• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.199-210
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• 2021

A number of management issues can be used as drivers for change in order to improve animal welfare and nursing capacity of the hyperprolific sow. Group housing of sows during gestation is a recommended practice from the perspective of animal welfare. Related health issues include reproductive health and the locomotor system. It appears that management of pregnant sows in groups is challenging for a producer and considerable skill is required. We explored the benefits and challenges of group housing, including feeding issues. Increasing litter size requires additional attention to the mammary gland and its ability to provide sufficient nursing for the growing litter. We discuss the fundamentals of mammary development and the specific challenges related to the hyperprolific sow. We also address challenges with the farrowing environment. It appears that the old-fashioned farrowing crate is not only outdated in terms of welfare from the public's perspective, but also fails to provide the environment that the sow needs to support her physiology of farrowing, nursing, and maternal behaviour. Studies from our group and others indicate that providing the sow with a loose housing system adequate in space and nesting material, along with reasonable chance for isolation, can be considered as fundamental for successful farrowing of the hyperprolific sow. It has also been shown that management strategies, such as split suckling and cross fostering, are necessary to ensure proper colostrum intake for all piglets born alive in a large litter. We thus conclude that welfare and nursing capacity of the sow can be improved by management. However, current megatrends such as the climate change may change sow management and force the industry to rethink goals of breeding and, for instance, breeding for better resilience may need to be included as goals for the future.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• Archives of Craniofacial Surgery
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• v.22 no.4
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• pp.183-192
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• 2021

Background: The purse-string suture (PSS) is a simple and rapid wound closure method that results in minimal scarring. It has been used to treat circular or oval skin defects caused by tumor excision or trauma. However, due to obscurity, it is not widely used, especially for the head and neck. This study aimed to modify the PSS to obtain predictable and acceptable results. Methods: A total of 45 sites in 39 patients with various types of skin and soft tissue defects in the head and neck were treated with PSS. We used PDS II (2-0 to 5-0), which is an absorbable suture. Minimal dissection of the subcutaneous layer was performed. The suture knot was hidden by placing it in the dissection layer. Depending on the characteristics of the skin and soft tissue defects, additional surgical interventions such as side-to-side advancement sutures, double PSS, or split-thickness skin graft were applied. Results: All wounds healed completely without any serious complications. Large defects up to 45 mm in diameter were successfully reconstructed using only PSS. Postoperative radiating folds were almost flattened after approximately 1-2 months. Conclusion: PSS is simple, rapid, and relatively free from surgical design. Owing to the circumferential advancement of the surrounding tissue, PSS always results in a smaller scar than the initial lesion and less distortion of the body structures around the wound in the completely healed defect. If the operator can predict the process of healing and immediate radiating folds, PSS could be a favorable option for round skin defects in the head and neck.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Microbiology and Biotechnology Letters
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• v.50 no.3
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• pp.436-440
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• 2022

In this study, yeast artificial chromosome Insert (YAC) harboring genes which related xylan metabolism was constructed by using chromosome manipulation technique. For efficient chromosome manipulation, each splitting fragment (DNA module) required for splitting process was prepared and these DNA modules were transformed into Saccharomyces cerevisiae strain YKY164. By two-rounds chromosome splitting, yeast chromosome VII (1,124 kb) was split 887 kb-YAC, 45 kb-mini YAC and 198 kb-YAC and YKY183 strain containing 18 chromosomes was constructed. Splitting efficiency for chromosome manipulation was 50- 78% and expression level of foreign genes on 45 kb-mini YAC and enzyme activity were indistinguishable from that of the YKY164 strain. Furthermore, xylan-degraded products by recombinant enzymes were confirmed and mini-yeast artificial chromosome maintained stable mitotic stability without chromosome loss during 160 generations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.115-119
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• 2023

The natural language processing models used in current artificial intelligence are huge, causing various difficulties in processing and analyzing data in real time. In order to solve these difficulties, we proposed a method to improve the efficiency of processing by using less memory and checked the performance of the proposed model. The technique applied in this paper to evaluate the performance of the proposed model is to divide the large corpus by adjusting the number of attention heads and embedding size of the BERT[1] model to be small, and the results are calculated by averaging the output values of each forward. In this process, a random offset was assigned to the sentences at every epoch to provide diversity in the input data. The model was then fine-tuned for classification. We found that the split processing model was about 12% less accurate than the unsplit model, but the number of parameters in the model was reduced by 56%.

