Dissociation between the potency and reversibility of the insulinotropic action of two meglitinide analogues

The non-sulphonylurea hypoglycaemic agent repaglinide is about one order of magnitude less potent, in terms of insulinotropic efficiency, than S3075, another meglitinide analogue. In the present study, the effects of these two drugs upon 86Rb outflow, 45Ca efflux and insulin release from prelabelled rat pancreatic islets were investigated in a perifusion system. At a concentration of 10 microM, which is sufficient to evoke a maximal secretory response in incubated islets, both agents inhibited 86Rb efflux from islets perifused in the absence of D-glucose, and stimulated both 45Ca efflux and insulin release from islets perifused in the presence of 6 mM D-glucose. The stimulation of 45Ca efflux from prelabelled islets was suppressed in the absence of extracellular Ca2+. The cationic and secretory response to repaglinide differed, however, from that evoked by S3075, in persisting for at least 20 min after removal of the drug from the perifusion medium, whilst the changes in 86Rb outflow, 45Ca efflux and insulin release caused by S3075 were rapidly reversible. These findings indicate that there is no parallel between the insulinotropic efficiency of distinct meglitinide analogues, and the reversibility of their functional effects. Since a comparable dissociation was recently documented in the case of hypoglycaemic sulphonylureas, the present observations reinforce the view that distinct molecular determinants may rule the relative insulinotropic potency of sulphonylureas and structurally related meglitinide analogues, on one hand, and the reversibility of their biological action, on the other hand.     

Methodological Challenges in Union Commitment Studies.

Methodological problems in studies of union commitment were identified and illustrated with data from 4,641 members and 479 stewards in 297 local teachers' unions. Using a 20-item union commitment scale, results confirmed the existence of 3 substantive factors and 1 method factor at the individual level of analysis: loyalty to the union, responsibility to the union, willingness to work for the union, and a factor of negatively worded items. Tests of measurement invariance showed that the scale captured commitment for rank-and-file members but not for union stewards. The authors also found partial measurement invariance between long-time and newer members and full measurement invariance between men and women. Finally, the authors found that violation of the statistical assumption of independence reduced model fit when individual commitment scores were analyzed without attention to the hierarchical nature of the data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

Fuzzy Bayesian reliability and availability analysis of production systems

To have effective production planning and control, it is necessary to calculate the reliability and availability of a production system as a whole. Considering only machine reliability in the calculations would most likely result unmet due dates. In this study, a new modelling approach for determining the reliability and availability of a production system is proposed by considering all the components of the system and their hierarchy in the system structure. Components of a production system are defined as production processes; components of the processes are defined as sub-processes. In this hierarchical structure we could model all kinds of failures such as material and supply, management and personnel, and machine and equipment. In the analysis, a fuzzy Bayesian method is used to quantify the uncertainties in the production environment. The suggested modelling approach is illustrated on an example. In the example, also a separate reliability and availability analysis is conducted which only considered machine failures, and the results of both analyses are compared.     

Adapting application execution in CMPs using helper threads

In parallel to the changes in both the architecture domain–the move toward chip multiprocessors (CMPs)–and the application domain–the move toward increasingly data-intensive workloads–issues such as performance, energy efficiency and CPU availability are becoming increasingly critical. The CPU availability can change dynamically due to several reasons such as thermal overload, increase in transient errors, or operating system scheduling. An important question in this context is how to adapt, in a CMP, the execution of a given application to CPU availability change at runtime. Our paper studies this problem, targeting the energy-delay product (EDP) as the main metric to optimize. We first discuss that, in adapting the application execution to the varying CPU availability, one needs to consider the number of CPUs to use, the number of application threads to accommodate and the voltage/frequency levels to employ (if the CMP has this capability). We then propose to use helper threads to adapt the application execution to CPU availability change in general with the goal of minimizing the EDP. The helper thread runs parallel to the application execution threads and tries to determine the ideal number of CPUs, threads and voltage/frequency levels to employ at any given point in execution. We illustrate this idea using four applications (Fast Fourier Transform, MultiGrid, LU decomposition and Conjugate Gradient) under different execution scenarios. The results collected through our experiments are very promising and indicate that significant EDP reductions are possible using helper threads. For example, we achieved up to 66.3%, 83.3%, 91.2%, and 94.2% savings in EDP when adjusting all the parameters properly in applications FFT, MG, LU, and CG, respectively. We also discuss how our approach can be extended to address multi-programmed workloads.     

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

An adaptive least squares channel estimation based on conjugate gradient approach for multipath fading CDMA systems

Multiple access interference (MAI) is the main factor affecting the performance of channel estimation techniques for code division multiple access (CDMA) systems. Although, several multi-user channel estimation algorithms have been proposed to mitigate MAI, these algorithms require high computational complexities. In this paper, we address the problem of iterative least squares (LS) mobile channel estimation at high channel efficiency that requires a short training sequence along with the spreading sequences. We employ an efficient iterative method based on conjugate gradient (CG) algorithm to reduce the computational complexity of the estimation method. Computer simulations illustrate that the proposed method performs almost identical to the exact LS estimate for reasonable training lengths.     

Automated construction of fuzzy event sets and its application toactive databases

Fuzzy sets and fuzzy logic research aims to bridge the gap between the crisp world of maths and the real world. Fuzzy set theory was applied to many different areas, from control to databases. Sometimes the number of events in an event-driven system may become very high and unmanageable. Therefore, it is very useful to organize the events into fuzzy event sets also introducing the benefits of fuzzy set theory. All the events that have occurred in a system can be stored in event histories which contain precious hidden information. We propose a method for automated construction of fuzzy event sets out of event histories via data mining techniques. The useful information hidden in the event history is extracted into a matrix called sequential proximity matrix. This matrix shows the proximities of events and it is used for fuzzy rule execution via similarity based event detection and construction of fuzzy event sets. Our application platform is active databases. We describe how fuzzy event sets can be exploited for similarity based event detection and fuzzy rule execution in active database systems