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), and nearest epicenter distance (REPi) to a seismic station. The derived PGA models are remarkably simple and straightforward and can be used reliably for pre-design purposes. The proposed PGA models (i.e., models I and II) obtained via the explicit formula produced using the PSO method are highly correlated to the actual PGA records owing to low coefficients of variation (CoV) of approximately 2.12% and 2.06%, and mean values (i.e., close to 1.0) of approximately 1.005 and 1.004. Lastly, high-frequency, low absolute relative error (ARE), which is below 5%, is recorded for the proposed models, thereby showing an acceptable error distribution.
The unconventional techniques called “the quick look techniques”, have been developed to present well log data calculations, so that they may be scanned easily to identify the zones that warrant a more detailed analysis, these techniques have been generated by service companies at the well site which are among the useful, they provide the elements of information needed for making decisions quickly when time is of essence. The techniques used in this paper are:
- Apparent resistivity Rwa
- Rxo /Rt
The above two methods had been used to evaluate Nasiriyah oil field formations (well-NS-3) to discover the hydrocarbon bearing formations. A compu
... Show MoreBackground: Scientific education aims to be inclusive and to improve students learning achievements, through appropriate teaching and learning. Problem Based Learning (PBL) system, a student centered method, started in the second half of the previous century and is expanding progressively, organizes learning around problems and students learn about a subject through the experience of solving these problems.Objectives:To assess the opinions of undergraduate medical students regarding learning outcomes of PBL in small group teaching and to explore their views about the role of tutors and methods of evaluation. Type of the study: A cross-sectional study.Methods: This study was conducted in Kerbala Medical Colleges among second year students
... Show MoreThis paper demonstrates the design of an algorithm to represent the design stages of fixturing system that serve in increasing the flexibility and automation of fixturing system planning for uniform polyhedral part. This system requires building a manufacturing feature recognition algorithm to present or describe inputs such as (configuration of workpiece) and built database system to represents (production plan and fixturing system exiting) to this algorithm. Also knowledge – base system was building or developed to find the best fixturing analysis (workpiece setup, constraints of workpiece and arrangement the contact on this workpiece) to workpiece.
The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreThe economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
... Show MoreProfessional learning societies (PLS) are a systematic method for improving teaching and learning performance through designing and building professional learning societies. This leads to overcoming a culture of isolation and fragmenting the work of educational supervisors. Many studies show that constructing and developing strong professional learning societies - focused on improving education, curriculum and evaluation will lead to increased cooperation and participation of educational supervisors and teachers, as well as increases the application of effective educational practices in the classroom.
The roles of the educational supervisor to ensure the best and optimal implementation and activation of professional learning soci
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