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Assessing Pullout Resistance of Earth Reinforced Embankment Model
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- The sandy soil with high gypsum content (usually referred to as gypseous soil) covers vast area in south, east, middle and west regions of Iraq, such soil possess a type of cohesive forces when attached with optimum amount of water, then compacted and allowed to cure, but losses its strength when flooded with water again. Much work on earth reinforcement was published which concentrate on the gain in bearing capacity in the reinforced layer using different types of cohesive or cohesion less soil and various types of reinforcement such as plastic, metal, grids, and synthetic textile. Little attention was paid to there enforce gypseous soil. The objective of this work is to study the interaction between such soil and reinforcement strips and determine the frictional stress between there enforcement strips and gypseous soil at its cured condition and at the asphalt stabilized condition through the pullout technique. This work presents a laboratory investigation on earth reinforced embankment model box. The box was filled with gypseous soil compacted in layers to a predetermined density. Aluminum and plastic reinforcement strips of variable geometric types were embedded at each layer. After compaction of each layer, and filling the box, the strips were subjected to pullout test to determine the frictional resistance between the soil and the strips at different spacing in the vertical and horizontal planes. The same procedure was repeated on another box after subjecting the embankment to curing for 10 days. A third embankment model was constructed using asphalt stabilized gypseous soil. Finally, the frictional behavior of the models was evaluated and the reinforcing strips behavior and capabilities were determined

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Publication Date
Sat Aug 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Image Compression based on Non-Linear Polynomial Prediction Model
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Publication Date
Thu Aug 13 2020
Journal Name
Journal Of Physics: Conference Series
Chaos in Beddington–DeAngelis food chain model with fear
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Abstract<p>In the current paper, the effect of fear in three species Beddington–DeAngelis food chain model is investigated. A three species food chain model incorporating Beddington-DeAngelis functional response is proposed, where the growth rate in the first and second level decreases due to existence of predator in the upper level. The existence, uniqueness and boundedness of the solution of the model are studied. All the possible equilibrium points are determined. The local as well as global stability of the system are investigated. The persistence conditions of the system are established. The local bifurcation analysis of the system is carried out. Finally, numerical simulations are used t</p> ... Show More
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Publication Date
Sat Aug 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Employ 6D-BIM Model Features for Buildings Sustainability Assessment
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Abstract<p>To translate sustainable concepts into sustainable structure, there is a require a collaborative work and technology to be innovated, such as BIM, to connect and organize different levels of industry e.g. decision-makers, contractors, economists, architects, urban planners, construction supplies and a series of urban planning and strategic infrastructure for operate, manage and maintain the facilities. This paper will investigate the BIM benefits as a project management tool, its effectiveness in sustainable decision making, also the benefit for the local industry key stakeholders by encouraging the BIM use as a project management tool to produce a sustainable building project. This p</p> ... Show More
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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Cybersecurity And Information Management
Machine Learning-based Information Security Model for Botnet Detection
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

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Publication Date
Sat Sep 02 2017
Journal Name
Journal Of Engineering
Estimation Curve Numbers using GIS and Hec-GeoHMS Model
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Recently, the development and application of the hydrological models based on Geographical Information System (GIS) has increased around the world. One of the most important applications of GIS is mapping the Curve Number (CN) of a catchment. In this research, three softwares, such as an ArcView GIS 9.3 with ArcInfo, Arc Hydro Tool and Geospatial Hydrologic Modeling Extension (Hec-GeoHMS) model for ArcView GIS 9.3, were used to calculate CN of (19210 ha) Salt Creek watershed (SC) which is located in Osage County, Oklahoma, USA. Multi layers were combined and examined using the Environmental Systems Research Institute (ESRI) ArcMap 2009. These layers are soil layer (Soil Survey Geographic SSURGO), 30 m x 30 m resolution of Digital Elevati

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Publication Date
Tue Dec 31 2024
Journal Name
Journal Of Soft Computing And Computer Applications
Enhancing Image Classification Using a Convolutional Neural Network Model
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In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

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Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
"Compared some of the semi-parametric methods in analysis of single index model "
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As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

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Publication Date
Wed Apr 01 2020
Journal Name
Civil Engineering Journal
Model Development for the Prediction of the Resilient Modulus of Warm Mix Asphalt
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Increasing material prices coupled with the emission of hazardous gases through the production and construction of Hot Mix Asphalt (HMA) has driven a strong movement toward the adoption of sustainable construction technology. Warm Mix Asphalt (WMA) is considered relatively a new technology, which enables the production and compaction of asphalt concrete mixtures at temperatures 15-40 °C lower than that of traditional hot mix asphalt. The Resilient modulus (Mr) which can be defined as the ratio of axial pulsating stress to the corresponding recoverable strain, is used to evaluate the relative quality of materials as well as to generate input for pavement design or pavement evaluation and analysis. Based on the aforementioned preface, it is

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Publication Date
Wed Jan 01 2020
Journal Name
Journal Of Building Engineering
Development of gravitational search algorithm model for predicting packing density of cementitious pastes
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Publication Date
Sun Dec 31 2017
Journal Name
College Of Islamic Sciences
Stylistic phenomena in the Quranic referral: (Pronouns of backbiting as a model)
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Research summary

Backbiting pronouns have gained great importance in contemporary textual linguistic studies, as they play a coordinating function in the text and link its parts.

Interpreters did not overlook the role of backbiting pronouns in achieving the function of consistency and coherence in the Qur’anic text, as they referred to this, including Ibn Ashour, who had many analytical practices related to referral issues and their complexities. The Qur'anic reference to the pronouns of backbiting, so the researcher traced these phenomena and their evidence in the Holy Qur'an, aiming to clarify their role in enriching the significance and in the coherence and harmony

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