A Discrimination Method Between Transformer Inrush and Fault Current Based on Chromatic Monitoring

or successful transformer differential protection employment, correct discrimination between inrush current and fault current is essential. It is one of the main focuses of research and one of the main challenges for transformer protection. In this paper, a discrimination method based on utilizing chromatic monitoring of the box dimension algorithm outcome curve for transformer differential current in time-domain analysis is proposed. The x-L chromatic mapping is employed for general detection of fault cases, while the x-y chromatic mapping result is used for distinguishing inrush current from the fault current cycles. The preliminary results show that the proposed method can effectively provide correct discrimination of the current type within quite a short time and thus help in providing efficient decision-making supportive protection tool.

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.

Characterization of wood popularly known as “Louros” in the Brazilian amazon by visible spectroscopy and CIELAB parameters

Aim of study: The objective of this work was to evaluate the potential of colorimetry to discriminate wood from the “louros” group, in particular to contribute to a database of Brazilian native species for future application in logging control actions. Area of study: The wooden discs were collected in a Sustainable Forest Management Area (AMFS) belonging to two extractivist communities, Paraiso and Arimum, both in the “Green Forever” Extractivist Reserve, in the municipality of Porto de Moz, Pará state, Brazil. Material and methods: Colorimetric evaluation was carried out based on data from the CIEL*a*b* system, analyzed using PCA and ANOVA in a completely randomized design in a two-factor scheme. Main results: PCA produced some evidence of color patterns and some parameters may be more useful in caracterization. Longitudinal surfaces were more indicated for this group caracterization. The data from CIEL*a*b* system produce helpful information for distinction of wood from “louros” group, and so, the colorimetric technique can be an auxiliary tool for separation of this group, complementary to other techniques as wood anatomy. Research highlights: Correct discrimination of wood from “louros” group in commerce control; Brazilian native species.

DISCRIMINATION OF VARIETAL GROUPS AND HYBRIDS OF COFFEA CANEPHORA SPECIES USING MULTIVARIATE ANALYSIS

Coffee growing is one of the most important agricultural activities in the world market. Among the commercially relevant species, there is Coffea canephora,which can be divided into the varietal groups Conilon and Robusta. These varietal groups have complementary agronomic interests. Because of this, hybrids are obtained through the crosses between these groups. Given the difficulty in differentiating between two varietal groups genotypes in the field, the correct discrimination is essential for the definition of crosses in breeding programs. In this context, the objective was to apply a discriminant analysis (DA) to define functions to differentiate between varietal groups and hybrids of canephora, as well as to identify the most relevant phenotypic traits in these functions. Data from 165 genotypes from the Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural e do Centro Agronómico Tropical de Investigación y Enseñanza were used for which different plant traits were measured. The quadratic DA applied was the one with the best performance for genotype discrimination, with an average apparent error rate of 0.0333. Cercosporiose incidence, rust incidence and vegetative vigor were the most important traits in the varietal groups' discrimination.

Implementation of MALDI-TOF Mass Spectrometry and Peak Analysis: Application to the Discrimination of Cryptococcus neoformans Species Complex and Their Interspecies Hybrids

Matrix-assisted laser desorption–ionization/time of flight mass spectrometry (MALDI-TOF MS) has been widely implemented for the rapid identification of microorganisms. Although most bacteria, yeasts and filamentous fungi can be accurately identified with this method, some closely related species still represent a challenge for MALDI-TOF MS. In this study, two MALDI-TOF-based approaches were applied for discrimination at the species-level of isolates belonging to the Cryptococcus neoformans complex, previously characterized by Amplified Fragment Length Polymorphism (AFLP) and sequencing of the ITS1-5.8S-ITS2 region: (i) an expanded database was built with 26 isolates from the main Cryptococcus species found in our setting (C. neoformans, C. deneoformans and AFLP3 interspecies hybrids) and (ii) peak analysis and data modeling were applied to the protein spectra of the analyzed Cryptococcus isolates. The implementation of the in-house database did not allow for the discrimination of the interspecies hybrids. However, the performance of peak analysis with the application of supervised classifiers (partial least squares-discriminant analysis and support vector machine) in a two-step analysis allowed for the 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. In addition, PCA analysis prior to support vector machine (SVM) provided 98.45% correct discrimination of the three analyzed species in a one-step analysis. This novel method is cost-efficient, rapid and user-friendly. The procedure can also be automatized for an optimized implementation in the laboratory routine.

