Northeast India is one of the world’s most seismically active regions. The event data included in this research for the period 1737–2012 is mostly obtained from worldwide database agencies such as ISC, NEIC, and GCMT. Historical seismicity is collected from published and unpublished documents and some earthquake events are collected from the Indian Meteorological Department Bulletins. As the M_w scale is developed and validated in the southern California region and overestimates the smaller magnitude earthquakes, therefore, recent literature suggested an improved version of the seismic moment magnitude scale (M_wg) applicable for the entire globe considering both long- and short-period frequency-spectra using modern instrumental data. To update the earthquake catalog of Northeast India, we prepared empirical relationships between different magnitudes to M_wg using robust statistical General Orthogonal Regression. A procedure is also suggested for converting different earthquake sizes towards seismic moment scale. The Magnitude of Completeness (M_c) and the Gutenberg–Richter (GR) recurrence parameter values for the declustered homogenized catalog in four time periods, namely 1737–1963, 1964–1990, 1964–2000, and 1964–2012, have been computed. Our analysis suggests that the use of the M_wg scale improves seismicity parameters ‘b’ up to 30%, ‘a’ up to 17%, and ‘M_c’ up to 18% for the Northeast India region. A complete unified earthquake catalog in terms of advanced seismic moment magnitude scale could help understand seismicity and earthquake engineering studies of the region.

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K‐nearest neighbors. The principal component analysis (PCA) and logistic regression (LR) methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F‐measure, accuracy, and area under the curve (AUC‐ROC) were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC‐ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC‐ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications.

In order to explore new options to optimize the low-grade copper ore bioleaching process, it is important to understand the kinetics of microbial oxidation at industrial level. This work studies the changes of iron and sulfur oxidation rates of microbial communities in solution from an industrial low grade copper bioleaching heap process at Escondida Mine in Chile. Pregnant leach solution (PLS) samples were analyzed periodically to determine physico-chemical parameters. The total numbers of the different microorganism species in industrial samples were determined by Real Time PCR. In addition, Most Probable Number assays (MPN) were performed for iron and sulfur oxidizing microorganisms. Kinetics incubation tests of PLS in the presence of iron or sulfur were performed to study the iron and sulfur oxidation, in total, 102 oxidation profile tests were obtained. Based on the oxidation profiles obtained, the tests were divided into four groups, labeled as fast, normal, stepped shape, and incomplete. The grouping system was established by considering oxidation time and rates, during the initial oxidation stages and accounted for any lag phase. A data mining technique, called decision trees was used to analyze the data and to generate rules that represented patterns in the data. Strong correlations were found between the predominant microorganisms and the behavior of the oxidation tests. Preliminary results indicate that the magnitude order of MPN of the iron oxidizing microorganisms is an important factor in the microbial oxidizing activity, followed by the predominant specie within the microbial population, PLS temperature and Eh.