• Asia pacific journal of information systems
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• v.22 no.4
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• pp.7-29
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• 2012

Smartphone and its applications (i.e. apps) are increasingly penetrating consumer markets. According to a recent report from Korea Communications Commission, nearly 50% of mobile subscribers in South Korea are smartphone users that accounts for over 25 million people. In particular, the importance of smartphone has risen as a geospatially-aware device that provides various location-based services (LBS) equipped with GPS capability. The popular LBS include map and navigation, traffic and transportation updates, shopping and coupon services, and location-sensitive social network services. Overall, the emerging location-based smartphone apps (LBA) offer significant value by providing greater connectivity, personalization, and information and entertainment in a location-specific context. Conversely, the rapid growth of LBA and their benefits have been accompanied by concerns over the collection and dissemination of individual users' personal information through ongoing tracking of their location, identity, preferences, and social behaviors. The majority of LBA users tend to agree and consent to the LBA provider's terms and privacy policy on use of location data to get the immediate services. This tendency further increases the potential risks of unprotected exposure of personal information and serious invasion and breaches of individual privacy. To address the complex issues surrounding LBA particularly from the user's behavioral perspective, this study applied the privacy calculus model (PCM) to explore the factors that influence the adoption of LBA. According to PCM, consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. Consistent with the principal notion of PCM, we investigated how individual users make a risk-benefit assessment under which personalized service and locatability act as benefit-side factors and information privacy risks act as a risk-side factor accompanying LBA adoption. In addition, we consider the moderating role of trust on the service providers in the prohibiting effects of privacy risks on user intention to adopt LBA. Further we include perceived ease of use and usefulness as additional constructs to examine whether the technology acceptance model (TAM) can be applied in the context of LBA adoption. The research model with ten (10) hypotheses was tested using data gathered from 98 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a LBA allows the participant to purchase time-and-location sensitive discounted tickets for nearby stores. Structural equations modeling using partial least square validated the instrument and the proposed model. The results showed that six (6) out of ten (10) hypotheses were supported. On the subject of the core PCM, H2 (locatability ${\rightarrow}$ intention to use LBA) and H3 (privacy risks ${\rightarrow}$ intention to use LBA) were supported, while H1 (personalization ${\rightarrow}$ intention to use LBA) was not supported. Further, we could not any interaction effects (personalization X privacy risks, H4 & locatability X privacy risks, H5) on the intention to use LBA. In terms of privacy risks and trust, as mentioned above we found the significant negative influence from privacy risks on intention to use (H3), but positive influence from trust, which supported H6 (trust ${\rightarrow}$ intention to use LBA). The moderating effect of trust on the negative relationship between privacy risks and intention to use LBA was tested and confirmed by supporting H7 (privacy risks X trust ${\rightarrow}$ intention to use LBA). The two hypotheses regarding to the TAM, including H8 (perceived ease of use ${\rightarrow}$ perceived usefulness) and H9 (perceived ease of use ${\rightarrow}$ intention to use LBA) were supported; however, H10 (perceived effectiveness ${\rightarrow}$ intention to use LBA) was not supported. Results of this study offer the following key findings and implications. First the application of PCM was found to be a good analysis framework in the context of LBA adoption. Many of the hypotheses in the model were confirmed and the high value of $R^2$ (i.,e., 51%) indicated a good fit of the model. In particular, locatability and privacy risks are found to be the appropriate PCM-based antecedent variables. Second, the existence of moderating effect of trust on service provider suggests that the same marginal change in the level of privacy risks may differentially influence the intention to use LBA. That is, while the privacy risks increasingly become important social issues and will negatively influence the intention to use LBA, it is critical for LBA providers to build consumer trust and confidence to successfully mitigate this negative impact. Lastly, we could not find sufficient evidence that the intention to use LBA is influenced by perceived usefulness, which has been very well supported in most previous TAM research. This may suggest that more future research should examine the validity of applying TAM and further extend or modify it in the context of LBA or other similar smartphone apps.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.256-266
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• 2022

