A full discretisation of the time-dependent Boussinesq (buoyancy) model with nonlinear viscosity

In this article, we study the time dependent Boussinesq (buoyancy) model with nonlinear viscosity depending on the temperature. We propose and analyze first and second order numerical schemes based on finite element methods. An optimal a priori error estimate is then derived for each numerical scheme. Numerical experiments are presented that confirm the theoretical accuracy of the discretization.     

Roh-, verdauliche und unverdauliche Fruchtkörperproteine in AusternseitlingenPleurotus ostreatus (Pilze)

Different kinds of protein (crude, digestible, non-digestible) were analysed in caps and stipe ofPleurotus ostreatus (oyster mushroom) in four phases of development. The phases examined were: A (cap diameter <5 cm); B (diameter 5–8 cm); C (diameter, 8–10 cm) and D (diameter >10 cm). ThePleurotus variety analysed (the cap and the stipe) has a relative high crude protein content, the main part of which is digestible (average, 92%). During the four stages of fruit body ripening, stage B was the best, with the highest crude and digestible protein concentrations. In stage D the highest nondigestible protein content was measured in the cap and stipe. These data can be used for optimal harvesting of fruit bodies of cultivatedP. ostreatus.     

Bio-Interactive Healthcare Service System Using Lifelog Based Context Computing

Intelligent bio-sensor information processing was developed using lifelog based context aware technology to provide a flexible and dynamic range of diagnostic capabilities to satisfy healthcare requirements in ubiquitous and mobile computing environments. To accomplish this, various noise signals were grouped into six categories by context estimation and effectively reconfigured noise reduction filters by neural network and genetic algorithm. The neural network-based control module effectively selected an optimal filter block by noise context-based clustering in running mode, and filtering performance was improved by genetic algorithm in evolution mode. Due to its adaptive criteria, genetic algorithm was used to explore the action configuration for each identified bio-context to implement our concept. Our proposed Bio-interactive healthcare service system adopts the concepts of biological context-awareness with evolutionary computations in working environments modeled and identified as bio-sensors based environmental contexts. We used an unsupervised learning algorithm for lifelog based context modeling and a supervised learning algorithm for context identification.     

Parametric Study of Posttensioned Inverted-T Bridge System for Improved Durability and Increased Span-to-Depth Ratio

Rehabilitation of the existing bridges is one of the most pressing needs in maintenance of the transportation infrastructure. As an example, more than 2,000 bridges in Kansas alone need to be replaced during the next decade. The majority of these bridges have spans of 30 m (100 ft) or less, and shallow profiles. The inverted-T (IT) bridge system has gained increasing popularity in recent years due to its lower weight and relatively larger span-to-depth ratio compared to the prestressed I-girder bridges. However, there are some limitations in replacing the existing cast in place (CIP) bridges with IT system. Implementation of posttensioning, which is the focus of this paper, is a promising solution for these limitations. This leads to a higher span-to-depth ratio and reduces potential transverse cracks in the CIP deck which is a major concern for corrosion of the reinforcement. An analytical research was conducted to identify the major parameters influencing the performance of a posttensioned IT bridge system. This was followed by a parametric study to explore the scope of these parameters and specify the design limits in terms of posttensioning stages, timing scenarios, and posttensioning forces. Concrete strength and different methods for estimating time-dependent restraining moments were addressed in this parametric study.     

Resolving Ambiguous Segmentation of Korean Compound Nouns Using Statistics and Rules

Korean compound nouns may be written as a sequence of characters without blanks between unit nouns. For Korean processing systems, Korean compound nouns have to be first segmented into a sequence of unit nouns. However, the segmentation task is difficult because a sequence of characters may be ambiguously segmented to several sequences of appropriate unit nouns. Moreover, this task is not trivial because Korean compound nouns may include many unknown unit nouns.
This paper proposes a new method for KCNS (Korean Compound Noun Segmentation) and reports on the appliccation of such a segmentationtechnique to enhance the performance of an information retrieval system. According to our method, compound nouns are first segmented by using a dictionary and structure patterns. If they are ambiguously segmented, we resolve the ambiguities by using statistical information and a preference rule. Moreover, we employ three kinds of heuristics in order to segment compound nouns with unknown unit nouns.
To evaluate KCNS, we use three kinds of data from various domains. Experimental results show that the precision of KCNS's output is approximately 96% on average, regardless of domains. The effectiveness of using the segmented unit nouns provided by KCNS for indexing is proved by improving retrieval performance of our information retrieval system.     

A position based kinematic method for the analysis of human gait

Human joint motion can be kinematically described in three planes, typically the frontal, sagittal, and transverse, and related to experimentally measured data. The selection of reference systems is a prerequisite for accurate kinematic analysis and resulting development of the equations of motion. Moreover, the development of analysis techniques for the minimization of errors, due to skin movement or body deformation, during experiments involving human locomotion is a critically important step, without which accurate results in this type of experiment are an impossibility. The traditional kinematic analysis method is the Angular-based method (ABM), which utilizes the Euler angle or the Bryant angle. However, this analysis method tends to increase cumulative errors due to skin movement. Therefore, the objective of this study was to propose a new kinematic analysis method, Position-based method (PBM), which directly applies position displacement data to represent locomotion. The PBM presented here was designed to minimize cumulative errors via considerations of angle changes and translational motion between markers occurring due to skin movements. In order to verify the efficacy and accuracy of the developed PBM, the mean value of joint dislocation at the knee during one gait cycle and the pattern of three dimensional translation motion of the tibiofemoral joint at the knee, in both flexion and extension, were accessed via ABM and via new method, PBM, with a Local Reference system (LRS) and Segmental Reference system (SRS), and then the data were compared between the two techniques. Our results indicate that the proposed PBM resulted in improved accuracy in terms of motion analysis, as compared to ABM, with the LRS and SRS.     

Inside Front Cover: Selective Construction of Supramolecular Nanotube Hosts with Cationic Inner Surfaces (Adv. Mater. 22/2005)

Selective construction of a supramolecular nanotube host with a cationic inner surface consisting of unsymmetrical bolaamphiphiles is reported on p. 2732 by Masuda, Shimizu, and co‐workers. The nanotube, shown schematically on the inside cover, has different inner and outer surfaces covered with amino and sugar functionalities, respectively, and is able to effectively encapsulate anionic nanomaterials, such as sulfate‐latex beads (see TEM image) and spherical proteins, in the cationic hollow cylinder without depending on capillary action.     

Effective paging procedure for the optical feeder microcellular system

The paper considers a paging problem in the optical feeder microcellular system where group simulcasting and dynamic channel allocation technologies are incorporated. The objective is to propose an effective paging procedure whose performance is measured in terms of paging load, average paging delay, and paging success probability. In the paging procedure, paging signals are transmitted via group simulcasting, for which the control channel is allocated dynamically. The performance of the procedure is analyzed in an approximate queueing network model. The proposed procedure is then compared in performance with a simulation approach, from which the procedure is found to outperform any previous methods in the literature.