多体非完全经典态的基于Affinity的关联测度

在量子信息理论中,量子关联是一种极其重要的物理资源。文章首先定义了多体非完全经典态基于Affinity的一种量子关联测度,它可以量化多体非完全经典态的关联程度;接下来证明了该关联测度满足量子关联度的一些必要物理性质,包括非负性、局部酉操作下的不变性、局部量子操作下的单调性、凸性以及连续性。

Strategies for Enhancing the Affinity of College Ideological and Political Course Teachers

This article aims to explore effective ways to enhance the affinity of ideological and political course teachers in universities.By analyzing the connotation of affinity,the factors that affect the affinity of ideological and political course teachers are analyzed,and corresponding improvement strategies are proposed.Research suggests that strengthening the construction of teacher ethics and conduct,improving teaching skills,enhancing emotional engagement,and enhancing practical training are key paths to enhance the affinity of ideological and political course teachers.The implementation of these paths will help improve the teaching quality and effectiveness of ideological and political courses,and promote the comprehensive development of students.

多体k不可分量子态基于Affinity的纠缠测度

量子纠缠是一种重要的物理资源,已被广泛应用于量子信息及量子计算的许多领域中。提出了一种多体复合系统量子态相对于k体分划的基于Affinity的纠缠测度,并证明了其满足纠缠测度的必要物理性质。

Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.

A Deep Learning Drug-Target Binding Affinity Prediction Based on Compound Microstructure and Its Application in COVID-19 Drug Screening

Drug target relationship(DTR)prediction is a rapidly evolving area of research in com-putational drug discovery.Despite recent advances in computational solutions that have overcome the challenges of in vitro and in vivo experiments,most computational methods still focus on binary classification.They ignore the importance of binding affinity,which correctly distinguishes between on-targets and off-targets.In this study,we propose a deep learning model based on the microstruc-ture of compounds and proteins to predict drug-target binding affinity(DTA),which utilizes topo-logical structure information of drug molecules and sequence semantic information of proteins.In this model,graph attention network(GAT)is used to capture the deep features of the compound molecular graph,and bidirectional long short-term memory(BiLSTM)network is used to extract the protein sequence features,and the pharmacological context of DTA is obtained by combining the two.The results show that the proposed model has achieved superior performance in both cor-rectly predicting the value of interaction strength and correctly discriminating the ranking of bind-ing strength compared to the state-of-the-art baselines.A case study experiment on COVID-19 con-firms that the proposed DTA model can be used as an effective pre-screening tool in drug discovery.

Secondary electron emission and photoemission from a negative electron affinity semiconductor with large mean escape depth of excited electrons

The formulae for parameters of a negative electron affinity semiconductor(NEAS)with large mean escape depth of secondary electrons A(NEASLD)are deduced.The methods for obtaining parameters such asλ,B,E_(pom)and the maximumδandδat 100.0 keV≥E_(po)≥1.0 keV of a NEASLD with the deduced formulae are presented(B is the probability that an internal secondary electron escapes into the vacuum upon reaching the emission surface of the emitter,δis the secondary electron yield,E_(po)is the incident energy of primary electrons and E_(pom)is the E_(po)corresponding to the maximumδ).The parameters obtained here are analyzed,and it can be concluded that several parameters of NEASLDs obtained by the methods presented here agree with those obtained by other authors.The relation between the secondary electron emission and photoemission from a NEAS with large mean escape depth of excited electrons is investigated,and it is concluded that the presented method of obtaining A is more accurate than that of obtaining the corresponding parameter for a NEAS with largeλ_(ph)(λ_(ph)being the mean escape depth of photoelectrons),and that the presented method of calculating B at E_(po)>10.0 keV is more widely applicable for obtaining the corresponding parameters for a NEAS with largeλ_(ph).

An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

A Remark on the Affine Coordinates for KdV Tau-Functions

We give a proof of an explicit formula for affine coodinates of points in the Sato’s infinite Grassmannian corresponding to tau-functions for the KdV hierarchy.

