SynopsisThe extensional and failure properties of polystyrene melts were studied by pulling sample rods in a special "weight dropping" extensiometer. This apparatus allows pulling to long final lengths and a t relatively high rates; except for the highest rates, the experiment is one of constant applied force. Various commercial (broad molecular weight distribution) and special (narrow molecular weight distribution) samples were studied a t various temperatures and applied forces. The striking result was that the former (BMWD) samples stretched reasonably uniformly and displayed what has been described as "viscoelastic failure"; the latter (NMWD) samples necked in the final stages and showed what might be called "viscous" failure. In the case of the BMWD material, the stress-time behavior was analyzed theoretically by independently determining the parameters in a nonlinear cohstitutive equation from GPC and rheogoniometer (shear) data. The theoretical tensile stresses compared quite well with the experimental values. An interesting result came from comparing the complete viscoelastic theory with a viscous (Trouton viscosity) asymptote. These two theoretical curves closely approximated the experimental data until just short of the failure point; at this incipient point, the stresses from the complete theory grew to very large values compared with the viscous stresses. That is, the material could not relax fast enough to allow steady stresses to develop, and the sample failed shortly thereafter.

No abstract

The exposure index (EI) was proposed as a new X-ray dose index by the International Electrotechnical Commission (IEC) and has since been implemented as an international standard. The EI is calculated by use of an approximation equation under IEC-specified calibration conditions. However, several factors encountered in clinical practice, including the patient's body thickness and the tube voltage, differ with regard to these calibration conditions. We, therefore, require a solid water phantom-based function that can incorporate the IEC-specified conditions and different subject thicknesses to evaluate the effects of subject thickness on the EI. Here, we assumed average thicknesses of 10 cm for a child, 15 cm for slender patients, and 21 cm for an average adult abdomen and we evaluated errors, that are included in the EI, which were calculated by use of the function. Our results suggested that the EI depends on the subject thickness. At the 21-cm thickness (average adult abdomen), the display EI exhibited a small error level. In contrast, EI values calculated from the calibration conditions exhibited maximum errors that were as high as 34 % at the lower subject thicknesses (10 and 15 cm), suggesting a significant influence of the subject thickness on the EI. In conclusion, the EI should be used cautiously during the examination of children and thin patients, with a complete understanding of the discrepancy revealed by our study results.

Al‐doped ZnO (AZO) nanopowders with different Al content were fabricated by a galvanostatic electrolytic method. The electrical resistivities were measured by a cell method, which reached its minimum (28 Ω·cm) at 0.93 at.% of Al with its grain size of ∼30 nm. Microstructures of powders were characterized by X‐ray diffraction and transmission electron microscopy (TEM), which showed a decrease in grain size with the increase of Al content. In addition, the distributions of Al and the chemical bonding nature of Al atoms were examined by STEM‐EDS and by X‐ray photoelectron spectra, respectively, which suggested the substituational incorporation of Al atom into the ZnO lattice. Absorption properties were investigated for wavelength ranging from 250 to 2500 nm, which showed that the film coated with AZO nanopowders exhibited a rapid decrease in transmittance below 370 nm to ∼0% and beyond 1250 nm to ∼40% (at 2000 nm).

The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt). Methods: The proposed method involves transfer learning to assess the lung fields, mediastinum, and spine using GoogLeNet, which is a type of DCNN that has been trained using conventional images. Three detectors were created, and the image quality of local regions was rated. Subsequently, the results were used to determine the overall quality of chest X-ray images using a rule-based technique that was in turn based on expert assessment. The minimum EI required for diagnosis was calculated based on the distribution of the EI values, which were classified as either suitable or non-suitable and then used to ascertain the EIt. Results: The accuracy rate using the DCNN and the rule base was 81%. The minimum EI required for diagnosis was 230, and the EIt was 288. Conclusion: The results indicated that the proposed method using the DCNN and the rule base could discriminate different image qualities without any visual assessment; moreover, it could determine both the minimum EI required for diagnosis and the EIt.