System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks.

The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10-5, RMSE of 0.4411% and a MAPE of 0.0643.

Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS).

A cerebral arteriovenous malformation (AVM) is a tangle of abnormal blood vessels that irregularly connects arteries and veins. Stereotactic radiosurgery (SRS) has been shown to be an effective treatment for AVM patients, but the factors associated with AVM obliteration remains a matter of debate. In this study, we aimed to develop a model that can predict whether patients with AVM will be cured 36 months after intervention by means of SRS and identify the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 0.92, sensitivity of 0.93, specificity of 0.89, and an area under the curve (AUC) of 0.98, which shows that ML models are suitable for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AVM would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.

Fourteen years of manifestations and factors of health insurance fraud, 2006-2020: a scoping review.

BACKGROUND: Healthcare fraud entails great financial and human losses; however, there is no consensus regarding its definition, nor is there an inventory of its manifestations and factors. The objective is to identify the definition, manifestations and factors that influence health insurance fraud (HIF). METHODS: A scoping review on health insurance fraud published between 2006 and 2020 was conducted in ACM, EconPapers, PubMed, ScienceDirect, Scopus, Springer and WoS. RESULTS: Sixty-seven studies were included, from which we identified 6 definitions, 22 manifestations (13 by the medical provider, 7 by the beneficiary and, 2 by the insurance company) and 47 factors (6 macroenvironmental, 15 mesoenvironmental, 20 microenvironmental, and 6 combined) associated with health insurance fraud. We recognized the elements of fraud and its dependence on the legal framework and health coverage. From this analysis, we propose the following definition: "Health insurance fraud is an act of deception or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by a health insurance company". Among the most relevant manifestations perpetuated by the provider are phantom billing, falsification of documents, and overutilization of services; the subscribers are identity fraud, misrepresentation of coverage and alteration of documents; and those perpetrated by the insurance company are false declarations of benefits and falsification of reimbursements. Of the 47 factors, 25 showed an experimental influence, including three in the macroenvironment: culture, regulations, and geography; five in the mesoenvironment: characteristics of provider, management policy, reputation, professional role and auditing; 12 in the microenvironment: sex, race, condition of insurance, language, treatments, chronic disease, future risk of disease, medications, morale, inequity, coinsurance, and the decisions of the claims-adjusters; and five combined factors: the relationships between beneficiary-provider, provider-insurance company, beneficiary-insurance company, managers and guanxi. CONCLUSIONS: The multifactorial nature of HIF and the characteristics of its manifestations depend on its definition; Identifying the influence of the factors will support subsequent attempts to combat HIF.

Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network.

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.

Bruise dating using deep learning.

The bruise dating can have important medicolegal implications in family violence and violence against women cases. However, studies show that the medical specialist has 50% accuracy in classifying a bruise by age, mainly due to the variability of the images and the color of the bruise. This research proposes a model, based on deep convolutional neural networks, for bruise dating using only images, by age ranges, ranging from 0-2 days to 17-30 days, and images of healthy skin. A 2140 experimental bruise photograph dataset was constructed, for which a data capture protocol and a preprocessing procedure are proposed. Similarly, 20 classification models were trained with the Inception V3, Resnet50, MobileNet, and MnasNet architectures, where combinations of learning transfer, cross-validation, and data augmentation were used. Numerical experiments show that classification models based on MnasNet have better results, reaching 97.00% precision and sensitivity, and 99.50% specificity, exceeding 40% precision reported in the literature. Also, it was observed that the precision of the model decreases with the age of the bruise.

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics.

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches-on the other hand-contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited-bootstrapping from the features returned by Word Embedding mechanisms-to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

Anthelmintic activity of Cratyliamollis leaves against gastrointestinal nematodes in goats

SUMMARY The present study assessed the in vivo anthelmintic activity of the C. mollis leaf decoction extract when administered orally to naturally infected goats with gastrointestinal nematodes. To this, animals were randomized into three groups:non-treated, control (treated with doramectin 1mL/50 kg b.w.) and C. mollis extract treated groups (2.5mg/kg b.w.). Blood and faecal samples were collected from each animal at day 0, and 30th day posttreatment to monitor immunological and parasitological parameters. A significant faecal egg reduction (61.1%) and an increase in IgA and eosinophils levels were observed in the C. mollis extract treated group, in comparison to the untreated and doramectin groups. Considering that gastrointestinal nematode infections in small ruminants are serious problems in the world, causing economic losses worldwide, associated to high anthelmintic cost, resistance to available anthelmintics and residue problems in meat and milk for human consumption, the plant extract use is an area of interest to search new anthelmintic agents. Thus, Cratyliamollis Mart. Ex Benth, an important medicinal plant from Brazilian Northeast semiarid region, is used to treat different types of diseases, and as forage supplementation. Therefore, the data indicated the potential anthelmintic activity of C. mollis extract; further research is thereby warranted to assess its value for therapeutic purposes.

