• Journal of Gastric Cancer
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• v.24 no.3
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• pp.246-256
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• 2024

Purpose: Peritoneal carcinomatosis (PC) presents a major challenge in the treatment of late-stage, solid tumors, with traditional therapies limited by poor drug penetration. We evaluated a novel hyperthermic pressurized intraperitoneal aerosol chemotherapy (HPIPAC) system using a human abdominal cavity model for its efficacy against AGS gastric cancer cells. Materials and Methods: A model simulating the human abdominal cavity and AGS gastric cancer cell line cultured dishes were used to assess the efficacy of the HPIPAC system. Cell viability was measured to evaluate the impact of HPIPAC under 6 different conditions: heat alone, PIPAC with paclitaxel (PTX), PTX alone, normal saline (NS) alone, heat with NS, and HPIPAC with PTX. Results: Results showed a significant reduction in cell viability with HPIPAC combined with PTX, indicating enhanced cytotoxic effects. Immediately after treatment, the average cell viability was 66.6%, which decreased to 49.2% after 48 hours and to a further 19.6% after 120 hours of incubation, demonstrating the sustained efficacy of the treatment. In contrast, control groups exhibited a recovery in cell viability; heat alone showed cell viability increasing from 90.8% to 94.4%, PIPAC with PTX from 82.7% to 89.7%, PTX only from 73.3% to 74.8%, NS only from 90.9% to 98.3%, and heat with NS from 74.4% to 84.7%. Conclusions: The HPIPAC system with PTX exhibits a promising approach in the treatment of PC in gastric cancer, significantly reducing cell viability. Despite certain limitations, this study highlights the system's potential to enhance treatment outcomes. Future efforts should focus on refining HPIPAC and validating its effectiveness in clinical settings.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Information Systems Review
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• v.12 no.1
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• pp.209-227
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• 2010

One type of innovative multimedia platform environments is mobile data services (MDS), exemplified by Nate, Show, and OZ. In the context of MDS, enhancing user's continuance intention is a significant challenge to the continuing growth and long-term viability of MDS. Because the cost of using MDS is borne mainly by users, they are likely to evaluate it based on perceptions of what is received and what is given. This study identifies perceived usefulness and perceived enjoyment as the 'get'components, and perceived fee and perceived anxiety as the 'give' components. To understand the role of get and give components in the MDS post-adoption environment, this study incorporates these components into expectation confirmation model. We collected data from 204 users who had direct experiences with MDS within recent 3 months. The data was analyzed by employing PLS (partial least squares). Theoretical and practical implications of our findings are discussed.

• The Journal of Industrial Distribution & Business
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• v.11 no.2
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• pp.41-48
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• 2020

Purpose: This study is to examine the intention of the elderly who live alone in the customized AI speaker for the elderly living alone to improve the quality of life service for the elderly living alone in the smart city environment. Based on the quality of life model of the elderly, this study is applied to the technology acceptance model to investigate the relationship between perceived usefulness and ease of use on the sustained use intention. Research design, data and methodology: Residents in Suwon, Gyeonggi-do, selected as candidate local governments for the Smart City Challenge Project of the Ministry of Land, Infrastructure and Transport in June 2019 to measure the perceived technology acceptance of potential users for the AI technology for the elderly living alone as part of the smart city technology. In order to evaluate the intention of using AI speaker, which is the target system of this study, a video of a chatbot using experience of elderly people living alone was produced. Results: First of all, in order for the elderly living alone to have an attitude to use AI-based speakers, there should be a perceived usefulness of the quality of life of the elderly. However, ease of use did not show any significant causal relationship to attitude toward use. In addition, the attitude toward use weakly influenced the intention to use. In other words, elderly people living alone were not likely to have a significant effect on their attitude toward use. However, feeling that AI speakers are easy to use will help to improve the quality of life, which in turn led to the attitude toward using AI speakers, which could lead to indirect effects. Finally, the perceived usefulness of quality of life was found to have a weak effect on direct use intentions. Conclusions: This study conducted a study on the technology acceptance of service environment to improve the quality of life for the specific user group who live alone in the smart seat environment. In this study, we examined the effects of AI speaker on the elderly living alone to improve the quality of life for the elderly living alone.

