Different Levels Of Dispersion Research Articles

On Designing a New Control Chart Using the Generalized Conway–Maxwell–Poisson Distribution to Monitor Count Data

Many researchers employed Poisson distribution-based control charts to monitor count data. Nevertheless, these charts can handle count data that deviate from the Poisson assumption of equal mean and variance. This paper suggests a new control chart (CC) that uses the generalized Conway–Maxwell–Poisson (GCOMP) distribution, which can deal with count data that have different levels of dispersion and zero-inflation (ZI). The proposed chart is designed considering the total number of counts. The main advantage of this study is that it pays attention to the tails of the count data when monitoring the process. The performance is measured by the average run length using L control limits at different sample sizes and parametric settings. The findings demonstrate that, for count data with varying tail behaviors, the proposed chart performs better compared to existing CCs. ZI count data can also be monitored with the proposed chart. The proposed chart can be applied in a variety of fields, as verified by the examples provided in this paper.

Bayesian estimation for median discrete Weibull regression model

<abstract><p>The discrete Weibull model can be adapted to capture different levels of dispersion in the count data. This paper takes into account the direct relationship between explanatory variables and the median of discrete Weibull response variable. Additionally, it provides the Bayesian estimate of the discrete Weibull regression model using the random walk Metropolis algorithm. The prior distributions of the coefficient predictors were carried out based on the uniform non-informative, normal and Laplace distributions. The performance of the Bayes estimators was also compared with the maximum likelihood estimator in terms of the mean square error and the coverage probability through the Monte Carlo simulation study. Meanwhile, a real data set was analyzed to show how the proposed model and the methods work in practice.</p></abstract>

Dark uncertainty in key comparisons in the gas analysis area

CCQM-K118 natural gas is among the first key comparisons in the gas analysis area where a model was chosen to fit the data that considered possible overdispersion (‘dark uncertainty’) of the submitted results. As the key comparison was operated with as many travelling standards as there were participants, a Bayesian hierarchical model was developed that also took into account the (small) differences between the measurands across the suite of standards. As there was no independent reference value, such as from static gravimetry, the key comparison was evaluated using a consensus value. In this paper, we assess the performance of the model used in CCQM-K118 using the data from two previous key comparisons about the natural gas composition, CCQM-K23 and CCQM-K16. These key comparisons were operated with independent reference values and showed different levels of dispersion and agreement in the results. From the re-evaluation of the data, we conclude that the model developed for CCQM-K118 is fit for purpose and captures aptly the differences across the measurands for the different travelling standards and the overdispersion of the data. We also conclude that if there is no overdispersion of the data, this is reflected in the posterior probability distribution of the excess standard deviation. The representative value (e.g. median) of this standard deviation becomes then small if not negligible in comparison to the uncertainties stated by the participants.

Social capital and landscape: effects of monoculture forest plantations on small producers in the Mediterranean dryland of south-central Chile

ABSTRACTThis research focuses on the socio-ecological context of small farmers in the Mediterranean dryland region of south-central Chile or Secano Interior. This area has been characterised by a long history of natural resource exploitation, especially soil and native vegetation, and the arrival and rapid expansion of monoculture forest plantations resulting in serious environmental degradation issues. Using a mixed methods approach including social network surveys, semi-structured interviews, and geospatial data, this article compares social capital indicators and socio-ecological interactions for rural households with different levels of encroachment due to forest plantations. The research objective is to contrast diverse socio-environmental scenarios with different levels of dispersion and proximity to forest plantations. Findings on local egocentric networks propose clues about the social effects of traditional agriculture loss, rural depopulation and other socio-economic and ecological issues which respondents associated with the introduction and expansion of monoculture plantations in the region.

An alternative hyper-Poisson integer-valued GARCH model with application to polio, internet protocol and COVID-19 data

<abstract> <p>Time series of counts are observed widely in actuarial science, finance, epidemiology and biology. These time series may exhibit over-, equi- and under-dispersion. The Poisson distribution is commonly used in count time series models, but it is restricted by the equality of mean and variance. Other distributions such as the generalized Poisson, double Poisson, hyper-Poisson, and COM-Poisson distributions have been proposed to replace the Poisson distribution to model the different levels of dispersion in time series of counts. These models have certain limitations such as complex expressions for the mean and variance which complicate the formulation as GARCH models. In this study, we propose an alternative hyper-Poisson (AHP) distribution, with simple forms of conditional mean and variance, for an integer-valued GARCH (INGARCH) model for time series of counts that also exhibit the different levels of dispersion. We demonstrate that the AHP-INGARCH model is comparable to some existing INGARCH models. Additionally, the model can cover a wider range of dispersion. The maximum likelihood estimation can be used to estimate the parameters of the proposed model. Applications to three real-life data sets related to polio, internet protocol and daily COVID-19 new deaths underscore the usefulness of the proposed model in studying both over-dispersed and under-dispersed time series of counts.</p> </abstract>

Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance

Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. This study aimed to assess the influence of data features and overdispersion on RF regression performance. We assessed the effect of types of predictors (100, 75, 50, and 20% continuous, and 100% categorical), the number of predictors (p = 816 and 24), and the sample size (N = 50, 250, and 1250) on RF parameter settings. We also compared RF performance to that of classical generalized linear models (Poisson, negative binomial, and zero-inflated Poisson) and the linear model applied to log-transformed data. Two real datasets were analysed to demonstrate the usefulness of RF for overdispersed data modelling. Goodness-of-fit statistics such as root mean square error (RMSE) and biases were used to determine RF accuracy and validity. Results revealed that the number of variables to be randomly selected for each split, the proportion of samples to train the model, the minimal number of samples within each terminal node, and RF regression performance are not influenced by the sample size, number, and type of predictors. However, the ratio of observations to the number of predictors affects the stability of the best RF parameters. RF performs well for all types of covariates and different levels of dispersion. The magnitude of dispersion does not significantly influence RF predictive validity. In contrast, its predictive accuracy is significantly influenced by the magnitude of dispersion in the response variable, conditional on the explanatory variables. RF has performed almost as well as the models of the classical Poisson family in the presence of overdispersion. Given RF’s advantages, it is an appropriate statistical alternative for counting data.

Experimental study of stern slamming and global response of a large cruise ship in regular waves

This paper presents an experimental investigation of stern slamming and global responses using a segmented model test. A model of large cruise ship with a long and flat stern is selected for the test, with a scale ratio of 1:60. Experimental random uncertainty is analyzed by considering the model responses in a series of regular waves. It is found that different types of response have different levels of dispersion. A calculation model that can reproduce ship–wave relative motions during stern slamming is established based on the experimental data. The influences of the instantaneous impact velocity and longitudinal deadrise angle on the distribution of local slamming pressure are analyzed. Finally, the effect of stern slamming on global responses is discussed. This investigation shows that the longitudinal deadrise angles are usually small in following waves, and this results in a large slamming force loaded on the stern in a short time. In addition, a whipping response of the hull girder will be excited.

Carbon Nanotubes Dispersion Assessment in Nanocomposites by Means of a Pulsed Thermographic Approach.

The extensive production of polymer composites reinforced by carbon nanotube is limited by the absence of non-destructive evaluation (NDE) methods capable of assessing product quality to guarantee compliance with specifications. It is well known that the level of dispersion of carbon nanotubes (CNTs) in the polymer matrix is the parameter that, much more than others, can influence their enhancement capabilities. Here an active Infrared Thermography Non Destructive Testing(IR-NDT) inspection, joined with pulsed phase thermography (PPT), were applied for the first time to epoxy-CNT composites to evaluate the level of dispersion of the nanoparticles. The PPT approach was tested on three groups of epoxy nanocomposite samples with different levels of dispersion of the nanoparticles. The phasegrams obtained with the presented technique clearly show clusters, or bundles, of CNTs when present, so a comparison with the reference sample is not necessary to evaluate the quality of the dispersion. Therefore, the new NDE approach can be applied to verify that the expected dispersion levels are met in products made from epoxy and Multi-Walled Carbon Nanotubes (MWCNTs). The mechanisms underlying the effects of the dispersion of carbon nanotube on the thermal response of polymer composites have been identified.

Modulation of the cutoff wavelength in the spectra for solar selective absorbing coating based on high-entropy films

This paper demonstrates an intrinsic modulation of the cutoff wavelength in the spectra for solar selective absorbing coating based on high-entropy films. The (NiCuCrFeSi)N ((NCCFS)N) films were deposited by a magnetron sputtering system. Rutherford backscattering spectroscopy analysis confirms the uniform composition and good homogeneity of these high-entropy films. The real and imaginary parts of the permittivity for the (NCCFS)N material are calculated on the basis of the reflectance spectral fitting results. A redshift cutoff wavelength of the reflectance spectrum with increasing nitrogen gas flow rate exists because of the different levels of dispersion when changing nitrogen content. To realize significant solar absorption, the film surface was reconstituted to match its impedance with air by designing a pyramid nanostructure metasurface. Compared with the absorptance of the as-deposited films, the designed metasurface obtains a significant improvement in solar absorption with the absorptance increasing from 0.74 to 0.99. The metasurfaces also show low mid-infrared emissions with thermal emittance that can be as low as 0.06. These results demonstrate a new idea in the design of solar selective absorbing surface with controllable absorptance and low infrared emission for high-efficiency photo-thermal conversion.

