Journal of The Korean Association For Science Education
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v.29
no.8
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pp.990-1010
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2009
To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.
With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.
Dong Hyun Kim;Hyung Jun Park;Young Jun Bang;Seung Oh Lee
Journal of Korean Society of Disaster and Security
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v.16
no.4
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pp.45-59
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2023
The global focus on mitigating climate change has traditionally centered on carbon dioxide, but recent attention has shifted towards methane as a crucial factor in climate change adaptation. Natural settings, particularly aquatic environments such as wetlands, reservoirs, and lakes, play a significant role as sources of greenhouse gases. The accumulation of organic contaminants on the lake and reservoir beds can lead to the microbial decomposition of sedimentary material, generating greenhouse gases, notably methane, under anaerobic conditions. The escalation of methane emissions in freshwater is attributed to the growing impact of non-point sources, alterations in water bodies for diverse purposes, and the introduction of structures such as river crossings that disrupt natural flow patterns. Furthermore, the effects of climate change, including rising water temperatures and ensuing hydrological and water quality challenges, contribute to an acceleration in methane emissions into the atmosphere. Methane emissions occur through various pathways, with ebullition fluxes-where methane bubbles are formed and released from bed sediments-recognized as a major mechanism. This study employs Biochemical Methane Potential (BMP) tests to analyze and quantify the factors influencing methane gas emissions. Methane production rates are measured under diverse conditions, including temperature, substrate type (glucose), shear velocity, and sediment properties. Additionally, numerical simulations are conducted to analyze the relationship between fluid shear stress on the sand bed and methane ebullition rates. The findings reveal that biochemical factors significantly influence methane production, whereas shear velocity primarily affects methane ebullition. Sediment properties are identified as influential factors impacting both methane production and ebullition. Overall, this study establishes empirical relationships between bubble dynamics, the Weber number, and methane emissions, presenting a formula to estimate methane ebullition flux. Future research, incorporating specific conditions such as water depth, effective shear stress beneath the sediment's tensile strength, and organic matter, is expected to contribute to the development of biogeochemical and hydro-environmental impact assessment methods suitable for in-situ applications.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.19
no.1
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pp.119-133
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2024
As a result of significant investments by the government in promoting public technology commercialization and fostering a venture startup ecosystem, there have been quantitative achievements, such as the registration of over 1,600 Innopolis Start-ups since 2006, generating a total revenue of 1.1 trillion won as of 2021. However, these achievements have been overshadowed by critical qualitative challenges, including a continuous decline in average revenue per Innopolis Start-up. This led to a focus on whether managers' technological and market orientations affect business performance. This study aims to provide insights into improving the qualitative growth of Innopolis Start-ups by analyzing the effects of technological and market orientations on business performance, as well as the moderating effects of adjusting marketing and research and development (R&D) costs on this relationship. Through prior research and empirical analysis, this study derives three main findings. First, technological excellence and innovation significantly influence the business performance of Innopolis Start-ups, while technological intensity does not. Second, customer orientation and competitive orientation significantly impact business performance, whereas entry barriers as a single factor do not. Third, adjusting marketing and R&D costs, as controlled variables obtained through general situations, has no direct impact on other variables. However, it interacts with entry barriers, influencing financial business performance, with R&D costs exhibiting a negative buffering effect and marketing costs showing a positive enhancing effect. This study confirms that both technological and market orientations directly influence the business performance of Innopolis Start-ups, thus being crucial factors affecting their growth. Moreover, it establishes that investments in marketing and R&D play significant roles in alleviating initial entry barriers and enhancing financial performance. Consequently, it underscores the importance of reinforcing technological and market orientations tailored to the characteristics of Innopolis Start-ups. Additionally, it proposes five theoretical contributions: strengthening institutional support systems for technology commercialization and innovation, improving qualitative evaluation criteria during the selection process of Innopolis Start-ups, conducting comprehensive analyses of technological and market aspects during startup selection, enhancing support for marketing education and consulting for smooth market entry, and supporting expenditure strategies and milestone setting tailored to the industrial characteristics of individual Innopolis Start-ups.
The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.
