• Animal cells and systems
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• v.7 no.4
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• pp.303-307
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• 2003

The aim of this study is to investigate whether alcohol alters learning and memory processes pertaining to emotional and spatial factors using the active avoidance and T-maze task in zebrafish. In the active avoidance task, zebrafish were trained to escape from one compartment to another to avoid electric shocks (unconditioned stimulus) following a conditioned light signal. Acquisition of active avoidance task appeared to be normal in zebrafish that were treated with 1% alcohol for 30 min for 17 days until the end of the behavioral test, and retention ability of learned behavior, tested 2 days later, was the same as control group. In the T-maze task, the time to find a reservoir was compared. While the latency was similar during the 1 st training session between control and alcohol-treated zebrafish, it was significantly longer in alcohol-treated zebrafish during retention test 24 h later. Furthermore, when alcohol was treated 30 min after 2nd session without prior treatment, zebrafish demonstrated similar retention ability compared to control. These results suggest that chronic alcohol treatment alters spatial learning of zebrafish, but not emotional learning.

• Journal of the Korean Chemical Society
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• v.60 no.5
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• pp.327-341
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• 2016

This study explores the effective visual resources in the “regularity in chemical reactions” unit of ninth grade science textbooks (2009 revised version). The frequency and role of visual resources were initially examined, and the students’ perceptions of visual resources were investigated. The results of the analysis represented the learning material presentation (68%), motivational categories (14%), guide to inquiry procedures (9%), and inquiry results and summaries (8%). According to the investigation of the students’ perceptions of visual resources, the most effective visual resource for motivation is a photograph depicting physical and chemical changes, such as in bread baking and the most effective for learning material presentations in mass conservation, definite proportion, and stoichiometric concept units were a cartoon, graph, and formula representing stoichiometric phenomena, respectively. The most effective resource for guide to inquiry (experimental) procedures were photographs of both instruments and sequential experiment processes; and in the inquiry results and summary category, incomplete tables and graphs for students to work on themselves. The aims of this research are to increase the usefulness of visual resources in the teaching-learning activity and provide informative supplements for the development and improvement of visual resources, according to the students’ perceptions.

• Journal of Korean Elementary Science Education
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• v.37 no.2
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• pp.173-187
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• 2018

The purpose of this study is to clarify the specific terms of epistemic and epistemological by reviewing the literature on epistemological understanding of science learning, examine the necessity of epistemic discourse analysis based on the view of social epistemology, and provide an exemplar of practical epistemology analysis for elementary children's science learning. The review was conducted in terms of meaning and terminology about epistemic or epistemological approach to science learning, epistemology of/for science, and methodologies for epistemic discourse analysis. As an alternative way of epistemic discourse analysis in science classroom I employed practical epistemology analysis (by Wickman), evidence-explanation continuum (by Duschl), and DREEC diagram (by Maeng et al.). The methods were administered to an elementary science class for the third grade where children observed sedimentary rocks. Through the outcomes of analysis I sought to understand the processes how children collected data by observation, identified evidence, and constructed explanations about rocks. During the process of practical epistemology analysis the cases of four categories, such as encounter, stand-fast, gap, and relation, were identified. The sequence of encounter, stand fast, gap, and relation showed how children observed sedimentary rocks and how they came to learn the difference among the rocks. The epistemic features of children's observation discourse, although different from scientists' discourses during their own practices, showed data-only conversation, evidence-driven conversation, or explanation inducing conversation. Thus I argue even elementary children are able to construct their own knowledge and their epistemic practices are productive.

• Journal of Korean Elementary Science Education
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• v.26 no.2
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• pp.141-148
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• 2007

This study proposed task-based teaming strategies as a means of fulfilling the demands and goals of the 7th national science curriculum. Task-based learning is based on the use of a series of activities whereby a teacher presents students with tasks related to daily lift and the students solve the tasks by themselves using various methods and thought processes and then present and discuss their results with each other. The tasks are selected from the 6-grade science textbook, are reconstructed and are then given to the classes. The tasks include whole class activities as well as individual activities related to the interests, abilities, and concerns of the students. The purpose of this study was to verify the effects of task-based learning classes on the science process skills and the scientific attitudes of elementary school students, when applied to 6th grade students. For this, the task-based learning activities were applied to an experimental group and expository teaching was applied to the comparison group. Both groups were given a pre-post test on science process skills and scientific attitudes. The results indicate that task-based loaming is very effective in the development of science process skills and scientific attitudes.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.26-38
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• 2017

The architecture, engineering, and construction (AEC) industry is yet to formulate a holistic strategy to realign the evolving technological infrastructures with organisational ambitions and adaptive knowledge of the workforce. This study attempts to create an understanding of the underlying processes adopted by technology-enhanced construction organisations to disseminate and maintain knowledge within the workforce in order to keep pace with the evolving construction technologies. The study adopted expansive learning and adaptive expertise constructs to help better explain workplace learning support structures for organisational effectiveness in a turbulent situation. The two theories were tailored to empirically evaluate three case study construction organisations that have embarked on technology-enabled organisational changes. The study concluded on the creation of a facilitating workplace learning environment to enable the workforce to adapt into and resolve any inherent contradictions and cognitive ambiguities of the changing organisational conditions. This could ensure that novel and conflicting features of the emerging technologies can be adapted across the myriad multi-functional project activities in order to expand the frontiers of the technological capabilities to address the eminent issues confronting the AEC sector.

• The Mathematical Education
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• v.49 no.4
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• pp.463-488
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• 2010

The purpose of this study was providing implications in teaching and learning activities to vitalize mathematical communication and to raise positive attitudes about mathematics. We analyzed mathematical communication, disposition and attitudes of the students who had lower mathematical achievement rather than that of Korean language in class using reading materials and strategies. We found that teaching-learning activities using the reading materials and strategies let the low achievers in mathematics communicate more about mathematical notions and problem-solving process actively. The activities triggered interests and attention of mathematics and self-study. In addition, the lessons with reading materials and strategies aroused confidence, will and responsibility to mathematics learning to the students. They made the learners notice mathematics' values and roles and gave the opportunity of reflection about students' learning processes. As a result, the teaching-learning using reading materials and strategies should be developed and accomplished actively in classroom to turn mathematical inclination and attitudes of the students who had had negative inclination and attitudes to mathematics into those of positive and to vitalize mathematical communication to the lower achievers in mathematics.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.