Mesoscopic Simulation of Dispersed Copolymers: Effects of Chain Length, Chemical Composition, and Block Length Distributions on Self‐Assembly

AbstractPolymer materials are normally constituted by chains of different sizes and compositions due to the stochastic nature of most polymerization mechanisms. For this reason, dissipative particle dynamics (DPD) simulations are carried out in the present work to investigate the effect of chain length distribution (CLD), chemical composition distribution (CCD), and block length distribution (BLD) on the process of self‐assembly of diblock copolymers. Flory and Poisson distributions are used to study CLD and BLD effects and bidispersed distributions are used for the study of CCD effects. Visual inspection and the static structure factor S(q) are used to evaluate the obtained structures. The results show that high dispersion in the CCD and different levels of dispersion in the BLDs of different components of the system increase the sizes of the segregated domains and decisively affect the structure and the purity of the formed mesophases.

Rheological Behaviour of PP Nanocomposites by Extrusion Process

The effect of organophilic clay (C15A) in PP compatibilized polymer (PP/PP-g-MA) through rheological experimental results is presented. This study focusses on the description of the rheological behaviour of the organophilic layers along the screws of a twin screw extruder by melting process, varying the screw profile as well as the processing conditions, namely screw speed, temperature and feed rate. Different levels of dispersion were found along the screws for all conditions and positions analyzed by rheology. The qualitative analysis of the images obtained by transmission electron microscopy, reinforced the results. Furthermore, the effects of the processing conditions on dispersion of C15A layers along the screw profile are analyzed using multiple linear regression techniques. The results show statistically significant differences of all rheology measures for all the processing conditions and positions along the extruder.

Efficient Bayesian inference for COM-Poisson regression models

COM-Poisson regression is an increasingly popular model for count data. Its main advantage is that it permits to model separately the mean and the variance of the counts, thus allowing the same covariate to affect in different ways the average level and the variability of the response variable. A key limiting factor to the use of the COM-Poisson distribution is the calculation of the normalisation constant: its accurate evaluation can be time-consuming and is not always feasible. We circumvent this problem, in the context of estimating a Bayesian COM-Poisson regression, by resorting to the exchange algorithm, an MCMC method applicable to situations where the sampling model (likelihood) can only be computed up to a normalisation constant. The algorithm requires to draw from the sampling model, which in the case of the COM-Poisson distribution can be done efficiently using rejection sampling. We illustrate the method and the benefits of using a Bayesian COM-Poisson regression model, through a simulation and two real-world data sets with different levels of dispersion.

Categorising Count Data into Ordinal Responses with Application to Ecological Communities

Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line.

Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model

The zero inflated hyper-Poisson regression model permits count data to be analysed with covariates that determine different levels of dispersion and that present structural zeros due to the existence of a non-users group. A simulation study demonstrates the capability of the model to detect over- and under-dispersion of the potential users group of the dataset in relation to the value of covariates, and to estimate the proportion of structural zeros with great accuracy. An application of the model to fit the number of children per family in relation to several covariates confirms the presence of structural zeros in fertility data at the same time as it detects under-dispersion in most of the levels determined by the covariates.

Mössbauer studies of magnetite nanoparticles

Magnetite Fe3O4 samples with different levels of dispersion (“bulk” and nanoscale particles with an average diameter of 24, 14 and 7 nm) are studied using Mossbauer spectroscopy. It is shown that this method of analysis makes it possible to obtain important information about the features of magnetic states of iron-containing nanoparticles and, more specifically, about the distinction in effective magnetic fields for iron nuclei located in different regions within them. The obtained results are fully consistent with known theoretical data and allow estimation of the thickness of the surface region of the nanoparticles.

Free vibrations of an uncertain energy pumping system

The aim of this paper is to study the energy pumping (the irreversible energy transfer from one structure, linear, to another structure, nonlinear) robustness considering the uncertainties of the parameters of a two DOF mass-spring-damper, composed of two subsystems, coupled by a linear spring: one linear subsystem, the primary structure, and one nonlinear subsystem, the so-called NES (nonlinear energy sink). Three parameters of the system will be considered as uncertain: the nonlinear stiffness and the two dampers. Random variables are associated to the uncertain parameters and probability density functions are constructed for the random variables applying the Maximum Entropy Principle. A sensitivity analysis is then performed, considering different levels of dispersion, and conclusions are obtained about the influence of the uncertain parameters in the robustness of the system.

