Appropriate fertilization in farmland is quite important. However, it takes a long time to analyze the concentration of fertilizer components. To solve this difficulty, the concentration analysis and fertilization in the farming area at the same time surely provide convenience and efficiency of the farm work. Near-infrared spectroscopy (NIR) provides one of the solutions: NIR is a spectroscopic method that uses the near-infrared region with wavelengths from 800 nm to 2500 nm and utilizes the harmonic and combinatorial vibrations of molecules. As fertilizer components, we focused on phosphorus (P₂O₅), calcium (CaO), magnesium (MgO), and potassium (K₂O). The relationship between NIR spectra and these concentrations in soil has been calibrated using the partial least squares (PLS) method. Furthermore, in principal component analysis on NIR spectra, the principal components were evaluated by the magnitude of eigenvalues and cumulative eigenvalues. The results were visualized using the t-SNE and K-means++ methods, and the samples were classified into clusters with similar NIR spectral characteristics. The results for each cluster were used to obtain a calibration curve to accurately determine the relationship between the NIR spectra and the concentration of fertilizer components in the soil.

Triple point of argon was estimated by constant energy and constant temperature molecular dynamics. The basic cell was cubic, where the central part was lattice at the initial stage. The number of molecules in the basic cell was 6912. The Lennard-Jones potential function was assumed. The pressure and potential energy were obtained as functions of temperature. The triple point was assigned by atomic configuration, potential energy and pressure. The estimated triple point temperature and pressure were reasonable compared with experimental data.

Condensation temperature of argon was estimated by constant temperature and constant pressure molecular dynamics. The number of molecules in the basic cell was 864. The Lennard-Jones potential function was assumed. By including of gradual cooling process from 200 K. reasonable condensation temperature was obtained, where the initial density should be 0.1 g/cm³ ~ 1 g/cm³.

In this paper, we propose an imaginary-time evolution method to obtain self-consistent static electronic states according to real-time TDDFT calculations. This method provides very good convergence accuracy and stability concerning the ground state, as well as a large number of computational steps due to obtaining all states to evolve simultaneously. Also, we can get a diffusion equation from this method as the same as the real-time TDDFT and can probably unify these time-evolution calculations in the same framework which is used in electronic structure calculations. In this study, we show that this method is effective in practical electronic state calculations. We will show several improvements and calculations with the computational process using the imaginary time evolution method in combination with the conventional DFT algorithm for practical use. The results of calculations of systems such as hydrogen atoms, hydrogen molecules, and ethylene can show the computational efficiency and accuracy of this method, which probably improves traditional self-consistent electronic structure calculations for practical systems.

In the present study, the steady incompressible turbulent flow in the ultra-highly loaded axial turbine linear cascade (UHLTC) with tip clearance was numerically analyzed by using the commercial CFD code focusing on the characteristic leakage flow in the tip clearance resulting in the formation of leakage vortex and the associated loss generation. The computed results indicated that the separation bubbles formed along the pressure surface and the first half of the suction surface on the blade tip surface influenced the loss generation related to the leakage vortex. Moreover, it was suggested that the effective loss reduction technique for the leakage flow was the squealer tip with the cavity distributed along the pressure surface on the blade tip surface.

The GPA averages in large classes are lower than those in small classes. This tendency is because large classes must be one-way teaching from teachers to students. For this reason, a system has been developed to realize active learning through group learning, even in large classes, since FY 2018. So far, a proof-of-concept of a group formation system has been conducted to evaluate group composition using a group discussion visualization system, support for LTI 1.3 in the LTI tool, and implementation of group formation using Word2Vec. As a result, initial research objectives were achieved by FY2021. Therefore, this fiscal year is positioned as the final year of our research, and the implementation of a system based on the results of the research to date is conducted. First, a summary of the research results to date and the development of the system reflecting these results are described. The research question is set - “How to implement the research results?” As a result, a system with REST architecture was developed as an LTI tool linked with an LMS and formed groups based on a similarity between keywords using Word2Vec. The significance of using this system in classes and developing a system for research purposes are discussed. For the former, the system’s significance is that group discussions can be realized in large classes without teachers’ and teaching assistants’ administrative work. For the latter, we proposed using the system in the research community, in classes as an OER (Open Educational Resources), and as a reference for companies.