• Journal of Distribution Science
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• v.9 no.4
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• pp.29-42
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• 2011

With the many advantages of the internet, online shopping has become one of the fastest growing types of retail businesses. However, internet-based firms are much more firmly required to retain existing customers rather than secure new ones, and to make them revisit the site by strengthening trust and loyalty, thereby improving profits and outrivaling competitors. Commitment is an essential part of successful long-term relationships between buyers and sellers. Although commitments by both parties in an exchange can provide the foundation for the development of relational social norms, disproportionate commitments can lead to opportunism by the less committed partner. Moreover, flow, which is characterized by intense concentration and enjoyment, was found to be significantly linked with exploratory use behavior, which in turn was linked to the extent of computer use. The level of flow was, itself, determined by the individual's sense of being in control, and the level of challenge perceived in maneuvering a website. Website attractiveness goes hand in hand with the attractiveness of an internet shopping mall, and it can be conceptualized as the persuasive effectiveness of a message by the use of familiarity, favor, similarity, etc. It occurs when information receivers try to achieve self-satisfaction when they actually or emotionally identify themselves with an information source. This study investigates the relationship between the perceived system characteristics of an internet shopping mall and the loyalty of online consumers, and it examines how perceived website attractiveness and flow play mediating roles between the perceived system characteristics of an internet shopping mall and the affective commitment in the context of a clothes internet shopping mall. For these purposes, a structural model comprising several variables was developed. That model was tested with an analysis of moment structure (AMOS) using data from respondents who had purchased clothing through the internet during the past three months. In this model, the perceived system characteristics of an internet shopping mall, such as familiarity, reputation, uniqueness, positive emotions, self-efficacy, and interactivity, were proposed to affect the website's attractiveness and flow, and lead to a higher affective commitment over time. Thus, the perceived website attractiveness and flow were proposed as core mediating variables between perceived system characteristics and affective commitment. The results of a reliability test using Cronbach's Alpha, and a confirmatory factor analysis warranted using unidimensionality for the measures for each construct. In addition, the nomological validity of the measures was warranted from the results of a correlation analysis. The results of empirical analyses indicated that systematic attributes resulting in website attractiveness and user's characteristics, thereby triggering customers' flow, play a crucial role in inducing customers' affective commitment, and a user's characteristics are twice as important as systematic attributes in this study. Moreover, familiarity, reputation, and uniqueness all have a significant effect on website attractiveness, and the research showed that uniqueness took the first place, and that familiarity and reputation followed in order of magnitude. The fact that reputation was not the most important factor that affects the attractiveness of an internet shopping mall, with uniqueness or familiarity having a greater impact, suggests much deeper implications. Finally, positive emotion, self-efficacy, and interactivity all have a significant effect on customers' flow. In particular, the fact that positive emotion, compared to self-efficacy or interactivity, has much more impact on flow is very suggestive.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Clinical and Experimental Pediatrics
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• v.54 no.9
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• pp.373-379
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• 2011

Purpose: 4-1BB (CD 137) is a costimulatory molecule expressed on activated T-cells. Repression by 4-1BB is thought to attenuate Th2-mediated allergic reactions. The aim of this study was to investigate the effect of 4-1BB on allergic airway inflammation in a murine asthma model. Methods: BALB/c mice were sensitized to and challenged with ovalbumin (OVA). Hu.4-1BB-Fc was administered 1 day before the first OVA sensitization or 1 day after the second OVA sensitization. Following antigen challenge, airway responsiveness to methacholine was assessed and bronchoalveolar lavage (BAL) fluid was analyzed. Total immunoglobulin (Ig) E, OVA-specific IgE, $IgG_1$, and $IgG_{2a}$ levels in sera were measured by enzyme-linked immunosorbent assay. Lung pathology was also evaluated. Results: In mice treated with Hu.4-1BB-Fc before the first OVA sensitization, there was a marked decrease in airway hyperresponsiveness, total cell count, and eosinophil count in the BAL fluid. In addition, Hu.4-1BB-Fc treatment decreased serum OVA-specific $IgG_1$ levels and increased serum $IgG_{2a}$ level significantly compared with the corresponding levels in mice sensitized to and challenged with OVA. Hu.4-1BB-Fc-treated mice also showed suppressed peribronchial and perivascular inflammatory cell infiltration. In contrast, treatment with Hu.4-1BB-Fc 1 day after sensitization had no effect on airway hyperresponsiveness and showed less suppression of inflammation in lung tissue. Conclusion: Administration of Hu.4-1BB-Fc can attenuate airway inflammation and hyperreactivity in a mouse model of allergic airway inflammation. In addition, administration before sensitization may be more effective. These findings suggest that 4-1BB may be a useful therapeutic molecule against asthma.

