Synthesis and characterization of functional fluorinated telomers

The synthesis and characterizations of fluorinated telomers based on vinylidene fluoride (VDF) by iodine transfer polymerization (ITP) are presented. These telomers were prepared in the presence of ω‐iodoperfluorinated functional chain transfer agents, C₆F₁₃I or C₄F₉I. ITPs were initiated by thermal decomposition of bis(4‐tert‐butylcyclohexyl) peroxydicarbonate (BBCHPDC), in solution (in the presence of acetonitrile). The obtained telomers were characterized by different analyses such as elemental analyses, nuclear magnetic resonance (NMR), Fourier transform infrared spectroscopy, size exclusion chromatography, X‐ray diffraction (XRD), and finally by thermal analyses such as differential scanning calorimetry (DSC) and thermogravimetric analyses. These technical analyses enabled one to assess their chemical structures, various thermal properties, and their crystalline forms. The assignments of the characteristic signals obtained by ¹⁹F NMR spectroscopy enabled one to calculate the average degrees of polymerization (DPn) and percentages of ? CH₂CF₂I end group functionalities that depend on the initiator and the fractionation process after reaction. A good control of polymerization was shown by the absence of reversed ? CH₂CF₂? CF₂CH₂? (VDF–VDF dyads) and narrow polydispersity indices (<1.2). The XRD and DSC evidenced the influence of the chain length of the telomers on the crystallinity rate (>70%) and indicated two crystalline forms, α and β, that depended on DPn values. © 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2010     

寻找自我的悲歌——浅析多丽丝·莱辛的《十九号房间》

英国著名女作家多丽丝·莱辛的《十九号房间》,讲述一名职业妇女婚后如何在为妻为母的主妇生活中找寻自我,最终自杀的故事。文章运用精神分析学说有关意识的理论来分析小说女主人公的心理世界,发现无意识是其自我迷失及最终崩溃的内在心理动机,从而揭示女主人公心理建构的内在实质。     

Locally strong endomorphisms of paths

We determine the number of locally strong endomorphisms of directed and undirected paths—direction here is in the sense of a bipartite graph from one partition set to the other. This is done by the investigation of congruence classes, leading to the concept of a complete folding, which is used to characterize locally strong endomorphisms of paths. A congruence belongs to a locally strong endomorphism if and only if the number l of congruence classes divides the length of the original path and the points of the path are folded completely into the l classes, starting from 0 to l and then back to 0, then again back to l and so on. It turns out that for paths locally strong endomorphisms form a monoid if and only if the length of the path is prime or equal to 4 in the undirected case and in the directed case also if the length is 8. Finally some algebraic properties of these monoids are described.     

Symmetric identities on Bernoulli polynomials

In this paper, we obtain a generalization of an identity due to Carlitz on Bernoulli polynomials. Then we use this generalized formula to derive two symmetric identities which reduce to some known identities on Bernoulli polynomials and Bernoulli numbers, including the Miki identity.     

Oriented Chow groups, Hermitian K-theory and the Gersten conjecture

We show that the oriented Chow groups of Barge–Morel appear in the E ₂-term of the coniveau spectral sequence for Hermitian K-theory. This includes a localization theorem and the Gersten conjecture (over infinite base fields) for Hermitian K-theory. We also discuss the conjectural relationship between oriented and higher oriented Chow groups and Levine’s homotopy coniveau spectral sequence when applied to Hermitian K-theory.     

Mahler measures and computations with regulators

In this work we apply the techniques that were developed in [M.N. Lalín, An algebraic integration for Mahler measure, Duke Math. J. 138 (2007), in press] in order to study several examples of multivariable polynomials whose Mahler measure is expressed in terms of special values of the Riemann zeta function or Dirichlet L-series. The examples may be understood in terms of evaluations of regulators. Moreover, we apply the same techniques to the computation of generalized Mahler measures, in the sense of Gon and Oyanagi [Y. Gon, H. Oyanagi, Generalized Mahler measures and multiple sine functions, Internat. J. Math. 15 (5) (2004) 425-442].     

Regioselectivity of fluorine substitution by alkoxides on unsymmetrical difluoroarenes

An efficient approach to unsymmetrical halogenated resorcinol diethers has been developed. This synthesis consists of two subsequent nucleophilic aromatic substitutions (S_NAr) of unsymmetrical difluoroarenes by alkoxides. The novelty of this approach is its control of regioselectivity during the first S_NAr, which occurs at room temperature. Interestingly, the reactivity of competing fluorines was correlated to their chemical shift in ¹⁹F NMR.     

A convenient preparation of 8R,25-dihydroxy-9,10-seco-4,6,10(19)-cholestatrien-3-one

Summary The title compound was prepared in three steps from 25-hydroxyvitamin D₃. The keystep in this sequence is the transformation of3b to4 by means of the LiBr/HMPT complex.Dedicated to Prof. Dr. Karl Schlögl on the occasion of his 65th anniversary     

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.