UHE Belo Monte: el estudio de impacto ambiental y sus contradicciones

Este número da Revista Brasileira de Iniciação Científica publica os artigos com os resultados das pesquisas de Iniciação Científica 2016/17 da Universidade Federal da Integração Latino-Americana (UNILA). A UNILA é uma das novas universidades federais brasileiras, iniciando suas atividades em 2010. Já em 2011, começa a funcionar seu Programa de Iniciação Científica (IC). A IC é uma das prioridades da política de pesquisa da UNILA. Seu aporte em bolsas de IC é superior ao de fontes externas como CNPq e Fundação Araucária. Em seus seis anos de existência, o Programa de IC tem promovido a difusão da cultura científica entre os estudantes, contribuído com sua formação e os estimulado ao ingresso na pós-graduação.Analisando o planejamento e execução do projeto UHE Belo Monte, o artigo compara as normas que regem o licenciamento de empreendimentos hidrelétricos com a sua controvertida implementação prática. complexidade do processo de licenciamento ambiental como mecanismo de proteção do meio ambiente dos possíveis impactos das atividades humanas é cotejada com o histórico de exploração de recursos naturais da região Amazônica, além do contexto das políticas econômicas desenvolvimentistas.Analyzing the planning and execution of the Belo Monte HPP project, the article aims to compare the environmental law framework valid in Brazil with their controversial enforcement. The environmental impact assessment as a mechanism to protect the environment from the possible impacts of human activities is studied, along with the historical perspective of Amazon natural resources exploitation and the context of Brazilian’s economic development policies.Analizando el planeamiento y la ejecución del proyecto UHE Belo Monte, el artículo compara las normas que rigen el licenciamiento de emprendimientos hidroeléctricos con su controvertida implantación práctica. La complejidad del proceso de licenciamiento ambiental como mecanismo de protección del medio ambiente de los posibles impactos de las actividades humanas es cotejada con el histórico de explotación de recursos naturales de la región Amazónica y el contexto de las políticas económicas del desarrollismo

Intercomparison of Hantzsch and fiber-laser-induced-fluorescence formaldehyde measurements

Two gas-phase formaldehyde (HCHO) measurement techniques, a modified commercial wet-chemical instrument based on Hantzsch fluorimetry and a custom-built instrument based on fiber laser-induced fluorescence (FILIF), were deployed at the atmospheric simulation chamber SAPHIR (Simulation of Atmospheric PHotochemistry In a large Reaction Chamber) to compare the instruments' performances under a range of conditions. Thermolysis of para-HCHO and ozonolysis of 1-butene were used as HCHO sources, allowing for calculations of theoretical HCHO mixing ratios. Calculated HCHO mixing ratios are compared to measurements, and the two measurements are also compared. Experiments were repeated under dry and humid conditions (RH 60%) to investigate the possibility of a water artifact in the FILIF measurements. The ozonolysis of 1-butene also allowed for the investigation of an ozone artifact seen in some Hantzsch measurements in previous intercomparisons. Results show that under all conditions the two techniques are well correlated (R2 ≥ 0.997), and linear regression statistics show measurements agree with within stated uncertainty (15% FILIF + 5% Hantzsch). No water or ozone artifacts are identified. While a slight curvature is observed in some Hantzsch vs. FILIF regressions, the potential for variable instrument sensitivity cannot be attributed to a single instrument at this time. Measurements at low concentrations highlight the need for a secondary method for testing the purity of air used in instrument zeroing and the need for further FILIF White cell outgassing experiments

Intercomparison of Hantzsch and fiber-laser-induced-fluorescence formaldehyde measurements

Two gas-phase formaldehyde (HCHO) measurement techniques, a modified commercial wet-chemical instrument based on Hantzsch Fluorimetry and a custom-built instrument based on Fiber-Laser Induced Fluorescence (FILIF), were deployed at the atmospheric simulation chamber SAPHIR to compare the instruments' performances under a range of conditions. Thermolysis of para-HCHO and ozonolysis of 1-butene were used as HCHO sources, allowing for calculations of theoretical HCHO mixing ratios. Calculated HCHO mixing ratios are compared to measurements, and the two measurements are also compared. Experiments were repeated under dry and humid conditions (RH < 2% and RH > 60%) to investigate the possibility of a water artifact in the FILIF measurements. The ozonolysis of 1-butene also allowed for the investigation of an ozone artifact seen in some Hantzsch measurements in previous intercomparisons. Results show that under all conditions the two techniques are well correlated (<i>R</i>² &ge; 0.997), and linear regression statistics show measurements agree with within stated uncertainty (15% FILIF + 5% Hantzsch). No water or ozone artifacts are identified

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

YesIn this work, machine learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CMEs catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flares and CMEs data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Different properties are extracted from all the associated (A) and not-associated (NA) flares representing the intensity, flare duration, duration of decline and duration of growth. Cascade Correlation Neural Networks (CCNN) are used in our work. The flare properties are converted to numerical formats that are suitable for CCNN. The CCNN will predict if a certain flare is likely to initiate a CME after input of its properties. Intensive experiments using the Jack-knife techniques are carried out and it is concluded that our system provides an accurate prediction rate of 65.3%. The prediction performance is analysed and recommendation for enhancing the performance are provided

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Assessment of prodfction service capacity by soil Qfality evalfations

The ability of a soil to provide the productivity service depends on the fulfillment of the functions that enable the realization of productivity service (PS). This study was conducted to determine and map the PS capacity of surface and subsurface soils in a 195-ha farmland located at Amasya province of Turkey. Functions that contribute to the provision of PS have been identified, and effective indicators ensuring the realization of functions have been identified. Indicator values were converted to unit-less scores using non-linear scoring functions defined in soil management assessment framework. Simple additive (SA) and weighted additive (WA) methods were used to calculate soil functions scores and PS index values. The weights representing the contribution ratio of each indicator to soil functions as well as each function to PS index were obtained by employing the Analytical Hierarchy Process (AHP). Soil functions scores were calculated by summing of the weighted indicator scores, and the PS index value was obtained by summing the weighted function scores. Ordinary kriging, inverse distance weighting and radial basis function methods were used to produce maps for functions and PS index values. Root mean squared error and mean absolute error values were used as criteria to determine the most accurate interpolation method. The AHP technique revealed that nutrient cycle function had the highest (34%) contribution to the provision of PS, while the durability and resistance function (15%) had the lowest contribution. The PS index value was calculated as 0.57 and 0.59 by SA and WA methods, respectively. The PS index values and soil functions, except the resistance and resilience, calculated both by SA and WA were slightly different for surface and sub-surface soils. The results revealed that organic carbon is the most influential indicator affecting the soil functions and consequently the PS of soils. © 2019 Parlar Scientific Publications. All rights reserved

Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

WOS: 000232985200004In this paper, we present a new system for the classification of electrocardiogram ( ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network