High resurgence of dengue vector populations after space spraying in an endemic urban area of Thailand:A cluster randomized controlled trial

Objective: To examine the resurgence rate, house density index(HDI) and parous rate of the Aedes aegypti vector after space spraying carried out by the routine spraying team,and compare with the rates after standard indoor ultra low volume(SID-ULV) spraying carried out by the trained research spraying team.Methods: Between March and September 2014, a cluster randomized controlled trial including 12 clusters(6 regular ULV, 6 SID-ULV) with totally 4 341 households was conducted, and around 20–31 houses in each cluster were selected for assessment. The parous rate and HDI of collected mosquitoes 2 days before and 1, 2 and 6 days after spraying were obtained and compared.Results: The HDI dropped significantly from the baseline 1 and 2 days after spraying to a non-zero value in the SID-ULV treated locations but not in the regular ULV group locations. However, by 6 days after spraying, the HDI of both groups had returned to the base value measured 2 days before spraying. There were no statistically significant differences in the parous rate between groups.Conclusions: SID-ULV is more effective in reducing Aedes aegypti populations.However, rapid resurgence of dengue vector after spraying in urban areas was observed in both groups.

Exact Solution for Clustering Coefficient of Random Apollonian Networks

By means of the series method, we obtain the exact analytical solution of clustering coefficient in random Apollonian networks [Phys. Rev. E 71 （2005）046141]. Our exact analytical result is identical with the simulation, whereas in the original work, there is a deviation of about 4% between their approximate analytical result and the simulation.

一种修正评分偏差并精细聚类中心的协同过滤推荐算法

协同过滤作为国内外学者普遍关注的推荐算法之一,受评分失真、数据稀疏等问题影响,算法推荐效果不尽如人意。为解决上述问题,文章提出了一种改进的聚类协同过滤推荐算法。首先,该算法利用无监督情感挖掘技术将评论情感映射为一个固定区间中的值,通过加权修正用户评分偏差;然后,构建修正后用户-产品评分矩阵的数据场,利用启发式寻优算法计算最佳聚类数和最优初始聚类中心,进而对用户进行划分聚类,结合最近邻用户相似性与评分产生推荐结果;最后,基于三个自建真实数据集对所提算法性能和有效性进行全面评估。实验结果表明,改进算法在精度Precision、召回率Recall和F1-Score评价指标上的表现均优于其他算法,能够有效应对数据稀疏的问题,提升推荐系统的推荐效果。

三种补液方式预防即发型及迟发型献血相关血管迷走神经反应的效果研究:基于群组随机试验

目的对比3种献血前补液方式对预防即发型及迟发型献血相关血管迷走神经反应(vasovagal response,VVR)的效果。方法2021年1—6月,在本中心6个固定献血点对符合献血条件,签署知情同意的6250名全血献血者,按献血点和日期分为198个群组,按随机数法随机分配到口服补液盐(ORS)组,糖水组或饮用水组,分别在献血前20 min内喝完500 mL饮用水,糖水及ORS,研究人员现场记录干预的实际完成情况,并登记现场发生的VVR及相关情况。献血后休息时,献血者完成包含社会人口学信息的电子问卷。献血后48 h,研究人员电话回访每位献血者,登记迟发型VVR及相关情况。基于意向性分析法(ITT)Logistic回归分析3组间VVR发生率差异,考虑到依从性的影响,计算干预组的平均处理效应(ATT)。用PASS 2021估算样本量,R(4.2.0)做统计分析。结果本中心固定点全血献血者在3种不同的献血前补液方式干预下,献血相关VVR的累计发生率为2.67%(2.29%~3.11%),其中,即发型和迟发型VVR的发生率分别为1.02%(0.79%~1.31%)和1.65%(1.36%~2.01%)。ITT分析仅发现ORS比水组可有效降低迟发型VVR发生率【OR=0.59,95%CI[0.37,0.94]】。ATT分析考虑了依从性的影响发现:任意两组间即发型VVR发生率差异均无统计学意义(P>0.05);糖水组对比饮用水组的迟发型VVR发生率差异无统计学意义(P>0.05)。补充ORS对比饮用水,迟发型VVR发生率平均下降值相差了-0.013,95%CI[-0.022,-0.004],补充ORS对比糖水,迟发型VVR发生率平均下降值相差了-0.008,95%CI[-0.017,-0.000],差异均有统计学意义(P<0.05)。3个组累计VVR显示了与迟发型VVR相似的结果。结论献血前补充ORS对预防迟发型VVR效果最好,下一步可建立迟发型VVR预测模型甄别易感人群,建议其献血前喝ORS,其他人群献血前可根据喜好任选液体,实现献血相关VVR的个性化防控。

