Comparison of R and Excel in the Field of Data Analysis

This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis tasks.It is also highly customizable,allowing users to create custom functions and packages to meet their specific needs.Additionally,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project simultaneously.These advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business applications.The findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data analysis.At the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences.

Bolzano Traffic: An Example of Open ITS Deployment for Advanced Traveller Information Services

This paper aims to present the experience gathered in the Italian alpine city of Bolzano within the project“Bolzano Traffic”whose goal is the introduction of an experimental open ITS platform for local service providers,fostering the diffusion of advanced traveller information services and the future deployment of cooperative mobility systems in the region.Several end-users applications targeted to the needs of different user groups have been developed in collaboration with local companies and research centers;a partnership with the EU Co-Cities project has been activated as well.The implemented services rely on real-time travel and traffic information collected by urban traffic monitoring systems or published by local stakeholders(e.g.public transportation operators).An active involvement of end-users,who have recently started testing these demo applications for free,is actually on-going.

LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space

Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.

基于数据流的K-S变化检测的动态多目标规划算法

为了更加准确地判断环境是否发生变化并快速追踪动态多目标规划问题(dynamicmulti-objectiveoptimization problem,DMOP)当前时刻的Pareto前沿,提出了一种基于数据流的Kolmogorov-Smirnov(K-S)变化检测的动态多目标规划(DSK-SDMOP)算法。该算法以NSGA-Ⅱ为基础,通过数据流建立2个时刻的检验窗口,再利用K-S检验基于数据流的Pareto最优前沿是否发生变化,检测2个窗口的数据是否服从同一分布来判断环境是否发生变化,并就环境变化的剧烈程度实行相应的应答机制,以提高对环境的适应程度。利用基于数据流的K-S检测方法,对环境变化不会过于敏感,而且不用提前假设对应目标值的分布,易于操作。通过5个动态多目标规划标准测试函数对该算法进行测试,并和现有的2种算法进行对比分析,结果表明该算法处理动态多目标规划问题具有良好的性能。

政府数据开放平台建设对促进数字政府发展效果的影响——基于PSM模型的计量分析

[目的/意义]政府数据开放是政府数字化发展的方向,探究政府数据开放平台(OGDP)对数字政府发展的影响,对推进实现国家治理体系和治理能力现代化目标具有指导意义。[方法/过程]文章基于OGDP的建设与否,运用倾向得分匹配(PSM)方法,以全国101个地级市2019年的截面数据为研究样本进行实证分析,探究OGDP建设对数字政府发展的促进作用。[结果/结论]在克服样本选择偏误以及尽可能消除不可观测因素带来的内生性影响的情况下,研究发现OGDP的建设能够正向影响数字政府发展,建设OGDP的城市的数字政府发展效应要比未建设的高15%—25%。因此,建议从加强OGDP建设、提升数据开放主动性和质量、优化平台服务、建设国家级OGDP等方面促进数字政府发展。

三维各向异性TI介质中的P/S波快速解耦技术及在弹性波逆时偏移中的应用

随着多分量采集技术的发展,弹性波逆时偏移技术在三维各向异性介质复杂地质构造成像中得到了广泛的应用.然而耦合的P波场和S波场,会在传播过程中产生串扰噪声,降低弹性波逆时偏移的成像精度.为了解决这一问题,本研究针对具有倾斜各向异性对称轴的三维横向各向同性(Transverse Isotropy,TI)介质,提出了一种矢量弹性波场快速解耦方法,可以有效提高偏移剖面的成像质量.该方法首先通过坐标转换,将观测系统坐标系的垂直轴旋转到TI介质的对称轴方向,在新坐标系下,根据具有垂直对称轴的三维横向各向同性(Vertical Transverse Isotropy,VTI)介质中的分解算子,推导出三维TI介质解耦算子表达式.接着引入一种在空间域快速计算分解波场的方法,来实现空间域矢量P波场和S波场分离,极大地提高了计算效率.最后,通过点积成像条件,将提出的P/S波分解方法引入到三维TI介质弹性波逆时偏移中,得到高精度的PP和PS成像.与以往的波场分解方法相比,本文方法具有数值稳定和计算效率高的特点.数值算例表明,应用上述三维TI分解算子得到的偏移剖面有效压制了噪声,提高了成像质量.

