This study focuses on improving the safety of embankment dams by considering the effects of vibration due to powerhouse operation on the dam body. The study contains two main parts. In the first part, ANSYS-CFX is used to create the three-dimensional (3D) Finite Volume (FV) model of one vertical Francis turbine unit. The 3D model is run by considering various reservoir conditions and the dimensions of units. The Re-Normalization Group (RNG) k-ε turbulence model is employed, and the physical properties of water and the flow characteristics are defined in the turbine model. In the second phases, a 3D finite element (FE) numerical model for a rock-fill dam is created by using ANSYS®, considering the dam connection with its powerhouse

In this study, an unknown force function dependent on the space in the wave equation is investigated. Numerically wave equation splitting in two parts, part one using the finite-difference method (FDM). Part two using separating variables method. This is the continuation and changing technique for solving inverse problem part in (1,2). Instead, the boundary element method (BEM) in (1,2), the finite-difference method (FDM) has applied. Boundary data are in the role of overdetermination data. The second part of the problem is inverse and ill-posed, since small errors in the extra boundary data cause errors in the force solution. Zeroth order of Tikhonov regularization, and several parameters of regularization are employed to decrease error

This article describes how to predict different types of multiple reflections in pre-track seismic data. The characteristics of multiple reflections can be expressed as a combination of the characteristics of primary reflections. Multiple velocities always come in lower magnitude than the primaries, this is the base for separating them during Normal Move Out correction. The muting procedure is applied in Time-Velocity analysis domain. Semblance plot is used to diagnose multiples availability and judgment for muting dimensions. This processing procedure is used to eliminate internal multiples from real 2D seismic data from southern Iraq in two stages. The first is conventional Normal Move Out correction and velocity auto picking and

Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

One of the challenges faced by traditional intrusion detection systems based on machine learning or deep learning is instability when dealing with unbalanced network traffic, leading to failure in detecting certain attacks (minority classifications). Additionally, they struggle with multi-stage attacks, resulting in an increase in false alarms. This paper presents a hierarchical intrusion detection system supported by a Prediction Verification Layer (PVL) and a Feature Inheritance Mechanism (FIM). Where PVL contributes to documenting the system’s final decision and increasing sensitivity to minority attacks, FIM also helps in inheriting features from previous layers and correcting errors as much as possible. Additionally, it allows for ad

Cardiovascular disease (CVD) remains the leading cause of mortality in women. Estimating cardiovascular risk using prediction models is essential for guiding preventive strategies. Despite progress, conventional risk models still omit critical women-specific factors, limiting their accuracy. Precision medicine, supported by artificial intelligence, provides a framework to integrate these overlooked determinants. This approach may help close existing gaps in cardiovascular risk prediction. Sex-specific biomarkers that contribute to overall cardiovascular risk can be incorporated into risk assessment tools to improve prevention strategies, early detection, and personalized intervention. The integration of imaging-derived variables enh

Peak ground acceleration (PGA) is one of the critical factors that affect the determination of earthquake intensity. PGA is generally utilized to describe ground motion in a particular zone and is able to efficiently predict the parameters of site ground motion for the design of engineering structures. Therefore, novel models are developed to forecast PGA in the case of the Iraqi database, which utilizes the particle swarm optimization (PSO) approach. A data set of 187 historical ground-motion recordings in Iraq’s tectonic regions was used to build the explicit proposed models. The proposed PGA models relate to different seismic parameters, including the magnitude of the earthquake (Mw), average shear-wave velocity (VS30), focal depth (FD

In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reforming reactors in series process.

The experimental information (Reformate composition and output temperature) for each four reactors collected at different operating conditions was used to predict the parameters of the proposed kinetic model. The kinetic model involving 24 components, 1 to 11 carbon atoms for paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 71 reactions. The pre-exponential Arrhenius constants and a