Information Security Management Measures for College Archives Under the Network Environment

The construction of archives in colleges and universities in China is in the process of development and improvement.With the development information technology,the informatization of college archives has been accelerated.Network technology is developing rapidly in our country,and the number of network users has increased significantly.The use of network technology in university archives management can improve the management efficiency and quality of archives,but the safety factor has dropped significantly.For example,the archival system may face many problems such as virus infection,system paralysis,or cyberattacks,which affects the security of the university archives.Therefore,this paper presents an analysis of these problems in detail,and proposes corresponding solutions,so as to optimize and improve the information security management of college archives.

Evolution of the Socio-cognitive Structure of Knowledge Management(1986–2015): An Author Co-citation Analysis

Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submitted to author co-citation analysis(ACA) following a longitudinal perspective as of the following time slices: 1986–1996, 1997–2006, and 2007–2015. The top 10% of most cited first authors by sub-periods were mapped in bibliometric networks in order to interpret the communities formed and their relationships.Findings: KM is a homogeneous field as indicated by networks results. Nine classical authors are identified since they are highly co-cited in each sub-period, highlighting Ikujiro Nonaka as the most influential authors in the field. The most significant communities in KM are devoted to strategic management, KM foundations, organisational learning and behaviour, and organisational theories. Major trends in the evolution of the intellectual structure of KM evidence a technological influence in 1986–1996, a strategic influence in 1997–2006, and finally a sociological influence in 2007–2015.Research limitations: Describing a field from a single database can offer biases in terms of output coverage. Likewise, the conference proceedings and books were not used and the analysis was only based on first authors. However, the results obtained can be very useful to understand the evolution of KM research.Practical implications: These results might be useful for managers and academicians to understand the evolution of KM field and to(re)define research activities and organisational projects.Originality/value: The novelty of this paper lies in considering ACA as a bibliometric technique to study KM research. In addition, our investigation has a wider time coverage than earlier articles.

Intensive management enhances mycorrhizal respiration but decreases free-living microbial respiration by affecting microbial abundance and community structure in Moso bamboo forest soils

Intensive management is known to markedly alter soil carbon(C)storage and turnover in Moso bamboo forests compared with extensive management.However,the effects of intensive management on soil respiration(RS)components remain unclear.This study aimed to evaluate the changes in different RScomponents(root,mycorrhizal,and free-living microorganism respiration)in Moso bamboo forests under extensive and intensive management practices.A1-year in-situ microcosm experiment was conducted to quantify the RScomponents in Moso bamboo forests under the two management practices using mesh screens of varying sizes.The results showed that the total RSand its components exhibited similar seasonal variability between the two management practices.Compared with extensive management,intensive management significantly increased cumulative respiration from mycorrhizal fungi by 36.73%,while decreased cumulative respiration from free-living soil microorganisms by 8.97%.Moreover,the abundance of arbuscular mycorrhizal fungi(AMF)increased by 43.38%,but bacterial and fungal abundances decreased by 21.65%and 33.30%,respectively,under intensive management.Both management practices significantly changed the bacterial community composition,which could be mainly explained by soil pH and available potassium.Mycorrhizal fungi and intensive management affected the interrelationships between bacterial members.Structural equation modeling indicated that intensive management changed the cumulative RSby elevating AMF abundance and lowering bacterial abundance.We concluded that intensive management reduced the microbial respiration-derived C loss,but increased mycorrhizal respiration-derived C loss.

A Prediction Framework for Turning Period Structures in COVID-19 Epidemic and Its Application to Practical Emergency Risk Management

The aim of this paper is first to establish a general prediction framework for turning(period)term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice,which allows us to conduct the reliable estimation for the peak period based on the new concept of“Turning Period”(instead of the traditional one with the focus on“Turning Point”)for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020.By a fact that emergency risk management is necessarily to implement emergency plans quickly,the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible.As applications,the paper also discusses how this“Turning Term(Period)Structure”is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020.Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020,from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020,as verified by the true observation in the practice.The method established in this paper for the prediction of“Turning Term(Period)Structures”by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate,providing adequate time for the government,hospitals,essential industry sectors and services to meet peak demands and to prepare aftermath planning,and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called“dynamic zero-COVID-19 policy”ongoing basis in the practice.

