Important points were concluded from this analysis related with the presence of the same variable CEs within multiple isolates with different time points being under the selection and the location of SNPs within the conserved functional pattern of CEs. In the 40 isolates, 9 out of 39 variable CEs conducted with multiple isolates

Ghar Formation outcrop at the Iraqi western desert was studied by microfacies analysis
of (13) thin sections collected from wadi Al-Ratgha ( west of Qaim ) . According to
petrographic com position and organisms content ,rocks were subdivided into (4)
microfacies units :bioclastic wackestone , mudstone , miliolids wackestone , and grainstone
with aggregate grains microfacies .Microfacies units reflect shallow marine environment of
low circulation of very warm water at the middle part . The lower and middle part
interbedded with quite open marine environment below the wave base . The upper part was
deposited at shallow marine environment of low circulation . The main diagenetic processes
were the transformation ( ty

The present work divided into two parts, first the experimental side which included the
measuring of the first natural frequency for the notched and unnotched cantilever composite beams
which consisted of four symmetrical layers and made of Kevlar- epoxy reinforced. A numerical
study covers the effect of notches on the natural frequencies of the same specimen used in the
experimental part. The mathematical model for the beam contains two open edges on the upper
surface. The effect of the location of cracks relative to the restricted end, depth of cracks, volume
fraction of fibers and orientation of the fiber on the natural frequencies are explored. The results
were calculated using the known engineering program (ANSY

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles

This work characterizes the fractographic features of the neat epoxy and ZrO₂ epoxy nanocomposites. All samples were subjected to a tensile test to determine the tensile strength and tensile modulus. SEM images were used to study the morphology of the fractured surface. The fractographic of the fracture surfaces were studied by microstructure analysis program (j-images) to specify the effect of ZrO₂ nanoparticles on tensile performance and failure mechanism for ZrO₂ epoxy nanocomposites. The tensile test results show that the addition of ZrO₂ nanoparticles (2, 4, 6, 8, and 10 vol.%) to the epoxy matrix leads to increase the tensile strength about 40% for optimal content of ZrO₂ nanop

Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory char

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet