A comparative study on characteristics of retracted publications across different open access levels

Purpose:Recently,global science has shown an increasing open trend,however,the characteristics of research integrity of open access(OA)publications have rarely been studied.The aim of this study is to compare the characteristics of retracted articles across different OA levels and discover whether OA level influences the characteristics of retracted articles.Design/methodology/approach:The research conducted an analysis of 6,005 retracted publications between 2001 and 2020 from the Web of Science and Retraction Watch databases.These publications were categorized based on their OA levels,including Gold OA,Green OA,and non-OA.The study explored retraction rates,time lags and reasons within these categories.Findings:The findings of this research revealed distinct patterns in retraction rates among different OA levels.Publications with Gold OA demonstrated the highest retraction rate,followed by Green OA and non-OA.A comparison of retraction reasons between Gold OA and non-OA categories indicated similar proportions,while Green OA exhibited a higher proportion due to falsification and manipulation issues,along with a lower occurrence of plagiarism and authorship issues.The retraction time lag was shortest for Gold OA,followed by non-OA,and longest for Green OA.The prolonged retraction time for Green OA could be attributed to an atypical distribution of retraction reasons.A comparative study on characteristics of retracted publications across different open access levels Research limitations:There is no exploration of a wider range of OA levels,such as Hybrid OA and Bronze OA.Practical implications:The outcomes of this study suggest the need for increased attention to research integrity within the OA publications.The occurrences offalsification,manipulation,and ethical concerns within Green OA publications warrant attention from the scientific community.Originality/value:This study contributes to the understanding of research integrity in the realm of OA publications,shedding light on retraction patterns and reasons across different OA levels.

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization,the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where informa-tion regarding all the buses connecting in a city will be gathered,processed and accurate bus arrival time prediction will be presented to the user.Various linear and time-varying parameters such as distance,waiting time at stops,red signal duration at a traffic signal,traffic density,turning density,rush hours,weather conditions,number of passengers on the bus,type of day,road type,average vehi-cle speed limit,current vehicle speed affecting traffic are used for the analysis.The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area.Multiple ELMs(MELM)for explicitly training dynamic,road and trajectory information are used in the proposed approach.A large-scale dataset(historical data)obtained from Kerala State Road Transport Corporation is used for training.Simulations are carried out by using MATLAB R2021a.The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week.It can manage huge volumes of data with less human intervention at greater learning speeds.It is found MELM yields prediction with accuracy in the range of 96.7%to 99.08%.The MAE value is between 0.28 to 1.74 minutes with the proposed approach.The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy.The research has proved that MELM is superior for arrival time pre-dictions in terms of accuracy and error,compared with other approaches.

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

Accessibility Comparison and Spatial Differentiation of Xi’an Scenic Spots with Different Modes Based on Baidu Real-time Travel

A study of the accessibility of a city’s scenic spots via different travel modes can contribute to optimization of tourism-related transportation while improving tourists’ travel-related satisfaction levels and advancing tourism. We systematically analyzed the accessibility of 56 scenic spots in Xi’an City, China, via car and public transport travel modes using the real-time travel function of the Baidu Maps API(Application Programming Interface) along with spatial analysis methods and the modal accessibility gap index of scenic spots. We obtained the following results. First, maximum and minimum travel times using public transport exceeded those using cars. Moreover, the accessibility of scenic spots via cars and public transport presented a circular spatial pattern of increasing travel time from the center to the periphery. Contrasting with travel by public transport, car travel showed a clear time-space compression effect. Second, accessibility of the scenic spots via cars and public transport showed some spatial heterogeneity, with no clear advantages of car accessibility in the central urban area. However, advantages of car accessibility were increasingly evident moving from the center to the periphery. Third, whereas the correlation of the modal accessibility gap index of scenic spots in Xi’an with global space was significantly positive, local spatial interdependence was only evident in some inner city areas and in marginal areas. Moreover, spatial heterogeneity was evident in two regions but was insignificant in other areas, indicating that the spatial interdependence of the modal accessibility gap index in most scenic spots was not apparent in terms of the overall effect of public transport routes, road networks, and the distribution of scenic spots. The improvement of public transport coverage in marginal areas and the optimization of public transport routes in central urban areas are essential tasks for improving travel using public transport in the future.