Open Data Services和Socket的应用研究——数据库服务器与应用程序间网络通信接口模块的设计

在数据库应用系统开发时常遇到的问题是如何合理的设计外部进程访问数据库的时间控制方案,在满足设计要求的响应时间的前提下,把握好系统与网络负载同系统事务响应处理速度之间的平衡关系。但在实际开发过程中对于某些类型数据的响应处理缺乏有效率的解决途径。此类数据采用通常的处理方式往往不能较好地得到解决。基于此,开发了1种由ODSAPI和Socket编程的数据库ESP扩展方法,由应用系统主动查询方式变为数据库系统主动通知或推送的方式,从而提供了1种高效率的解决方案。

基于SFIC模型的公民个人信息开放与保护的协同治理研究

数据开放在带来公众知情权便利的同时,也存在着公民个人隐私暴露的风险,引发个人信息安全问题。结合数据开放的环境特点,构建有效的多主体协同治理架构,有助于推动个人信息保护的多主体协同发展。基于SFIC协同治理模型构建理论分析框架,从外部环境、起始条件、催化领导、制度设计、协同过程5个方面分别对公民个人信息保护展开分析,提出在数据开放背景下,公民个人信息保护需要构建以政府为主导,社会组织、企业、公民等多主体共同参与的协同治理架构,要加强数据开放平台建设,把握好数据开放和信息保护的相互制衡,致力提升个人信息保护能力。

基于D-S证据理论的农作物气候品质预测方法研究:以晚熟杂交柑橘春见为例

【目的】基于多源气象数据构建果实品质(糖含量等级)预测模型,为科学评价果实气候品质及深入挖掘农产品气候资源提供科学依据。【方法】以晚熟柑橘春见果实为研究对象,利用多源数据融合技术、人工神经网络(BP神经网络、RBF神经网络和Elman神经网络)和D-S证据理论,包括气象数据质量控制、特征选取、特征级融合、决策级融合4个步骤,构建基于多源气象数据的果实品质(糖含量等级)预测模型。【结果】春见果实品质预测模型采用BP神经网络预测结果总体准确率为87.50%,平均绝对误差(MAE)为0.150,均方根误差(RMSE)为0.447;RBF神经网络预测结果总体准确率为85.00%,MAE为0.175,RMSE为0.474;Elman神经网络预测结果总体准确率为87.50%,MAE为0.150,RMSE为0.447;D-S证据理论决策融合总体预测准确率达95.20%,分别较BP神经网络、RBF神经网络和Elman神经网络提升7.7百分点、10.2百分点和7.7百分点,MAE和RMSE分别为0.040和0.214,均明显降低。【结论】D-S证据理论决策融合后的果实品质预测准确率相比单一神经网络预测更高、误差更小。

AIGC视域下科学数据开放共享风险及对策——基于WSR理论分析框架

科学数据是国家战略性资源和重要生产力,基于AIGC技术将其开放共享顺应了开放科学和数字经济发展的趋势,有助于实现数据价值及增值,但同时也产生了不可避免的风险。本文基于WSR方法论,从“物理”“事理”“人理”三个维度构建了理论分析框架,在此基础上,分析了AIGC视域下科学数据开放共享的风险因素,并从技术治理、主体协同、伦理遵循三个方面提出了解决路径,以期为AIGC视域下科学数据的开放共享工作提供有益参考。

An accessible strong-motion dataset(PGA,PGV,and site v_(S30))of 2022 M_(S)6.8 Luding,China Earthquake

A M_(S)6.8 earthquake occurred on 5th September 2022 in Luding county,Sichuan,China,at 12:52 Beijing Time(4:52 UTC).We complied a dataset of PGA,PGV,and site vS30 of 73 accelerometers and 791 Micro-Electro-Mechanical System(MEMS)sensors within 300 km of the epicenter.The inferred v_(S30)of 820 recording sites were validated.The study results show that:(1)The maximum horizontal PGA and PGV reaches 634.1 Gal and 71.1 cm/s respectively.(2)Over 80%of records are from soil sites.(3)The v_(S30)proxy model of Zhou J et al.(2022)is superior than that of Wald and Allen(2007)and performs well in the study area.The dataset was compiled in a flat file that consists the information of strong-motion instruments,the strong-motion records,and the v_(S30)of the recording sites.The dataset is available at https://www.seismisite.net.

基于S⁃G滤波算法的航空器着陆阶段滑行轨迹修正研究

对航空器着陆阶段运行轨迹分析是客观评价飞行员技术水平与航司运行品质的重要手段,在保障航空器安全高效运行等方面具有重要意义。通过对QAR数据中经纬度参数在地图软件中打点分析发现,航空器在跑道上减速滑行阶段轨迹出现明显的曲折、错位与偏移现象。采用S⁃G滤波算法,构建航空器着陆阶段轨迹修正模型,对航迹进行修正处理。结果表明,S⁃G滤波算法修正模型可对原始航迹进行良好修正。

A data assimilation-based forecast model of outer radiation belt electron fluxes

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Open Meta-Analyst软件及其功能简介

Meta分析是生产证据的重要方法,其合并效应可以为临床实践指南提供有效的数据支持,分析过程依赖于性能良好的软件。Open Meta-Analyst软件是近年来新研发出来的一款具备多种Meta分析功能、适用于多种类型Meta分析的免费、非编程软件,操作简便,易于学习与掌握。本文以二分类数据为例,介绍Open Meta-Analyst软件的功能及其实现Meta分析的过程,同时分析该软件的优势与不足。

Government Data Openness and Coverage. How do They Affect Trust in European Countries?

Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.

How does a data strategy enable customer value?The case of FinTechs and traditional banks under the open finance framework

The coupling of data and digital innovation opens the way for new business in the financial services sector,where customers are placed at the centre of decisions and data can help to develop customer knowledge.To carry out our research,we adopted a multi-case study approach to explore how a data strategy is developed in the retail banking industry,together with its relationship with customer value,paying particular attention to the heterogeneity between traditional banks and financial technology companies(FinTechs).Two main points emerged from the study.Firstly,there are three possible approaches to Open Finance,which are mainly defined by their different corporate cultures,organisational configurations,technological architecture and data value.Secondly,it is not enough to be a FinTech to be best placed to exploit the market,as some traditional banks share the FinTechs’approach to Open Finance.Designing new tailored products,customising their prices and offering them over the right channels through targeted communication are all data-driven initiatives that stem from cross-or up-selling potential,core to the retail banking industry for turning a customer into a cash flow,thus enabling value to be created for customers.Our findings additionally revealed that there is a form of external information asymmetry between the customer and the bank,and that there is also an internal asymmetry between bank departments,as their visibility on information about the same customer may differ.

HiLog:OpenHarmony的高性能日志系统

日志是计算机系统中记录事件状态信息的的重要载体,日志系统负责计算机系统的日志生成、收集和输出.OpenHarmony是新兴的、面向全设备、全场景的开源操作系统.在所述工作之前,包括日志系统在内OpenHarmony有许多关键子系统尚未构建,而OpenHarmony的开源特性使第三方开发者可以为其贡献核心代码.为了解决Open Harmony日志系统缺乏的问题,主要开展如下工作:(1)分析当今主流日志系统的技术架构和优缺点;(2)基于OpenHarmony操作系统的异构设备互联特性设计HiLog日志系统模型规范;(3)设计并实现第1个面向OpenHarmony的日志系统HiLog,并贡献到OpenHarmony主线;(4)对HiLog日志系统的关键指标进行测试和对比试验.实验数据表明,在基础性能方面,HiLog和Log的日志写入阶段吞吐量分别为1500 KB/s和700 KB/s,相比Android日志系统吞吐量提升114%;在日志持久化方面,HiLog可以3.5%的压缩率进行持久化,并且丢包率小于6‰,远低于Log.此外,HiLog还具备数据安全、流量控制等新型实用能力.

Why Open Government Data? The Case of a Swedish Municipality

Purpose: The purpose of this exploratory study is to provide modern local governments with potential use cases for their open data, in order to help inform related future policies and decision-making. The concrete context was that of the Vaxjo municipality located in southeastern Sweden.Design/methodology/approach: The methodology was two-fold: 1) a survey of potential end users(n=151) from a local university;and, 2) analysis of survey results using a theoretical model regarding local strategies for implementing open government data.Findings: Most datasets predicted to be useful were on: sustainability and environment;preschool and school;municipality and politics. The use context given is primarily research and development, informing policies and decision making;but also education, informing personal choices, informing citizens and creating services based on open data. Not the least, the need for educating target user groups on data literacy emerged. A tentative pattern comprising a technical perspective on open data and a social perspective on open government was identified. Research limitations: In line with available funding, the nature of the study was exploratory and implemented as an anonymous web-based survey of employees and students at the local university. Further research involving(qualitative) surveys with all stakeholders would allow for creating a more complete picture of the matter. Practical implications: The study determines potential use cases and use contexts for open government data, in order to help inform related future policies and decision-making.Originality/value: Modern local governments, and especially in Sweden, are faced with a challenge of how to make their data open, how to learn about which types of data will be most relevant for their end users and what will be different societal purposes. The paper contributes to knowledge that modern local governments can resort to when it comes to attitudes of local citizens to open government data in the context of an open government data perspective.

Research on the Opening of Adverse Drug Reaction Data in the EU

Systematically analyze the composition of post-marketing adverse drug reaction data and the open mode in the EU, and summarize its characteristics. EU post-marketing ADR data is open to six categories of stakeholders: EMA, EC, medicines regulatory authorities in EEA member states, healthcare professionals and the public, Marketing Authorization Holders, academia, WHO and medicines regulatory authorities in third countries. The EU has implemented hierarchical opening for ADRs, with different levels containing different data and facing different stakeholders. Openness is divided into active and passive openness. In opening up data, the EU complies with relevant personal data protection laws to protect the privacy of individuals. The EU’s post-marketing adverse drug reaction data openness is characterized by a combination of data openness and privacy protection, active and passive openness, and a hierarchy of data openness. It is hoped that this can provide a reference for the opening up of post-marketing adverse drug reaction data in China.