High-performance self-aligned SiGeC HBT with selectively grown Si/sub 1-x-y/Ge/sub x/C/sub y/ base by UHV/CVD

Si/sub 1-x-y/Ge/sub x/C/sub y/ selective epitaxial growth (SEG) was performed by cold-wall, ultrahigh-vacuum chemical vapor deposition, and the effects of incorporating C on the crystallinity of Si/sub 1-x-y/Ge/sub x/C/sub y/ layers and the performance of a self-aligned SiGeC heterojunction bipolar transistor (HBT) were evaluated. A Si/sub 1-x-y/Ge/sub x/C/sub y/ layer with good crystallinity was obtained by optimizing the growth conditions. Device performance was significantly improved by incorporating C, as a result of applying Si/sub 1-x-y/Ge/sub x/C/sub y/ SEG to form the base of a self-aligned HBT. Fluctuations in device performance were suppressed by alleviating the lattice strain. Furthermore, since the B out diffusion could be suppressed by incorporating C, the cutoff frequency was able to be increased with almost the same base resistance. A maximum oscillation frequency of 174 GHz and an emitter coupled logic gate-delay time of 5.65 ps were obtained at a C content of 0.4%, which shows promise for future ultrahigh-speed communication systems.     

Rates for intramolecular excimer formation of poly(α-methylstyrene) measured by using picosecond pulse radiolysis

Summary The rate constants for intramolecular excimer formation, k_DM, of poly(α-methylstyrene) with different molecular weight were determined by using picosecond pulse radiolysis. Values of k_DM for poly(α-methylstyrene) are a little smaller than those for polystyrene with nearly same molecular weight. It appears to be mainly due to steric hindrance by methyl substituent of main chain.     

Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

     

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.     

Rewired Cellular Metabolic Profiles in Response to Metformin under Different Oxygen and Nutrient Conditions

Metformin is a metabolic disruptor, and its efficacy and effects on metabolic profiles under different oxygen and nutrient conditions remain unclear. Therefore, the present study examined the effects of metformin on cell growth, the metabolic activities and consumption of glucose, glutamine, and pyruvate, and the intracellular ratio of nicotinamide adenine dinucleotide (NAD⁺) and reduced nicotinamide adenine dinucleotide (NADH) under normoxic (21% O₂) and hypoxic (1% O₂) conditions. The efficacy of metformin with nutrient removal from culture media was also investigated. The results obtained show that the efficacy of metformin was closely associated with cell types and environmental factors. Acute exposure to metformin had no effect on lactate production from glucose, glutamine, or pyruvate, whereas long-term exposure to metformin increased the consumption of glucose and pyruvate and the production of lactate in the culture media of HeLa and HaCaT cells as well as the metabolic activity of glucose. The NAD⁺/NADH ratio decreased during growth with metformin regardless of its efficacy. Furthermore, the inhibitory effects of metformin were enhanced in all cell lines following the removal of glucose or pyruvate from culture media. Collectively, the present results reveal that metformin efficacy may be regulated by oxygen conditions and nutrient availability, and indicate the potential of the metabolic switch induced by metformin as combinational therapy.     

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.     

Study on chemical structures of poly (tetrafluoroethylene-co-perfluoroalkylvinylether) by soft-EB irradiation in solid and molten state

Poly (tetrafluoroethylene-co-perfluoroalkylvinylether) (PFA) was irradiated by soft electron beam (soft-EB) under nitrogen gas atmosphere in solid-state and its molten state, respectively. The changes of thermal property and chemical structures of irradiated PFA in solid-state and molten state were studied by differential scanning calorimetric analysis (DSC) and solid-state ¹⁹F magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy. By DSC analysis, the melting temperature shifted to lower temperatures, and crystallinity decreased with increasing soft-EB dose. By solid-state ¹⁹F MAS NMR spectroscopy, the new signals was observed and the detected new signals in irradiated PFA at 315 °C and at 30 °C were due to the tertiary carbon group with branching site (Y-type crosslinking site), perfluoro-propylene site and chain end methylene groups, respectively.Moreover, the molar ratio of perfluoroalkylvinylether (FAVE) structure to -CF₂- units decreased with increasing dose.     

Development of sulfonated FEP-Nafion hybrid proton exchange membranes for PEFC

The performance of polymer electrolyte fuel cell (PEFC) is affected by an interfacial property between a proton exchange membrane (PEM) and electrodes. Thus, to develop a well-laminated membrane electrode assembly (MEA), a hybrid PEM (FN) was fabricated by mixing a radiation grafted membrane (sulfonated FEP) with ionomer (Nafion^® dispersion) which is applied to coat the interface of the PEM and electrodes.The obtained FN, sulfonated FEP and Nafion^®112 were characterized in terms of water uptake, ion exchange capacity (IEC), polarization performance and electrochemical impedance. FN showed high IEC and water uptake, which would induce the highest ionic conductivity (IC) among tested PEMs. In terms of FN, the interface between the PEM and electrodes should have been improved because FN showed the lowest charge transfer resistance than other tested PEMs. The high IC and improved interface between the PEM and electrodes resulted in the best cell performance of FN in tested PEMs.     

Improving the photovoltaic performance of co‐evaporated Cu2ZnSnS4 thin‐film solar cells by incorporation of sodium from NaF layers

We have investigated the influence of sodium (Na) on the properties of co‐evaporated Cu₂ZnSnS₄ (CZTS) layer microstructures and solar cells. The photovoltaic performance and diode properties were improved by incorporating Na from NaF layers into the CZTS layers, while Na had a negligible effect on the microstructural properties of the layer. The best cell fabricated by using an optimal CZTS layer (Cu/(Zn + Sn) = 0.70, Zn/Sn = 1.8) yielded an active area efficiency of 5.23%. The analysis of device properties suggests that charge‐carrier recombination at CZTS/CdS interface is suppressed by intentional Na incorporation from NaF layers. Copyright © 2016 John Wiley & Sons, Ltd.