Corrugated flat band as an origin of large thermopower in hole doped PtSb2_2

The origin of the recently discovered large thermopower in hole-doped PtSb$_2$ is theoretically analyzed based on a model constructed from first principles band calculation. It is found that the valence band dispersion has an overall flatness combined with some local ups and downs, which gives small Fermi surfaces scattered over the entire Brillouin zone. The Seebeck coefficient is calculated using this model, which gives good agreement with the experiment. We conclude that the good thermoelectric property originates from this "corrugated flat band", where the coexistence of large Seebeck coefficient and large electric conductivity is generally expected.Comment: 4 pages, 4 figure

Spatiotemporal forecasting of track geometry irregularities with exogenous factors

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with a higher spatiotemporal frequency. For forecasting with a high spatiotemporal frequency, it is necessary to capture spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, we propose a method to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory (ConvLSTM). In the experiment, we compared the proposed method with other methods in terms of the forecasting performance. Additionally, we conducted an ablation study on exogenous factors to examine their contribution to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of the vertical alignment.Comment: 16 pages, 6 figure

Relapse of Neuromyelitis Optica Spectrum Disorder Associated with Intravenous Lidocaine

Lidocaine unmasks silent symptoms and eases neuropathic pain in multiple sclerosis patients; however, the effects of lidocaine in neuromyelitis optica have never been reported. We describe the case of a 59-year-old Japanese woman with neuromyelitis optica spectrum disorder who developed optic neuritis 1 day after intravenous lidocaine injection for treating allodynia. Her symptom seemed to result from a relapse of neuromyelitis optica induced by lidocaine administration, and not because of the transient effects of intravenous lidocaine administration. The possibility that lidocaine administration results in relapse of neuromyelitis optica due to its immunomodulating effects cannot be ruled out

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.

PurposeTo validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset.MethodThe training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic.ResultsOLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05).ConclusionsVBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings