Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Enzymatic processing of protein-based fibers

Wool and silk are major protein fiber materials used by the textile industry. Fiber protein structure-function relationships are briefly described here, and the major enzymatic processing routes for textiles and other novel applications are deeply reviewed. Fiber biomodification is described here with various classes of enzymes such as protease, transglutaminase, tyrosinase, and laccase. It is expected that the reader will get a perspective on the research done as a basis for new applications in other areas such as cosmetics and pharma.

The role of social cognition and motor imagery in gesture processing: a patients' study

Dissertação de mestrado integrado em Psicologia

The role of deviant-letter position in cognate word processing

Dissertação de mestrado integrado em Psicologia

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

On the routines of wild-type silk fibroin processing toward silk-inspired materials: a review

For years, silk fibroin of a domestic silkworm, Bombyx mori, has been recognized as a valuable material and extensively used. In the last decades, new application fields are emerging for this versatile material. Those final, specific applications of silk dictate the way it has been processed in industry and research. This review focuses on the description of various approaches for silk downstream processing in a laboratory scale, that fall within several categories. The detailed description of workflow possibilities from the naturally found material to a finally formulated product is presented. Considerable attention is given to (bio-) chemical approaches of silk fibroin transformation, particularly, to its enzyme-driven modifications. The focus of the current literature survey is exclusively on the methods applied in research and not industry.

Processing and characterization of plates made from granulate of waste electrical cables

This article is intended to evaluate the density and the mechanical, acoustic and thermal properties of compression moulded plates composed of granulate from electrical cables wastes. Those cable wastes are the insulation part from the electric cables, and are composed of PVC, PE, EMP and PEX rubber. After these materiais lose their initial properties and cease to be useful as insulation material, due to safety requirements, it is possible to reuse them into new applications like industrial or playground floorings, as sound insulation material to be applied in walls or floors, or to dampen vibrations from equipments. Recovering electric cable waste has been a major concern to the European Commission due to its leveis of toxicity when incineration and land fill ing is the solution to dispose this material. Such as the European Commission's study for DG Xl[1] suggested that recycling may be the most favourable future waste management option.

Abnormalities in the processing of emotional prosody from single words in schizophrenia

Background: Abnormalities in emotional prosody processing have been consistently reported in schizophrenia and are related to poor social outcomes. However, the role of stimulus complexity in abnormal emotional prosody processing is still unclear. Method: We recorded event-related potentials in 16 patients with chronic schizophrenia and 16 healthy controls to investigate: 1) the temporal course of emotional prosody processing; and 2) the relative contribution of prosodic and semantic cues in emotional prosody processing. Stimuli were prosodic single words presented in two conditions: with intelligible (semantic content condition—SCC) and unintelligible semantic content (pure prosody condition—PPC). Results: Relative to healthy controls, schizophrenia patients showed reduced P50 for happy PPC words, and reduced N100 for both neutral and emotional SCC words and for neutral PPC stimuli. Also, increased P200 was observed in schizophrenia for happy prosody in SCC only. Behavioral results revealed higher error rates in schizophrenia for angry prosody in SCC and for happy prosody in PPC. Conclusions: Together, these data further demonstrate the interactions between abnormal sensory processes and higher-order processes in bringing about emotional prosody processing dysfunction in schizophrenia. They further suggest that impaired emotional prosody processing is dependent on stimulus complexity.

The music of language: an ERP investigation of the effects of musical training on emotional prosody processing

Recent studies have demonstrated the positive effects of musical training on the perception of vocally expressed emotion. This study investigated the effects of musical training on event-related potential (ERP) correlates of emotional prosody processing. Fourteen musicians and fourteen control subjects listened to 228 sentences with neutral semantic content, differing in prosody (one third with neutral, one third with happy and one third with angry intonation), with intelligible semantic content (semantic content condition--SCC) and unintelligible semantic content (pure prosody condition--PPC). Reduced P50 amplitude was found in musicians. A difference between SCC and PPC conditions was found in P50 and N100 amplitude in non-musicians only, and in P200 amplitude in musicians only. Furthermore, musicians were more accurate in recognizing angry prosody in PPC sentences. These findings suggest that auditory expertise characterizing extensive musical training may impact different stages of vocal emotional processing.

