Nuclear matter and neutron matter for improved quark mass density- dependent model with ρ\rho mesons

A new improved quark mass density-dependent model including u, d quarks, $\sigma$ mesons, $\omega$ mesons and $\rho$ mesons is presented. Employing this model, the properties of nuclear matter, neutron matter and neutron star are studied. We find that it can describe above properties successfully. The results given by the new improved quark mass density- dependent model and by the quark meson coupling model are compared.Comment: 18 pages, 7 figure

Quark deconfinement phase transition for improved quark mass density-dependent model

By using the finite temperature quantum field theory, we calculate the finite temperature effective potential and extend the improved quark mass density-dependent model to finite temperature. It is shown that this model can not only describe the saturation properties of nuclear matter, but also explain the quark deconfinement phase transition successfully. The critical temperature is given and the effect of $\omega$- meson is addressed.Comment: 18 pages, 7 figure

Improved quark mass density- dependent model with quark-sigma meson and quark-omega meson couplings

An improved quark mass density- dependent model with the non-linear scalar sigma field and the $\omega$-meson field is presented. We show that the present model can describe saturation properties, the equation of state, the compressibility and the effective nuclear mass of nuclear matter under mean field approximation successfully. The comparison of the present model and the quark-meson coupling model is addressed.Comment: 15 pages, 6 figure

Estimating Costs Associated with Disease Model States Using Generalized Linear Models: A Tutorial

Estimates of costs associated with disease states are required to inform decision analytic disease models to evaluate interventions that modify disease trajectory. Increasingly, decision analytic models are developed using patient-level data with a focus on heterogeneity between patients, and there is a demand for costs informing such models to reflect individual patient costs. Statistical models of health care costs need to recognize the specific features of costs data which typically include a large number of zero observations for non-users, and a skewed and heavy right-hand tailed distribution due to a small number of heavy healthcare users. Different methods are available for modelling costs, such as generalized linear models (GLMs), extended estimating equations and latent class approaches. While there are tutorials addressing approaches to decision modelling, there is no practical guidance on the cost estimation to inform such models. Therefore, this tutorial aims to provide a general guidance on estimating healthcare costs associated with disease states in decision analytic models. Specifically, we present a step-by-step guide to how individual participant data can be used to estimate costs over discrete periods for participants with particular characteristics, based on the GLM framework. We focus on the practical aspects of cost modelling from the conceptualization of the research question to the derivation of costs for an individual in particular disease states. We provide a practical example with step-by-step R code illustrating the process of modelling the hospital costs associated with disease states for a cardiovascular disease model

A framework of human–robot coordination based on game theory and policy iteration

In this paper, we propose a framework to analyze the interactive behaviors of human and robot in physical interactions. Game theory is employed to describe the system under study, and policy iteration is adopted to provide a solution of Nash equilibrium. The human’s control objective is estimated based on the measured interaction force, and it is used to adapt the robot’s objective such that human-robot coordination can be achieved. The validity of the proposed method is verified through a rigorous proof and experimental studies

Personalized Risk Assessment in Never, Light, and Heavy Smokers in a prospective cohort in Taiwan.

The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives

Adaptive optimal control for coordination in physical human-robot interaction

In this paper, a role adaptation method is developed for human-robot collaboration based on game theory. This role adaptation is engaged whenever the interaction force changes, causing the proportion of control sharing between human and robot to vary. In one boundary condition, the robot takes full control of the system when there is no human intervention. In the other boundary condition, it becomes a follower when the human exhibits strong intention to lead the task. Experimental results show that the proposed method yields better overall performance than fixed-role interactions