The Test of Combining Ability and Heterosis on the Silkworm(Bombyx mori) Breeding (누에 견.사질에 관한 잡종강세 및 조합능력검정)
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- Journal of Sericultural and Entomological Science
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- v.36 no.1
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- pp.8-25
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- 1994
The study was conducted to obtain the genetic information on heterosis and combining ability of the quantitative characters for F1 hybrid breeding in silkworms. Six parental varieties and each set of 30 diallel crosses in F1's were used as materials, and bred on the randomized complete block design with three replications. Fourteen characters were observed with the twenty samples in each tray. The data were analyzed for (1) heterosis and combining ability in F1 hybrid. The heterosis in the weight and the length of cocoon showed positively high at 24.51%, and 23.4%, respectively and the weight of the whole cocoon as well as the weight of the whole cocoon layer showed a siginificant heterosis ranging from 15.56% to 15.71% and from 17.14% to 19.01%, but the fifth and the total instar period showed negative heterosis. It was found that the combination between, C70XRomogua and N9 X Romogua showed highly a negative heterosis on the fifth instar period and for the cocoon weight. The female of N9+Sansuian and the male of Romogua X Sansurian have a high heterosis effect, on the cocoon shell weight, and Sansurian X Romogua(reciprocal) on the length and the weight of cocoon filament with no regard to sexuality. The significant maternal and cytoplasmic effect on heterosis of the cocoon weight and the cocoon shell weight were observed with the combinations, N9 X C5, N63 X C70 and on the length of the cocoon filament with the combinations, Sansurian X N63, Sansurian X C5, Sansurian X C70 and N9 X C70, N63 X C70 on the weight of cocoon filament. As mean squared of GCA, SCA and RCA were significant with these combining ability for all characters resulted from additive and non-additive altogether and there is a significant difference between reciprocals. Sansurian showed a negative GCA effect on the fifth and total larval duration, but the higher positive GCA effects took places with varieties N9 and C5 on the length, width, weight of cocoon, cocoon shell weight, percentage of cocoon shell weight, length and weight of cocoon filament, percentage of raw-silk with no regard to both generations and silkworm sexuality. The values of SCA between the cross combinations varied generation-wise and sex-wise. It was shown that SCA value for the fifth instar period was highly negative for Sansurian X C70, Romogua X C70, Sansurian X C5, Romogua X C5, but it was positive effect on the cocoon weight, cocoon shell weight with N9 X C5, and C70 X Sansurian, on the length of cocoon filament with N9 X C5, Romogua X Sansurian on the weight of cocoon filament between Romogua and N63 and on the percentage of raw-silk between the combination of Sansurian X Romoga.
To calculate the total mass flux that change in dry and flood season in the Yeomha Channel of Gyeonggi Bay, the 13 hour bottom tracking observation was performed from the southern extremity. The value of the total mass flux(Lagrange flux) was calculated as the sum of the Eulerian flux value and stroke drift value and the tidal residual flow was harmonically analyzed through the least-squares method. Moreover, the average during the tidal cycle is essential to calculate the mass flux and the tidal residual flow and there is the need to equate the grid of repeatedly observed data. Nevertheless, due to the great differences in the studied region, the number of vertical grid tends to change according to time and since the horizontal grid differs according to the transport speed of the ship as a characteristic of the bottom tracking observation, differences occur in the horizontal and vertical grid for each hour. Hence, the present study has vertically and horizontally normalized(sigma coordinate) to equate the grid per each hour. When compared to the z-level coordinate system, the Sigma coordinate system was evaluated to have no irrationalities in data analysis with 5% of error. As a result of the analysis, the tidal residual flow displayed the flow pattern of sagging in the both ends in the main waterway direction of dry season. During flood season, it was confirmed that the tidal residual flow was vertical 2-layer flow. As a result of the total mass flux, the ebb properties of 359 cm/s and 261 cm/s were observed during dry and flood season, respectively. The total mass flux was moving the intertidal region between Youngjong-do and Ganghwa-do.
The development and proliferation of the mandibular condyle can be altered by changes in the biomechanical environment of the temporomandibular joint. The biomechanical loads were varied by feeding diets of different consistencies. The purpose of the present study was to determine whether changes of masticatory forces by feeding a soft diet can alter the trabecular bone morphology of the growing mouse mandibular condyle, by means of micro-computed tomography. Thirty-six female, 21 days old, C57BL/6 mice were randomly divided into two groups. Mice in the hard-diet control group were fed standard hard rodent pellets for 8 weeks. The soft-diet group mice were given soft ground diets for 8 weeks and their lower incisors were shortened by cutting with a wire cutter twice a week to reduce incision. After 8 weeks all animals were killed after they were weighed. Following sacrifice, the right mandibular condyle was removed. High spatial resolution tomography was done with a Skyscan Micro-CT 1072. Cross-sections were scanned and three-dimensional images were reconstructed from 2D sections. Morphometric and nonmetric parameters such as bone volume(BV), bone surface(BS), total volume(TV), bone volume fraction(BV/TV), surface to volume ratio(BS/BV), trabecular thickness(Tb. Th.), structure model index(SMI) and degree of anisotropy(DA) were directly determined by means of the software package at the micro-CT system. From directly determined indices the trabecular number(Tb. N.) and trabecular separation(Tb. Sp.) were calculated according to parallel plate model of Parfitt et al.. After micro-tomographic imaging, the samples were decalcified, dehydrated, embedded and sectioned for histological observation. The results were as follow: 1. The bone volume fraction, trabecular thickness(Tb. Th.) and trabecular number(Tb. N.) were significantly decreased in the soft-diet group compared with that of the control group (p<0.05). 2. The trabecular separation(Tb. Sp.) was significantly increased in the soft-diet group(p<0.05). 3. There was no significant differences in the surface to volume ratio(BS/BV), structure model index(SMI) and degree of anisotropy(DA) between the soft-diet group and hard-diet control group (p>0.05). 4. Histological sections showed that the thickness of the proliferative layer and total cartilage thickness were significantly reduced in the soft-diet group.
60 nm and 20 nm thick hydrogenated amorphous silicon(a-Si:H) layers were deposited on 200 nm
Objectives: The purpose of this study was to evaluate
Objectives: This study investigated the effect of infection control barrier thickness on power density, wavelength, and light diffusion of light curing units. Materials and Methods: Infection control barrier (Cleanwrap) in one-fold, two-fold, four-fold, and eightfold, and a halogen light curing unit (Optilux 360) and a light emitting diode (LED) light curing unit (Elipar FreeLight 2) were used in this study. Power density of light curing units with infection control barriers covering the fiberoptic bundle was measured with a hand held dental radiometer (Cure Rite). Wavelength of light curing units fixed on a custom made optical breadboard was measured with a portable spectroradiometer (CS-1000). Light diffusion of light curing units was photographed with DSLR (Nikon D70s) as above. Results: Power density decreased significantly as the layer thickness of the infection control barrier increased, except the one-fold and two-fold in halogen light curing unit. Especially, when the barrier was four-fold and more in the halogen light curing unit, the decrease of power density was more prominent. The wavelength of light curing units was not affected by the barriers and almost no change was detected in the peak wavelength. Light diffusion of LED light curing unit was not affected by barriers, however, halogen light curing unit showed decrease in light diffusion angle when the barrier was four-fold and statistically different decrease when the barrier was eight-fold (p < 0.05). Conclusions: It could be assumed that the infection control barriers should be used as two-fold rather than one-fold to prevent tearing of the barriers and subsequent cross contamination between the patients.
As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.
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.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70