Search Processes in the Exploration of Complex Data under Different Display Conditions

The study investigated user experience, display complexity, display type (tables versus graphs), and task difficulty as variables affecting the user’s ability to navigate through complex visual data. A total of 64 participants, 39 undergraduate students (novice users) and 25 graduate students (intermediate-level users) participated in the study. The experimental design was 2 × 2 × 2 × 3 mixed design using two between-subject variables (display complexity, user experience) and two within-subject variables (display format, question difficulty). The results indicated that response time was superior for graphs (relative to tables), especially when the questions were difficult. The intermediate users seemed to adopt more extensive search strategies than novices, as revealed by an analysis of the number of changes they made to the display prior to answering questions. It was concluded that designers of data displays should consider the (a) type of display, (b) difficulty of the task, and (c) expertise level of the user to obtain optimal levels of performance.

Linear mixed-effects model for longitudinal complex data with diversified characteristics

The increasing richness of data encourages a comprehensive understanding of economic and financial activities,where variables of interest may include not only scalar(point-like)indicators,but also functional(curve-like)and compositional(pie-like)ones.In many research topics,the variables are also chronologically collected across individuals,which falls into the paradigm of longitudinal analysis.The complicated nature of data,however,increases the difficulty of modeling these variables under the classic longitudinal frame-work.In this study,we investigate the linear mixed-effects model(LMM)for such complex data.Different types of variables arefirst consistently represented using the corresponding basis expansions so that the classic LMM can then be conducted on them,which gener-alizes the theoretical framework of LMM to complex data analysis.A number of simulation studies indicate the feasibility and effectiveness of the proposed model.We further illustrate its practical utility in a real data study on Chinese stock market and show that the proposed method can enhance the performance and interpretability of the regression for complex data with diversified characteristics.

Source complexity of the 2016 M_W7.8 Kaikoura (New Zealand) earthquake revealed from teleseismic and InSAR data

On November 13, 2016, an MW7.8 earthquake struck Kaikoura in South Island of New Zealand. By means of back-projection of array recordings, ASTFs-analysis of global seismic recordings, and joint inversion of global seismic data and co-seismic In SAR data, we investigated complexity of the earthquake source. The result shows that the 2016 MW7.8 Kaikoura earthquake ruptured about 100 s unilaterally from south to northeast(~N28°–33°E), producing a rupture area about 160 km long and about 50 km wide and releasing scalar moment 1.01×1021 Nm. In particular, the rupture area consisted of two slip asperities, with one close to the initial rupture point having a maximal slip value ~6.9 m while the other far away in the northeast having a maximal slip value ~9.3 m. The first asperity slipped for about 65 s and the second one started 40 s after the first one had initiated. The two slipped simultaneously for about 25 s.Furthermore, the first had a nearly thrust slip while the second had both thrust and strike slip. It is interesting that the rupture velocity was not constant, and the whole process may be divided into 5 stages in which the velocities were estimated to be 1.4 km/s, 0 km/s, 2.1 km/s, 0 km/s and 1.1 km/s, respectively. The high-frequency sources distributed nearly along the lower edge of the rupture area, the highfrequency radiating mainly occurred at launching of the asperities, and it seemed that no high-frequency energy was radiated when the rupturing was going to stop.

Data Driven Uncertainty Evaluation for Complex Engineered System Design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

Analysis of Complex Correlated Interval-Censored HIV Data from Population Based Survey

In studies of HIV, interval-censored data occur naturally. HIV infection time is not usually known exactly, only that it occurred before the survey, within some time interval or has not occurred at the time of the survey. Infections are often clustered within geographical areas such as enumerator areas (EAs) and thus inducing unobserved frailty. In this paper we consider an approach for estimating parameters when infection time is unknown and assumed correlated within an EA where dependency is modeled as frailties assuming a normal distribution for frailties and a Weibull distribution for baseline hazards. The data was from a household based population survey that used a multi-stage stratified sample design to randomly select 23,275 interviewed individuals from 10,584 households of whom 15,851 interviewed individuals were further tested for HIV (crude prevalence = 9.1%). A further test conducted among those that tested HIV positive found 181 (12.5%) recently infected. Results show high degree of heterogeneity in HIV distribution between EAs translating to a modest correlation of 0.198. Intervention strategies should target geographical areas that contribute disproportionately to the epidemic of HIV. Further research needs to identify such hot spot areas and understand what factors make these areas prone to HIV.

