3 research outputs found

    A Multidisciplinary Approach for Evaluating Spatial and Temporal Variations in Water Quality

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    The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality

    A Multidisciplinary approach to treat massive recurrent hematochezia from a jejunal Dieulafoy lesion: A case report

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    Jejunal Dieulafoy lesion (DL) is an exceedingly rare, life-threatening cause of gastrointestinal bleeding. Due to its rarity, intermittent bleeding symptoms that often necessitate prompt clinical intervention, variability in detection and treatment methods, and the risk of rebleeding, this condition frequently presents a diagnostic and therapeutic conundrum. We report a case of severe, intermittent, recurrent hematochezia due to a jejunal DL that was difficult to localize. In this case, the metallic coils used as a radiopaque marker allowed surgeons to accurately identify the bleeding site during intraoperative enteroscopy and successfully manage the lesion with minimally invasive laparoscopic surgery
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