Two practical approaches to monitoring the zooplanktonic community at Lago Grande do Curuai, Pará, Brazil

The use of substitute groups in biomonitoring programs has been proposed to minimize the high financial costs and time for samples processing. The objectives of this study were to evaluate the correlation between (i) the spatial distribution among the major zooplankton groups (cladocerans, copepods, rotifers, and testaceans protozoa), (ii) the data of density and presence/absence of species, and (iii) the data of species, genera, and families from samples collected in the Lago Grande do Curuai, Pará, Brazil. A total of 55 sample of the zooplanktonic community was collected, with 28 samples obtained in March and 27 in September, 2013. The agreement between the different sets of data was assessed using Mantel and Procrustes tests. Our results indicated high correlations between genus level and species level and high correlations between presence/absence of species and abundance, regardless of the seasonal period. These results suggest that zooplankton community could be incorporated in a long-term monitoring program at relatively low financial and time costs.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Automatic distinction of Fernando Pessoas’ heteronyms

Text Mining has opened a vast array of possibilities concerning automatic information retrieval from large amounts of text documents. A variety of themes and types of documents can be easily analyzed. More complex features such as those used in Forensic Linguistics can gather deeper understanding from the documents, making possible performing di cult tasks such as author identi cation. In this work we explore the capabilities of simpler Text Mining approaches to author identification of unstructured documents, in particular the ability to distinguish poetic works from two of Fernando Pessoas' heteronyms: Alvaro de Campos and Ricardo Reis. Several processing options were tested and accuracies of 97% were reached, which encourage further developments.

Effects of bariatric surgery on the central nervous system and eating behavior in humans: a systematic review on the neuroimaging studies

INTRODUCTION: Neuroimaging studies suggest that obese people might show hyperactivity of brain areas regarding reward processing, and hypoactivity of brain areas concerning cognitive control, when exposed to food cues. Although the effects of bariatric surgery on the central nervous system and eating behavior are well known, few studies have used neuroimage techniques with the aim of investigating the central effects of bariatric surgery in humans. OBJECTIVES: This paper systematically and critically reviews studies using functional neuroimaging to investigate changes on the patterns of activation of central areas related to the regulation of eating behavior after bariatric surgery. METHOD: A search on the databases Medline, Web of Science, Lilacs and Science Direct on Line, was conducted in February 2013, using the keywords "Neuroimaging", "Positron-Emission Tomography", "Magnetic Resonance Imaging", "Gastric Bypass", "Gastroplasty", "Jejunoileal Bypass", "Bariatric Surgery". RESULTS: Seven manuscripts were included; the great majority studied the central effects of Roux en Y gastric bypass, using positron emission tomography or functional magnetic resonance. CONCLUSIONS: Bariatric surgery might normalize the activity of central areas concerned with reward and incentive salience processing, as the nucleus accumbens and mesencephalic tegmental ventral area, as well as circuitries processing behavioral inhibition, as the dorsolateral prefrontal cortex.

Investigation of cognition in schizophrenia: psychometric properties of instruments for assessing working memory updating

Objective This study describes the development of two updating measures of working memory (WM): Letter Updating Test (LUT) and Word Updating Test (WUT). Methods In stage 1, items were created and the instruments were assessed by experts and laymen. In stage 2, tests were given to 15 patients with schizophrenia and 15 paired controls. All were able to understand and respond to the instruments. In stage 3, 141 patients with schizophrenia and 119 healthy controls aged 18 to 60 took part; they were assessed on WM, processing speed (PS) and functional outcome. Results The results showed adequate rates of internal consistency for both measures developed, for both the total sample and each group separately, as well as evidence of convergent validity, discriminant validity and sensitivity to differentiate performance among the groups. Principal component analysis yielded two components, one for updating tests and other for PS measures, indicating factorial validity. Positive and significant, yet low, correlations were found with functionality measures. Conclusion These results provide adequate psychometric parameters for the measures developed, applicable to cognitive research settings in schizophrenia.