• Quality Improvement in Health Care
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• v.28 no.2
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• pp.14-29
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• 2022

Purpose:In order to improve the quality of cardiac rehabilitation, it is important to examine the experiences of the patients and how they feel about each part of the process. We used a qualitative research methodology to conduct an in-depth review of the experiences of patients who participated in cardiac rehabilitation. Methods: According to semi-structured guidelines, in-depth interviews were conducted with a total of five patients undergoing cardiac rehabilitation. Results: A total of 232 codes was derived; these codes were split into 4 categories and 10 subcategories. Although the patients were apprehensive about the diagnosis of heart disease, it also gave them a chance to reflect on their health behaviors of the past. In addition, they began to appreciate their families and support networks more as the disease progressed. The participants did not know about cardiac rehabilitation until the medical staff recommended it, but they participated in the program with the expectation that they could become healthier. The participants felt that they became more physically and mentally healthy while undergoing cardiac rehabilitation. Rehabilitation served as an opportunity to improve other health behaviors as well. However, they emphasized that it is necessary to continuously maintain improved health behavior, find an exercise method that is suitable for one's physical ability and not monotonous, and prepare a plan to reduce the time and economic burden of cardiac rehabilitation. Conclusion: The participants' positive experiences confirmed in this study will be used as evidence for the expansion of cardiac rehabilitation programs.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.257-269
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• 2024

Purpose: The purpose of this study is to explain the pivotal role of the travel forecasting process in urban transportation planning. This study emphasizes the use of travel forecasting models to anticipate future traffic. Method: This study examines the methodology used in urban travel demand modeling within transportation planning, specifically focusing on the Urban Transportation Modeling System (UTMS). UTMS is designed to predict various aspects of urban transportation, including quantities, temporal patterns, origin-destination pairs, modal preferences, and optimal routes in metropolitan areas. By analyzing UTMS and its operational framework, this research aims to enhance an understanding of contemporary urban travel demand modeling practices and their implications for transportation planning and urban mobility management. Result: The result of this study provides a nuanced understanding of travel dynamics, emphasizing the influence of variables such as average income, household size, and vehicle ownership on travel patterns. Furthermore, the attraction model highlights specific areas of significance, elucidating the role of retail locations, non-retail areas, and other locales in shaping the observed dynamics of transportation. Conclusion: The study methodically addressed urban travel dynamics in a four-ward area, employing a comprehensive modeling approach involving trip generation, attraction, distribution, modal split, and assignment. The findings, such as the prevalence of motorbikes as the primary mode of transportation and the impact of adjusted traffic patterns on reduced travel times, offer valuable insights for urban planners and policymakers in optimizing transportation networks. These insights can inform strategic decisions to enhance efficiency and sustainability in urban mobility planning.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.3
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• pp.6-13
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• 2000

Ultra-thin gate oxide was grown on nitrogen implanted silicon substrates. For nitrogen implantation, the energy was fixed at 25keV, but the dose was split into 5.0$\times$10$^{13}$ /c $m^{2}$ and 1.0$\times$10$^{14}$ /c $m^{2}$. The grown gate oxide thickness were 2nm, 3nm and 4nm. The oxidation time to grow 3nm was increased by 20% and 50% for the implanted wafers of 5.0$\times$10$^{13}$ /c $m^{2}$ and 1.0$\times$10$^{14}$ /c $m^{2}$ doses, respectively, when it was compared with control wafers which were not implanted by nitrogen. The value of charge-to-breakdown ( $Q_{BD}$ ) is decreased with increasing nitrogen doses. If an annealing process( $N_{2}$, 85$0^{\circ}C$, 60min.) is peformed after nitrogen implantation, $Q_{BD}$ is increased. It is indicated that nitrogen implantation damage affect gate oxide reliability and the damage can be removed by post-implantation annealing process.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.