A Preliminary Study of Just-in-Time Methods for a Seamless Public Transportation Information Framework

A seamless multimodal transit system has been a goal for transportation planners and users. Owing to the developments of advanced public transportation and telecom technologies such as automatic vehicle location (AVL) and real-time passenger information system, key just-in-time (JIT) concepts can now be realized in process design and coordinated scheduling to shape a seamless multimodal transit system. JIT refers to a production system that times both movements of goods during production and delivery from suppliers together. It meets the same logic for seamless multimodal services. Therefore, this study innovatively analyzes possible public transportation technologies for JIT uses;moreover, to layout the possible application frame-work of a transportation technology based JIT multimodal transit system for main station area in Taipei city. With this innovative framework, multimodal transit services can deliver to the right place at right time.

京津冀城市群综合承灾能力时空异质性研究

基于2010—2020年京津冀城市群13个城市的调查数据,应用Kernel密度估计方法、空间自相关模型及Dagum基尼系数,分析不同时期京津冀城市群综合承灾能力时空动态演进分布格局及地区差异。结果表明:(1)在时间维度,2010—2020年京津冀城市群综合承灾能力总体呈现逐渐上升趋势,但京津冀城市群各市之间的承灾能力水平差距逐渐增大,区域整体呈现非均衡特征;(2)在空间维度,2010—2020年京津冀城市群综合承灾能力在空间分布上关联度较低,不存在显著的集聚或分散特征,但有局部地区产生聚集特点,属于“高-高”“低-低”空间聚集型城市较多,在此期间京津冀城市群综合承灾能力呈现空间集聚与空间分异并存;(3)京津冀城市群综合承灾能力的总体差异呈现上升趋势,北京-河北地区间差异最大,地区间差异是总体差异的主要来源。掌握京津冀城市群综合承灾能力的时空分布变化规律,并制订相应的防灾策略是京津冀城市群有效应对外部冲击以及实现京津冀地区协同与高质量发展国家战略的重要支撑。

基于网络开放数据的区域消防救援总体有效覆盖率评估

高效准确地掌握消防救援有效覆盖率是优化消防站点资源配置的前提。将判断是否有效覆盖的消防救援行程时间阈值定为240 s,建立了基于时间加权的区域消防救援总体有效覆盖率评估模型。基于网络开放数据收集了长沙的50座消防站作为消防救援供给点、5746家被抽查到的社会单位作为消防救援需求点,并调用网络地图应用程序编程接口基于实时路况来仿真消防救援行程时间。在连续7 d内设置197个评估场景,共获得了1131962个有效样本,进而得出:长沙消防救援总体有效覆盖率为21.22%,结果等级为“C”,消防救援水平一般,需加强消防站建设。这种基于网络开放数据的区域消防救援有效覆盖率评估,具备高效和准确的特点,可为优化消防站点资源配置、提升公共安全水平提供关键理论和方法支撑。

择时训练理论下公安院校警察体能课程时段设置研究

择时训练理论依托运动生物学的核心——生物节律,通过更加符合人体生物节律的运动时段进行训练,同时公安院校承载着为国家培养预备警官的重要任务,更加突出在公安院校警察体能课程中进行课程时段设置的重要性。本研究基于择时训练理论,旨在探讨公安院校警察体能课的时段设置对学员体能训练效果的影响。为此,本研究采用文献资料法、专家访谈法、实验法等对公安院校警察体能课程时段设置进行研究,并结合择时训练理论给出切实可行的训练方案,从而为公安院校学员在体能训练方面提供研究思路。

美国公共图书馆夜间服务实践调研及启示

文章选取美国排名前25的公共图书馆作为研究对象,对美国公共图书馆夜间服务实践展开网络调研,从服务群体、服务空间和读者活动三方面分析了美国公共图书馆的夜间服务现状及服务特点。基于研究结果阐述了对我国公共图书馆夜间服务高质量发展的启示:创新思维,积极策划服务项目;细分受众,精准把控用户需求;吸引社会力量,加强跨界融合;深挖地域特色文化,打造夜间经济文化IP。

航空货物分类分级安检模式安全性分析

为保障航空安检环节安全高效,部分机场探索实施航空货物分类分级安检模式。研究以安全性为目标,综合考虑安检错误报警概率及货物安检处理时间,分析传统安检模式和分类分级安检模式在安全性方面的差异。使用错误放行概率作为衡量安检模式安全性的指标,利用目视搜索-决策模型分析安检员判图过程,为两种安检模式建立非线性规划模型。研究显示:与传统安检模式相比,分类分级安检模式中高风险货物的比例m在(0,0.45)内才能提高系统安全性,且m最优取值为0.12;与传统安检模式相比,分类分级安检模式在货量较大、对安检处理时间较为敏感或是对安检错误报警概率容忍度较高时更能显著提高系统安全性。