This study aims to elucidate the historical origins of the long-term demand for the integration of "science subject classes" and "science-related events" within the context of science education for teachers. During the 1970s, science education in elementary schools faced a dual challenge marked by the tension between implementing the third curriculum, which emphasized fundamental science, and the "Pan-National Scientification Movement," which focused on technology education. The Ministry of Education was compelled to integrate the sudden demands of the Yushin regime into the ongoing third curriculum. As these demands emerged from dual policy directives, activities related to elementary science education were subsequently categorized into formal science subject classes and extracurricular science-related events. Although the movement did not directly alter the curriculum, it instigated modifications in personnel structure, activity spaces, and evaluation systems within schools. The introduction of the Pan-National Scientification Movement in elementary schools resulted in changes including the establishment of a new "science lead teacher system," the creation of a dedicated "science corner," and the implementation of a "science badge system." Although the movement was abruptly introduced, it ostensibly contributed to the advancement of the inquiry-oriented approach promoted by the third curriculum. Paradoxically, this advancement was facilitated by the integration of the consequences of the movement into schools' autonomous, extracurricular activities spearheaded by frontline education offices and schools. Although the movement represented a government-driven policy at a particular juncture in time, the manner in which science education practitioners responded to urgent governmental mandates, while preserving the integrity of the long-established third curriculum framework, involved dividing education activities into subject-specific classes and extracurricular science activities. Examining how science education practitioners in the 1970s proactively addressed these challenges offers valuable insights for the science education community in adapting to the current rapidly evolving educational landscape.
Hee Joon Kwon;Geun soo Lee;Jin Hwa Kim;Soon Woo Kwon;Hyung seo Hwang
Journal of Applied Biological Chemistry
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v.66
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pp.416-423
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2023
Figs has known to have antioxidant, whitening, anti-inflammatory, and antibacterial effects in their leaves, roots, stems, latex, and fruits. In order to develop cosmetic materials based on natural products, we have studied on the skin activity of the ficin in latex as well as the whitening function of the fruit extract with 70% ethanol, and used it as a raw material for released cosmetic product. However, there is little research on the demand for the development of new eutectic solvent extraction methods and its ability to control skin inflammation and psoriasis regulation. Thus, in this study, we evaluated the effectiveness of fig fruit extracts and fractions using eutectic solvent extraction for skin inflammation control and psoriasis. First, fig fruits were extracted under optimal eutectic solvent conditions and fractionated with n-hexane, dichloromethane, ethyl acetate, and butanol. First, the antioxidant activity and inhibition of nitric oxide (NO) production were confirmed in mouse macrophage RAW264.7 cells. In addition, as a result of observing the mRNA expression through RT-PCR, pro-inflammatory cytokines such as TNF-α, IL1α, and IL-1β were suppressed significantly in the hexane, dichloromethane, and ethyl acetate fractions. In addition, it was confirmed in TNF-α stimulated HaCaT keratinocyte model. Finally, chemokine CC motif ligand 20 (CCL20), marker gene of human psoriasis skin disease, was significantly suppressed in the hexane, dichloromethane, and ethyl acetate fractions. These results suggested its anti-inflammatory and skin soothing effect and the possibility of development as an excellent skin soothing natural cosmetic material in the future through future clinical trials.
