A Distribution‐freeCUSUMChart for Monitoring Variability of AutocorrelatedProcesses

We present a distribution‐free tabular cumulative sum chart for monitoring the variability of an autocorrelated process. A quantity known as the asymptotic variance parameter is employed as a measure of the variability, and a distribution‐free tabular cumulative sum chart is applied to variance estimates calculated from batches of nonoverlapping samples. The proposed chart is applicable to a stationary process with a general marginal distribution and a general autocorrelation structure. It also determines control limits analytically without trial‐and‐error simulations. The performance of the proposed chart is tested on stationary processes with both normal and nonnormal marginals with various autocorrelation structures. Copyright © 2014 John Wiley & Sons, Ltd.     

An Analysis of Citation Counts of ETRI‐Invented US Patents

From its foundation until 2004, ETRI has registered over 1,000 US patents. This letter analyzes the characteristics of these patents and addresses the explanatory factors affecting their citation counts. For explanatory variables, research team related variables, invention specific variables, and geographical domain related variables are suggested. Zero‐altered count data models are used to test the impact of independent variables. A key finding is that technological cumulativeness, the scale of invention, outputs in the electronic field, and the degree of dependence on the US technology domain positively affect the citation counts of ETRI‐invented US patents. The magnitude of international presence appears to negatively affect the citation counts of ETRI‐invented US patents.     

Bacteriological Safety of Swine Carcasses Treated with Reconditioned Water

Swine carcass microflora were evaluated for selected foodborne pathogens after exposure to reconditioned water during scalding, dehairing, and polishing operations. Reused water had been reconditioned and chlorinated. Rodac plates applied to hams were used to assess carcass microflora. Water samples were enumerated using membrane filtration or spiral plating. Sampling was at mid-week throughout the year. Total aerobic plate counts on hams were unaffected by treating with potable or reconditioned waters. No differences were observed for staphylococci, enterics, fecal streptococci, Listeria monocytogenes, coliforms, and Aeromonas levels. A preevisceration potable water carcass wash reduced the bacterial load, regardless of initial treatment. Bacterial counts on carcasses paralleled those in water. Reuse is an alternative to potable water for initial slaughter operations without diminishing bacteriologic safety.     

Disinfection of wastewaters: high-energy electron vs gamma irradiation

A study was undertaken to examine the sensitivity of a wastewater population of coliphage, total coliforms and total flora present in raw sewage and secondary effluent after irradiating with similar doses delivered by a high-energy electron beam and y -radiation. The electron beam study was conducted on a large scale at the Virginia Key Wastewater Treatment Plant, Miami, Fla. The facility is equipped with a 1.5 MeV, 50 mA electron accelerator, with a wastewater flow rate of 8 ls⁻¹. Concurrent y-radiation studies were conducted at laboratory scale using a 5000 Ci, ⁶⁰Co y -source. Three logs reduction of all three test organisms were observed at an electron beam dose of 500 krads, while at least four logs reduction were observed at the same dose utilizing the y-source.     

隐层神经元数对基于神经网络的自相关过程控制的影响

将传统修哈特控制图应用于自动化连续生产过程,经常会引发大量虚发报警。使用神经网络方法对存在相关性的连续生产过程进行研究时,取得了良好效果。本文在使用BP神经网络对自相关过程进行监控的基础之上,对隐层神经元数对于神经网络训练收敛性及识别率的影响进行分析研究。针对一定的输入层神经元数,选择合适的隐层神经元数将极大的改善网络的训练效果并提高网络的识别率。     

均值渐进式漂移下的过程控制方法研究

实践证明，使用传统休哈特控制图控制自相关过程会造成大量虚发报警，使得控制图的灵敏度大大降低。神经网络方法以其独特的模式识别技术，在对自相关过程进行识别的尝试中，显示出了极大的优越性。文章针对自相关过程中渐进式均值漂移的模型，以神经网络方法进行检测，取得了良好效果。     

Detecting Periods of Significant Increased Communication Levels for Subgroups of Targeted Individuals