Heteroclinic Cycles in a Class of 3-Dimensional Piecewise Affine Systems

This paper explores the existence of heteroclinic cycles and corresponding chaotic dynamics in a class of 3-dimensional two-zone piecewise affine systems. Moreover, the heteroclinic cycles connect two saddle foci and intersect the switching manifold at two points and the switching manifold is composed of two perpendicular planes.

Characteristics and Affinity of Tropical Flora in Southern China

The tropical flora in southern China is represented mainly by three regional flora, i.e. southern Yunnan (southwestern China), southwestern Guangxi (southwestern China) and Hainan Island (southern China). The floristic composition of each is concisely enumerated. Their geographical elements at generic level are analyzed. Furthermore, a comparison of floristic similarities between southern Yunnan and Hainan Island and Malay Peninsula and Brunei of western Malesia is made, based on existing regional flora treatments. The following is concluded: (1) The flora of southern China consists mainly of tropical floristic elements which contribute about 60% at the family level and more than 80 % at the generic level of its total flora. The dominant distribution type of the flora of southern China at the generic level is the tropical Asian distribution pattern. This reveals that the flora of southern China is of tropical nature with a strong tropical Asian affinity; (2) Most of the dominant families from the flora of southern China are also dominant in the Malesian flora. The floristic similarities between the regional flora of southern China and the regional flora of western Malesia are over 70% at the family level and more than 50% at the generic level. This suggests that the tropical flora of southern China belongs to the Malesian floristic region; (3) Situated at the northern margin of tropical Asia, on the other hand it is also obvious that the flora of southern China comprises less strictly tropical elements compared to the Malesian flora, and is consequently only a marginal type of the latter; (4) The close affinity of the flora of southern China to the Malesian flora can be explained by the geological history of southeast Asia.

Affinity Chromatography Purification of Recombinant Human Interleukin-6 from Its Fusion Protein with GST

Recombinant E.coli JM109, containing pHZ1818 plasmid which included the fused gene encoding human interleukin 6(IL 6), expressed a fusion protein with glutathion S transferase(GST). The fusion protein existed both in the supernatant and inside the bacterial cell,but the insoluble protein had no biological activity and could not be refolded. The rotative speed of the shaker and the temperature of induction were optimized to maximize the expression of the soluble fusion protein. From the supernatant of the cell sonicates Glutathion Sephrose 4B affinity column chromatography was employed to isolate the fusion protein which could be purified to >80 0 0 in a single step. The yield of soluble GST IL 6 was about 10 mg per liter culture. The GST was site specifically cloven by 6 hours of treatment with thrombin and from the thrombin digest mixture IL 6 was purified by Q high performance ion exchange chromatography. From 1 liter of E.coli culture 2 mg refined IL 6 was obtained. The purified IL 6 had a purity of more than 95 0 0 and a biological activity of 1.02×10 8 IU/mg.

Affinity and fluorescent detection of surfactants/ssDNA and single-walled carbon nanotube

A new biosensor platform was explored for detection of surfactant based on fluorescence changes from single strand DNA （ssDNA） and single-walled carbon nanotubes （SWNTs）. Thermodynamics assay was performed to value the stability of probe. The affinities of SWNT to five common surfactants （SDS, DBS, Triton X-100, Tween-20 and Tween-80） were investigated by real-time fluorescence method. The effects of Mg^2＋ and pH on the fluorescence intensity of self-assembled quenched sensor were performed. The fluorescent emission spectra were used to measure the responses of self-assembled quenched fluorescent of ssDNA/SWNTs to different concentration surfactant（Triton X-100）. The FAM-DNA wrapped SWNTs probe was stable in a wide temperature range （5 ℃ to 80℃）. The binding strength of surfactants and single-stranded DNA （ssDNA） on SWNTs surfaces was shown as follows： Triton X-100〉DBS〉Tween-20〉Tween-80〉ssDNA〉SDS, and the optimized reaction conditions included pH 7.4 and 10 mmol/L Mg2＋. The fluorescence of FAM-ssDNA wrapped SWNTs was proportionally recovered as a result of adding different concentrations of Triton X- 100, which realizes the quantitative detection of Triton X- 100.

