• Earthquakes and Structures
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• 제22권2호
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• 한국산업보건학회지
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• 제31권4호
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• pp.396-416
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• 2021

Objectives: This study was conducted to analyze the trends of government R&D (R&D) projects related to laboratory safety over the past 20 years. Methods: We collected publications from various databases(DBs) with words such as laboratory(ies), lab(s), researcher(s), laboratory worker(s), safety, environment, hazard(s), risk(s), and so on. Selected publications were analyzed by the research funds and the number of projects according to the investment subject and research characteristics. Results: About 93% of the total R&D budget went to government policy projects, not scientific research. Second, from the perspective of 'safety management activities', most of the research is related to management and inspection at the organizational level. Issues that need to be discussed at the national level like policy governance are not included. Third, focusing on the 'safety management cycle', there were few studies related to 'prediction' or 'post-response'. Fourth, when an analysis framework combining the perspectives of 'safety management activities' and 'safety management cycle' is applied, most of the budget is spent on infrastructure such as digital management systems, whereas basic knowledge for prevention and production of evidence was very few. Conclusions: In order to prevent policy planning without policy evaluation, implementation without strategy, and evaluation without evidence, it is necessary to expand investment in empirical research on risks, research on the effectiveness of current application methods, and research on theory development. The government budget for laboratory safety-related projects should be managed separately from the R&D budget for scientific research. Although less than 5% of the budget allocated to scientific research is the total budget, an optical illusion occurs because both the project budget and the scientific research budget are counted as R&D budgets.

• 한국간호교육학회지
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• 제27권4호
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• pp.423-435
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• 2021

Purpose: The purpose of this study is to identify the educational needs of a severe trauma treatment simulation program based on mixed reality which combines element of both virtual reality and augmented reality. Methods: Focus group interviews were conducted with ten military hospital nurses on February 4 and 5, 2021. The collected data were analyzed using a qualitative content analysis. As a framework for data analysis, the educational needs were clustered into the following four categories: teaching contents, teaching methods, teaching evaluation, and teaching environment. Results: The educational needs for each category that emerged were as follows: three subcategories including "realistic education reflecting actual clinical practice" and "motivating education" for teaching contents; five subcategories including "team-based education," "repeated education that acts as embodied learning," and "stepwise education" for teaching methods; six subcategories including "debriefing through video conferences," "team evaluation and evaluator in charge of the team," "combination of knowledge and practice evaluation" for teaching evaluation; six subcategories including "securing safety," "similar settings to real clinical environments," "securing of convenience and accessibility for learners," and "operating as continuing education" for teaching environment. Conclusion: The findings of this study can provide a guide for the development and operation of a severe trauma treatment simulation program based on mixed reality. Moreover, it suggests that research to identify the educational needs of various learners should be conducted.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.160-170
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• 2022

COVID pandemic has reshaped the world as it has been known to us and the education system is one of the most affected by it. Due to social distancing, quarantines and isolations have made it impossible for the knowledge transition to the masses using conventional methods. For cope with pandemic, the only other way available for some of the fortunate countries is the use of E-learning having somewhat the same traditional teaching method. This paper is concerned with the study of the preparedness of the learning system in some Sudanese universities due to the impact of the COVID-19 pandemic. Critical analysis has been performed to evaluate the current developing scenario, usage of the facilities available in open-source platforms, and the interaction of the universities folks with e-learning systems. The impact of such measures has been thoroughly investigated in this paper for Sudan which is already deprived of a proper education system. The investigation shows that the interact of the staff and the students with the system was acceptable where more than 85% of those enrolled to the system were interact properly and efficiently. The lecturers conducted through the platform were attended with more than 75% of the students. We also found that most of the lecturer were avoid to exam students by utilize the platform; where only 45% of the uploaded courses were conducted exams over Moodle platform. As Moodle is an open source and still need to be improved to be used for high examination credibility.

• Journal of Acupuncture Research
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• 제40권3호
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• pp.238-244
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• 2023

Background: Occipital neuralgia (ON) is an established risk factor for headaches in the posterior cervical region. Several conservative treatments by nerve decompression and pain relief are available for ON, but these treatments have limitations. Acupuncture treatment, which is known to demonstrate analgesic effects, involves various stimulation methods, and several studies have reported their clinical benefit. No recent systematic review (SR) has compared each acupuncture type for ON treatment. Thus, this SR aims to investigate the clinical effectiveness of each acupuncture type for treating ON. Methods: We will identify relevant studies using electronic databases, including EMBASE, MEDLINE, Cochrane Library, China National Knowledge Infrastructure (CNKI), Korean Studies Information Service System (KISS), Korean Medical Database, KoreaMed, and National Digital Science Library (NDSL) from the inception until August 2023. The primary outcome will include the numerical change of pain symptoms (visual analog scale and numerical rating scale) and effective rate. Safety and secondary outcomes will include adverse events and quality of life. We will compare the conservative treatment with the acupuncture treatment using network meta-analysis. The Cochrane Collaboration "risk of bias" tools will be used to assess the quality of included trials. The Grades of Recommendation, Assessment, Development, and Evaluation will be used to examine the evidence level. Conclusion: This study will provide clinical evidence of several acupuncture types for ON and help clinicians decide on the best.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Steel and Composite Structures
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• 제45권3호
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• pp.389-408
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• 2022

Residual capacity is defined as the load carrying capacity of an RC column after undergoing severe damage. Evaluation of residual capacity of RC columns is necessary to avoid damage initiation in RC structures. The central aspect of the current research is to propose an empirical formula to estimate the residual capacity of RC columns after undergoing severe damage. This formula facilitates decision making of whether a replacement or a repair of the damaged column is adequate for further use. Available literature mainly focused on the simulation of explosion loads by using simplified pressure time histories to develop residual capacity of RC columns and rarely simulated the actual explosive. Therefore, there is a gap in the literature concerning general relation between blast damage of columns with different explosive loading conditions for a reliable and quick evaluation of column behavior subjected to blast loading. In this paper, the Arbitrary Lagrangian Eulerian (ALE) technique is implemented to simulate high fidelity blast pressure propagations. LS-DYNA software is utilized to solve the finite element (FE) model. The FE model is validated against the practical blast tests, and outcomes are in good agreement with test results. Multivariate linear regression (MLR) method is utilized to derive an analytical formula. The analytical formula predicts the residual capacity of RC columns as functions of structural element parameters. Based on intensive numerical simulation data, it is found that column depth, longitudinal reinforcement ratio, concrete strength and column width have significant effects on the residual axial load carrying capacity of reinforced concrete column under blast loads. Increasing column depth and longitudinal reinforcement ratio that provides better confinement to concrete are very effective in the residual capacity of RC column subjected to blast loads. Data obtained with this study can broaden the knowledge of structural response to blast and improve FE models to simulate the blast performance of concrete structures.

• Journal of Preventive Medicine and Public Health
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• 제56권2호
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• pp.128-144
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• 2023

Objectives: The health system reform process is highly political and controversial, and in most cases, it fails to realize its intended goals. This study was conducted to synthesize factors underlying the failure of health system reforms. Methods: In this systematic review and meta-synthesis, we searched 9 international and regional databases to identify qualitative and mixed-methods studies published up to December 2019. Using thematic synthesis, we analyzed the data. We utilized the Standards for Reporting Qualitative Research checklist for quality assessment. Results: After application of the inclusion and exclusion criteria, 40 of 1837 articles were included in the content analysis. The identified factors were organized into 7 main themes and 32 sub-themes. The main themes included: (1) reforms initiators' attitudes and knowledge; (2) weakness of political support; (3) lack of interest group support; (4) insufficient comprehensiveness of the reform; (5) problems related to the implementation of the reform; (6) harmful consequences of reform implementation; and (7) the political, economic, cultural, and social conditions of the society in which the reform takes place. Conclusions: Health system reform is a deep and extensive process, and shortcomings and weaknesses in each step have overcome health reform attempts in many countries. Awareness of these failure factors and appropriate responses to these issues can help policymakers properly plan and implement future reform programs and achieve the ultimate goals of reform: to improve the quantity and quality of health services and the health of society.

• Journal of Veterinary Science
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• 제24권3호
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• pp.48.1-48.13
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• 2023

Background: Senecavirus A (SVA), a member of the family Picornaviridae, is newly discovered, which causes vesicular lesions, lameness in swine, and even death in neonatal piglets. SVA has rapidly spread worldwide in recent years, especially in Asia. Objectives: We conducted a global meta-analysis and systematic review to determine the status of SVA infection in pigs. Methods: Through PubMed, VIP Chinese Journals Database, China National Knowledge Infrastructure, and Wanfang Data search data from 2014 to July 26, 2020, a total of 34 articles were included in this analysis based on our inclusion criteria. We estimated the pooled prevalence of SVA in pigs by the random effects model. A risk of bias assessment of the studies and subgroup analysis to explain heterogeneity was undertaken. Results: We estimated the SVA prevalence to be 15.90% (1,564/9,839; 95% confidence interval [CI], 44.75-65.89) globally. The prevalence decreased to 11.06% (945/8,542; 95% CI, 28.25-50.64) after 2016. The highest SVA prevalence with the VP1-based RT-PCR and immunohistochemistry assay was 58.52% (594/1,015; 95% CI, 59.90-83.96) and 85.54% (71/83; 95% CI, 76.68-100.00), respectively. Besides, the SVA prevalence in piglet herds was the highest at 71.69% (119/166; 95% CI, 68.61-98.43) (p < 0.05). Moreover, our analysis confirmed that the subgroups, including country, sampling year, sampling position, detected gene, detection method, season, age, and climate, could be the heterogeneous factors associated with SVA prevalence. Conclusions: The results indicated that SVA widely exists in various countries currently. Therefore, more prevention and control policies should be proposed to enhance the management of pig farms and improve breeding conditions and the environment to reduce the spread of SVA.

• Earthquakes and Structures
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• 제22권3호
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• pp.245-261
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• 2022

Key questions to researchers interested in nonlinear analysis of skeletal structures are whether the distributed plasticity approach - albeit computationally demanding - is more reliable than the concentrated plasticity to adequately capture the extent and severity of the inelastic response, and whether force-based formulation is more efficient than displacement-based formulation without compromising accuracy. The present research focusing on performance-based seismic response of mid-span concrete bridges provides a pilot holistic investigation opting for some hands-on answers. OpenSees software is considered adopting different modeling techniques, viz. distributed plasticity (through either displacement-based or force-based elements) and concentrated plasticity via beam-with-hinges elements. The pros and cons of each are discussed based on nonlinear pushover analysis results, and fragility curves generated for various performance levels relying on incremental dynamic analyses under real earthquake records. Among prime conclusions, distributed plasticity modeling albeit inherently not relying on prior knowledge of plastic hinge length still somewhat depends on such information to ensure accurate results. For instance, displacement-based and force-based approaches secure optimal accuracy when dividing, for the former, the member into sub-elements, and satisfying, for the latter, a distance between any two consecutive integration points, close to the expected plastic hinge length. On the other hand, using beam-with-hinges elements is computationally more efficient relative to the distributed plasticity, yet with acceptable accuracy provided the user has prior reasonable estimate of the anticipated plastic hinge length. Furthermore, when intrusive performance levels (viz. life safety or collapse) are of concern, concentrated plasticity via beam-with-hinges ensures conservative predicted capacity of investigated bridge systems.