• Animal Systematics, Evolution and Diversity
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• v.29 no.1
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• pp.23-30
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• 2013

This study reports the discovery of two oxytrichid ciliates, Cyrtohymena primicirrata (Berger and Foissner, 1987) and Oxytricha granulifera Foissner and Adam, 1983, in Jeju Island, Korea. The morphology of the two species was studied using live observation and protargol impregnation. These species are described as follows: Cyrtohymena primicirrata has a body size in live specimens $90-140{\times}40-60{\mu}m$, length : width ratio 2.3 : 1 on average; elongated and slender obovate in outline of body. Cortical granules are shiny yellow on the ventral and dorsal side. Adoral zone of membranelles (AZM) is covering about 48% of the cell with about 38 adoral membranelles. Arrangement of undulating membranes is ordinary Cyrtohymena pattern. Dorsal kineties is six rows with $5{\mu}m$ long bristles. Oxytricha granulifera has a body size in live specimens $90-115{\times}25-38{\mu}m$, length : width ratio 3.31 on average; elongated ellipsoidal in outline of body. Cortical granules are colorless on the ventral and dorsal side. AZM is covering 28% of the cell length in vivo with about 24 adoral membranelles. Arrangement of undulating membranes is Oxytricha pattern. Dorsal kineties is five rows with about $3{\mu}m$ long dorsal bristles.

• Safety and Health at Work
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• v.6 no.2
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• pp.104-113
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• 2015

Background: Given the long hours on the road involving multiple and interacting work stressors (i.e., delivery pressures, irregular shifts, ergonomic hazards), commercial drivers face a plethora of health and safety risks. Researchers goal was to determine whether and to what extent long-haul trucker work schedules influence sleep duration and quality. Methods: Survey and biometric data collected from male long-haul truck drivers at a major truckstop in central North Carolina over a six month period. Results: Daily hours worked (mean = 11 hours, 55 minutes) and frequency of working over government-mandated daily HOS regulations (23.8% "frequently or always") were statistically significant predictors of sleep duration. Miles driven per week (mean = 2,812.61), irregular daily hours worked (63.8%), and frequency of working over the daily hour limit (23.8% "frequently or always") were statistically significant predictors of sleep quality. Conclusion: Implications of findings suggest a comprehensive review of the regulations and operational conditions for commercial motor vehicle drivers be undertaken.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.433-444
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• 2020

The purpose of this study is to analyze integrated reporting disclosure and its implications on investor reactions. The population in this study is all manufacturing companies listed on the Indonesia stock exchange from 2017 to 2019, totaling 171 companies, and the sampling technique used is purposive sampling method. The method used in this research is a quantitative description using the financial statements of manufacturing companies listed on the Indonesia stock exchange. The data analysis method used is multiple regression analysis with intervening variables using AMOS 24 software. The results of this study show a positive and significant effect of profitability (X1) and company size (X2) on integrated reporting (IR); a positive and insignificant effect of stakeholder pressure (X3) on integrated reporting (IR); a positive and significant effect of profitability (X1) and stakeholder pressure (X3) on investor reactions (Y); a positive and insignificant effect of firm size (X2) and integrated reporting (IR) on investor reactions (Y). Suggestions are that in further studies, we can increase the sample size by including other industries, and in addition to using annual reporting, we can also use other sources such as websites, press releases, and prospectuses to improve the robustness of this study by relying on other data sources.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• Smart Structures and Systems
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• v.19 no.2
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• pp.225-236
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• 2017

Railroad bridges in North America are an integral but aging part of the railroad network and are typically only monitored using visual inspections. When quantitative information is required for assessment, railroads often monitor bridges using accelerometers. However without a sensor to directly measure displacements, it is difficult to interpret these results as they relate to bridge performance. Digital Image Correlation (DIC) is a non-contact sensor technology capable of directly measuring the displacement of any visible bridge component. In this research, a railroad bridge was monitored under load using DIC and accelerometers. DIC measurements are directly compared to serviceability limits and it is observed that the bridge is compliant. The accelerometer data is also used to calculate displacements which are compared to the DIC measurements to assess the accuracy of the accelerometer measurements. These measurements compared well for zero-mean lateral data, providing measurement redundancy and validation. The lateral displacements from both the accelerometers and DIC at the supports were then used to determine the source of lateral displacements within the support system.

