State-Space Interpretation of Model Predictive Control

A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optimal filter gain is parametrized explicitly in terms of a single parameter between 0 and 1, thus removing the requirement for solving a Riccati equation and equipping the control system with useful on-line tuning parameters. Parallels are drawn to the existing MPC techniques such as Dynamic Matrix Control (DMC), Internal Model Control (IMC) and Generalized Predictive Control (GPC)

Robust Control Structure Selection

Screening tools for control structure selection in the presence of model/plant mismatch are developed in the context of the Structured Singular Value (μ) theory. The developed screening tools are designed to aid engineers in the elimination of undesirable control structure candidates for which a robustly performing controller does not exist. Through application on a multicomponent distillation column, it is demonstrated that the developed screening tools can be effective in choosing an appropriate control structure while previously existing methods such as the Condition Number Criterion can lead to erroneous results

Metformin use in adolescents

There are no studies evaluating whether metformin prevents or delays the onset of diabetes in adolescents who are obese. In adults, metformin is as effective as lifestyle interventions in preventing increases in A1C and fasting glucose levels, but it is less effective in preventing or delaying the onset of overt type 2 diabetes. Metformin use reduces body mass index (BMI) for up to six months in adolescents who are obese (Strength of Recommendation [SOR]: C, based on disease-oriented randomized controlled trials [RCTs]) and reduces insulin resistance, the prevalence and severity of steatosis, and nonalcoholic fatty liver disease. (SOR: C, based on disease-oriented RCTs.

Is guaifenesin safe during pregnancy?

It's not clear; little evidence supports or refutes the safety of guaifenesin, a common expectorant, in pregnancy. A small number of observational and case-control studies suggest a weak association between guaifenesin use and inguinal hernias and neural tube defects in newborns. However, substantial methodological flaws, the absence of statistical significance, and low rates of prevalence cast a shadow of a doubt over the data (strength of recommendation [SOR]: B, based on observational and case- control studies)

Estimation of local concentration from measurements of stochastic adsorption dynamics using carbon nanotube-based sensors

This paper proposes a maximum likelihood estimation (MLE) method for estimating time varying local concentration of the target molecule proximate to the sensor from the time profile of monomolecular adsorption and desorption on the surface of the sensor at nanoscale. Recently, several carbon nanotube sensors have been developed that can selectively detect target molecules at a trace concentration level. These sensors use light intensity changes mediated by adsorption or desorption phenomena on their surfaces. The molecular events occurring at trace concentration levels are inherently stochastic, posing a challenge for optimal estimation. The stochastic behavior is modeled by the chemical master equation (CME), composed of a set of ordinary differential equations describing the time evolution of probabilities for the possible adsorption states. Given the significant stochastic nature of the underlying phenomena, rigorous stochastic estimation based on the CME should lead to an improved accuracy over than deterministic estimation formulated based on the continuum model. Motivated by this expectation, we formulate the MLE based on an analytical solution of the relevant CME, both for the constant and the time-varying local concentrations, with the objective of estimating the analyte concentration field in real time from the adsorption readings of the sensor array. The performances of the MLE and the deterministic least squares are compared using data generated by kinetic Monte Carlo (KMC) simulations of the stochastic process. Some future challenges are described for estimating and controlling the concentration field in a distributed domain using the sensor technology.Korea (South). Ministry of Science, ICT and Future Planning (Advanced Biomass R&D Center (ABC) of Global Frontier Project