Upon contact with a polymeric material, microparticles from the polymer may adhere to a silicon (Si) substrate during device processing. The adhesion contaminates the surface and, in turn, leads to defects in the fabricated Si-based microelectronic devices. In this study, Si substrates with artificially induced high-density polyethylene (HDPE) contamination was exposed to 13.56 MHz radio frequency (RF) plasma utilizing argon and oxygen gas admixtures at a power density of 5.6 W/cm2 and a working pressure of 110 Pa for up to 6 min of treatment. Optical microscopy studies revealed the removal of up to 74% of the polymer contamination upon plasma exposure. Surface free energy (SFE) increased owing to the removal of contaminants as well as the formation of polar groups on the Si surface after plasma treatment. Atomic force microscopy scans showed a decrease in surface roughness from 12.25 nm for contaminated samples to 0.77 nm after plasma cleaning. The smoothening effect can be attributed to the removal of HDPE particles from the surface. In addition, scanning electron microscope images showed that there was a decrease in the amount of HDPE contaminants adhering onto the surface after plasma exposure.
We postulate a dynamic spatio-temporal model with constant covariate effect but with varying spatial effect over time and varying temporal effect across locations. To mitigate the effect of temporary structural change, the model can be estimated using the backfitting algorithm embedded with forward search algorithm and bootstrap. A simulation study is designed to evaluate structural optimality of the model with the estimation procedure. The fitted model exhibit superior predictive ability relative to the linear model. The proposed algorithm also consistently produced lower relative bias and standard errors for the spatial parameter estimates. While additional neighbourhoods do not necessarily improve predictive ability of the model, it trims down relative bias on the parameter estimates, specially for spatial parameter. Location of the temporary structural change along with the degree of structural change contributes to lower relative bias of parameter estimates and in better predictive ability of the model. The estimation procedure is able to produce parameter estimates that are robust to the occurrence of temporary structural change.
In modeling count data with multivariate predictors, we often encounter problems with clustering of observations and interdependency of predictors. We propose to use principal components of predictors to mitigate the multicollinearity problem and to abate information losses due to dimension reduction, a semiparametric link between the count dependent variable and the principal components is postulated. Clustering of observations is accounted into the model as a random component and the model is estimated via the backfitting algorithm. Simulation study illustrates the advantages of the proposed model over standard poisson regression in a wide range of scenarios.
High dimensional predictors in regression analysis are often associated with multicollinearity along with other estimation problems. These problems can be mitigated through a constrained optimization method that simultaneously induces dimension reduction and variable selection that also maintains a high level of predictive ability of the fitted model. Simulation studies show that the method may outperform sparse principal component regression, least absolute shrinkage and selection operator, and elastic net procedures in terms of predictive ability and optimal selection of inputs. Furthermore, the method yields reduced models with smaller prediction errors than the estimated full models from the principal component regression or the principal covariance regression.
We introduce panel models and identify its link to spatial-temporal models. Both models are characterized and differentiated through the variance-covariance matrix of the disturbance term. The resulting estimates or tests are as complicated as the nature of the said variance-covariance matrix. Some iterative methods typically used in computational statistics are also presented. These methods are used in conducting statistical inference for spatial-temporal models.
We introduce panel models and identify its link to spatial-temporal models. Both models are characterized and differentiated through the variance-covariance matrix of the disturbance term. The resulting estimates or tests are as complicated as the nature of the said variance-covariance matrix. Some iterative methods typically used in computational statistics are also presented. These methods are used in conducting statistical inference for spatial-temporal models.
Same-sex marriage in the Philippines remains a highly contentious issue due to the influence of religion in this predominantly Catholic country, where it is often framed as an issue of morality. However, the psychological underpinnings of this religious influence still merits further exploration. Thus, we examined the role of religious behaviors and moral foundations in predicting attitudes toward same-sex marriage among Filipinos. Data from 385 participants revealed that the particular behavior of reading the main sacred text of one’s religion, rather than the often-used predictor of religious attendance predicts negative attitudes. Beyond these religious variables, the moral foundation of Purity/sanctity also predicts negative attitudes. Recommendations for further research and possible implications on attitude change are discussed in light of these findings.
We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes.
Jose Wendell P. Capili. 2016. “Cronulla Beach (poetry).” In 21st Century Literature (Marikit Alto-Uychoco, editor), Pp. 50. Quezon City: Rex Book Store.
Carbon molecular ions were extracted from an ion source operated with a carbon filament cathode. The mass spectrum of the extracted beam showed a higher current of diatomic carbon molecular ions than atomic carbon ions. The magnetic filter field successfully reduced extraction of smaller mass ions, to make molecular carbon ion current comparable to the ion current of helium, which was used as a discharge support gas.
