Many past researchers have ignored the multi-objective nature of the transit route network design problem (TrNDP), recognizing user or operator cost as their sole objective. The main purpose of this study is to identify the inherent conflict among TrNDP objectives in the design process. The conventional scheme for transit route design is addressed. A route constructive genetic algorithm is proposed to produce a vast pool of candidate routes that reflect the objectives of design, and then, a set covering problem (SCP) is formulated for the selection stage. A heuristic algorithm based on a randomized priority search is implemented for the SCP to produce a set of nondominated solutions that achieve different tradeoffs among the identified objectives. The solution methodology has been tested using Mandl's benchmark network problem. The test results showed that the methodology developed in this research not only outperforms solutions previously identified in the literature in terms of strategic and tactical terms of design, but it is also able to produce Pareto (or near Pareto) optimal solutions. A real-scale network of Rivera was also tested to prove the proposed methodology's reliability for larger-scale transit networks. Although many efficient meta-heuristics have been presented so far for the TrNDP, the presented one may take the lead because it does not require any weight coefficient calibration to address the multi-objective nature of the problem.

Wastewater production is a serious problem in Egypt, which increases as the population grows and the demand on fresh water increases. The lack of water used in agriculture in arid and semi-arid regions is being an important reason to use wastewater in agriculture. In this research, a wastewater stabilization ponds (WSPs) system with a design flow of 40000 m3/day in El-Qusiyyah city located in Upper Egypt is evaluated to make sure that the effluent from this system is inconformity with Egyptian and Food and Agriculture Organization of the United Nations (FAO) quality standards for wastewater reuse in agriculture. Monthly samples were collected from the inlet, anaerobic ponds, facultative ponds, and maturation ponds. Some physicochemical parameters as pH, biochemical oxygen demand (BOD), and total suspended solids (TSS) were measured. In addition, levels of some heavy metals were taken into consideration. Concentrations of copper (Cu), chromium (Cr), and zinc (Zn) were measured. Results showed a moderate efficiency of the system and the effluent did not match the recommended characteristics to be reused in agricultural purposes. Increasing in pH, and TSS values above the permissible limits prevent the agricultural reuse of the effluent from El-Qusiyyah wastewater treatment plant.

In this study, a series of reinforced concrete beams were carried out to determine the size effect of the beams strengthened with different types of fiber reinforced polymer sheets (FRP). Two types of FRP sheets were considered in this study; Carbon and Glass fiber reinforced polymer sheets (CFRP and GFRP). FRP sheets were bonded to the soffit of the beams using a two-part Epoxy. These two types of FRP sheets were used to allow a variety of fabric stiffnesses and strengths to be studied. Also, this study introduced a new type of anchor to improve the efficiency of the FRP-strengthened beams and to prevent the anchor delamination of the FRP sheets. The results show that the strengthening system increases the ultimate capacity of the FRP-strengthened beams. Also, this increase depends on many parameters such as the type of the FRP sheets and size of the tested beams. Moreover, the beam size has significantly effected on the ductility of testing beams which increases as the size of beams decreases.

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This paper presents a real time simulation for virtual end milling process. Alyuda NeuroIntelligence was used to design and implement an artificial neural network. Artificial neural networks (ANN’s) is an approach to evolve an efficient model for estimation of surface roughness, based on a set of input cutting conditions. Neural network algorithms are developed for use as a direct modeling method, to predict surface roughness for end milling operations. Prediction of surface roughness in end milling is often needed in order to establish automation or optimization of the machining processes. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling processes. The training of the networks is preformed with experimental machining data. The neural network is used to predict surface roughness of the virtual milling machine to analyze and preprocess pre measured test data. The simulation for the geometrical modeling of end milling process and analytical modeling of machining parameters was developed based on real data from experiments carried out using Prolight2000 (CNC) milling machine. This application can simulate the virtual end milling process and surface roughness Ra (µm) prediction graphs against cutting conditions simultaneously. The user can also analyze parameters that influenced the machining process such as cutting speed, feed rate of worktable. Key words: Surface roughness, virtual reality, simulation, surface roughness, virtual end milling process, neural

Urban sprawl on agricultural lands considers one of the permanent problems, which face all the governments that govern Egypt last decades, especially with the fact that all urban areas distributed on 6% of all Egyptian territories accompanied with accelerated population growth (2% yearly). Therefore, this research aims to study factors affecting urban sprawl on agricultural lands in Egypt, theoretical studies focused on understanding all aspects of the research problem (economical, demographical, political and spatial aspects