Multiscale Characteristics and Connection Mechanisms of Attraction Networks:A Trajectory Data Mining Approach Leveraging Geotagged Data

Urban tourism is considered a complex system,and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism,so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning.This paper uses geotagging data to compare the links between attractions in Beijing,China during four different periods:the pre-Olympic period(2004–2007),the Olympic Games and subsequent‘heat period’(2008–2013),the post-Olympic period(2014–2019),and the COVID-19(Corona Virus Disease 2019)pandemic period(2020–2021).The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination.The results show that the macro,meso-,and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks.The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure,suggesting that new entrants are more likely to be associated with attractions that already have high value.The mesoscale links attractions according to the common purpose of tourists,and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect,and the weak links between clusters result from attractions bound by incomplete information and distance,and the functional polycentric structure with a generally more efficient network of clusters.The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern,and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism,which aids in understanding the attraction network pattern at both macro and micro scales.Important approaches and practical implications for planners and managers are presented.

A methodological framework for improving air quality monitoring network layout.Applications to environment management

This work aims to provide a methodology framework which allows to improve the performance and efficiency of an air quality monitoring network(AQMN).It requires to be constituted by a minimum and reliable number of measurement sites.Nevertheless,the AQMN efficiency should be assessed over time,as a consequence of the possible emergence of new emission sources of air pollutants,which could lead to variations on their spatial distribution within the target area.PM_(10)particles data monitored by the Community of Madrid's(Spain)AQMN between 2008 and 2017 were used to develop a methodology to optimize the AQMN performance.The annual spatial distribution of average PM_(10)levels over the studied period monitored by all current stations vs those more representative was provided by a geographic information system(GIS),and the percentage of similarity between both postulates was quantified using simple linear regression(>95%).As one innovative tool of this study,the practical application of the proposed methodology was validated using PM_(10)particles data measured by AQMN during 2007 and 2018,reaching a similitude degree higher than 95%.The influence of temporal variation on the proposed methodological framework was around 20%.The proposed methodology sets criteria for identifying non-redundant stations within AQMN,it is also able to appropriately assess the representativeness of fixed monitoring sites within an AQMN and it complements the guidelines set by European legislation on air pollutants monitoring at fixed stations,which could help to tackle efforts to improve the air quality management.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Characterizing the urban spatial structure using taxi trip big data and implications for urban planning

Urban spatial structure is an important feature for assessing the effects of urban planning.Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments.Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures.However,these methods cannot efficiently reflect the influence of human activities.With the wide application of big data,analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning.In this study,we constructed a human-activity space network using the taxi trip big data.Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning.This method was applied to a case study based on one-month taxi trip data of Dongguan City.Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020,which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan.We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure.The analysis demonstrated that the taxi trip data are important big data on social spatial perception,and taxi data should be used for evaluating spatial structures in future urban planning.

基于BP神经网络的钢铁工业建(构)筑物混凝土结构耐久性环境区划研究

由于高温、潮湿、腐蚀等各种不利生产环境的影响,工业建(构)筑物混凝土结构耐久性问题非常突出,大量在役工业建(构)筑物混凝土结构钢筋锈蚀、混凝土劣化严重,使其使用寿命远小于设计使用年限。以烧结、焦化、炼铁、炼钢及轧钢为代表的钢铁行业的五大主要生产工艺工序的主要混凝土建(构)筑物为研究对象,调研不同年代117个建(构)筑物的特点和耐久性损伤状况;选取不同地区的两个钢铁工业区A、B,通过对其各工序建(构)筑物混凝土结构耐久性状况调研,以及局部环境调查、检测和分析,确定了钢铁工业建(构)筑物混凝土结构耐久性环境划分的指标体系;基于BP神经网络算法对各工艺流程主要建(构)筑物混凝土结构耐久性环境进行区划,给出了以烧结室、焦化厂、高炉出铁场平台、炼钢厂为例的耐久性环境等级划分图,为钢铁工业建(构)筑物混凝土结构耐久性设计、评估及修复提供可靠依据。

