• Title/Summary/Keyword: learning physics

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The Theoretical and Practical Aspects of Science Talented Education--The Case of Chonnam National University (과학영재교육의 목표와 실제-전남대학교 과학영재교육센터 프로그램)

  • 조정일;이종백;김인수;박종원;윤석태;주동기;임형석
    • Journal of Gifted/Talented Education
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    • v.8 no.2
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    • pp.175-191
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    • 1998
  • The purpose and content of Chonnam National University Center for Science Talented Education program and students' responses were described. The program was developed with the purpose of providing various learning opportunities for science talented students according to the level of their learning abilities. Students are given a variety of activities based on their potentials and interest. The program was developed in four subjects, such as mathematics, information science, science Ⅰ (physics and earth science), and science Ⅱ (biology and chemistry). Each subject consisted of simple inquiry, advanced one, and project, even though it had its own distinctions. Students were selected for each subject based on two criteria, that is, achievements in school science or mathematics (the upper 3 percent of the 8th grade students) and examination scores. Means and standard deviations for each subject were as follows: 51.8 and 13.3 for Science Ⅰ, 53.1 and 13.9 for Science Ⅱ, 36.7 and 10.7 for mathematics and 36.4 and 12.5 for information science. Thirty hours of summer classes were performed, and a survey was administered to obtain students' responses concerning difficulty, interest, teaching and content of the program. They gave relatively favourable responses in most area, but lack of time for studying was revealed in mathematics and information science. Further study in needed to get detailed and more accurate results of our program.

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The High School Common Science Textbook and Classes by the Point of Science Teacher's View (교사들에 의한 공통과학 교과서 평가와 수업내용 현황)

  • Kim, Sung-Won;Jin, Yoo-Jung
    • Journal of The Korean Association For Science Education
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    • v.17 no.4
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    • pp.405-413
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    • 1997
  • High school common science is introduced by the sixth national curriculum. It consists of physics, chemistry, biology and earth science like the secondary school science. In this paper, textbooks are analyzed by the science teachers and the status of the present teaching and learning methods is reported. The detailed results are as follows; 1. Almost high school teachers choose textbook that included little the STS material. More than two teachers are teaching the high school common science and when they are chosen, they are independent with their major. 2. According to the national curriculum, they evaluated the textbooks as it is below the middle level. This evaluations are not dependent on teachers' comparement and textbook's class except the several matters based on STS (science-technology-society). 3. The teacher teaching the high school common science thought that teaching the textbook in school is worse than analysizing it. they must have emphasised on learning of inquire method than system of knowledge, introduction to material connected with real life and STS in the high school common science.

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Deep Learning Model on Gravitational Waves of Merger and Ringdown in Coalescence of Binary Black Holes

  • Lee, Joongoo;Cho, Gihyuk;Kim, Kyungmin;Oh, Sang Hoon;Oh, John J.;Son, Edwin J.
    • The Bulletin of The Korean Astronomical Society
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    • v.44 no.1
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    • pp.46.2-46.2
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    • 2019
  • We propose a deep learning model that can generate a waveform of coalescing binary black holes in merging and ring-down phases in less than one second with a graphics processing unit (GPU) as an approximant of gravitational waveforms. Up to date, numerical relativity has been accepted as the most adequate tool for the accurate prediction of merger phase of waveform, but it is known that it typically requires huge amount of computational costs. We present our method can generate the waveform with ~98% matching to that of the status-of-the-art waveform approximant, effective-one-body model calibrated to numerical relativity simulation and the time for the generation of ~1500 waveforms takes O(1) seconds. The validity of our model is also tested through the recovery of signal-to-noise ratio and the recovery of waveform parameters by injecting the generated waveforms into a public open noise data produced by LIGO. Our model is readily extendable to incorporate additional physics such as higher harmonics modes of the ring-down phase and eccentric encounters, since it only requires sufficient number of training data from numerical relativity simulations.

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The Development of Digital Age Literacy: A Case Study in Indonesia

  • MUJTAHID, Iqbal Miftakhul;BERLIAN, Mery;VEBRIANTO, Rian;THAHIR, Musa;IRAWAN, Dedi
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.2
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    • pp.1169-1179
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    • 2021
  • This research aims to develop the instrument of feasible and reliable digital-age literacy to be used in the learning process. This needs to be done due to a different region, tribe, and gender involved in this research. The digital-age literacy developed in this research consisting of 8 constructs, including 1) basic literacy, 2) scientific skill, 3) economy skill, 4) information skill, 5) technology skill, 6) visual skill, 7) various cultures skill, and 8) global awareness. As many as 650 respondents were chosen through stratified and random sampling in this survey. Those respondents were students at Universitas Terbuka based on gender and Ethnicity comparison. To see the internal consistency, the data were then analyzed using SPSS 23.00 version for Windows. It was obtained that all questionnaire constructs were valid and reliable, proven by obtaining a high mean reliability value of Cronbach Alpha (0.816 > 0.6), in which each item had a high value (0.778-0.841). Therefore, the number obtained the results proved that this research had produced a quality instrument that can be used to evaluate the students' mastery of the digital-age literacy of the learning process in Universitas Terbukain Asia, especially in Indonesia.