细微粒度视域下国内外跨社交网络用户对齐研究综述

针对多源复杂网络用户管理所面临的冷启动、信息孤岛问题难以满足企业在不同社交网络中识别同一用户的困境,传统的仅使用单一维度特征提取源的跨社交网络用户对齐方法难以实现迅速、精准的用户识别,而使用细微粒度视域下的多维特征提取源可以有效耦合各类用户信息的优势以提升识别效率和精准度。论文首先以国内外跨社交网络用户对齐的相关文献为研究对象进行系统性分析,从细微粒度视域出发对文献主要内容进行汇总与梳理;然后逐一细化地对比分析以总结现有不同研究所采用的特征提取源之间的差异、优势和桎梏;最后从隐私数据集获取、多维数据集成和多源社交网络数据耦合3个方面来对未来用户对齐领域中的研究前沿方向与研究深入挖掘点作出展望。

Multilevel Modeling of Binary Outcomes with Three-Level Complex Health Survey Data

Complex survey designs often involve unequal selection probabilities of clus-ters or units within clusters. When estimating models for complex survey data, scaled weights are incorporated into the likelihood, producing a pseudo likeli-hood. In a 3-level weighted analysis for a binary outcome, we implemented two methods for scaling the sampling weights in the National Health Survey of Pa-kistan (NHSP). For NHSP with health care utilization as a binary outcome we found age, gender, household (HH) goods, urban/rural status, community de-velopment index, province and marital status as significant predictors of health care utilization (p-value < 0.05). The variance of the random intercepts using scaling method 1 is estimated as 0.0961 (standard error 0.0339) for PSU level, and 0.2726 (standard error 0.0995) for household level respectively. Both esti-mates are significantly different from zero (p-value < 0.05) and indicate consid-erable heterogeneity in health care utilization with respect to households and PSUs. The results of the NHSP data analysis showed that all three analyses, weighted (two scaling methods) and un-weighted, converged to almost identical results with few exceptions. This may have occurred because of the large num-ber of 3rd and 2nd level clusters and relatively small ICC. We performed a sim-ulation study to assess the effect of varying prevalence and intra-class correla-tion coefficients (ICCs) on bias of fixed effect parameters and variance components of a multilevel pseudo maximum likelihood (weighted) analysis. The simulation results showed that the performance of the scaled weighted estimators is satisfactory for both scaling methods. Incorporating simulation into the analysis of complex multilevel surveys allows the integrity of the results to be tested and is recommended as good practice.

A Complexity Analysis and Entropy for Different Data Compression Algorithms on Text Files

In this paper, we analyze the complexity and entropy of different methods of data compression algorithms: LZW, Huffman, Fixed-length code (FLC), and Huffman after using Fixed-length code (HFLC). We test those algorithms on different files of different sizes and then conclude that: LZW is the best one in all compression scales that we tested especially on the large files, then Huffman, HFLC, and FLC, respectively. Data compression still is an important topic for research these days, and has many applications and uses needed. Therefore, we suggest continuing searching in this field and trying to combine two techniques in order to reach a best one, or use another source mapping (Hamming) like embedding a linear array into a Hypercube with other good techniques like Huffman and trying to reach good results.