This paper investigates detecting significant increases in communication patterns and levels between small groups of individuals within a moderate‐size targeted group. Potential applications range from trying to establish emerging thought leaders within an organisation to the detection of the planning stages of a crime. The scan statistic is a popular choice for monitoring and detecting spatio‐temporal outbreaks, but it is difficult to apply to large‐scale target groups because of the computational effort required. When monitoring communication levels between thousands of people, the number of combinations of people whose communication may have increased is very high, and to scan through all of these to find which combinations have increased communications significantly is an enormous task. A successful surveillance plan will have early communication outbreak detection properties and good diagnostic capabilities for identifying individuals contributing to this outbreak. This paper proposes a new computationally feasible approach for detecting communication outbreaks based on exponentially weighted moving average smoothed communication counts between individuals within the network. We apply a cumulative sum of ordered signal‐to‐noise (SN) ratios for communication counts to flag significant departures from their respective median values. This plan is demonstrated to be efficient at detecting changes in communication levels for a small part of the network and diagnosing who is involved in the outbreak. Copyright © 2015 John Wiley & Sons, Ltd.     

Conducting inferential statistics for low microbial counts in foods using the Poisson-gamma regression

Mixed Poisson distributions have been shown to be able to represent low microbial counts more efficiently than the lognormal distribution because of its greater flexibility to model microbial clustering even when data consist of a large proportion of zero counts. The objective of this study was to develop an alternative modelling framework for low microbial counts based on heterogeneous Poisson regressions. As an illustration, Poisson-gamma regression models were used to assess the effect of chilling on the concentration of total coliforms from beef carcasses (n = 600) sampled at eight large Irish abattoirs. Three Poisson-gamma and three zero-modified (hurdle and zero-inflated) models were appraised with a series of random-effects variants in order to extract any variability in microbial mean concentration, dispersion and/or proportion of zero counts. Models were compared and validated in their ability to predict the coliforms counts on carcasses after chilling. In all five test batches, the hurdle Poisson-gamma distributions predicted the observed post-chill counts closer than the Poisson-gamma distributions. This is justified by the better capacity of the hurdle model to represent a higher proportion of zero counts, which were in fact observed in the post-chill batches. Thus, with a coded variable (pre-chill/post-chill) as treatment, and extracting the significant variability of batches nested in abattoirs for the coliforms mean concentration (σ²_u = 2.68), the dispersion measure (σ²_v = 2.39) and the probability of zero counts (σ²_w = 0.89), the validated hurdle Poisson-gamma model confirmed that chilling has a decreasing effect on the viability of coliforms from beef carcasses, and that the concentration is reduced by an average (pre-chill to post-chill) factor of 2.2 (95% CI: 2.15–2.24) at batch level. The model also indicated that chilling increases the odds of producing a zero count from a carcass swab in about 13.5 times, and that the higher the coliforms concentration in a batch, the weaker the effect that chilling has to reduce such contamination on the beef carcasses.     

Assessment of chemical and microbiological parameters of indoor swimming pool atmosphere using multiple comparisons

The aim of this study was to evaluate the air quality of an indoor swimming pool, analyzing diurnal and seasonal variations in microbiological counts and chemical parameters. The results indicated that yeast and bacteria counts, as well as carbon dioxide (CO₂), nitrogen oxides (NO_x) and O₃ concentrations, showed significant diurnal difference. On the other hand, temperature, relative humidity (R.H.), yeast counts and concentrations of CO₂, particles, O₃, toluene, and benzene showed seasonal differences. In addition, the relationship between indoor and outdoor air and the degree of correlation between the different parameters have been calculated, suggesting that CO₂, fine particles and NO_x would have indoor origin due to the human activity and secondary reactions favored by the chemical and environmental conditions of the swimming pool; while O₃, benzene and toluene, would come from outside, mainly. The overall results indicated that indoor air quality (IAQ) in the swimming pool building was deficient by the high levels of CO₂ and microorganisms, low temperatures, and high R.H., because frequently the limits established by the legislation were exceeded. This fact could be due to the poor ventilation and the inadequate operation of heating, ventilation, and air‐conditioning systems.     

Monitoring Process Variability for Stationary Process Data

Processes that arise naturally, for example, from manufacturing or the environment, often exhibit complicated autocorrelation structures. When monitoring such a process for changes in variance, accounting for that structure is critical. While charts for monitoring the variance of processes of independent observations and some specific autocorrelated processes have been proposed in the past, the chart presented in this article can handle a general stationary process. The performance of the proposed chart was examined through simulations for the first‐order autoregressive and first‐order autoregressive‐moving average processes and demonstrated with examples. Copyright © 2014 John Wiley & Sons, Ltd.