Red yeast barley reduces plasma glucose levels and activates AMPK phosphorylation in <Emphasis Type="Italic">db/db</Emphasis> mice

Red yeast (Monascus purpureus) fermented over rice has a limited hypoglycemic activity. To enhance its glucose-lowering effect, we fermented red yeast over waxy barley, a hypoglycemic grain with high levels of fibers and β-glucans, and investigated the metabolic effects of red yeast barley (RYB) in high-fat-fed hyperglycemic db/db mice for 6 weeks. The fasting glucose levels were significantly reduced in the RYB group at 6 weeks by 25% (p<0.05), as was the glucose tolerance (−27% of area under the curve in RYB vs. controls, p<0.05). Plasma insulin levels and the expression of PPAR-γ were unaltered, however, the phosphorylation activation of hepatic AMP-activated protein kinase (AMPK) was increased significantly in RYB group compared with controls suggesting that hypoglycemic effect of RYB may be achieved by AMPK-dependent mechanism. RYB may be used as a hypoglycemic functional food modulating cellular AMPK activity.     

An inspection-maintenance model for systems with multiple competing processes

In some applications, the failure rate of the system depends not only on the time, but also upon the status of the system, such as vibration level, efficiency, number of random shocks on the system, etc., which causes degradation. In this paper, we develop a generalized condition-based maintenance model subject to multiple competing failure processes including two degradation processes, and random shocks. An average long-run maintenance cost rate function is derived based on the expressions for the degradation paths & cumulative shock damage, which are measurable. A geometric sequence is employed to develop the inter-inspection sequence. Upon inspection, one needs to decide whether to perform a maintenance, such as preventive or corrective, or to do nothing. The preventive maintenance thresholds for degradation processes & inspection sequences are the decision variables of the proposed model. We also present an algorithm based on the Nelder-Mead downhill simplex method to calculate the optimum policy that minimizes the average long-run maintenance cost rate. Numerical examples are given to illustrate the results using the optimization algorithm.     

Survey of reliability and availability evaluation of complex networks using Monte Carlo techniques

Generally there are four main difficulties in evaluating complex large-scale system reliability, availability and MTBF: the system structure may be very complex; subsystems may follow various failure distributions; subsystems may conform to arbitrary failure and repair distributions for maintained systems; the failure data of subsystems are sometimes not sufficient, reliability test sample sizes tend to be small. It is difficult and often impossible to obtain s-confidence limits of them by classical statistics. Monte Carlo technique combined with Bayes method is a powerful tool to solve this kind of problems. In this survey, the typical existing Monte Carlo reliability, availability and MTBF simulation procedures, variance reduction methods, and random variate generation algorithms are analyzed and summarized. The advantages, drawbacks, accuracy and computer time of Monte Carlo simulation in evaluating reliability, availability and MTBF of a complex network are discussed. Finally, some conclusions are drawn and a general Monte Carlo reliability and MTTF assessment procedure is recommended.     

High-throughput sample preparation for gene expression profiling and in vitro target validation

The demand for high-throughput RNA isolation has been dramatically increasing with wide applications of RNAi, expression profiling, and molecular diagnosis. A comparison of various RNA isolation methods that have been adapted to high-throughput platforms, focusing on consistently high yield and quality of isolated RNA, reduction of cross-contamination, and simplicity and robustness of the protocol is presented. The streamlining of RNA isolation with RNA quantification by qRT-PCR and amplification for microarray analysis is also discussed. In general, a microspheric bead-based approach results in more consistent RNA recovery than glass fiber filter-based RNA isolation method, and RNA can be eluted in a smaller volume. This is because beads can be fully re-suspended in solution to enable more thorough mixing, washing, and elution, whereas the glass fiber matrix is fixed in a filter plate.     

