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).

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.

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.

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.

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.

Research on Verification Method of Motor Startups in Nuclear Power Plants Based on Topology Recognition

There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.

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.

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.

Fusion of Type-2 Neutrosophic Similarity Measure in Signatures Verification Systems: A New Forensic Document Analysis Paradigm

Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signatures from thematching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy,a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertaintiesand ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values,which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work exploresthe type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy,and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophiclogic allows the assessment of many sources of ambiguity and conflicting information, decision-making is moreflexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signatureverification by demonstrating its superior handling of uncertainty and variability over type-1, which eventuallyresults in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In acomparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similaritymeasure yields a better accuracy rate of 98% than the type-1 95%.

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.

Construction and verification of a model for predicting fall risk in patients with maintenance hemodialysis

Objective:To construct a risk prediction model for fall in patients with maintenance hemodialysis(MHD)and to verify the prediction effect of the model.Methods:From June 2020 to December 2020,307 patients who underwent MHD in a tertiary hospital in Chengdu were divided into a fall group(32 cases)and a non-fall group(275 cases).Logistic regression analysis model was used to establish the influencing factors of the subjects.Hosmer–Lemeshow and receiver operating characteristic(ROC)curve were used to test the goodness of fit and predictive effect of the model,and 104 patients were again included in the application research of the model.Results:The risk factors for fall were history of falls in the past year(OR=3.951),dialysis-related hypotension(OR=6.949),time up and go(TUG)test(OR=4.630),serum albumin(OR=0.661),frailty(OR=7.770),and fasting blood glucose(OR=1.141).Hosmer–Lemeshow test was P=0.475;the area under the ROC curve was 0.907;the Youden index was 0.642;the sensitivity was 0.843;and the specificity was 0.799.Conclusions:The risk prediction model constructed in this study has a good effect and can provide references for clinical screening of fall risks in patients with MHD.

小麦抗条锈基因Yr5、Yr9和Yr18分子标记的特异性评估

Yr5、Yr9(1B/1R)、Yr18基因的自身性状或连锁性状在中国小麦育种中具有重要的应用前景。本研究运用以Avocet S为背景的单基因近等基因系及其对应基因的载体品种,对Yr5基因的连锁标记STS9/10,Yr9(1B/1R)基因的分子标记AF1/4、D15、20H,Yr18基因的分子标记csLV34、cssfr1~cssfr5进行有效性验证。结果表明,Yr5基因的特异性分子标记STS9/10、Yr9(1B/1R)基因的特异性分子标记AF1/4、D15和20H均能够准确识别不同遗传背景材料中的相应抗性基因;Yr18基因分子标记cssfr2能够准确检测Yr18的非载体材料,并检测出Avocet S*6/Yr5、Avocet S*6/Yr24、Avocet S*6/Yr27三个材料具有Yr18的等位变异。这说明Yr5基因的连锁标记STS9/10,Yr9(1B/1R)基因的分子标记AF1/4、D15、20H能对目的基因进行有效检测,Yr18基因的几个分子标记结合使用不仅能够对目的基因进行有效检测,而且能够识别该位点的等位变异。

电气化铁路接触网冲击振动除冰仿真分析

接触线覆冰严重影响电气化铁路的正常运行。通过Ansys有限元分析软件创建简单链形悬挂接触网有限元模型,采用负驰度法分析接触网模型的静力初始形态,在此基础上建立接触网覆冰模型。对模型进行模态分析和瞬态动力学分析,通过承力索驰度、模型固有频率以及网格收敛性3方面验证模型的合理性。基于人工敲打机械除冰方法,结合除冰准则,研究不同加载点、不同载荷大小以及不同覆冰层厚度对除冰效果的影响。综合分析各种载荷方式,归纳总结接触网除冰的一般规律,使除冰效果达到最佳。

