Fiber reinforced polymer composite is an important material for structural application. The diversified application of FRP composite has taken center of attraction for interdisciplinary research. However, improvements on mechanical properties of this class of materials are still under research for different applications. In this paper we have modified the epoxy matrix by Al₂O₃, SiO₂ and TiO₂ nano particles in glass fiber/epoxy composite to improve the mechanical and physical properties. The composites are fabricated by hand lay-up method. It is observed that mechanical properties like flexural strength, hardness are more in case of SiO₂ modified epoxy composite compare to other nano

Gypseous soil, which covers vast area in west, middle, east and south west regions of Iraq exhibit acceptable strength properties when dry, but it is weak and collapsible when it comes in touch with moisture from rain or other sources. When such weak soil is adopted for earth reinforced embankment construction, it may exhibit hazardous situation. Gypseous soil was investigated for the optimum liquid asphalt requirements of both cutback and emulsion using the one-dimensional unconfined compression strength test. The optimum fluid content was 13% (7% of cutback with 6% water content), and 17% (9% of emulsion with 8% water content). A laboratory model box of 50x50x25 cm was used as a representative of embankment; soil or asphalt stabilize

The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video str