• Korean Journal of Environmental Agriculture
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• v.40 no.1
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• pp.1-12
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• 2021

BACKGROUND: G protein-coupled receptors (GPCRs) are widely distributed in various organisms. Insect GPCRs shown as in vertebrate GPCRs are membrane receptors that coordinate or involve in various physiological processes such as learning/memory, development, locomotion, circadian rhythm, reproduction, etc. This study aimed to identify GPCRs expressed in fat body and compare the expression pattern of GPCRs in different temperature conditions. METHODS AND RESULTS: To identify GPCRs genes and compare their expression in different temperature conditions, total RNAs of fat body in Plutella xylostella larva were extracted and the transcriptomes have been analyzed via next generation sequencing method. From the fat body transcriptomes, genes that belong to GPCR Family A, B, and F were identified such as opsin, gonadotropin-releasing hormone receptor, neuropeptide F (NPF) receptor, muthuselah (Mth), diuretic hormone receptor, frizzled, etc. Under low temperature, expressions of GPCRs such as C-C chemokine receptor (CCR), opsin, prolactin-releasing peptide receptor, substance K receptor, Mth-like receptor, diuretic hormone receptor, frizzled and stan were higher than those at 25℃. They are involved in immunity, feeding, movement, odorant recognition, diuresis, and development. In contrast to the control (25℃), at high temperature GPCRs including CCR, gonadotropin-releasing hormone receptor, moody, NPF receptor, neuropeptide B1 receptor, frizzled and stan revealed higher expression whose biological functions are related to immunity, blood-brain barrier formation, feeding, learning, and reproduction. CONCLUSION: Transcriptome of fat body can provide understanding the pools of GPCRs. Identifications of fat body GPCRs may contribute to develop new targets for the control of insect pests.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.1-9
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• 2023

This study was conducted for the purpose of improving the usability of the product through the usability evaluation of the voice tag reader to improve the life convenience of the visually impaired. Perceived usability evaluation was conducted for 19 evaluation items based on the evaluation model considering the usability principle and the characteristics of the visually impaired. A total of 50 participants were included for the analysis. As a result of the perceived usability evaluation of the visually impaired, the safety of the voice tag reader, voice and sound quality, and accuracy of voice information were relatively satisfactory. It was found that the reader received a low evaluation in terms of efficiency in use, including the size and weight of the reader, and the convenience of carrying and storing. For the usability improvement, the procedure for using a product needs to be more simplified, and it would be helpful to input and supply tags for commonly used objects in advance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.871-880
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• 2017

Recently, service based on internet is inter-connected and integrated with a variety of connection. This kind of internet of things consist of heterogenous devices such as sensor node, devices and end-to end equipment which used in conventional protocols and services. The representative system is healthcare system. From healthcare appliance used by IoT, patient and doctor can utilize healthcare information with safety and high speed management. It is very convenient management to operate mobility. But it induced security and vulnerability issues because it has small memory capacity, low power supply and low computing power. This made impossible to implement security algorithm with embedded engine based on hardware. Nowdays, we can't realize conventional standard algorithm due to these kinds of reasons. From the critical issues, it occurred security and vulnerability issues. Therefore, we analysed and compared with conventional method and proposed techniques. Finally, we evaluated security issues and requirement for end-to-end point healthcare system based on internet of things.

• 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.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.30 no.3
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• pp.252-263
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• 2023

Purpose: To assess the feasibility, safety, and preliminary estimates of effectiveness of Tai Chi on the functional outcomes of older adults in the community. Methods: This was a mixed-method study that employed a single-group repeated measure design and in-depth interviews. Nine older adults were recruited from the community were recruited to participate in a Tai Chi program, conducted twice weekly for 6 months. Research outcomes included physical function, cognitive function, and quality of life, measured at intervals of 3 and 6 months. Findings: Tai Chi exercises were gradually conducted based on the health status of the older adults. All participants actively participated in the program with an average attendance of 90%. Consequently, the participants showed significant improvements in mobility and their memory recall ability at both 3 and 6 months. Additionally, the results of the Stroop test exhibited improvement 3 months after the commencement of the study program. Quality of life of the participants improved according to the mild cognitive impairment questionnaire, but it did not show significant improvement in health-related quality of life. Conclusion: The Tai Chi exercise program was a safe and, feasible program to improve the physical function, cognitive function, quality of life among the older adults in the community.

• Clinical and Experimental Vaccine Research
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• v.12 no.2
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• pp.156-171
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• 2023

Purpose: The development of vaccines that confer protection against multiple avian influenza A (AIA) virus strains is necessary to prevent the emergence of highly infectious strains that may result in more severe outbreaks. Thus, this study applied reverse vaccinology approach in strategically constructing messenger RNA (mRNA) vaccine construct against avian influenza A (mVAIA) to induce cross-protection while targeting diverse AIA virulence factors. Materials and Methods: Immunoinformatics tools and databases were utilized to identify conserved experimentally validated AIA epitopes. CD8⁺ epitopes were docked with dominant chicken major histocompatibility complexes (MHCs) to evaluate complex formation. Conserved epitopes were adjoined in the optimized mVAIA sequence for efficient expression in Gallus gallus. Signal sequence for targeted secretory expression was included. Physicochemical properties, antigenicity, toxicity, and potential cross-reactivity were assessed. The tertiary structure of its protein sequence was modeled and validated in silico to investigate the accessibility of adjoined B-cell epitope. Potential immune responses were also simulated in C-ImmSim. Results: Eighteen experimentally validated epitopes were found conserved (Shannon index <2.0) in the study. These include one B-cell (SLLTEVETPIRNEWGCR) and 17 CD8⁺ epitopes, adjoined in a single mRNA construct. The CD8⁺ epitopes docked favorably with MHC peptidebinding groove, which were further supported by the acceptable ∆G_bind (-28.45 to -40.59 kJ/mol) and Kd (<1.00) values. The incorporated Sec/SPI (secretory/signal peptidase I) cleavage site was also recognized with a high probability (0.964814). Adjoined B-cell epitope was found within the disordered and accessible regions of the vaccine. Immune simulation results projected cytokine production, lymphocyte activation, and memory cell generation after the 1st dose of mVAIA. Conclusion: Results suggest that mVAIA possesses stability, safety, and immunogenicity. In vitro and in vivo confirmation in subsequent studies are anticipated.