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This paper presents simulation and analysis of a grid connected multi-level converter topologies. In this paper, converter circuit works as an inverter by controlling the switching angle (α). This paper, presents a MATLAB/SIMULINK model of multi-level converter topologies (topology1 & topology2). Topology1 is without transformer while topology2 with transformer. Both the topologies are simulated and analyzed for three level converters in order to reduce the total harmonic distortion (THD). A comparative study of topology1 and topology2 is also presented in this paper for different switching angles (α) and battery voltages. The results have been tabulated and discussed.

This paper describes the development of an optimal water quality tool for managing wet weather discharges that uses a macrolevel simulation model (i.e., the Streeter-Phelps equation) linked with a highly efficient optimization algorithm (i.e., a modified version of the Box complex method). The proposed approach can be applied to an urban watershed impaired by multiple sources of wet weather discharges. As an application, the proposed approach is used to develop optimal water quality management strategies for the Beargrass Creek watershed in Louisville, Ky. DOI: 10.1061/(ASCE)0733-9496(2009) 135:4(276) CE Database subject headings: Optimization; Computation; Mathematical models; Simulation models; Surface water; Watersheds.

This paper describes the use of inductive models developed using two artificial intelligence (AI)-based techniques for fecal coliform prediction and classification in surface waters. The two AI techniques used include artificial neural networks (ANNs) and a fixed functional set genetic algorithm (FFSGA) approach for function approximation. While ANNs have previously been used successfully for modeling water quality constituents, FFSGA is a relatively new technique of inductive model development. This paper will evaluate the efficacy of this technique for modeling indicator organism concentrations. In scenarios where process-based models cannot be developed and/or are not feasible, efficient and effective inductive models may be more suitable to provide quick and reasonably accurate predictions of indicator organism concentrations and associated water quality violations. The relative performance of M-based inductive models is compared with conventional regression models. When raw data are used in the development of the inductive models described in this paper, the AI models slightly outperform the traditional regression models. However, when log transformed data are used, all inductive models show comparable performance. While the work validates the strength of simple regression models, it also validated FFSGA to be an effective technique that competes well with other state-of-the-art and complex techniques such as ANNs. FFSGA comes with the added advantage of resulting in a simple, easy to use, and compact functional form of the model sought. This work adds to the limited amount of research on the use of data-driven modeling methods for indicator organisms. DOI: 10.1061/(ASCE)0733-9372(2008) 134:9(789) CE Database subject headings: Artificial intelligence; Neural networks; Computation; Pathogens; Regression models; Surface waters; Watersheds; Predictions.

Extensive research has been carried out to predict bridge pier scour, with laboratory and field data, using different modelling techniques. This study introduces a new soft computing technique called gene expression programming (GEP) for pier scour depth prediction using field data. A functional relationship has been established using GEP and its performance is compared with other inductive modelling techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Field data comprising 370 data sets were collected from the published literature and divided into calibration and validation (testing) data sets. The performance of GEP was found to be satisfactory and encouraging when compared with regression and ANN models in predicting bridge pier scour depth. GEP has the unique capability of providing a compact and explicit mathematical expression for computing bridge scour. This advantage of GEP over ANN is one of the main motivations for this work. The resulting GEP models add to the existing literature on artificial intelligence based inductive models that can be used effectively for bridge scour modelling. [ABSTRACT FROM AUTHOR]

Uncontrolled bleeding is the leading cause of mortality in the trauma victims. Massive bleeding after traumatic injury is a result of surgical and coagulopathic bleeding. We describe a case of gun shot injury brought to the hospital in a collapsed state because of massive blood loss in the abdominal cavity. Surgical intervention secured the surgical bleeding but coagulopathic bleeding continued which was controlled with Recombinant activated factor VII (rFVIIa). Guidelines of the use of factor VII in trauma are presented.