基于弱光环境的车辆识别研究

由于图像在弱光环境下具有低曝光、前景背景融合、对比度低等问题,因此难以在弱光环境下有效、实时检测图像中的目标车辆。目前为了提高检测效果,通常需要设计较为复杂的神经网络结构或建立额外的对比数据集,但这不仅降低了网络速度,也会提升网络训练成本。为了解决这一问题,提出一种弱光环境下的车辆识别网络,在该网络中一方面设计了特征提取模块与特征融合模块以提高网络在弱光环境下的检测能力,另一方面使用模块结构重参数化的方法以提高网络的检测速度。实验表明,该网络可在保证检测速度的同时有效地识别弱光环境下的车辆。

大数据环境下基于BIM与CNN的电力工程造价优化算法

针对大数据环境下电力工程造价在精准化、动态化等方面存在的不足,提出了一种基于BIM与CNN的电力工程造价优化算法。利用BIM技术的特点进行电力工程全生命周期的造价管理,实现了造价的动态化管控。并且采用Levenberg-Marquardt规则算法改进卷积神经网络,通过改进后的CNN网络对每个工程环节的造价完成预测,从而优化整个工程的施工方案。结合相关的电力工程造价数据,基于Matlab对所提算法进行实验测试。结果表明,当学习率为0.010时CNN网络的性能最佳,所提算法的预测准确率为94%,并且与造价的真实值最为接近。

高校一站式学生社区建设对学生综合能力提升的影响

对160名大学生进行问卷调查,了解高校一站式学生社区建设对学生综合能力提升的影响。设计详尽的李克特量表,覆盖社区建设的质量与范围、学生参与度、社区支持系统、社区管理与组织结构、社区文化与环境等维度。研究结果显示,量表在测量学生综合能力方面具有极高的可靠性。描述性统计显示,受访者对一站式学生社区建设各方面的正面评价中,社区文化与环境维度的均值最高,社区支持系统维度的评价存在较大差异。由回归分析可知,学生参与度对学生综合能力提升的影响最显著。高校需不断优化学生社区服务与管理策略,打造积极、包容的社区环境,让学生意识到积极参与社区活动的重要性。

流域水网环境异质性及驱动力分析

【目的】水网结构和类型的产生与多种环境因素相关,对流域水网的环境异质性及驱动力方面的分析,是调整水系结构、合理恢复和保护水网的重要基础工作。【方法】以长江流域、黄河流域、淮河流域和海河流域为研究对象,基于典型水网类型,系统分析对比四大流域的环境异质性。进一步采用Pearson相关性分析、冗余分析、地理探测器分析方法,定量梳理各因子对于水网结构和类型的解释度。【结果】结果表明:网状和混合状水网流域内,坡度为0.0°~5.0°的比例大于55.05%,地形起伏度为0~70 m的比例大于67.98%,土地利用类型多为耕地和人造地表。矩形状、平行状、树枝状和扇状水网流域内,坡度为5.0°~35.0°的比例大于62.42%,地形起伏度为70~500 m的比例大于49.63%,土地利用类型多为耕地、林地和草地。土壤侵蚀强度、降水量、湿润指数、气温和水利设施数量也在不同水网类型中表现出异质性。气候因子与河流弯曲度显著正相关,地形因子、开发程度与水网的密度和流向最大频率均显著负相关。所选因子对四大流域水网结构特征的总解释率达53.47%。【结论】水网结构和类型受到多种因素的综合影响。坡度和湿润指数对水网结构有较大影响,解释率分别为21.40%和16.50%。降水量对水网类型的解释度最高,为16.61%。开发程度和气候因子的交互作用对水网类型的形成有重要影响,解释度接近50%。

基于贝叶斯网络的生态环境脆弱性评估方法研究

为提高生态环境脆弱性评估结果的准确性,提出基于贝叶斯网络的生态环境脆弱性评估方法。选择秦岭地区作为研究对象,采集该地区的生态环境数据,通过模糊估计提取生态环境扰动特征,并对生态环境脆弱性数据采样结果进行归一化处理,以提高评估结果的真实性。根据数据处理结果给出秦岭地区生态环境脆弱性评估体系,并通过贝叶斯网络节点模拟生态环境结构状态,描述状态转移过程,明确外界扰动攻击和环境可靠度间关系,实现对生态环境脆弱性的评估。经实验证明所提方法具备准确评估生态环境脆弱性的能力,置信度水平高。

