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

Double hydrothermal method was used to prepare nano gamma alumina   using aluminum nitrate nano hydrate and sodium aluminate as an aluminum source, CTAB (cetyltrimethylammonium bromide) as surfactant, and variable acids: weak acids like; citric, and acitic acids, and strong acids like; hydrochloric and nitric acids as a bridge between aluminum salts and surfactant. Different crystallization times  12, 24, 48, and 72 hrs were applied. All the batches were prepared at pH equals to 9. XRD diffraction technique was used to investigate the crystalline nano gamma alumina pure from surfactant. N₂ adsorption-desorption (BET) was used to measure the surface area and pore volume of the prepared nano alumina, the average p

In this study, SnO2 nanoparticles were prepared from cost-low tin chloride (SnCl2.2H2O) and ethanol by adding ammonia solution by the sol-gel method, which is one of the lowest-cost and simplest techniques. The SnO2 nanoparticles were dried in a drying oven at a temperature of 70°C for 7 hours. After that, it burned in an oven at a temperature of 200°C for 24 hours. The structure, material, morphological, and optical properties of the synthesized SnO2 in nanoparticle sizes are studied utilizing X-ray diffraction. The Scherrer expression was used to compute nanoparticle sizes according to X-ray diffraction, and the results needed to be scrutinized more closely. The micro-strain indicates the broadening of diffraction peaks for nano

This study aims to find the effect of water-cement ratio on the compressive strength of concrete by using ultrasonic pulse velocity test (UPVT). Over 230 standard cube specimens were used in this study, with dimensions of 150mm, and concrete cubes were cured in water at 20 °C. Also, the specimens used in the study were made of concrete with varied water-cement ratio contents from 0.48 to 0.59. The specimens were taken from Diyarbakir-Turkey concrete centers and tested at the structure and material science lab, civil engineering, faculty of engineering from Dicle University.  The UPV measurement and compressive strength tests were carried out at the concrete age of 28 days. Their UPV and compressive strength ranged

Aim: To evaluate the effect of two bonding systems and two curing systems on sealing ability of class V composite restorative materials. Materials and methods: This study was performed in vitro on 40 caries free upper first premolar teeth. The Standardized class V cavity preparation on buccal and lin- gual surfaces of each tooth was done. Then the teeth were randomly divided into two major groups each of twenty. 40 cavities were performed on these teeth and the first group7th generation bonding agent (i Bond) were applied according to the manufacturer instructions and single increment of univer- sal composite (XRV Herculite) from kerr were applied and twenty of the cavities were cured with con- ventional light cure device (astralis-5) and t

S Ali…, Journal of Physical Education, 2019 - Cited by 1

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