Single-state distributed k-winners-take-all neural network model

Distributed k-winners-takes-all (k-WTA) neural network (k-WTANN) models have better scalability than centralized ones. In this work, a distributed k-WTANN model with a simple structure is designed for the efficient selection of k winners among a group of more than k agents via competition based on their inputs. Unlike an existing distributed k-WTANN model, the proposed model does not rely on consensus filters, and only has one state variable. We prove that under mild conditions, the proposed distributed k-WTANN model has global asymptotic convergence. The theoretical conclusions are validated via numerical examples, which also show that our model is of better convergence speed than the existing distributed k-WTANN model.</p

The studies on gas adsorption properties of MIL-53 series MOFs materials

Molecular dynamics (MD), grand canonical Monte Carlo (GCMC) and ideal adsorbed solution theory (IAST) were used to study the structures and gas adsorption properties of MIL-53(M)[M=Cr, Fe, Sc, Al] metal organic framework (MOF) materials. The results show that the volumes of those MOF materials increase significantly at high temperature. By analyzing the adsorption isotherms, we found that the temperature had a paramount effect on the gas adsorption behaviors of these MOF materials. For MIL-53(Cr), the orders of the quantities of adsorbed gases were CH4>N2>CO2>H2S, CH4>H2S>CO2>N2 and CH4>CO2>H2S>N2 at 100K, 293K and 623K, respectively. We also calculated the adsorption of several combinations of two gases by MIL-53(Cr) at 293K, the results indicate that the material had selective adsorption of CH4 over CO2, H2S and N2. Our calculations provide microscopic insights into the gas adsorption performances of these MOFs and may further guide the practice of gas separation

Self-assembled structure of sulfonic gemini surfactant solution

Sulfonate gemini surfactant is a new type of anionic gemini surfactant. The unique structure of double sulfonate endows the sulfonate gemini surfactant with superior surfactant properties, including lower critical micelle concentration (CMC), unusual decontamination ability, excellent stability in strong acid/alkali solution. In this paper, the self-assembled structure of gemini dodecyl sulfonate sodium, abbreviated as 12-2-12(SO3Na)2, is studied by using of dissipative particle dynamics (DPD) method. We have constructed a spring structure model of surfactant molecules, and the effect of length hydrophobic chain, the concentration of surfactants, ethanol addictive on the self-assembly behavior and critical micelle concentration (CMC) was investigated. The results show that with the increase of the concentration of surfactants in aqueous solution, spherical, wormlike and layered micelles appear in turn. With the increase of the length of the hydrophobic chain, the clusters of the surfactants become tighter and the larger clusters are presented at the lower concentration. It was found that the addition of ethanol molecule can enhance the solubility of hydrophobic group and thus inhibit the formation of the micelles

Single-state distributed k-winners-take-all neural network model

Abstract Distributed k-winners-takes-all (k-WTA) neural network (k-WTANN) models have better scalability than centralized ones. In this work, a distributed k-WTANN model with a simple structure is designed for the efficient selection of k winners among a group of more than k agents via competition based on their inputs. Unlike an existing distributed k-WTANN model, the proposed model does not rely on consensus filters, and only has one state variable. We prove that under mild conditions, the proposed distributed k-WTANN model has global asymptotic convergence. The theoretical conclusions are validated via numerical examples, which also show that our model is of better convergence speed than the existing distributed k-WTANN model

Uplift modeling to predict individual treatment effects of renal replacement therapy in sepsis-associated acute kidney injury patients

Abstract Renal replacement therapy (RRT) is a crucial treatment for sepsis-associated acute kidney injury (S-AKI), but it is uncertain which S-AKI patients should receive immediate RRT. Identifying the characteristics of patients who may benefit the most from RRT is an important task. This retrospective study utilized a public database and enrolled S-AKI patients, who were divided into RRT and non-RRT groups. Uplift modeling was used to estimate the individual treatment effect (ITE) of RRT. The validity of different models was compared using a qini curve. After labeling the patients in the validation cohort, we characterized the patients who would benefit the most from RRT and created a nomogram. A total of 8289 patients were assessed, among whom 591 received RRT, and 7698 did not receive RRT. The RRT group had a higher severity of illness than the non-RRT group, with a Sequential Organ Failure Assessment (SOFA) score of 9 (IQR 6,11) vs. 5 (IQR 3,7). The 28-day mortality rate was higher in the RRT group than the non-RRT group (34.83% vs. 14.61%, p < 0.0001). Propensity score matching (PSM) was used to balance baseline characteristics, 458 RRT patients and an equal number of non-RRT patients were enrolled for further research. After PSM, 28-day mortality of RRT and non-RRT groups were 32.3% vs. 39.3%, P = 0.033. Using uplift modeling, we found that urine output, fluid input, mean blood pressure, body temperature, and lactate were the top 5 factors that had the most influence on RRT effect. The area under the uplift curve (AUUC) of the class transformation model was 0.068, the AUUC of SOFA was 0.018, and the AUUC of Kdigo-stage was 0.050. The class transformation model was more efficient in predicting individual treatment effect. A logistic regression model was developed, and a nomogram was drawn to predict whether an S-AKI patient can benefit from RRT. Six factors were taken into account (urine output, creatinine, lactate, white blood cell count, glucose, respiratory rate). Uplift modeling can better predict the ITE of RRT on S-AKI patients than conventional score systems such as Kdigo and SOFA. We also found that white blood cell count is related to the benefits of RRT, suggesting that changes in inflammation levels may be associated with the effects of RRT on S-AKI patients

Effect of ethanol concentration on methane hydrate decomposition: MD simulation insights

The controllability of mining is a key factor affecting the commercial application of methane hydrates, and the addition of chemical additives can significantly accelerate the mining process. However, the effect of additive concentration on hydrate decomposition is not yet well understood. In this study, we systematically investigate the effect of ethanol concentration on the decomposition of methane hydrate under varying thermodynamic conditions using molecular dynamics (MD) simulations. To quantitatively characterize the decomposition process and mechanism of methane hydrates, the combination of angular order parameter (AOP), radial distribution function (RDF), mean square displacement (MSD), diffusion coefficients and system energy was for the first time used. The results showed that the addition of ethanol contributed to the formation of methane bubbles and accelerated the decomposition of hydrates. The mass transfer effect of ethanol molecules and the reconstruction of the hydrogen bond network of water molecules determined the stability of hydrates. From 0 to 40 mol% ethanol concentration, the hydrate decomposition increased with increasing the concentration of ethanol. Both increasing the temperature and decreasing the pressure are beneficial to the decomposition of the hydrate system. These results provide the selection of optimal ethanol concentration for the decomposition of methane hydrate and reveal its decomposition mechanism, and shed important light for the controllable production of gas hydrates