In practical engineering problems, uncertainty exists not only in external excitations but also in structural parameters. This study investigates the influence of structural geometry, elastic modulus, mass density, and section dimension uncertainty on the stochastic earthquake response of portal frames subjected to random ground motions. The North-South component of the El Centro earthquake in 1940 in California is selected as the ground excitation. Using the power spectral density function, the two-dimensional finite element model of the portal frame’s base motion is modified to account for random ground motions. A probabilistic study of the portal frame structure using stochastic finite elements utilizing Monte Carlo simulation

Abstract

Since period the developing economies  including, Iraq economy faced undesirable double economic which is spread unemployment beside continual rising  in price levels this phenomenon called stagflation , there is researches  deal with this phenomenon and the elements participate in it, but it seems to the researcher there is important element from These elements which did not give sufficient concern and this lead to unhealthy situation  which can be represent to the rush creating establishments to University education in Iraq during period ( 2004- 2014) with excessive the3 truly need of the outputs which the Iraq market need which result increase the numbers unemployed on one

Several parameters affect the properties of Portland cement and one of these parameters is the cooling rate of the clinker. If the effectiveness of the cooling method of the clinker increases, a good enhancement in the properties of Portland cement will be found. Depending on the new cooling method suggestion by Nasr et. al. [20], the counter pattern of air clinker flow was studied using (FLUENT 6.3.26). The dimensions of the cooling room in grate cooler, the constant mass flow rate of both clinker and air, different height ratios, and different clinker porosity were considered in this numerical work. The results show that the heat transfers in the first half of the cooling room (0 < X < 0.9 m) is larger than that in the secon

Aspartate aminotransferase was purified from urine and serum of patients with type 2 diabetes in a 2 steps procedure involving dialysis bag and sephadex G-25 gel filtration (column chromatography). The enzyme was purified 346.23 fold with 1467% yield and 3.46 fold with 142.85% yield in urine and serum of patients with type 2 diabetes respectively. The purified enzyme showed single peak. The results of this study revealed that AST activity of type 2 diabetes urine and serum increased significantly (p<0.001) compared with control group.

Background: Since carbapenems are currently the preferred treatment for severe infections brought on by multidrug-resistant bacteria which can create Extended-Spectrum β-Lactamases (ESBLs), it is extremely concerning that Gram-negative bacteria are becoming resistant to carbapenem. It has been demonstrated that Klebsiella pneumoniae produces a beta-lactamase that hydrolyses the β-lactam ring in antibiotics, making it one of the few bacteria which are currently exhibiting a high value of resistance because of changing in the organism's core genome. Methods: For the current study, 50 samples were gathered from different water sources, and based on morphological and biochemical testing, 10 isolates were determined to be K. pneumoniae. Accord

Um-Al-Naaj region in Al-Hawiezah Marsh, Southern Iraq was chosen to study the environmental variations of some water characteristics during 2008, seasonally. The results showed clear seasonal changes in values of some environmental variables (temperature, depth, light penetration, turbidity, total suspended solids, pH, dissolved oxygen, reactive phosphate, reactive nitrite, and reactive nitrate), while there were no clear seasonal changes in electrical conductivity and salinity values. In addition, high nutrients concentrations and light penetration were noted. Statistical analysis showed significant positive relationship between air and water temperature; electrical conductivity and salinity. Water turbidity was significantly affecte

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an