Classifications of Hadiths based on Supervised Learning Techniques

  • AbdElaal, Hammam M.;Bouallegue, Belgacem;Elshourbagy, Motasem;Matter, Safaa S.;AbdElghfar, Hany A.;Khattab, Mahmoud M.;Ahmed, Abdelmoty M.
    • International Journal of Computer Science & Network Security
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    • v.22 no.11
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    • pp.1-10
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    • 2022
  • This study aims to build a model is capable of classifying the categories of hadith, according to the reliability of hadith' narrators (sahih, hassan, da'if, maudu) and according to what was attributed to the Prophet Muhammad (saying, doing, describing, reporting ) using the supervised learning algorithms, with a view to discover a relationship between these classifications, based on the outputs of this model, which might be useful to avoid the controversy and useless debate on automatic classifications of hadith, using some of the statistical methods such as chi-square, information gain and association rules. The experimental results showed that there is a relation between these classifications, most of Sahih hadiths are belong to saying class, and most of maudu hadiths are belong to reporting class. Also the best classifier had given high accuracy was MultinomialNB, it achieved higher accuracy reached up to 0.9708 %, for his ability to process high dimensional problems and identifying the most important features that are relevant to target data in training stage. Followed by LinearSVC classifier, reached up to 0.9655, and finally, KNeighborsClassifier reached up to 0.9644.

Data Science and Machine Learning Approach to Improve E-Commerce Sales Performance on Social Web

  • Hussain Saleem;Khalid Bin Muhammad;Altaf H. Nizamani;Samina Saleem;M. Khawaja Shaiq Uddin;Syed Habib-ur-Rehman;Amin Lalani;Ali Muhammad Aslam
    • International Journal of Computer Science & Network Security
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    • v.23 no.8
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    • pp.137-145
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    • 2023
  • E-Commerce is a buzzword well known for electronic commerce activities including but not limited to the online shopping, digital payment transactions, and B2B online trading. In today's digital age, e-commerce has been playing a very important and vital role in areas such as retail shopping, sales automation, supply chain management, marketing and advertisement, and payment services. With a huge amount of data been collected from various e-commerce services available, there are multiple opportunities to use that data to analyze graphs and trends. Strategize profitable activities, and forecast future trade. This paper explains a contemporary approach for collecting key data metrics and implementing cost-effective automation that will support in improving conversion rates and sales performance of the e-commerce websites resulting in increased profitability.

Learning Model for Avoiding Drowsy Driving with MoveNet and Dense Neural Network

  • Jinmo Yang;Janghwan Kim;R. Young Chul Kim;Kidu Kim
    • International Journal of Internet, Broadcasting and Communication
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    • v.15 no.4
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    • pp.142-148
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    • 2023
  • In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

Differences among Sciences and Mathematics Gifted Students: Multiple Intelligence, Self-regulated Learning Ability, and Personal Traits (과학·수학 영재의 다중지능, 자기조절학습능력 및 개인성향의 차이)

  • Park, Mijin;Seo, Hae-Ae;Kim, Donghwa;Kim, Jina;Nam, Jeonghee;Lee, Sangwon;Kim, Sujin
    • Journal of Gifted/Talented Education
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    • v.23 no.5
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    • pp.697-713
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    • 2013
  • The research aimed to investigate characteristics of middle school students enrolled in a science gifted education center affiliated with university in terms of multiple intelligence, self-regulated learning and personality traits. The 89 subjects in the study responded to questionnaires of multiple intelligence, self-regulated learning ability and a personality trait in October, 2011. It was found that both science and math gifted students presented intrapersonal intelligence as strength and logical-mathematical intelligence as weakness. While physics and earth science gifted ones showed spatial intelligence as strength, chemistry and biology gifted ones did intrapersonal intelligence. For self-regulated learning ability, both science and mathematics gifted students tend to show higher levels than general students, in particular, cognitive and motivation strategies comparatively higher than meta-cognition and environment condition strategies. Characteristics of personal traits widely distributed across science and mathematics gifted students, showing that each gifted student presented distinct characteristics individually. Those gifted students showing certain intelligence such as spatial, intrapersonal, or natural intelligences as strength also showed different characteristics of self-regulated learning ability and personal traits among students showing same intelligence as strength. It was concluded that science and mathematics gifted students showed various characteristics of multiple intelligences, self-regulated learning ability, and personal traits across science and mathematics areas.

Exploring Application Ways of Virtual Reality Technology in Science Education (과학교육에서 가상현실 기법의 활용 모색)

  • Shim, Kew-Cheol;Park, Jong-Seok;Kim, Hyun-Sup;Kim, Jae-Hyun;Park, Young-Chul;Ryu, Hai-Il
    • Journal of The Korean Association For Science Education
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    • v.21 no.4
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    • pp.725-737
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    • 2001
  • Virtual reality technology is very useful for the 21C science education, and is able to contribute to the development of new teaching and learning methods in science education. One of these computer-based technologies, virtual reality, is possible to use in many directions. It is a new communication medium that is receiving a lot of attention, and is usually identified by a collection of technological hardware. Virtual reality is defined as a highly interactive, computer-based, multimedia environment in which the user becomes the participant, with the computer in a virtual real world. A key feature of virtual reality is real-time interactivity, in that the computer is able to detect user inputs and instantaneously modify the virtual world. It is being used in a wide variety of fields including physics, chemistry, human biology, biomedical sciences, military, architecture, industry and the entertainment. In classroom, using science educational program developed by virtual reality technology can increase the interests of students, promote understanding of basic science concepts, help laboratory skills, and encourage creative learning for them.

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A SE Approach for Real-Time NPP Response Prediction under CEA Withdrawal Accident Conditions

  • Felix Isuwa, Wapachi;Aya, Diab
    • Journal of the Korean Society of Systems Engineering
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    • v.18 no.2
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    • pp.75-93
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    • 2022
  • Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.