• Korean Journal of Clinical Pharmacy
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• v.31 no.3
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• pp.171-179
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• 2021

Background: Need for regulatory science is emerging with the development of pharmaceutical industry. It is essential to train regulatory science experts to meet the needs of technology and regulations to evaluate advanced products. Major regulatory science countries are conducting the regulatory science activities and fostering the experts. Methods: Published literature and the relevant website of European Union (EU) were reviewed and criteria were developed. In particular, we focused on in depth descriptions of the Innovative Medicines Initiative program, which was conducted twice. Results: EU is striving to provide funding and training experts for the development of the regulatory science by horizon 2020 and regulatory science to 2025. Innovative medicines initiative (IMI) is a public-private partnership aimed at the development of the pharmaceutical industry, including the regulatory science. IMI education and training projects have provided various education and training course including short-term curriculum and master and doctoral course. The difference between South Korea's regulatory science expert training project in 2021 and the EU's IMI education and training projects is participation of pharmaceutical companies. While the pharmaceutical companies participate in the IMI project to select project topics and form a community, South Korea's project is focused on the Ministry of Food and Drug Safety and universities. Conclusion: Through successful active networks with regulatory party, pharmaceutical companies, and universities, a great innovative advance of regulatory science in South Korea is expected.

• Journal of the Korean Society of Radiology
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• v.12 no.7
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• pp.887-893
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• 2018

The purpose of this study is to compare the performance of X-ray generators installed in hospitals and universities and apply the quality control items of diagnostic X-ray generators to recognize the importance of periodic performance management. First, the reproducibility and linearity test results showed that the PAE of the reproducibility evaluation was high for the GX-650 devices that met the acceptance criteria in all the experimental conditions and lacked the periodic quality control. In the linearity evaluation, when the tube voltage was set to 100 kVp, It was measured to deviate from the error. In addition, it was found that the PAE in the low-accuracy evaluation results relative to an X-ray tube voltage and tube current of the device low occurrence frequency. The HVL experiment was included in all of the devices at the HVL by tube voltage. Therefore, it is necessary to recognize the importance of quality control of all devices rather than hospital and laboratory, and to manage the device performance by actively managing the device, and to establish a short - term quality control system like special medical devices.

• Journal of Neurogastroenterology and Motility
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• v.24 no.4
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• pp.528-535
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• 2018

Background/Aims To evaluate the efficacy and safety of Aloe vera (AV) in patients with irritable bowel syndrome (IBS). Methods We searched the MEDLINE, EMBASE, and Cochrane databases for studies dated between 1st January 1960 and 30th December 2017. Eligible randomized controlled trials (RCTs) compared AV to placebo in patients with IBS. The primary outcome was standardized mean difference of the change in severity of IBS symptoms as measured by patient-rated scales. Secondary outcomes included response rate of IBS symptoms and adverse events. Heterogeneity among studies was assessed using Cochrane's Q and $I^2$ statistics. Results Three RCTs with a total of 151 patients with IBS were included. The meta-analysis showed a significant difference for patients with AV compared to those with placebo regarding improvement in IBS symptom score (standardized mean difference, 0.41; 95% CI, 0.07-0.75; P = 0.020). Using intention-to-treat analysis, the AV patients showed significantly better response rates of IBS symptoms compared to placebo (pooled risk ratio, 1.69; 95% CI, 1.05-2.73; P = 0.030). No adverse events related with AV were found in included studies. There was no significant heterogeneity of effects across studies (P = 0.900; $I^2=0%$). Conclusion AV is effective and safe for the treatment of patients with IBS compared to placebo.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.4
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• pp.27-34
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• 2018

Flat or Multiple Dwelling Houses are relatively vulnerable to crime safety. Crime prevention measures are urgently needed because crime is 2.6 times higher than in real apartments. Through the analysis of domestic and foreign crime prevention design standards, field survey, and interviews with experts, 27 items of crime prevention design criteria for flat or multiple dwelling house were derived, and ten design criteria that should be considered first by the expert AHP were derived. As a result of the pilot evaluation of existing flat or multiple dwelling house, the houses completed after 2015 have relatively high level of crime prevention, but the houses constructed before that are very vulnerable. The policy and system improvement plan based on the analysis result is as follows. First, new housing should be promoted to meet minimum criteria by supplying and educating public officials, architects, and building owners in the short term to provide criteria for flat or multiple dwelling house crime prevention plans. Second, existing housing should be supported with basic crime prevention support projects such as security windows for flat or multiple dwelling house where security of residential environment such as urban renewal policy is poor. Third, the Enforcement Decree of the Building Act shall be revised to make the crime prevention environment design of flat or multiple dwelling house obligatory, and the criteria of flat or multiple dwelling house crime prevention plan should be reflected in the notice of crime prevention building standard.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Earthquakes and Structures
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• v.24 no.4
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• pp.303-316
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• 2023

Rapid post-earthquake damage estimation of subway stations is particularly necessary to improve short-term crisis management and safety measures of urban subway systems after a destructive earthquake. The conventional Performance-Based Earthquake Engineering (PBEE) framework with constant earthquake occurrence rate is invalid to estimate the aftershock risk because of the time-varying rate of aftershocks and the uncertainty of mainshock-damaged state before the occurrence of aftershocks. This study presents a time-varying probabilistic seismic risk assessment framework for underground structures considering mainshock and aftershock hazards. A discrete non-omogeneous Markov process is adopted to quantify the time-varying nature of aftershock hazard and the uncertainties of structural damage states following mainshock. The time-varying seismic risk of a typical rectangular frame subway station is assessed under mainshock-only (MS) hazard and mainshock-aftershock (MSAS) hazard. The results show that the probabilities of exceeding same limit states over the service life under MSAS hazard are larger than the values under MS hazard. For the same probability of exceedance, the higher response demands are found when aftershocks are considered. As the severity of damage state for the station structure increases, the difference of the probability of exceedance increases when aftershocks are considered. PSDR=1.0% is used as the collapse prevention performance criteria for the subway station is reasonable for both the MS hazard and MSAS hazard. However, if the effect of aftershock hazard is neglected, it can significantly underestimate the response demands and the uncertainties of potential damage states for the subway station over the service life.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.