Dynamic Update Scheme of Spectrum Information Based on Spectrum Opportunity Incentive in the Database-Assisted Dynamic Spectrum Management

To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

Scoping review update on somatic symptom disorder that includes additional Chinese data

Somatic symptom disorder(SSD)is a new diagnosis introduced into the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5),which is expected to solve the diagnostic difficulties of patients with medically unexplained symptoms.Based on the previous work,this review aims to comprehensively synthesise updated evidence related to SSD from recent years in English publications and,more extensively,from data published in Chinese language journals.The scoping review update was based on an earlier scoping review and included Chinese language publication data from China National Knowledge Internet(CNKI),WANFANG and WEIPU between January 2013 and May 2022 and data from PubMed,PsyclNFO,and Cochrane Library between June 2020 and May 2022.Initially,2984 articles were identified,of which 63 full texts were included for analysis.In China,SSD is mainly applied in scientific research,but it also shows good predictive validity and clinical application potential.The mean frequency of SSD was 4.5%in the general population,25.2%in the primary care population and 33.5%in diverse specialised care settings.Biological factors,such as brain region changes and heart rate variability,are associated with the onset of sSD.Psychological impairment related to somatic symptoms is the best predictor of prognosis.While adolescent SSD is significantly associated with family function,SSD overall is associated with an increased dysfunction of cognition and emotion,decreased quality of life,and high comorbidity with anxiety and depressive disorders.Further research is needed on suicide risk and cultural and gender-related issues.Updating the data of Chinese language studies,our research enriches the evidence-based findings related to the topics addressed in the text sections of the SSD chapter of DSM-5.However,research gaps remain about SSD reliability,population-based prevalence,suicide risk,and cultural and gender-related issues.

Updates in the Diagnosis of Gestational Trophoblast Disease

GTD (Gestational Trophoblastic Disease) is a pathology that encompasses benign and malignant clinical forms, affects women of childbearing age, has a variable incidence and is more frequent in developing or underdeveloped countries, colliding with the economic barrier. The frequent absence of clear protocols and guidelines for the correct diagnosis of the pathology results in inadequate classification, imprecise treatment and failed post-therapeutic observation, increasing the risk of relapses, morbidity and mortality. The present study aims to point out updated national and international practice protocols of diagnosis of GTD, through an integrative review. Seven articles were selected and it was observed that the main international reference centers are agreed with the management suggested by the IFGO (International Federation of Gynecology and Obstetrics), being the conduct in the Hydatidiform Mole (HM): evacuation by suction and curettage under ultrasound guidance, followed by hCG monitoring every 1 - 2 weeks until normalized (usually one month for Partial Hydatidiform Mole six months for Complete Hydatidiform Mole and one year for Gestational Trophoblastic Neoplasia). Unfortunately, regarding the diagnosis of MH, the guidelines of some countries show the absence or difficulty of access to the karyotype test and ploid p57 or pelvic ultrasound accompanying the uterine curettage, contrary to what is proposed by the IFGO guideline. Establishing and complying with consistent guidelines can improve patient care, with early diagnosis of the pathology and its complications, reducing the rate of recurrence, morbidity and mortality, especially in less developed countries.

基于改进UpdateNet的行人视觉跟踪算法

目前,基于孪生网络的跟踪器主要是把跟踪过程看作是目标模板分支和待检测区域分支的一种互相关计算,它能够在速度和精度方面达到一个良好的均衡。然而,在跟踪过程中当前帧模板是对前一帧累积模板的线性结合,目标遮挡的问题一直很难被解决。针对该问题,本文采用改进SiamRPN跟踪器并融合UpdateNet网络的方案实现对行人单目标的跟踪。首先利用改进SiamRPN网络模块生成线性模板,然后在此基础上融合UpdateNet网络生成更新模板并进行多阶段训练,最后根据数据集的评价指标选取最优参数模型完成行人跟踪任务。本文在基准数据集OTB2015和其子集上进行实验,实验结果表明,本文所采用的方法取得的效果相比原来方法有明显提升,精度和成功率分别提高2.1和1.6个百分点,而且保持了实时跟踪帧率,同时在解决遮挡问题方面超过了DaSiamRPN、SiamDW等先进方法.

