• Animal Bioscience
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• v.36 no.1
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• pp.144-155
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• 2023

Objective: Adipocyte differentiation is regulated by a variety of functional genes and noncoding RNAs. However, the role of miRNAs in lipid deposition of goat white adipose tissue is still unclear. Therefore, this study revealed the miRNA expression profile in goat subcutaneous adipocytes by sRNA-seq. Methods: The miRNA expressed in goat subcutaneous preadipocytes and the mature adipocytes were sequenced by sRNA-seq. The differentially expressed miRNAs (DEm) were screened and gene ontology (GO) and Kyoto encyclopedia for genes and genomes (KEGG) analyses were performed. Gain-of-function and loss-of-function combined with oil red O staining, Bodipy staining, and quantitative reverse-transcription polymerase chain reaction (qPCR) were utilized to determine the effect of miR-133a-3p on adipocyte differentiation. Results: A total of 218 DEm were screened out. The target genes of these DEm were significantly enriched in GO items such as biological regulation and in KEGG terms such as FAK signaling pathway and MAPK signaling pathway. qPCR verified that the expression trend of miRNA was consistent with miRNA-seq. The gain-of-function or loss-of-function of miR-133a-3p showed that it promoted or inhibited the accumulation of lipid droplets, and CCAAT enhancer binding protein α (C/EBPα) and C/EBPβ were extremely significantly up-regulated or down-regulated respectively (p<0.01), the loss-of-function also led to a significant down-regulation of peroxisome proliferator activated receptor gamma (PPARγ) (p<0.01). Conclusion: This study successfully identified miRNAs expression patterns in goat subcutaneous adipocytes, and functional identification indicates that miR-133a-3p is a positive regulator of the differentiation process of goat subcutaneous adipocytes. Our results lay the foundation for the molecular mechanism of lipid deposition in meat-source goats from the perspective of miRNA.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.211-221
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• 2022

Recently, the demand for the cultivation of upland soil has been increasing, and the rate of conversion of paddy soil into upland soil is also increasing. Theincrease in uneven precipitation due to climate change has resulted in dramatic effects of waterlogging stress on upland crops. Therefore, the present study was conducted to investigate the changes in growth characteristics and the expression patterns of proteins at the two-leaf stage of adzuki beans. The domestic cultivar, Arari (Miryang No. 8), was used to test waterlogging stress. At the two-leaf stage of adzuki beans, plant height slightly decreased androot fresh weight showed significant changes after 3 days of waterlogging treatment. Chlorophyll content was also significantly different after 3 days of waterlogging treatment compared to its content in control plants. Using two-dimensional gel electrophoresis, more than 400 protein spots were identified. Twenty-one differentially expressed proteins from the two-leaf stage were analyzed using linear trap quadrupole-Fourier transform-ion cyclotron resonance mass spectrometry. Of these 21 proteins, 9 were up-regulated and 12 were down-regulated under waterlogging treatment. Protein information resource (https://pir.georgetown.edu/) categories were assigned to all 49 proteins according to their molecular function, cellular component localization, and biological processes. Most of the proteins were found to be involved in the biological process, carbohydrate metabolism and were localized in chloroplasts.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.30-42
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• 2023

The purpose of this study is to quantitatively and qualitatively analyze the science journals written by pre-service elementary school teachers, and to categorize the view on the nature of science and the process of their change. For this purpose, 112 science journals written by 13 pre-service elementary school teachers were analyzed. The frequency of each area was analyzed using the research framework of the four areas of the nature of science, and the pattern of change in perspective on the nature of science was inductively derived and classified using the VNOS-C test analysis framework. As a result, The nature of scientific thinking, nature of scientific knowledge, nature of STS, and nature of scientific inquiry were described in relatively similar proportions, but among them, The nature of scientific thinking appeared in the largest percentage, and the nature of scientific inquiry was described in the smallest percentage. The variability of scientific knowledge, the importance of empirical evidence, and the positive and negative effects of science were especially intensively addressed. In addition, the changing aspects of pre-service elementary school teachers' perspectives on the nature of science could be categorized into 'naive view maintenance type', 'informed view maintenance type', 'regression type', 'development type', and 'mixed type'. The element of 'the empirical nature of scientific knowledge' showed various patterns of change depending on the students, and most of the students maintained a informed view on the tentativeness of scientific knowledge for several sessions.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.

