• International Area Studies Review
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• v.15 no.1
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• pp.571-583
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• 2011

We use the properties of competitive location equilibrium to study the relationship between the demand for education and the choice of primary residential location. Consumers can work and live in a comparatively high wage place where there are few education opportunities, or live in a place where education is available and commute to work. If education and employment are each location-specific, there are pooling equilibria in which consumers locate according to their preference for education. In general, the stronger the taste for education, the greater the attraction of living close to the education site and the lower the demand for other goods, including housing. Exploring the effects of the model parameters on the spatial distribution of consumers, we find that a higher frequency of trips taken to the education site, a shorter distance between the work place and the education site, or a greater out-of-pocket education cost each leads to a wider range of consumer types selecting to reside at the education location. We also find that a higher wage lowers the range of consumers who select to live near the education site.

• Korea Trade Review
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• v.48 no.4
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• pp.263-283
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• 2023

Companies must strengthen core competencies by concentrating resources to secure a competitive edge and operate efficient processes from a company-wide perspective. To this end, it is seeking to concentrate its capabilities and reduce costs by pooling non-essential tasks or facilities that require a lot of time and capital at a strategic level. Therefore, logistics companies should actively utilize logistics coorperate system in order to maximize the use of logistics resources according to the limitations of human resources, physical resources, and time. This study is an empirical analysis of the strategy of logistics companies and the impact of logistics coorperate on corporate performance, and a survey and analysis was conducted on domestic logistics companies. The results of the empirical analysis showed that the cost·relationship·information-oriented strategy of logistics has a positive(+) effect on the financial·operation·strategic performance indicators of companies through logistics coorperate. The results derived from this paper will be used as an important determining factor in establishing a logistics strategy and logistics coorperate to improve the performance of logistics companies and logistics service companies.

• ETRI Journal
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• v.46 no.2
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.649-661
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• 2019

Objective: To systematically review the evaluation of the diagnostic accuracy of pre-treatment apparent diffusion coefficient (ADC) and change in ADC during the intra- or post-treatment period, for the prediction of locoregional failure in patients with head and neck squamous cell carcinoma (HNSCC). Materials and Methods: Ovid-MEDLINE and Embase databases were searched up to September 8, 2018, for studies on the use of diffusion-weighted magnetic resonance imaging for the prediction of locoregional treatment response in patients with HNSCC treated with chemoradiation or radiation therapy. Risk of bias was assessed by using the Quality Assessment Tool for Diagnostic Accuracy Studies-2. Results: Twelve studies were included in the systematic review, and diagnostic accuracy assessment was performed using seven studies. High pre-treatment ADC showed inconsistent results with the tendency for locoregional failure, whereas all studies evaluating changes in ADC showed consistent results of a lower rise in ADC in patients with locoregional failure compared to those with locoregional control. The sensitivities and specificities of pre-treatment ADC and change in ADC for predicting locoregional failure were relatively high (range: 50-100% and 79-96%, 75-100% and 69-95%, respectively). Meta-analytic pooling was not performed due to the apparent heterogeneity in these values. Conclusion: High pre-treatment ADC and low rise in early intra-treatment or post-treatment ADC with chemoradiation, could be indicators of locoregional failure in patients with HNSCC. However, as the studies are few, heterogeneous, and at high risk for bias, the sensitivity and specificity of these parameters for predicting the treatment response are yet to be determined.

• Korean Journal of Radiology
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• v.20 no.6
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• pp.880-893
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• 2019

Objective: To assess the technical performance of two-dimensional shear wave elastography (2D-SWE) for measuring liver stiffness. Materials and Methods: The Ovid-MEDLINE and EMBASE databases were searched for studies reporting the technical performance of 2D-SWE, including concerns with technical failures, unreliable measurements, interobserver reliability, and/or intraobserver reliability, published until June 30, 2018. The pooled proportion of technical failure and unreliable measurements was calculated using meta-analytic pooling via the random-effects model and inverse variance method for calculating weights. Subgroup analyses were performed to explore potential causes of heterogeneity. The pooled intraclass correlation coefficients (ICCs) for interobserver and intraobserver reliability were calculated using the Hedges-Olkin method with Fisher's Z transformation of the correlation coefficient. Results: The search yielded 34 articles. From 20 2D-SWE studies including 6196 patients, the pooled proportion of technical failure was 2.3% (95% confidence interval [CI], 1.3-3.9%). The pooled proportion of unreliable measurements from 20 studies including 6961 patients was 7.5% (95% CI, 4.7-11.7%). In the subgroup analyses, studies conducting more than three measurements showed fewer unreliable measurements than did those with three measurements or less, but no intergroup difference was found in technical failure. The pooled ICCs for interobserver reliability (from 10 studies including 517 patients) and intraobserver reliability (from 7 studies including 679 patients) were 0.87 (95% CI, 0.82-0.90) and 0.93 (95% CI, 0.89-0.95), respectively, suggesting good to excellent reliability. Conclusion: 2D-SWE shows good technical performance for assessing liver stiffness, with high technical success and reliability. Future studies should establish the quality criteria and optimal number of measurements.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.1
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• pp.31-39
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• 2022

