Semi-empirical insertion probability for hard-spheres and hard-sphere chains

In spite of its simplicity and a well-defined theoretical basis, the Flory–Guggenheim approach is conventionally regarded as inapplicable to off-lattice system since the insertion probability of the approach does not account for the excluded region, existing in the off-lattice system. In this work, we propose the insertion probability accounting for the excluded region of off-lattice fluids and derive a new version of equation of state (EOS) for hard-sphere chains basing on the Flory–Guggenheim approach. To investigate the behavior of the excluded regions, a Monte Carlo sampling was performed for hard disks and the various excluded regions were found to have different density dependence. On the basis of the simulation result, we formulated the insertion probability for hard-sphere and that of hard-sphere chain which accounts for the effect of chain-connectivity on the monomer insertion. The proposed insertion probability was found to correctly predict the simulation data for monomer and correctly correlate the simulation data for chain fluids. The resulting EOS was found to meet closed-packed limit and predict the simulation data of compressibility factor for monomer and chains with a reasonable degree of accuracy. When compared with other off-lattice based EOS, it shows a comparable or better result. For second virial coefficient of chain molecules, the model was found to reasonably predict the simulation data.     

General construction of mean-field potential and its application to the multiphase equations of state of tin

The mean-field potential (MFP) approach is an efficient way to evaluate the free energy contribution of ion motions for both solid and liquid states. In this paper the MFP is generally constructed with a volume-dependent term and a shape function. The former is derived in accordance with quasi-harmonic approximation. The latter is given semi-empirically. Application to multiphase equations of state for β-, γ- and liquid-tin has been examined. The theoretical phase diagram and thermodynamic properties of isotherm, thermal expansion, heat capacity, Hugoniot states as well as phase transitions are all in excellent agreement with experiments.     

Neutron–proton elliptic flow difference as a probe for the high density dependence of the symmetry energy

We employ an isospin dependent version of the QMD transport model to study the influence of the isospin dependent part of the nuclear matter equation of state and in-medium nucleon–nucleon cross-sections on the dynamics of heavy-ion collisions at intermediate energies. We find that the extraction of useful information on the isospin-dependent part of the equation of state of nuclear matter from proton or neutron elliptic flows is obstructed by their sensitivity to model parameters and in-medium values of nucleon–nucleon cross-sections. Opposite to that, neutron–proton elliptic flow difference shows little dependence on those variables while its dependence on the isospin asymmetric EoS is enhanced, making it more suitable for a model independent constraining of the high-density behaviour of asy-EoS. Comparison with existing experimental FOPI-LAND neutron–hydrogen data can be used to set an upper limit to the softness of asy-EoS. Successful constraining of the asy-EoS via neutron–proton elliptic flow difference will require experimental data of higher accuracy than presently available.     

Strange stars properties calculated in the framework of the Field Correlator Method

We calculate the strange star properties in the framework of the Field Correlator Method. We find that for gluon condensate values G₂$G_2$ in the range 0.006–0.007 GeV⁴$0.006\text{–}0.007{\text{GeV}}^4$, which give a critical temperature Tc∼170 MeV$T_c\sim 170\text{MeV}$ at μc=0${\mu }_c=0$, the sequences of strange stars are compatible with some of the semi-empirical mass–radius relations and data obtained from astrophysical observations.     

Calibration of a stochastic health evolution model using NHIS data

This paper presents and calibrates an individual’s stochastic health evolution model. In this health evolution model, the uncertainty of health incidents is described by a stochastic process with a finite number of possible outcomes. We construct a comprehensive health status index (HSI) to describe an individual’s health status, as well as a health risk factor system (RFS) to classify individuals into different risk groups. Based on the maximum likelihood estimation (MLE) method and the method of nonlinear least squares fitting, model calibration is formulated in terms of two mixed-integer nonlinear optimization problems. Using the National Health Interview Survey (NHIS) data, the model is calibrated for specific risk groups. Longitudinal data from the Health and Retirement Study (HRS) is used to validate the calibrated model, which displays good validation properties. The end goal of this paper is to provide a model and methodology, whose output can serve as a crucial component of decision support for strategic planning of health related financing and risk management.     

Confidence-based optimisation for the newsvendor problem under binomial,Poisson and exponential demand

We introduce a novel strategy to address the issue of demand estimation in single-item single-period stochastic inventory optimisation problems. Our strategy analytically combines confidence interval analysis and inventory optimisation. We assume that the decision maker is given a set of past demand samples and we employ confidence interval analysis in order to identify a range of candidate order quantities that, with prescribed confidence probability, includes the real optimal order quantity for the underlying stochastic demand process with unknown stationary parameter(s). In addition, for each candidate order quantity that is identified, our approach produces an upper and a lower bound for the associated cost. We apply this approach to three demand distributions in the exponential family: binomial, Poisson, and exponential. For two of these distributions we also discuss the extension to the case of unobserved lost sales. Numerical examples are presented in which we show how our approach complements existing frequentist—e.g. based on maximum likelihood estimators—or Bayesian strategies.     

分层性物质的组合取样误差与份样数目之间的关系及其Monte Carlo模拟

本文从理论上研究了分层性物质的组合取样误差与份样数目之间的定量关系，表明份样数目与组合取样相对标准偏差的平方之积为一常数，称为组合取样常数，它与Ｉｎｇａｍ ｅｌｌｓ 和Ｓｗ ｉｔｚｅｒ 提出的取样常数是相似的，表示６８％ 置信度下组合取样的相对标准偏差达到１％ 时的份样数目。应用Ｍｏｎｔｅ Ｃａｒｌｏ 模拟技术对该理论进行了验证，结果令人满意。     

Turbulence Closure with a Topography-parameter-free Single Equation Model

A new topography-parameter-free turbulence closure, based on a transport equation for the pseudo eddy viscosity, is described. The model is tested against experimental data of several flow cases across the Mach number range and compared to Menter's single equation model and to the Spalart-Allmaras model. The main conclusion is that the new closure outperforms the other two models in both low and high speed flows.     

A modification to the Peng–Robinson-fitted equation of state for pure substances

In this work we present two modifications to the Peng–Robinson-Fitted equation of state where pure component parameters are regressed to vapor pressure and saturated liquid density data. The first modification (PR-f-mod) is a method that enhances the equation of state pure component property predictions through simple temperature dependent pure component parameters. In the second modification (PR-f-prop) we propose a temperature dependency for co-volume b in the repulsive parameter of the EoS, and revise the temperature function in the attractive term. The agreement with experimental data for 72 pure substances, including highly polar compounds, is remarkably good. We obtain average absolute deviations in saturated liquid density of less than 1% for all substances studied.     

Recent advances in clinical microdialysis

Clinical microdialysis (MD) is a minimally invasive sampling technique that offers selective in-vivo measurement of free, active drug or biomolecule concentrations in human tissues and organs. From a regulatory perspective, MD can thus be seen as a suitable scientific tool that meets regulatory requirements for the study of tissue distribution or bioequivalence during drug development. From a clinical perspective, the use of MD in different applications has shown the potential to rationalize drug-dosing regimens and to influence clinical decision-making, although validation and correlation of MD-derived results with clinical response are required to promote routine clinical use of the technique. From an analytical perspective, highly sensitive analytical systems have increasingly become available for MD-sample analysis, and these have further improved the quality and the power of MD-derived information. Given the constant development in recent years, MD data might become an important part of new drug submissions and clinical treatment algorithms, and might positively influence patient benefit in the future.