基于ITA法和Mann-Kendall法的漓江流域极端降水变化解析

为了分析全球气候变化背景下漓江流域极端降水的变化特征,基于流域内气象站点1963-2019年逐日降水资料,采用修改后的ETCCDI指数,通过ITA(innovative trend analysis)法和Mann-Kendall法2种非参数检验方法从站点和流域尺度研究了漓江流域极端降水的变化趋势,并分析了流域年降水量、汛期降水量和极端降水事件发生时间的变化情况。结果表明:1963-2019年漓江流域年降水量及汛期降水量均呈增长趋势;空间上,各极端降水指数呈现出由流域西北向东南递减的分布格局;时间上,各站点极端降水指数均呈上升趋势;极端降水事件在年内最早发生时间总体表现为流域北部站点时间推迟,南部站点时间提前;ITA法和Mann-Kendall法对漓江流域极端降水指数的趋势显著性检验结果存在明显差异,ITA法的敏感性更强。

Refinement modeling and verification of secure operating systems for communication in digital twins

In traditional digital twin communication system testing,we can apply test cases as completely as possible in order to ensure the correctness of the system implementation,and even then,there is no guarantee that the digital twin communication system implementation is completely correct.Formal verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented correctly.In this paper,we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture,and to model the related assembly instructions.The verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states,indicating that the system meets the design expectations.

基于Mann-Kendall方法的闽江流域降水趋势分析

基于闽江流域1965年~2020年逐日降水资料,利用Mann-Kendall方法进行趋势分析。结果表明:①闽江流域1965年~2020年平均降水量约为1653 mm,有明显的时间规律和区域分布特点。汛期降水量占全年降水量的70%左右,降水在空间上呈现由上游至下游,从西北至东南减少的趋势;②闽江流域全年降水量呈上升趋势,但趋势不显著。汛期及非汛期期间内,多数站点的降水量呈现递增变化,少数站点表现为弱减少;③闽江流域汛期有雨日的日平均雨强有增加趋势,提高了洪水风险,增加了防洪难度。

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Mann-Kendall法在怒江大峡谷降水量分级组成演变规律中的应用

利用怒江流域4个长系列、38个短系列站点降水量数据,运用Mann-Kendall检验法,分析近几十年来怒江流域峡谷段降水量分级组成变化特征,探讨了年降水量分级组成对年降水的贡献情况。结果表明:①怒江流域年降水量以贡山为中点随河流南北两端递减;②怒江大峡谷左右岸年降水量差距明显,年降水量随海拔升高而增加,高黎贡山背风坡降水量随高程增加的幅度为95~99 mm/100m,碧罗雪山迎风坡的降水量随高程增加的幅度为7~30 mm/100m;③流域内小雨、中雨、大雨级别的日降水量每年均有发生;而暴雨仅分布于部分年份,其中峡谷中上段的暴雨发生率高于下段,无特大暴雨发生;④在峡谷段,中雨量对年降水量贡献最大,小雨、大雨对年降水量的贡献次之,之后为暴雨、大暴雨,对于年降水量发生显著增加的贡献,从大到小依次为大雨量、中雨量、小雨量、暴雨量;⑤年降水量在区域存在一个显著增加的时段,这个年降水显著增加的时段主要分布于1995—2023年,其中峡谷段年降水量均在1999—2003年发生显著增加。

The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites

Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constellations often rely on bent-pipe architecture,resulting in high communication costs.Existing onboard inference architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit applications.To address these challenges,we propose a cloud-native-based satellite design specifically tailored for Earth Observation tasks,enabling diverse computing paradigms.In this work,we present a case study of a satellite-ground collaborative inference system deployed in the Tiansuan constellation,demonstrating a remarkable 50%accuracy improvement and a substantial 90%data reduction.Our work sheds light on in-orbit energy,where in-orbit computing accounts for 17%of the total onboard energy consumption.Our approach represents a significant advancement of cloud-native satellite,aiming to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.