基于改进模糊聚类算法的大数据随机挖掘仿真

大数据挖掘是从大量有噪声的、随机模糊的大数据中提取有价值信息的过程,由于海量大数据具有多维性、稀疏性以及动态性等特点,准确获取其分布特征的难度较大,随机挖掘难以直接实现。为此提出基于改进模糊聚类算法的大数据随机挖掘方法。利用建立的语义概念树模型获取大数据的特征分布关系,并根据模糊语义分析法得出大数据的语义相似性、关联性条件,提取大数据特征。优先确定最佳聚类数,采用改进模糊聚类算法对其聚类,实现基于改进模糊算法的大数据随机挖掘。实验结果表明,上述方法的大数据模糊聚类效果较好,随机挖掘准确率可达到95%以上,实验所得结果验证了上述方法较强的应用有效性。

扫描电镜/能谱法对粉底液的检测分类研究

为建立一种将扫描电镜/能谱法与多元统计学结合检测粉底液物证的方法。对收集到的50个粉底液样本进行前处理,利用扫描电镜/能谱仪对样本进行分析测试,将50个样本大致分成2大类;结合K-Means聚类对实验数据进行处理,将2大类样本细化分为4类;最后利用随机森林算法搭建分类模型,40个样本作为训练集,10个样本作为测试集,测试集的预测正确率达到86.6%,预测效果良好,可实现样本分类自动化。此方法操作简单,可实现无损检材、分类效果良好,可直接用于公安机关侦查破案,在法庭科学领域有广阔的应用前景。

结合指数函数改进的随机近邻嵌入式短文本聚类

近年来深度学习在短文本聚类方面发挥巨大作用,最近提出的短文本聚类(Short Text Clustering, STC)算法在此方面取得不错的成效。为进一步提高聚类准确率并优化算法性能,基于指数函数提出改进的随机近邻嵌入算法。该算法用指数函数度量样本点与聚类中心差距,放大不同特征差别,并在后期使用k-means++算法预先确定聚类中心与聚类数目。在Stackoverflow数据集上的实验证明,随机指数嵌入聚类模型(e-STC)在准确率与标准互信息上均优于原STC模型,准确率相对提高3.2%,互信息相对提高2.9%。

空海自组网随机接入仿真研究

针对无人机在远海远域协同任务过程中的空海信息协同共享需求,提出了一种基于优先级传输的动态随机接入协议。首先,通过设计高效集群组网信令保证集群测控通信自组网的遥测、遥控信息的稳定传输;然后,结合改进的TDMA(时分多址)组网时隙结构实现节点快速入网与动态时隙分配协议;最后,通过OPNET(网络仿真技术软件包)分析所设计的集群空海自组网系统的随机接入低时延、业务传输高吞吐量和端到端传输低时延性能。结果表明:该组网信令下建网时间只需要2~3个飞控周期,在32节点场景下节点完成随机Aloha入网时延平均不到0.6 s,改进的TDMA协议在端到端时延比传统TDMA协议降低1/3。

基于模糊聚类算法的玻璃文物分析

模糊聚类作为一种自然语言评语集下的统计方法,通过数理方式,将难以量化的模糊评估转化成具体的数据评判标准,再利用线性回归的方式,得到风化与否与化学成分之间的关联,通过已得出函数式,反预测对于未知样本点的数据可能,最后使用随机森林算法将得到的样本数据加以不断学习、优化,模拟出更加贴合文物本身的化学成分。本文对基于模糊聚类算法的玻璃文物做出具体分析。

基于三维成像声呐的无人艇水下目标检测

针对无人艇水下目标探测过程中声呐图像受干扰多、单个目标可能分裂为多个亮斑等问题,开展了基于三维成像声呐的无人艇水下目标检测技术研究。首先,考虑水面无人艇水下目标检测跟踪识别的需要,建立了三维点云图像模型;然后,提出了基于随机抽样一致性(RANSAC)平面拟合的海底检测算法,以完成三维点云图中海底平面提取与分离,通过基于欧式聚类的三维点云检测技术和多特征数据关联技术等手段提高了目标的检测概率;最后,在湖上进行了试验,试验结果验证了该算法对水下目标检测识别的有效性。