In patients with neuroendocrine tumors with liver metastases (NETLMs), complete resection of both the primary and liver metastases is a potentially curative option. When complete resection is not possible, debulking of the tumour burden has been proposed to prolong survival. The objective of this systematic review was to evaluate the effect of curative surgery (R0-R1) and debulking surgery (R2) on overall survival (OS) in NETLMs. For the subgroup of R2 resections, outcomes were compared by the degree of hepatic debulking (≥ 90% or ≥ 70%). A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines using PubMed, Medline, CINAHL, Cochrane, and Embase databases. Hazard ratios (HRs) were estimated for each study and pooled using a random-effects inverse-variance meta-analysis model. Of 538 articles retrieved, 11 studies (1,729 patients) reported comparisons between curative and debulking surgeries. After pooling these studies, OS was found to be significantly shorter in debulking resections, with an HR of 3.49 (95% confidence interval, 2.70-4.51; p < 0.001). Five studies (654 patients) compared outcomes between ≥ 90% and ≥ 70% hepatic debulking approaches. Whilst these studies reported a tendency for OS and progression-free survival to be shorter in those with a lower degree of debulking, they did not report sufficient data for this to be assessed in a formal meta-analysis. In patients with NETLM, OS following surgical resection is the best to achieve R0-R1 resection. There is also evidence for a progressive reduction in survival benefit with lesser debulking of tumour load.

• Korean Journal of Veterinary Research
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• v.23 no.2
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• pp.137-148
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• 1983

This study was made for the better information of the male reproductive system on the meat-type drake, Cherry Belly X White Golden. The epithelium of ductules of epididymal region and deferent duct were observed histologically and histochemically with the progress of their development. India-ink absorbability on the luminal epithelium was also investigated after the administration of India-ink. The results are as follows; 1. Rete testis and various round ductules in immature form appeared in epididymis within 6 weeks after hatching, and simple cuboidal and simple columnar epithelium were found in the epithelia of the ductules within 8 weeks after hatching. Larger ductules were found on epididymal surface which was in the developing stage near to the immature efferent ductule. From 10th to 20th week, various ductules appeared in epididymis, and developing form of efferent ductules were much more increased on epididymal surface. The luminal epithelium of the ductules were composed of ciliated simple columnar and pseudostratified ciliated columnar cells. At the same time, deferent duct appeared. From the 21th week, various ductules in epididymis became abruptly matured. Lumen of rete testis was lined by simple squamous or simple cuboidal epithelium, and that of efferent ductules, having many folds and being larger than any others were lined by pseudostratified ciliated columnar epithelium in which ciliated columnar cells, non-ciliated cells(clear cells) and basal cells were noted. Connecting tubules of star shaped lumen were composed of pseudostratified ciliated columnar epithelium in which ciliated columnar cells, nonciliated cells, and basal cells were observed. The luminal surface of epididymal ducts was smooth and has thick pseudostratified columnar epithelium which was composed of high columnar cells and basal cells. From 26th week after hatching, sperm pooling was started in various ductules. 2. From 4th to 10th week, simple cuboidal epithelium of deferent duct transformed to simple columnar epithelium with the progress of aging. At the basement of epithelium, clear round cells were noted. From 12th to 20th week, high columnar cells with enlongated nucleus were noted on the luminal border of deferent ducts, forming folds of pseuclostratified columnar epithelium. From 20th week, the deferent duct started to have septa in it's lumen and composed mainly of pseudostratified columnar epithelium, and round cells disappeared. From 20th week, the lumen diameter of deferent duct became wider with the progress of aging, but there was no difference among the values of lumen diameter in upper, middle, and lower part of deferent ducts. At 26th week, the pooling period of sperms in deferent ducts, the lumen diameter became rapidly widen, especially in the lower part of deferent ducts. Thickness of muscular layer of ductus deferens showed gradual growth within 24 weeks but did abrupt thickening from 26th week. 3. Saliva resistant PAS granules were dotted on the top of nucleus in efferent ductules epithelium but the amount of the granules were little in the connecting ductules's epithelium. The granules reactive to acid phosphatase were abundant in the some epithelial cells of efferent ductules and connecting ductules, especially above the nucleus of cells. The granules reactive to alkaline phosphatase were noted on the luminal border of efferent ductules. Parts of free border of efferent ductules and middle portion of deferent ducts were stained slightly by alcian blue technique. India ink granules were found mainly in the epithelium of efferent ductules but were few in that of connecting ductules.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.