61 research outputs found

    ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) APPROACH TO EVALUATE THE DEBUTANIZER TOP PRODUCT

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    Abstract This paper proposed an ANFIS estimator to evaluate the top product from secondary measurements. Real debutanizer column in one of the Iranian refineries has been purchased and the adaptive neuro-fuzzy inference system is trained and validated with real data. According to results, ANFIS can be used with acceptable approximation in replace of costly measurement instruments as gas chromatographs

    Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images

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    Purpose: The novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance. Material and methods: Herein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision. Results: All proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively. Conclusions: Our data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation

    UGROŽENE RIBE SVIJETA: Paracobitis rhadinaeus (Regan, 1906) (NEMACHEILIDAE)

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    Paracobitis rhadinaeus is an endemic Nemacheiline loach in the Sistan basin, southeast Iran. The population is declining probably due to habitat loss or degradation, damming, drought and poaching. Urgent habitat protection with bans on further regulation of the Hamoun wetland and related reservoirs is suggested. Captive breeding of the fish should be initiated.Fishing activities should be forbidden or limited. A detailed study of current population status, biology and ecology of P. rhadinaeus is required.Paracobitis rhadinaeus je endemski vijun iz Sistanskog slijeva na jugoistoku Irana. Populacija je u opadanju najvjerojatnije zbog gubitka i narušavanja staništa, podizanja brana, isušivanja i potapanja zemljišta. Predlaže se hitna zaštita staništa sa zabranom daljnje regulacije slijeva Hamuni njegovih akumulacijskih jezera. Potrebno je potaknuti kontrolirani mrijest ove vrste te zabraniti ili ograničiti ribolov. Nužna je detaljna studija sadašnjeg stanja populacije, biologije i ekologijevrste P. rhadinaeus

    A historical literature review on the role of posterior axillary boost field in the axillary lymph node coverage and development of lymphedema following regional nodal irradiation in breast cancer

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    To elucidate whether (1) a posterior axillary boost (PAB) field is an optimal method to target axillary lymph nodes (LNs); and (2) the addition of a PAB increases the incidence of lymphedema, a systematic review was undertaken. A literature search was performed in the PubMed database. A total of 16 studies were evaluated. There were no randomized studies. Seven articles have investigated dosimetric aspects of a PAB. The remaining 9 articles have determined the effect of a PAB field on the risk of lymphedema. Only 2 of 9 articles have prospectively reported the impact of a PAB on the risk of lymphedema development. There are conflicting reports on the necessity of a PAB. The PAB field provides a good coverage of level I/II axillary LNs because these nodes are usually at a greater depth. The main concern regarding a PAB is that it produces a hot spot in the anterior region of the axilla. Planning studies optimized a traditional PAB field. Prospective studies and the vast majority of retrospective studies have reported the use of a PAB field does not result in increasing the risk of lymphedema development over supraclavicular-only field. The controversies in the incidence of lymphedema suggest that field design may be more important than field arrangement. A key factor regarding the use of a PAB is the depth of axillary LNs. The PAB field should not be used unless there is an absolute indication for its application. Clinicians should weigh lymphedema risk in individual patients against the limited benefit of a PAB, in particular after axillary dissection. The testing of the inclusion of upper arm lymphatics in the regional LN irradiation target volume, and universal methodology measuring lymphedema are all areas for possible future studies
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