The relationship between non-suicidal self-injury and suicidality

The overall purpose of this study was to explain the overlap and distinctiveness of non-suicidal self-injury (NSSI) and suicidality from a diathesis-stress perspective. The first part of this study evaluated the third variable theory as an explanation for the high rates of lifetime co-occurrence between NSSI and suicidality. Specifically, it was hypothesized that these forms of self-harm share a common vulnerability profile comprised of five affective, cognitive and behavioural diatheses. The second part of this study tested the hypothesis that NSSI and suicidality become distinguishable on the basis of immediate, proximal stressors, namely psychache and survival and coping beliefs (SCB). Participants (N = 262) were community individuals aged 16-24 years who reported either no history of self-harm (i.e., no history of NSSI, suicidality, or both), a history of NSSI, suicidality or both, or current NSSI-only or current NSSI+suicidality. They were recruited online to complete an online battery of questionnaires. Using a set of discriminant function analyses, it was found that the vulnerability profile was unable to distinguish between the three self-harm groups, but was able to differentiate the no self-harm group from a collated self-harm group. Result patterns were also analyzed for gender differences. It was also found that a current NSSI+suicidality group exhibited significantly higher psychache and lower SCB (for women only) than a current NSSI-only group. These results suggest that NSSI and suicidality may tend to co-occur because they have similar long-term diatheses, but that they may become more distinct with respect to immediate psychological stressors.

Expressing Sentiments in Game Reviews

Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.

A Quantitative analysis of pulsed signals emitted by wild bottlenose dolphins

Common bottlenose dolphins (Tursiops truncatus), produce a wide variety of vocal emissions for communication and echolocation, of which the pulsed repertoire has been the most difficult to categorize. Packets of high repetition, broadband pulses are still largely reported under a general designation of burst-pulses, and traditional attempts to classify these emissions rely mainly in their aural characteristics and in graphical aspects of spectrograms. Here, we present a quantitative analysis of pulsed signals emitted by wild bottlenose dolphins, in the Sado estuary, Portugal (2011-2014), and test the reliability of a traditional classification approach. Acoustic parameters (minimum frequency, maximum frequency, peak frequency, duration, repetition rate and inter-click-interval) were extracted from 930 pulsed signals, previously categorized using a traditional approach. Discriminant function analysis revealed a high reliability of the traditional classification approach (93.5% of pulsed signals were consistently assigned to their aurally based categories). According to the discriminant function analysis (Wilk's Λ = 0.11, F3, 2.41 = 282.75, P < 0.001), repetition rate is the feature that best enables the discrimination of different pulsed signals (structure coefficient = 0.98). Classification using hierarchical cluster analysis led to a similar categorization pattern: two main signal types with distinct magnitudes of repetition rate were clustered into five groups. The pulsed signals, here described, present significant differences in their time-frequency features, especially repetition rate (P < 0.001), inter-click-interval (P < 0.001) and duration (P < 0.001). We document the occurrence of a distinct signal type-short burst-pulses, and highlight the existence of a diverse repertoire of pulsed vocalizations emitted in graded sequences. The use of quantitative analysis of pulsed signals is essential to improve classifications and to better assess the contexts of emission, geographic variation and the functional significance of pulsed signals.

Evolução da capacidade preditiva da informação contábil : um estudo a partir dos disclosures das Companhias listadas na BM&FBovespa após a adoção das Normas Internacionais de Contabilidade no Brasil

Dissertação (mestrado)—Universidade de Brasília, Universidade Federal da Paraíba, Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação Multiinstitucional e Inter-Regional de Pós-Graduação em Ciências Contábeis, 2016.

