Fundamental vibrational frequencies of 109 molecular forms of 38 polyatomic chain molecules consisting of the CH3, CD3, CH2 CD2, CHD, O, and S groups are given as an extension of tables of molecular vibrational frequencies published in the NSRDS-NBS publication series and in this journal. On preparing the tables in this part, an approach, different from that in the previous parts, based on the calculations of normal vibration frequencies was adopted. A set of force constants which explains all the frequencies of small molecules for which the assignments had been established was obtained and then the frequencies of larger molecules was calculated and compared with the frequencies observed in the infrared and Raman spectra. The tables provide a convenient source of information for those who require vibrational energy levels and related properties in molecular spectroscopy, thermodynamics, analytical chemistry, and other fields of physics and chemistry.

Fundamental vibrational frequencies of 94 molecular forms of 23 polyatomic chain molecules of halogenoalkanes and halogenoalkyl ethers consisting of the CH₃, CH₂, O, F, Cl, Br, and I groups are given as an extension of tables of molecular vibrational frequencies published in the NSRDS‐NBS publication series and in this journal. On preparing the tables in this part, an approach, similar to that in Part 9 but different from that in earlier parts, based on the calculations of normal vibration frequencies was adopted. A set of force constants which explains all the frequencies of small molecules for which the assignments had been established was obtained and then the frequencies of larger molecules were calculated and compared with the frequencies observed in the infrared and Raman spectra. The tables provide a convenient source of information for those who require vibrational energy levels and related properties in molecular spectroscopy, thermodynamics, analytical chemistry, and other fields of physics and chemistry.

The built year and structure of individual buildings are crucial factors for estimating and assessing potential earthquake and tsunami damage. Recent advances in sensing and analysis technologies allow the acquisition of high-resolution street view images (SVIs) that present new possibilities for research and development. In this study, we developed a model to estimate the built year and structure of a building using omnidirectional SVIs captured using an onboard camera. We used geographic information system (GIS) building data and SVIs to generate an annotated built year and structure dataset by developing a method to automatically combine the GIS data with images of individual buildings cropped through object detection. Furthermore, we trained a deep learning model to classify the built year and structure of buildings using the annotated image dataset based on a deep convolutional neural network (DCNN) and a vision transformer (ViT). The results showed that SVI accurately predicts the built year and structure of individual buildings using ViT (overall accuracies for structure = 0.94 [3 classes] and 0.96 [2 classes] and for age = 0.68 [6 classes] and 0.90 [3 classes]). Compared with DCNN-based networks, the proposed Swin transformer based on ViT architectures effectively improves prediction accuracy. The results indicate that multiple high-resolution images can be obtained for individual buildings using SVI, and the proposed method is an effective approach for classifying structures and determining building age. The automatic, accurate, and large-scale mapping of the built year and structure of individual buildings can help develop specific disaster prevention measures.