• Horticultural Science & Technology
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• v.34 no.6
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• pp.845-859
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• 2016

This study was conducted to examine the effect of nighttime ventilation and heating on changes in temperature and humidity, the occurrence of white rust, and growth of standard chrysanthemum (Dendranthema grandiflorum) 'Baekma' in a greenhouse. For the ventilation treatments, the mean nighttime humidity in the control greenhouse, which had a closed side window, was higher (94.5%), but the humidity in the natural and natural+fan ventilation treatments was lower (74.3% and 72.8%, respectively). The rate of occurrence of white rust at 34 days after treatment was 100, 98.3, 75.6, and 43.3% for the control, fan ventilation, natural ventilation, and natural+fan ventilation treatments, respectively. The number of infected leaves and telia were the lowest in the natural+fan ventilation treatment compared with the other treatments. The growth of the chrysanthemum 'Baekma' was significantly suppressed in the control because of the occurrence of white rust and high humidity, while plant height, number of leaves, stem diameter, and fresh weight were the greatest in the natural+fan ventilation treatment. For the heating treatments, the mean nighttime temperature of the control (RH 95% heating), which was heating and held at 95% humidity, was $18.4^{\circ}C$, while the temperature of the heating treatment, which was held at 70% relative humidity (RH 70% heating) was $25.8^{\circ}C$. The rate of occurrence of white rust (34.4%), number of infected leaves (0.9), and telia (1.0) were the lowest in the RH 70% heating treatment compared with the other heating conditions. Also, the RH 70% heating treatment showed the best growth in terms of plant height, stem diameter, number of leaves, and fresh weight. Therefore, the natural+fan ventilation and RH 70% heating treatments were effective for the control of white rust and the growth of standard chrysanthemum 'Baekma' in a greenhouse.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Chest Surgery
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• v.38 no.4 s.249
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• pp.291-300
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• 2005

One-stage repair of aortic arch anomalies and intracardiac defects through median sternotomy has been recently adopted by many institutions since it is known to be safer than the staged repair. The early and midterm results of the one-stage repair of aortic arch anomalies and intracardiac defects were retrospectively evaluated. Material and Method: 45 patients who underwent one-stage repair of aortic arch anomalies and intracardiac defects performed by one surgeon from January 1996 to July 2003 were included in this retrospective study. The median age of repair was 16 days (range, 3 days$\~$23.7 months) and the mean weight was $3.62\;\pm\;1.30 kg$. Thirty one $(68.9\%)$ had coarctation and 14 $(31.1\%)$ had interrupted aortic arch. Associated intracardiac anomalies were VSD in 31 patients (VSD group), TGA or Taussig-Bing anomaly in 10 (TGA group), and others in 4 (ASD in 1, aortopulmonary window 1, truncus arteriosus 1, aortic and mitral stenoses 1, miscellaneous group). The arch obstruction was repaired with end-to-side anastomosis in 23 patients and end-to-end anastomosis in 22. Result: Overall postoperative hospital mortality was $22.2\%\;(10/45);\;16.1\%$ (5/31) in VSD group, $40\%$ (4/10) in TGA group, and$25\%$ (1/4) in miscellaneous group. There was no mortality in VSD group since 1998, and the mortality in TGA group has remarkably reduced since technical modification for coronary transfer was adopted $(75\%\;vs\;16.7\%)$. There was no postoperative seizure or other neurological complications. Postoperative aortic restenosis occurred in 5 patients $(5/35,\;14.3\%)$. Two patients underwent balloon aortoplasty with successful results. There was no reoperation. There was one late death caused by pneumonia 5 months after the operation. Two-year actuarial survival rate including operative death was $72.9\%$. Conclusion: The operative mortality of one-stage repair has been reduced with time and aortic restenosis rate was also acceptable. We concluded that this procedure is a reproducible procedure for aortic arch anomalies associated with intracardiac defects.

• Journal of radiological science and technology
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• v.32 no.1
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• pp.101-106
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• 2009

Purpose of this study is to compare the signal intensity (SI) and CNR with T1 weighted image using FLASH at 3T abdominal MRI by varying flip angle (FA). Totally 20 patients (male : 12, female : 8, Age : $28{\sim}63$ years with mean : 51) were examined by 3 Tesla MR scanner (Magnetom Tim Trio, SIEMENS, Germany) with 8 channel body array coil between september and October 2008. Imaging parameters were as follows : FLASH sequence, TR : 120 ms, TE : minimum, FOV (field of view) : $360{\times}300\;mm$, Matrix : $256{\times}224$, slice : 6 mm, scan time : 15 sec and Breath-hold technique. Abdominal image, with a 50 ml syringe filled with water placed in the FOV measuring the water signal, were acquired with varying FA through $10^{\circ}$ to $90^{\circ}$ with $10^{\circ}$ interval. SI's were measured three times at liver parenchyme, water, spleen and background and averaged. The CNR's were measured between the ROIs (region of interest). Statistic analysis was performed with ANOVA test using SPSS software (version 17.0). Less than FA $30^{\circ}$, abdominal images were severely inhomogeneity. Especially, T1 effect of water signal was weak. As the flip angle increased, the signal intensity decreased at all the regions. Especially, flip angle of the highest signal intensity was observed with $40^{\circ}$ at the liver parenchyme, $20^{\circ}$ at water, $30^{\circ}$ at the spleen, respectively. The CNR between liver and water was -60.92 at FA $10^{\circ}$ and 15.16 at FA $80^{\circ}$. The CNR between liver and spleen was -3.18 at FA $10^{\circ}$ and 9.65 at $80^{\circ}$. In conclusion, FA $80^{\circ}$ is optimal for T1 weighted effect using FLASH pulse sequence at 3.0 T abdominal MRI.

• Asian Oncology Nursing
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• v.3 no.2
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• pp.145-154
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• 2003

Purpose: The study was to furnish basic raw materials that evaluate the efficacy of meatal care according to the form and the relative importance of symptom distress which most of cancer sufferers have been experienced. For that, an investigation of five diverse major cancer symptom distress made a comparison between symptom distress and degree of suffering. Method: Study subjects were 138 inpatients with stomach cancer, lung cancer, hepatocellular carcinoma(HCC), large intestine cancer and breast cancer, except those in the terminal-stage, in 'H' university hospital in Seoul and 'K' center in Ilsan gathered from November 20, 2002 to February 20, 2003. To measure the correlation between feeling of discomfort and agony caused by cancer, 5 point scale (from zero to four), stood on the basis of Symptom Distress Scale (SDS, Rodes & Watson, 1987), was used for this study and the Cronbach's coefficient alpha was 0.95. Accumulated data was analyzed with SPSS 10.0 for window, also used by ANOVA and Duncan's Multiple Range Test. Pearson's Correlation Analysis. Results: 1. Symptom distress of cancer patients was noted and defined in their severity-fatigue, anorexia, pain, depression, dyspepsia, changing appearance and nausea. The degree of symptom distress was fatigue, dyspepsia, depression, anorexia, pain, changing appearance and the degree of suffering was nausea, pain, anorexia, dyspepsia, vomiting, breathing difficulty, changing appearance and fatigue. 2. Examining the difference of degree of symptom distress in each cancer cases, it takes the precedence of them. First, in case of stomach cancer, depression, pain, vomiting and nausea were shown in sequence. In case of lung cancer depression, pain, sleeping problem, anxiety, changing appearance, inattentiveness and vomiting were showed in sequence, depression, changing appearance, sleeping problem, pain in case of HCC, depression, pain in case of large intestine cancer and lastly in case of breast cancer changing appearance, depression, pain and anxiety were shown in sequence. The category of the degree of symptom distress that has a signifiant difference was anorexia, activity discomfort, fatigue, constipation or diarrhea, breathing difficulty, dyspepsia, caughing, fever or chillness, scotoma and urinary disorder. Verifying the highest degree of symptom distress in each cancer cases, anorexia was 1.94(F=4.00, p<.01) in stomach cancer, activity discomfort was 0.97(F=3.08, p<.01) in lung cancer and HCC, fatigue was 2.32(F=4.64, p<.01) in HCC, constipation or diarrhea was 1.83(F=22.31, p<.001) in large intestine cancer, breathing difficulty was 1.83(F=4.00, p<.01) in lung cancer, dyspepsia was 2.69(F=9.98, p<.001) in stomach cancer, coughing was 1.53(F=20.49, p<.001) in lung cancer, fever or chillness was 1.23(F=6.88, p<.001) in lung cancer, scotoma was 1.20(F=3.02, p<.05) in lung cancer and urinary disorder was 1.54(F=11.56, p<.001) in HCC. 3. Examining the difference degree of suffering on cancer cases, the result was as follows; depression of lung cancer was 1.17(F=3.76, p<.01), anorexia of stomach cancer was 1.61(F=3.89, p<.01), constipation or diarrhea of large intestine cancer was 1.42(F=10.43, p<.001), changing appearance of breast cancer was 1.65(F=5.43, p<.001), breathing difficulty of lung cancer was 2.27(F=18.57, p<.001), dyspepsia of stomach cancer was 1.97(F=13.56, p<.001), coughing of lung cancer was 1.70(F=22.07, p<.001), fever or chillness of lung cancer was 1.13(F=4.41, p<.01), scotoma of lung cancer was 0.87(F=3.34, p<.05), anxiety of lung cancer was 0.87(F=4.50, p<.001) and urinary disorder of HCC was 1.43(F=16.71, p<.001). 4. In consequence, comparing between symptom distress and degree of suffering on cancer patients undergoing chemotherapy, lung cancer patients showed the highest feeling of discomfort following stomach cancer, HCC, breast cancer and large intestine cancer(F=2.88, p<.05). On those undergoing radiotherapy, lung cancer, HCC, breast cancer, large intestine cancer was in sequence(F=3.78, p<.05) and those resisting radiotherapy, lung cancer, HCC, stomach cancer, large intestine cancer and breast cancer was in sequence(F=2.72, p<.05). 5. Correlation between symptom distress and degree of suffering on cancer patients was generally significant. Conclusion: this study not only defines a significant correlation between symptom distress and degree of suffering but also proffers basic data to evaluate the efficient meatal care depending upon diverse spectrums of symptom distress and degree of suffering.