• Herbal Formula Science
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• v.20 no.2
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• pp.83-92
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• 2012

Objectives : The aim of this study was to evaluate the efficacy of Hwangryunhaedok-tang (HHT) and Gungangbuja-tang (GBT) on lipopolysaccharide (LPS)-induced mouse model of inflammation. HHT and GBT are one of the representative prescriptions of cold drug and one of the representative prescriptions of hot drug, respectively. For experimental evaluation of their efficacy, we investigated the anti-inflammatory effects of HHT and GBT on LPS-induced inflammation and the mechanisms of their action. Methods : ICR mice were given a HHT (50, 500 mg/kg), GBT (100, 1000 mg/kg) extract orally on three consecutive days. On the third day, they were administered LPS intraperitoneally (35 mg/kg), 1 h after the last sample administration. Blood and liver samples were taken 6 h after the LPS challenge. Cytokine expression and inflammation-related protein factor analyses were performed by Western blotting. Results : Oral administration of HHT significantly reduced pro-inflammatory cytokines, including interleukin (IL)-6, and interferon (IFN)-${\gamma}$ in the serum. While GBT inhibited an increase of IL-6, IFN-${\gamma}$ was not affected. Immunoblot analysis showed that LPS-induced NF-${\kappa}b$ activation was inhibited by GBT, meanwhile HHT only inhibited NF-${\kappa}b$ expression at high does (500 mg/kg). In addition, HHT and GBT inhibited LPS-induced phosphorylation of Erk1/2, Jnk and p38 MAPKs. GBT also significantly inhibited i-Nos and Cox-2 expression, and HHT inhibited only i-Nos expression. Conclusions : Both of HHT and GBT showed anti-inflammatory effects against LPS-induced endotoxemia. However, HHT significantly decreased inflammatory cytokine levels, such as IL-6 and IFN-${\gamma}$ more than GBT, while GBT significantly inhibited inflammatory proteins, including NF-${\kappa}b$, MAP Kinases, i-Nos and Cox-2, more than HHT. These results suggest that HHT and GBT regulate the different mechanisms of action and pathways, presumably by regulating cytokine levels (IL-6, IFN-${\gamma}$), NF-${\kappa}b$ activation, and several pro-inflammatory gene expression, although both of HHT and GBT have anti-inflammatory effects.

• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.649-665
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• 2018

In this study, we extracted the core competencies of makers through the analysis of critical incident technique and behavioral event interview to explore the nature and attributes of maker education, and then we developed a maker competency model based on these core competencies. As a result, six competency groups and 23 sub-competencies were extracted. In other words, we were able to confirm the existence of integrated thinking competency group consisting of four competencies made up of 'analytic thinking', 'intuitive thinking', 'visual thinking', and 'empirical thinking' and that of collaborative competency group with four competencies of 'sharing', 'communication', 'conflict management', and 'scrupulosity'. In addition, we could also confirm the existence of making mind competency group, which is composed of four competencies namely 'interest in various areas', 'challenge consciousness', 'failure management', and 'pleasure of the making process'. We could also confirm that human-centered competence group consisting of two competencies of 'humanity' and 'user-oriented' and the problem-finding competence group consisting of two competencies of 'observation' and 'recognition of discomfort in daily life'. Lastly, the making practice competency group is composed of seven competencies: 'understanding making tool', 'understanding electricity', 'understanding programming', 'planning', 'hand knowledge', 'information search', and 'direct execution'. We discussed educational implications of these findings.