基于现行中医证候标准的证素模型验证、评价与思考

目的对证素模型进行系统验证与评价,并针对存在问题提出解决思路与方法。方法以53项通用证素和10项五官专科证素为标准,提取《GB∕T 16751.2-2021中医临床诊疗术语第2部分:证候》中所有符合纳入标准的证候名称病位、病性证素,对证素模型进行验证。结果共纳入证候名称术语2023条,标明病性证素的有1579条(78.1%),标明病位证素的有971条(48.0%),只标明病性证素的有795条,只标明病位证素的有187条,两者均包含的有784条(38.8%),均未包含的有257条(12.7%);证候术语层级划分越细,证素覆盖率越高。结论证素模型存在一定的局限性,限制了证素研究发展。证素模型需要在权衡复杂度与实用性基础上,进行多学科交叉融合以支持其研究的继承与创新,“多维多层”证素模型可能是其未来发展方向。

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.

多腔室复合弯曲气动网格软体驱动器解析建模与实验研究

软体机器人具有灵活度高、人机交互安全等优势,在操作易碎物体和非结构化环境中具有广阔应用前景.气动软体驱动器是构建软体机器人的重要部件之一,其特性直接影响到软体机器人的性能.针对气动网格型软体驱动器,从腔室侧壁膨胀角和驱动器弯曲角度的非线性关系出发,基于赫兹接触理论和Yeoh超弹性不可压缩材料的非线性本构方程,建立了多腔室气动网格软体驱动器的准静态力学模型.该模型考虑了超弹性材料变形和多腔室侧壁膨胀接触几何非线性特点,能够准确描述不同输入气压与驱动器弯曲角度和顶端输出力的关系.根据仿生思想设计了一种多腔室复合弯曲多腔室气动网格驱动器结构,并基于该结构分别通过有限元仿真和实验对提出的解析模型进行验证.结果表明,解析模型计算结果与有限元仿真结果、实验结果最大差异均不超过10%.该气动网格软体驱动器解析模型具有较好的准确性.

A Novel Verification Development Platform for Passive UHF RFID Tag

This paper introduces a novel verification development platform for the passive UHF RFID tag,which is compatible with the ISO/IEC 18000-6B standard,operating in the 915MHz ISM band. This platform efficiently reduces the design and development time and cost, and implements a fast prototype design of the passive UHF RFID tag. It includes the RFID analog front end and the tag control logic, which is implemented in an Altera ACEX FPGA. The RFID analog front end, which is fabricated using a Chartered 0.35μm two-poly four-metal CMOS process, contains a local oscillator, power on reset circuit, matching network and backscatter, rectifier, regu- lator,AM demodulator, etc. The platform achieves rapid, flexible and efficient verification and development, and can also be fit for other RFID standards after changing the tag control logic in FPGA.

Dynamic business process modeling and verification for inter-organizational collaboration

To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new business process model which is multi-role, multi-dimensional, integrated and dynamic is proposed relying on inter-organizational collaboration. Compatible with the traditional linear sequence model, the new model is an M x N multi-dimensional mesh, and provides horizontal and vertical formal descriptions for the collaboration business process model. Finally, the pi-calculus theory is utilized to verify the deadlocks, livelocks and synchronization of the example models. The result shows that the proposed approach is efficient and applicable in inter-organizational business process modeling.

自贸试验区设立能否提升地区共同富裕水平——基于宏微观数据的双重验证

新时代推进中国式现代化建设,走共同富裕道路,需全面深化改革与扩大高水平对外开放。自贸试验区作为高水平对外开放体系的基础和平台,对促进共同富裕具有重要意义。基于我国2006—2021年276个地级市面板数据及中国家庭追踪调查数据,通过构建多期双重差分模型,实证检验自贸试验区设立对共同富裕的影响。研究发现,自贸试验区设立显著提升了地区共同富裕水平,且经系列检验后结果依然稳健。异质性分析表明,该政策效应在经济基础较好的地区更显著,且效应随自贸试验区成立批次先增后减。机制检验表明,自贸试验区通过产业、贸易和要素三渠道促进共同富裕。通过从自贸试验区设立的视角探究共同富裕水平提升的路径,能够为地区资源统筹与协调发展、促进共同富裕提供政策启示。