Comorbidity Distribution Based on the Charlson Comorbidity Index (n = 4095).

<p>Comorbidity Distribution Based on the Charlson Comorbidity Index (n = 4095).</p

Predictive Method for the Change in Equilibrium Conditions of Gas Hydrates with Addition of Inhibitors and Electrolytes

Here we present a predictive method for the change in the three-phase (vapor–liquid–hydrate) equilibrium condition of gas hydrates upon the introduction of organic inhibitors and electrolytes. The Peng–Robinson–Stryjek–Vera (PRSV) equation of state, combined with the COSMO-SAC activity coefficient liquid model through the modified Huron–Vidal (MHV1) mixing rule, is used to describe the fluid phase, and the van der Waals and Platteeuw (vdW–P) model is used to describe the hydrate crystalline phase. The temperature-dependent Langmuir absorption constants for the vdW–P model are determined by fitting to the equilibrium condition of pure gas hydrates. Once determined, the method contains no adjustable binary interaction parameters and can be used for prediction of the phase behaviors of gas hydrates with additives that do not enter the cages of the clathrate hydrates (e.g., most inhibitors and electrolytes). We examined the accuracy of this method using five pure gas hydrates, five organic inhibitors, and nine electrolytes, and over ranges of temperature (259.0–303.6 K) and pressure (1.37 × 10⁵–2.08 × 10⁸ Pa). The average relative deviations in the predicted equilibrium temperatures are found to be 0.23% for pure gas hydrates, 0.72% with organic inhibitors, and 0.18% with electrolytes, respectively. We believe that this method is useful for many gas hydrate related engineering problems such as the screening of inhibitors for gas hydrates in flow assurance

Patient Characteristics.

<p>Abbreviations: nasopharyngeal carcinoma, NPC;+CT, chemotherapy; ‡RT, Radiation therapy; SD, standard deviation; CCI, Charlson Comorbidity Index; ACCI, Age-Adjusted Charlson Comorbidity Index; HN-CCI, revised head and neck Charlson Comorbidity Index</p><p>Demographic characteristics for NPC patients from 2007 to 2011 (n = 4095).</p><p>Patient Characteristics.</p

Medical cost in the last one month of life of Taiwanese oral cancer decedents from 2009 to 2011 by hierarchical generalized linear model using a random-intercept model.

<p>*Medical cost of aggressive care in the last one month of life US dollars 2,611±3,329.</p><p>**95% CI, 95% confidence interval.</p><p>Medical cost in the last one month of life of Taiwanese oral cancer decedents from 2009 to 2011 by hierarchical generalized linear model using a random-intercept model.</p

Comorbidity Distribution Based on the HN-CCI (n = 4095).

<p>Comorbidity Distribution Based on the HN-CCI (n = 4095).</p

Prediction of Phase Equilibrium of Methane Hydrates in the Presence of Ionic Liquids

In this work, a predictive method is applied to determine the vapor–liquid-hydrate three-phase equilibrium condition of methane hydrate in the presence of ionic liquids and other additives. The Peng–Robinson–Stryjek–Vera Equation of State (PRSV EOS) incorporated with the COSMO-SAC activity coefficient model through the first order modified Huron–Vidal (MHV1) mixing rule is used to evaluate the fugacities of vapor and liquid phases. A modified van der Waals and Platteeuw model is applied to describe the hydrate phase. The absolute average relative deviation in predicted temperature (AARD-T) is 0.31% (165 data points, temperature ranging from 273.6 to 291.59 K, and pressure ranging from 1.01 to 20.77 MPa). The method is further used to screen for the most effective thermodynamic inhibitors from a total of 1722 ionic liquids and 574 electrolytes (combined from 56 cations and 41 anions). The valence number of ionic species is found to be the primary factor of inhibition capability, with the higher valence leading to stronger inhibition effects. The molecular volume of ionic liquid is of secondary importance, with the smaller size resulting in stronger inhibition effects

The influence of comorbidity according to CCI, ACCI and HN-CCI on patient survival.

<p>The influence of comorbidity according to CCI, ACCI and HN-CCI on patient survival.</p

Patient selection flowchart.

<p>Patient selection flowchart.</p

Factors associated with increased or decreased EOL expenditure in oral cancer decedents.

<p>Factors associated with increased or decreased EOL expenditure in oral cancer decedents.</p

Receiver operating characteristic curve compared the discriminating ability for predicting survival of the ACCI (area = 0.693; 95% CI 0.670 to 0.715), CCI (area = 0.619; 95% CI 0.593 to 0.644) and HN-CCI (area = 0.545; 95% CI 0.519 to 0.570).

<p>Receiver operating characteristic curve compared the discriminating ability for predicting survival of the ACCI (area = 0.693; 95% CI 0.670 to 0.715), CCI (area = 0.619; 95% CI 0.593 to 0.644) and HN-CCI (area = 0.545; 95% CI 0.519 to 0.570).</p