Small amphiphilic organics, coordination extended solids, and constant curvature structures

In this Account, we explore the role constant curvature structures play in amphiphilic small molecule crystals and extended coordination solids. A constant curvature structure is one in which there is a surface or interface that has the same curvature throughout its surface. Simple examples of such structures contain spheres (micelles), columns, or layers. Yet another family are cubic as is found in the gyroid topology. For amphiphilic systems, there are two domains, one generally hydrophobic and the other hydrophilic. We find that the interfaces between these two domains in small molecule structures resemble those in larger scale systems and adopt topologies related to constant curvature structures. The hydrophobic-to-total volume ratio, a molecular parameter, can be used to predict which type of constant curvature structure is adopted. In the case of three coordinate extended solids, constant curvature plays a role both in the extended net topology and in the hydrophobic-to-hydrophilic interface.     

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Cooperative adaptive control for synchronization of second‐order systems with unknown nonlinearities

This paper studies synchronization to a desired trajectory for multi‐agent systems with second‐order integrator dynamics and unknown nonlinearities and disturbances. The agents can have different dynamics and the treatment is for directed graphs with fixed communication topologies. The command generator or leader node dynamics is also nonlinear and unknown. Cooperative tracking adaptive controllers are designed based on each node maintaining a neural network parametric approximator and suitably tuning it to guarantee stability and performance. A Lyapunov‐based proof shows the ultimate boundedness of the tracking error. A simulation example with nodes having second‐order Lagrangian dynamics verifies the performance of the cooperative tracking adaptive controller. Copyright © 2010 John Wiley & Sons, Ltd.     

Closed-loop identification using direct approach and high order ARX/GOBF-ARX models

Model accuracy plays a key role in the performance of advanced, model predictive control algorithms. Model fidelity is usually affected by routine operating condition changes, which necessitate reidentification. From several theoretical and practical considerations, it is recommended that such re-identification be performed under closed-loop conditions. The direct approach for closed-loop identification, owing to its simplicity, is better suited for MPC. In order to yield unbiased and consistent parameter estimates, however, this approach requires the noise model to be sufficiently parameterized. Towards this objective, high order ARX models are the most suitable candidates from the viewpoint of ease of parameter estimation. For multivariable systems, however, the identification of high order ARX models would require longer experiments to be performed. This being undesirable from a practical viewpoint, there is a need for a parsimonious parameterization that would retain the benefits of high order ARX models. In this work, we propose to use generalized orthonormal basis filters (GOBFs) to achieve this parsimonous parameterization. Further, we propose an approach to obtain reduced order models by emphasizing important frequencies so as to suitably shape the bias. We also show that the choice of the GOBF parameterization has another important merit, viz. their ability to perform well even with minimal perturbation data or short experiment times. The efficacy of the proposed approach is demonstrated via simulations on the benchmark Shell Control Problem and a laboratory quadruple tank setup.     

Differential regulation of redox proteins and chaperones in HbEβ-thalassemia erythrocyte proteome

Purpose: In (hemoglobin, Hb) HbEβ‐thalassemia, HbE (β‐26 Glu→Lys) interacts with β‐thalassemia to produce clinical manifestation of varying severity. This is the first proteomic effort to study changes in protein levels of erythrocytes isolated from HbEβ‐thalassemic patients compared to normal. Experimental design: We have used 2‐DE and MALDI‐MS/MS‐based techniques to investigate the differential proteome profiling of membrane and Hb‐depleted fraction of cytosolic proteins of erythrocytes isolated from the peripheral blood samples of HbEβ‐thalassemia patients and normal volunteers. Results: Our study showed that redox regulators such as peroxiredoxin 2, Cu‐Zn superoxide dismutase and thioredoxin and chaperones such as α‐hemoglobin stabilizing protein and HSP‐70 were upregulated in HbEβ‐thalassemia. We have also observed larger amounts of membrane associated globin chains and indications of disruption of spectrin‐based junctional complex in the membrane skeleton of HbEβ‐thalassemic erythrocytes upon detection of low molecular weight fragments of β‐spectrin and decrease in β‐actin and dematin content. Conclusion and clinical relevance: We have observed interesting changes in the proteomic levels of redox regulators and chaperons in the thalassemic hemolysates and have observed strong correlation or association of the extent of such proteomic changes with HbE levels. This could be important in understanding the role of HbE in disease progression and pathophysiology.     

Temporal scale effect of loading data on instream nitrate-nitrogen load computation

The temporal scale effect of loading data on nitrate-nitrogen load computation was examined using outputs of watershed modeling tool Hydrologic Simulation Program-FORTRAN (HSPF) for the Amite River in Louisiana, USA. The daily nitrate-nitrogen concentrations simulated using the HSPF were employed first to obtain daily, weekly, bi-weekly, and monthly average data and then to develop load duration curves for the data with four different temporal scales. The duration curves exhibited high variability in the load estimated using daily data as compared with those based on bi-weekly and monthly data. According to daily data, the nitrate-nitrogen load in the winter was found to be 2,780 kg. The nitrate-nitrogen load decreased with increasing temporal (daily, weekly, bi-weekly, and monthly) scale (commonly used in water quality monitoring) of the data. The coefficient of variation, used to quantify the effect of temporal scale on the load, was found to be linearly and inversely correlated with the logarithm of the time scale. Based on the finding, empirical equations were proposed to extrapolate near real-time data for flow and nitrate-nitrogen, greatly simplifying nutrient monitoring and reducing the cost involved in water quality monitoring.     

一种可编程ExpressCard解决方案

ExpressCard模块是由PCMCIA推出的一种更薄、更快、更轻的抽屉式插卡,用以取代现有的PC卡和CardBus模块,主要用于笔记本计算机后继市场中的功能性升级和扩展.同样,ExpressCard模块最初旨在服务于笔记本计算机后继市场(图1a).     

Analysis of Clinic Layouts and Patient-Centered Procedural Innovations Using Discrete-Event Simulation

Abstract

Given that healthcare costs in the U.S. continue to climb, engineering managers can help address this situation by identifying designs for cost savings. Using discrete-event simulation, the layout of four different healthcare clinic scenarios considered in a redesign program named Patient Aligned Care Teams were studied. Model 1 is an existing layout in many clinics and Model 2 is staff-centered, while Models 3 and 4 provide additional space including common hallway(s). Our main conclusion is that Model 4 reduces patient travel distances and average queueing waits, while also providing the best privacy in accordance with HIPAA and the most security for the staff.     

Consumer shopping behaviour on the Internet: insights from Malaysia

Shopping on the Internet enables consumers to search for information on products or services and make purchases through direct interaction with online retailers. This study investigates consumer shopping behaviour on the Internet based on four aspects, i.e. the Internet marketing environment, product characteristics, familiarity, and promotional offers. The impact of influencing factors was checked by questionnaire survey, which was then processed and evaluated. Two hundred randomly selected respondents (students and employees in a public higher learning institution in the Federal Territory of Labuan, Malaysia) participated in the survey. Multiple regression analysis was used as a statistical measure to determine the most influential relationship aspect among a series of independent variables of consumer shopping on the Internet. The results revealed that familiarity has a great influence on consumer shopping on the Internet, followed by promotional offers. The assimilation of these aspects, and the empirical examination of the factors that influence consumer shopping on the Internet, advanced understanding of these constructs and their link to dynamic technology deployment for shopping. The paper suggests managerially actionable implications, and future research directions.