• Journal of the Korean Geotechnical Society
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• v.24 no.5
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• pp.65-78
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• 2008

In case of cut-slopes or shallow-depth tunnels, sliding along with discontinuities or rotation could play a critical role in judging stability. Although numerical analysis is widely used to check the stability of these cut-slopes and shallow-depth tunnels in early design process, common analysis programs are based on continuum model. Performing continuum model analysis regarding discontinuities is possible by reducing overall strength of jointed rock mass. It is also possible by applying ubiquitous joint model to Mohr-Coulomb failure criteria. In numerical analysis of cut-slope, main geotechnical properties such as cohesion, friction angle and elastic modulus can be evaluated by empirical equations. This study tried to compare two main systems, RMR and GSI system by applying them to in-situ hazardous cut-slopes. In addition, this study applied ubiquitous joint model to simulation model with inputs derived by RMR and GSI system to compare with displacements obtained by in-situ monitoring. To sum up, numerical analysis mixed with GSI inputs and ubiquitous joint model proved to provide most reliable results which were similar to actual displacements and their patterns.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.217-228
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• 2009

Data analysis applications typically aggregate data across many dimensions looking for unusual patterns in data. Even though such applications are usually possible with standard structured query language (SQL) queries, the queries may become very complex. A complex query may result in many scans of the base table, leading to poor performance. Because online analytical processing (OLAP) queries are usually complex, it is desired to define a new operator for aggregation, called the data cube or simply cube. Data cube supports OLAP tasks like aggregation and sub-totals. Many aggregate functions can be used to construct a data cube. Those functions can be classified into three categories, the distributive, the algebraic, and the holistic. It has been thought that the distributive functions such as SUM, COUNT, MAX, and MIN can be used to construct a data cube, and also the algebraic function such as AVG can be used if the function is replaced to an intermediate function. It is believed that even though AVG is not distributive, but the intermediate function (SUM, COUNT) is distributive, and AVG can certainly be computed from (SUM, COUNT). In this paper, however, it is found that the intermediate function (SUM COUNT) cannot be applied to OLAP cubes, and consequently the function leads to erroneous conclusions and decisions. The objective of this study is to identify some problems in applying aggregate function AVG to OLAP cubes, and to design a process for solving these problems.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Environmental and Resource Economics Review
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• v.31 no.1
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• pp.113-139
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• 2022

The iron and steel industry is a manufacturing industry with the largest greenhouse gases emissions and has a great ripple effect on the national economy as a core material industry. This study internationally compared the decoupling patterns of greenhouse gases emissions in the iron and steel industry from 1990 to 2019, focusing on Korea, Japan, and Germany. In particular, unlike previous studies that considered only fuel combustion emissions, this study considered all fuel combustion emissions, industrial process emissions, and indirect emissions from the use of electricity and heat. As a result of the analysis, Korea is interpreted as expansive coupling, Japan as decoupling, and Germany as unclear. Therefore, the decoupling path that the Korean iron and steel industry should take should not be in Germany, but in the form of seeking a decoupling method similar to Japan or more effective than Japan. In addition, this study considered the characteristics of the iron and steel industry as much as possible and presented the causes of the decoupling analysis results and implications for the Korean iron and steel industry through comparison with Japan and Germany. In particular, four factors were suggested as factors which has promoted decoupling in Japan: high value-added of Japanese iron and steel products, development of energy efficiency technology in the Japanese iron and steel industry, strategic M&A of the Japanese iron and steel industry, and maintaining competitiveness according to the closed distribution structure of Japanese iron and steel products. The Korean iron and steel industry should also use the case of Japan as a benchmark to further increase added value through quality uprade and product diversification of iron and steel products, while at the same time making efforts to fundamentally reduce greenhouse gas emissions through the development of new technologies.