《印度斯坦时报》对华舆论战:以“印太”语境下的策略性叙事为例

[研究目的]当前,在中印边境争端凸显、印度将中国塑造为假想敌、借加入“印太”战略提升竞争筹码的背景下,印度主流媒体对华舆论攻势愈演愈烈,研究“印太”语境下的策略性叙事对中国夺取舆论战主动权意义重大。[研究方法]通过定量与定性相结合的方法,利用语义网络的内容分析和话语分析考察《印度斯坦时报》对华舆论战的阶段性叙事框架和策略性文本重构。[研究结论]研究发现,印媒利用策略性叙事进行的舆论战兼具政策叙事、体系叙事和身份叙事的框架特征,善用利益捆绑、情感操纵、科学伪装的叙事文本。中国对印舆论交锋应着力拓展叙事对象、提升叙事技巧、设置叙事议程,夺取舆论传播的制度性话语权。

基于HPO-LSTM的公交周转时间预测

公交周转时间的准确预测是公交智能排班的基础和前提,是制定行车时刻表的关键。为提高公交周转时间的预测精度,提出了基于猎人猎物优化长短时记忆神经网络(HPO-LSTM)的公交周转时间预测模型,将长短时记忆神经网络(LSTM)中的超参数(隐含层节点数、迭代循环数以及初始学习率)映射为猎人猎物优化算法(HPO)种群的位置;以LSTM模型预测值与真实值产生的均方根误差E_(RMS)作为种群适应度函数,优化种群位置,实现LSTM神经网络超参数寻优;用最优超参数构建LSTM神经网络,进行公交周转时间预测。采用某市公交1号线数据对模型进行验证分析,结果表明:相比于BP、LSTM、FA-BP、HPO-BP模型,HPO-LSTM模型平均绝对百分比误差E_(MAP)分别降低10.44%、4.00%、3.61%、2.04%。

张恨水北京叙事中的“公共时间”

现代通俗小说是反映社会的一座富矿。20世纪二三十年代,北京的商业活动日益活跃、娱乐业逐渐兴旺,北京市民的生活节奏加快,社会交往和互动增多。为进行公共娱乐活动,市民们共享公共时间,娱乐活动的时间安排也更集中。通俗小说大家张恨水的北京叙事对北京公共时间的设定与反映,彰显了当时北京城市生活的现代特质,成为研究现代通俗小说社会学价值的典型范例。

分数卷积灰色预测模型及其在信息传播中的应用

通过构造累加生成卷积序列的离散卷积运算实现了数据序列的累加生成,并推广到分数阶卷积幂以实现分数阶累加生成。离散序列卷积运算统一了累加生成和累减生成,给定阶数即可生成相应的卷积序列。引入离散卷积运算,可以从信号处理的角度来理解累加生成。累加生成系统可视为一个线性滤波器,累加卷积序列的本质是滤波器的单位冲击响应,卷积运算对应滤波过程。在分数累加卷积序列的基础上构建了分数阶灰色预测模型。最后给出数值实验和信息传播中的实际应用案例验证理论的正确性和方法的有效性。

文化综合体全时开放的公共空间设计研究——以深圳为例

全时开放公共空间设计是影响文化综合体公共空间层次和市民行为活动的重要因素,注重其设计质量对于城市公共空间和市民日常活动都具有重要意义。本文从公共活动的相关理论出发,探讨深圳文化综合体全时开放的公共空间承载市民活动的机制。从激活市民日常公共活动的角度,综合深圳市具有代表性的案例与实践,解析新趋势下文化综合体全时开放公共空间的具体设计方法,反思设计结果,提出了边界消隐、连接加强、尺度适宜、功能复合、流线立体、激发活力的设计策略。

江苏省2004—2022年传染病突发公共卫生事件趋势分析

目的:分析江苏省传染病突发公共卫生事件的流行特征,为防控工作开展提供科学依据。方法:采用joinpoint回归模型分析时间维度变化趋势,采用时空重排扫描探测时空聚集性。结果:2004—2022年累计报告传染病突发事件3641起,累计报告发病131880例,死亡73例,涉及25种法定传染病突发事件1868起,其中甲类76起、乙类391起、丙类1401起;涉及8种其他传染病突发事件1773起。Joinpoint回归分析显示全省事件数呈增加趋势[平均年度变化百分比(average APC,AAPC=16.28,P<0.05)],甲类自2006年呈下降趋势(AAPC=-18.23,P<0.05),乙类、丙类、其他均呈增加趋势(AAPC>0,P<0.05),苏南、苏中、苏北各区域事件数全局均呈增加趋势(AAPC>0,P<0.05),苏南分为“快速增加—慢速增加—快速增加—减少”4个阶段,苏中事件数单调增加,苏北分为“增加—慢速减少—快速增加—减少”4个阶段。2006年、2015年、2018年为主要转折点。时空重排扫描共探测出8个时空聚集区,按发生顺序分别分布在苏中+苏南—苏北—苏南—苏南+苏北。结论:江苏省传染病突发事件总体呈现增加趋势,苏南和苏北聚集,苏中相对分散,重点地区需做好水痘、流行性感冒、手足口病等重点传染病的疫情监测和防控。