Empirical models including the Angstrom-Prescott (AP) model have been used to estimate solar radiation at sites, which would support a wide use of crop models. The objective of this study was to estimate two sets of solar radiation estimates using the AP coefficients derived for climate zone (AP_Frere) and specific site (AP_Choi), respectively. The daily solar radiation was estimated at 18 sites in Korea where long-term measurements of solar radiation were available. In the present study, daily solar radiation and sunshine duration were collected for the period from 2012 to 2021. Daily weather data including maximum and minimum temperatures and rainfall were also obtained to prepare input data to a process-based crop model, CERES-Rice model included in Decision Support System for Agrotechnology Transfer (DSSAT). It was found that the daily estimates of solar radiation using the climate zone specific coefficient, SFrere, had significantly less error than those using site-specific coefficients S_Choi (p<0.05). The cumulative values of S_Frere for the period from march to September also had less error at 55% of study sites than those of S_Choi. Still, the use of S_Frere and S_Choi as inputs to the CERES-Rice model resulted in slight differences between the outcomes of crop growth simulations, which had no significant difference between these outputs. These results suggested that the AP coefficients for the temperate climate zone would be preferable for the estimation of solar radiation. This merits further evaluation studies to compare the AP model with other sophisticated approaches such as models based on satellite data.

• 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.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.9
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• pp.955-960
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• 2008

We investigated and estimated at the characteristics of decomposition and mineralization of benomyl using a design of experiment(DOE) based on the general factorial design in an E-beam process, and also the main factors(variables) with benomyl concentration(X$_1$) and E-beam irradiation(X$_2$) which consisted of 5 levels in each factor was set up to estimate the prediction model and the optimization conditions. At frist, the benomyl in all treatment combinations except 17 and 18 trials was almost degraded and the difference in the decomposition of benomyl in the 3 blocks was not significant(p > 0.05, one-way ANOVA). However, the % of benomyl mineralization was 46%(block 1), 36.7%(block 2) and 22%(block 3) and showed the significant difference of the % that between each block(p < 0.05). The linear regression equations of benomyl mineralization in each block were also estimated as followed; block 1(Y$_1$ = 0.024X$_1$ + 34.1(R$^2$ = 0.929)), block 2(Y$_2$ = 0.026X$_2$ + 23.1(R$^2$ = 0.976)) and block 3(Y$_3$ = 0.034X$_3$ + 6.2(R$^2$ = 0.98)). The normality of benomyl mineralization obtained from Anderson-Darling test in all treatment conditions was satisfied(p > 0.05). The results of prediction model and optimization point using the canonical analysis in order to obtain the optimal operation conditions were Y = 39.96 - 9.36X$_1$ + 0.03X$_2$ - 10.67X$_1{^2}$ - 0.001X$_2{^2}$ + 0.011X$_1$X$_2$(R$^2$ = 96.3%, Adjusted R$^2$ = 94.8%) and 57.3% at 0.55 mg/L and 950 Gy, respectively. A Microtox test using V. fischeri showed that the toxicity, expressed as the inhibition(%), was reduced almost completely after an E-beam irradiation, whereas the inhibition(%) for 0.5 mg/L, 1 mg/L and 1.5 mg/L was 10.25%, 20.14% and 26.2% in the initial reactions in the absence of an E-beam illumination.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.139-160
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• 2011

