Nanorheometry of Molecularly Thin Liquid Lubricant Films Coated on Magnetic Disks

Molecularly thin lubricant films are used for the lubrication of head disk interfaces in hard disk drives. The film thickness is reduced to 1-2 nm to minimize the magnetic spacing, and optimal, precise design is required to obtain sufficient lubrication. However, until now, there was no generally applicable method for investigating such thin films. Therefore, we developed a highly sensitive shear force measuring method and have applied it to the viscoelastic measurement of lubricant films coated on magnetic disk surfaces. In this paper, we review the method and summarize the useful findings we have demonstrated so far

The Newport Manual on the Law of Naval Warfare

The Newport Manual on the Law of Naval Warfare is the first effort to restate the law of naval warfare as a purely lex lata exercise since 1955. It is designed to provide a practical guide for commanders and seafarers, lawyers and officials, and educators and students. In doing so, the Manual also factors in the developments in warfighting technologies in recent decades, which have significantly influenced the nature of war at sea

Impact of general practice / family medicine training on Japanese junior residents:a descriptive study

Background: Despite international recognition of the impact of general practice / family medicine training on postgraduate training outcomes, there have been few reports from Japan. Methods: Junior residents who participated in community medicine training for one month between 2019 and 2022 were enrolled in the study. The settings were five medical institutions (one hospital and four clinics) that had full-time family doctors. The junior residents were assigned to one of these institutions. The training content mainly consisted of general ambulatory care, home medical care, community-based care, and reflection. The junior residents evaluated themselves at the beginning and end of their training, and the family doctors evaluated the junior residents at the end. The evaluation items were 36 items in 10 areas, based on the objectives outlined in the Guidelines for Residency Training - 2020 Edition, and were rated on a 10-point Likert scale. In the statistical analysis, Wilcoxon signed rank test of two related groups was performed to analyze changes between pre and post self-evaluation, and the effect size r was calculated. Results: Ninety-one junior residents completed the study. Their self-evaluations showed statistically significant increases in all 36 items. The effect size was large in 33 items. The family doctors' evaluation was 8-9 points for all 36 items. Conclusion: General practice / family medicine training may greatly contribute to the acquisition of various required clinical abilities in postgraduate training even in Japan

Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region

A New Rough Set Classifier for Numerical Data Based on Reflexive and Antisymmetric Relations

The grade-added rough set (GRS) approach is an extension of the rough set theory proposed by Pawlak to deal with numerical data. However, the GRS has problems with overtraining, unclassified and unnatural results. In this study, we propose a new approach called the directional neighborhood rough set (DNRS) approach to solve the problems of the GRS. The information granules in the DNRS are based on reflexive and antisymmetric relations. Following these relations, new lower and upper approximations are defined. Based on these definitions, we developed a classifier with a three-step algorithm, including DN-lower approximation classification, DN-upper approximation classification, and exceptional processing. Three experiments were conducted using the University of California Irvine (UCI)’s machine learning dataset to demonstrate the effect of each step in the DNRS model, overcoming the problems of the GRS, and achieving more accurate classifiers. The results showed that when the number of dimensions is reduced and both the lower and upper approximation algorithms are used, the DNRS model is more efficient than when the number of dimensions is large. Additionally, it was shown that the DNRS solves the problems of the GRS and the DNRS model is as accurate as existing classifiers