Pinning sampled-data synchronization for complex networks with probabilistic coupling delay

We deal with the problem of pinning sampled-data synchronization for a complex network with probabilistic time-varying coupling delay. The sampling period considered here is assumed to be less than a given bound. Without using the Kronecker product, a new synchronization error system is constructed by using the property of the random variable and input delay approach. Based on the Lyapunov theory, a delay-dependent pinning sampled-data synchronization criterion is derived in terms of linear matrix inequalities （LMIs） that can be solved effectively by using MATLAB LMI toolbox. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

Block-based test data adequacy measurement criteria and test complexity metrics

On the basis of software testing tools we developed for progrmnming languages, we firstly present a new control flowgraph model based on block. In view of the notion of block, we extend the traditional program-based software test data adequacy measurement criteria, and empirically analyze the subsume relation between these measurement criteria. Then, we define four test complexity metrics based on block. They are J-complexity 0; J-complexity 1 ; J-complexity 1 + ; J-complexity 2. Finally, we show the Kiviat diagram that makes software quality visible.

随机矩阵理论在高速路关键路径辨识中的应用

高速公路网络是我国各地区相互连接的重要纽带,高速公路网络关键路径辨识对确保高速网络的可靠运行具有重要意义。传统的关键路径分析方法基于拓扑结构,未考虑交通网络的运输量特性;而现有的基于运输量数据的分析方法只考虑部分路径的运输量特性,难以反映交通网络的实际运行情况。利用路径运输量数据,搭建运输量随机矩阵模型,针对高速公路网络异常后的运输量变化特性,定义关键路径评估指数,实现异常影响程度的量化评估,在此基础上提出一种基于数据驱动的高速公路网络关键路径辨识方法。最后,采用辽宁省高速公路网络进行分析,验证了所提方法的合理性和有效性,并将该方法应用于城市路网案例中,进一步证明该方法具有普适性。

基于数字孪生与元宇宙技术的能源互联网态势感知系统论方法研究(一):概念、挑战与研究框架

态势感知是能源互联网的核心任务,为运管调控各项决策提供重要辅助信息。能源互联网的复杂定性对其态势感知任务提出严峻挑战——受其所辖单元日益增长的规模、种类、主动性、耦合性以及外界环境等不确定因素的影响,能源互联网其复杂性与日俱增。系统复杂性所衍生的一些问题超出经典简化论的讨论范畴,亟需一种新的认知手段。该文通过关联数据、数据科学、物理系统与具体应用,提出能源互联网数字孪生系统;进一步,延伸出一种新型数据驱动的态势感知方法论,即数字孪生态势感知(digitaltwinsituation awareness,DT-SA)。其核心思想是将真实世界的固有难题转化到虚拟空间,继而借助复杂系统理论和大数据分析等工具予以解决,具体涉及孪生体建模、分析和认知等核心环节。此外,该文也讨论元宇宙技术对孪生体互联的增强作用,以及相关的科学问题。该系列研究有益于推进能源系统领域和数据科学领域的交叉融合,对于企业数字化转型、数字城市建设等具备参考价值。

Baddeleyite from Large Complex Deposits: Significance for Archean-Paleozoic Plume Processes in the Arctic Region (NE Fennoscandian Shield) Based on U-Pb (ID-TIMS) and LA-ICP-MS Data

Baddeleyite is an important mineral geochronometer. It is valued in the U-Pb (ID-TIMS) geochronology more than zircon because of its magmatic origin, while zircon can be metamorphic, hydrothermal or occur as xenocrysts. Detailed mineralogical (BSE, KL, etc.) research of baddeleyite started in the Fennoscandian Shield in the 1990s. The mineral was first extracted from the Paleozoic Kovdor deposit, the second-biggest baddeleyite deposit in the world after Phalaborwa (2.1 Ga), South Africa. The mineral was successfully introduced into the U-Pb systematics. This study provides new U-Pb and LA-ICP-MS data on Archean Ti-Mgt and BIF deposits, Paleoproterozoic layered PGE intrusions with Pt-Pd and Cu-Ni reefs and Paleozoic complex deposits (baddeleyite, apatite, foscorite ores, etc.) in the NE Fennoscandian Shield. Data on concentrations of REE in baddeleyite and temperature of the U-Pb systematics closure are also provided. It is shown that baddeleyite plays an important role in the geological history of the Earth, in particular, in the break-up of supercontinents.

