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- Adem, Abdu, et al.
(author)
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ANP and BNP Responses to Dehydration in the One-Humped Camel and Effects of Blocking the Renin-Angiotensin System
- 2013
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:3, s. e57806-
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Journal article (peer-reviewed)abstract
- The objectives of this study were to investigate and compare the responses of atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) in the circulation of hydrated, dehydrated, and dehydrated losartan - treated camels; and to document the cardiac storage form of B-type natriuretic peptide in the camel heart. Eighteen male camels were used in the study: control or hydrated camels (n = 6), dehydrated camels (n = 6) and dehydrated losartan-treated camels (n = 6) which were dehydrated and received the angiotensin II (Ang II) AT-1 receptor blocker, losartan, at a dose of 5 mg/kg body weight intravenously for 20 days. Control animals were supplied with feed and water ad-libitum while both dehydrated and dehydrated-losartan treated groups were supplied with feed ad-libitum but no water for 20 days. Compared with time-matched controls, dehydrated camels exhibited a significant decrease in plasma levels of both ANP and BNP. Losartan-treated camels also exhibited a significant decline in ANP and BNP levels across 20 days of dehydration but the changes were not different from those seen with dehydration alone. Size exclusion high performance liquid chromatography of extracts of camel heart indicated that proB-type natriuretic peptide is the storage form of the peptide. We conclude first, that dehydration in the camel induces vigorous decrements in circulating levels of ANP and BNP; second, blockade of the renin-angiotensin system has little or no modulatory effect on the ANP and BNP responses to dehydration; third, proB-type natriuretic peptide is the storage form of this hormone in the heart of the one-humped camel.
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| 2. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(author)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
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In: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
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Journal article (peer-reviewed)abstract
- Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.
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