Search results for: Sonya Barzgar

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Barzgar</title> <meta name="description" content="Search results for: Sonya Barzgar"> <meta name="keywords" content="Sonya Barzgar"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Sonya Barzgar"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Sonya Barzgar</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Effect of Sulphur Concentration on Microbial Population and Performance of a Methane Biofilter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonya%20Barzgar">Sonya Barzgar</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Patrick"> J. Patrick</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hettiaratchi"> A. Hettiaratchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Methane (CH4) is reputed as the second largest contributor to greenhouse effect with a global warming potential (GWP) of 34 related to carbon dioxide (CO2) over the 100-year horizon, so there is a growing interest in reducing the emissions of this gas. Methane biofiltration (MBF) is a cost effective technology for reducing low volume point source emissions of methane. In this technique, microbial oxidation of methane is carried out by methane-oxidizing bacteria (methanotrophs) which use methane as carbon and energy source. MBF uses a granular medium, such as soil or compost, to support the growth of methanotrophic bacteria responsible for converting methane to carbon dioxide (CO₂) and water (H₂O). Even though the biofiltration technique has been shown to be an efficient, practical and viable technology, the design and operational parameters, as well as the relevant microbial processes have not been investigated in depth. In particular, limited research has been done on the effects of sulphur on methane bio-oxidation. Since bacteria require a variety of nutrients for growth, to improve the performance of methane biofiltration, it is important to establish the input quantities of nutrients to be provided to the biofilter to ensure that nutrients are available to sustain the process. The study described in this paper was conducted with the aim of determining the influence of sulphur on methane elimination in a biofilter. In this study, a set of experimental measurements has been carried out to explore how the conversion of elemental sulphur could affect methane oxidation in terms of methanotrophs growth and system pH. Batch experiments with different concentrations of sulphur were performed while keeping the other parameters i.e. moisture content, methane concentration, oxygen level and also compost at their optimum level. The study revealed the tolerable limit of sulphur without any interference to the methane oxidation as well as the particular sulphur concentration leading to the greatest methane elimination capacity. Due to the sulphur oxidation, pH varies in a transient way which affects the microbial growth behavior. All methanotrophs are incapable of growth at pH values below 5.0 and thus apparently are unable to oxidize methane. Herein, the certain pH for the optimal growth of methanotrophic bacteria is obtained. Finally, monitoring methane concentration over time in the presence of sulphur is also presented for laboratory scale biofilters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20warming" title="global warming">global warming</a>, <a href="https://publications.waset.org/abstracts/search?q=methane%20biofiltration%20%28MBF%29" title=" methane biofiltration (MBF)"> methane biofiltration (MBF)</a>, <a href="https://publications.waset.org/abstracts/search?q=methane%20oxidation" title=" methane oxidation"> methane oxidation</a>, <a href="https://publications.waset.org/abstracts/search?q=methanotrophs" title=" methanotrophs"> methanotrophs</a>, <a href="https://publications.waset.org/abstracts/search?q=pH" title=" pH"> pH</a>, <a href="https://publications.waset.org/abstracts/search?q=sulphur" title=" sulphur"> sulphur</a> </p> <a href="https://publications.waset.org/abstracts/46369/effect-of-sulphur-concentration-on-microbial-population-and-performance-of-a-methane-biofilter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46369.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">236</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> On the End-of-Life Inventory Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hans%20Frenk">Hans Frenk</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonya%20Javadi"> Sonya Javadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Semih%20Onur%20Sezer"> Semih Onur Sezer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We consider the so-called end of life inventory problem for the supplier of a product in its final phase of the service life cycle. This phase starts when the production of the items stops and continues until the warranty of the last sold item expires. At the beginning of this phase, the supplier places a final order for spare parts to serve customers coming with defective items. At any time during the final phase, the supplier may also decide to switch to an alternative and more cost-effective policy. This alternative policy may be in the form of replacing a defective item with a substitutable product or offering discounts / rebates on new generation products. In this setup, the objective is to find a final order quantity and also a switching time which will minimize the total expected discounted cost. We study this problem under a general cost structure in a continuous-time framework where arrivals of defective items are given by a non-homogeneous Poisson process. We consider four formulations which differ by the nature of the switching time. These formulations are studied in detail and properties of the objective function are derived in each case. Using these properties, we provide exact algorithms for efficient numerical implementations. Numerical examples are provided illustrating the application of these algorithms. In these examples, we also compare the costs associated with these different formulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=End-of-life%20inventory%20control" title="End-of-life inventory control">End-of-life inventory control</a>, <a href="https://publications.waset.org/abstracts/search?q=martingales" title=" martingales"> martingales</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20parts" title=" service parts"> service parts</a> </p> <a href="https://publications.waset.org/abstracts/71462/on-the-end-of-life-inventory-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Ensemble Methods in Machine Learning: An Algorithmic Approach to Derive Distinctive Behaviors of Criminal Activity Applied to the Poaching Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zachary%20Blanks">Zachary Blanks</a>, <a href="https://publications.waset.org/abstracts/search?q=Solomon%20Sonya"> Solomon Sonya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ensemble%20methods" title="ensemble methods">ensemble methods</a>, <a href="https://publications.waset.org/abstracts/search?q=imputation" title=" imputation"> imputation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forests" title=" random forests"> random forests</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20gradient%20boosting" title=" stochastic gradient boosting"> stochastic gradient boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=wildlife%20protection" title=" wildlife protection"> wildlife protection</a> </p> <a href="https://publications.waset.org/abstracts/66907/ensemble-methods-in-machine-learning-an-algorithmic-approach-to-derive-distinctive-behaviors-of-criminal-activity-applied-to-the-poaching-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66907.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul 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Search results for: Sonya Barzgar