Gasification of polyethylene using steam plasma generated by microwave discharge

Gasification of polyethylene (PE) pellet was studied using atmospheric argon-steam plasma generated by microwave discharge and the feasibility of the process was examined. The experimental results showed that additional steam to argon plasma promoted the weight decrease of PE and enhanced the production of H₂, CO, CO₂ and CH₄. The results confirmed that the treatment of plastics with the steam plasma was effective to obtain synthesis gas.     

Thermal decomposition of uranium peroxide hydrates

The thermal decomposition of uranium peroxide hydrates, UO₄·4H₂O and UO₄·2H₂O, has been investigated by thermal analyses, X-ray diffraction study and infrared spectroscopy. As a result, it is concluded that the thermal decomposition of uranium peroxide hydrates proceeds in the following sequence: where 3 ≤ x ≤ 3.5.     

Generalized multiplicative stochastic processes arising from one-dimensional maps with noise

Artificial Life and Robotics - We propose a class of generalized multiplicative stochastic processes obtained by introducing an endo-perspective into one-dimensional maps with additive noise. We...     

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

Computational Visual Media - The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning. While voxel-based deep learning frameworks have been proposed for this...     

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

Basket Analysis, which is a standard method for data mining, derives frequent itemsets from database. However, its mining ability is limited to transaction data consisting of items. In reality, there are many applications where data are described in a more structural way, e.g. chemical compounds and Web browsing history. There are a few approaches that can discover characteristic patterns from graph-structured data in the field of machine learning. However, almost all of them are not suitable for such applications that require a complete search for all frequent subgraph patterns in the data. In this paper, we propose a novel principle and its algorithm that derive the characteristic patterns which frequently appear in graph-structured data. Our algorithm can derive all frequent induced subgraphs from both directed and undirected graph structured data having loops (including self-loops) with labeled or unlabeled nodes and links. Its performance is evaluated through the applications to Web browsing pattern analysis and chemical carcinogenesis analysis.     

Phase Relations and Volume Changes of Hafnia under High Pressure and High Temperature

Using multi-anvil high-pressure devices and synchrotron radiation, X-ray in situ observations of HfO₂ under high pressure and high temperature have been performed to investigate its phase relations and compression behavior. An orthorhombic phase (orthoI) is stable from 4 to 14.5 GPa below 1250°–1400°C and transforms to a tetragonal phase, which is one of the high-temperature forms of HfO₂, above these temperatures. Another orthorhombic phase (orthoII) with a cotunnite-type structure appears above 14.5 GPa. OrthoII is stable up to 1800°C at 21 GPa. OrthoII is quenchable to ambient conditions. The orthoI-to-orthoII transition is accompanied by ∼8 vol% decrease. The bulk moduli of orthoI and orthoII at room temperature are 220 and 312 GPa, respectively. This low compressibility of orthoII indicates that it is a potential candidate for very hard materials.     

Multi-structure information retrieval method based on transformation invariance

The needs of efficient and flexible information retrieval on multi-structural data stored in database and network are significantly growing. Especially, its flexibility plays one of the key roles to acquire relevant information desired by users in retrieval process. However, most of the existing approaches are dedicated to a single content and data structure respectively, e.g., relational database and natural text. In this work, we propose “Multi-Structure Information Retrieval” (MSIR) approach applicable to various types of contents and data structures by adapting a small part of the approach to data structures. The power of this approach comes from the use of the invariant feature information obtained from byte patterns in the files through some mathematical transformation. The experimental evaluation of the proposed approach for both artificial and real data indicates its high feasibility. Fuminori Adachi: He received his Master of engineering from Osaka University in ’03. He is enrolled in the doctoral course of Osaka University from ’03. His current research interest includes scientific discovery, data mining and machine learning techniques. Takashi Washio, Ph.D.: He received his Ph.D. from Tohoku University in ’88. In ’88, he became a visiting reseacher in Massachusetts Institute of Technology. In ’90, he joined Mitsubishi Research Institute Inc., and is working for Osaka University from ’96. His current research interest includes scientific discovery, data mining and machine learning techniques. Atsushi Fujimoto: He is enrolled in the master cource of Osaka University from ’03. His Current research interest includes correlation analysis, data mining and machine learning techniques. Hiroshi Motoda, Ph.D.: He received his Ph.D. from University of Tokyo in ’72. In ’67, he joined Hitachi Ltd. and has been working for Osaka University since ’96. His current research interest includes scientific discovery, data mining and machine learning. Hidemitsu Hanafusa: He received Master of Engineering from Keio University in ’83. In ’83, he joined The Kansai Electric Power Co. Ins. (KEPCO). He researched on Maintenance Support System at INSS from ’97 to ’02. Now, he is working in KEPCO.     

Numerical prediction of fraction of eutectic phase in Sn−Ag−Cu soldring using the phase-field method

A combination of macroscale solidification simulation and phase-field calculation is employed to predict the volume fraction of the eutectic phase in Sn-4.0 mass% Ag-XCu solder alloys (X=0.5–1.1 mass%). The solidification simulation incorporates the cooling rate in the phase-field simulation. We assume the residual liquid solidifies as eutectic phase when the driving force for the nucleation of Cu₆Sn₅ amounts to a critical value, which is determined based on the experimental data. Though the calculation results depend on the experimental data, the obtained fractions are about 40% for 0.5 mass% Cu and more than 90% for 1.1 mass% Cu alloy, which shows good agreement with the experimental data.