基于Affinity Propagation算法的半监督微博水军识别

对微博网络空间中水军账户的识别研究,有助于清朗网络空间和维护社会安定。首先,文章针对微博水军不断进化、传统特征集无法覆盖现有水军特征,结合水军定义与原始特征,构造了新特征。然后,针对水军账户标注困难,无标注数据又没能充分利用的问题,提出了一种基于Affinity Propagation算法的半监督微博水军识别方法(APDHW)。该方法通过在Affinity Propagation算法中引入欧氏距离Radius阈值,再结合支持向量机分类算法,实现对微博水军识别。通过多组实验对比及实证研究,结果表明文章所提的微博水军识别方法在牺牲少量算法时间的情况下得到较好的识别效果,提升了水军识别的准确率和召回率。

Method and Progress of the Research on Virus Affinity Peptide

With the continuous progress on affinity peptide research, it has become more and more popular in pharmacology and medicine. lt is promising to study these viruses affinity peptide to treat infectious diseases. And the analysis on the virus affinity peptide with high selectivity and high sensitivity could provide valuable means for disease detection, treatment as wel as the study on the molecular mechanism of virus affinity peptide. Therefore, we reviewed the bioinformatics pre-diction technologies of computer simulation, molecular docking and homology model-ing, as wel as the research method on analyzing and screening virus affinity pep-tide, such as Phage display technology.

基于Affinity Propagation聚类方法的图像检索技术在数字图书馆中的应用

随着数字图书馆包含的内容逐渐丰富,数字图像也越来越多。为了有效地检索这些图像,迫切需要一种效率更高的检索方法。目前的基于内容的图像检索算法在检索时间和效率上都还不能满足这一需求。本文采用最新提出的Af-finity Propagation聚类方法和颜色-形状直方图特征,提出一种新的检索方法应用到数字图书馆进行图像检索。经过试验证明在查准率、查全率和检索时间上均有较大的提高。

Local and global approaches of affinity propagation clustering for large scale data

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.

Dyes with high affinity for polylactide

Attempts were made to develop dyes with high affinity for polylactide as an alternative to the existent commercial disperse dyes. The dyes synthesized according to the affinity concept of dye to polylactide exhibited excellent dyeing properties on polylactide compared with the commercial disperse dyes.

Male spike strobiles with Gnetum affinity from the Early Cretaceous in western Liaoning,Northeast China

A fossil with Gnetum affinity was found in the Jianshangou Member （Barremian Age） of the Yixian Formation （Lower Cretaceous Epoch） of the Jehol Group in western Liaoning, northeastern China. The single fossil specimen is represented by both elongate-cylindrical male spike strobiles which borne within a nodal bract of cauliflorous branch. The spike strobiles have apparent nodes, invisible internodes, and numerous verticillate involucral collars. The microsporangiate units within involucral collars are not seen. The male spike strobiles with verticillate involucral collars occur exclusively in Gnetum; hence, the fossil strobiles are attributed to a new taxon, Khitania columnispicata gen. ＆ sp. nov., being closely related to Gnetum. The general isotopic dating suggests an age of Barremian, ca. 125-122 million years （Myr） ago for the Jianshangou Member. The palaeoecological and palaeoclimatic inference based on the compositions of flora and fauna, and lithological characters of the fossil locality suggests that the fossil plants grew in a subtropical mesophytic forest and under a warmer climate. The remains of male spike strobiles are the first record of gnetalean macrofossil. It documents the evolution of the distinct gnetoid morphology and indicates a wider range of distribution of Gnetaceae in the Early Cretaceous than present day.

Synthesis of a New Series of Bone Affinity Compounds

A new series of bone affinity compounds were synthesized by linking chrysophanol with 5-fluorouracil derivatives. Their bone affinity was established by hydroxyapative （HA） affinity experiment in vitro, and their cytostatic effects were shown by the MTT assay.