• Tuberculosis and Respiratory Diseases
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• v.63 no.5
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• pp.423-429
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• 2007

Background: Alveolar recruitment (RM) is one of the primary goals of respiratory care for an acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The purposes of alveolar recruitment are an improvement in pulmonary gas exchange and the protection of atelectrauma. This study examined the effect and safety of the alveolar RM using pressure control ventilation (PCV) in early ALI and ARDS patients. Methods: Sixteen patients with early ALI and ARDS who underwent alveolar RM using PCV were enrolled in this study. The patients data were recorded at the baseline, and 20 minutes, and 60 minutes after alveolar RM, and on the next day after the maneuver. Alveolar RM was performed with an inspiratory pressure of $30cmH_2O$ and a PEEP of $20cmH_2O$ in a 2-minute PCV mode. The venous $O_2$ saturation, central venous pressure, blood pressure, pulse rate, $PaO_2/FiO_2$ ratio, PEEP, and chest X-ray findings were obtained before and after alveolar RM. Results: Of the 16 patients, 3 had extra-pulmonary ALI/ARDS and the remaining 13 had pulmonary ALI/ARDS. The mean PEEP was 11.3 mmHg, and the mean $PaO_2/FiO_2$ ratio was 130.3 before RM. The $PaO_2/FiO_2$ ratio increased by 45% after alveolar RM. The $PaO_2/FiO_2$ ratio reached a peak 60 minutes after alveolar RM. The Pa$CO_2$ increased by 51.9 mmHg after alveolar RM. The mean blood pressure was not affected by alveolar RM. There were no complications due to pressure injuries such as a pneumothorax, pneumomediastinum, and subcutaneous emphysema. Conclusion: In this study, alveolar RM using PCV improved the level of oxygenation in patients with an acute lung injury and acute respiratory distress syndrome. Moreover, there were no significant complications due to hemodynamic changes and pressure injuries. Therefore, alveolar RM using PCV can be applied easily and safely in clinical practice with lung protective strategy in early ALI and ARDS patients.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.2
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• pp.98-105
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• 2020

This experiment was conducted to a comparison of the productivity according to variety and forage quality by plant parts of imported silage corn (Zea mays, L) in Pyeongchang. The corns evaluated in this experiment were 8 varieties (P1184, P1151, P1194, P1543, P1345, P1429, P1443, and P2105) introduced from the United States, Pioneer Hybrid Co. The harvested corn was divided into 5 plant parts (leaf, stem, cob, husk, and grain), and the ratio of each part was calculated using dry weight and the feed value was analyzed. The emergence rate of corn was generally good except for the P1151 and P2105 varieties. The average tasseling date was July 24^th and the silking date was July 27^th, but the P2105 variety was late to July 28^th and August 1^st, and the remaining varieties were similar. P1345 was the highest (289 and 123 cm), and P1151 varieties were the lowest (267 and 101 cm) in the plant and ear height. Disease resistance was low in P1184, P1443 and P1429, and P1197 and P1345 were high. In the case of stover, the dry matter (DM) content was the lowest at 19.6% in the P1151 and the highest at 24.9% in the P1429. DM content of ear was the highest in the P2105 (55.5%), and P1184 (54.2%) and P1345 (54.3%) were also significantly higher (p<0.05). The DM yield of stover of P2105, P1429 and P1194 varieties was significantly higher (p<0.05), and ear yield of P2105, P1345 and P1443 was higher. The proportions of each part of plants (leaf, stem, cob, husk, and grain) divided by 5 was high, with 50-60% of the ear(grain+cob) ratio. The ratio of husk and cob was roughly similar, and the leaf and stem part showed a ratio of about 20%. The crude protein (CP) content was highest in leaf, followed by grain. The CP content of the stem was the lowest, and the husk was not significantly different among the varieties (p>0.05). The acid detergent fiber (ADF) content was similar to the rest parts except grain, but the leaf part tended to be lower, and other parts except the stem and leaf showed no significant difference between varieties (p>0.05). There was no significant difference in NDF (neutral detergent fiber) content in husk, but there was a difference between varieties in other parts (p<0.05). In addition, there was a special difference by plant parts for each variety, P2015 on the stem, P1197 on the leaf, P1151 on the cob, P1197 on the husk, and P1197 on the grains with high NDF content. IVDMD (in vitro dry matter digestibility) was not significantly different between stems and grains, but there was a difference between varieties in cobs and husks. According to the results, DM yield of P2105 variety was the best in the experiment, and the ratio of grain was excellent in P1543 and P1345. In addition, it was found that the feed value was higher in the leaves and grains, and the leaf and stem had higher feed values than husk or cob.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Applied Biological Chemistry
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• v.9
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• pp.125-147
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• 1968

