Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

Isospin Effects in Nuclear Multifragmentation

We develop an improved Statistical Multifragmentation Model that provides the capability to calculate calorimetric and isotopic observables with precision. With this new model we examine the influence of nuclear isospin on the fragment elemental and isotopic distributions. We show that the proposed improvements on the model are essential for studying isospin effects in nuclear multifragmentation. In particular, these calculations show that accurate comparisons to experimental data require that the nuclear masses, free energies and secondary decay must be handled with higher precision than many current models accord.Comment: 46 pages, 16 figure

Genetic insights into resting heart rate and its role in cardiovascular disease.

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development

PRMT5 Modulates Splicing for Genome Integrity and Preserves Proteostasis of Hematopoietic Stem Cells

10.1016/j.celrep.2019.02.001Cell Reports2692316-232800000

Influence of high temperature on the tribological properties of hybrid PTFE/Kevlar fabric composite

The tribological properties of PTFE/Kevlar fabric are extremely important for the service behavior of self-lubricating and maintenance-free joint bearings in aviation, aerospace and other fields that require high wear performance at different temperatures. This paper aims at investigating the influence of the ambient temperature on the tribological properties of hybrid PTFE/Kevlar fabric through reciprocating wear tests. The wear loss, surface damage morphology and tribo-chemical behaviors of PTFE/Kevlar at the temperature range of 25–200 °C are first analyzed. The wear mechanisms, damage behaviors and their transitions with the increase in the ambient temperature and reciprocating cycles are then systematically discussed. This information can provide a guide to master the range of safe use and the optimization of materials

Combined temozolomide and radiation as an initial treatment for anaplastic glioma

10.1111/ajco.12038Asia-Pacific Journal of Clinical Oncology93220-22

Impact of Cryotherapy on Sensory, Motor, and Autonomic Neuropathy in Breast Cancer Patients Receiving Paclitaxel: A Randomized, Controlled Trial

10.3389/fneur.2020.604688Frontiers in Neurology1160468

Effect of cell microenvironment on the drug sensitivity of hepatocellular cancer cells

10.18632/oncotarget.27910Oncotarget127674-68