Novel alternating copolyoxamides with high crystallinity and heat resistance

Two series of novel alternating copolyoxamides (PAnT-alt-n2 and PAn2-alt-62) are synthesized via solution/solid-state polycondensation (SSP). The alternating structures are analyzed carefully with ¹H NMR and ¹³C NMR spectra. The melting behaviors, thermal stabilities, crystal structures and crystallinities are systematically evaluated by DSC, TGA and WAXD. The results reveal that these alternating copolyoxamides possess almost perfect alternating chain structures and have high melting temperature (T_m > 270 °C), high crystallinity (X_c > 32%) and high decomposition temperature (T₅ > 405 °C) as well as low saturated water absorption (<3.5 wt%), which suggests that they have high potential as engineering plastic of high heat resistant.     

A Novel Biaryl Derivative from Hydrangea chinensis

Chemistry of Natural Compounds - A novel bibenzyl derivative, hydrangchinenin (1), was isolated from the stems of Hydrangea chinensis Maxim. (Hydrangeaceae). Its structure was determined on the...     

Isoflavonoids from Astragalus membranaceus Hairy Roots

Chemistry of Natural Compounds -     

Effect of temperature on the interactions between cellulose and lignin via molecular dynamics simulations

Cellulose - Heating is essential in various biomass pre-treatments and thermal conversion processes. It is of practical significance to study the characteristics of cellulose-lignin and...     

Auto-deposition of 210Bi and 210Po on nickel discs and their application in the analysis of natural waters

Journal of Radioanalytical and Nuclear Chemistry - This study presents a time-efficient method of analysing 210Pb, 210Bi, and 210Po in natural waters. The optimum pH (1.00), temperature...     

Excellent UV-absorbing cotton fabric with high durability and hydrophobicity based on surface-initiated ATRP of polymerizable organic UV-absorber

Cellulose - Ultraviolet (UV) protective cotton fabric is a convenient and reliable way to protect human body from sunlight. Herein, a polymerizable UV-absorber,...     

Special John-Nirenberg-Campanato spaces via congruent cubes

Let p ∈ [1, ∞), q ∈ [1, ∞), α∈ R, and s be a non-negative integer. Inspired by the space JNp introduced by John and Nirenberg(1961) and the space B introduced by Bourgain et al.(2015), we introduce a special John-Nirenberg-Campanato space JN^con_(p,q,s) over Rⁿ or a given cube of R;with finite side length via congruent subcubes, which are of some amalgam features. The limit space of such spaces as p →∞ is just the Campanato space which coincides with the space BMO(the space of functions with bounded mean oscillations)when α = 0. Moreover, a vanishing subspace of this new space is introduced, and its equivalent characterization is established as well, which is a counterpart of the known characterization for the classical space VMO(the space of functions with vanishing mean oscillations) over Rⁿ or a given cube of Rⁿ with finite side length.Furthermore, some VMO-H¹-BMO-type results for this new space are also obtained, which are based on the aforementioned vanishing subspaces and the Hardy-type space defined via congruent cubes in this article. The geometrical properties of both the Euclidean space via its dyadic system and congruent cubes play a key role in the proofs of all these results.     

基于深度学习的中医古文献临床经验抽取

中医古文献蕴藏着丰富的临床经验，是古代中医在行医过程中对临床诊疗的经验性总结，体现了中医学形成和发展的理论框架和思想基础.然而这些宝贵的临床经验不仅量大，而且分散在不同的文献中，使得中医从业者手工很难快速全面地获取它们，文献检索工具也只能提供文档级别的信息筛选，无法为这种细粒度的信息获取提供支持.此外，古汉语相对于现代汉语的不同特点也限制了主流文本分析工具的使用效果.为此本文提出面向临床经验获取的中医古文献信息抽取任务，用于识别古文献中描述临床经验的文本片段，手工标注了样本数据用于这种抽取模型的训练和测试，并设计了基于深度学习的序列标注器用于完成该任务.考虑到标注数据量小可能带来的过度拟合问题，本文引入对抗训练和虚拟对抗训练来增强模型的泛化能力.一系列充分的实验验证了模型的有效性，表明利用信息抽取技术从古文献获取中医临床经验具有可行性，为这一新的信息抽取任务提供了有希望的研究基线和可复用的标注数据集.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

Fully Piecewise Linear Vector Optimization Problems

Journal of Optimization Theory and Applications - We distinguish two kinds of piecewise linear functions and provide an interesting representation for a piecewise linear function between two normed...