Research on rework strategies for reconfigurable manufacturing system considering mission reliability

Rework strategies that involve different checking points as well as rework times can be applied into reconfigurable manufacturing system (RMS) with certain constraints, and effective rework strategy can significantly improve the mission reliability of manufacturing process. The mission reliability of process is a measurement of production ability of RMS, which serves as an integrated performance indicator of the production process under specified technical constraints, including time, cost and quality. To quantitatively characterize the mission reliability and basic reliability of RMS under different rework strategies, rework model of RMS was established based on the method of Logistic regression. Firstly, the functional relationship between capability and work load of manufacturing process was studied through statistically analyzing a large number of historical data obtained in actual machining processes. Secondly, the output, mission reliability and unit cost in different rework paths were calculated and taken as the decision variables based on different input quantities and the rework model mentioned above. Thirdly, optimal rework strategies for different input quantities were determined by calculating the weighted decision values and analyzing advantages and disadvantages of each rework strategy. At last, case application were demonstrated to prove the efficiency of the proposed method.

Physicochemical characterization of grewia polysaccharide gum:effect of drying method

Grewia polysaccharide gum, a potential pharmaceutical excipient was extracted from the inner stem bark of Grewia mollis, thereupon drying was achieved by three techniques: air-drying, freeze-drying and spray-drying. Analysis of the monosaccharide composition including 1H and 13C NMR spectroscopic analysis of the polysaccharide gum was carried out. The effect of the drying methods on the physicochemical properties of the gum was evaluated by Fourier transformed infra-red (FT-IR) spectroscopy, solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis, differential scanning calorimetry and gel permeation chromatography. Monosaccharide sugar analysis revealed that the gum is composed of glucose, rhamnose, galactose, arabinose and xylose as the main neutral sugars. These were supported by the results from 1H and 13C NMR spectroscopic analysis. FT-IR and solid-state NMR results indicated that drying technique has little effect on the structure of the polysaccharide gum but XPS showed that surface chemistry of the gum varied with drying methods. Thermogravimetric analyses showed that oxidation onset varied according to the drying method. The molecular weight was also dependent on the drying technique. For industrial extrapolation, air-drying may be preferable to spray-drying and freeze-drying when relative cost, product stability and powder flow are required, for example in tablet formulation. © 2010 Elsevier Ltd. All rights reserved.

Characterization of Grewia gum, a potential pharmaceutical excipient

Grewia gum was extracted from the inner stem bark of Grewia mollis and characterized by several techniques such as gas chromatography (GC), gel permeation chromatography (GPC), scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and thermogravimetric analysis of the extracted sample. Spectroscopic techniques such as x-ray photoelectron spectroscopy (XPS), fourier-transformed infrared (FT-IR), solid-state nuclear magnetic resonance (NMR), and 1H and 13C NMR techniques were also used to characterize the gum. The results showed that grewia gum is a typically amorphous polysaccharide gum containing glucose, rhamnose, galactose, arabinose and xylose as neutral sugars. It has an average molecular weight of 5925 kDa expressed as the pullulan equivalent. The gum slowly hydrated in water, dispersing and swelling to form a highly viscous dispersion exhibiting pseudoplastic flow behaviour. The polysaccharide gum is thermally stable and may have application as stabilizer or suspending agent in foods, cosmetics and in pharmaceuticals. It may have application as a binder or sustained-release polymer matrix in tablets or granulations. © IPEC-Americas Inc.

Statnote 36: Do the data fit the Poisson distribution

In previous Statnotes, many of the statistical tests described rely on the assumption that the data are a random sample from a normal or Gaussian distribution. These include most of the tests in common usage such as the ‘t’ test ), the various types of analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’) . In microbiology research, however, not all variables can be assumed to follow a normal distribution. Yeast populations, for example, are a notable feature of freshwater habitats, representatives of over 100 genera having been recorded . Most common are the ‘red yeasts’ such as Rhodotorula, Rhodosporidium, and Sporobolomyces and ‘black yeasts’ such as Aurobasidium pelculans, together with species of Candida. Despite the abundance of genera and species, the overall density of an individual species in freshwater is likely to be low and hence, samples taken from such a population will contain very low numbers of cells. A rare organism living in an aquatic environment may be distributed more or less at random in a volume of water and therefore, samples taken from such an environment may result in counts which are more likely to be distributed according to the Poisson than the normal distribution. The Poisson distribution was named after the French mathematician Siméon Poisson (1781-1840) and has many applications in biology, especially in describing rare or randomly distributed events, e.g., the number of mutations in a given sequence of DNA after exposure to a fixed amount of radiation or the number of cells infected by a virus given a fixed level of exposure. This Statnote describes how to fit the Poisson distribution to counts of yeast cells in samples taken from a freshwater lake.

What determines the size frequency distribution of β-amyloid (Aβ) deposits in Alzheimer's disease patients?

The factors determining the size of individual β-amyloid (A,8) deposits and their size frequency distribution in tissue from Alzheimer's disease (AD) patients have not been established. In 23/25 cortical tissues from 10 AD patients, the frequency of Aβ deposits declined exponentially with increasing size. In a random sample of 400 Aβ deposits, 88% were closely associated with one or more neuronal cell bodies. The frequency distribution of (Aβ) deposits which were associated with 0,1,2,...,n neuronal cell bodies deviated significantly from a Poisson distribution, suggesting a degree of clustering of the neuronal cell bodies. In addition, the frequency of Aβ deposits declined exponentially as the number of associated neuronal cell bodies increased. Aβ deposit area was positively correlated with the frequency of associated neuronal cell bodies, the degree of correlation being greater for pyramidal cells than smaller neurons. These data suggested: (1) the number of closely adjacent neuronal cell bodies which simultaneously secrete Aβ was an important factor determining the size of an Aβ deposit and (2) the exponential decline in larger Aβ deposits reflects the low probability that larger numbers of adjacent neurons will secrete Aβ simultaneously to form a deposit. © 1995.

Statnote 37 : the negative binomial distribution

An organism living in water, and present at low density, may be distributed at random and therefore, samples taken from the water are likely to be distributed according to the Poisson distribution. The distribution of many organisms, however, is not random, individuals being either aggregated into clusters or more uniformly distributed. By fitting a Poisson distribution to data, it is only possible to test the hypothesis that an observed set of frequencies does not deviate significantly from an expected random pattern. Significant deviations from random, either as a result of increasing uniformity or aggregation, may be recognized by either rejection of the random hypothesis or by examining the variance/mean (V/M) ratio of the data. Hence, a V/M ratio not significantly different from unity indicates a random distribution, greater than unity a clustered distribution, and less then unity a regular or uniform distribution . If individual cells are clustered, however, the negative binomial distribution should provide a better description of the data. In addition, a parameter of this distribution, viz., the binomial exponent (k), may be used as a measure of the ‘intensity’ of aggregation present. Hence, this Statnote describes how to fit the negative binomial distribution to counts of a microorganism in samples taken from a freshwater environment.