Prediction of elastic parameters has an important role in the development of oil field.Elastic parameters are important properties, so by knowing them, we can find some variable parameters in reservoir, like pressure, to find the location of oil. In addition, elastic parameters have an important part in the study of isotropy and anisotropy in the earth.Moreover, elastic parameters are a basic way in computing geomechanics parameters in reservoir. For calculation of elastic parameters, we should know compressional wave velocity, shear wave velocity and total density. However, measuring shear wave is difficult and expensive.Therefore in this study we used artificial neural networks for better result. The neural network, which we used, is a feed forward back propagation with three layers and it is made of four neurons in input layer, twelve neurons in hidden layer and one neuron in output layer.With appropriate correlation coefficient in validation process (R=0.91).We used compressional wave velocity, gamma ray, porosity and density for input and shear wave velocity as output, till our network is trained. Then, we can use every well data for input for estimating shear wave velocity and finally elastic parameters.

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary, multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpose, multinomial log it regression (MLR) is considered as the basis. Then, the MLR is converted to Poisson GLM with log link. Two methods including Multivariate exponentially weighted moving average (MEWMA) statistics, and Likelihood ratio test (LRT) statistics are proposed to monitor MLR profiles in phase II. Performances of these three methods are evaluated by average run length criterion (ARL). A case study from alloy fasteners manufacturing process is used to illustrate the implementation of the proposed approach. Results indicate satisfactory performance for the proposed method.

Information extraction (IE) is a process of automatically providing a structured representation from an unstructured or semi-structured text. It is a long-standing challenge in natural language processing (NLP) which has been intensified by the increased volume of information and heterogeneity, and non-structured form of it. One of the core information extraction tasks is relation extraction which aims at extracting semantic relations among entities from natural language text. Traditional relation extraction techniques were relation-specific, producing new instances of relations determined a priori. While effective, this model is not applicable in cases where the relations are not defined a priori or when the number of relations is high. Open Relation Extraction (ORE) methods were developed to elicit instances of arbitrary relations while requiring fewer training examples. Since ORE systems are employed by the applications depended on large-scale relation extraction, high performance and low computational cost are major requirements for ORE methods. This is particularly important in the large scales such as the Web. Many OIE systems have been proposed in recent years. These approaches range from shallow (such as part-of-speech tagging) to deep (such as semantic role labeling), therefore they differ in their performance level and computational cost. In this paper, we use the state-of-the-art shallow NLP tools to extract instances of relations. A supervised log-linear model for OIE is presented which is based on using advantages of shallow NLP tools, as they are fast and lead to a low computational time. Extractor which is the main core of proposed approach integrates a high performance subset of the shallow NLP tools with the strength of the deep NLP tools by using a supervised log linear model and produces a high performance method that is scalable. This causes efficient use of time and therefore reduces computational cost and increases precision. Proposed approach achieves higher precision and recall than ReVerb, one of the most successful shallow OIE system.

The Shirbit polymetal prospect area is located in 30 km north-east of Ahar city within the Ahar-ore filed in around of Shivardagh batholith. Intrusion of granodiorite igneous bodies into the Eocene trachyte to andesite associated with pyroclastic interlayers caused widespread silicic, argillic and propylitic hydrothermal alteration associated with the Cu, Mo, Pb mineralization as quartz-sulfide (± oxide, carbonate, sulphate) vein and veinlets. Most of chalcopyrite, pyrite and molybdenite veins together with malachite, azurite and iron hydroxides were concentrated in the trachytic-andesitic units with silicic-argillic alteration. The turning point of this study includes investigating the possibility of log-log C-P method for separating geochemical anomalies of ore forming elements from the background, accurately determining the cut-off grade, decreasing the area of anomalies, and determining the number of stages of enrichment of elements in the study area compared to the multivariate statistical methods. For this purpose, 85 stream sediment samples taken from a systematic geochemical network were analyzed using ICP-MS method. In order to identify the susceptible mineralization areas by using of concentration-perimeter double logarithmic method, thee background values and anomalies were separated for each element in the region. Then, elemental anomaly maps were prepared and finally the distribution maps of the ore-forming elements were mapped and the relevant anomalies were distinguished. The results of using C-P fractal method indicates the presence of possible anomalies of Cu, Mo and Ti elements in the eastern, northeastern, and southwest parts of Shirbit area.

Introduction: Evaluation of hospital information system (HIS) is a complex endeavor, in which all human, technical and organizational aspects should be considered. This study aimed to develop indicators for HIS evaluation.Methods: Present qualitative study was carried out through a cross-sectional method in 2012 in Kerman province, using Delphi technique. Given the objectives of this study, three independent phases were performed including literature review, providing draft indicators for HIS evaluation and reaching consensus. Required data were obtained through interviews and designed forms. Twenty-three experts composed the study population in interview and reaching consensus phases. Validity and reliability were confirmed through content validity and test-retest method, respectively. Data were analyzed using descriptive statistics.Results: Final set of indicators for HIS evaluation consisted of ninety-one indicators under 8 main topics, i.e. technical quality, software quality, architecture and interface quality, vendor quality, after-sale services quality, workflow support quality, support department, outcome quality and HIS cost.Conclusion: Given the complexity of information system evaluation, all human, technical and organizational aspects have to be taken into account in any evaluation. Proposed indicators provide the possibility of comprehensive evaluation of HIS.