation and choice of the inoculum level and method of contamination, to the experimental design and data interpretation. The objective of the present document is to provide an exhaustive and practical guideline for laboratories that want to implement L. monocytogenes challenge testing on RTE food

Antibiotic Resistance in Staphylococcus aureus and Coagulase Negative Staphylococci Isolated from Goats with Subclinical Mastitis

Antimicrobial resistance patterns and gene coding for methicillin resistance (mecA) were determined in 25 S. aureus and 75 Coagulase Negative Staphylococci (CNS) strains isolates from half-udder milk samples collected from goats with subclinical mastitis. Fourteen (56.0%) S. aureus and thirty-one (41.3%) CNS isolates were resistant to one or more antimicrobial agents. S. aureus showed the highest resistance rate against kanamycin (28.0%), oxytetracycline (16.0%), and ampicillin (12.0%). The CNS tested were more frequently resistant to ampicillin (36.0%) and kanamycin (6.7%). Multiple antimicrobial resistance was observed in eight isolates, and one Staphylococcus epidermidis was found to be resistant to six antibiotics. The mecA gene was not found in any of the tested isolates. Single resistance against β-lactamics or aminoglicosides is the most common trait observed while multiresistance is less frequent

A Survey on aflatoxin M₁ content in sheep and goat milk produced in Sardinia region, Italy (2005-2013)

In the present work the results of a survey conducted in Sardinia Region on Aflatoxin M1 (AFM1) contamination in milk of small ruminants from 2005 to 2013 are reported. A total of 517 sheep and 88 goat milk samples from bulk tank, tank trucks and silo tank milk were collected. Analyses were performed by the Regional Farmers Association laboratory using high-performance liquid chromatography following the ISO 14501:1998 standard. None of the sheep milk samples analysed during 2005- 2012 showed AFM1 contamination. In sheep milk samples collected in 2013, 8 out of 172 (4.6%) were contaminated by AFM1 with a concentration (mean±SD) of 12.59±14.05 ng/L. In one bulk tank milk sample 58.82 ng/L AFM1 was detected, exceeding the EU limit. In none of goat milk samples analysed from 2010 to 2012 AFM1 was detected. In 2013, 9 out of 66 goat milk samples (13.6%) showed an AFM1 concentration of 47.21±19.58 ng/L. Two of these samples exceeded the EU limit, with concentrations of 62.09 and 138.6 ng/L. Higher contamination frequency and concentration rates were detected in bulk tank milk samples collected at farm than in bulk milk truck or silo samples, showing a dilution effect on AFM1 milk content along small ruminants supply chain. The rate and levels of AFM1 contamination in sheep and goat milk samples were lower than other countries. However, the small number of milk samples analysed for AFM1 in Sardinia Region in 2005-2013 give evidence that food business operators check programmes should be improved to ensure an adequate monitoring of AFM1 contamination in small ruminant dairy chain