Emotions play a key role in the way humans learn. However, understanding emotions in the context of online learning is complex, as many factors affect them. MOEMO (Motion and Emotion) is a classroom monitoring system that monitors online learners’ affective states and generates feedback to the teachers about the learners’ engagement and concentration level based on emotional data. The system captures data from a laptop or a desktop PC’s built-in camera. When capturing emotional data using the camera function, a few factors affected the performance of emotion detection. In this paper, we highlighted six factors that affected the result of emotion detection in the MOEMO system. Although these factors are common and, to some extent, inevitable, we aim to bring those to the learning analytics community.

In this paper, we propose an optimal method for transmitting and sharing information on various communication and reporting items that occur in university laboratory activities between a small number of laboratory faculty members and a large number of undergraduate students using a smartphone app. This is the report of research results on the method from 2020 to 2022. The practiced method is as follows: (a) We classify the content of information transmitted in laboratory activities into a total of 8 categories from the viewpoints of the high frequency of use and the extent of impact and urgency, (b) by using the functions of the smartphone app which sets the push notification method and the contents of the transmission information as the standard statements for each category, the user can create various communication and reporting items from the standard statements, so that the information can be sent easily and reliably, and (c) the operation rules for each laboratory activity are established, thoroughly disseminated and operated with continuity. This method can be judged to be effective from the usage data of the smartphone app and the results of questionnaires for all users. (d) Furthermore, from the usage data, it was found that this method is more effective in the face-to-face lab activities than in the remote lab activities.

Relative gas-phase stabilities (Total Stabilization Energy, TSE) of Meisenheimer complexes generated by addition of hydride anion to 4- or/and 5-substituted 2-nitrobenzenes carrying various functional groups (Y) at position 1 were computationally determined utilizing hydride transfer reactions with parent 1-′Y′-2-nitrobenzene at the B3LYP/6-311+G(2d,p) level of theory. As for the Y, electronically various groups were introduced. Nine sets of substituent effects obtained were compared with that of benzoate anions that are used as the σ⁰-reference system. It has become apparent that the gas-phase stabilities of these Meisenheimer complexes are governed by three kinds of electronic effects of the basic, through-resonance, and saturation effects and all of the substituent effects are excellently correlated by the three-term extended Yukawa-Tsuno equation: . Anion Stabilization Energies (ASE) of these Meisenheimer complexes were also calculated utilizing Y– transfer reactions in a similar manner, and the substituent effects obtained are also found to be accurately correlated by the extended Yukawa-Tsuno equation. The r ^– values on TSE are found to be very similar to those on ASE, and did not show significant dispersion by the change of the Y. On the other hand, the s values on TSE showed significant variation, although those on ASE distributed in a narrow range. This shows that the s value depends not only on the anion but also on the nature of neutral species. These results revealed a wide applicability of the extended Yukawa-Tsuno equation not only to the anions of benzene derivatives but also to non-aromatic anions.

The 2023 Symposium of Research Center for Computing and Multimedia Studies, Hosei University was held hybrid on 7 March 2023. This short article introduces outline of the 2023 symposium.

The author HS has been conducting, since April 2022, a “Hosei University — RCCMS (Research Center for Computing and Multimedia Studies)” research project entitled “Development of Educational Tools using xR Technologies”. During the fiscal year 2022-2023, we have carried out a survey on the history of virtual reality (VR), the hardware and software technologies to produce VR applications, and the possibility of developing educational tools using these technologies. We have also experimented developing an VR software applied to chemistry education.

The author has been conducting, since April 2022, a “Hosei University — RCCMS (Research Center for Computing and Multimedia Studies)” research project entitled “Numerical Computation and Deep Learning Libraries Development on lab2022 Computers”. During the fiscal year 2022-2023, the author has focused on linear algebra computations, and carried out research and development on numerical linear algebra libraries available on the lab2022 computers along with the library utilization techniques. In this manuscript, the author explains these research activities and achievements.