Ex) Article Title, Author, Keywords
Abstract : The ﬁrst steam engine, the Aeolipile, was powered by action–reaction and rotational forces. This study presented the Aeolipile model experiments ‘Spinning water bottle’ and ‘Spinning straw’ to gifted science students and instructed them to generate corresponding explanatory models. Results showed that the gifted students most commonly provided explanatory models that explain the spinning phenomenon based on the concept of action–reaction. Additionally, the most common groups included students who either maintained a level 3 model that correctly explained the rotational phenomenon based on the law of action and reaction or progressed to a level 4 model that discusses the rotational phenomenon based on the law of action and reaction and rotational force. This ﬁnding conﬁrms that science-gifted students can generate explanatory models for the Aeolipile model based on their comprehension of the law of action and reaction. Moreover, the generation of explanatory models for the ‘Spinning straw’ experiment can aid science-gifted students in comprehending and expressing rotational forces.
Abstract : The nonlinear evolution of large scale structures (LSSs) can disclose key cosmological information for understanding the physics beyond the standard model. In recent years, the use of deep neural networks in the direct extraction of cosmological information from LSS maps has gained increasing attention among research community. As the evolution of LSSs is governed by a growth factor that depends on contents of the universe combined with nonlinear eﬀects, if neural networks can capture correlations at various epochs, then precision measurements of cosmological parameters can be improved. In this paper, we perform N-body simulations and demonstrate that image-based transformer networks conﬁgured for time-series data can be enhanced for the accurate extraction of Ωm and σ8 parameters.
Abstract : In this study, we explore the spectral discrimination of fluorescence images of liquid scintillators acquired by complementary metal oxide semiconductor (CMOS) image sensors using the discriminative ability of deep convolutional neural networks without requiring any special effort. With the continuous advancement of semiconductor fab-processing technology, the processing technology of optical elements in image sensors has also advanced. However, there exists a trade-off between the pixel size of an image sensor, signal noise ratio, and high color reproduction. Furthermore, commercial CMOS image sensor manufacturers typically do not provide users with spectral response data for their CMOS sensors. To address these challenges, we generated training images using a light-emitting diode module programmable on a single-board computer and demonstrated the feasibility of inferring the spectral response backward from the discriminant values of a deep convolutional neural network. Building on the previous study and considering the operational characteristics of neutrino experiments, we evaluated the feasibility of employing a deep convolutional neural network for monitoring the attenuation distance and spectral response of light in a liquid scintillator through supervised learning. In future, we aim to optimize transformer implantation that is efficient with limited required computational resources for the characteristics of the Internet of Things.
Abstract : The optical properties of white light-emitting diodes (LEDs) with a yellow phosphor plate and a red quantum dot film applied sequentially on top of a blue LED chip were investigated as a function of phosphor thickness, quantum dot concentration, and the presence or absence of the microprism film. It was found that an excessively thick yellow phosphor plate leads to an increased absorption of the blue LED, resulting in a higher proportion of yellow light and deviation from the Planckian locus. This deviation arises due to the absence of long-wavelength red component in the emitting spectrum of yellow phosphor plates. Particularly, the application of one-dimensional microprism film to the quantum dot film demonstrates notable enhancement. The reflection at the prism interface facilitates the reciprocating motion of light within the vertical cavity formed by the prism film and the bottom reflector. This phenomenon considerably increases the color conversion efficiency of the red quantum dot film, resulting in improved on-axis brightness and color rendering index. This study provides insights into improving the color rendering performance of lighting with remote color-changing components.
Abstract : The structure of physics education research papers was explored by analyzing and comparing empirical research papers from academic fields closely related to physics education research. A total of 100 papers were studied, including 20 studies each in physics, chemistry, education, and psychology, all of which were recently published in Korean journals. According to the results, IM[RD]C for physics and chemistry, ILMR[DC] for education, and IMRD for psychology were the most common. However, structural patterns in physics education were found to be diverse. Moreover, titles such as “Conclusions and suggestions” toward the end of the paper was identified as a characteristic trait of physics education papers as it was rarely observed in other fields. As in education, tables were used more often than pictures and the characteristics of natural and social science research articles appeared complex in physics education papers. Furthermore, some characteristics were unique to physics education papers.
Geun Taek YU, Geun Hyeong PARK, Eun Been LEE, Min Hyuk PARK*
New Phys.: Sae Mulli 2021; 71(11): 890-900
Aekyung Shin, Donggeul Hyun, Jeongwoo Park
New Phys.: Sae Mulli 2023; 73(1): 37-43
Chang Won AHN, Jin San CHOI, Muhammad SHEERAZ, Hwan Min KIM, Ill Won KIM, Tae Heon KIM*
New Phys.: Sae Mulli 2021; 71(12): 991-1003