The aim of this research is to develop mechanical properties of a new aluminium-lithium-copper alloy. This alloy prepared under control atmosphere by casting in a permanent metal mould. The microstructure was examined and mechanical properties were tested before and after heat treatment to study the influence of heat treatment on its mechanical properties including; modulus of elasticity, tensile strength, impact, and fatigue. The results showed that the modulus of elasticity of the prepared alloy is higher than standard alloy about 2%. While the alloy that heat treated for 6 h and cooled in water, then showed a higher ultimate tensile stress comparing with as-cast alloy. The homogenous heat treatment gives best fatigue

Salt stress negatively affects germination and seedling growth. Sorghum cultivars (Bohuth70, Inqath and Rabeh), seed soaking in dry yeast extract (3, 6 and 9 g l-1) in addition to dry seeds and electrical conductivity (4, 10 and 16 dS m-1) were studied. Traits of germination ratio at first and final counts, lengths of radicle and plumule, seedling dry weight and seedling vigour index were studied. The cultivar of Bohuth70 and concentration of yeast extract (9 g l-1) were superior at all studied traits, while all traits values were reduced with increased saline stress. The combination (Bohuth70×9×4) was superior to most other treatments at first and final counts, radicle length and seedling dry weight, while superiority of plumule length a

The deterioration of buried sewers during their lifetime can be affected by several factors leading to bad performance and can damage the infrastructure similar to other engineering structures. The Hydraulic deterioration of the buried sewers caused by sewer blockages while the structural deterioration caused by sewer collapses due to sewer specifications and the surrounding soil characteristics and the groundwater level. The main objective of this research is to develop deterioration models, which are used to predict changes in sewer condition that can provide assessment tools for determining the serviceability of sewer networks in Baghdad city. Two deterioration models were developed and tested using statistical software SPSS, the

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve