• Journal of Soil and Groundwater Environment
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• v.8 no.1
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• pp.81-86
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• 2003

Understanding the penetration behavior of the spilled oil is very important to remove itself and to minimize its impact on intertidal biological communities by earlier treatment of the oil. The purpose of this study is to clarify the effects of wave and tidal actions on the penetration of spilled oil and to evaluate main factors of oil penetration using a sandy-beach model. Infiltration processes into the sediments showed significant difference between seawater and crude oil. Seawater was infiltrated by both wave action and tidal fluctuation into the sediments in sandy beach. However, spilled crude oil penetrated into the sediments only by falling tides and not by wave action, and the first tide is most important for the penetration of stranded oil. Over 70% of bulk fraction in penetrated crude oil was concentrated to the top 2 cm sediment-layer when spilled oil volume was 1 L/$\textrm{m}^2$. Moreover, the penetration of stranded oil into the sandy beach sediments was strongly correlated with the oil viscosity affected by temperature.

• Polymer(Korea)
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• v.29 no.5
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• pp.481-485
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• 2005

In this work, the thermal and mechanical properties of vapor grown carbon nanofibers (VGCNFs)-reinforced difunctional epoxy (EP) composites were investigated in the presence of the 0, 0.1, 0.5, 1.0, and $2wt\%$ VGCNFs. The thermal properties of the VGCNFs/EP composites were studied by thermo-mechanical analysis (TMA) and dynamic mechanical analysis (DMA). The mechanical properties of the VGCNFs/EP composites were also examined by universal testing machine (UTM), falling impact test, and the friction and wear tests. From experimental results, the thermal and mechanical properties of the VGCNFs/EP composites were improved with increasing the VGCNFs contents. This was due to the increase of crosslinking structure of the composites, resulting in improving the mechanical interlockings between VGCNFs and epoxy resins in the present composite system.

• Fire Science and Engineering
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• v.5 no.3
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• pp.5-14
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• 1991

Now a days, according that the occurrence of industrial disaster is on the increase, the necessity of protective goods is increasing. Specially estimate of safety helmet for protect of head is very important. On this, in this paper, amplification ratio and natural frequencies of fire safety helmet and general safety helmet are estimated by falling impect test and frequency analysis. Also. trend of damping is estimated by using these test results. And we know that the fire safety helmet is more safe than general safety helmet for protect of head.

• The Journal of Internal Korean Medicine
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• v.39 no.6
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• pp.1272-1280
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• 2018

Introduction: The aim of this study is to report the effect of Ling-Gui-Gan-Zao-Tang (LGGZT) effectively improves anxiety disorder following traumatic brain injury (TBI). Case Presentation: 50-year-old female with traumatic brain injury after falling down from golf cart. After injury, symptoms like anxiety disorder, diarrhea, dizziness, headache were occurred. She took medications like antidepressants, antianxiety drugs and antipsychotic agent, but symptoms deteriorated consistently. After being prescribed LGGZT, patients' symptoms were significantly improved. Result of Impact Event Scale-Revised (IES-R-K) was changed from 24 to 5 and Beck Anxiety Inventory (BAI) was changed from 21 to 3. Also, side effects were not observed during the treatment period. Conclusion: LGGZT can be considered as an effective treatment for anxiety disorder following traumatic brain injury.

• Physical Therapy Korea
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• v.28 no.2
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• pp.123-131
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• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.

• Journal of the Korean GEO-environmental Society
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• v.24 no.1
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• pp.25-36
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• 2023

In Korea, the rockfall prevention fence is designed with 50kJ of rockfall kinetic energy in order to prevent damages such as falling rocks and landslides. In the case of rockfall kinetic energy, it is highly dependent on the shape of the slope on which it occurs. As a previous study, a fence performance evaluation was conducted for 100kJ rockfall impact energy using ETAG 27. However, previous studies have focused on newly installed rockfall prevention fences. In this study, a reinforcing materials was installed on the existing rockfall prevention fence through numerical analysis, and the structural performance of the high-strength rockfall prevention fence capable of defending against 120kJ of rockfall kinetic energy was evaluated.