The purpose of this study is twofold: (i) to develop a teaching and learning plan and practical performance assessment tools for the improvement of teenager-parent communication and relationships as well as explore their effects on the communication in the everyday family life; and (ii) to find the underlying problems of teenager- parent communication through conversation analysis and to provide a improved dialogue model. We provided the experimental group with a performance task of communication training between teenagers and their parents in the real family situation while the control group practiced communication skills in a learning situation. However for both classes, before and after performance tasks were equally provided. The experimental group exhibited a longer conversation time with their parents, better communication skills, and higher degrees of relational satisfaction than the control group. Conversation analysis revealed that the experimental group reduced the use of blocking techniques in the teenager-parent conversations more than the control group, and all so raised the frequency of functional communications more than the control group. In both areas of communication in the experimental group was significantly improved, Most notably, a problem-solving case through no-lose conflict resolution methods was effective, succeeding by 70% in the e experimental group and 43.3% in the control group. Parents use blocking techniques like admonition, lecturing, blaming. sarcastic remarking, ordering and so forth, while teenagers use dispute, avoidance, blaming, and teasing in this order. The communication problems during the conversation process, teenagers' evasive and rebellious way of speaking instigates adverse communication responses from parents, so their conversation tends to unfold as ambiguous evasion opposed to: inquiring or evasion by short answers vs. ordering-preaching, or disputing vs. criticizing-making sarcastic, disputing vs. disputing-teaching, and criticizing vs. criticizing.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Journal of Soil and Groundwater Environment
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• v.12 no.4
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• pp.25-31
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• 2007

Characterizing the risk posed by a mixture of chemicals is a challenging task due to the chemical interactions of individual components that may affect their physical behavior and hence alter their exposure to receptors. In this study, cell tests that represent subsurface environment were carried out using benz[a]anthracene (BaA) and p-xylene focusing on phasetransforming interaction to verify increased mobility and risk of highly sorbed pollutants in the presence of less sorbed, mobile liquid pollutants. A transport model was also developed to interpret results and to simulate the same process on a field scale. The experimental results showed that BaA had far greater mobility in the presence of p-xylene than in the absence of that. The main transport mechanisms in the vadose zone were by dissolution to p-xylene or water. The transport model utilizing Defined Time Steps (DTS) was developed and tested with the experimental results. The predicted and observed values showed similar tendency, but the more work is needed in the future study for more precise modeling. The field-scale simulation results showed that transport of BaA to groundwater table was significantly faster in the presence of NAPL, and the oral carcinogenic risk of BaA calculated with the concentration in groundwater was 15${\sim}$87 times larger when mixed with NAPL than when solely contaminated. Since transport rate of PAHs is very slow in the subsurface without NAPL and no degradation of PAHs was considered in this simulation during the transport, the increase of risk in the presence of NAPL is expected to be greater for the actual contaminated site.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.126-133
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• 2020

In accordance with the implementation of the sunset provision to cancel the designations of urban park sites that remained unexecuted for a prolonged period until 2020, the park sites in the city center, which account for 90% of the long-term unexecuted urban facilities subjected to the provision, are currently on the verge of development. The total area of the 204 park sites that will disappear in Seoul as a result of this provision is 95 ㎢; moreover, 116 of these are privately-owned. It is expected that the possible changes in the use of these park sites could result in reckless development and reduction of green space, which would ultimately affect the ecosystem. This study applied the InVEST model to calculate the changes in the fixed carbon amount before and after the implementation of the sunset provision to estimate the economic value of these changes. The study focused on Jongno-gu in Seoul because it has the most unexecuted park sites subjected to the lifting of the designation. The research findings show that the fixed carbon amount provided by the unexecuted park sites in Jongno-gu was 374,448 mg, prior to the implementation of the sunset provision; however, the amount was estimated to decrease by 18% to 305,564 mg after its execution. When calculated in terms of average value of the real carbon price, this translated into a loss of approximately 700 million won. In addition, considering the social costs including both climate change and the impact on the ecosystem, an economic loss of approximately 98 billion won was projected. This study is meaningful because its predictions are based on the estimation of fixed carbon amount according to the implementation of the sunset provision in Jongno-gu and scientifically calculates the value of ecological services provided by the parks in the city. This study can serve not only as a basis during the decision-making process for policies related to ecosystem conservation and development, but also as an evidentiary material for the compensation of privately-owned land that is designated as urban park sites and was unexecuted for a prolonged period.