Inference of interactions between players based on asynchronously updated evolutionary game data

The interactions between players of the prisoner's dilemma game are inferred using observed game data.All participants play the game with their counterparts and gain corresponding rewards during each round of the game.The strategies of each player are updated asynchronously during the game.Two inference methods of the interactions between players are derived with naive mean-field(n MF)approximation and maximum log-likelihood estimation(MLE),respectively.Two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick models,varying the data length,asymmetric degree,payoff,and system noise(coupling strength).We find that the mean square error of reconstruction for the MLE method is inversely proportional to the data length and typically half(benefit from the extra information of update times)of that by n MF.Both methods are robust to the asymmetric degree but work better for large payoffs.Compared with MLE,n MF is more sensitive to the strength of couplings and prefers weak couplings.

Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously optimized.However,the data update performance is often bottlenecked by the constrained cross-rack bandwidth.Various techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch update.These techniques were largely proposed individually previously,and in this work,we seek to use them jointly.To mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch update.Meanwhile,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based update.The proposed DXR-DU is evaluated via trace-driven local testbed experiments.Comprehensive experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

System Reliability Analysis for Truss Structures Based on Automatic Updated Model

Multiple failure modes tend to be identified in the reliability analysis of a redundant truss structure.This identification process involves updating the model for identifying the next potential failure members.Herein we intend to update the finite element model automatically in the identification process of failure modes and further perform the system reliability analysis efficiently.This study presents a framework that is implemented through the joint simulation of MATLAB and APDL and consists of three parts:reliability index of a single member,identification of dominant failure modes,and system-level reliability analysis for system reliability analysis of truss structures.Firstly,RSM(response surface method)combines with a constrained optimization model to calculate the reliability indices ofmembers.Then theβ-unzipping method is adopted to identify the dominant failuremodes,and the system function in MATLAB,as well as the EKILL command in APDL,is used to facilitate the automatic update of the finite element model and realize load-redistribution.Besides,the differential equivalence recursion algorithmis performed to approximate the reliability indices of failuremodes efficiently and accurately.Eventually,the PNET(probabilistic network evaluation technique)is used to calculate the joint failure probability as well as the system reliability index.Two illustrative examples demonstrate the accuracy and efficiency of the proposed system reliability analysis framework through comparison with corresponding references.

Fast Verification of Network Configuration Updates

With the expansion of network services,large-scale networks have progressively become common.The network status changes rapidly in response to customer needs and configuration changes,so network configuration changes are also very frequent.However,no matter what changes,the network must ensure the correct conditions,such as isolating tenants from each other or guaranteeing essential services.Once changes occur,it is necessary to verify the after-changed network.Whereas,for the verification of large-scale network configuration changes,many current verifiers show poor efficiency.In order to solve the problem ofmultiple global verifications caused by frequent updates of local configurations in large networks,we present a fast configuration updates verification tool,FastCUV,for distributed control planes.FastCUV aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring correctness.This paper presents a method to determine the network range affected by the configuration change.We present a flow model and graph structure to facilitate the design of verification algorithms and speed up verification.Our scheme verifies the network area affected by obtaining the change of the Forwarding Information Base(FIB)before and after.FastCUV supports rich network attributes,meanwhile,has high efficiency and correctness performance.After experimental verification and result analysis,our method outperforms the state-of-the-art method to a certain extent.

Multi-scale Incremental Analysis Update Scheme and Its Application to Typhoon Mangkhut(2018)Prediction

In the traditional incremental analysis update(IAU)process,all analysis increments are treated as constant forcing in a model’s prognostic equations over a certain time window.This approach effectively reduces high-frequency oscillations introduced by data assimilation.However,as different scales of increments have unique evolutionary speeds and life histories in a numerical model,the traditional IAU scheme cannot fully meet the requirements of short-term forecasting for the damping of high-frequency noise and may even cause systematic drifts.Therefore,a multi-scale IAU scheme is proposed in this paper.Analysis increments were divided into different scale parts using a spatial filtering technique.For each scale increment,the optimal relaxation time in the IAU scheme was determined by the skill of the forecasting results.Finally,different scales of analysis increments were added to the model integration during their optimal relaxation time.The multi-scale IAU scheme can effectively reduce the noise and further improve the balance between large-scale and small-scale increments in the model initialization stage.To evaluate its performance,several numerical experiments were conducted to simulate the path and intensity of Typhoon Mangkhut(2018)and showed that:(1)the multi-scale IAU scheme had an obvious effect on noise control at the initial stage of data assimilation;(2)the optimal relaxation time for large-scale and small-scale increments was estimated as 6 h and 3 h,respectively;(3)the forecast performance of the multi-scale IAU scheme in the prediction of Typhoon Mangkhut(2018)was better than that of the traditional IAU scheme.The results demonstrate the superiority of the multi-scale IAU scheme.

基于多源数据融合的水利工程测量信息动态更新系统

现有的水利工程测量信息动态更新系统,在传输测量信息时没有最佳的路径概念,采用车轮搜索,导致信息更新所需时间较长,为优化系统运行效率,设计基于多元数据融合设计水利工程测量信息动态更新系统。设计可以增强数据更新速度的芯片作为系统硬件;构造有效路径代价函数,整编水利工程测量多源数据;选择数据传输最短路径;建立最小二乘估计数学模型,引入多元数据融合设计信息更新算法。实验结果显示:在系统更新时间的测试中,面对不同数量的并行服务器,三个对照组用时均高于实验组,可见该系统具备更好的运行效率。

基于MEC边缘云的智慧商城数据更新控制算法

针对智慧商城中数据量较大的问题,提出基于MEC边缘云的智慧商城数据更新控制算法。在MEC边缘云环境中将数据信息经过整合分类处理后直接推送至用户端,将万有引力搜索算法和布谷鸟搜索算法融合构建CS-GSA算法,用于MEC边缘云智慧商城数据库数据清洗。在差分数据更新方法中引入游程编码和哈夫曼编码,采用游程编码将连续重复的数据压缩处理,降低数据规模,然后通过哈夫曼编码再次压缩,降低传输过程中的通信开销,实现MEC边缘云智慧商城数据更新。实验结果表明,所提方法的解编码时间、数据更新时间更短、通信开销较小,说明其能够提高数据更新质量。

基于实测数据的半潜式平台贝叶斯模型修正方法研究

随着半潜式平台的长期海上服役,平台的基本设计参数会发生变化,如仍基于原始设计参数对平台进行分析将影响结果的准确性。本文以半潜式平台服役中易发生变化的基本参数(平台排水量、重心高度和三个自由度回转半径)作为修正参数,以平台横摇、纵摇和垂荡自由度的方差和波频功率谱密度构建目标函数,建立可依据实测数据的半潜式平台贝叶斯参数修正方法。以我国某半潜式平台实测数据为例,开展平台的参数修正研究,并基于修正后的参数对平台多年一遇环境荷载下的响应进行对比评估。

基于随机抽样的衰变热不确定度量化研究

为研究核数据引起的核素存量及导出量的不确定度,在自主程序GNET上实现了基于随机抽样的不确定度量化方法。利用贝叶斯更新方法获得裂变产物独立产额的协方差数据,弥补裂变产额协方差数据缺失。对热中子引起的235U一次裂变后衰变热不确定度进行了计算。结果表明,裂变产物产额的不确定度贡献占主要部分。该算例表明GNET程序具备了核素存量的不确定度量化功能。

结合数字孪生的斜拉桥系统更新和响应预测

由于传统建模方法难以反馈桥梁系统参数和响应的细微变化,为此提出了以数字孪生体作为结构系统的高保真映射模型。首先,定义数字孪生体包含物理孪生层、数字孪生层和信息交互媒介3部分,数字孪生层的孪生模型是对斜拉桥物理实体的虚拟映射,通过信息交互媒介实现不同层间信息的实时传递;其次,针对具体应用提出了结构信息化、信息数据化和数据模型化3条建模准则,实现对斜拉桥物理实体的信息勾勒和可视化过程,建立高保真的斜拉桥数字孪生模型;最后,以一座实桥端锚索的监测数据为感知信息,将变化的索参数实时反馈给孪生模型,实现模型更新和响应预测。研究结果表明,所提出的数字孪生建模方法能及时反馈实桥的参数变化,并预测由此造成的索力、塔顶偏位及主梁跨中挠度的细微改变。

