Fibrous support for immobilization of enzymes

A poly(vinyl alcohol) (PVA) fiber support incorporating various aminoacetal functional groups has been developed for immobilizing enzymes. The aminated PVA fiber seems to adsorb enzymes with electrostatic force of attraction; thus the immobilization procedure is simple. By the use of this fiber having immobilized enzymes, the reaction between enzymes and substrates is nearly independent of the size of subtstrates. This newly developed type of fiber, which is formed by a mass superfine fibers (SFF), each measuring 1 μm or less in diameter, permits much more increased surface area than the conventional enzyme immobilization supports. Our studies of the properties of the fiber for immobilization of enzymes show the following results: (1) SFF has a greater ability for the immobilization of invertase than ordinary fibers; (2) dimethyl-aminated SFF has the best performance for the immobilization of invertase. From these results, it is concluded that the dimethyl-aminated SFF is an excellent support for the immobilization of invertase.     

Temperature Dependence of Young's Modulus and Internal Friction in Alumina, Silicon Nitride, and Partially Stabilized Zirconia Ceramics

The temperature dependence of Young's modulus and internal friction (Q⁻¹)in alumina, silicon nitride, and partially stabilized zirconia (Y-PSZ) ceramics was studied. Little change in Q⁻¹ was found for alumina, whereas Q⁻¹ for silicon nitride ceramics increased above 700°C. The Q⁻¹ of Y-PSZ increased markedly with increasing temperature up to a peak at ∼200°C.     

TMN implementation strategy based on OSI management standards

In order to facilitate the implementations ofTMN interface protocols/services studied inITU-T, it is very important to define profiles for supportingTMN management service. This paper proposes a concrete method for achieving this based on osi management standards as a promisingTMN implementation method. It proposes an idea of structuring theTMN ISP’S based on the structure of the osi managementISP’S. The paper discusses aTMN based on the osi managementISP’S. Finally the implementation as software is discussed and a software architecture for efficient application development is proposed.     

Damage to seagrass and seaweed beds in Matsushima Bay,Japan, caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011

Damage to seagrass and seaweed beds caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011 was investigated in Matsushima Bay, Miyagi Prefecture, Japan, by comparing satellite images taken before the tsunami in November 2009 and after the tsunami in May 2011. The results showed that the tsunami destroyed 220 ha of seagrass and seaweed beds, from 320 ha in 2009 to 100 ha just after the tsunami. Zostera marina beds on the sandy and muddy bottom were rather severely damaged compared with brown seaweed beds of Sargassum horneri on the rocky substrate. Substrates of the beds and directions of the tsunami seem to be responsible for the magnitude of the damage to the beds. Maps of the spatial distributions of the beds before and after the tsunami can provide information for the restoration of seagrass and seaweed beds as natural infrastructures of coastal fisheries resources.     

Detection of environmental changes from network structure

We studied the networks of the temperature record in the atmosphere. They are made by the strength of the synchronization, including the delay between temperatures at locations on Earth. We consider these locations as nodes, and we consider a pair of locations as a link if the synchronization between them is stronger than a threshold. The network is scale-free, which is thought to contribute to the stability of the climate. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

The analysis of an adaptive workload balancing strategy in computing system resources management

An adaptive workload balancing scheme is analyzed. This scheme is intended to automatically dissolve the imbalance in resource utilization of computing systems and to bring about the proper balance between throughput and turnaround time. This is done by means of the invisible hand due to the bidding of priorities of resource use, with no contradiction between the flexible bid for each resource and the stable price for the entire service. The result of this analysis shows that this scheme would regulate those imbalances, and that it includes the load balancing policy of IBM OS/VS2-2 as a special case. Some considerations and methods on an actual implementation are also presented.This paper is a revised version of material which appeared as Research Report RC5072 of IBM Thomas J. Watson Research Center, Yorktown Heights, New York.On leave from the University of Electro-Communications, Chofu, Tokyo, Japan.     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

Dm-KDE: dynamical kernel density estimation by sequences of KDE estimators with fixed number of components over data streams

In many data stream mining applications, traditional density estimation methods such as kernel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kernels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required.We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the proposed method.     

Depth from Refraction Using a Transparent Medium with Unknown Pose and Refractive Index

In this paper, we introduce a novel method for depth acquisition based on refraction of light. A scene is captured directly by a camera and by placing a transparent medium between the scene and the camera. A depth map of the scene is then recovered from the displacements of scene points in the images. Unlike other existing depth from refraction methods, our method does not require prior knowledge of the pose and refractive index of the transparent medium, but instead can recover them directly from the input images. By analyzing the displacements of corresponding scene points in the images, we derive closed form solutions for recovering the pose of the transparent medium and develop an iterative method for estimating the refractive index of the medium. Experimental results on both synthetic and real-world data are presented, which demonstrate the effectiveness of the proposed method.