Tank Monitoring: A pAMN Case Study

The introduction of probabilistic behaviour into the B-Method is a recent development. In addition to allowing probabilistic behaviour to be modelled, the relationship between expected values of the machine state can be expressed and verified. This paper explores the application of probabilistic B to a simple case study: tracking the volume of liquid held in a tank by measuring the flow of liquid into it. The flow can change as time progresses, and sensors are used to measure the flow with some degree of accuracy and reliability, modelled as non-deterministic and probabilistic behaviour respectively. At the specification level, the analysis is concerned with the expectation clause in the probabilistic B machine and its consistency with machine operations. At the refinement level, refinement and equivalence laws on probabilistic GSL are used to establish that a particular design of sensors delivers the required level of reliability.     

Reliability analysis of nuclear fail-safe redundancy

In many critical nuclear safety system applications, fault tolerance has been an essential design attribute for achieving high reliability. Generally, operating systems cannot achieve the intended reliability without employing redundancy. Redundant systems, however, require additional resources in terms of both labor and materials. Therefore, the redundancy level needed to achieve fault tolerance must be carefully determined by balancing the reliability and the cost of the systems. In this paper, the optimization problem is formulated and solved for the minimum average total cost of nuclear fail-safe systems. We also determine the optimal system size which minimizes the average total system cost subject to a restricted type I design error. Application and numerical examples are given to illustrate the results.     

CLASSIFICATION OF ON-LINE POULTRY CARCASSES WITH BACKPROPAGATION NEURAL NETWORKS1

A transportable system equipped with an overhead shackle conveying line and a visible/near-infrared (Vis/NIR) spectrophotometer system was assembled and used at a poultry slaughter plant. The reflectance spectra of each poultry carcass hung on the moving shackle was measured with a stationary fiber optic probe, which was set 2 to 5 cm away from the carcass, depending on the size. Reflectance spectra of wholesome and unwholesome poultry carcasses on the moving shackle, set at 60 or 90 birds/min, were measured, either under room light or in a dark environment. The scanning time for each carcass was 0.32 s. Most of the unwholesome poultry carcasses for this study were septicemic and air-sacculitic. The average accuracy in classifying wholesome and unwholesome carcasses was above 94%. All the misclassified carcasses were air-sacculitic. With a shackle speed of 90 birds/min, the highest average accuracy was obtained when the reflectance was measured in the dark (97.5%). The results showed that the accuracy of classification could be improved with the maintenance of a consistent lighting environment. All results indicated the Vis/NIR spectrophotometer system would be a highly accurate, robust tool for on-line, real-time classification of wholesome and unwholesome carcasses.     

Controllability of one class of nonlinear singularly perturbed systems

Sufficient conditions of complete controllability are derived for quasilinear singularly perturbed systems.Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 61–66, July–August, 1992.     

PID-controller with predictor and auto-tuning algorithm: study of efficiency for thermal plants

The problem of efficiency estimation of an automatic control system (ACS) with a Smith predictor and PID-algorithm for thermal plants is considered. In order to use the predictor, it is proposed to include an auto-tuning module (ATC) into the controller; the module calculates parameters for a second-order plant module with a time delay. The study was conducted using programmable logical controllers (PLC), one of which performed control, ATC, and predictor functions. A simulation model was used as a control plant, and there were two variants of the model: one of them was built on the basis of a separate PLC, and the other was a physical model of a thermal plant in the form of an electrical heater. Analysis of the efficiency of the ACS with the predictor was carried out for several variants of the second order plant model with time delay, and the analysis was performed on the basis of the comparison of transient processes in the system when the set point was changed and when a disturbance influenced the control plant. The recommendations are given on correction of the PID-algorithm parameters when the predictor is used by means of using the correcting coefficient k for the PID parameters. It is shown that, when the set point is changed, the use of the predictor is effective taking into account the parameters correction with k = 2. When the disturbances influence the plant, the use of the predictor is doubtful, because the transient process is too long. The reason for this is that, in the neighborhood of the zero frequency, the amplitude-frequency characteristic (AFC) of the system with the predictor has an ascent in comparison with the AFC of the system without the predictor.