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This research investigates the effect of tin addition on corrosion resistance and micro structure of Cu-Al-Ni shape memory alloy with addition three different percentages of Tin (0.3, 1 &3)wt% to the base alloy. These alloys were prepared by casting and pouring in the metallic mold under argon atmosphere, then the prepared ingots were homogenized and machined by wire cutting into a small samples for corrosion test. X-ray diffraction inspection and scanning electron microscope were used for microstructural observations. Corrosion tests were carried out in sea water (3.5 wt% NaCl solution) at three different temperatures (25, 40 and 60) °C. The results showed that the corrosion resistance increased with addition percentage of tin metal and with rising temperature, It was observed that the 3% tin addition gave the best results in corrosion rate with rising temperature upto 60 °C and also in microstructure the same percentage gave a fine grain size Then the 0.3% Tin Addition and Finally the 1% Tin Addition.

This study examines the effect of Sn additions on Cu-14%Al-4.5%Ni shape memory alloy. Sn was added in three different percentages (0.3,1,3) %. The alloys were mechanically tested both in compression test and micro hardness test. Also, a thermo-mechanical test was performed on the alloys. Results showed an increase in the transformation temperature outside the domain and also a better recovery strain with the increase of Sn percentage of 3% Sn addition showed the best results in mechanical properties while the 3% Sn showed a better Shape Memory Properties near to super elastic.

This investigation examines the influences of the (Ce) addition on the mechanical properties and the shape memory effect (SME) of the Cu-based shape memory alloys (SMAs), in which (Ce) is added in three percentages (0.3, 1.0 and 3 wt%) to the base material (Cu-14%Al-4.5%Ni). Different tests and inspections, including X-ray diffraction, compression, HV hardness, and the thermomechanical tests were conducted on the (Ce-modified) alloys. In addition, a differential scanning calorimetry test, microstructure examination via optical measurements, and (SEM) for the whole alloys were performed. Results revealed an increment in the hardness, the yield strength and the maximum strain (e max%) occurred with the increase of the (Ce %) in the base shape memory alloy (with exception of the 3% Ce case). The thermomechanical characteristics depicted an increment in the recovery of strain by as much (98.72%) with a reduction in the modulus of elasticity of martensite and an increment in the modulus of elasticity of austenite. Moreover, the temperatures of transformation shifted to beyond the base shape memory alloy domain (100–170 °C), and the modified (SMA) with (3% Ce) exhibited better results than the other materials.

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

This article presented several main stages of timeline of evolutionary history of logarithmic barrier functions along with interior point methods and affine connections on manifolds. The crucial idea is many optimization problems can be better stated on manifolds rather than Euclidean space, such as interior point methods, which in turns based on self-concordant functions (logarithmic barrier functions). This led us to the truth, that there is a logical evidence to study the relationships between self-concordant functions and Riemannian manifolds including affine connections.

The content-based image retrieval (CBIR) based on sparse visual features is a challenging research problem to categorize images into semantically meaningful classes. Different robust feature representation methods have been proposed to address the semantic gap issue of CBIR. In this article, we propose a novel robust image representation for CBIR, which is based on complementary visual words integration of speeded up robust features (SURF) and co-occurrence histograms of oriented gradients (CoHOG). The SURF is a local feature descriptor, while CoHOG is a global feature descriptor. The local features give better performance for images belonging to different semantic classes and having close visual appearance among their visual contents, while global features give better performance to retrieval images at large scale. To ensure image retrieval accuracy, the proposed method build two smaller size dictionaries, each containing visual words of SURF and CoHOG descriptors, which are assimilated to form one larger size dictionary. In the CBIR system, a dictionary of smaller size produces better sensitivity, while a dictionary of larger sizes produces better specificity. The dictionary of the larger sizes also yields an overfitting problem to affect CBIR performance, which is addressed by introducing a linear discriminant analysis (LDA) and relevance feedback with the proposed method. The comparative performance analysis of the proposed method is performed with the competitor CBIR methods (i.e., feature integration of SURF–CoHOG descriptors’ based on the bag-of-visual-words (BoVW), a single feature of SURF–BoVW and CoHOG–BoVW methods). The quantitative and qualitative analysis carried out on four standard image databases proves the robust performance of the proposed method as compared to its competitor and recent CBIR methods.

Microwave heating is caused by the ability of the materials to absorb microwave energy and convert it to heat. The aim of this study is to know the difference that will occur when heat treating the high strength aluminum alloys AA7075-T73 in a microwave furnace within different mediums (dry and acidic solution) at different times (30 and 60) minutes, on mechanical properties and fatigue life. The experimental results of microwave furnace heat energy showed that there were variations in the mechanical properties (ultimate stress, yielding stress, fatigue strength, fatigue life and hardness) with the variation in mediums and duration times when compared with samples without treatment. The ultimate stress, yielding stress and fatigue strength decreased for all states compared with standard values. With reference to fatigue life, the results showed that the increment in fatigue life occurred for test states of dry 60 minutes in a microwave furnace, which were only 25%. Moreover, the increment in hardness range was reached for test state of acid in 60 minutes which was about 7.3%, and 5 % for dry in 30 minutes and only 3%.

This study to investigate the effect of adding 0.3% ,1% and 3% Te to Cu-14%Al-4.5%Ni shape memory alloy in three deferent percentages (0.3,1,3)% and to analyze the effect of addition on transformation temperature and thermal properties , the alloys were fabricated by casting the homogenized and machined by EDM , also Chemical Analysis ,SEM-EDS , XRD and DSC were performed to the alloys Then Enthalpy was calculated by means of area under the curve form the DSC charts also specific heat and Grain Size by Debye–Scherer equations then the calculations of activation energy both by Ozawa and Kissinger equations Results showed an increase in the transformation temperature and in enthalpy and a decrease in the activation energy calculated using Ozawa and Kissinger equations.

