Design optimization using ANOVA

This paper describes the design optimization of a robot sensor used for locating 3-D objects employing the Taguchi method in a computer simulation scenario. The location information from the sensor is to be utilized to control the movements of an industrial robot in a 'pick-and-place' or assembly operation. The Taguchi method, which is based on the Analysis-of-Variance (ANOVA) approach, is utilized to improve the performance of the sensor over a wider operating range. A review of the Taguchi method is presented along with step-by-step implementation details to identify and optimize the design parameters of the sensor. The method allows us to gauge the impact of various interactions present in the sensor system exclusively and permits us to single out those factors that have a dominant influence on the overall performance of the sensor. The investigation suggests that the Taguchi method is a more structured and efficient approach for achieving a robust design compared with the classical full factorial design approach.     

Still-births among the offspring of male radiation workers at the Sellafield nuclear reprocessing plant: detailed results and statistical aspects

Summary. This study investigates whether there was evidence of increasing risk of still-birth with increasing paternal exposure to ionizing radiation received during employment at the Sellafield nuclear installation before the child was conceived. A significant positive association is found between the total paternal preconceptional exposure to external ionizing radiation and the risk of still-birth (after adjustment for year of birth, social class, birth order and paternal age, odds ratio at 100 mSv 1.24 (95% confidence interval 1.04–1.45)). A summary of the principal scientific findings of this study has been published in the Lancet . This paper describes in detail the statistical methods that were used in the investigation and presents the results in full.     

Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

Stjepan Metrović and Michel Maffesoli's 'implosive' defence of the Durkheimian tradition: theoretical convergences around Baudrillard's thesis on the 'end' of the social

This paper critically examines Metrovi? and Maffesoli's attempt to understand postmodernity through Émile Durkheim's nonrational link between society, religion and morality. Metrovi? (1991, 1997) and Maffesoli's (1996) work draws upon this emotional element when attempting to refute Baudrillard's (1983) cognitively focused, if implicit, critique of the Durkheimian tradition. Despite their best intentions, Metrovi? and Maffesoli still fail to exploit the partialities of Baudrillard's critique to the full. While both have some appreciation of the link between emotion and religion as found in Durkheim's The Elementary Forms of Religious Life (1912) they, nevertheless, fall short of grasping its full conceptual importance. This leads them to an implicit acceptance of Baudrillard's thesis on the ‘end’ of the social. This pitfall could have been avoided if Metrovi? and Maffesoli had built their respective analyses of the postmodern condition on a closer reading of The Elementary Forms. Reading this text alongside those other insights on emotion and social life as contained within Durkheim and Mauss's Primitive Classification (1903) and Talcott Parsons's subsequent writings on the sociological problem of religion, would have helped to distance the work of Metrovi? and Maffesoli from that of Baudrillard, and allowed them to offer a stronger and more comprehensive defence of the said tradition.     

The Effect of Repetition on Infants' Imitation From Picture Books Varying in Iconicity

Although picture‐book reading is commonplace during infancy, little is known about the impact of this activity on learning. A previous study showed that 18‐ and 24‐month‐olds imitated a novel action sequence presented in a book that was illustrated with realistic color photos, whereas they failed to imitate from books illustrated with less realistic drawings. In the research reported here, we hypothesized that increasing infants' exposure to a picture book would increase learning from books illustrated with both color photos and drawings. Independent groups of 18‐and 24‐month‐olds were exposed to a picture book either twice in succession or 4 times in succession. The results showed that, regardless of the iconicity of the illustrations, increasing the number of reading sessions significantly improved the infants' imitation scores, compared to age‐matched, no‐demonstration controls. The results are discussed in relation to representational insight and cognitive flexibility.     

A class of count models and a new consistent test for the Poisson distribution

We define a class of count distributions which includes the Poisson as well as many alternative count models. Then the empirical probability generating function is utilized to construct a test for the Poisson distribution, which is consistent against this class of alternatives. The limit distribution of the test statistic is derived in case of a general underlying distribution, and efficiency considerations are addressed. A simulation study indicates that the new test is comparable in performance to more complicated omnibus tests.     

Overcoming the Effects of Variation in Infant Speech Segmentation: Influences of Word Familiarity

Previous studies have shown that 7.5‐month‐olds can track and encode words in fluent speech, but they fail to equate instances of a word that contrast in talker gender, vocal affect, and fundamental frequency. By 10.5 months, they succeed at generalizing across such variability, marking a clear transition period during which infants' word recognition skills become qualitatively more mature. Here we explore the role of word familiarity in this critical transition and, in particular, whether words that occur frequently in a child's listening environment (i.e., “Mommy” and “Daddy”) are more easily recognized when they differ in surface characteristics than those that infants have not previously encountered (termed nonwords). Results demonstrate that words are segmented from continuous speech in a more linguistically mature fashion than nonwords at 7.5 months, but at 10.5 months, both words and nonwords are segmented in a relatively mature fashion. These findings suggest that early word recognition is facilitated in cases where infants have had significant exposure to items, but at later stages, infants are able to segment items regardless of their presumed familiarity.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.