非正式组织视角下煤矿工人不安全行为形成路径研究

为了探究非正式组织视角下煤矿工人不安全行为的形成路径,找寻影响煤矿工人不安全行为的因素,在现有研究基础上,提出组织环境和社会网络关系对煤矿工人不安全行为具有导向性的假设,通过编制量表、发放问卷的方式收集数据资料,运用SPSS分析软件、AMOS建模工具进行数据处理和模型拟合,确定了煤矿工人不安全行为的形成路径。结果表明:组织环境和社会网络直接或间接作用于煤矿工人不安全行为;不安全行为形成有2条主路径,分别是组织影响→不安全监督→不安全行为前提条件→煤矿工人不安全行为、关联程度→依赖关系→煤矿工人不安全行为;通过对改善的组织环境和行为监管社会网络关系,提升煤矿工人非正式组织氛围,可以有效减少煤矿工人不安全行为的发生,为煤矿企业管理决策提供新思路。

新时代我国森林城市群建设现状及对策

我国自改革开放以来,经济快速发展,城市化进程加快,森林城市群的建设也取得了一定的成就。然而,随着城市化的不断推进,森林城市群的建设也面临着诸多挑战。本文将从新时代我国森林城市群建设的现状、问题及对策三个方面进行详细论述,并从优化城市森林结构、加强城市森林管理维护以及推进智慧森林城市建设等方面提出了具体的对策建议。

基于TOE理论的数实经济深度融合影响因素模型构建与分析

文章运用TOE理论框架建立数实经济深度融合影响因素指标体系,采用ISM-ANP-MICMAC模型对数实经济融合影响因素的网络层级、优先级、驱动力及依赖性进行剖析,结果表明:数实经济深度融合影响因素之间存在着紧密且复杂的关联关系,影响因素网络层级结构中包含表层直接、中层过渡、深层关键及根层决定四个层次,优先级排序中数字技术创新、人才资源、基础设施、政府行为属于核心关键因素,数据要素、网络安全、国际合作、营商环境、数字化产业、数字经济治理属于次要因素,数字社会、数字政府、产业数字化因素驱动力较弱,但依赖性极强。基于此对数实经济融合提出短中长期发展对策,及时疏通融合路径中的急、难、重点,为推动数实经济深度融合高质量发展提供理论指导。

基于改进YOLOv4算法的工程实验室信息化管理系统构建与应用

融合物联网技术、图像检测技术和深度学习算法,提出一种基于改进YOLOv4算法的工程检测实验室信息管理系统,通过WIFI+NB-IoT实现数据传递。为了弥补YOLOv4算法在尺度分布不均匀时精度降低的问题,提出利用IK-means++算法,引入ECA注意力模块和阶梯状特征融合网络结构对算法进行改进,算法改进后浮点运算数量、模型参数量分别降低了25.1%和43.1%,FPS和mAP分别提高6.8帧/s和3.65%,改进后算法不仅收敛速度更快,而且在不同光线环境下的设备检测准确率均高于改进前。将系统应用到工程实验室检测中,设备和环境各参数检测结果与标准仪器检测结果误差控制在±5%以内。

配电网目标网架实施策略下的电力质量管理优化研究与应用

本研究旨在探讨配电网目标网架实施策略下的电力质量管理优化方法,并通过应用实例展示其潜在价值。首先,文章介绍了配电网目标网架的定义、特点以及主要实施策略,突出了其在电力分配中的优势。随后,分析了当前电力质量管理方法存在的局限性,并探讨了目标网架实施策略下电力质量管理所面临的挑战。接着,文章详细讨论了电力质量管理的优化方法,包括数据驱动的电力质量预测、监控与控制策略,以及电力质量问题的故障定位与诊断技术。最后,通过实际应用案例,展示了目标网架实施策略下的电力质量管理的优化成果。本研究为电力行业提供了有力的参考,以改善电力质量管理和提高配电网的效率与可靠性。