Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

Detecting network intrusions by data mining and variable-length sequence pattern matching

Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.

Multidimensional Visualization of Bikeshare Travel Patterns Using a Visual Data Mining Technique: Data Cubes

In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users,an online analytical processing(OLAP)tool called data cube was used for treating and displaying multi-dimensional data.We extended and modified the traditionally threedimensional data cube into four dimensions,which are space,date,time,and user,each with a user-specified hierarchy,and took transaction numbers and travel time as two quantitative measures.The results suggest that there are two obvious transaction peaks during the morning and afternoon rush hours on weekdays,while the volume at weekends has an approximate even distribution.Bad weather condition significantly restricts the bikeshare usage.Besides,seamless smartcard users generally take a longer trip than exclusive smartcard users;and non-native users ride faster than native users.These findings not only support the applicability and efficiency of data cube in the field of visualizing massive smartcard data,but also raise equity concerns among bikeshare users with different demographic backgrounds.

A Test Pattern Identification Algorithm and Its Application to CINRAD/SA(B) Data

A variety of faulty radar echoes may cause serious problems with radar data applications,especially radar data assimilation and quantitative precipitation estimates.In this study,＂test pattern＂ caused by test signal or radar hardware failures in CINRAD （China New Generation Weather Radar） SA and SB radar operational observations are investigated.In order to distinguish the test pattern from other types of radar echoes,such as precipitation,clear air and other non-meteorological echoes,five feature parameters including the effective reflectivity data percentage （Rz）,velocity RF （range folding） data percentage （RRF）,missing velocity data percentage （RM）,averaged along-azimuth reflectivity fluctuation （RNr,z） and averaged along-beam reflectivity fluctuation （RNa,z） are proposed.Based on the fuzzy logic method,a test pattern identification algorithm is developed,and the statistical results from all the different kinds of radar echoes indicate the performance of the algorithm.Analysis of two typical cases with heavy precipitation echoes located inside the test pattern are performed.The statistical results show that the test pattern identification algorithm performs well,since the test pattern is recognized in most cases.Besides,the algorithm can effectively remove the test pattern signal and retain strong precipitation echoes in heavy rainfall events.

An Efficient Outlier Detection Approach on Weighted Data Stream Based on Minimal Rare Pattern Mining

The distance-based outlier detection method detects the implied outliers by calculating the distance of the points in the dataset, but the computational complexity is particularly high when processing multidimensional datasets. In addition, the traditional outlier detection method does not consider the frequency of subsets occurrence, thus, the detected outliers do not fit the definition of outliers (i.e., rarely appearing). The pattern mining-based outlier detection approaches have solved this problem, but the importance of each pattern is not taken into account in outlier detection process, so the detected outliers cannot truly reflect some actual situation. Aimed at these problems, a two-phase minimal weighted rare pattern mining-based outlier detection approach, called MWRPM-Outlier, is proposed to effectively detect outliers on the weight data stream. In particular, a method called MWRPM is proposed in the pattern mining phase to fast mine the minimal weighted rare patterns, and then two deviation factors are defined in outlier detection phase to measure the abnormal degree of each transaction on the weight data stream. Experimental results show that the proposed MWRPM-Outlier approach has excellent performance in outlier detection and MWRPM approach outperforms in weighted rare pattern mining.

东北三省耕地利用格局变化对粮食全要素生产率的影响

耕地是粮食生产最基本的要素,当前粮食需求扩大与粮食生产区域性不足矛盾突出,寻求更合理的耕地利用格局来实现粮食全要素生产率的提升具有重要意义。本研究基于土地利用/土地覆被变化(LUCC)遥感监测数据,应用GIS软件提取东北三省2000、2005、2010、2015年耕地利用格局相关数据,同时运用数据包络分析模型估算了2000、2005、2010年和2015年东北三省区域和市域粮食全要素生产率。在耕地利用格局变化分析和粮食全要素生产率测算的基础上,从耕地利用格局变化的角度选取指标构建面板数据模型,定量分析其对粮食全要素生产率的影响。结果表明:东北三省市域粮食全要素生产率4个时期均值分别为0.81、0.78、0.82和0.83,粮食全要素生产率空间分布从最初的相对均匀到局部集聚;耕地转出率和耕地斑块破碎度与粮食全要素生产率呈负相关关系,耕地面积比例、耕地转入率和耕地斑块聚合度与粮食全要素生产率呈正相关关系;东北三省粮食全要素生产率提高主要源于耕地面积增加、林地和未利用地向耕地的转入以及耕地在流域的聚合,粮食全要素生产率的降低主要源于耕地转入减少和建设用地对耕地的嵌入式占用。研究表明,严格规划耕地利用格局、持续开展土地整理、提高耕地聚合度,可在保证耕地有效数量的同时,提高耕地利用格局的集聚性和合理性,促进粮食全要素生产率的提升。

“stppSim”: A Novel Analytical Tool for Creating Synthetic Spatio-Temporal Point Data

In crime science, understanding the dynamics and interactions between crime events is crucial for comprehending the underlying factors that drive their occurrences. Nonetheless, gaining access to detailed spatiotemporal crime records from law enforcement faces significant challenges due to confidentiality concerns. In response to these challenges, this paper introduces an innovative analytical tool named “stppSim,” designed to synthesize fine-grained spatiotemporal point records while safeguarding the privacy of individual locations. By utilizing the open-source R platform, this tool ensures easy accessibility for researchers, facilitating download, re-use, and potential advancements in various research domains beyond crime science.

Spatial-Temporal Features of Wuhan Urban Agglomeration Regional Development Pattern—Based on DMSP/OLS Night Light Data

Based on the night light data, urban area data, and economic data of Wuhan Urban Agglomeration from 2009 to 2015, we use spatial correlation dimension, spatial self-correlation analysis and weighted standard deviation ellipse to identify the general characteristics and dynamic evolution characteristics of urban spatial pattern and economic disparity pattern. The research results prove that: between 2009 and 2013, Wuhan Urban Agglomeration expanded gradually from northwest to southeast and presented the dynamic evolution features of “along the river and the road”. The spatial structure is obvious, forming the pattern of “core-periphery”. The development of Wuhan Urban Agglomeration has obvious imbalance in economic geography space, presenting the development tendency of “One prominent, stronger in the west and weaker in the east”. The contract within Wuhan Urban Agglomeration is gradually decreased. Wuhan city and its surrounding areas have stronger economic growth strength as well as the cities along The Yangtze River. However, the relative development rate of Wuhan city area is still far higher than other cities and counties.

基于中医传承辅助平台对布鲁氏菌病肝肾亏虚证用药规律的分析

目的:分析中医治疗布鲁氏菌病肝肾亏虚证的用药规律,挖掘有效的药物组合及新处方。方法:检索相关数据库并查阅资料,收集关于布鲁氏菌病肝肾亏虚证的文献和医案,筛选治疗该病证的方剂。基于中医传承辅助平台(V2.5)建立方药特征数据库,并通过关联规则分析法、熵层次聚类等方法,对处方用药进行数据分析,包括对用药频次统计、组方规律进行挖掘等。结果:肝肾亏虚的症状出现54条,症状频次总共224次,其中频率在5以上的18条,总共出现147次,占总数的65.6%。筛选出关于布鲁氏菌病肝肾亏虚证的组方17首,中药38味,使用频次最高的5味中药为牛膝、枸杞子、当归、续断、桑寄生,并取得核心药物组合14条,药物规则主要为牛膝、党参、续断、当归、桑寄生、枸杞子、黄精之间的组合,核心组合4条、得出2个新的处方。结论:中医治疗布鲁氏菌病肝肾亏虚证用药以补肝肾、强筋骨、祛风湿、养气血之品为主,新方组成体现了祛邪扶正、标本兼顾,可使肝肾强、风湿除而血气足、痹痛愈。

基于数据挖掘探讨《普济方》所载中药治疗高血压相关眼病的用药规律

目的 基于数据挖掘,探讨《普济方》中中药治疗高血压相关眼病的用药规律,并探讨高频核心药物运用思路及理论基础。方法 以《普济方》中治疗头目昏、头目眩、眼痛方为数据来源,使用“中医传承计算平台v 3.0”进行数据挖掘,对纳入的中药组方进行关联分析和聚类分析,探寻高血压相关眼病的高频药物及使用规律。结果 (1)一般情况:最终纳入的101个中药组方,212味中药。(2)性味归经:药性以温性居多,使用频次为367次,占总频次的40.60%。药味以辛为主,使用频次为456次,占总频次的33.48%。高频归经为肺经,使用频次为426次,占总频次的10.92%。药物功效主要是祛风、补虚、平肝熄风,使用频率最高的单味药依次为防风、川芎、甘草、人参、细辛。(3)聚类分析:高频药物组合有,防风、细辛-川芎;川芎、防风-天麻;防风-炙甘草,新核心类组方3个,组合I为人参、茯苓、炙甘草、天麻、川芎;组合II为菊花、防风、炙甘草、半夏、大黄;组合III为防风、川芎、天麻、羌活、冰片。结论《普济方》治疗高血压相关眼病多以发散风邪、补虚固中、清热育阴、平肝息风为主要治法,以达“明目、止眩”的治疗效果。

城市休闲产业聚类模式APM算法模型开发与校验

城市休闲相关产业的高质量发展对当前我国城市消费升级以及人居环境质量提升具有重要现实意义。但是,现有研究未能精准地捕捉海量广域分布的城市休闲产业的基本空间分布规律与结构,而已有的空间聚类算法较多适用于城市用地分析,未能很好地适用于离散分布的城市休闲产业研究。为此,文章基于空间兴趣点数据,开发距离通达值及空间集群中心点等算法,构建城市休闲旅游产业聚类模式空间算法模型(APM)。在以广州为例的研究中,APM模型捕捉出3170个以500 m步行生活圈为范围的城市休闲产业集群,校验了APM模型的科学性与应用价值。整体上,APM算法可以较好地捕捉城市休闲业态集群的空间结构,清晰识别城市休闲产业空间冷、热点分布的基本结构,由其捕捉行程的聚类边界与实际道路和建筑走向、水系边界、区域范围等重合度高,聚类集群符合实际情况,具备可信度与有效性。该研究是休闲产业集聚机制研究的一次方法创新,在算法精度、实际应用、可视化效率上均做出了创新性推进。与Fishnet方法相比,可以更科学精准地识别城市内部多个休闲消费商圈的边界,实现了高效率的城市休闲产业集群捕捉;与同位模型相比,可以呈现多类别的城市休闲业态结构,突破了现有研究只能捕捉两类业态组团的局限。

理论驱动与数据驱动相结合的学习者绩效预测研究

对学生的学习成绩进行精准预测,可以尽早识别风险学生并采取有针对性的干预措施。文章以某高校修读计算机网络与云计算课程的160名本科生为研究对象,结合理论驱动和数据驱动的方法,运用聚类分析及交叉分析法,研究了学生主观自评和在线学习行为数据对预测学生成绩的作用。研究发现,理论驱动方法识别出了“深度学习”和“浅层学习”2种在线学习模式,且这2种学习模式下的学生在各项学习成绩上存在显著差异;数据驱动方法识别出了4种(积极主动型、任务型、被动型、消极被动型)在线学习模式,且这4类学习模式下的学生在学习行为及各项成绩上均存在显著差异。本研究进一步采用交叉分析法细分出7种学习模式,并发现这7类学生在学习投入、学习策略、学习行为及各项学习成绩上均存在显著差异。

异常检测在网络安全防护中的应用研究

图像异常数据作为网络安全检测的核心监控对象,面临着样本不均衡、数据缺乏标注以及异常形式多样化等挑战,针对这些问题,创新性地提出了自信息量挖掘模块,旨在学习已知类别样本的数据模式;同时提出了三元组信息量学习策略,优化类别信息学习和已知类别的数据模式学习,最终实现了在网络安全防护场景中对图像的未知类别样本的异常检测。实验结果表明,异常检测算法可以有效提升网络安全防护的准确性,在实际应用中表现出色。

数智技术赋能统战工作发展透视

数智技术与统战工作在价值理念和政治定位等层面具有高度契合性,前者可以满足后者信息化、智能化、精准化、动态化等发展需求,为后者高质量发展提供持续动能。数智技术能有效支撑和服务于统战工作、开拓统战工作数据应用新空间、激发统战工作新活力。当前我国数智统战工作取得了一些进展,但也面临数字化平台和工具应用不足、数据共享和协同体系不完善以及数智化人才短缺和统战干部理念更新不足等问题。因此,未来数智统战应综合运用数智技术,通过构建面向统战对象的大数据智能分析平台、开发数智化统战工作工具、丰富宣传内容和形式,推动统战工作凝聚人心、汇聚力量;通过制定统一的数智统战顶层设计和规划、构建统一开放的统战云平台、完善数智安全保障体系,赋能大统战工作格局的构建;通过推进协商数智化体系构建、建立政协系统数智人才培训与考核机制、加大新技术在协商业务流程中的应用,深化协商民主。

基于Voronoi图的空间点事件统计聚类方法

挖掘地理空间数据中点事件聚集模式对于揭示流行疾病、犯罪分布热点区域及城市基础设施空间分布格局等具有重要意义。针对不同形状、密度和大小的显著空间点聚集模式的识别,目前以空间扫描统计为代表的方法虽然可以对空间点聚类的显著性进行统计推断,减少虚假聚类结果,但其主要用于识别球形或椭圆形状的聚簇,对于沿着街道或河道分布的任意形状、不同密度的显著空间点聚簇识别还存在局限。因此,本研究提出一种基于Voronoi图的空间点聚集模式统计挖掘方法。首先,采用Voronoi图来度量空间点分布的聚集性,将空间点聚类问题转化为热点区域探测问题;其次,结合局部Gi*统计量探测统计上显著的空间点聚簇;最后,通过模拟数据和真实犯罪事件数据进行实验与对比分析。结果表明:本方法能够有效探测任意形状的空间点聚类,并对空间点簇的显著性进行统计判别,识别显著的空间点簇,减少随机噪声点的干扰;聚类识别结果优于现有代表性方法,如DBSCAN算法、空间扫描统计方法等。

基于夜光数据的昆明市城市扩张格局演变分析

基于夜光数据采用POI&NPP综合指数提取建成区,然后进行建成区面积精度评定,从定性和定量两个角度进行城市扩张空间形态及格局演变分析。结果表明:1)POI融入夜光数据后,提高了提取的精度,提取边界与实际的分布范围更为接近。2)扩张速度和强度较快区域主要集中在呈贡区、嵩明县等,而较慢区域集中在北部的禄劝县、东川区、寻甸县等;嵩明县的动态变化率最高,中心城区的区域动态变化率较低,北部的寻甸县、东南部的宜良县、石林县动态变化率反而较高。3)昆明市整体上经济重心经度方向为向东偏移,纬度方向为向南偏移。

基于生态安全格局的云南世居民族所在地生态韧性评价研究

云南世居民族所在地是基于传统生态智慧世代延续,以自然系统与社会系统相互嵌套为特点的自然–文化遗产,而现代性和社会经济的快速发展使得世居民族所在地不断被蚕食和消解.研究基于韧性理念,利用多源数据,从稳定性、适宜性、冗余度、多样性4个层面构建云南世居民族所在地生态安全格局和生态韧性耦合框架,对藏、彝、傣族所在地2000—2020年生态韧性进行评价研究.结果表明,近20 a来,世居民族所在地总体生态韧性水平为中等,呈现逐年降低的趋势.高水平韧性区多分布于林地,存在连续但细碎化的分布特征.低水平生态韧性多分布于道路缓冲区2 km以内,路网的社会扰动内涵很大程度上影响生态韧性强度.20 a来平均生态韧性指数傣族所在地(0.6420)>彝族所在地(0.5602)>藏族所在地(0.5344).影响各世居民族所在地生态韧性的原因存在差异,地质灾害防护格局和水生环境安全格局是影响生态韧性的主要因素.

基于2020年版《中华人民共和国药典》(一部)活血类制剂用药规律分析

目的分析2020年版《中华人民共和国药典》(一部)中具有活血功效制剂的组方规律,为新药的研发提供参考。方法构建2020年版《中华人民共和国药典》(一部)成方制剂数据库,从中筛选具有“活血”功效的制剂,对活血类制剂进行经胃肠道给药和非经胃肠道给药两大类剂型分析;根据功效将活血类制剂分为活血化瘀、活血止痛、行气活血、益气活血、活血调经和活血解毒6大类,采用Microsoft Excel 2016中函数对制剂组成药物的频数进行分析,采用Vosviewer 1.6.17软件构建药物网络视图,利用IBM SPSS Modeler 18.0提供的Apriori算法进行关联规则分析并制作网络图。结果2020年版《中华人民共和国药典》(一部)共筛选出367个具有活血功效的制剂,264个不同组方,涉及417味中药。其中经胃肠道给药制剂329个,剂型较多的是胶囊剂(29%)和片剂(23%),非经胃肠道给药制剂38个。活血化瘀类、活血止痛类、行气活血类、益气活血类、活血调经类和活血解毒类制剂分别有115、99、56、44、35和18个。从264个组方频数统计发现,川芎(87次)、当归(85次)、丹参(80次)、红花(73次)、赤芍(57次)、三七(55次)、黄芪(49次)的频数最高,药物多苦、辛、甘、温,归肝经。从主治病证发现,组方主要用于13类疾病,其中心脑血管病(36%)所占比例最大,其次是骨伤病(20%)、妇科病(14%)和风湿免疫病(10%)。结论2020年版《中华人民共和国药典》(一部)中活血类制剂主要以胶囊剂为主,主要针对心脑血管疾病,用药以当归、川芎、丹参为主,组方以苦味、辛味、甘味为主,药物多性温,归肝经。

基于数据挖掘和网络药理学分析刘鑫教授治疗肺结节用药规律及作用机制

目的基于数据挖掘和网络药理学分析刘鑫教授治疗肺结节处方用药规律及核心药物组合作用机制。方法收集2020年1月至2022年1月就诊于南华大学附属第一医院中医科刘鑫教授门诊的肺结节患者198例处方,应用中医传承辅助平台(V 2.5)挖掘四气、五味、归经、核心药物组合、潜在新药物组合及新方等用药规律。采用网络药理学方法预测核心药物组合的有效成分、作用靶点和通路等作用机制。结果198例处方中涉及163味中药,药性以温、平、寒居多,药味以甘、辛、苦居多,归经以肺经、肝经、脾经和心经为主。高频药物组合为甘草-桔梗、甘草-石上柏等,核心药物为柴胡、生地黄、川牛膝、当归、红花、桃仁、赤芍、桔梗、枳壳、川芎、甘草、浙贝母、桑白皮、石见穿和石上柏,得到潜在新药物组合10个、新处方5个。网络药理学分析显示,核心药物组合治疗肺结节有效成分为槲皮素、山柰酚等,通过作用TP53、Akt1、JUN等靶点,调控AGE-RAGE、TNF、IL-17、HIF-1、PI3K-Akt、FoxO等信号通路起作用。结论刘鑫教授治疗肺结节以活血化瘀、疏肝理气、化痰散结和温肺化饮为主,兼以健脾益肾,体现其基于“龙虎回环”理论、“从肝论治”肺结节的学术特点,得出潜在新的药物组合及新方,为临床治疗肺结节提供新的思路,其核心药物组合治疗肺结节潜在靶点和作用机制主要参与炎症反应、血管生成、细胞增殖与凋亡等生物学过程,为进一步作用机制研究提供参考。