Efficient method of adaptive sign detection for 4×4 determinants using a standard arithmetic processing unit

We propose an efficient and exact method for the adaptive sign detection of 4×4 determinants using a standard arithmetic unit. The entities of determinants are variable length integers (integers of arbitrary bit length). The integers are expressed in 16-bit data units, and the sign detection is reduced to the computation of 4×4 determinants of 16-bit integers. To accelerate the computation, the calculation is performed by using a standard arithmetic unit. We have implemented our method and confirmed that it significantly improves the computation time of 4×4 determinants. The method can be applicable to many geometric algorithms that need the exact sign evaluation of 4×4 determinants, especially to construct robust geometric algorithms.     

Hypolipidemic action of the soybean isoflavones genistein and genistin in glomerulonephritic rats

Effects of genistein and its glycoside genistin were studied in nephritic rats with endogenous hyperlipidemia. Male Wistar rats with glomerulonephritis caused by a single intravenous injection of nephrotoxic serum were orally given 5 mg of genistein or 8 mg of genistin/d/100 g body weight for 12 d. These isoflavones suppressed nephritis-induced severe hypercholesterolemia and hypertriglyceridemia, and their hypolipidemic action was almost identical. Fecal steroid excretion was unchanged by administration of the two isoflavones. Genistein inhibited the incorporation of [1-¹⁴C]acetate into cholesterol and FA in liver slices from nephritic rats when added to an incubation buffer, whereas genistin did not. These results suggest that genistin may be hydrolyzed to genistein and that genistein itself and/or its metabolite(s) may be intracorporal entities suppressing hepatic lipid syntheses. They also suggest that the suppression of hepatic lipid synthesis may be one mechanism of the hypolipidemic action of genistein.     

Exploring overlapping clusters using dynamic re-scaling and sampling

Until recently, the aim of most text-mining work has been to understand major topics and clusters. Minor topics and clusters have been relatively neglected even though they may represent important information on rare events. We present a novel method for exploring overlapping clusters of heterogeneous sizes, which is based on vector space modeling, covariance matrix analysis, random sampling, and dynamic re-weighting of document vectors in massive databases. Our system addresses a combination of difficult issues in database analysis, such as synonymy and polysemy, identification of minor clusters, accommodation of cluster overlap, automatic labeling of clusters based on their document contents, and the user-controlled trade-off between speed of computation and quality of results. We conducted implementation studies with new articles from the Reuters and LA Times TREC data sets and artificially generated data with a known cluster structure to demonstrate the effectiveness of our system. Mei Kobayashi received a Bachelors degree in Chemistry from Princeton and Masters and Ph.D. degrees in Pure and Applied Mathematics from UC Berkeley. She was a student intern in Frick Chemical Laboratory at Princeton, the Biochemical and Math-Physics divisions of Lawrence Berkeley Laboratories, and IBM Research. She has been a Researcher at IBM since 1988 and has been involved in projects ranging from inverse problems, airflow simulation and graphics to speech signal analysis using wavelets. Her most recent work has been on information retrieval, data mining, and unstructured information management. She has served on the Editorial Board of the Bulletin of Japan SIAM and Technical Program Committees of the SIAM Data Mining Conference, SIAM Text Mining Workshops, and Symposiums on Wavelets sponsored by the Japanese Ministry of Education. From 1996 to 1999, she was a Visiting Associate Professor at the Graduate School for Mathematical Sciences of the University of Tokyo. Masaki Aono received Bachelors and Masters in Science degrees in Information Science from the University of Tokyo and a Ph.D. in Computer Science from Rensselaer Polytechnic Institute. He worked for IBM Research, Tokyo Research Laboratory from 1984 to 2003. He is currently a Professor in the Information and Computer Sciences. Department at the Toyohashi University of Technology, where he is teaching object-oriented programming, logic circuit, computer architecture, and knowledge data engineering. His current research interests include text and data mining, information extraction, semantic web, and information visualization. His most recent work on time series data mining from human body bio-signals obtained by microsensors, was been selected to be part of the 21st century Center Of Excellence Program sponsored by Japanese government. He has been a Japanese delegate of the ISO/IEC JTC1 SC24 Standard Committee since 1996.     

