1,075 research outputs found
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
) 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 βproductivityβ 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)Tβββ) 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
Π ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΊΡΠΈΠ·ΠΈΡΠ΅
Π£ ΡΡΠ°ΡΡΡ Π΄Π»Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ ΠΏΠΎΠ·ΠΈΡΡΡ ΠΌΠΎΠ»ΠΎΠ΄ΠΈΡ
ΡΡΠ΅Π½ΠΈΡ
, ΡΡΠΎΡΠΎΠ²Π½ΠΎ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΎ-Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎΡ ΠΊΡΠΈΠ·ΠΈ 2008β2010 Ρ. Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½ΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π½Π΅ΡΡΡΠΊΠΎΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΡΡ Π΄Π°Π½ΠΈΡ
, ΡΠΎ ΠΏΡΠ°ΡΡΡ Π² ΡΠ΅ΠΆΠΈΠΌΡ ΠΏΠ°ΡΠ°Π»Π΅Π»ΡΠ½ΠΎΡ ΡΡ
Π½ΡΠΎΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ. ΠΠ°Π²Π΅Π΄Π΅Π½ΠΎ Π·Π°Ρ
ΠΎΠ΄ΠΈ ΡΠΎΠ΄ΠΎ Π·Π½ΠΈΠΆΠ΅Π½Π½Ρ Π½Π°ΡΠ»ΡΠ΄ΠΊΡΠ² ΠΊΡΠΈΠ·ΠΈ Π΄Π»Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ.Π ΡΡΠ°ΡΡΠ΅ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌΡ ΠΊΡΠΈΠ·ΠΈΡΡ 2008β2010 Π³Π³. ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ ΠΌΠ΅ΡΠΎΠ΄ Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
, ΠΊΠΎΡΠΎΡΡΠΉ ΡΠ°Π±ΠΎΡΠ°Π΅Ρ Π² ΡΠ΅ΠΆΠΈΠΌΠ΅ ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΠΎΠΉ ΠΈΡ
ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡ ΠΏΠΎ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΉ ΠΊΡΠΈΠ·ΠΈΡΠ° Π΄Π»Ρ Π£ΠΊΡΠ°ΠΈΠ½Ρ.In an article for determining the position of young scientists, in relation to financial and economic crisis, 2008β 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 (0β18 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 [0.07β0.29], LS 0 [0β0], HN 0 [0β0], and HS 0.03 [0.0β0.17] 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
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