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Supercritical carbon dioxide (SCCO2) is a non-toxic and environmentally friendly fluid and has been used in polymerization reactions, processing, foaming, and plasticizing of polymers. Exploring the behavior and data of SCCO2 sorption and dissolution in polymers provides essential information for polymer applications. This study investigated the sorption and diffusion of SCCO2 into polyetherimide (PEI). The sorption and desorption processes of SCCO2 in PEI samples were measured in the temperature range from 40 to 60 °C, the pressure range from 20 to 40 MPa, and the sorption time from 0.25 to 52 h. This study used the ex situ gravimetric method under different operating conditions and applied the Fickian diffusion model to determine the mass diffusivity of SCCO2 during sorption and desorption processes into and out of PEI. The equilibrium mass gain fraction of SCCO2 into PEI was reported from 9.0 wt% (at 60 °C and 20 MPa) to 12.8 wt% (at 40 °C and 40 MPa). The sorption amount increased with the increasing SCCO2 pressure and decreased with the increasing SCCO2 temperature. This study showed the crossover phenomenon of equilibrium mass gain fraction isotherms with respect to SCCO2 density. Changes in the sorption mechanism in PEI were observed when the SCCO2 density was at approximately 840 kg/m3. This study qualitatively performed FTIR analysis during the SCCO2 desorption process. A CO2 antisymmetric stretching mode was observed near a wavenumber of 2340 cm−1. A comparison of loss modulus measurements of pure and SCCO2-treated PEI specimens showed the shifting of loss maxima. This result showed that the plasticization of PEI was achieved through the sorption process of SCCO2.

The Shewhart X¯- and S-charts are most commonly used for monitoring the process mean and variability based on the assumption of normality. However, many process distributions may follow a positively skewed distribution, such as the lognormal distribution. In this study, we discuss the construction of three combined X¯- and S-charts for jointly monitoring the lognormal mean and the standard deviation. The simulation results show that the combined lognormal X¯- and S-charts are more effective when the lognormal distribution is more skewed. A real example is used to demonstrate how the combined lognormal X¯- and S-charts can be applied in practice.

Background: Phenotype prediction with genome-wide markers is a critical but difficult problem in biomedical research due to many issues such as nonlinearity of the underlying genetic mapping and high-dimensionality of marker data. When using the deep learning method in the small-n-large-p data, some serious issues occur such as over-fitting, over-parameterization, and biased prediction. Methods: In this study, we propose a split-and-merge deep learning method, named SM-DL method, to learn a neural network on the dimension reduce data by using the split-and-merge technique. Conclusions: Numerically, the proposed method has significant performance in phenotype prediction for a simulated example. A real example is used to demonstrate how the proposed method can be applied in practice.

Developing materials for use in photovoltaic (PV) systems requires knowledge of their performance over the warranted lifetime of the PV system. Poly(ethylene-terephthalate) (PET) is a critical component of PV module backsheets due to its dielectric properties and low cost. However, PET is susceptible to environmental stressors and degrades over time. Changes in the physical properties of nine PET grades were modeled after outdoor and accelerated weathering exposures to characterize the degradation process of PET and assess the influence of stabilizing additives and weathering factors. Multivariate multiple regression (MMR) models were developed to quantify changes in color, gloss, and haze of the materials. Natural splines were used to capture the non-linear relationship between predictors and responses. Model performance was evaluated via adjusted-R2 and root mean squared error values from leave-one-out cross validation analysis. All models described over 85% of the variation in the data with low relative error. Model coefficients were used to assess the influence of weathering stressors and material additives on the property changes of films. Photodose was found to be the primary degradation stressor and moisture was found to increase the degradation rate of PET. Direct moisture contact was found to impose more stress on the material than airbone moisture (humidity). Increasing the concentration of TiO2 was found to generally decrease the degradation rate of PET and mitigate hydrolytic degradation. MMR models were compared to physics-based models and agreement was found between the two modeling approaches. Cross-correlation of accelerated exposures to outdoor exposures was achieved via determination of cross-correlation scale factors. Cross-correlation revealed that direct moisture contact is a key factor for reliable accelerated weathering testing and provided a quantitative method to determine when accelerated exposure results can be made more aggressive to better approximate outdoor exposure conditions.

Control charts, one of the powerful tools in statistical process control (SPC), are widely used to monitor and detect out-of-control processes in the manufacturing industry. Many researchers have pointed out the effects of using estimated parameters on the average run length (ARL) performance metric. Most of the previous literature has studied the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL) to evaluate the performance of control charts. In this article, we study the performance of three S control charts, the Shewhart S-chart, the median absolute deviation (MAD) control chart, and the lognormal S control chart, for a lognormal distribution in terms of the AARL and SDARL. Simulation results indicate the sample size to reach the specified in-control ARL value is very large. The lognormal S control chart has a smaller SDARL value than the other two S-charts.

Control charts, one of the powerful tools in statistical process control (SPC), are widely used to monitor and detect out-of-control processes in the manufacturing industry. Many researchers have pointed out the effects of using estimated parameters on the average run length (ARL) performance metric. Most of the previous literature has studied the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL) to evaluate the performance of control charts. In this article, we study the performance of three S control charts, the Shewhart S-chart, the median absolute deviation (MAD) control chart, and the lognormal S control chart, for a lognormal distribution in terms of the AARL and SDARL. Simulation results indicate the sample size to reach the specified in-control ARL value is very large. The lognormal S control chart has a smaller SDARL value than the other two S-charts.

Questionnaires are important surveying tools that are used in numerous studies. Analyses of multiple-response questions are not as well established in detail compared with single-response questions. Wang has proposed several methods for ranking responses in multiple-response questions under the frequentist set-up. However, prior information may exist for ranks of responses in numerous situations. Therefore, establishing a methodology that combines updated survey data and past information for ranking responses is an essential issue in questionnaire data analysis. This study develops Bayesian ranking methods based on several Bayesian multiple-testing procedures to rank responses by controlling the posterior expected false discovery rate. Moreover, a simulation is conducted to compare these approaches, and a real data example is presented to show the effectiveness of the methods proposed.

Developing materials for use in photovoltaic (PV) systems requires knowledge of their performance over the warranted lifetime of the PV system. Poly(ethylene-terephthalate) (PET) is a critical component of PV module backsheets due to its dielectric properties and low cost. However, PET is susceptible to environmental stressors and degrades over time. Changes in the physical properties of nine PET grades were modeled after outdoor and accelerated weathering exposures to characterize the degradation process of PET and assess the influence of stabilizing additives and weathering factors. Multivariate multiple regression (MMR) models were developed to quantify changes in color, gloss, and haze of the materials. Natural splines were used to capture the non-linear relationship between predictors and responses. Model performance was evaluated via adjusted-R.sup.2 and root mean squared error values from leave-one-out cross validation analysis. All models described over 85% of the variation in the data with low relative error. Model coefficients were used to assess the influence of weathering stressors and material additives on the property changes of films. Photodose was found to be the primary degradation stressor and moisture was found to increase the degradation rate of PET. Direct moisture contact was found to impose more stress on the material than airbone moisture (humidity). Increasing the concentration of TiO.sub.2 was found to generally decrease the degradation rate of PET and mitigate hydrolytic degradation. MMR models were compared to physics-based models and agreement was found between the two modeling approaches. Cross-correlation of accelerated exposures to outdoor exposures was achieved via determination of cross-correlation scale factors. Cross-correlation revealed that direct moisture contact is a key factor for reliable accelerated weathering testing and provided a quantitative method to determine when accelerated exposure results can be made more aggressive to better approximate outdoor exposure conditions.

BackgroundThere are several studies comparing the difference between adenocarcinoma (AC) and squamous cell carcinoma (SqCC) of lung cancer. However, seldom studies compare the different overall survival (OS) between AC and SqCC at same clinical or pathological stage. The aim of the study was to investigate the 5-year OS between AC and SqCC groups.MethodsData were obtained from the Taiwan Society of Cancer Registry. There were 48,296 non-small cell lung cancer (NSCLC) patients analyzed between 2009 and 2014 in this retrospective study. We analyzed both the AC and SqCC groups by age, gender, smoking status, Charlson co-morbidity index (CCI) score, clinical TNM stage, pathological stage, tumor location, histologic grade, pleura invasion, performance status, treatment, stage-specific 5-year OS rate in each clinical stage I–IV and causes of death. We used propensity score matching to reduce the bias.ResultsThe AC and SqCC groups are significantly different in age, gender, smoking status, CCI score, clinical TNM stage, pathological stage, tumor location, histologic grade, pleura invasion, performance status, treatment, stage-specific 5-year OS rate in each clinical stage and causes of death (p < 0.0001). The stage-specific 5-year OS rates between AC and SqCC were 79% vs. 47% in stage I; 50% vs. 32% in stage II; 27% vs. 13% in stage III; 6% vs. 2% in stage IV, respectively (all p values < 0.0001).ConclusionsAC and SqCC have significantly different outcomes in lung cancer. We suggest that these two different cancers should be analyzed separately to provide more precise outcomes in the future.