Smart Trust Management for Vehicular Networks

Spontaneous networks such as VANET are in general deployed in an open and thus easily accessible environment. Therefore, they are vulnerable to attacks. Trust management is one of a set of security solutions dedicated to this type of networks. Moreover, the strong mobility of the nodes (in the case of VANET) makes the establishment of a trust management system complex. In this paper, we present a concept of ‘Active Vehicle’ which means an autonomous vehicle that is able to make decision about trustworthiness of alert messages transmitted about road accidents. The behavior of an “Active Vehicle” is modeled using Petri Nets

Comparison of some probabilistic methods for analyzing slope stability problem

Abstract The study aims at comparing the results of different probabilistic methods such as the perturbation method, Spectral Stochastic Finite Element Method (SSFEM

Improving Malaria Detection Using L1 Regularization Neural Network

Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value

Influence of water stress on the nutritional quality of peach fruits

Climate change, especially in arid and semi-arid areas, affects the production of fruit trees. In this region, fruit tree production requires an efficient water supply that maintaining safe and stable yields. The aim of this work is to study the influence of irrigation modes on the nutritional peach fruit quality and the control of water stress indicators rates. Our experiment was carried out at the CRRA Sidi Bouzid (Central-West of Tunisia). It focused on four varieties of peach (Prunus persica L), two early varieties (Flordastar (FS) and Early Maycrest (EMC)), a seasonal variety (Rubirich (RUB)) and a late variety (O'Henry (O'H)). Three different irrigation treatments were applied to the experimental plot: full irrigation (T1; 100% ETc), sustained deficit irrigation (T2; 50% ETc) and cyclical deficit irrigation (T3). The contents of total sugar, protein, and proline as well as some bioactive compounds and stress indicators (MDA, H2O2) were quantified in the exocarp and mesocarp of the fruit. The results showed that O’H fruits are the richest in phenolic compounds, as well as they have significant antioxidant activity. While, both FS tissues accumulated more sugar (55.15 and 81.31g/100g in the mesocarp and exocarp, respectively). Protein level was much higher under T2 and T3 treatments compared to the control treatment (T1) in all varieties. Water stress mainly T2 had significantly stimulated the accumulation of proline in the mesocarp of FS (the content increased from 0.61 to 2.1 µmol/100 g MS). In addition, in the four varieties, the cyclic water treatment (T3) has a significant effect on the accumulation of sugar and phenolic compounds. In conclusion, T3 seems to be the most adequate water regime to be applied in semi-arid region, saving water resources and maintaining fruit quality

Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG

Soil salinity related to physical soil characteristics and irrigation management in four Mediterranean irrigation districts

25 Pag., 6 Tabl., 1 Fig. The definitive version is available at: http://www.sciencedirect.com/science/journal/03783774Irrigated agriculture is threatened by soil salinity in numerous arid and semiarid areas of the Mediterranean basin. The objective of this work was to quantify soil salinity through electromagnetic induction (EMI) techniques and relate it to the physical characteristics and irrigation management of four Mediterranean irrigation districts located in Morocco, Spain, Tunisia and Turkey. The volume and salinity of the main water inputs (irrigation and precipitation) and outputs (crop evapotranspiration and drainage) were measured or estimated in each district. Soil salinity (ECe) maps were obtained through electromagnetic induction surveys (ECa readings) and district-specific ECa–ECe calibrations. Gravimetric soil water content (WC) and soil saturation percentage (SP) were also measured in the soil calibration samples. The ECa–ECe calibration equations were highly significant (P 0.1) with WC, and was only significantly correlated (P Morocco (2.2 dS m−1) > Spain (1.4 dS m−1) > Turkey (0.45 dS m−1). Soil salinity was mainly affected by irrigation water salinity and irrigation efficiency. Drainage water salinity at the exit of each district was mostly affected by soil salinity and irrigation efficiency, with values very high in Tunisia (9.0 dS m−1), high in Spain (4.6 dS m−1), moderate in Morocco (estimated at 2.6 dS m−1), and low in Turkey (1.4 dS m−1). Salt loads in drainage waters, calculated from their salinity (ECdw) and volume (Q), were highest in Tunisia (very high Q and very high ECdw), intermediate in Turkey (extremely high Q and low ECdw) and lowest in Spain (very low Q and high ECdw) (there were no Q data for Morocco). Reduction of these high drainage volumes through sound irrigation management would be the most efficient way to control the off-site salt-pollution caused by these Mediterranean irrigation districts.This study was supported by the European Commission research project INCO-CT-2005-015031.Peer reviewe

Temporal variability of mineral dust in southern Tunisia : analysis of 2 years of PM10 concentration, aerosol optical depth, and meteorology monitoring

International audienceThe south of Tunisia is a region very prone to wind erosion. During the last decades, changes in soil management have led to an increase in wind erosion. In February 2013, a ground-based station dedicated to the monitoring of mineral dust (that can be seen in this region as a proxy of the erosion of soils by wind) was installed at the Institut des Régions Arides (IRA) of Médenine (Tunisia) to document the temporal variability of mineral dust concentrations. This station allows continuous measurements of surface PM10 concentration (TEOM™), aerosol optical depth (CIMEL sunphotometer), and total atmospheric deposition of insoluble dust (CARAGA automatic sampler). The simultaneous monitoring of meteorological parameters (wind speed and direction, relative humidity, air temperature, atmospheric pressure, and precipitations) allows to analyse the factors controlling the variations of mineral dust concentration from the sub-daily to the annual scale. The results from the two first years of measurements of PM10 concentration are presented and discussed. In average on year 2014, PM10 concentration is 56 µg/m3. However, mineral dust concentration highly varies throughout the year: very high PM10 concentrations (up to 1,000 µg/m3 in daily mean) are frequently observed during wintertime and springtime, hardly ever in summer. These episodes of high PM10 concentration (when daily average PM10 concentration is higher than 240 µg/m3) sometimes last several days. By combining local meteorological data, air-masses trajectories, sunphotometer measurements, and satellite imagery, the part of the high PM10 concentration due to local emissions and those linked to an advection of dusty air masses by medium and long range transport from the Sahara desert is quantified

Determinants of Positive Word of Mouth in the Tunisian Tourism Sector

Our study aims to support a critical view on a large literature on relational fidelity. It seeks to demonstrate that, to understand the loyalty in the context of an experiential consumption, the analytical framework must be changed. It is beyond the simplistic view considering consumption as an instantaneous act and positions themselves within a more holistic approach with consumption as an experience for the consumer. We try to investigate to what extent the novelty, control and commitment help explain the strength of the relationship that could bind a consumer to the provider. We discuss and test the validity of a complex network of relationships breaking with the theoretical approaches. Then, we test, through an empirical study, the validity of the relationships identified in the literature in the context of a consumption experience of hotel services in North West Tunisia