Colloid-oil-water-interface interactions in the presence of multiple salts: charge regulation and dynamics

We theoretically and experimentally investigate colloid-oil-water-interface interactions of charged, sterically stabilized, poly(methyl-methacrylate) colloidal particles dispersed in a low-polar oil (dielectric constant $\epsilon=5-10$) that is in contact with an adjacent water phase. In this model system, the colloidal particles cannot penetrate the oil-water interface due to repulsive van der Waals forces with the interface whereas the multiple salts that are dissolved in the oil are free to partition into the water phase. The sign and magnitude of the Donnan potential and/or the particle charge is affected by these salt concentrations such that the effective interaction potential can be highly tuned. Both the equilibrium effective colloid-interface interactions and the ion dynamics are explored within a Poisson-Nernst-Planck theory, and compared to experimental observations.Comment: 13+2 pages, 5+3 figures; V2: small clarifications in the tex

Trading-off payments and accuracy in online classification with paid stochastic experts

We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influences the accuracy of their prediction through some unknown Lipschitz “productivity” function. In each round, the learner must decide how much to pay each expert and then make a prediction. They incur a cost equal to a weighted sum of the prediction error and upfront payments for all experts. We introduce an online learning algorithm whose total cost after T rounds exceeds that of a predictor which knows the productivity of all experts in advance by at most O(K2(lnT)T−−√) where K is the number of experts. In order to achieve this result, we combine Lipschitz bandits and online classification with surrogate losses. These tools allow us to improve upon the bound of order T2/3 one would obtain in the standard Lipschitz bandit setting. Our algorithm is empirically evaluated on synthetic data

О финансово-экономическом кризисе

У статті для визначення позиції молодих учених, стосовно фінансово-економічної кризи 2008–2010 р. використаний метод нечіткої кластеризації даних, що працює в режимі паралельної їхньої обробки. Наведено заходи щодо зниження наслідків кризи для України.В статье для определения позиции молодых ученых, применительно к финансово-экономическому кризису 2008—2010 гг. использован метод нечеткой кластеризации данных, который работает в режиме параллельной их обработки. Приведены мероприятия по снижению последствий кризиса для Украины.In an article for determining the position of young scientists, in relation to financial and economic crisis, 2008— 2010. used the method of fuzzy clustering, which operates in parallel processing. Shows the measures to reduce the impact of the crisis in Ukraine

The occurrence of adverse events in low-risk non-survivors in pediatric intensive care patients: an exploratory study

We studied the occurrence of adverse events (AEs) in low-risk non-survivors (LNs), compared to low-risk survivors (LSs), high-risk non-survivors (HNs), and high-risk survivors (HSs) in two pediatric intensive care units (PICUs). The study was performed as a retrospective patient record review study, using a PICU-trigger tool. A random sample of 48 PICU patients (0–18 years) was chosen, stratified into four subgroups of 12 patients: LNs, LSs, HNs, and HSs. Primary outcome was the occurrence of AEs. The severity, preventability, and nature of the indentified AEs were determined. In total, 45 AEs were found in 20 patients. The occurrence of AEs in the LN group was significantly higher compared to that in the LS group and HN group (AE occurrence: LN 10/12 patients, LS 1/12 patients; HN 2/12 patients; HS 7/12 patients; LN-LS difference, p < 0.001; LN-HN difference, p < 0.01). The AE rate in the LN group was significantly higher compared to that in the LS and HN groups (median [IQR]: LN 0.12 [0.07–0.29], LS 0 [0–0], HN 0 [0–0], and HS 0.03 [0.0–0.17] AE/PICU day; LN-LS difference, p < 0.001; LN-HN difference, p < 0.01). The distribution of the AEs among the four groups was as follows: 25 AEs (LN), 2 AEs (LS), 8 AEs (HN), and 10 AEs (HS). Fifteen of forty-five AEs were preventable. In 2/12 LN patients, death occurred after a preventable AE. Conclusion: The occurrence of AEs in LNs was higher compared to that in LSs and HNs. Some AEs were severe and preventable and contributed to mortality.(Table presented.

The impact of delirium on the prediction of in-hospital mortality in intensive care patients

Introduction: predictive models, such as acute physiology and chronic health evaluation II (APACHE-II), are widely used in intensive care units (ICUs) to estimate mortality. Although the presence of delirium is associated with a higher mortality in ICU patients, delirium is not part of the APACHE-II model. The aim of the current study was to evaluate whether delirium, present within 24 hours after ICU admission, improves the predictive value of the APACHE-II score.Methods: in a prospective cohort study 2116 adult patients admitted between February 2008 and February 2009 were screened for delirium with the confusion assessment method-ICU (CAM-ICU). Exclusion criteria were sustained coma and unable to understand Dutch. Logistic regression analysis was used to estimate the predicted probabilities in the model with and without delirium. Calibration plots and the Hosmer-Lemeshow test (HL-test) were used to assess calibration. The discriminatory power of the models was analyzed by the area under the receiver operating characteristics curve (AUC) and AUCs were compared using the Z-test.Results: 1740 patients met the inclusion criteria, of which 332 (19%) were delirious at the time of ICU admission or within 24 hours after admission. Delirium was associated with in-hospital mortality in unadjusted models, odds ratio (OR): 3.22 (95% confidence interval [CI]: 2.23 - 4.66). The OR between the APACHE-II and in-hospital mortality was 1.15 (95% CI 1.12 - 1.19) per point. The predictive accuracy of the APACHE-II did not improve after adding delirium, both in the total group as well as in the subgroup without cardiac surgery patients. The AUC of the APACHE model without delirium was 0.77 (0.73 - 0.81) and 0.78 (0.74 - 0.82) when delirium was added to the model. The z-value was 0.92 indicating no improvement in discriminative power, and the HL-test and calibration plots indicated no improvement in calibration.Conclusions: although delirium is a significant predictor of mortality in ICU patients, adding delirium as an additional variable to the APACHE-II model does not result in an improvement in its predictive estimate