Ricerc@Sapienza

The aim of the present study was to identify sub-types of moped riders based on a cluster analysis of specific personality characteristics (i.e., driving anger, anxiety, angry hostility, excitement-seeking, altruism, normlessness, and driving locus of control) within a large sample of Italian adolescents. The study had also the aim to compare the emerged sub-types of moped riders on measures of attitudes toward safe driving, risky driving behaviors (e.g., rule's violations and speeding, not using helmet, drinking and driving, etc.), and self-reported tickets and accident involvement.

The statistical structure of intrinsic parietal and parieto-frontal connectivity in monkeys was studied through hierarchical cluster analysis. Based on their inputs, parietal and frontal areas were grouped into different clusters, including a variable number of areas that in most instances occupied contiguous architectonic fields. Connectivity tended to be stronger locally: that is, within areas of the same cluster. Distant frontal and parietal areas were targeted through connections that in most instances were reciprocal and often of different strength.

Mutations in CDCA7, the SNF2 family protein HELLS (LSH), or the DNA methyltransferase DNMT3b cause immunodeficiency–centro-meric instability–facial anomalies (ICF) syndrome. While it has been speculated that DNA methylation defects cause this disease, little is known about the molecular function of CDCA7 and its functional relationship to HELLS and DNMT3b.

In this paper, we propose a novel implementation for solving the large-scale k-medoids clustering problem. Conversely to the most famous k-means, k-medoids suffers from a computationally intensive phase for medoids evaluation, whose complexity is quadratic in space and time; thus solving this task for large datasets and, speci?cally, for large clusters might be unfeasible.

Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality.

The possibility of clustering objects represented by structured data with possibly non-trivial geometry certainly is an interesting task in pattern recognition. Moreover, in the Big Data era, the possibility of clustering huge amount of (structured) data challenges computer science and pattern recognition researchers alike. The aim of this paper is to bridge the gap on large-scale structured data clustering.

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation.

Workers healthcare gained a lot of attention recently as many countries are increasingly concerning about welfare. This paper faces the problem of predicting occupational disease risks by means of computational intelligence and pattern recognition techniques. Specifically, three different machine learning approaches are compared: the first one is based on the k-means algorithm, in charge to determine a set of meaningful labelled clusters as the final model. The latter two are based on fully supervised techniques, namely Support Vector Machines and K-Nearest Neighbours.

The number of accidents and victims in the construction sector has not decreased significantly despite the increasingly stricter laws and regulations. The analysis of accidents, as well as their root causes and determinants can certainly contribute to the development of more effective preventive interventions.