Continuous-time Mean-Variance Portfolio Selection with Stochastic Parameters

This paper studies a continuous-time market {under stochastic environment} where an agent, having specified an investment horizon and a target terminal mean return, seeks to minimize the variance of the return with multiple stocks and a bond. In the considered model firstly proposed by [3], the mean returns of individual assets are explicitly affected by underlying Gaussian economic factors. Using past and present information of the asset prices, a partial-information stochastic optimal control problem with random coefficients is formulated. Here, the partial information is due to the fact that the economic factors can not be directly observed. Via dynamic programming theory, the optimal portfolio strategy can be constructed by solving a deterministic forward Riccati-type ordinary differential equation and two linear deterministic backward ordinary differential equations

Pharmacokinetics and Disposition of Green Tea Catechins

Green tea reportedly possesses many health beneficial effects as a beverage. Its usage has even been elevated to therapeutic level for treatment of diseases, including cancer, after increasing the catechin constituents in green tea extract or through purified catechins compounds. However, the therapeutic effectiveness of green tea extract or catechin formulae on different diseases is still questionable and inconsistent in reported studies. One reason is the low and variable bioavailability of catechins or unknown constituents in green tea extract. The plasma levels of total catechins are usually at submicromolar level which is well below the effective dose in many in vitro studies. Besides their variable chemical structures that cause heterogeneity of absorption, green tea catechins are subject to extensive metabolism by phase II process and catabolism by colonic microbes that result in complicated pharmacokinetics. It is essential to understand the factors affecting the pharmacokinetics and metabolic profiles in plasma and tissues based on animal and human studies before green tea catechins can be applied for therapeutic use

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

Simulation on Phase Change Thermal Storage Panel Based on Capillary Network

AbstractStorage technology has the advantage to solve the problem of the not matching in time and space of energy's supply and demand, making it an effective way to the rational use of resources and reducing environmental pollution. What is used in this paper is a regular flat phase change heat storage module packaging with capillary network and phase change material. In this paper, the software Fluent was used to simulate the impact factors and the ways to enhance heat transfer in the process of phase change. The simulation result indicates that: During the melting process, because of the influence of natural convection, the top of phase change regional melts faster, the temperature contour ramp to top. The higher the horizon is, the temperature contour is more stable. Natural convection play different role in the melting and solidification process, which accelerated melting rate in the melting process, and slowed down in solidification. The surface temperature of phase change thermal storage panel will be maintained at 26.5°C, within the limits of human body comfort and is able to improve the air temperature within a certain space