Research on Some Bus Transport Networks with Random Overlapping Clique Structure

On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of alot of cliques,which intensively connect and overlap with each other.We study the network properties,which includethe degree distribution,multiple edges' overlapping time distribution,distribution of the overlap size between any twooverlapping cliques,distribution of the number of cliques that a node belongs to.Naturally,the cliques also constitute anetwork,with the overlapping nodes being their multiple links.We also research its network properties such as degreedistribution,clustering,average path length,and so on.We propose that a BTN has the properties of random cliqueincrement and random overlapping clique,at the same time,a BTN is a small-world network with highly clique-clusteredand highly clique-overlapped.Finally,we introduce a BTN evolution model,whose simulation results agree well withthe statistical laws that emerge in real BTNs.

Clustering-based label estimation for network anomaly detection

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses.In this work,we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings.The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users.To this end,we estimate the labels of each connection in the training phase using clustering.The“estimated”labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage.We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels.Through our extensive experiments with a public dataset(NSL-KDD),we will prove that the proposed method can achieve performance comparable to one with the “original”labels provided in the dataset.We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.

Random Route and Quota Sampling: Do They Offer Any Advantage over Probably Sampling Methods?

The aim of this paper is to compare sample quality across two probability samples and one that uses probabilistic cluster sampling combined with random route and quota sampling within the selected clusters in order to define the ultimate survey units. All of them use the face-to-face interview as the survey procedure. The hypothesis to be tested is that it is possible to achieve the same degree of representativeness using a combination of random route sampling and quota sampling (with substitution) as it can be achieved by means of household sampling (without substitution) based on the municipal register of inhabitants. We have found such marked differences in the age and gender distribution of the probability sampling, where the deviations exceed 6%. A different picture emerges when it comes to comparing the employment variables, where the quota sampling overestimates the economic activity rate (2.5%) and the unemployment rate (8%) and underestimates the employment rate (3.46%).

ON THE RANDOM-ORIENTED PERCOLATION

Let Ld=(Zd, Ed) be the d-dimensional lattice, suppose that each edge of Ld be oriented in a random direction, i.e., each edge being independently oriented positive direction along the coordinate axises with probability p and negative direction otherwise. Let Pp be the percolation measure, η(p) be the probability that there exists an infinite oriented path from the origin. This paper first proves η(p) θ(p) for d 2 and 1/2 p 1, where θ(p) is the percolation probability of bond model; then, as corollaries, the author gets η(1/2) = 0 for d = 2 and dc(1/2) = 2, where dc(1/2) = sup{d: η(1/2) = 0}. Next, based on BK Inequality for arbitrary events in percolation (see[2]), two inequalities are proved, which can be used as FKG Inequality in many cases (note that FKG Inequality is absent for Random-Oriented model). Finally, the author proves the uniqueness of infinite cluster and a theorem on geometry of the infinite cluster (similar to theorem (6.127) in [1] for bond percolation).

基于用户特征聚类的综合能源套餐推荐方法

针对庞大的综合能源用户群体在购买能源服务套餐时难以选择的问题,提出一种基于用户特征聚类的综合能源套餐推荐方法,以提高用户粘性。首先,将收集的综合能源用户信息进行知识图谱构建,对缺失的用户信息进行补充完善,同时分析用户之间的关系。然后,采用谱聚类的方法对构建好的用户知识图谱进行图谱分割聚类,进行用户之间的相似度计算,提取代表综合能源用户用能行为多样性的兴趣特征。最后,通过随机森林模型计算综合能源用户对各能源服务套餐的预测评分,将预测评分进行排序后,取评分最高的部分套餐通过线上平台为用户呈现套餐服务内容,实现对用户的精准推荐。将所提套餐推荐模型与传统推荐模型进行比较分析,结果表明基于用户特征聚类的综合能源套餐推荐方法能够为综合能源服务公司实现有效的用户精准化能源服务推荐,有利于提升能源服务公司的市场竞争力,同时为电力企业向综合能源服务提供商转型提供技术支撑。

Free clustering optimal particle probability hypothesis density(PHD) filter

As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.