Amphibians, sensitive to external environmental changes, serve as bioindicator species for assessing alterations or disturbances in local ecosystems. It is known that one-third of amphibian species within the order Anura are at risk of extinction due to anthropogenic threats such as habitat destruction and fragmentation caused by urbanization. To develop effective protection and conservation strategies for anuran amphibians, species surveys that account for population characteristics are essential. This study aimed to investigate the potential for citizen participation in ecological monitoring using the mating calls of anura species. We also proposed suitable quality control measures to mitigate errors and biases, ensuring the extraction of reliable species occurrence data. The Citizen Science project was carried out nationwide from April 1 to August 31, 2022, targeting 12 species of anura amphibians in Korea. Citizens voluntarily participated in voice signal monitoring, where they listened to anura species' mating calls and recorded them using a mobile application. Additionally, we established a quality control process to extract reliable species occurrence data, categorizing errors and biases from citizen-collected data into three levels: omission, commission, and incorrect identification. A total of 6,808 observations were collected during the citizen participation in anura species vocalization monitoring. Through the quality control process, errors and biases were identified in 1,944 (28.55%) of the 6,808 data. The most common type of error was omission, accounting for 922 cases (47.43%), followed by incorrect identification with 540 cases (27.78%), and commission with 482 cases (24.79%). During the Citizen Science project, we successfully recorded the mating calls of 10 out of the 12 anuran amphibian species in Korea, excluding the Asian toads (Bufo gargarizans Cantor), Korean brown frog (Rana coreana). Difficulties in collecting mating calls were primarily attributed to challenges in observing due to population decline or discrepancies between the breeding season of non-emergent individuals and the timing of the citizen science project. This study represents the first investigation of distribution status and species emergence data collection through mating calls of anura species in Korea based on citizen participation. It can serve as a foundation for designing future bioacoustic monitoring that incorporates citizen science and quality control measures for citizen science data.
Journal of Korean Home Economics Education Association
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v.35
no.3
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pp.47-66
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2023
The purpose of this study was to develop an educational smart app for classes by reconstructing some of the teaching-learning contents of the clothing preparation planning within the 'clothing preparation planning and selection' curriculum unit. To this end, a teaching-learning process plan was planned for the classes, a smart app was developed for classes, and feedback from home economics teachers and app development experts was received for the developed app. The main composition of the developed app consists of five steps. The first step is to set up a profile using a real photo, ZEPETO or Galaxy emoji, or iPhone Memoji. In the second step, students make a list of clothes by figuring out the types, quantities and conditions of their exisitng wardrobe items. Each piece of clothing is assigned an individual registration number, and stduents can take pictures of the front and back, along with describing key attributes such as type, color, season-appropriateness, purchase date, and current status. Step three guides students in deciding which garments to retain and which to discard. Building on the clothing inventory from the previous step, students classify items to keep and items to dispose of. In Step 4, Deciding How to Arrange Clothing, students decide how to arrange clothing by filling out an alternative scorecard. Through this process, students can learn in advance the subsection of resource management and self-reliance, laying the foundationa for future learning in 'Practice of Rational Consumption Life'. Lastly, in the fifth stage of determining the disposal method, this stage is to develop practical problem-oriented classes on how to dispose of the clothes to be discarded in the thirrd stage by exploring various disposal methods, engaging in group discussions, and sharing opinions. This study is meaningful as a case study as an attempt to develop a smart app for education by an instructor to align teaching plans and educational content with achievement standards for the class. In the future, upgrades will have to be made through user application.
The purpose of this study is to analyze products for good taste and convenience, which become an engine to constantly create customers. In addition, this study is aimed at investigating the relationship between the selective attributes of Home Meal Replacement, the perceived utilitarian value, and the repurchase intention, and drawing new suggestions on the Home Meal Replacement market from a new marketing perspective. Based on a total of 215 samples, this study reviewed the reliability and fitness of the research model and verified a total of 5 hypothesized using the Amos program. The result of study modeling was GFI=0.905, AGFI=0.849, NFI=0.889, CFI=0.945, and RMR=0.0.092 at the level of $x^2$=230.22 (df=126, p<0.001). First, the food quality (${\beta}$=0.221), convenience (${\beta}$=0.334), packing (${\beta}$=0.278), and employee service (${\beta}$=0.204) of home meal replacement consideration attributes had a positive (+) influence on perceived utilitarian value. Second, perceived utilitarian value (${\beta}$=0.584) had a positive (+) influence on repurchase intention. The factors to differentiate one company from other competitors in terms of the utilitarian value are the quality of food, convenience, wrapping, and services by employees. This study has illustrated the need to focus on the development of a premium menu to compete with other companies and to continue to research and develop nutritious foods that are easy to cook. Moreover, the key factors to have a distinct and constant competitive edge over other companies are the alleviation of consumer anxiety over wrapping container materials, the development of more designs, and the accumulation of service know-how. Therefore, it is necessary for a company to strongly develop the key factors based on its resources as a core capability.
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