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Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

Objective - To evaluate the utility of perfusion in computed tomography (CT) and magnetic resonance (MR) in the diagnosis of acute ischemic stroke. Methods - An electronic literature research was performed, looking for prospective studies, regarding an adult population at risk of cerebral ischemia with a diagnostic evaluation of perfusion studies, having perfusion sequences by CT (CPT) and MR (MRP) performed after a stroke. Of the 2,224 articles found, 28 matched the inclusion criteria for analysis of definitions (15 CT, 11 MRI, and 2 CT and MR). Furthermore, protocols and percentage levels of accuracy, sensitivity, and specificity were compared through metrics of frequency analysis and central expression. Results - In these articles, parameters and techniques for the acquisition of perfusion imaging either in CT or MRI were analyzed. Regarding it, was found that the accuracy, sensitivity, and specificity was of 88.5%, 91.5% and 90.5% for MRI and of 83%, 86% and 91.7% for CT, respectively. Conclusion - MRI remains the imaging method with greater clinical value, however, the CT study also presents as being an advantage, with better accessibility and rapidity, reducing the waiting time between the diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Time series pattern discovery is of great importance in a large variety of environmental and engineering applications, from supporting predictive models to helping to understand hidden underlying processes. This work develops a multiresolution time series method for extracting patterns in weather records, particular temperature data. The topic is important, as, given a warming climate, morbidity andmortality are expected to rise as heatwave frequency and intensity increase. By analysing summer temperature quantiles at different levels of coarseness, it was found that compounding models can contain a complete description of severe weather events. This new multiresolution quantile approach is developed as an extension of the symbolic aggregate approximation of the temperature time series in which quantiles are computed at every stretch of the piecewise partition. The process is iterated at different scales of the partition, and it was found to be a very useful approach for finding patterns related to both heatwave periods and intensities. The method is successfully tested using real weather records from Brazil (Recife) and the UK (London), and it was found that in both locations heatwave intensity and frequency are increasing at a substantial rate. In addition, it was found that the rate of increase in intensity of the heatwaves is far outstripping the rate of increase in mean summer temperature: by a factor of 2 in Recife and a factor of 6 in London. The approach will be of use to those looking at the impact of future climates on civil engineering, water resources, energy use, agriculture and health care, or those looking for sustained extreme events in any time series. [ABSTRACT FROM AUTHOR]

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series. [ABSTRACT FROM AUTHOR]

Abstract: Reservoir computing is a framework for computation like a recurrent neural network that allows for the black box modeling of dynamical systems. In contrast to other recurrent neural network approaches, reservoir computing does not train the input and internal weights of the network, only the readout is trained. However it is necessary to adjust parameters to create a “good” reservoir for a given application. In this study we introduce a method, called RCDESIGN (reservoir computing and design training). RCDESIGN combines an evolutionary algorithm with reservoir computing and simultaneously looks for the best values of parameters, topology and weight matrices without rescaling the reservoir matrix by the spectral radius. The idea of adjust the spectral radius within the unit circle in the complex plane comes from the linear system theory. However, this argument does not necessarily apply to nonlinear systems, which is the case of reservoir computing. The results obtained with the proposed method are compared with results obtained by a genetic algorithm search for global parameters generation of reservoir computing. Four time series were used to validate RCDESIGN. [Copyright &y& Elsevier]

Biologically inspired controllers demonstrate great success in several applications, mainly in situations that present disturbances and uncertainties in the dynamics of the system. In recent times, several works have appeared in the area of emotional learning which occurs in the human brain, thus allowing to the emergence of new theories and applications in control engineering. In control engineering, it is possible to highlight the BELBIC (Brain Emotional Learning-Based Intelligent Controller). However, the design and commissioning of this type of controller still represents a major challenge for researchers, since it is necessary to determine some characteristic signals to this system (stimuli), which can vary from application to application. This work presents a methodology for the construction of architectures for BELBIC stimulus signals, using as a basis the DRL (Deep Reinforcement Learning) techniques. The DRL allows extracting characteristic patterns from the dynamics of systems which, perhaps, may have high dimensionality and possibly nonlinear dynamics, as is the case of most problems involving real-world dynamic systems. The resulting controller model is validated by applying an inverted pendulum dynamic system in order to demonstrate a new approach to the architectures of the BELBIC that allows to achieve a greater generalization in its application, as well as providing a viable alternative to the traditional models in use. [ABSTRACT FROM AUTHOR]

