Geodetic model of the 2016 Central Italy earthquake sequence inferred from InSAR and GPS data

We investigate a large geodetic data set of interferometric synthetic aperture radar (InSAR)and GPS measurements to determine the source parameters for the three main shocks of the 2016Central Italy earthquake sequence on 24 August and 26 and 30 October (Mw6.1, 5.9, and 6.5,respectively). Our preferred model is consistent with the activation of four main coseismic asperitiesbelonging to the SW dipping normal fault system associated with the Mount Gorzano-Mount Vettore-Mount Bove alignment. Additional slip, equivalent to aMw~ 6.1–6.2 earthquake, on a secondary (1) NEdipping antithetic fault and/or (2) on a WNW dipping low-angle fault in the hanging wall of the mainsystem is required to better reproduce the complex deformation pattern associated with the greatestseismic event (theMw6.5 earthquake). The recognition of ancillary faults involved in the sequencesuggests a complex interaction in the activated crustal volume between the main normal faults and thesecondary structures and a partitioning of strain releas

RESPIRATORY RESPONSES TO ACUTE INTERMITTENT HYPOXIA AND HYPERCAPNIA IN AWAKE RATS

This article deals with the recognition of early changes in the breathing pattern, in response to acute intermittent stimuli in awake rats. Two different types of stimuli were given: 9% hypoxia in N 2 and 10% hypercapnia in O 2 . Animals were exposed to 3 consecutive cycles consisting of 3-min stimulus period separated by 8-min normoxic recovery intervals. Features of the breathing pattern, such as respiratory frequency, tidal volume, minute ventilation, inspiration and expiration times, peak inspiratory and expiratory flows, were measured by whole body plethysmography. The data were analyzed with the use of pattern recognition methods. We conclude that the overall respiratory changes were rather slight. However, computerized analysis using a k-nearest neighbor decision rule (k-NN) allowed for a good recognition of the respiratory responses to the stimuli. The misclassification rate (E r ) varied from 5 to 10%. After feature selection, E r decreased below 1%. The k-NN classifier differentiated correctly also the type of intermittent stimulus. Our experimental results demonstrate usefulness of pattern recognition algorithms in studying respiratory effects in biological models

Hippocampal Subfield Volumes: Age, Vascular Risk, and Correlation with Associative Memory

Aging and age-related diseases have negative impact on the hippocampus (HC), which is crucial for such age-sensitive functions as memory formation, maintenance, and retrieval. We examined age differences in hippocampal subfield volumes in 10 younger and 19 older adults, and association of those volumes with memory performance in the older participants. We manually measured volumes of HC regions CA1 and CA2 (CA1–2), sectors CA3 and CA4 plus dentate gyrus (CA3–4/DG), subiculum, and the entorhinal cortex using a contrast-optimized high-resolution PD-weighted MRI sequence. Although, as in previous reports, the volume of one region (CA1–2) was larger in the young, the difference was due to the presence of hypertensive subjects among the older adults. Among older participants, increased false alarm rate in an associative recognition memory task was linked to reduced CA3–4/DG volume. We discuss the role of the DG in pattern separation and the formation of discrete memory representations

Fuzzy inference networks for pattern recognition.

This dissertation presents a study of fuzzy inference networks for pattern recognition problems. In this research, fuzzy neurons are defined and five types of fuzzy neurons are introduced. Three fuzzy inference models for pattern recognition systems, min-max inference model, min-sum inference model, and min-competitive inference model, are developed. Fuzzy inference networks based on the inference models and their learning algorithms are presented. The proposed fuzzy inference networks can learn fuzzy inference rules directly from training data. Two of the proposed fuzzy inference networks, Min-Max Fuzzy Inference Network and Min-Sum Fuzzy Inference Network, are applied to pattern classification problems. These two networks can learn the membership functions of all the classes and find out the soft and hard partitions according to the membership values. Another two fuzzy inference networks based on a min-competitive inference method are developed for invariant pattern recognition systems. These two Min-Competitive Fuzzy Inference Networks have been constructed for 2-D visual pattern recognition problems and have been tested with letter patterns with black and white pixel values. The learning speed of the proposed fuzzy inference networks is very fast. The structures of the proposed fuzzy inference networks are simple and they perform well when used in pattern classification and pattern recognition problems.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1995 .C34. Source: Dissertation Abstracts International, Volume: 56-11, Section: B, page: 6284. Adviser: H. K. Kwan. Thesis (Ph.D.)--University of Windsor (Canada), 1995