28th SCEJ Students Meeting (online, 2026): Session programs

`28th SCEJ Students Meeting (online, 2026)`

Session programs

Fundamental properties, Simulation & prediction

A01-A06, A08-A13, A27-A32

Most recent update: 2026-03-06 17:03:49

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall A
(9:30–10:42) (Chair: Konishi Tomoya, Nakagawa K.)
9:30– 9:42	A01	Does the thermal conductivity change by a kind of the meat? (Ikuno) Kimura K., Kitano S., Nakano K., Kuroiwa S., Yoshida S.	meat heat conductivity	2-g	151
9:42– 9:54	A02	Relationship between freezing point depression and molar concentration in highly concentrated aqueous solutions (Masuda H.S) Ueda K., Gobara S., *Hirano K., Fukui M.	freezing point depression highly concentrated solution glucose aqueous solution	1-e	84
9:54– 10:06	A03	The impact of geographical conditions and human activities on ant ecology (Hyogo H.S) Akagawa Megumi, Katayama Naoya, Hosooka Mone, Yamasaki Mahana, *Watanabe Shotaro, Shiga Toshiki	Ant Ecology Geographical situation	7-g	22
10:06– 10:18	A04	The effect of how to carry a bag on a physical and brain performance (Hyogo H.S) Akashi Naoya, Kobayashi Kazumichi, Tada Tomoka, Fujinaka Azuki, *Hotta Ikumi, Shiga Toshiki	Athletic ability Muscle activity Brain activity	9-f	19
10:18– 10:30	A05	How to create rainbow colors in a test tube by the universal indicator (Kitamifujikoukou) *Hirabayashi K, Yamaki S, Uto Y.	acid-base indicator hydrogen ion exponent rainbow-collar	5-i	164
10:30– 10:42	A06	Quantitative analysis of the solubility of sucrose in aqueous urea solutions (Tachikawa H.S.) Hatakeyama Koki, Matsumoto Kakeru, Takano Shunsuke	solubility urea sucrose	1-a	100

(10:50–12:02) (Chair: Yamauchi N., Sato Takafumi)
10:50– 11:02	A08	Improving Accuracy of Machine Learning Models for Predicting the Properties of Polymer Aerogels Using Transfer Learning (Kyoto U.) *Yanagi Kiyoshi, Yoshikawa Itsuki, Tonomura Osamu, Sotowa Ken-Ichiro	Polyimide Aerogel Transfer Learning Materials Informatics	6-g	161
11:02– 11:14	A09	Doubt a textbook!Is the iodoform reaction caused? (Ikuno) Katayama D., Nakazawa K., Fujiwara S., Iwamoto K., Yoshida S.	iodoform chemical structure iodine	1-b	96
11:14– 11:26	A10	Exploring the possibility of Flame Color reaction (Tachikawa H.S.) Simizu syuji, Imai Tsubasa, *Nakagawa Koei, Yoshida Takashi	Flame color reaction wavelength color change	1-a	138
11:26– 11:38	A11	Development of a property estimation method for ternary systems using gradient-constrained machine learning (Shizuoka U.) *Hatoyama Atsushi, Murakami Yuya	PINNs mixture machine learning	1-a	208
11:38– 11:50	A12	Effects of changing conditions on the iodine clock reaction (Tamagawa Academy) *Sasaki Yui, Kinouchi Mikiko	chemical reaction clock reaction Iodine	5-i	141
11:50– 12:02	A13	Study on Creating Rainbow Colors in a Test Tube Using Natural Pigments (Kitamifujikoukou) *Hayashi Setuka, Tuji Ruki, Uto Y.	Natural Pigments Rainbow Colors Red cabbage	5-i	168

(14:34–15:46) (Chair: Yoshikawa Itsuki, Horie Takafumi)
14:34– 14:46	A27	Optimization of a particle detection algorithm in a flow field for softsensor development (Shizuoka U.) *Ibuki Ryoto, Nihei Kosuke, Murakami Yuya	softsensor particle tracking machine learning	6-f	210
14:46– 14:58	A28	Driving Simulation of Electric Vehicles Using MATLAB/Simulink (Toyohashi Tech) *Hori Kyota, Hamada Shu, Xiao Yan, Watanabe Takato, Watanabe Takahiro, Takikawa Hirofumi, Tojo Tomohiro	MATLAB/Simulink Electric Vehicles Driving Simulation	6-c	34
14:58– 15:10	A29	Dynamic Prediction of Marine Microorganisms Using a 3D Simulation Incorporating Predation Processes for Environmental Analysis of Suruga Bay (Shizuoka U.) *Shinada Mana, Yokojima Satoshi, Takeda Kazuhiro	3D simulation microorganism dynamics prediction	6-g	30
15:10– 15:22	A30	Development of a property estimation method using descriptors for multicomponent mixtures (Shizuoka U.) *Ikeda Keisuke, Hatoyama Atsushi, Murakami Yuya	multi-component mixture molecular descriptor machine learning	1-d	214
15:22– 15:34	A31	DFT Study of the Anomeric Ratio of ｦﾁ- and ｦﾂ-D-Glucopyranose in Aqueous Solution (Ikeda.H.S) Shimizu Koichi, Morimoto Iroha, Abe Hinako, Takagi Akihiro, Nakazono Mio, *Nitta Anri, Nishimura Haruka, Onishi Keita	Density Functional Theory Glucopyranose Anomeric Ratio	6-e	69
15:34– 15:46	A32	Development of an Alarm Necessity Prediction Model Using HAZOP Data and Large Language Models (Shizuoka U.) *Kaneshima Yosei, Takeda Kazuhiro	Alarm Classification HAZOP LLM	10-c	216

Technical program
28th SCEJ Students Meeting (online, 2026)

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Most recent update: 2026-03-06 17:03:49
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