改进的密度峰值聚类算法的差分隐私保护方案

针对改进的密度峰值聚类(AdDPC)算法在计算局部密度时产生的隐私泄露问题以及算法的一次分配策略,提出一种改进的密度峰值聚类算法的差分隐私保护方案。该方案在算法计算局部密度的过程中添加Laplace随机噪声,使得即使攻击者拥有最大背景知识,也无法通过添加或者删除数据集中的某一点来获取相应的信息,从而利用差分攻击获取目标数据点的信息,达到保护隐私数据的目的,并且在分配非聚类中心点时引入可达定义改进AdDPC算法的分配策略,避免因为一次分配策略导致数据点分配错误的问题。实验对比了DP-rcCFSFDP算法、AdAPC-rDP算法、IDP K-means算法的F-Measure和ARI,结果表明:当隐私预算大于1.5时,所提算法的F-Measure和ARI优于其他算法,所提算法能够在保护敏感数据的同时保证数据的可用性。

VANET随机部署环境下基于改进型共享最近邻密度峰聚类的快速分簇算法

针对车辆高速移动场景下,网络拓扑变化过大导致网络分簇结果不稳定的问题,提出一种基于改进型共享最近邻密度峰聚类的快速成簇算法SNNCA(shared nearest neighbor clustering algorithm);通过综合考虑节点的链路生存周期和移动相似性,提出一种全新的节点连接稳定程度评估指标,并将该评估指标应用于节点共享最近邻的计算过程,以组织网络节点为划分合理的多跳簇结构;为适应网络环境的动态变化,提出一种簇维护策略,其中每个层级的簇成员承担着维护下一层级簇成员的任务,该策略能够对簇成员进行批量分离或合并,从而实现了算法的分布式快速收敛;根据随机部署场景中进行的仿真实验结果显示,相比其他较新算法,SNNCA算法降低了74%的簇数量,并且簇成员的平均存活时间增加了近1倍,表现出更好的网络稳定性和健壮性。

基于区域生长顺序聚类-RANSAC的水稻苗带中心线检测

为提高水稻苗带中心线检测的适应性和实时性,满足巡田机器人导航的低成本、轻量级计算、高实时性需求,针对水稻苗带中心线检测结果容易受到光照变化和机器震动等原因产生图像噪声影响的问题,该研究以返青期和分蘖初期水稻秧苗为研究对象,提出基于区域生长顺序聚类-随机抽样一致性算法(random sample consensus,RANSAC)的水稻苗带中心线检测方法。首先,对采集的水稻秧苗图像运用归一化超绿特征法(excess green,ExG)和最大类间方差法(Otsu)分割水田背景和秧苗区域,应用先腐蚀后开运算的形态学方法去除秧苗图像噪声点;然后,采用基于水平带的秧苗轮廓质心检测方法提取秧苗特征点,利用区域生长顺序聚类方法将同一秧苗行的特征点聚成一类;最后,通过RANSAC算法拟合苗带中心线,从而得到巡田机器人视觉导航基准线。试验结果表明:该方法对返青期和分蘖初期水稻苗带中心线检测率均在97%以上,比已有YOLOv3算法提高6.12个百分点,比基于区域生长均值漂移聚类算法降低2.41个百分点;平均误差角度为2.34°,比已有YOLOv3算法高1.37°,比基于区域生长均值漂移聚类算法低0.12°,平均每帧图像检测时间为15.53 ms,比已有YOLOv3算法缩短81.19%,比基于区域生长均值漂移聚类算法缩短82.74%,本文方法在保证检测精度的基础上,大幅提升了检测速度,具有良好的适应性和实时性。研究结果可为巡田机器人视觉导航提供参考。

红外光谱法结合化学计量学对快递包装纸盒的检验研究

目的研究一种红外光谱法与化学计量学相结合的方法,以对现场提取的快递包装纸盒样品进行快速检验分类。方法利用红外光谱法对53个快递包装纸盒样品进行检验,依据其主要填料差异进行分类,并利用系统聚类进行分组。基于该分组,训练随机森林模型、多层感知器判别、Fisher判别3种预测模型,实现对新样品组别的分类预测。结果53个快递包装纸盒样品被分为3类,而后进一步细分为9组,训练得到的3种判别模型中的Fisher判别预测准确率较高。结论该检验方法快速、无损、准确,依据化学计量学实现对快递包装纸盒样品的快速检验,为公安机关检验此类物证提供依据。