Cell adhesion characteristics of chemically modified silicon nitride surfaces

We investigated the fundamental characteristics of cell adhesion during cell culture on the Si₃N₄ surface for using it as the gate insulator in field‐effect devices. The culture medium and the surface condition of Si₃N₄ were controlled for cell culture on the Si₃N₄ substrate. When we measure electrical characteristics of cell adhesion and cell function using field‐effect devices, we require a stable measurement of the electrical signals. The serum in the culture medium includes a number of proteins, which cause instability of the potential behavior. In this paper, we show that the cell‐adhesive molecules on the Si₃N₄ surface used as a gate insulator should be designed and controlled when cell culture is performed in the culture medium without the serum. Copyright © 2007 Institute of Electrical Engineers of Japan© 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Anomalous Mixing State in Room-Temperature Ionic Liquid-Water Mixtures: <Emphasis Type="Italic">N,N</Emphasis>-diethyl-<Emphasis Type="Italic">N</Emphasis>-methyl-<Emphasis Type="Italic">N</Emphasis>-(2-methoxyethyl) Ammonium Tetrafluoroborate

The mixing states of room-temperature ionic liquid (RTIL) H₂O mixtures (x = 0.0 mol pct to 99.5 mol pct H₂O) were investigated using wide-angle X-ray scattering (WAXS), small-angle X-ray scattering (SAXS), and optical absorption in an ultraviolet and visible (UV-vis) region. The RTIL is N, N-diethyl-N-methyl-N-(2-methoxyethyl) ammonium tetrafluoroborate, [DEME][BF₄]. In a “prepeak” region of the WAXS, the scattered intensities increased at 85 mol pct to 95 mol pct. A medium-range order (MRO) in the liquid structure as observed in network-forming materials developed markedly. In the SAXS experiments, we can detect nanoscale fluctuations relating to polar and nonpolar regions. At 65 mol pct to 85 mol pct, the SAXS intensity increased unexpectedly. Furthermore, entirely different optical absorption spectra in the UV-vis region were observed as a macroscopic property from 90 mol pct to 95 mol pct. We suppose that these anomalies relate to the MRO of the liquid structure. All anomalies probably are induced by an intrinsic property in [DEME][BF₄]-H₂O mixtures.     

Auction-based production planning considering operators' skill criterion

With the penetration of the IoT and data science, new manufacturing initiatives are accelerating. However, conventional approaches have emphasized the use of machine-based data. A need exists to realize more productive work styles considering the abilities and physical condition of operators. In this paper, we propose a production planning method to allocate machines and operators with different skill level optimally. After formulating a combinatorial auction mechanism, developing a schedule that minimizes the total production cost, we verify that the proposed method can realize human-centered production planning by combining the schedules with different evaluation indices.     

Tunable morphology from 2D to 3D in the formation of hierarchical architectures from a self-assembling dipeptide: thermal-induced morphological transition to 1D nanostructures

Construction of complex three-dimensional (3D) architectures through hierarchical self-assembly of peptide molecules has become an attractive approach of fabricating multifunctional advanced materials due to their various potential applications in bionanotechnology. This paper describes the tunable formation of flower-like 3D hierarchical architectures of intricate morphology from a simple self-assembling dipeptide phenylalanine–tyrosine with a facile preparative method by applying a range of voltages through a drop of peptide solution. The fine-tuning of voltages and their application time enable to produce morphological changes of the microstructures from 2D to 3D and also control their formation. The morphology has been characterized by the gradual change in the height-to-diameter ratio of the microstructures with change in the applied voltages. Moreover, these microstructures show significant thermal stability over a wide range of temperatures, whereas adequately high temperature promotes the morphological transformation of the microstructures into different types of ultrathin 1D nanostructures such as nanowires, nanofibrils, etc. Furthermore, we have suggested a possible growth model for the fabrication of unique hierarchical architectures through diffusion-limited aggregation mechanism.     

Infrared Aluminum Metamaterial Perfect Absorbers for Plasmon‐Enhanced Infrared Spectroscopy

Matured surface chemistry and excellent chemical stability have enabled gold to become the material‐of‐choice for plasmonic sensing in both visible and infrared wavelength range. Here, successful surface functionalization of metamaterials made from a low‐cost abundant plasmonic material, aluminum, with phosphonic acid and subsequent detection of the C?O vibration mode via surface‐enhanced infrared absorption spectroscopy is demonstrated. The metamaterial consists of infrared perfect absorbers fabricated by colloidal lithography. Near perfect absorption is achieved at resonance wavelengths, which can be readily tuned by changing the diameters of the Al disk resonators, enabling excellent overlapping with the molecular vibration. Separately, the detection of a physically adsorbed protein layer on the Al metamaterial is also demonstrated. Surface functionalization with phosphonic acid provides various functional groups to the Al surfaces. Combined with tunable metamaterials, the work herein opens up great opportunities for Al‐based plasmonic nanostructures for biochemical sensing applications.