A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Efficacy of Juanbi capsule on ameliorating knee osteoarthritis:a network pharmacology and experimental verification-based study

Background:The purpose of the study was to investigate the active ingredients and potential biochemical mechanisms of Juanbi capsule in knee osteoarthritis based on network pharmacology,molecular docking and animal experiments.Methods:Chemical components for each drug in the Juanbi capsule were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,while the target proteins for knee osteoarthritis were retrieved from the Drugbank,GeneCards,and OMIM databases.The study compared information on knee osteoarthritis and the targets of drugs to identify common elements.The data was imported into the STRING platform to generate a protein-protein interaction network diagram.Subsequently,a“component-target”network diagram was created using the screened drug components and target information with Cytoscape software.Common targets were imported into Metascape for GO function and KEGG pathway enrichment analysis.AutoDockTools was utilized to predict the molecular docking of the primary chemical components and core targets.Ultimately,the key targets were validated through animal experiments.Results:Juanbi capsule ameliorated Knee osteoarthritis mainly by affecting tumor necrosis factor,interleukin1β,MMP9,PTGS2,VEGFA,TP53,and other cytokines through quercetin,kaempferol,andβ-sitosterol.The drug also influenced the AGE-RAGE,interleukin-17,tumor necrosis factor,Relaxin,and NF-κB signaling pathways.The network pharmacology analysis results were further validated in animal experiments.The results indicated that Juanbi capsule could decrease the levels of tumor necrosis factor-αand interleukin-1βin the serum and synovial fluid of knee osteoarthritis rats and also down-regulate the expression levels of MMP9 and PTGS2 proteins in the articular cartilage.Conclusion:Juanbi capsule may improve the knee bone microstructure and reduce the expression of inflammatory factors of knee osteoarthritis via multiple targets and multiple signaling pathways.

Verification and Application Evaluation of Intelligent Audit Rules for The UN9000 Urine Analysis System

Objective:To apply and verify the application of intelligent audit rules for urine analysis by Cui et al.Method:A total of 1139 urine samples of hospitalized patients in Tai’an Central Hospital from September 2021 to November 2021 were randomly selected,and all samples were manually microscopic examined after the detection of the UN9000 urine analysis line.The intelligent audit rules(including the microscopic review rules and manual verification rules)were validated based on the manual microscopic examination and manual audit,and the rules were adjusted to apply to our laboratory.The laboratory turnaround time(TAT)before and after the application of intelligent audit rules was compared.Result:The microscopic review rate of intelligent rules was 25.63%(292/1139),the true positive rate,false positive rate,true negative rate,and false negative rate were 27.66%(315/1139),6.49%(74/1139),62.34%(710/1139)and 3.51%(40/1139),respectively.The approval consistency rate of manual verification rules was 84.92%(727/856),the approval inconsistency rate was 0%(0/856),the interception consistency rate was 12.61%(108/856),and the interception inconsistency rate was 0%(0/856).Conclusion:The intelligence audit rules for urine analysis by Cui et al.have good clinical applicability in our laboratory.

伊犁河谷旅游气候变化特征分析——基于Mann-Kendall检验

旅游气候对旅游业发展的影响显著,分析旅游气候规律对于科学合理制定旅游业发展对策具有重要意义。基于Mann-Kendall检验伊犁河谷旅游气候变化规律,发现近59年来伊犁河谷年平均气温总趋势呈现波动升高态势,年降水量略呈波动增长趋势,年平均风速呈波动减小趋势,平均相对湿度变化幅度较小;气温、降水呈“V”型变化趋势;各县市年平均气温均表现为波动增长趋势,但其年平均气温差异较大,且风寒指数、着衣指数差异较大,对旅游具有一定区域性影响。

浅谈Mann-Kendall法分析蚌埠市降水量变化趋势

研究降水的趋势演变规律一直以来都是水文、气象领域的热点,其研究结果对于地区防洪减灾具有非常重要的意义,可以为降水和水资源长期预测提供依据,也为水资源合理开发与利用提供了参考。以安徽省蚌埠水文局辖区内23个水文基本雨量站1950—2022年各月、年降水量资料序列为基础,采用Mann-Kendall趋势分析法,分析了蚌埠市近73年春、夏、秋、冬四季及年降水量序列的趋势。分析结果为淮河流域蚌埠-洪泽湖区间降水预测及水资源合理调配等提供了最新的理论依据。

基于Mann-Kendall和Wavelet分析的唐山市近60年来降水量时空变化研究

降水量时空变化研究,对了解和预测气候变化趋势、合理规划水资源,以及应对极端天气事件具有重要意义。基于唐山市12个标准气象站点的1961-2020年观测资料,采用Mann-Kendall和Wavelet方法相结合的方式,对唐山市降水量时变性进行分析。结果表明,近60年来研究区降水量变化斜率为-1.59mm/a,经Mann-Kendall检测的趋势值Sen’slo值为-1.23mm/a;年际降水量于2014年发生突变,但并不显著;利用Wavelet分析发现,区域降水量存在1~8、7~10、14~16年的变化周期。

