Series of incubation experiments were conducted to study the effect of three different organic amendments (olive waste, compost and lemon waste) on some chemical properties of calcareous soil. Soil samples were collected from northwestern coast of Egypt (Borg El Arab) from the surface soil (0-30 cm). The samples were then air-dried and then grinded, sieved (2 mm) and analyzed some physical and chemical properties. Samples were used in laboratory experiment, where organic amendments were applied to soil with different rates (0, 0.5 , 1, 1.5 and 2% (w w-1). The prepared pots were incubated for four months at temperature between 23 to 30°C.  After 4, 8, 12 and 16 weeks of the incubation, the soil samples were taken for analysis soil pH, electrical conductivity (EC) and total equivalent calcium carbonate. The results showed that compost, olive waste and lemon waste could change the chemical properties of the calcareous soils. All organic amendments caused an increase in soil EC while they slightly decreased soil pH and CaCO3 content. The compost was showed to have effectivity effect on soil pH comparing to other amendments, while lemon waste and olive waste were showed to have effectivity effect on CaCO3 content after few weeks of application. This can be explained on the basis that the olive waste and lemon waste are less biodegradable than that of compost. Finally, it is concluded that olive waste and or lemon waste can be used in preparing any compost to be more useful.

Abstract Rapid Prototyping of embedded hardware/software systems is important, because it shortens the path from specification to the final product. Prototypes play a major role in decision making, concept and design validation, feature and limit exploration, as well as design verification in every phase of the product development cycle, including product planning, requirement engineering, and product development. Rapid prototyping of embedded systems can emulate different kind of processes, through the mathematical modelling that represent their dynamic characteristics. This make easier to emulate different control strategies, which can interact with the real signals of the embedded process, making a better approach of the real response than a simple simulation. This paper presents the results of the emulation of the dynamic behaviour in the study case to work, in order to validate control strategies like PID and Fuzzy, using the concepts of rapid prototyping and hardware-in-the-loop (HIL). To achieve this objective, two embedded systems were employed, the first one to emulate the dynamics of the process, and the second one to implement the control strategy. Both systems were interconnected using the Controller Area Network protocol (CAN). The principal contribution of this work is the methodological development in the application of the control strategies through HIL 

Abstract The characteristics of non-minimum phase and static unstable of a tail controlled tactical missile are presented firstly. Then, in order to eliminate the static error, a cascade PI compensator was introduced to the classic two loop autopilot. Due to the slow tracking for command acceleration, the longitudinal three-loop autopilot design is driven based on LTI model of missile plant to stabilize the non-minimum phase static unstably missile airframe. The focus is to explain the performance and the control effect at different values of velocity and stability derivative (𝑀𝜶) of two algorithms on missile plant. The analysis is executed by establishing a standard algorithm in virtue of MATLAB/Simulink for autopilot design. The simulation results indicated that three-loop topology gives better tracking than two-loop with a cascade PI compensator at different value of stability derivative 𝑀𝜶. On the other hand, two-loop has a better response and less control effort at different velocities. fin angle and fin angle rate are less than the three loop for static unstable and stable missile. 

This is an exploratory study aiming at investigating integration of technology in writing and supervising graduation projects in the department of English at the Faculty of Arts/University of Zawia. Two questionnaires were distributed: one to teachers and the other to students to obtain relevant data. The results have shown that most of the teachers and students investigated are not using technology in their graduation project works including writing, editing, correcting, reviewing, sending and receiving these works. The results have also shown that those who integrate technology have very limited knowledge and experience related to the use of computer applications and other technological tools like using email and Windows features. It has also been found that the reasons behind this problem include lack of awareness of the importance and usefulness of using technology, negative attitudes, indifference in addition to other limitations related to the unavailability of internet service and necessary technological equipment. To solve this problem several procedures have been suggested including raising the awareness of both teachers and students of the importance and usefulness of integrating technology, providing educational institutions with the necessary equipment and providing teachers and students with the necessary training to update their knowledge and skills related to the use of computers and other technological tools.  

We present the results of predictive modelling for the detection of anti-social behaviour in online communication in Arabic, such as comments which contain obscene or offensive words and phrases. We collected and labelled a large dataset of YouTube comments in Arabic which contains a broad range of both offensive and inoffensive comments. We used this dataset to train a Support Vector Machine classifier and experimented with combinations of word-level features, N-gram features and a variety of pre-processing techniques. We summarise the pre-processing steps and features that allow training a classifier which is more precise, with 90.05% accuracy, than classifiers reported by previous studies on Arabic text.

In recent years, many studies target anti-social behaviour such as offensive language and cyberbullying in online communication. Typically, these studies collect data from various reachable sources, the majority of the datasets being in English. However, to the best of our knowledge, there is no dataset collected from the YouTube platform targeting Arabic text and overall there are only a few datasets of Arabic text, collected from other social platforms for the purpose of offensive language detection. Therefore, in this paper we contribute to this field by presenting a dataset of YouTube comments in Arabic, specifically designed to be used for the detection of offensive language in a machine learning scenario. Our dataset contains a range of offensive language and flaming in the form of YouTube comments. We document the labelling process we have conducted, taking into account the difference in the Arab dialects and the diversity of perception of offensive language throughout the Arab world. Furthermore, statistical analysis of the dataset is presented, in order to make it ready for use as a training dataset for predictive modelling.