Single-Phase Motors for Household Applications

Single-phase motors are widely used in household applications. Shaded-pole and split-phase capacitor-start single-phase induction motors are very popular for their ruggedness and their comparatively low cost. Recently, line-start single-phase motors are gaining market shares. However, their superior efficiency and torque density are counterbalanced by the higher cost of the rotor construction due to the magnets. This chapter compares the main structures of single-phase line-start motors, presenting their lumped parameter models and the finite element analysis. The equivalent circuits of the single-phase induction motor and of the line-start permanent magnet are derived. Different rotor structures for single-phase line-start permanent magnet (PM) motors are compared. The finite element method (FEM) is used to compare the characteristics of the motors. Motors with the same stator have been tested. No-load and load tests have been performed and compared to the FEM simulations and to the analytical model. Finally, the performances of line-start PM motors are compared to the shaded-pole induction motors in terms of torque density and efficiency

MAP Inference in Probabilistic Answer Set Programs

Reasoning with uncertain data is a central task in artificial intelligence. In some cases, the goal is to find the most likely assignment to a subset of random variables, named query variables, while some other variables are observed. This task is called Maximum a Posteriori (MAP). When the set of query variables is the complement of the observed variables, the task goes under the name of Most Probable Explanation (MPE). In this paper, we introduce the definitions of cautious and brave MAP and MPE tasks in the context of Probabilistic Answer Set Programming under the credal semantics and provide an algorithm to solve them. Empirical results show that the brave version of both tasks is usually faster to compute. On the brave MPE task, the adoption of a state-of-the-art ASP solver makes the computation much faster than a naive approach based on the enumeration of all the worlds

Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities

Type 1 statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest. These are of the form ''x of the elements share the same property''. The recently proposed language PASTA (Probabilistic Answer set programming for STAtistical probabilities) extends Probabilistic Logic Programs under the Distribution Semantics and allows the definition of this type of statements. To perform exact inference, PASTA programs are converted into probabilistic answer set programs under the Credal Semantics. However, this algorithm is infeasible for scenarios when more than a few random variables are involved. Here, we propose several algorithms to perform both conditional and unconditional approximate inference in PASTA programs and test them on different benchmarks. The results show that approximate algorithms scale to hundreds of variables and thus can manage real world domains

Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments

A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting

Digital Identity into Practice: The Case of UniCam

Identity management is a set of technologies and processes supporting identity information. Its adoption in Public Administration, in particular in the domain of university, maintains organization autonomy giving at the same time students and staff support to access the services that are delivered. In this paper we present a project lead by University of Camerino with the Italian Banking Group UBI and the Namirial Certification Authority. The project consists in the issue of Enjoy my UniCam card allowing users to have, on a single physical card, several functionalities about facilitated banking account, university services and digital signature certificate. First results about the testing phase are presented as well as the next steps of the project

Ontological domain coding for cultural heritage mediation

Abstract. An ontology-based representation of information about a domain is flexible enough to support different strategies in presenting the information. In this paper, we present two applications for creating interactive presentations in a cultural heritage domain, that share the same database of informative units and the same representation of the domain, encoded in a light-weight ontology. An application for drama-based guided tours assembles the informative units in a location-aware fashion, by exploiting the structure of the ontology to enforce the notion of discourse focusing in the generated presentation. A browsing-based application for accessing the informative units supports semantic search, consulting the ontology to suggest modifications of the user's search to circumscribe or enlarge the result sets

Evaluation of quantity and purity of miRNAs extracted from different matrices collected from dogs with Mast Cell Tumours.

MicroRNAs (miRNAs) are a class of short non-coding RNA, which interact with the 3’ UTR region of complementary mRNA to decrease or inhibit the translation of proteins (Lai, 2002). MiRNAs regulate pathways in various pathophysiological status, and are regarded as biomarkers for early diagnosis of several diseases, including cancer (Di Leva et al., 2014).The study aims to evaluate the quality and purity of miRNAs extracted from a) 11 archival Formalin Fixed and Paraffin Embedded (FFPE) samples of Mast Cell Tumour (MCT) at stage I, II, III and IV, and 8 intra-patient healthy controls; b) samples collected during surgery, including 6 samples of saliva, primary tumour biopsy and serum/plasma. The quality of miRNA largely influence the downstream experiments, and must be carefully evaluated before performing for examples, the sequencing reaction. MiRNA extraction was carried out using commercial kits (Qiagen) and quantify using Small RNA Kit (Agilent) on Agilent 2100 Bioanalyzer. The results showed that the concentration of miRNAs from FFPE, saliva,  primary tumor biopsy and serum was acceptable with a Median (Me)= 56,91 ng/ml, Me=10,30 ng/ml, Me=3,44 ng/ml and  Me=0,71 ng/ml, and a miRNA/Small RNA ratio of 48%, 61%, 17% and 76%, respectively. The concentration of miRNAs from plasma was not detectable. Studies reveal that plasma ranks as the first choice source for diagnostic purpose, much more than serum (Aung et al., 2014), but the debate remains open and subsequent analyses are needed.The concentration of miRNAs from FFPE and saliva samples is higher than that from other matrices. Possible explanations include a) different quantity and quality of starting materials; b) nucleic acids fragmentation, due to the formalin fixation and paraffin embedded procedure; c) presence of nucleases in saliva, which produce small fragments recognized as miRNAs or smallRNAs.In conclusion, the quantity and the purity of miRNAs, obtained using Qiagen commercial kits, are reliable for further NGS analysis