基于Mann-Kendall检验的香港鲗鱼涌地区降水与潮位变化趋势分析

香港鲗鱼涌地区是受气候与潮位变化影响较大的敏感区域。为研究降水量与潮位变化对该区域的影响情况,收集2006—2023年鲗鱼涌潮汐站观测的降水量与潮位数据,运用Mann-Kendall检验法分析降水量和潮位的年际、年内变化规律及突变特征。研究结果表明:2006—2023年,鲗鱼涌地区多年平均降水量2274.11 mm,平均潮位1.46 m,年内降水量分配不均匀,而年内平均潮位较均匀。年际尺度上,降水量变化较大,平均潮位变化较小,总体呈上升趋势;季际尺度和月际尺度上,潮位和降水量的变化趋势各异;年际潮位在2007—2008年出现突变特征,而年际降水量未出现突变特征。研究结果可为香港鲗鱼涌地区水资源管理和气候变化的应对决策提供科学依据。

基于Mann-Kendall法的湖泊稳态转换突变分析

采用变化趋势与倾向率、Mann-Kendall趋势检验法和突变点分析法对1981~2008年间太湖湖泊稳态转换关键因子总氮（TN）、总磷（TP）和叶绿素a（Chla）进行突变识别,结果表明,（1）TN、TP和Chla分别在0.05、0.10和0.05水平上呈显著增加趋势;（2）TN浓度在1990~1991年间和1994~1995年间发生了两次突变;TP浓度突变点发生在1987~1988年;Chla浓度历史变化存在三个阶段,1981~1989年为第一阶段,尚未产生突变阶段,第二阶段为1990~1996年,突变过渡阶段,第三阶段为1997~2008年,属于突变后的状态;（3）综合TN、TP和Chla浓度历史变化存在不同阶段,结合各因子的历史变化序列,太湖湖泊稳态转换突变点为1988年和1997年,并把太湖划分为三个阶段,第一阶段为1981~1987年,TP浓度为0.025mg/L,属于草藻共存,接近于清水稳态阶段;第二阶段为1988~1996年,TP浓度为0.086mg/L,属于藻草共存阶段;第三阶段为1997到2008年,TP浓度为0.103mg/L,属于藻型浊水稳态研究结果表明Mann-Kendall法在湖泊稳态转换突变分析中具有一定的适用性.

Synoptic Verification of Precipitation Forecast of Three NWP Models from May to August of 2008 in Liaoning Province

In order to evaluate the precipitation forecast performance of mesoscale numerical model in Northeast China,mesoscale model in Liaoning Province and T213 model,and improve the ability to use their forecast products for forecasters,the synoptic verifications of their 12 h accumulated precipitation forecasts of 3 numerical modes from May to August in 2008 were made on the basis of different systems impacting weather in Liaoning Province.The time limitations were 24,36,48 and 60 h.The verified contents included 6 aspects such as intensity and position of precipitation center,intensity,location,scope and moving velocity of precipitation main body.The results showed that the three models had good forecasting capability for precipitation in Liaoning Province,but the cupacity of each model was obviously different.

运用Mann-Kendall方法探究地表植被变化趋势及其对地形因子的响应机制——以太湖苕溪流域为例

利用Mann-Kendall方法对浙江太湖苕溪流域植被随时间、高程、坡度序列变化的特征与机制进行分析.结果表明:在2000—2010年间,植被随时间变化波动大,但该流域植被覆盖和空间变异均呈不显著增长趋势,2004年植被覆盖最低,植被空间异质性在2002年至2004年间达到最高水平;植被覆盖随高程、坡度的增加而增长,植被空间变异随高程、坡度的增加呈下降趋势,在高程200 m和坡度15°区域有一突变点.该研究结果不仅揭示了植被随时间、高程、坡度的变化趋势,还揭示了植被发生突变的时间段、高程带、坡度带,为进一步研究植被时空变异规律及驱动因子奠定了基础.

基于Mann-Kendall法和小波分析的降水变化特征研究——以重庆市沙坪坝区为例

以重庆市沙坪坝区1959—2008年50年的日降水资料为基础数据,以Mann-Kendall法和小波分析为主要研究方法,分析了沙坪坝区的降水变化特征.结果表明:近50年来沙坪坝区年降水量和汛期降水量成上升趋势,但变化不显著,非汛期降水量有较显著的上升,将有助于解决区域水资源在时间尺度上供需不平衡问题;年内分配的年代际变化分析表明,该地区年内分配极为不均,汛期多年平均降水量为843.9 mm,约占年平均降水量的77%;根据年降水和汛期降水的小波方差图的比较可知,年降水的变化在很大程度上受汛期降水变化的控制.