中红外光谱法在无创血糖检测技术中的应用

构建了中红外无创血糖检测的实验方案,利用傅里叶红外光谱仪对葡萄糖溶液及手指进行了中红外光谱检测并进行分析,得到葡萄糖的中红外特征吸收峰在1000cm^-1附近,证实了中红外光谱在无创血糖检测中的可实现性,选择激光器和光导探测器进行特征峰处的光电检测实验,并得到了有效信号。     

关于集合的粗相似度量的注记

举例说明文献"集合的粗相似度量"中定理3.1(2)的结论是不严谨的,从而给出一个严谨的结论。     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

A Comprehensive Review on the Benefits and Problems of Curcumin with Respect to Human Health

Curcumin is the most important active component in turmeric extracts. Curcumin, a natural monomer from plants has received a considerable attention as a dietary supplement, exhibiting evident activity in a wide range of human pathological conditions. In general, curcumin is beneficial to human health, demonstrating pharmacological activities of anti-inflammation and antioxidation, as well as antitumor and immune regulation activities. Curcumin also presents therapeutic potential in neurodegenerative, cardiovascular and cerebrovascular diseases. In this review article, we summarize the advancements made in recent years with respect to curcumin as a biologically active agent in malignant tumors, Alzheimer’s disease (AD), hematological diseases and viral infectious diseases. We also focus on problems associated with curcumin from basic research to clinical translation, such as its low solubility, leading to poor bioavailability, as well as the controversy surrounding the association between curcumin purity and effect. Through a review and summary of the clinical research on curcumin and case reports of adverse effects, we found that the clinical transformation of curcumin is not successful, and excessive intake of curcumin may have adverse effects on the kidneys, heart, liver, blood and immune system, which leads us to warn that curcumin has a long way to go from basic research to application transformation.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

室温铁磁性Sr3YCo4O10.5+δ多晶有序化的烧结工艺及电磁性能

采用固相反应法制备了不同烧结温度(950～1180℃)、烧结时间、烧结次数共7种工艺的Sr3 YCo4 O10.5+δ多晶块材,通过热分析、XRD、SEM确定了有序化相变和最佳烧结工艺(1180℃/24 h+1180℃/24 h),并研究了多晶的电磁性能.结果表明,964℃完全晶化的四方相Sr3 YCo4 O10.5在...     

1,3,5-三(羧基甲氧基)苯镍配位聚合物的合成、晶体结构及性质研究

采用溶剂热法,1,3,5-三(羧基甲氧基)苯为定向配体和乙酸镍反应构筑了一个新型的金属配位聚合物[Ni(TB)₂(H₂O)₂]_n·2H₂O,其中H₃TB=1,3,5-三(羧基甲氧基)苯,通过元素分析、IR及X射线单晶衍射对配合物结构进行表征,并研究其荧光性质、热稳定性及Hirshfeld表面作用力。单晶结构分析表明,该配合物属于三斜晶系,空间群$P \overline{1}$,配合物中心离子Ni(Ⅱ)分别与来自两个水分子上的氧原子及四个不同1,3,5-三(羧基甲氧基)苯配体的羧酸氧原子配位,形成六配位的NiO₆八面体构型,并通过与1,3,5-三(羧基甲氧基)苯配体的氧原子配位不断延伸形成具有孔洞结构的一维链状构型。配合物具有良好的荧光性能和热稳定性。Hirshfeld表面作用分析表明配合物分子中O…H/H…O作用占主导且占比为39.0%,而H…H的作用力占比为25.9%,O…O的作用力占比为13.6%。     

电感耦合等离子体原子发射光谱法同时测定槲寄生中的微量元素

采用硝酸-双氧水对槲寄生全药及煎煮液进行湿法消解,用电感耦合等离子体原子发射光谱法(ICP-AES)同时测定试样中Cu,Mg,Zn,Mn,Ni,Se,K,Na,Ca和Fe等元素的含量。该法的的加标回收率为91.6％～108.9％,变异系数为0.47％～4.59％,具有良好的准确度和精密度。结果表明：槲寄生中含有丰富的K和Ca元素,Na和Mg的含量也相对丰富,为进一步研究槲寄生的药理提供了科学的数据。     

基于IBIS5-A-1300 CMOS图像传感器的噪音抑制技术研究

为了提高图像质量,在分析APS CMOS图像传感器工作时序的基础上,对标准时序做了改进,并对改进后的时序进行了实验仿真.结果表明,改进后的时序可有效消除固定模式噪音,达到改进图像质量的目的.     

基于截断δ-冲击模型的不完全维修更换策略

本文在截断δ-冲击模型的基础上,考虑了因系统劣化而导致的冲击失效门限值与维修时间的变化,扩充失效状态,从而提出一种新的截断δ-冲击模型。以最小费用为目标,稳态可用度为约束条件,建立N型更换策略的不完全维修更换策略模型,并给出三种常用冲击到达间隔分布的期望寿命。最后通过算例验证模型的有效性,并对参数进行灵敏度分析。