Abstract

Abstract
Diabetes mellitus (DM) is one of the world's most prevalent medical conditions. Contemporary management focuses on lowering mean blood glucose values toward a normal range, but largely ignores the dynamics of glucose fluctuations. We probed analyte time series obtained from continuous glucose monitor (CGM) sensors. We show that the fluctuations in CGM values sampled every 5 min are not uncorrelated noise. Next, using multiscale entropy analysis, we quantified the complexity of the temporal structure of the CGM time series from a group of elderly subjects with type 2 DM and age-matched controls. We further probed the structure of these CGM time series using detrended fluctuation analysis. Our findings indicate that the dynamics of glucose fluctuations from control subjects are more complex than those of subjects with type 2 DM over time scales ranging from about 5 min to 5 h. These findings support consideration of a new framework, dynamical glucometry, to guide mechanistic research and to help assess and compare therapeutic interventions, which should enhance complexity of glucose fluctuations and not just lower mean and variance of blood glucose levels. © 2014 AIP Publishing LLC.

Abstract

Abstract
In the last decade, SAR interferometry techniques, especially those that use time series analysis experienced a strong development in both, methodologies and applications, becoming an operational tool for deformation monitoring. The emergence of a growing number of SAR dedicated missions combined with the increasing interest from academics, but also private research groups, reflected in the number of available software packages developed with interferometric analysis purposes, were the major responsible for the InSAR/MTI achievements occurred over the past few years. Many free-of-charge (freeware or open-source) and commercial software packages exist. Due to its proven reliability and freeware distribution among the scientific community, Stanford Method for Persistent Scatterers/Multi-Temporal Interferometry (StaMPS/MTI) implementation, is widely used for ground deformation monitoring. This paper presents viStaMPS v1.2, a collaborative scientific project that appeared with three major purposes: (1) facilitate the usage by users nonfamiliar with the specificities of the programming language that supports StaMPS; (2) implement several visualization tasks not available in the StaMPS standard approach requiring that each user to develop its own code for visualization and interpretation purposes and (3) create a collaborative research project, continually under development counting on the dynamism of its users to improve and/or add new features. (C) 2014 Published by Elsevier Ltd.

Abstract
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.

Abstract