Design and Implementation Intelligent Adaptive Front-lighting System of Automobile using Digital Technology on Arduino Board

The automatic light AFS (Adaptive Front - Lighting System) is added to the capabilities of modern vehicles that will improve the safety of vehicle drivers and passengers traveling at night. A new architecture of the AFS has proposed in this paper. This architecture is powerful and intelligent using the PWM technique on ARDUINO Board replaces the old mechanical system based on stepper motors

A New Photovoltaic Energy Sharing System between Homes in Standalone Areas

Today, global energy consumption is dominated by fossil fuels such as oil, coal and gas. The intensive consumption of these energy sources gives rise to greenhouse gas emissions and therefore an increase in CO2 emissions. Photovoltaic energy has persistently been considered as a green and pollution-free renewable energy source to overcome greenhouse effect and energy crisis. This paper describes a new method of photovoltaic energy sharing in standalone micro-grids using photovoltaic panels. This approach is based on automatic electrical energy sharing depending on the state of charge (SOC) of the electrical storage unit using by each home and on the electrical power consumption of each home.The monitoring system is connected to each home in micro-grid, it manage each home’s energy use, and assigns more energy to a large energy-consuming home. This architecture contributes to reducing total energy lost

An Environmental Genocide: Counting the Human and Environmental Cost of Oil in Bayelsa, Nigeria

NoBayelsa, in the Niger Delta, in Southern Nigeria, is in the grip of a human and environmental catastrophe of unimaginable proportions. At one time, the area was home to one of the largest mangrove forests on the planet; an area of unrivalled ecological value. Today, it is one of the most polluted places on Earth. Oil extraction and its impact is the overwhelmingly evident cause of this disaster

Prediction of the steel-concrete bond strength from the compressive strength of Portland cement and geopolymer concretes

The oldest and simplest bond test, which is the standard concentric pull out test, is usually used as a comparative test for different concretes in order to assess the bond with deformed bars. In this paper, two types of concrete are considered: Ordinary Portland cement (OPC) concrete and a novel concrete technology, namely geopolymer concrete (GPC). Bond strength was investigated by conducting pull-out tests on ribbed bars with a nominal diameter of 10 mm and/or 12 mm. The specimens were tested at various ages ranging from 1 to 28 days. Compression tests were performed at all ages as well. The main objective of the extensive research program involving 260 pull-out tests was to develop empirical models correlating the steel-concrete bond strength to the mean compressive strength of concrete for both OPC and geopolymer concretes. The models developed are compared to the existing model adopted by FIP Committee

L'Egypte sous pression ? : des mobilisations au verrouillage politique

En 1992, la Convention sur la diversité biologique a garanti les droits des populations autochtones sur la nature. Dans les Aires marines protégées (AMP) ouest-africaines, l'approche conservatoire tend à encourager des formes autochtones de régulation de l'accès aux territoires et aux ressources. Si l'autochtonie s'est imposée comme un référent global, elle n'en soulève pas moins un certain nombre d'ambiguïtés que révèlent ici les exemples de deux AMP en Mauritanie et au Sénégal. L'autochtonie serait-elle davantage un produit du capitalisme global contemporain qu'une résistance à la modernité marchande

Application de la théorie des problèmes inverses à l'estimation des paramètres des modèles rhéologiques

Nous présentons dans cette étude une méthode d'assistance à la détermination des paramètres des modèles rhéologiques. Cette méthode est basée sur une théorie probabiliste des problèmes inverses (TARANTOLA, 1987). Elle tient compte des incertitudes des mesures au Laboratoire et des connaissances a priori des paramètres des modèles rhéologiques. La méthode présentée donne un algorithme général qui permet de déterminer les paramètres des modèles rhéologiques sophistiqués à partir des essais multiaxiaux non conventionnels, voire des essais non homogènes

Arabic Aspect-Based Sentiment Classification Using Seq2Seq Dialect Normalization and Transformers

Sentiment analysis is one of the most important fields of natural language processing due to its wide range of applications and the benefits associated with using it. It is defined as identifying the sentiment polarity of natural language text. Researchers have recently focused their attention on Arabic SA due to the massive amounts of user-generated content on social media and e-commerce websites in the Arabic world. Most of the research in this fieldwork is on the sentence and document levels. This study tackles the aspect-level sentiment analysis for the Arabic language, which is a less studied version of SA. Because Arabic NLP is challenging and there are few available Arabic resources and many Arabic dialects, limited studies have attempted to detect aspect-based sentiment analyses on Arabic texts. Specifically, this study considers two ABSA tasks: aspect term polarity and aspect category polarity, using the text normalization of the Arabic dialect after making the classification task. We present a Seq2Seq model for dialect normalization that can serve as a pre-processing step for the ABSA classification task by reducing the number of OOV words. Thus, the model’s accuracy increased. The results of the conducted experiments show that our models outperformed the existing models in the literature on both tasks and datasets

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications

Realization of tin oxide like anode for the manufacture of the organic solar cells

The transparent oxides such as SnO2, In2O3 and ZnO continue to arouse a private interest for their various applications. The objective of the various studies being to carry out the layers which are simultaneously most transparent and most conducting possible. Thus in the field of the solar spectrum, the transmission of the layers must be higher than 80% and their conductivity exceeding 103 (Ohm.cm)-1. Their transparency which is related to the value of their forbidden band must be higher than 3.7 e V. Their electric properties as for them depend on the composition of the layers and a possible doping. In this work, one characterized layers of SnO2 deposited by chemical pulverization, one carried out measurements by, electronic scan microscopy, diffraction of x-rays and also of the optical measurements and electronic. It results from it that the layers are conducting and transparent in the visible one but they are relatively rough, following its characterizations, one carried out organic photovoltaic cells using these layers of SnO2 and also of the commercial ITO like anode in these components. More particularly one was interested in the influence of the presence of a fine layer of gold between the anode and organic material