ROMP-based thermosetting polymers from modified castor oil with various cross-linking agents

Polymers derived from bio-renewable resources are finding an increase in global demand. In addition, polymers with distinctive functionalities are required in certain advanced fields, such as aerospace and civil engineering. In an attempt to meet both these needs, the goal of this work aims to develop a range of bio-based thermosetting matrix polymers for potential applications in multifunctional composites. Ring-opening metathesis polymerization (ROMP), which recently has been explored as a powerful method in polymer chemistry, was employed as a unique pathway to polymerize agricultural oil-based reactants. Specifically, a novel norbornyl-functionalized castor oil alcohol (NCA) was investigated to polymerize different cross-linking agents using ROMP. The effects of incorporating dicyclopentadiene (DCPD) and a norbornene-based crosslinker (CL) were systematically evaluated with respect to curing behavior and thermal mechanical properties of the polymers. Isothermal differential scanning calorimetry (DSC) was used to investigate the conversion during cure. Dynamic DSC scans at multiple heating rates revealed conversion-dependent activation energy by Ozawa-Flynn-Wall analysis. The glass transition temperature, storage modulus, and loss modulus for NCA/DCPD and NCA/CL copolymers with different cross-linking agent loading were compared using dynamic mechanical analysis. Cross-link density was examined to explain the very different dynamic mechanical behavior. Mechanical stress-strain curves were developed through tensile test, and thermal stability of the cross-linked polymers was evaluated by thermogravimetric analysis to further investigate the structure-property relationships in these systems

Research on Current Private Higher Education in Shandong Province

After 30 years’ development, private higher education in China has gradually entered into a sustainable and benign development stage. As a large educational province, the development of Shandong’s private higher education plays an important role in the national development. In this paper, through the investigation and study of 39 private colleges (including independent institutes) in Shandong on the layer and level of running school, talent training quality and educational modes and types, I try to find the problems in private higher education in Shandong Province during the period of last thirty years and give countermeasures to them.Key words: Private higher education; Optimization; Strateg

f-Betas and Portfolio Optimization with f-Divergence induced Risk Measures

In this paper, we build on using the class of f-divergence induced coherent risk measures for portfolio optimization and derive its necessary optimality conditions formulated in CAPM format. We have derived a new f-Beta similar to the Standard Betas and previous works in Drawdown Betas. The f-Beta evaluates portfolio performance under an optimally perturbed market probability measure and this family of Beta metrics gives various degrees of flexibility and interpretability. We conducted numerical experiments using DOW 30 stocks against a chosen market portfolio as the optimal portfolio to demonstrate the new perspectives provided by Hellinger-Beta as compared with Standard Beta and Drawdown Betas, based on choosing square Hellinger distance to be the particular choice of f-divergence function in the general f-divergence induced risk measures and f-Betas. We calculated Hellinger-Beta metrics based on deviation measures and further extended this approach to calculate Hellinger-Betas based on drawdown measures, resulting in another new metric which we termed Hellinger-Drawdown Beta. We compared the resulting Hellinger-Beta values under various choices of the risk aversion parameter to study their sensitivity to increasing stress levels.Comment: 17 pages, 6 figure

Statistical Depth Function Random Variables for Univariate Distributions and induced Divergences

In this paper, we show that the halfspace depth random variable for samples from a univariate distribution with a notion of center is distributed as a uniform distribution on the interval [0,1/2]. The simplicial depth random variable has a distribution that first-order stochastic dominates that of the halfspace depth random variable and relates to a Beta distribution. Depth-induced divergences between two univariate distributions can be defined using divergences on the distributions for the statistical depth random variables in-between these two distributions. We discuss the properties of such induced divergences, particularly the depth-induced TVD distance based on halfspace or simplicial depth functions, and how empirical two-sample estimators benefit from such transformations.Comment: 15 pages, 3 figure

Wireless Communications in the Era of Big Data

The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications Magazin

A General Theory for Kernel Packets: from state space model to compactly supported basis

It is well known that the state space (SS) model formulation of a Gaussian process (GP) can lower its training and prediction time both to \CalO(n) for $n$ data points. We prove that an $m$-dimensional SS model formulation of GP is equivalent to a concept we introduce as the general right Kernel Packet (KP): a transformation for the GP covariance $K$ such that $\sum_{i=0}^{m}a_iD_t^{(j)}K(t,t_i)=0$ holds for any $t \leq t_1$, 0 $\leq j \leq m-1$, and $m+1$ consecutive points $t_i$, where ${D}_t^{(j)}f(t)$ denotes $j$-th derivative acting on $t$. We extend this idea to the backward SS model formulation, leading to the left KP for next $m$ consecutive points: $\sum_{i=0}^{m}b_i{D}_t^{(j)}K(t,t_{m+i})=0$ for any $t\geq t_{2m}$. By combining both left and right KPs, we can prove that a suitable linear combination of these covariance functions yields $m$ KP functions compactly supported on $(t_0,t_{2m})$. KPs improve GP prediction time to $\mathcal{O}(\log n)$ or $\mathcal{O}(1)$, enable broader applications including GP's derivatives and kernel multiplications, and can be generalized to multi-dimensional additive and product kernels for scattered data