Dans le roman moderne, le type du héros est depuis longtemps devenu suspect.  Il risque même de disparaitre dans ce qu´on désigne le Nouveau Roman qui, se concentrant plutôt sur les objets, décrits minutieusement, refuse la fonction épistémologique traditionnelle de la littérature. Cette conception se manifeste, sur le plan formel, par certains traits typiques, comme la relativisation des points de vue, la décomposition de l´action, la destruction du temps, la décomposition de l´espace et la désintégration du personnage romanesque dont les liens avec la société sont coupés.

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

        The study aims to show the correlation between inflation rates and the volume of private sector deposits in Jordan on one hand, and Amman Stock Exchange index for the period of 1999-2017. The study used the “Vector Auto Regression” model. Different types of tests are used such as: The “Johansen Co integration” test of joint integration, “Granger causality” test, the “Analysis of Variance Decomposition”, and “Impulse response Function” test.

The results showed there is a positive-one-way causal effect from Amman Stock Exchange index to inflation. Also, a one-way causal effect that comes from Amman Stock Exchange index to the size of private sec

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

In recent years, the Global Navigation Satellite Services (GNSS) technology has been frequently employed for monitoring the Earth crust deformation and movement. Such applications necessitate high positional accuracy that can be achieved through processing GPS/GNSS data with scientific software such as BERENSE, GAMIT, and GIPSY-OSIS. Nevertheless, these scientific softwares are sophisticated and have not been published as free open source software. Therefore, this study has been conducted to evaluate an alternative solution, GNSS online processing services, which may obtain this privilege freely. In this study, eight years of GNSS raw data for TEHN station, which located in Iran, have been downloaded from UNAVCO website

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce

This work represents study the rock facies and flow unit classification for the Mishrif carbonate reservoir in Buzurgan oil Field, which located n the south eastern Iraq, using wire line logs, core samples and petrophysical data (log porosity and core permeability). Hydraulic flow units were identified using flow zone indicator approach and assessed within each rock type to reach better understanding of the controlling role of pore types and geometry in reservoir quality variations. Additionally, distribution of sedimentary facies and Rock Fabric Number along with porosity and permeability was analyzed in three wells (BU-1, BU-2, and BU-3). The interactive Petrophysics - IP software is used to assess the rock fabric number, flow zon