Increasing tropospheric ozone concentration is a big threat to food security due to its phytotoxicity. It causes a huge damage to crop production across the globe, especially in the C3 plants (paddy (Oryza sativa)). T...Increasing tropospheric ozone concentration is a big threat to food security due to its phytotoxicity. It causes a huge damage to crop production across the globe, especially in the C3 plants (paddy (Oryza sativa)). The present study focuses on exposure-plant response index over different O3 concentration. In this study, two metrics viz. the average ozone for 7 h during daytime (M7) and accumulated exposure above a threshold of X ppb (AOTX) have been used in examining crop yield decline in Delhi, India. Eight AOTX indices (AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30 and AOT40) were analysed and potential crop reduction was predicted. The regular monitoring of O3 was done for 24 hours in year 2013. As per the European benchmark, a 5% yield loss was expected when AOT40 values crosses 3000 ppb·h, however this study revealed that AOT40 threshold value ranged between 695 ppb to 17645 ppb which had exceeded the European benchmark in most of the months. The crop reduction was found to be ~6.3% as evaluated by AOT40 index, whereas, total AOTX contributed up to 23% of rice yield reduction in Delhi NCR. On the other side, only 2% of rice yield loss has been predicted using M7 index, which is not comparable with AOTX indices. The M7 index was also found incomparable to the calculated net yield loss (13%) for year 2013 to 2016. Hence, AOT40 may be a better index to predict the vulnerable impact of O3 into the crop production. The total vulnerability of O3 calculated as 57% in the crops reduction, while impacts of O3 was calculated and summed up for both the significant and non-significant paddy growing seasons. Hence, this study highlights an alarming situation in crop yield reduction due to O3 exposure in Delhi NCR which further threatens food security.展开更多
A pair of stainless steel (ss) plates separates the source and target regions of a double plasma device. Two sets of tungsten filaments, placed at different distances from the ss plates, are then used to produce plasm...A pair of stainless steel (ss) plates separates the source and target regions of a double plasma device. Two sets of tungsten filaments, placed at different distances from the ss plates, are then used to produce plasma alternately and the plasma parameters in the source and target regions for different discharge voltage, discharge current and plate separation are measured using Langmuir probes. It is found that plasma density and electron temperature are considerably affected and respond differently to changes in the plate separations and the position of the filaments.展开更多
Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which ...Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which can lead to the fabrication of a probabilistic mathematical model. As an alternative, a convolutional Long shortterm memory architecture-based image analysis approach is presented herein. This technique can predict the electrodeposition growth of the electrolytes, without prior detailed knowledge of the system. The captured images of the electrodeposition from the experiments are used to train and test the model. A comparison between the expected output image and predicted image on a pixel level, percentage mean squared error, absolute percentage error, and pattern density of the electrodeposit are investigated to assess the model accuracy. The randomness of the electrodeposition growth is outlined by investigating the fractal dimension and the interfacial length of the electrodeposits. The trained model predictions show a significant promise between all the experimentally obtained relevant parameters with the predicted one. It is expected that this deep learning-based approach for predicting random electrodeposition growth will be of immense help for designing and optimizing the relevant experimental scheme in near future without performing multiple experiments.展开更多
文摘Increasing tropospheric ozone concentration is a big threat to food security due to its phytotoxicity. It causes a huge damage to crop production across the globe, especially in the C3 plants (paddy (Oryza sativa)). The present study focuses on exposure-plant response index over different O3 concentration. In this study, two metrics viz. the average ozone for 7 h during daytime (M7) and accumulated exposure above a threshold of X ppb (AOTX) have been used in examining crop yield decline in Delhi, India. Eight AOTX indices (AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30 and AOT40) were analysed and potential crop reduction was predicted. The regular monitoring of O3 was done for 24 hours in year 2013. As per the European benchmark, a 5% yield loss was expected when AOT40 values crosses 3000 ppb·h, however this study revealed that AOT40 threshold value ranged between 695 ppb to 17645 ppb which had exceeded the European benchmark in most of the months. The crop reduction was found to be ~6.3% as evaluated by AOT40 index, whereas, total AOTX contributed up to 23% of rice yield reduction in Delhi NCR. On the other side, only 2% of rice yield loss has been predicted using M7 index, which is not comparable with AOTX indices. The M7 index was also found incomparable to the calculated net yield loss (13%) for year 2013 to 2016. Hence, AOT40 may be a better index to predict the vulnerable impact of O3 into the crop production. The total vulnerability of O3 calculated as 57% in the crops reduction, while impacts of O3 was calculated and summed up for both the significant and non-significant paddy growing seasons. Hence, this study highlights an alarming situation in crop yield reduction due to O3 exposure in Delhi NCR which further threatens food security.
文摘A pair of stainless steel (ss) plates separates the source and target regions of a double plasma device. Two sets of tungsten filaments, placed at different distances from the ss plates, are then used to produce plasma alternately and the plasma parameters in the source and target regions for different discharge voltage, discharge current and plate separation are measured using Langmuir probes. It is found that plasma density and electron temperature are considerably affected and respond differently to changes in the plate separations and the position of the filaments.
文摘Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which can lead to the fabrication of a probabilistic mathematical model. As an alternative, a convolutional Long shortterm memory architecture-based image analysis approach is presented herein. This technique can predict the electrodeposition growth of the electrolytes, without prior detailed knowledge of the system. The captured images of the electrodeposition from the experiments are used to train and test the model. A comparison between the expected output image and predicted image on a pixel level, percentage mean squared error, absolute percentage error, and pattern density of the electrodeposit are investigated to assess the model accuracy. The randomness of the electrodeposition growth is outlined by investigating the fractal dimension and the interfacial length of the electrodeposits. The trained model predictions show a significant promise between all the experimentally obtained relevant parameters with the predicted one. It is expected that this deep learning-based approach for predicting random electrodeposition growth will be of immense help for designing and optimizing the relevant experimental scheme in near future without performing multiple experiments.
