In patients with HF and LV systolic dysfunction without clinical suspicion of CAD, LGE-CMR is an excellent tool for classifying patients in relation to the presence or absence of underlying CAD. Thus, CMR might offer a valid alternative to coronary angiography for the detection of CAD in these patients.

Today's society, which is strongly based on knowledge and interaction with information, has a key component in technological innovation, a fundamental tool for the development of the current teaching methodologies. Nowadays, there are a lot of online resources, such as MOOCs (Massive Open Online Courses) and distance learning courses. One aspect that is common to all of these is a high dropout rate: about 90% in MOOCs and 50% in the courses of the Spanish National Distance Education University, among other examples. In this paper, we analyze a number of actions undertaken in the Master's Degree in Computational Mathematics at Universitat Jaume I in Castellón, Spain. These actions seem to help decrease the dropout rate in distance learning; the available data confirm their effectiveness.

We present a patient with nodular regenerative hyperplasia of the liver (NRH) and portal vein absence studied with CT, MR imaging, and MR angiography. The most striking feature was exuberant hemorrhoids due to a giant hepatofugal inferior mesenteric vein. A relationship between unbalanced portal blood flow and nodular regenerative transformation of the liver is suggested in this patient.

No abstract

A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional data (FD). In the first stage, the data are validated (false data are detected) and reconstructed, since there could be not only false data, but also missing and noisy data. FDA tools are used such as tolerance bands for FD and smoothing for dense and sparse FD. In the second stage, functional outlier detection tools are used in two phases. In Phase I, the data are cleared of anomalies to ensure that data are representative of the in-control system. The objective of Phase II is system monitoring. A new functional outlier detection method is also proposed based on archetypal analysis. The methodology is applied and illustrated with real data. A simulated study is also carried out to assess the performance of the outlier detection techniques, including our proposal. The results are very promising.