Aim of the study - To compare the thickness of subcutaneous, preperitoneal and visceral adipose tissue measured by ultrasonography (US) and relate them to the value of Body Mass Index (BMI). Methods - Weight, height and the abdominal perimeter were determined in 218 volunteers (177 females and 41 males, aged between 18 and 33 years, with a body mass index between 20.03 and 37.27kg/m²), later submitted to abdominal ultrasonography. Further, four lifestyle questions were answered by the volunteers. Results - The US allowed to quantify and classify objectively and reproducibly subcutaneous adipose tissue, preperitoneal and visceral, for p<0.01. Pearson's correlation (p<0.01) did not show inter-observer variability in US measurements of subcutaneous adipose tissue (r=0.9871), preperitoneal (r=0.9003), and visceral (r=0.9407). A strong linear correlation between BMI with subcutaneous adipose tissue (r=0.64) and with preperitoneal (r=0.56) was identified. It was verified that the US can classify the genus based on the thickness of the intra-abdominal adipose tissue, abdominal perimeter and BMI with a total accuracy of 86.69%. Conclusions - US shows to be an objective and capable method in the characterization and differentiation of intra-abdominal adipose tissue. The combined use of biometric (except weight and height) and US data allows a correct estimation of BMI. Future studies are needed to understand the usefulness of the Deep Learning frameworks in the automatic detection of different types of abdominal adipose tissue, thus guaranteeing the possibility of the US becoming a quick and preventive method for assessing obesity. [ABSTRACT FROM AUTHOR]

Abstract Heat waves give rise to order of magnitude higher mortality rates than other weather-related natural disasters. Unfortunately both the severity and amplitude of heat waves are predicted to increase worldwide as a consequence of climate change. Hence, meteorological services have a growing need to identify such periods in order to set alerts, whilst researchers and industry need representative future heat waves to study risk. This paper introduces a new location-specific mortality risk focused definition of heat waves and a new mathematical framework for the creation of time series that represents them. It focuses on identifying periods when temperatures are high during the day and night, as this coincidence is strongly linked to mortality. The approach is tested using observed data from Brazil and the UK. Comparisons with previous methods demonstrate that this new approach represents a major advance that can be adopted worldwide by governments, researchers and industry. Highlight • A novel weather file for building assessment facing high temperature scenarios is proposed. • The method relies on KDD involving quantile regression ensemble and data integration. • It provides additional knowledge of inputs that affect temperature extremes. • The outcome is validated w.r.t. standards and is ready to use by industry. [ABSTRACT FROM AUTHOR]

This article suggests the application of multiresolution analysis by Wavelet Transform—WT and Echo State Networks—ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine’s power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error—NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model—PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks—ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting. [ABSTRACT FROM AUTHOR]

Aims – The aim of this study was i) to evaluate the potential of the DWI sequence in the characterization of malignant breast lesions; ii) to verify if this sequence should incorporate the breast MRI protocol and iii) to correlate the apparent diffusion coefficients (ADC) values and histological results. Methodology – The sample includes 18 female patients between the ages of 38 and 71 years, who presented with malignant breast lesion confirmed by histology. The DWI sequence was added to the MRI standard protocol to calculate the ADC values. Results – In the results obtained we observed that the range of the ADC values calculated in the center of the malignant lesions, showed a mean and standard deviation of 0.89 ± 0.14 x10-3 mm2 / s. This method of using the ADC values for the detection of malignant lesions showed a sensitivity of 100%. Conclusion – The DWI technique proved to be a useful method in the characterization of malignant breast lesions, as it showed a sensitivity of 100%, so we suggest its inclusion in the Breast MR standard protocol. [ABSTRACT FROM AUTHOR]

Data security and integrity of the transactions, are important objects to the companies that have online systems working directly or indirectly with their assets. The interest in ecommerce has spread in many market types due to low operating costs and also to its global reach, causing a need for constant improvements regarding the safety of these systems. This paper demonstrates the construction of a Web system using Aspect Oriented Programming (AOP), based on a model called multilevel access methods using the AspectJ language. It was used NetBeans IDE as the development environment. It was adopted an alternative syntax, known as Annotations through interfaces, which served to connect the called method and the allowed user profile. This model was implemented in a system of the Federal Institute of Education, named as Programa Monitoria.The Aspect-Oriented Programming, used in this system, has contributed directly to the reduction of scattering and entanglement of source codes between the system modules and non-functional requirements, reducing the coupling and consequently the complexity of the source code. The Aspect-Oriented Programming also promoted the centralization of responsibility for security, which is usually assigned to all developers on the team. [ABSTRACT FROM AUTHOR]