Metodologias estatísticas aplicadas a dados de análises químicas da água produzida em um campo maduro de petróleo

Produced water is characterized as one of the most common wastes generated during exploration and production of oil. This work aims to develop methodologies based on comparative statistical processes of hydrogeochemical analysis of production zones in order to minimize types of high-cost interventions to perform identification test fluids - TIF. For the study, 27 samples were collected from five different production zones were measured a total of 50 chemical species. After the chemical analysis was applied the statistical data, using the R Statistical Software, version 2.11.1. Statistical analysis was performed in three steps. In the first stage, the objective was to investigate the behavior of chemical species under study in each area of production through the descriptive graphical analysis. The second step was to identify a function that classify production zones from each sample, using discriminant analysis. In the training stage, the rate of correct classification function of discriminant analysis was 85.19%. The next stage of processing of the data used for Principal Component Analysis, by reducing the number of variables obtained from the linear combination of chemical species, try to improve the discriminant function obtained in the second stage and increase the discrimination power of the data, but the result was not satisfactory. In Profile Analysis curves were obtained for each production area, based on the characteristics of the chemical species present in each zone. With this study it was possible to develop a method using hydrochemistry and statistical analysis that can be used to distinguish the water produced in mature fields of oil, so that it is possible to identify the zone of production that is contributing to the excessive elevation of the water volume.

Insights into population structure of Diplodus vulgaris along the SW Portuguese coast from otolith elemental signatures

The common two-banded sea bream (Diplodus vulgaris) is an important fish in the marine ecosystems of the NW Atlantic and Mediterranean. In southern Portugal it is a major fishery resource being targeted mainly by the artisanal fleets. Although there is some knowledge of the age, growth and reproductive biology of the species, information about its population structure is scarce and somewhat limited to the Mediterranean Sea. In this study the otolith elemental signatures of 90 specimens of D. vulgaris of the same age group (2+ years) and cohort collected from the important fishery regions of SW Portugal (Sesimbra, Sagres and Faro) have been analysed by inductively coupled plasma mass spectrometry (ICP-MS). Two different methodologies have been applied: solution based analysis of the whole otoliths; representative of the entire life-history prior to capture, and laser ablation analysis of otolith cores; representative of the larval and early post-settlement phase. Whole otolith comparisons utilised Sr/Ca, Ba/Ca, Mn/Ca, Li/Ca and Ni/Ca to demonstrate regional population structure. Classification accuracy rates from linear discriminant function analyses (LDFA) of whole otolith chemistry data were high for each region; Faro - 93%, Sagres - 90% and Sesimbra - 80%. Comparison of the otolith core chemistry utilised Sr/Ca, Ba/Ca, Mn/Ca and Mg/Ca and Zn/Ca. LDFA for the otolith core chemistry also achieved accurate classification for samples from Sesimbra (73%), but there was high overlap of otolith chemistry between samples from Faro and Sagres (47 and 43% classification accuracy respectively). The whole otolith results suggest that D. vulgaris are resident in the regional fishing areas during the juvenile phase. Both the core and whole otolith chemistry data supported separation of the Sesimbra fishery region from the more southern and closely associated Faro and Sagres regions for management purposes. However, while the whole otolith data indicated that the populations at Faro and Sagres likely remained separated in the juvenile stage, the otolith core chemistry data was inconclusive as to whether recruitment to these two areas was derived, or not, from different spawning areas.

Communications inspired linear discriminant analysis

We study the problem of supervised linear dimensionality reduction, taking an information-theoretic viewpoint. The linear projection matrix is designed by maximizing the mutual information between the projected signal and the class label. By harnessing a recent theoretical result on the gradient of mutual information, the above optimization problem can be solved directly using gradient descent, without requiring simplification of the objective function. Theoretical analysis and empirical comparison are made between the proposed method and two closely related methods, and comparisons are also made with a method in which Rényi entropy is used to define the mutual information (in this case the gradient may be computed simply, under a special parameter setting). Relative to these alternative approaches, the proposed method achieves promising results on real datasets. Copyright 2012 by the author(s)/owner(s).

Measure Fields for Function Approximation

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.

Validity and reliability of the Portuguese version of Mandibular Function Impairment Questionnaire

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.

Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.