Effect of cooking on the chemical composition of lentils,rice and their blend (Koshary)

Lentils, rice and their blend (Koshary) were cooked by traditional Egyptian methods. Cooking resulted in slight changes in the total nitrogen, as well as in the non-protein nitrogen (NPN), ether extract and crude fibre of the three products, compared with raw grains. In contrast, significant decreases were found in the mineral elements, indicating losses through leaching, on washing and cooking. The mineral composition of lentils' cooking liquor confirmed this.     

Window materials for high power gyrotron

The room temperature application of sapphire as window material at higher frequencies is not feasible since its absorption coefficient increases almost linearly with increasing frequency in the millimeter wavelength region. At cryogenic temperature the absorption coefficient value decreases only by a few factors (factor of 2 to 3) in the 90 – 200 GHz region. The earlier reported temperature squared dependence (decrease) in the absorption coefficient or the loss tangent value is totally absent in our broad band continuous wave data we are reporting here (at 6.5 K, 35K, 77K and 300K) and one we reported at conferences earlier. Our results are verified by another technique. We utilize our precision millimeter wave dispersive Fourier transform spectroscopic techniques at room temperature and at cryogenic temperatures The extra high resistivity single crystal compensated silicon is no doubt the lowest loss material available at room temperature in the entire millimeter wavelength region At higher millimeter wave frequencies an extra high resistivity silicon window or an window made with extra high resistivity silicon coated with diamond film would certainly make a better candidate in the future. A single free standing synthetic diamond window seems to have higher absorption coefficient values at millimeter wavelength region at this time although it is claimed that it possesses good mechanical strength and higher thermal conductivity characteristics. It certainly does not rule out the use of diamond film on a single crystal high resistivity silicon to improve its mechanical strength and thermal conductivity     

An incentive scheduling mechanism for peer-to-peer video streaming

P2P video streaming networks are found as a scalable solution and an alternative for traditional client–server based video streaming over the Internet. One of the significant issues affecting the success of any P2P streaming network is cooperation between peers. Practical observations have proved the prevalence of free riders in P2P networks that degrade their performance. To solve this problem, using incentive mechanisms, which encourage peers to contribute more in the network, is necessary. In this paper, we designed and proposed a distributed and scalable incentive mechanism for mesh based P2P video streaming networks. In the proposed approach the contribution of the peers is measured and maintained in a distributed fashion. Furthermore, we proposed an incentive sending side scheduler in which peers are served based on their contribution in the network. Our simulation evaluations show the efficiency of the proposed approach in improving the overall perceived video quality by the non-free rider nodes and consequently in the whole network.     

File size models for shared content over the BitTorrent Peer-to-Peer network

Peer-to-Peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. In this work we perform a measurement study of the content shared over the popular BitTorrent P2P file sharing network. We mathematically model the file size distributions of shared files after categorizing them into Audio, Video, Archive and CD image classes. For each of these categories we look into the most popular shared file formats and investigate their file size statistics. This provides an important milestone to building a realistic simulation framework for P2P systems, and for future analytical modeling of P2P networks.     

A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems

The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems.

     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Robust H ∞ static output feedback stabilization of T-S fuzzy systems subject to actuator saturation

This paper proposes a method for designing robust H_?? static output feedback stabilization of Takagi-Sugeno (T-S) fuzzy systems under actuator saturation. In this paper, the input saturation is represented by a polytopic model and the modeling error is assumed a norm-bounded uncertainty. A set invariance condition for robust H_?? static output feedback system under actuator saturation is first established. Then, the estimation of the largest domain of attraction for the system is formulated and solved as a Linear Matrix Inequality (LMI) optimization problem. Two examples are used to demonstrate the effectiveness of the proposed design method.     

Tensile Stress Relaxation of Alumina at High Temperature

The development of a tensile testing methodology for ceramics which enables a stress vs strain-rate response to be measured at high temperature is described. The test involves a carefully controled stress relaxation test at constant total strain using an experimental procedure and phenomenological analysis previously developed for metallic materials. It is demonstrated here with preliminary tests on alumina at 1050° and 1150°C. This offers, with further development, the possibility of establishing design stresses associated with low strain-rate behavior for structural applications. The results demonstrate that data covering four decades of strain rate may be generated in tests lasting a few hours. The inelastic strain consists of substantial anelastic recoverable strain in addition to a permanent creep strain.