Development of Pd Alloy Hydrogen Separation Membranes with Dense/Porous Hybrid Structure for High Hydrogen Perm-Selectivity

For the commercial applications of hydrogen separation membranes, both high hydrogen selectivity and permeability (i.e., perm-selectivity) are required. However, it has been difficult to fabricate thin, dense Pd alloy composite membranes on porous metal support that have a pore-free surface and an open structure at the interface between the Pd alloy films and the metal support in order to obtain the required properties simultaneously. In this study, we fabricated Pd alloy hydrogen separation membranes with dense/porous hybrid structure for high hydrogen perm-selectivity. The hydrogen selectivity of this membrane increased owing to the dense and pore-free microstructure of the membrane surface. The hydrogen permeation flux also was remarkably improved by the formation of an open microstructure with numerous open voids at the interface and by an effective reduction in the membrane thickness as a result of the porous structure formed within the Pd alloy films

Effectiveness of a Virtual Reality Head-Mounted Display System-based Developmental Eye Movement Test

By transplanting the Developmental Eye Movement (DEM) test chart to a virtual reality head-mounted display (VR HMD) system, this study sought to evaluate the effectiveness of the DEM test for measuring dynamic visual acuity.Thirty-nine adults aged 20–39 years of both genders were the subjects of the study. After undergoing measurement of their visual function, through medical questionnaire, interpupillary distance, near point of convergence (NPC), near point of accommodation (NPA), and far and near phoria, the correlation between the tests was analyzed performing DEM vertical, horizontal test and VR HMD DEM (VHD) vertical, horizontal test.NPC and NPA decreased significantly after the VHD test, while phoria did not. The horizontal was quicker than the vertical in the DEM test, and vice versa in the VHD test. DEM was quicker than VHD in both the vertical and horizontal directions. There was no notable difference in error frequency between DEM and VHD. In terms of DEM and VHD test, there was no notable difference in the short-range IPD and subjective symptoms of the top 10 and bottom 10 subjects. There was also no notable difference between the exercise and non-exercise groups and the game and non-game groups.The performance time for VHD, in which the chart must be read while moving the body, was longer than that of DEM. Therefore, based on the consistency of the results of both tests and the lack of a difference in error frequency and subjective symptoms, the VHD equipment proposed in this thesis is as effective as dynamic visual acuity measurement equipment. In addition, the lack of a difference between the exercise and non-exercise groups and the game and non-game groups demonstrated that the amount of exercise and game by an ordinary person does not influence their dynamic visual function

Low-temperature synthesis of CuO-interlaced nanodiscs for lithium ion battery electrodes

In this study, we report the high-yield synthesis of 2-dimensional cupric oxide (CuO) nanodiscs through dehydrogenation of 1-dimensional Cu(OH)2 nanowires at 60°C. Most of the nanodiscs had a diameter of approximately 500 nm and a thickness of approximately 50 nm. After further prolonged reaction times, secondary irregular nanodiscs gradually grew vertically into regular nanodiscs. These CuO nanostructures were characterized using X-ray diffraction, transmission electron microscopy, and Brunauer-Emmett-Teller measurements. The possible growth mechanism of the interlaced disc CuO nanostructures is systematically discussed. The electrochemical performances of the CuO nanodisc electrodes were evaluated in detail using cyclic voltammetry and galvanostatic cycling. Furthermore, we demonstrate that the incorporation of multiwalled carbon nanotubes enables the enhanced reversible capacities and capacity retention of CuO nanodisc electrodes on cycling by offering more efficient electron transport paths

Eyelid Squamous Cell Carcinoma in a Dog

A 10-year-old, female, Yorkshire Terrier was presented with a left lower eyelid mass. No other abnormality was detected on affected eye in a general eye examination. The mass was surgically removed and histologically diagnosed as a squamous cell carcinoma. The advancement flap used in this case may be an appropriate therapeutic choice for eyelid squamous cell carcinoma in dogs

Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales

Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings