The current study is performed to identify the flow regimes of oil-gas two-phase flow experimentally in a vertical pipe has an internal diameter of 6.7 cm. It also aims to provide more details about the possibility of using Differential Pressure Transducers (DPT) for indicating flow patterns. A flow development of oil and gas has been investigated in a vertical pipe of 6 m in length and operated at atmospheric pressure. A series of experiments have been run to cover a range of inlet oil superficial velocities from 0.262 to 0.419 m/s, and inlet gas superficial velocities from 0.05 to 4.7 m/s. Wire Mesh Sensors (WMS) have been used to collect the obtained void fraction values of the flow. The Differential Pressure Transducer (DPT) is utilized to measure the pressure drop values of a one-meter along the pipe. The flow patterns are classified according to the analysis of void fractions, pressure gradients regarding time series, tomographic images, probability density functions of the void fractions, and pressure gradients. A bubbly flow is observed at low superficial velocities of gas and liquid, slug flow is observed at the lower flow rate of liquid and moderate flow rates of gas, while the churn flow pattern is recognized at the higher rates of liquid and gas. Also, the result has revealed the possibility of using Differential Pressure Transducers (DPT) to classify the gas-oil flow patterns in vertical pipes.

Facial expression recognition as a recently developed method in computer vision is founded upon the idea of analyzing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result determination. The highest ranked six features are applied on six classifiers including multi-layer preceptor, support vector machine, decision tree, random forest, radial biased function, and K-Nearest neighbor to figure out the most accurate one when the minim number of features are utilized. This is done via analyzing and appraising the classifiers' performance. CK+ is used as the research's datasets. random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier among st the rest. Keywords: Base function Facial expression recognition Chi-square feature selection K-Nearest neighbor Multi-layer perception This is an open access article under the CC BY-SA license.

The demand for setting up a wireless LAN internet connectivity is almost mandatory for every commercial building, home, company, and educational institutions. With the growing number and height of buildings as well as the number of users, it has become essential to apply new techniques to provide better wireless network services, especially for security issue with many applications used by an attacker that can decrypt the traditional password to use network resources, that’s lead to poor network performance for providing network services for people authorized to use it. All of that is almost a very challenge issue when applying outdated techniques. In this research a wireless network has been created considering a large number of users in a multi-floor building using a new control system which can solve the problems by setting up RADIUS authentication via that wireless network with a web page that automatically appears to the user immediately after connecting to the wireless radio signal and automatically gaining an IP address, lead user directly to the temporary page asking him for authentication, if the user has the right username and password or even sometimes a code called Voucher, he will get a package assigned to his priority. This technology will eliminate the vulnerability on the wireless connection and the unauthorized user will be discarded from the router, in addition of that authorized users will get authenticated to make better use of network resources.

Relational databases have a wide range of applications and use in many areas over the past decades. After the emergence of social networks, the increase in the number of users of these networks, the evolution of data types and relationships between records have formed the main reasons behind the need to represent and store data in a dynamic way. One of the best ways to handle big data is document-oriented databases. This approach is characterized by the absence of a design to represent entities, as each entity in the entity set may have different private information from other entities in the same set. For the users of the data, data is displayed and manipulated by an easy graphical user interface which with its functionality (such as adding, deleting, updating a record) are designed during and after the design of the database. The design of the end-user interface depends on the customer's requirements as well as the design of the database. In the case of document-oriented databases, there is no static database design. Each entity has its own embedded design. It is possible at any time to add new fields or change the data type for some fields, in addition to the possibility of deleting some fields from some documents. Therefore, the user interface designing process for document-oriented databases is difficult and requires deep intuition in addition to a continuous modification of the design. In this research, it is suggested to use the tree to represent and manipulate the information of each entity separate from that of other entities in the same collection. This method represents each field name and field value as a node. In addition to the fact that the embedded documents are represented in the form of an embedded tree, this method facilitates the representation and handling of any structure of the document. A document management tool has also been built on the proposed managing MongoDB documents.