H-BSP: A Hierarchical BSP Computation Model

This paper presents a new parallel computing model, called H-BSP, which adds a hierarchical concept to the BSP(Bulk Synchronous Parallel) computing model. An H-BSP program consists of a number of BSP groups which are dynamically created at run time and executed in a hierarchical fashion. H-BSP allows algorithm designers to develop more efficient algorithms by utilizing processor locality in the program. Based on the distributed memory model, H-BSP provides a group-based programming paradigm and supports Divide & Conquer algorithms efficiently. This paper describes the structure of the H-BSP model, complexity analysis and some examples of H-BSP algorithm. Also presented is the performance characteristics of H-BSP algorithms based on the simulation analysis. Simulation results show that H-BSP takes advantages of processor locality and performs well in low bandwidth networks or in a constant-valence architecture such as 2-dimensional mesh. It is also proved that H-BSP can predict algorithm performance better than BSP, due to its locality-preserving nature.     

A flexible encapsulated MEMS pressure sensor system for biomechanical applications

 The use of pressure sensors made of conductive polymers is common in biomechanical applications. Unfortunately, hysteresis, nonlinearity, non-repeatability and creep have a significant effect on the pressure readings when such conductive polymers are used. The objective of this paper is to explore the potential of a new flexible encapsulated micro electromechanical system (MEMS) pressure sensor system as an alternative for human interface pressure measurement. A prototype has been designed, fabricated, and characterized. Testing has shown that the proposed packaging approach shows very little degradation in the performance characteristics of the original MEMS pressure sensor. The much-needed characteristics of repeatability, linearity, low hysteresis, temperature independency are preserved. Thus the flexible encapsulated MEMS pressure sensor system is very promising and shows superiority over the commercially available conductive polymer film sensors for pressure measurement in biomechanical applications. Received: 1 December 1999/Accepted: 17 August 2000     

Dynamic planning model for determining cutting parameters using neural networks in feature-based process planning

Although feature-based computer-aided process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prohibit the shop floor controllers from rapidly coping with unexpected production errors. The objective of the paper is to suggest a neural network-based dynamic planning model, by which the shop floor controllers determine cutting parameters in real-time based on shop floor status. At off-line is the dynamic planning model constructed as a neural network form, and then embedded into each removal feature. The dynamic planning model will be executed by the shop floor controllers to determine the cutting parameters. A prototype system is constructed to validate whether the dynamic planning model is capable of determining dynamically and efficiently the cutting parameters for a particular set of shop operating factors. Owing to the dynamic planning model, the shop floor controller will increase flexibility and robustness by rapidly and adaptively determining the cutting parameters in unexpected errors occurring.     

A multi-agent intelligent system for efficient ERP maintenance

The Enterprise Resource Planning (ERP) system is an enterprise-wide integrated software package designed to uphold the highest quality standards of business process. However, for the time being, when the business condition has been changed, the system may not guarantee that the process embedded in ERP is still best. Moreover, since the ERP system is very complex, maintaining the system by trial and error is very costly. Hence, this paper aims to construct a support system that adjusts ERP system to environmental changes. To do so, we adopt multi-agent intelligent technology that enables autonomous cooperation with one another to monitor ERP databases and to find any exceptional changes and then analyze how the changes will affect ERP performance. Moreover, Petri net is applied to manage the complexity and dynamics of agents’ behavior. To show the feasibility of the idea, a prototype agent system, ERP/PN, is proposed and an experiment is conducted.     

Optimization of 3.5-in. HDD spindle motors for OP-vibration performance: theoretical prediction and experimental verification

The purpose of this paper is to optimize OP-vibration performance of 3.5-in. hard disk drive (HDD) spindle motors through theoretical prediction and experimental verification. OP-vibration performance of HDD is closely related to the first rocking vibration of spindle motors because excited frequencies of 3.5-in. HDD from the environment are mostly below 500 Hz and the first rocking vibration is the only resonance in the corresponding frequencies. Therefore, minimizing first rocking vibration leads to improve OP-vibration performance of the spindle motors. In order to minimize the first rocking vibration key parameters of FDB spindle motors were selected from a previous work done by Heo and Shen (Microsyst Technol 11:1204–1213, 2005). Then, the selected parameters have been optimized to minimize the first rocking vibration through a theoretical model developed at University of Washington. Then, experiments with ten prototype FDB spindle motors have been conducted to verify the theoretical results. Each prototype motor has different spindle parameter configurations including bearing coefficients, bearing locations, and center of gravity location, etc. Also, this paper demonstrated that radial measurements of spindle rocking vibration have better correlation with OP-vibration performance than axial measurements through PES measurements. Finally, the optimized design has been manufactured by a motor maker and has also successfully verified the theoretical prediction experimentally.     