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

This paper aims to study the effect of 0.3% addition of Ge, Te and Ce on Cu-14%Al-4.5%Ni Shape memory alloys by building a computer model via MATLAB of two direction strain temperature with the calculation of enthalpy(ΔH) and entropy (ΔS) results showed the Ge addition gave the best results in the thermodynamic properties and also in strain ring ,also the use of a mathematical model via MATLAB to calculate the shape memory strain with compared with the actual results , it showed a close ratio with the actual results of the thermo-mechanical testing also results shown an increase in enthalpy entropy and heat flow for all alloy the Ge addition gave the best results as for the hysteresis loops the Ce and Ge Addition gave the best results in general the computer model results and the thermo mechanical showed about 2.7%deffernece in results.

Owing to their significant popularity, Facebook and other social network sites have gained considerable importance. The popularity of Facebook can be signified through more than 1.2 billion users, i.e. one in every five persons in the world is a Facebook user. It is being used for a variety of activities including sharing of opinions, announcing something or sending invites to parties and events, meeting new people, etc. This paper examines the legal aspects of resolving disputes, which occur in the world of social networking. The social matters are pertinent to the legal aspects. Thus, this article will focus on social science in most areas to present a more thorough approach. For example, the association between disclosure of private information and the friends list, the relation between disputes and social activities and the limitations on viewing others' private profiles will be reviewed.

This paper aims to study the effect of Ge addition in different percentages (0.3%wt,1%wt, 3%wt) on the micro structure and mechanical properties of Cu-14wt%Al-4.5wt%Ni shape memory alloy which prepared by casting method in vacuum induction furnace. Many tests and inspections such as optical and SEM examinations, DSC Measurement,XRD inspection, compression and Vickers hardness tests were investigated. The results show that there is an increase in the transformation temperatures, shape memory limits and decrease in hardness and increase in Young Modulus Also it was found that the increase of Ge content to 3%wt Ge gave better properties due to the formation of martensitic phase and Ge particles distribution in martensite matrix of SMA.

In the era of the Fourth Industrial Revolution, one of the key drivers of change is a sharing economy or so called 'uberization'. Uberization is especially rapidly developing in the service sector, in particular - when organizing individual passenger transportation by taxi services. Such taxi services as Uber and Gett, that use technological Internet platforms, allow faster and cheaper than traditional taxi companies to order the taxi. From the other point of view, they have the same disadvantage as the usage of individual transport is often associated with. It is the incomplete load of vehicles, which causes an additional negative impact on the environment as well as on the road network. This article presents a decision support system for taxi dispatch services based on the model of optimal route choice. Optimization is carried out with the help of multifactor analysis of transportation requirements and selection of the optimal route in accordance with given priorities. Such a system will reduce the cost of transportation and negative impact on the environment through the selection of fellow travelers by using a mobile application.

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature.

Visible light communications (VLC) have been pro- posed as a means of moving part of the indoor traffic to the optical spectrum. The deployment of LED lights for data transmission forming attocells in the cellular networks known as Light-Fidelity (LiFi) interacts with other radiofrequency (RF) based networks such as WiFi or femtocells. Since LED lights illuminate a small confined area and interfere with other LED lights, the hybrid network composed of the LiFi and RF systems can be employed for resource management allocating each user to a specific network. In this work, we consider the use of a novel inference management scheme for LiFi known as Blind Interference Alignment (BIA) that avoids both the multi-user and the intercell interference. However, BIA schemes are limited by noise increase due to interference subtraction and the channel coherence block requirement, which increase with the number of user served by the LiFi network. It is shown that the hybrid LiFi/RF network allow to relax the requirements for BIA implementation and increase the sum- rate of the whole network.

Sn was added to Cu-14%Al-4.5%Ni shape memory alloy in three Percentages (0.5,1,3)%to study the effect of the addition on microstructure, Transformation temperature, thermal properties and activation energythermal properties. SEM-EDS, XRD tests were performed on the specimens; DSC Measurement was performed on the alloys. A computer model was built via MATLAB to calculate the thermal properties by means of graphical integration for the DSC data. Results showed an increase in the transformation temperature and Equilibrium temperature outside the domain, also an increase in hysterics and spread with the increase of Sn Percentage, also results shown an increase in thermal properties (enthalpy and entropy and free energy), results showed also a decrease in activation energy more than the base alloy the 3% Sn Addition gave the best results in both transformation temperature and activation energy.

In this work, friction stir spot welding (FSSW) was performed for dissimilar aluminum alloys (AA2024-T3 and AA5754-H114) sheets of 2 mm thick at different tool rotational speeds (800, 1000 and 1250 rpm), plunging times (30, 60 and 90 s) and tool pin profile or geometry (threaded cylindrical with flute, tapered cylindrical and straight cylindrical). Process parameters were optimized by using Taguchi technique and depending on design of experiment (DOE), and data analysis based on the Taguchi method is performed by utilizing the Minitab 17 to estimate the significant factors of the FSSW and main effects using few experimental tests only. It was found that maximum shear force was (2860 N) obtained at best welding process parameters: 800 rpm of rotation speed, 60 s of plunging time and taper cylindrical pin which are obtained from the DOE. Pareto chart of the standardized effects of tensile shear results showed that the pin profile was the most effective parameter than other welding parameters (rotation speed and plunging time). Also it was found that the contribution percentage was 61.5% for pin profile followed by tool rotation speed 20.1% and plunging time 18.4%.