Design Support of Transportation Routing for a Multi Layered Factory

Recently, it becomes important to improve transportation capability to attain high productivity in manufacturing. One of the necessary schemes to attain the purpose is that the floor layout is initially to be optimized. After the floor layout is determined, reasonable equipment position for transportation is decided. Then the design of the transportation route is to be determined. In this paper, design support system for transportation routing, based on agent method is proposed. To optimize the transportation route, the working space by the side of the equipment is to be prepared. Starting from given floor layout of a three layered factory, an appropriate transportation route is pursuit using Ant algorithm. To check the applicability of the proposed method, case studies are carried out under various constraints and objective functions, and its effects were verified

The Influence of End Frictions on Stresses in Compressed Specimens

The present paper concerns the influences of end frictions on stresses in compressed rectangular and cylindrical specimens. In numerical calculations, the finite element method was employed. The following conclusions are made on the influences of the end frictions on stresses in the compressed specimens. (i) The more the end friction between the end of the specimen and the platten is reduced the more uniform stresses are developed in the specimen. (ii) When Poissoin's ratio is 1/6 and the coefficients of the end friction are approximately larger than 0.25, no lubrication can be practically expected, in other terms stresses in specimens with such coefficients of end friction are practically the same as stresses in specimens completely restrained at the ends. (iii) Stresses in both rectangular and cylindrical specimens are similar. The only difference is that the deviations of the axial stresses for various coefficients of frictions from the average are larger in cylindrical specimens than those in rectangular ones. (iv) Stresses in the mid-height region of the specimens are not so sensitive to the end friction as the compressed ends. Uniformity of stresses depends on the width-height or radius-height ratios as well as the end frictions. (v) For sufficiently small coefficients of the end friction, some portions of the end of the specimen slide and the shear stresses on the end become terrace-like and axial stressses become more or less uniform

Study of superconductivity of very thin FeSe1−xTex\mathrm{FeSe}_{1-x}\mathrm{Te}_x films investigated by microwave complex conductivity measurements

Complex conductivity measurements spanning the entire temperature range, including the vicinity of $T_c$, were conducted on systematically varied FeSe$_{1-x}$Te$_x$ ($x$ = 0 - 0.5) very thin films. By applying a novel cavity measurement technique employing microwave electric fields parallel to FeSe$_{1-x}$Te$_x$ films, we observed distinct temperature-dependent alterations in superfluid fraction and quasiparticle scattering rate at the nematic boundary. These changes in the nematic boundary suggests variations in the superconducting gap structure between samples in the nematic and non-nematic phase. Moreover, fluctuation is visible up to 1.2 $T_c$ irrespective of nematic order, consistent with large superconducting fluctuations in iron chalcogenide superconductors reported previously in [H. Takahashi $\textit{et al}$, Phys. Rev. B 99, 060503(R) (2019)] and [F. Nabeshima $\textit{et al}$, Phys. Rev. B 97, 024504(R) (2018)]

Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning

Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea behind predictive pattern mining is to build a prediction model by considering substructures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the Safe Pattern Pruning (SPP) method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences