Braces in straight bridge systems improve the lateral-torsional buckling resistance of the girders by reducing the unbraced length, while in horizontally curved and skew bridges, the braces are primary structural elements for controlling deformations by engaging adjacent girders to act as a system to resist the potentially large forces and torques caused by the curved or skewed geometry of the bridge. The cross-frames are usually designed as torsional braces, which increase the overall strength and stiffness of the individual girders by creating a girder system that translates and rotates as a unit along the bracing lines. However, when they transmit the truck’s live load forces, they can produce fatigue cracks at their connection

In this study, successive electrocoagulation (EC) and electro-oxidation (EO) processes were used to minimize some of the major pollutants in real wastewater, such as organics (detected by chemical oxygen demand (COD)), and turbidity. The wastewater utilized in the present study was collected from the Midland Refinery Company in Baghdad-Iraq. The performance of the successive batch EC-EO processes was studied by utilizing Graphite and Aluminum (Al) as monopolar anode electrodes and stainless steel (st.st.) as the cathode. The Taguchi experimental design approach was used to attain the best experimental conditions for COD reduction as a major response. Starting from chemical oxygen demand COD of (600 ppm), the effects of current density (C

Symmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a rand

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Background: One way to target polypharmacy and inappropriate medication in hemodialysis (HD) patients is with medication deprescribing. Objective: To assess the impact of implementing a pharmacist-led deprescribing program on medication adherence among HD patients. Method: A prospective interventional, one-group pretest-posttest-only design study was conducted at a hemodialysis center in Wasit Governorate, Iraq. Medication reconciliation followed by medication review based on the deprescribing program was done for all eligible patients, and the patients were monitored for three months for any possible complications. Results: Two hundred and seventy patients were screened for eligibility. Only one hundred and eighteen were enrolled i

The necessary optimality conditions with Lagrange multipliers  are studied and derived for a new class that includes the system of Caputo–Katugampola fractional derivatives to the optimal control problems with considering the end time free. The formula for the integral by parts has been proven for the left Caputo–Katugampola fractional derivative that contributes to the finding and deriving the necessary optimality conditions. Also, three special cases are obtained, including the study of the necessary optimality conditions when both the final time  and the final state  are fixed. According to convexity assumptions prove that necessary optimality conditions are sufficient optimality conditions.