海南岛抱板杂岩记录的多期构造热事件:锆石和独居石U-Pb年代学的制约

抱板杂岩(抱板群)是海南岛目前已发现的最古老前寒武纪结晶基底,记录了多期构造热事件,是研究海南岛大地构造属性的重要窗口。本文通过对海南岛冲卒岭地区抱板杂岩变质沉积岩、变质基性岩和伟晶岩脉的锆石和独居石LA-ICP-MS U-Pb年代学研究,来确定抱板杂岩所经历的多期构造热事件及其所代表的构造意义。研究结果显示,抱板杂岩主要由中元古代变质沉积岩、花岗质片麻岩和变质基性岩组成,经历了中元古代晚期-新元古代早期(1.3~0.9Ga)、早古生代(470~440Ma)、三叠纪(~230Ma)等多期构造热事件的改造。中元古代晚期-新元古代早期构造热事件与罗迪尼亚超大陆的汇聚有关;早古生代构造热事件与东冈瓦纳古陆北缘的增生造山作用有关;三叠纪构造热事件是印支陆块与华南陆块碰撞拼合的远程响应。综合上述研究成果和区域对比分析,认为海南岛与印支陆块具有相似的前寒武纪结晶基底和多期构造热事件的记录,表明二者具有明显的亲缘关系。

基于复杂网络及数据挖掘探析针灸治疗癫痫的选穴规律

目的运用复杂网络方法探析针灸治疗癫痫的核心腧穴及配伍规律。方法检索中国知网、维普、万方、Web of Science、EMBASE及Pubmed数据库,依据纳入与排除标准筛选文献并建立处方数据库。运用SPSS Modeler软件分析腧穴频次与关联性,运用Gephi0.10.1软件建立复杂网络模型,探析针灸治疗癫痫处方核心腧穴与选穴规律。结果最终纳入有效文献144篇,提取199个处方,涉及102个腧穴。百会穴使用频次最高,特定穴以五腧穴、八脉交会穴、背俞穴为主,经络选择上督脉选择的腧穴最多。关联规则分析显示,百会-太冲支持度及置信度最高。复杂网络拓扑结构分析表明,百会、大椎、腰奇、丰隆等36个腧穴为针灸治疗癫痫的核心腧穴。腧穴社团分析显示,督脉循经治痫群、四肢头部远近配穴群及脏腑津液辨证群为针灸治疗癫痫的3大腧穴群。结论针灸治疗癫痫的腧穴配伍要以督脉为主结合脏腑津液辨证选穴,要注重远近配穴的选穴思路。

针刺治疗抑郁症的选穴特点及腧穴配伍规律

目的运用数据挖掘技术分析针刺治疗抑郁症的选穴特点和腧穴配伍规律。方法检索自建库以来至2021年7月的中文数据库有关针刺治疗抑郁症的文献,包括中国知网期刊全文数据库(CNKI)、中国学术期刊数据库(万方数据)、中文科技期刊数据库(维普网)以及中国生物医学文献数据库(CBM)。经筛选后形成针刺治疗抑郁症的处方数据库,并利用关联规则、聚类分析、复杂网络分析等实现数据挖掘以及可视化展示。结果针刺治疗抑郁症腧穴以百会应用频次最高;腧穴归经督脉使用频次最高;关联规则中以百会-印堂、太冲-百会和太冲-内关关联性最强。结论治疗抑郁症的针刺处方以多穴配伍为主,配伍原则为远近配穴、按部位配穴与按经脉配穴相结合,针刺核心处方的核心腧穴为百会、太冲、印堂、内关、三阴交、神门、四神聪、神庭。

基于复杂网络分析吴力群治疗抽动障碍用药规律

目的探究吴力群教授治疗小儿抽动障碍的用药规律。方法收集吴力群教授2016年9月-2022年10月诊治抽动障碍患儿病案,建立数据库,使用SPSS Statistics 23进行频数统计,使用Liquorice软件进行多尺度骨干网络分析及多层核心网络分析,总结用药规律。结果共纳入病案709则,涉及处方834首,中药203味。药物类别多为补虚药、清热药、平肝熄风药,药性以温、寒、平性为主,归经多归肺经、肝经、脾经。复杂网络分析显示核心处方由当归、白芍、川芎、党参、等20味中药组成,得到30组常用药对,以及不同症状及共患病常用药物。结论吴力群教授治疗小儿抽动障碍重在从“肝风”论治,疏散外风截断病程,平肝潜阳以熄内风,并重视调达肝气,滋阴柔肝,养血熄风。

前交通动脉动脉瘤112例诊治真实世界数据分析:单中心报告

目的对前交通动脉动脉瘤(anterior communicating artery aneurysm,ACoAA)病例资料进行真实世界数据(real world data,RWD)分析,总结救治经验。方法回顾性分析112例ACoAA病例资料。男69例,女43例,年龄30~80岁、平均(45.00±11.05)岁。发生蛛网膜下腔出血就诊110例,未破裂就诊2例。行开颅夹闭66例,血管内介入治疗46例,其中应用支架辅助栓塞5例。统计病人的人口学资料、基础疾病、个人史、家族史、入院状况及影像学资料、动脉瘤解剖学特征、病人预后等资料。结果依据改良Rankin量表(modified Rankin Scale,mRS)评分:良好(mRS 0~2分)82例,残疾(mRS 3~5分)22例,死亡(mRS 6)8例。RWD将预后不良的相关因素,纳入有序多分类Logistic回归分析,显示年龄与饮酒史是ACoAA术后预后的独立危险因素,病人居住地、高血压病史及不同治疗术式(开颅夹闭和栓塞治疗)不是病人预后的独立危险因素。结论ACoAA确诊后接受积极治疗(开颅夹闭或栓塞治疗),病人术后恢复良好率较高。ACoAA不良预后可能与年龄、饮酒等因素相关。

基于复杂网络的交通序列数据特性

为了进一步研究交通流特性,采用复杂网络方法对交通序列数据进行分析。提出了箱型图-聚类算法模型用于识别和填充初始数据中的缺失值和异常值;通过相空间重构方法将一维数据重构为网络节点,选取连接阈值确定网络节点的连接关系,将交通序列数据构建为复杂网络,对复杂网络的结构和定量指标进行分析。研究结果表明交通序列数据复杂网络的结构一定程度上可以反映路段的交通流状态。该结果有助于优化数据预处理方法,拓展复杂网络在交通序列数据研究中的应用。

基于手机信令数据的小城市居民出行时空特征研究——以铜仁市为例

居民出行特征与交通设施建设、居民生活质量、疫情防控等密切相关,是城市规划领域研究的重要主题之一。当前居民出行特征研究主要集中于特大城市,对中小城市的研究相对较少。本文以贵州省铜仁市为例,基于2019年5月、2019年11月、2020年5月三个时段,61543902条铜仁市常住人口出行OD手机信令数据,对小城市居民出行的规模、时耗、距离、分布空间与网络特征进行研究。结果发现:一、小城市出行规模在一天中有3-4个高峰,大城市则仅有2个高峰,小城市高峰出行量占比在冬季更高;2020年初新冠疫情后,铜仁市人口密度较大的区县内部出行比例上升。二、铜仁市的区县内部每次出行平均时耗与人口规模之间有较强的正相关性。三、铜仁市每次出行距离分布在群体层面上服从幂律分布;且冬季的区县内长距离出行的次数衰减较快。四、小城市居民出行空间分布呈现明显的向心性,大城市向心性相对较弱。五、铜仁市城镇化率较高地区在出行网络中的重要程度在冬季往往上升。研究结论进一步认识了小城市居民出行特征,为交通规划和交通政策制定提供了参考。