PLA and Organoclays Nanocomposites: Degradation Process and Evaluation of ecotoxicity Using Allium cepa as Test Organism

In this study, nanocomposites of PLA and organoclays Cloisite 20A and Cloisite 30B were prepared by the melt intercalation method and the obtained samples were characterized by transmission electron microscopy (TEM). Since composting is an important proposal to the final disposal of biopolymers, the influence of clays on the hydrolytic degradation process of PLA was evaluated by visual analysis and monitoring of molecular weight after periods of 15 and 30 days of degradation in compost. After degradation of the materials in composting environment, the evaluation of cytotoxic, genotoxic and mutagenic effects of compost aqueous extract was carried out using a bioassay with Allium cepa as test organism. The TEM micrographs permitted the observation of different levels of dispersion, including exfoliated regions. In the evaluation of hydrolytic degradation it was noted that the presence of organoclays can decrease the rate of degradation possibly due to the barrier effect of clay layers and/or the higher degree of crystallinity in the nanocomposite samples. Nevertheless, even in the case of nanocomposites, the molecular weight reduction was significant, indicating that the composting process is favorable to the chain scission of PLA in studied materials. In the analysis performed by the bioassay using A. cepa as test organism, it was found that after degradation of the PLA and its nanocomposites the aqueous extract of compost samples induced a decreasing in the mitotic index and an increasing in the induction of chromosomal abnormalities. These results were statistically significant in relation to the negative control (distilled water). By comparing the results obtained for the nanocomposites in relative to pure polymer, there were no statistically significant differences. The types of the observed chromosomal aberrations indicated a possible genotoxic effect of the materials, which may be related to an aneugenic action of PLA degradation products.

In situ polymerization of polyamide 66 nanocomposites utilizing interfacial polycondensation. II. Sodium montmorillonite nanocomposites

AbstractInterfacial polycondensation (IPC) is used to generate polyamide 66 (PA66) nanocomposite using sodium montmorillonite (NaMMT), which offers better thermal stability than organically modified montmorillonite. Several approaches are used to obtain different levels of dispersion for studying the factors affecting dispersion of NaMMT layered‐silicates. These approaches include dispersing NaMMT in either aqueous media or in a compatible nonaqueous medium. Moreover, clay slurry was added to the reaction media separately or in combination with the aqueous hexamethylenediamine solution, which includes either excess amine or sodium carbonate as the by‐product scavenger, in order to study the effect of sequencing on the dispersion of NaMMT. Several characterization techniques including dynamic mechanical analysis, wide angle X‐ray diffraction, and transmission electron microscopy are used to examine the structure and relate it to the mechanical properties of the nanocomposites. Results show that in situ polymerization techniques predominantly give rise to hybrid exfoliated–intercalated NaMMT structure. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers

Effect of clay dispersion on the rheological properties and flammability of polyurethane‐clay nanocomposite elastomers

AbstractPolyurethane‐clay nanocomposite elastomers were synthesized using polyol‐clay blends with different levels of dispersion, which affected the final elastomer microstructure. A PU‐clay microcomposite elastomer containing partially dispersed clay showed poorer mechanical and similar fire properties to the unmodified polyurethane. More complete dispersion of the clay into the polyol led to an exfoliated structure in the final elastomer. This showed a higher modulus and kept a viscoelastic behavior to higher temperature than the pristine PU. The enhancement of mechanical and thermal properties in the nanocomposite elastomer can be attributed to the degree of clay exfoliation, and this also prevented dripping during the UL 94 fire test. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

Structural analysis of multicomponent nanoclay-containing polymer blends through simple model systems

A systematic approach was adopted to study multicomponent clay-containing nanocomposites using compatibilized and non-compatibilized blends of polyamide 6 (PA6)/acrylonitrile–butadiene–styrene terpolymer (ABS) and their organoclay (OMMT) nanocomposites. For this purpose PA6/styrene–acrylonitrile copolymer (SAN) based blends and nanocomposites were selected as simple model systems. In this way the role of each component of the systems, especially the clay, compatibilizer, and polybutadiene fraction on the formation of intercalated or exfoliated OMMT structures as well as resulting dynamic mechanical properties (DMA) could be elucidated. Structural analysis of the model systems using theoretical approach, and X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and DMA revealed that the most crucial factor in controlling the morphology and achieving different levels of dispersion is the extent of interaction between clay and the polymer matrix. Morphological analysis revealed that the OMMT layers were dispersed and exfoliated largely in the PA6 phase but, some were also accumulated at the rubber particle surface which remained non-intercalated. The effect of a compatibilizer on the dispersion of OMMT was not completely clear. The SAN based nanocomposites containing PA6 showed fully exfoliated OMMT structures, whereas the ABS based nanocomposites, having an additional rubber fraction, showed a mixed exfoliated and also partly non-intercalated morphology. The OMMT did not change the general occurrence of the co-continuous structures but refined the structures and led to mechanical stiffening as indicated by the DMA results. A correlation was established between the changes in the morphological states and the DMA properties.