Pupil size changes signal hippocampus-related memory functions

A major task of episodic memory is to create unique, distinguishable representations of highly overlapping perceptual inputs. Several studies on this basic function have shown that it is based on the intact functioning of certain subregions of the hippocampus and is among the most sensitive behavioral indicators of mild cognitive impairment (MCI) and dementia. Here we assessed pupil dilation associated with performance in a widely used recognition paradigm that aims to uncover the intactness of fine-graded mnemonic discrimination. A sample of healthy undergraduate students was used. First, we showed that the correct discrimination between highly similar lure items and target items elicit larger pupil response than correct target identification. Second, we found that mnemonic discrimination is associated with larger pupil response in general as compared to target identification, regardless of whether the response was correct or not. These results suggest the pupil changes differentiate mnemonic discrimination and memory identification processes in recognition performance.

Quantitative Imagery Analysis of Spot Patterns for Haplogroup Classification of Triatoma Dimidiata (Latreille, 1811) (Hemiptera: Reduviidae) an Important Vector of Chagas Disease

Background: Spots and coloring patterns evaluated quantitatively can be used to discrimination and identify possible cryptic species. Triatoma dimidiata (Reduviidae: Triatominae) is one of the main complexes of Chagas disease vector species. Phylogenetic studies have defined three haplogroups for Mexico and part of Central America. In this work, we evaluate the possibility of correctly discriminating among these T. dimidiata haplogroups using the pattern of dorsal spots.Methods: Digital images of the dorsal region of individuals from the three haplogroups were used. We used image processing to extract primary and secondary variables characterizing the dorsal spot pattern. The statistical analysis of the variables included descriptive statistics, non-parametric Kruskal-Wallis tests, Discriminant Function Analysis (DFA), and a neural classification network.Results: A distinctive spot pattern was found for each haplogroup. The most differentiated pattern was presented by haplogroup 2, mainly apparent in the notably larger central spots. Haplogroups 1 and 3 were more similar to each other, but there were consistent differences in the shape and orientation of the spots. Significant differences were found among haplogroups in almost all the variables analyzed, these differences being greater in relative spot area, mean relative area of central spots, central spots Feret diameters, lateral spots Feret diameters and aspect ratio. Both the DFA and the neural network had correct discrimination values above 90%. Conclusions: We conclude that the spot pattern can be reliably used to discriminate among haplogroups, and possibly among triatomine species.

Quantitative Imagery Analysis of Spot Patterns for Haplogroup Classification of Triatoma Dimidiata (Latreille, 1811) (Hemiptera: Reduviidae) an Important Vector of Chagas Disease

Background: Spots and coloring patterns evaluated quantitatively can be used to discrimination and identify possible cryptic species. Triatoma dimidiata (Reduviidae: Triatominae) is one of the main complexes of Chagas disease vector species. Phylogenetic studies have defined three haplogroups for Mexico and part of Central America. In this work, we evaluate the possibility of correctly discriminating among these T. dimidiata haplogroups using the pattern of dorsal spots.Results: A distinctive spot pattern was found for each haplogroup. The most differentiated pattern was presented by haplogroup 2, mainly apparent in the notably larger central spots. Haplogroups 1 and 3 were more similar to each other, but there were consistent differences in the shape and orientation of the spots. Significant differences were found among haplogroups in almost all the variables analyzed, these differences being greater in relative spot area, mean relative area of central spots, central spots Feret diameters, lateral spots Feret diameters and aspect ratio. Both the DFA and the neural network had correct discrimination values above 90%. Conclusions: We conclude that the spot pattern can be reliably used to discriminate among haplogroups, and possibly among triatomine species.

Implementation of MALDI-TOF Mass Spectrometry and Peak Analysis: Application to the Discrimination of Cryptococcus Species and Their Interspecies Hybrids

MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization Time-of Flight) is a type of mass spectrometry (MS) that has been widely implemented for the rapid identification of microorganisms over the last decade. The accuracy and flexibility of this method has encouraged researchers to implement the analysis of protein spectra obtained by MALDI-TOF for the discrimination of close-related species and bacterial typing. In this study, a standardized methodology based on the detection of species-specific protein peaks from the spectra obtained with MALDI-TOF is described. The methodology was applied to a collection of Cryptococcus spp. (n=70) previously characterized by Amplified Fragment Length Polymorphism (AFLP) and sequencing of the ITS1-5.8S-ITS2 region. An expanded ad-hoc database was also built for their discrimination with MALDI-TOF. This approach did not allow the discrimination of the interspecies hybrids. However, the performance of peak analysis with the application of the PLS-DA and SVM algorithms in a two-step analysis allowed 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. Besides, PCA analysis prior to SVM provided 98.45% correct discrimination of the 3 analyzed species in a one-step analysis. The method is cost-efficient, rapid and user-friendly. The procedure can also be automatized for an optimized implementation in the laboratory routine.