This experiment was carried out as a part of the studies on reasonable application of nitrogen in rice plant to determine: (I) Nitrogen absorption. and rooting of rice seedlings as affected by urea foliar application at late seedling stage (II) Effect of leaf prunning and foliar application of urea at late heading stage on the maturation and yield of rice (III) Effect of foliar application of urea and its time during the stage of ear formation on yield of rice plant. Results obtained are summarized as follows. Exp.I: Nitrogen absorption and rooting of rice seedlings as affected be urea foliar application at late seedling stage. 1 : The foliar application of urea plots$(T_{1},T_2)$ snowed mare N-content than non-urea foliar application plot(T0) at lane seedling stage, being significant among treatments and foliar application of urea seemed more effective in increasing the N-content of seedlings. and promoted root settlement and early growth alter the transplanting. 2 : The carbon contents of the plants of $T_1$, and $T_2$ at late seedling stage increased than T0, and the carbon contents. of $T_1$ and $T_2$ plots became higher in amount in proportion to the nitrogen absorption as compared with those of $T_0$. 3 : C/N ratio appeared significant among soil application plots($N_1, \;N_2$) and foliar application of urea plots ($T_1$, $T_2$ and $T_0$). C/N ratio was lower in case of increased amount of nitrogen. The higher contents of nitrogen and carbon and lower C/N ratio resulted in the increment of root numbers and root lengths. Exp.II: Effect of leaf prunning and foliar application of urea at late heading stage on the maturation and yield of rice. 1 : There was a highly significant decrease in the maturing rate by severe leaf prunning. In the mean time, significant increase in maturing rate was observed with urea foliar application and it was found the more frequent application the more effective for higher maturing rate with a moderate significance. A correlationship between the level of prunning and maturing rate was enumerated to 0.961 of correlation coefficient, which indicated an increased maturing rate by the increased number of remaining leaves. 2 : The 1.000 grain weight, grain weight and hulled rice yield increased by leaf prunning in order (plot a$A_1$, $A_3$, $A_2$ and $A_0$ were 89.8%, 89.4%, 87.8% and 87.5% respectively, showing the highest of rate in $A_1$ and $A_3$ in methods of ear fertilization and being highly significant between its treatment. 3 : 1000 grain weights were highly significant between time of application, showing a tendency of increase of weights with the time lagging until days before earings as that of maturing rates. High significance was recognized between methods of ear fertilization, showing the highest in $A_2$ 23.18 gr. 4 : Yields per $3.3m^2$ were not significant between time of ear fertilization, whereas were highly significant between methods of ear fertilization. Those of $A_1$, $A_3$, $A_2$ and $A_0$ were 1.486 kg, 1.491 kg, 1.381 kg and 1.328 kg, respectively, showing the highest in $A_1$ and $A_3$. 5 : Hulling ratios showed significant different between time of ear fertilization, showing the highest in $T_2$, whereas those of methods of ear fertilization were highly significant between its treatment, Those of $A_1$, $A_3$, $A_2$ and $A_0$ were 84.72%, 84.06%, 83.29%, and 82.56% respectively, showing the highest m $A_2$ and $A_3$ among others. 6 : Yields of hulled rice per $3.3m^2$ showed significant different between time of ear fertilization, showing the highest in $T_1$ 1.192 kg. Whereas, those were highly significant between methods of ear fertilization. Those of $A_1$, $A_3$, $A_2$ and $A_0$ were 1.259 kg, 1.254 kg, 1.149 kg and 1.095 kg, respectively, showing the highest in $A_1$ and $A_2$. 7 : Contents of nitrogen on rice plant increased in case of nitrogen application as ear fertilizer and showed that the case of urea foliar application was more effective than that of soil application, showing the increased nitrogen content of rice plant was accompanied by carbon content.