Finite element analysis of the transient motion of stratified viscous fluids under gravity

The present paper deals with the finite element analysis of two-dimensional two-layer density flows in a gravitational field. A fluid in each layer is replaced with a large number of discrete particles, and the motion and deformation of each layer is represented by moving those particles in a Lagrangian manner. The velocity distribution in the whole fluid region is given as the finite element solution of the Navier-Stokes equations and the equation of continuity. In the finite element calculation, free-slip conditions are used on solid wall boundaries because no-slip conditions may cause sticking of some particles to walls. Then, a new technique for the implementation of free-slip conditions on arbitrary curved boundaries is presented. As numerical examples, density flows in a rectangular closed container and Rayleigh-Taylor instability in the container with a circular cross-section have been computed.     

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.

     

A Recommendation System Keeping Both Precision and Recall by Extraction of Uninteresting Information

A recommendation system which recommends interesting information to the target user must guarantee high precision and recall. However, there is trade-off between precision and recall. In this paper, we propose a web page recommendation method balancing both of them by take advantage of uninteresting information. The proposed method extracts the interest and uninterest indicators from not only historical interesting web pages but also uninteresting ones in a target genre. The historical interesting and uninteresting information is derived based on the browsing time and bookmarking. The proposed method can keep precision and recall by excluding the uninteresting information from the recommended ones based on the interest and uninterest indicators. The experimental result proved that the proposed method can improve the precision and recall than an existing method.     

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.     

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.     

Mutants of the nematode Caenorhabditis elegans that are defective specifically in their attraction to cycloheximide

The nematode Caenorhabditis elegans exhibits chemotaxis toward a wide variety of chemicals including water-soluble molecules and volatile organic compounds. We have previously discovered that C. elegans wild-type strain N2 is strongly attracted by cycloheximide which has long been known as a bitter tastant for humans and other mammals. We describe here the isolation and initial characterization of the first mutants which were defective specifically in their attraction to cycloheximide. In our screenings, we selected two mutants that were defective in chemotaxis to cycloheximide but normal in their attraction to NH4Cl and histidine. These mutants also avoided quinine hydrochloride, CuSO4 and high concentrations of NaCl similar to the wild-type strain N2. Furthermore, no observable defect was detected in their attraction to volatile odorants such as isoamyl alcohol and diacetyl. Dye-filling experiments suggested that they have no morphological defect in the sensory endings of the amphid neurons.     

Vivid image projection system using e‐Paper active screen

Projected image on a screen is not always vivid enough when it is projected in a bright room. We generally have to choose either vivid image in a dark room or dull image in a bright room. We have suggested a new projection system which can realize high contrast image projection in a bright room. It is consisted of electronic paper screen, projector, and light‐emitting diode room light. The electronic paper (e‐Paper) screen alternates its whole surface white/black with 120 Hz. Room lights are controlled with the same frequency of 120 Hz with the opposite phase as that of the e‐Paper screen. We have confirmed that our new system has achieved higher contrast than the conventional projection system and also enough readability of paper documents in the room simultaneously. We have thus confirmed that our new system can bring an ideal condition, for audience, which can realize a vivid image projection in a bright room.     

Extraction of Road Information from Guidance Map Images

A function of image recognition is indispensable to an intelligent robot which can coexist with a human being. Furthermore, the intelligent robot needs to understand the environment of their action range by getting information of characters and maps on advertisements and signboards in order to move autonomously. In this research, a method to search the route from the starting point to the destination on a guidance map, by extracting the road area on the map and revising degradation portions because of overlapping with characters or other figures is proposed. And the validity of this method is shown by the experiment using maps which were collected from advertisements or pamphlets.     

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.     

Augmented reality-based block piling game with superimposed collapse prediction

Understanding what cannot be seen is difficult. Physical behavior can be explained on the basis of physical theories even if the behavior cannot be observed. Explanation of what is physically happening in the real world would become easy, however, if annotations were superimposed on the real objects. Herein, the authors demonstrate how an understanding of a physical event can be facilitated by overlapping a real-world situation with a simulation that predicts a future state. This idea is demonstrated in a game application in which a player stacks blocks into a pile until it collapses. In general, it is easy to estimate whether a block on the edge of a table will fall or not. However, it is more difficult to predict whether a stack of many blocks will collapse, and in what manner the stack will collapse. Even though previous research has demonstrated that the problem of how two-dimensionally stacked blocks collapse can be reduced to solving a sequence of convex quadratic programs, algorithms for convex quadratic programs require massive computational resources. Hence, the authors developed a fast and new algorithm based on a linear program. The proposed algorithm realizes real-time simulation based on physics that superimposes predicted collapse. The block that is predicted to fall is superimposed on the real block with a lit background projection. The system was evaluated in an experiment, and superimposed augmented reality annotation was observed to be efficient. The system was also demonstrated in game contests and received positive feedback and comments.