a present address. SPring -8 , JAERI, Kamigori, Aiko gun, Hyogo 678-12, Japan X-ray diffraction for liquid Rb and liquid Na has been measured under pressure up to 6 GPa using synchrotron radiation. Volume dependences of static structure factor S(Q) and pair distribution function g(r) were obtained to the volume range v(=V/V0) of 0.52 for liquid Rb and to v of 0.73 for liquid Na to investigate the effect of the electronic change on the structure of liquid metals. With increasing pressure, the peaks of S(Q) of both liquid Rb and liquid Na shift towards higher Q, and the heights of the first peak increase. These volume dependences of the structural data are compared with those for other metals under pressure and expanded fluids.[liquid metals, structure, alkali metals, synchrotron radiation, x-ray diffraction]

The electrical conductivity o has been measured at pressures P to 8 GPa and temperatures T of 77-300K in evaporated amorphous Ge(a-Ge), a-Ge-Cu alloys and a-Ge-AI alloys. The T dependence of o is well described by a power law at low temperatures below 150 K, which is expected from a multi-phonon tunneling transition process model with weak electron-lattice coupling, rather than the Mott's variable range hopping conduction model. The exponent n in the power law changes with increasing pressure. For both a-Ge1-xCux and a-Ge1-xCux alloys, d(ln n)/dP show positive values in the low pressure region and negative values in the high pressure region. Results are discussed from several hopping conduction models.

EXAFS measurements on Te k-edge were carried out on crystalline and liquid Te under high pressure and temperature up to 2.4 GPa and 823K using a large volume Paris-Edinburgh press. The pressure dependence of the first and second nearest neighbor distances and that of the mean-square displacements were obtained. The results were discussed in connection with pressure dependence of intra-and inter-chain bonding of Te.[EXAFS, high pressure, high temperature, tellurium, structure]

Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20–50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision–recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.