A quantum chemical study of the mechanism of action of Vitamin K epoxide reductase (VKOR) II. Transition states

A reaction path including transition states is generated for the Silverman mechanism [R.B. Silverman, Chemical model studies for the mechanism of Vitamin K epoxide reductase, J. Am. Chem. Soc. 103 (1981) 5939-5941] of action for Vitamin K epoxide reductase (VKOR) using quantum mechanical methods (B3LYP/6-311G**). VKOR, an essential enzyme in mammalian systems, acts to convert Vitamin K epoxide, formed by Vitamin K carboxylase, to its (initial) quinone form for cellular reuse. This study elaborates on a prior work that focused on the thermodynamics of VKOR [D.W. Deerfield II, C.H. Davis, T. Wymore, D.W. Stafford, L.G. Pedersen, Int. J. Quant. Chem. 106 (2006) 2944-2952]. The geometries of proposed model intermediates and transition states in the mechanism are energy optimized. We find that once a key disulfide bond is broken, the reaction proceeds largely downhill. An important step in the conversion of the epoxide back to the quinone form involves initial protonation of the epoxide oxygen. We find that the source of this proton is likely a free mercapto group rather than a water molecule. The results are consistent with the current view that the widely used drug Warfarin likely acts by blocking binding of Vitamin K at the VKOR active site and thereby effectively blocking the initiating step. These results will be useful for designing more complete QM/MM studies of the enzymatic pathway once three-dimensional structural data is determined and available for VKOR.     

On aggregate available bandwidth in many-to-one data transfer—modeling and applications

This work investigates the modeling of aggregate available bandwidth in multi-sender network applications. Unlike the well-established client–server model, where there is only one server sending the requested data, the available bandwidth of multiple senders when combined together does exhibit consistent properties and thus can be modeled and estimated. Through extensive experiments conducted in the Internet this work proposed to model the aggregate available bandwidth using a normal distribution and then illustrates its application through a hybrid download-streaming algorithm and a playback-adaptive streaming algorithm for video delivery under different bandwidth availability scenarios. This new multi-source bandwidth model opens a new way to provide probabilistic performance guarantee in best-effort networks such as the Internet, and is particularly suitable for the emerging peer-to-peer applications, where having multiple sources is the norm rather than the exception.

The discovery of biomarkers for type 2 diabetic nephropathy by serum proteome analysis

Diabetic nephropathy (DN) is a serious kidney complication of diabetes, and constitutes the leading cause of end-stage renal disease. The earliest clinical evidence of DN is microalbuminuria, a term which refers to the appearance of small but abnormal amounts of albumin in the urine. However, screening methods for DN, such as biomarker assays, are yet to be developed for type 2 DN. In the present study, in an attempt to identify the biomarkers for initial diagnoses of type 2 DN, the protein profiles of human sera collected from 30 microalbuminuric type 2 diabetic patients were compared with those collected from 30 normoalbuminuric type 2 diabetic patients, via 2-DE. As a result, a total of 18 spots were determined to have different protein levels in the microalbuminuric patients. Twelve spots had lower protein levels of approximately 50%, and the other six had higher levels of approximately 100-300% as compared to the spots of normoalbuminuric patients. These spots were identified with ESI-Q-TOF (ESI-quadrupole-TOF) MS. Among the identified proteins, vitamin D-binding protein (DBP) and pigment epithelium-derived factor (PEDF) were verified by Western blotting. The results of this study indicate that the DBP may be employed as diagnostic and monitoring biomarkers of type 2 DN, contingent on further study into the matter.     

Interface stability influences torso muscle recruitment and spinal load during pushing tasks

Handle or interface design can influence torso muscle recruitment and spinal load during pushing tasks. The objective of the study was to provide insight into the role of interface stability with regard to torso muscle recruitment and biomechanical loads on the spine. Fourteen subjects generated voluntary isometric trunk flexion force against a rigid interface and similar flexion exertions against an unstable interface, which simulated handle design in a cart pushing task. Normalized electromyographic (EMG) activity in the rectus abdominus, external oblique and internal oblique muscles increased with exertion effort. When using the unstable interface, EMG activity in the internal and external oblique muscle groups was greater than when using the rigid interface. Results agreed with trends from a biomechanical model implemented to predict the muscle activation necessary to generate isometric pushing forces and maintain spinal stability when using the two different interface designs. The co-contraction contributed to increased spinal load when using the unstable interface. It was concluded that handle or interface design and stability may influence spinal load and associated risk of musculoskeletal injury during manual materials tasks that involve pushing exertions.     

Genetic Parallel Programming: design and implementation

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.