• BMB Reports
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• v.43 no.5
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• pp.375-381
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• 2010

In this study, the cDNA library of Chang-liver cells was immunoscreened using common ADAMs antibody to obtain ADAM related genes. We found one positive clone that was confirmed as a new gene by Blast, which is an uncharacterized helical and coil protein and processes protease activity, and named protease-related protein 1 (ARP1). The submitted GenBank accession number is AY078070. Molecular characterizations of ARP1 were analyzed with appropriate bioinformatics software. To analyse its expression and function, ARP1 was subcloned into glutathione S-transferase fusion plasmid pGEX-2T and expressed by E. coli system. The in vitro expression product of ARP1 was recognized by common ADAMs antibody with western blot. Interestingly, ARP1 cleaves gelatine at pH9.5, which suggests it is an alkaline protease. Semi-quantitative RT-PCR result indicates that ARP1 mRNA is strongly transcribed in the liver and the treated Chang-liver cells.

• Bulletin of the Korean Mathematical Society
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• v.55 no.6
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• pp.1859-1868
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• 2018

In the present paper, we have proved the sharp inequality ${\mid}H_{3,1}(f){\mid}{\leq}4$ and ${\mid}H_{3,1}(f){\mid}{\leq}1$ for analytic functions f with $a_n:=f^{(n)}(0)/n!$, $n{\in}{\mathbb{N}},$, such that $$Re\frac{f(z)}{z}>{\alpha},\;z{\in}{\mathbb{D}}:=\{z{\in}{\mathbb{C}}:{\mid}z{\mid}<1\}$$ for ${\alpha}=0$ and ${\alpha}=1/2$, respectively, where $$H_{3,1}(f):=\left|{\array{{\alpha}_1&{\alpha}_2&{\alpha}_3\\{\alpha}_2&{\alpha}_3&{\alpha}_4\\{\alpha}_3&{\alpha}_4&{\alpha}_5}}\right|$$ is the third Hankel determinant.

• Computers and Concrete
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• v.14 no.2
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• pp.145-161
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• 2014

The article presents the statistical method of grouping the results of the compressive strength of concrete in continuous production. It describes the method of dividing the series of compressive strength results into batches of statistically stable strength parameters at specific time intervals, based on the standardized concept of "concrete family". The article presents the examples of calculations made for two series of concrete strength results, from which sets of decreased strength parameters were separated. When assessing the quality of concrete elements and concrete road surfaces, the principal issue is the control of the compressive strength parameters of concrete. Large quantities of concrete mix manufactured in a continuous way should be subject to continuous control. Standardized approach to assessing the concrete strength proves to be insufficient because it does not allow for the detection of subsets of the decreased strength results, which in turn makes it impossible to make adjustments to the concrete manufacturing process and to identify particular product or area on site with decreased concrete strength. In this article two independent methods of grouping the test results of concrete with statistically stable strength parameters were proposed, involving verification of statistical hypothesis based on statistical tests: Student's t-test and Mann - Whitney - U test.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.1
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• pp.99-104
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• 2009

In this paper, the drop characteristic of the small aircraft landing gear of SUAV has been analyzed and performed on orifice optimal design for shock absorption efficiency. The SUAV landing gear was simple oleo pneumatic type without metering pin. The landing gear was modelled by MSC ADAMS software. Drop test evaluation was conducted to confirm the analysis model. As a result of correlation between analysis and test results, it was verified that these results were coincided with very well. After confidence review of analysis model through the correlation between test and analysis results, design parametric study was performed by using confirmed analysis model. Optimal orifice size with best efficiency have been decided in this study.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.97-102
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• 2016

Deep learning enables machines to have perception and can potentially outperform humans in the medical field. It can save a lot of time and reduce human error by detecting certain patterns from medical images without being trained. The main goal of this paper is to build the optimal model for breast mass classification by applying various methods that influence the performance of Convolutional Neural Network (CNN). Google's newly developed software library Tensorflow was used to build CNN and the mammogram dataset used in this study was obtained from 340 breast cancer cases. The best classification performance we achieved was an accuracy of 0.887, sensitivity of 0.903, and specificity of 0.869 for normal tissue versus malignant mass classification with augmented data, more convolutional filters, and ADAM optimizer. A limitation of this method, however, was that it only considered malignant masses which are relatively easier to classify than benign masses. Therefore, further studies are required in order to properly classify any given data for medical uses.