Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

Language and semantics of expressions for Grafcet model synthesis in a MDE environment

The GRAphe Fonctionnel de Commande Étapes Transitions (GRAFCET) is a powerful graphical modeling language for the pecification of controllers in discrete event systems.It uses expressions to express the conditions of transitions and conditional actions as well as the logical and arithmetic expressions assigned to stored actions. However, several research works has focused on the transformation of Grafcet specifications (including expressions) into control code for embedded systems. To make it easier to edit valid Grafcet models and generate code, it is necessary to propose a formalization of the Grafcet expression language permitting to validate its constructs and provide an appropriate semantics. For this, we propose a context-free grammar that generates the whole set of Grafcet expressions, by extending the usual grammars of logical and arithmetic expressions. We also propose a metamodel and an associated semantics of Grafcet expressions to facilitate the implementation of the Grafcet language. A parser of the expressions Grafcet emph G7Expr is then obtained thanks to the generator of parsers ANTLR, while the metamodel is implemented in the Eclipse EMF Model Driven Engineering (MDE) environment. The combination of the two tools makes it possible to analyze and automatically build Grafcet expressions when editing and synthesizing Grafcet models. Le GRAphe Fonctionnel de Commande Étapes Transitions (GRAFCET) est un puissant lan-gage de modélisation graphique pour la spécification de contrôleurs dans des systèmes à événe-ments discrets. Il fait usage des expressions pour exprimer les conditions de franchissement des transitions et des actions conditionnelles ainsi que les expressions logiques et arithmétiques assi-gnées aux actions stockées. Cependant, de nombreux travaux se sont penchés sur la transformation de spécifications Grafcet (y compris les expressions) en code de contrôle pour systèmes embar-qués. Pour faciliter l'édition de modèles Grafcet valides et la génération du code de contrôle, il est judicieux de proposer une formalisation du langage des expressions Grafcet, permettant de valider ses constructions et d'en pourvoir une sémantique appropriée. Pour cela, nous proposons une gram-maire hors-contexte qui génère tout l'ensemble des expressions Grafcet, en étendant les grammaires usuelles des expressions arithmétiques et logiques. Nous proposons également un métamodèle et une sémantique associée des expressions Grafcet pour faciliter la mise en oeuvre du langage Grafcet sous la forme d'un parseur des expressions Grafcet G7Expr obtenu grce au générateur d'analyseurs syntaxiques ANTLR, alors que le métamodèle est mis en oeuvre dans l'environnement d'Ingénie-rie Dirigée par les Modèles (IDM) Eclipse EMF. L'association des deux outils permet d'analyser et de construire automatiquement les expressions Grafcet lors de l'édition et la synthèse des modèles Grafcet.

Spatiotemporal Rates of Tectonic Deformation and Landscape Evolution above a Laterally Propagating Thrust Fault: Wheeler Ridge Anticline, California, USA

The Wheeler Ridge anticline, located in the southern San Joaquin Valley of California, USA, is a well-studied and classic example of a laterally growing fault propagation fold. New high-resolution lidar elevation data combined with nine infrared stimulated luminescence (IRSL) ages of discrete geomorphic surfaces that are bounded by prominent transverse wind and river gaps allow for investigation of tectonic topography through time. Luminescence ages from four of the six surfaces yield depositional ages that range from 32 ka to 153 ka, which are broadly consistent with a previously published soil chronosequence. Our graphical modeling indicates an average surface uplift rate of ~2.1 mm/yr and an average along-strike fold propagation rate of ~20 mm/yr. However, our probabilistic modelling and topographic analysis suggest a rate decrease of both uplift and lateral propagation toward the fault tip from ~2.4 to 0.7 mm/yr and from ~49 to 14 mm/yr, respectively. Rate decreases are not progressive but rather occur in punctuated deformational intervals across previously documented structural barriers (tear faults) resulting in a fold that is characterized by discrete segments that exhibit a systematic deformational decrease toward the east. The punctuated tectonic growth of Wheeler Ridge has also locally controlled the topographic evolution of the anticline by effecting the formational timing and position of at least seven wind and river gaps that result from multiple north-flowing antecedent streams that traverse the growing structure. We quantify the timing of wind and river gap formation, based on IRSL results and inferred incision rates, and present a model for the spatiotemporal evolution of transverse drainages and the topographic development of Wheeler Ridge. Our chronology of gap formation broadly correlates with regional Late Pleistocene dry climate intervals suggesting that both tectonics and climate were integral to the geomorphic development of the Wheeler Ridge anticline.

How COVID-19 Quarantine(s) Can Generate Poverty?

This research paper attempts to show visually how the COVID-19 quarantines can generate massive unemployment, constant expansion of inflation, reduction of the purchasing power parity, and poverty expansion from a multidimensional perspective. This visualization is only possible by creating a new multivariate graphical modeling called “The Multidimensional Poverty Kaleidoscope Graph.” The multidimensional poverty kaleidoscope graph is not intended to use a forecasting model in any case. However, its application is not limited to the study of a particular group of countries. It is not constrained by issues about the region or countries interested in applying the multidimensional poverty kaleidoscope graph. There are four primary phases in the implementation of the multidimensional poverty kaleidoscope graph. The first phase is the design of the input-output-table. The second phase is divided into two sections of analysis: the first section of analysis assumes that the COVID-19 quarantine time framework growth rate (Y = Independent variable) impacts directly on our four variables in analysis, such as the inflation growth rate (X1); the unemployment growth rate (X2); purchasing power parity growth rate (X3); the government budget deficit (X4). In the second section of the analysis, the last past four variables in analysis became our dependent variables and directly affected the poverty growth rate (Z). The third phase is the construction of the multidimensional poverty kaleidoscope graph. Finally, the multidimensional poverty kaleidoscope graph was applied to three countries, such as the U.S., Malaysia, and Guatemala.

Optical and computational dissection of emergent prefrontal rewiring to encode fear memory

The prefrontal cortex regulates various emotional behaviors and memories, and prefrontal dysfunction can trigger psychiatric disorders. While untangling the internal network may provide clues to the neural architecture underlying such disorders, it is technically difficult due to the complexity and heterogeneity of the network. Here we propose an optical and computational dissection of the internal prefrontal network based on chronic two-photon imaging and a sparse modeling algorithm, which enabled the discrimination of newly emerged neuronal ensembles specifically encoding conditioned fear responses. Further graphical modeling revealed that neurons responding to the unconditioned stimulus during fear conditioning became a core of the ensembles with an enhanced capability for pattern completion, demonstrating the activity dependent rewiring upon the associative learning.