RESUMO Este estudo avaliou a capacidade anti-helmíntica in vivo do extrato aquoso de folhas de C. mollis, administrado por via oral em caprinos naturalmente infectados com nematódeos gastrintestinais. Para isto, os animais foram aleatoriamente distribuídos em três grupos (grupo não tratado, grupo controle tratado com doramectina (1mL/50 kg de peso corpóreo) e grupo tratado com o extrato aquoso de C. mollis (2,5mg/ kg de peso corpóreo). Amostras de sangue e fezes foram colhidas de cada animal nos dias 0 e 30 após o tratamento para verificar parâmetros imunológicos e parasitológicos. Uma redução significativa na oviposição (61,1%) e um aumento dos níveis de IgA e eosinófilos foram observados no grupo tratado com o extrato aquoso, em comparação aos grupos controle (não tratado e tratado com doramectina). Considerando que nematódeos gastrintestinais de pequenos ruminantes causam perdas econômicas ao nível mundial, devido ao custo e à resistência aos anti-helmínticos disponíveis, além do efeito residual nos derivados animais para consumo humano, os extratos vegetais apresentam-se como uma fonte alternativa de anti-helmínticos. Assim, Cratyliamollis Mart. ExBenth é uma planta usada no semi-áridodo Nordeste brasileiro para tratardoenças e como forragem. Portanto, os resultados indicaram o potencial anti-helmíntico do extrato, embora mais estudos sejam necessários para validar seu uso para fins terapêuticos.

Anthelmintic activity of Cratyliamollis leaves against gastrointestinal nematodes in goats

SUMMARY The present study assessed the in vivo anthelmintic activity of the C. mollis leaf decoction extract when administered orally to naturally infected goats with gastrointestinal nematodes. To this, animals were randomized into three groups:non-treated, control (treated with doramectin 1mL/50 kg b.w.) and C. mollis extract treated groups (2.5mg/kg b.w.). Blood and faecal samples were collected from each animal at day 0, and 30th day posttreatment to monitor immunological and parasitological parameters. A significant faecal egg reduction (61.1%) and an increase in IgA and eosinophils levels were observed in the C. mollis extract treated group, in comparison to the untreated and doramectin groups. Considering that gastrointestinal nematode infections in small ruminants are serious problems in the world, causing economic losses worldwide, associated to high anthelmintic cost, resistance to available anthelmintics and residue problems in meat and milk for human consumption, the plant extract use is an area of interest to search new anthelmintic agents. Thus, Cratyliamollis Mart. Ex Benth, an important medicinal plant from Brazilian Northeast semiarid region, is used to treat different types of diseases, and as forage supplementation. Therefore, the data indicated the potential anthelmintic activity of C. mollis extract; further research is thereby warranted to assess its value for therapeutic purposes.

RESUMO Este estudo avaliou a capacidade anti-helmíntica in vivo do extrato aquoso de folhas de C. mollis, administrado por via oral em caprinos naturalmente infectados com nematódeos gastrintestinais. Para isto, os animais foram aleatoriamente distribuídos em três grupos (grupo não tratado, grupo controle tratado com doramectina (1mL/50 kg de peso corpóreo) e grupo tratado com o extrato aquoso de C. mollis (2,5mg/ kg de peso corpóreo). Amostras de sangue e fezes foram colhidas de cada animal nos dias 0 e 30 após o tratamento para verificar parâmetros imunológicos e parasitológicos. Uma redução significativa na oviposição (61,1%) e um aumento dos níveis de IgA e eosinófilos foram observados no grupo tratado com o extrato aquoso, em comparação aos grupos controle (não tratado e tratado com doramectina). Considerando que nematódeos gastrintestinais de pequenos ruminantes causam perdas econômicas ao nível mundial, devido ao custo e à resistência aos anti-helmínticos disponíveis, além do efeito residual nos derivados animais para consumo humano, os extratos vegetais apresentam-se como uma fonte alternativa de anti-helmínticos. Assim, Cratyliamollis Mart. ExBenth é uma planta usada no semi-áridodo Nordeste brasileiro para tratardoenças e como forragem. Portanto, os resultados indicaram o potencial anti-helmíntico do extrato, embora mais estudos sejam necessários para validar seu uso para fins terapêuticos.