一种分析Timed-Release公钥协议的扩展逻辑

在Coffey和Saidha提出的CS逻辑 (CS逻辑将时间与逻辑结构相结合 ,可用于形式化分析Timed release公钥协议的时间相关性秘密的安全性 )的基础上 ,提出了CS逻辑的扩展逻辑 ,它更好地反映了Timed release公钥协议的特性 ,并对一个协议实例进行了有效的形式化分析 .

时变路网下考虑碳排放的需求响应型公交调度优化模型

以往对需求响应型公交的研究中,鲜有考虑到时变路网、碳排放等因素对车辆调度的影响,需要对现有研究的局限性进行改进。针对当前“双碳”背景下存在传统燃油公交与电动公交混合运行的现状,结合两者特性分别给出约束条件、成本和碳排放测算方法,建立包含延误时间、碳排放和运营成本作为优化目标的调度优化模型,并提出了自适应遗传-萤火虫算法用以求解该模型。实验结果表明:a)所提算法改善了传统遗传算法中易陷入局部最优的问题,在基于仿真路网的实验中能使目标函数减少9.1%,平均车辆使用数、平均途经节点数和平均行驶里程数分别减少了0.3辆、4.9个和104.57 km,提高了求解精度;b)模型考虑碳排放影响最高能减少9%的碳排放量,运营成本降低2.9%;c)动态阻抗下的车辆调度方案既贴近实际情况,又能同时降低7.5%的碳排放以及节约5%的运营成本;d)电动公交的引入能得到显著的碳减排效果,但由此带来的成本上升也是不容忽视的。

公立医院科室绩效方案改革对DIP患者住院费用影响——基于中断时间序列分析

目的 探讨医保按病种分值付费(Diagnosis-Intervention Packet,DIP)背景下公立医院科室绩效方案改革对DIP患者住院费用的影响,为优化公立医院科室绩效管理制度、降低DIP患者住院费用提供参考。方法 收集广东省某县级人民医院2020年7月—2023年7月DIP患者的住院费用数据,采用中断时间序列分析法描述例均住院费用变化趋势,分析该院科室绩效方案改革对DIP患者住院费用的影响。结果 医院科室绩效方案改革后,DIP患者总费用下降1 071.93元,并且每月继续下降116.39元。同时,科室绩效方案改革主要对居民医保DIP患者发挥作用,对职工医保DIP患者没有明显影响。结论 公立医院科室绩效方案改革明显降低了DIP患者的总费用及其分项费用,但是对不同险种DIP患者的影响不同。应进一步完善公立医院科室绩效管理机制,建立与医疗效能、医疗质量等相关的多维度绩效考核指标体系,因地制宜推动公立医院科室绩效方案改革,促进公立医院高质量发展。

基于换乘时间成本的客流分类及空间分布研究——以厦门市为例

文章基于公交IC刷卡数据,构建收入法模型评估厦门市换乘优惠政策实施后客流换乘的时间成本,与政策规定所减免的费用进行对比,根据乘客的换乘时间评估换乘优惠政策实施后乘客出行的整体效益,将换乘客流划分为换乘零成本客流、换乘额外费用成本客流、换乘额外时间成本客流、换乘额外时间+费用成本客流4类,并从站点、线路、交通小区、OD点密度、通勤与非通勤5方面分析了各类客流的空间分布特征,站点以岳阳小区为中心的区域聚集大量不同类型的客流;线路以24路为主聚集大量不同类型的客流;交通小区以地铁及岛内BRT线路周边的交通小区聚集大量不同类型客流;OD点密度以轨道一号线各站点和BRT各站点作为起点或讫点,聚集大量换乘客流;通勤时各类客流的活动范围较小,跨距较短,跨岛客流相对较少,非通勤时客流呈较明显的跨岛趋势,且跨距普遍较长。文章量化乘客的换乘时间成本以直观反映换乘的实际损耗,为进一步提升公交用户体验,降低乘客出行成本,制定科学有效的换乘优惠政策提供依据。