战略节奏推动酒店企业惯例更新的过程与机制——以本土酒店碧水湾为例

伴随着中国经济全面而深刻的转型和数字技术的快速应用与发展,如何实现惯例更新成为酒店业转型发展的关键议题。文章以本土酒店碧水湾为研究对象,运用纵向案例研究方法,基于“认知-行动-结果”的理论逻辑,剖析酒店企业通过战略节奏推动惯例更新的动态过程与作用机制。研究发现:(1)高管认知是互动行为产生的根源,两者之间存在适配机制塑造战略节奏。高管通过对外部环境的分析形成了经验导向型认知、市场导向型认知和技术导向型认知,而与其相适配的互动行为分别为基于认知引导的互动行为、基于文化桥梁的互动行为和基于业务编排的互动行为。(2)战略节奏推动酒店惯例更新,需循“储势蓄能-调整转变-发展升级”的演化轨迹,在不同阶段呈现“快速度低变异-快速度高变异-慢速度低变异”的战略节奏特征。(3)高管认知和与之相适配的互动行为塑造战略节奏,这一过程作用于组织惯例更新,从构建新惯例制度结构,向形成新惯例流程数字化,再向固化新惯例业务数字化演变。文章创新性构建并解析了战略节奏推动酒店惯例更新的理论框架,拓宽了战略节奏和组织惯例研究理论,为本土酒店企业转型和产业结构高质量发展提供了案例借鉴。

网络异常检测领域概念漂移问题研究综述

【目的】随着网络技术的快速发展和广泛应用,网络异常检测作为保护网络安全和维护系统正常运行的手段变得越来越重要。然而,网络中异常行为和攻击手段不断变化,给异常检测带来了新的挑战。其中,概念漂移问题是网络异常检测领域中受到广泛关注的难点之一。【方法】本综述旨在对网络异常检测领域中概念漂移问题进行研究分析和总结。与前人的研究相比,本文将专注于网络异常检测领域的流数据。【文献范围】首先,对概念漂移进行详细介绍,包括定义、产生原因和特点。通过对概念漂移的全面理解,可以为后续的检测方法提供指导。其次,系统性地介绍了概念漂移检测方法,主要包括基于统计的方法、机器学习方法和深度学习方法等,并对比了各类方法的优缺点和适用场景。最后,探讨了概念漂移在未来可能的研究方向。【结论】本文聚焦于网络异常检测领域的概念漂移问题,通过详细介绍概念漂移的定义、产生原因和特点,以及深入分析和总结针对流数据概念漂移的检测方法,为未来研究方向提供了有价值的参考和指导。

具有切换特性和免疫控制的计算机病毒传播模型

针对目前计算机病毒传播建模研究中没有综合考虑安全防护以及病毒持续更新的影响,提出具有切换特性和免疫控制的计算机病毒SEIR(Susceptible-Exposed-Infected-Recovery)传播模型。考虑安全防护措施和病毒更新的影响,构建切换型病毒传播模型,该模型由低防护子系统和高防护子系统构成。考虑免疫控制策略,构建切换脉冲型病毒传播模型。为验证模型的有效性,分别在BA无标度网络和SW小世界网络进行数值仿真,并且进行对比分析。实验结果表明:在SW小世界网络中免疫控制策略更能有效控制病毒的传播;低防护传播期与高防护传播期的比值越小,达到平稳时潜伏节点和感染节点的密度越小;病毒的更新周期越长,潜伏节点和感染节点的峰值密度越低。

基于倾斜三维模型与机载LiDAR点云的DEM快速更新

随着倾斜摄影技术的发展和实景三维建设的不断推进,地形级实景三维地理场景对DEM现势性要求日益提高。为了解决传统方法更新DEM成本高、工作量大的问题,本文提出了利用倾斜三维模型成果转换点云,融合两期DSM和DOM进行变化检测,替换已有机载LiDAR点云,快速更新DEM的方法,并通过项目验证了该方法的可行性,极大地节约了生产成本,为DEM更新提供了思路。

基于粗糙分析的大学英语考试质量提升路径

大学英语考试是检验大学英语教学质量和效果的有效手段。将粗糙集理论应用于大学英语考试命题,基于粗糙集的相对正域、冗余属性和属性重要度等概念,给出试题冗余性的定性判别方法,提出试题重要度的定量度量方法,建立基于粗糙分析的大学英语考试质量提升模型。实验结果表明,新型方法发现并修正了部分冗余和重要度较低的试题,有效提高了试卷命题质量,对指导大学英语教学工作、提升教学质量具有重要指导意义。