Man’ s requirements to the use of Internet and the development of an interdisciplinary field called Computational linguistics, which has allocated a separate position to itself by combining two majors such as linguistics and Computational sciences on the one hand as well as the interested and the students’ needs to getting familiar with and being informed of the function of Computational linguistics along with the lack of Persian resources in line with describing the field on the other hand, made the author to write down the results of her research on this major in which it has been introduced and to provide the students particularly the general linguistics researchers and the scholars of the relevant areas with it. Computational linguistics, artificial intelligence as well as natural language processing are considered as the key concepts the description and function of each of which have been elaborated on. Additionally, pointing out the machine translation which is considered as one of the most important achievements of the field and has allocated a high frequency of use among the students and professors especially those ones whose major is not the English Language field of study as well as refering to the frame semantics theory proposed by Fillmore (1975, 1982, 1985) and his project which has had an outstanding impact on Computational linguistics, the current article will end.

Thermodynamic Computational packages MTDATA and Fact Sage have been used to carry out calculations on the variation with temperature of the phases precipitated on cooling in both oxidising and reducing conditions of a typical ladle slag composition, in the temperature range 1700 - 900°C. The current coverage of the databases associated with the Computational packages is discussed in relation to their application to slag - refractory interaction and the validity of the results is compared with some relevant experimental data and phase equilibrium studies.

1. Introduction: Knowing the quantity of generated solid waste play a very significant role in solid waste management programs in a region. Due to lack of measured data as well as unavoidable errors in measurements, assessment of volume of generated solid waste is always challenging. Also, field measurement and continues monitoring of the volume of solid waste is usually costly, difficult and time-consuming. Accurate prediction of solid waste generation can be regarded as a key factor in future solid waste management system planning. Conventional forecasting methods in solid waste generation forecasting frequently use the demographic and socioeconomic factors in a per capita basis. In most cases, insufficient funds, the limited measuring equipment, lack of appropriate management systems and due to the lack of recorded data for the volume of generated solid waste cause many problems in integrated solid waste systems management (Dyson and Chang, 2005). In this study, three Computational intelligence techniques including M5P model trees, support vector machines (SVM) and multi-layer perceptron (MLP) artificial neural network are utilized to predict solid waste generation in Hormozgan Province, Iran. After a sensitivity analysis, four more influential factors including elevation, population, urban development index (measures the level of development in cities based on infrastructure, the municipality established year, the metropolitan area, population, city product and income, health and education) and the frequency of garbage collection were considered in developing models. The performance of proposed models in solid waste generation forecasting are assessed via different error evaluation indices and finally the results are compared...

Neurotrophic factor is a member of the neurotrophin family of growth factors. The neurotrophic factor proteins is present in high concentration in cerebral cortex.This protein have 247 amino acids .The mature neurotrophic factor is secreted to extracellular and bind to TrkB. Interaction to TrkB can lead to a variety of intracellular signaling cascades such as Ras-MAP kinase cascade. These cascades help to support the survival of existing neurons.computers techniques or bioinformatic techniques were applied to investigate the interaction of mutate neurotrophic factor to TrkB compairing to Native neurotrophic factor. Amino acids of neurotrophic factor that were involved in interaction were identified .one of the amino acids was Gln84. This amino acid was mutated to Lys84 amino acid,by swiss pdb viewer software. This software produced pdb file format and the pdb file uploaded to 3Drefine server. This server consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization and modelled 5 structure. RAMPAGE server and Qmean server was used to select best structure. Finally binding energy was calculated by Hex.8.0.0 software. Etotale of. Native neurotrophic factor is -675 and mutate BDNF is -678. The results showed the mutant structure interaction with TrkB is better than native protein. Neurotrophic factor shows pharmaceutical properties and modelling of new proteins and produce them can help in the development of the drug industry and mutant forms a good choice for treatment of Diseases of the nervous system.

The draft tube is one of the main components that integrate a turbine, since it has the function of recovering the residual kinetic energy after the runner by the pressure energy. The search for a draft tube design that increases the efficiency of the turbine is always an engineering challenge. The hydromechanics components geometry optimization can be accomplished through the integration of optimization methods and CFD tools. In this work, the geometric optimization of a double diffuser draft tube of a Bulb turbine applied to ultra-low heads is presented, with the objectives of maximizing the pressure recovery coefficient, Cp, and increasing the hydraulic efficiency of the turbine, ηh. These improvements would make it possible to reduce the longitudinal length of the draft tube, thereby, making an easier insertion of this kind of turbines in water transport systems, with pressures around 3 [mH2O]. The optimization methodology was performed in the meridional plane, using twelve geometric variables in the draft tube through the integration of optimization methods and Computational fluid dynamics. The optimized geometry obtained showed an increase in the Cp value of 0. 71516, from the original geometry, to 0. 83080. The results were extended to the 3D flow analysis, where the optimized turbine showed efficiency gains of 82% to 84%, when compared to the original turbine considering that its total length was reduced and its geometry simplified, resulting in a more compact and versatile equipment. The study also concluded that the applied methodology can be extended to other similar optimization problems in the design of hydraulic machines‎.