• Journal of the Korean Society of Physical Medicine
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• v.18 no.4
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• pp.109-119
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• 2023

PURPOSE: The definition and scope of biofeedback are broad and lack a clear framework. Therefore, efforts are needed to clearly understand the exact range and definition of biofeedback based on the research and development conducted to date. Thus, the purpose of this study was to arrive at the definition and scope of biofeedback through a literature review and analysis of its application methods. METHODS: This study is a systematic literature review conducted to understand the various types and effects of biofeedback. International databases such as Google Scholar and PubMed were used. Domestic databases utilized for keyword searches included the Research Information Sharing Service (RISS) and the National Digital Science Library (NDSL). Quality assessment of the selected studies in the selection process was done using the Cochrane risk of bias, and the research was analyzed according to the population, intervention, control, and outcomes (PICO) format. RESULTS: Studies conducted between 2019 and 2021 were selected, with 4 papers falling under physiological classifications and 7 under biomechanical classifications. The quality assessment results showed that random sequence generation, allocation concealment, performance bias, and reporting bias were unclear. Detection bias was moderate, and attrition bias and other biases were low. Out of the 11 papers, 9 dealt with physical function outcomes, 5 with daily life activities, and 3 with mental functions. CONCLUSION: Physiological biofeedback tended to influence psychological factors more than physical functions, while biomechanical biofeedback tended to have a positive impact on physical functions.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.821-830
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• 2023

This research aims to identify risk factors for fall accidents at small and medium-sized construction sites through a comprehensive regression analysis. Initially, the study involved collecting a decade's worth of fall accident data from these sites. A t-test confirmed a significant variation in the treatment duration following fall accidents between two distinct groups: small and medium-sized versus large construction sites. Subsequently, a regression analysis was conducted to establish a model highlighting the risk factors associated with safety accidents. The factors influencing fall accidents were determined to be, in descending order of impact, the time of the accident, the day of the accident, and the occupational classification. The findings from this study are expected to serve as foundational data for enhancing policies and conducting statistical analyses tailored to construction site sizes. They also provide crucial information for future research on risk quantification at small and medium-sized construction sites.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.193-216
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• 2020

Due to the recent mid- to long-term slump and falling growth rates in the global economy, interest in organizational structures that create new products or services as a new alternative to survive and develop in an opaque environment both internally and externally, and enhance organizational sustainability through changes in production methods and business innovation is increasing day by day. In this atmosphere, we agree that the growth of small and medium-sized venture companies has a significant impact on the national economy, and various efforts are being made to enhance the technological innovation capabilities of the members so that these small and medium-sized venture companies can enhance and sustain their performance. The purpose of this study is also to investigate how the technological innovation capabilities of small and medium-sized venture companies correlate with the performance of knowledge management and to analyze the role of network capabilities to organize the strategic activities of enterprise to obtain the resources and organizational capabilities to be used for value creation from external networks. In other words, research was conducted on the impact of technological innovation capabilities of small and medium venture companies on knowledge management performance by using network capabilities as parameters. Therefore, in this study, we would like to verify the hypothesis that innovation capabilities will have a positive impact on knowledge management performance by using network capabilities of small and medium venture companies. Economic activities based on technological innovation capabilities should respond quickly to new changes in an environment where uncertainty has increased, and lead to macro-economic growth and development as well as overcoming long-term economic downturns so that they can become the nation's new growth engine as well as sustainable growth and survival of the organization. In addition, this study was conducted by setting the most important knowledge management performance within the organization as a dependent variable. As a result, R&D and learning capabilities among technological innovation capabilities have no impact on financial performance. In contrast, it was shown that corporate innovation activities have a positive impact on both financial and non-financial performance. The fact that non-financial factors such as quality and productivity improvement are identified in the management of small and medium-sized venture companies utilizing their technological innovation capabilities is contrary to a number of studies by those corporate innovation activities affect financial performance during prior research. The reason for this result is that research companies have been out of start-up companies for more than seven years, but sales are less than 10 billion won, and unlike start-up companies, R&D and learning capabilities have more positive effects on intangible non-financial performance than financial performance. Corporate innovation activities have been shown to have a positive (+) impact on both financial and non-financial performance, while R&D and learning capabilities have a positive (+) impact on financial performance by parameters of network capability. Corporate innovation activities have been shown to have no impact on both financial and non-financial performance, and R&D and learning capabilities have no impact on non-financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance.