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.

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.

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.

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.

“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.

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。

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。

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

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。

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。

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。

数据要素市场体系建构与价值实现路径探索

数据要素市场体系的顶层设计是加快推进数字要素市场化配置、推动我国数字经济高质量发展的前提基础与关键部署。通过剖析当前全国数据要素统一大市场建设面临的瓶颈与挑战,本文分析数据要素市场建设的特殊性,提出全国统一数据要素市场体系“一体两翼”“三基”“七要点”的总体架构思路,以及战略布局、结构布局和空间布局相统一的总体布局方案,并从市场体系建设新特征、内在结构与运行模式解析、数据要素市场与传统要素市场一体化联动机理揭示与场景应用示范等方面探索促进数据要素市场价值升级与高质量发展的实现路径,从而为推动数据资源化、资产化和资本化的可持续运营提供参考借鉴。

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.