In this paper, we discuss an evidence reasoning model that support digital forensics of cloud computing. Unlike previous work, we aim to encapsulate the science of Probabilistic Graphical Model (PGM) to conclude highly accurate set of events using Bayesian theorem and Causal models. Our contribution is to achieve the highly accurate set of events without the need to set multiple hypothesis.
Trust and reputation is an emerging topic in high velocity ad-hoc networks. However, very few techniques in the literature specialise in building trust in a highly dynamic network as older approaches suffer from efficiency and speed limitations. This paper explores some of the most notable specialised trust and reputation techniques for highly mobile ad-hoc networks and the implications of using them in high velocity mobile ad- hoc networks.
In this paper, we address the problem of mobile phishing via the implementation of a Trojan that commits phishing through the mobile's pre-installed applications, which are naturally trusted. It utilizes task interception along with lack of identity indicators, and it overrides the default behavior of some functions to succeed with the attack. We also study the impact of this Trojan on the device's performance. Finally, we propose some security enhancements that do not rely on the human factor, such as identity indicators and comparing running processes.
As ubiquitous computing becomes pervasive, low computation devices are deployed in critical activities in our daily life such as the used of smart card for bank transaction. Since these devices contain sensitive information related to it is owner the confidentiality and the integrity of these devices must be considered during the designing phase. Thus, recently we have seen a lot of new proposed ciphers that are design for low computation devices. The aim of theses ciphers is to provide a sufficient security level with less computation power. Thus, we implement our proposed solution on 8 bits AVR microcontroller in order to study the required memory and speed. Also, the paper study and implement new proposed stream ciphers which are Grain and Trivium for comparison purpose. Our proposed SGCA algorithm proves to have less memory and time consuming than the other two.
Desert atmospheric boundary layers present extremely complex local structures that have been scarcely addressed in the literature, and whose understanding is essential in modeling processes for various environmental, economic and societal applications. In this study, we explore the potential of the joint usage of Lidar ceilometer backscattering profiles and sun-photometer optical depth retrievals to quantitatively determine the vertical aerosol profile over dusty hot desert regions. At this goal, we analyze a continuous record of observations of the atmospheric boundary layer (ABL) height from a single lens Lidar ceilometer operated at Masdar Institute Field Station (Abu Dhabi, U.A.E.) and the concurrent measurements of aerosol optical depth derived independently from the Masdar Institute AERONET sun-photometer. The main features of the desert ABL are obtained from the ceilometer range corrected backscattering profiles and therefore calibrated to obtain a full diurnal cycle climatology of the aerosol optical depth and aerosol profiles.
Selective catalytic reduction (SCR) of NOx is currently a well-used method of NOx reduction from industrial plants. It entails the application of reducing agents to convert harmful NOx to harmless gases in the presence of a catalyst. SCR for NOx reduction is currently seen as the most promising technology for the reduction of NOx from chemical and power plants. However, there is a need to study these systems to improve their performance to meet the continuously stricter NOx regulations. In this work, a 3-dimensional CFD model of the single channel SCR system was used to study the effect of key parameters such as inlet gas temperature, NH3/NOx ratio and NO2/NOx ratios on the performance of the system. The results showed that as the NO2/NOx is increased, the NOx reduction is increased until a ratio of unity. Further increase in the NO2 concentration results in a decline in NOx reduction.
Since 2001, the Harmful Algal Blooms (HABs) occur frequently over the Arabian Gulf mainly during winter seasons. These HABs cause death, poison the fish and birds and affect the desalination plants. Tracking those algae batches is mandatory to protect the desalination plants since it provides a significant amount of fresh water to the Gulf countries. Time series modeling is one of the forecasting approaches that can be used to predict the remotely sensed water quality parameters such as chlorophyll concentration, SST and FLH. In this study, three times series models were used to estimate and forecast the satellite water quality parameters measured during 10 years from 2003 to 2012. These models are univariate model (SARIMA), multivariate model (regression) and neural network. It is found that SARIMA model performs well in forecasting the SST and FLH. However, neural network (NAR) and regression are the best that fit the Chl data set.
The dynamics of dust in combination to land and atmospheric forcing has a major influence on landatmosphere interactions by modulating the radiative balance of our planet, reducing visibility, affecting human health, and boosting marine life in seas and oceans. Therefore, to understand the regional dust transport pathways and estimate the impact of regional dust on climate, economy, and human life, it is crucial to identify the sources, location, extent, magnitude, and geomorphological characteristics. Given this premise, this research deploys an enhanced automated dust mapping and monitoring tool for the MENA region using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This tool used for routine detection and mapping of Sand and Dust Strom in the region. Historic MSG-SEVIRI data will be analyzed to develop time series of dust storm extent and magnitude. The maps will be compared with AOD, MODIS, and situ observations from Masdar Institute.
Mangroves represent one of the most carbon-rich ecosystems in the Tropics, noticeably impacting ecosystem services and the economy of these regions. Whether the ability of mangroves to exclude and tolerate salt has been extensively investigated in the literature, eco-hydrological characteristics of these ecosystems remains largely understudied, despite the crucial link with efficient carbon-storage, biomass productivity and water-energy fluxes. Here, we develop a Soil-Plant-Atmosphere Continuum (SPAC) model for Avicennia Marina, a mangrove able to adapt to hyper-arid intertidal zones. Among mangroves, Avicennia marina is one of the most tolerant to salinity and arid climatic conditions. Our model takes into account the specific characteristics of the mangrove ecosystem and in particular, the effects of salt-stress. Mangrove transpiration is hence obtained by solving the plant and leaf water balance and the leaf energy balance, taking explicitly into account the role of osmotic water potential and salinity in governing plant resistance to water fluxes.
The purpose of this paper is to present the research work on HEMT/FET device modeling using measurement-based behavioral modeling techniques. The target of this research is to obtain more accurate transistor model to have better AC and DC simulations. Also, the accurate proposed model will be transformed into a geometrically scalable one using two-step optimization technique based on Genetic Algorithm. Finally, future work will be presented.
