Liquid–phase transport of plasma-generated reactive species toward large-scale plasma treatment for agricultural applications is experimentally studied. The liquid-phase reactive species in this study are generated by the contact of water solution with air–plasma effluent gas containing mainly low-solubility reactive species under elevated pressure, followed by spraying the plasma effluent gas dissolved solution into a target pathogenic conidium suspension. Low-solubility ozone in the liquid phase at the target is found to dominate the observed germination suppression effect, which also correlates with the gas-phase ozone density. The measured ozone concentration at the target is found to be ten times lower than the ozone concentration at saturation, estimated from Henry’s law with the measured gas-phase ozone density. The liquid-phase ozone loss mechanism during the transport of the sprayed liquid is interpreted as volatilization and reactions with co-dissolved species. The deduced control parameters for precise ozone transport are the droplet diameter of the sprayed solution, sprayed jet flow, and co-dissolved reactant with the liquid-phase ozone such as nitrite.
Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.
Real-time MRI can be used to obtain videos that describe articulatory movements during running speech. For detailed analysis based on a large number of video frames, it is necessary to extract the contours of speech organs, such as the tongue, semi-automatically. The present study attempted to extract the contours of speech organs from videos using a machine learning method. First, an expert operator manually extracted the contours from the frames of a video to build training data sets. The learning operators, or learners, then extracted the contours from each frame of the video. Finally, the errors representing the geometrical distance between the extracted contours and the ground truth, which were the contours excluded from the training data sets, were examined. The results showed that the contours extracted using machine learning were closer to the ground truth than the contours traced by other expert and non-expert operators. In addition, using the same learners, the contours were extracted from other naive videos obtained during different speech tasks of the same subject. As a result, the errors in those videos were similar to those in the video in which the learners were trained.
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