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.

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).

Metagenomic Insight Reveals the Microbial Structure and Function of the Full-Scale Coking Wastewater Treatment System:Gene-Based Nitrogen Removal

Microbial communities play crucial roles in pollutant removal and system stability in biological systems for coking wastewater(CWW)treatment,but a comprehensive understanding of their structure and functions is still lacking.A five month survey of four sequential bioreactors,anoxic 1/oxic 1/anoxic 2/oxic 2(A1/O1/A2/O2),was carried out in a full-scale CWW treatment system in China to elucidate operational performance and microbial ecology.The results showed that A1/O1/A2/O2 had excellent and stable performance for nitrogen removal.Both total nitrogen(TN;(17.38±6.89)mgL1)and ammonium-nitrogen(NH4 t-N;(2.10±1.34)mg·L^(-1))in the final biological effluent satisfied the Chinese national standards for CWW.Integrated analysis of 16S ribosome RNA(rRNA)sequencing and metagenomic sequencing showed that the bacterial communities and metagenomic function profiles of A1 and O1 shared similar functional structures,while those of A2 significantly varied from those of other bioreactors(p<0.05).The results indicated that microbial activity was strongly connected with activated sludge function.Nitrosospira,Nitrosomonas,and SM1A02 were responsible for nitrification during the primary anoxic-oxic(AO)stage and Azoarcus and Thauera acted as important denitrifiers in A2.Nitrogen cycling-related enzymes and genes work in the A1/O1/A2/O2 system.Moreover,the hao genes catalyzing hydroxylamine dehydrogenase(EC 1.7.2.6)and the napA and napB genes catalyzing nitrate reductase(EC 1.9.6.1)played important roles in the nitrification and denitrification processes in the primary and secondary AO stages,respectively.The mixed liquor suspended solids(MLSS)/total solids(TS),TN removal rate(RR),total organic carbon(TOC)(RR),and NH_(4)^(+)t-N(RR)were the most important environmental factors for regulating the structure of core bacterial genera and nitrogen-cycling genes.Proteobacteria were the potential main participants in nitrogen metabolism in the A1/O1/A2/O2 system for CWW treatment.This study provides an original and comprehensive understanding of the microbial community and functions at the gene level,which is crucial for the efficient and stable operation of the full-scale biological process for CWW treatment.

Resistance of full-scale beams against close-in explosions.Numerical modeling and field tests

This paper explores the performances of a finite element simulation including four concrete models applied to a full-scale reinforced concrete beam subjected to blast loading. Field test data has been used to compare model results for each case. The numerical modelling has been, carried out using the suitable code LS-DYNA. This code integrates blast load routine(CONWEP) for the explosive description and four different material models for the concrete including: Karagozian & Case Concrete, Winfrith, Continuous Surface Cap Model and Riedel-Hiermaier-Thoma models, with concrete meshing based on 10, 15, and 20 mm. Six full-scale beams were tested: four of them used for the initial calibration of the numerical model and two more tests at lower scaled distances. For calibration, field data obtained employing pressure and accelerometers transducers were compared with the results derived from the numerical simulation. Damage surfaces and the shape of rupture in the beams have been used as references for comparison. Influence of the meshing on accelerations has been put in evidence and for some models the shape and size of the damage in the beams produced maximum differences around 15%. In all cases, the variations between material and mesh models are shown and discussed.

Full-Scale Isogeometric Topology Optimization of Cellular Structures Based on Kirchhoff-Love Shells

Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio.In this paper,a full-scale isogeometric topology optimization(ITO)method based on Kirchhoff-Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed.This method utilizes high-order continuous nonuniform rational B-splines(NURBS)as basis functions for Kirchhoff-Love shell elements.The geometric and analysis models of thin shells are unified by isogeometric analysis(IGA)to avoid geometric approximation error and improve computational accuracy.The topological configurations of thin-shell structures are described by constructing the effective density field on the controlmesh.Local volume constraints are imposed in the proximity of each control point to obtain bone-like cellular structures.To facilitate numerical implementation,the p-norm function is used to aggregate local volume constraints into an equivalent global constraint.Several numerical examples are provided to demonstrate the effectiveness of the proposed method.After simulation and comparative analysis,the results indicate that the cellular thin-shell structures optimized by the proposed method exhibit great load-carrying behavior and high damage robustness.

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.

Behavior of transporting pipeline sections without and with hydrogen exposure based on full-scale tests

Pipeline transport of hydrogen is one of today’s economic and environmental challenges.In order to find safe and reliable application of both existing gas and build new pipelines,it is essential to carry out tests on full-scale pipeline section,including the potentially more dangerous places than the main pipe,the girth welds.For the investigations,pipeline sections of P355NH steel with girth welds were prepared and exposed to pure hydrogen at twice the maximum allowable operating pressure for 41 days.Subsequently,full-scale burst tests were carried out and specimens were cut and prepared from the typical locations of the failed pipeline sections for mechanical,and macro-and microstructural investigations.The results obtained were evaluated and compared with data from previous full-scale tests on pipeline sections without hydrogen exposure.The results showed differences in the behavior of pipeline sections loaded in different ways,with different characteristics of the materials and the welded joints,both in the cases without hydrogen exposure and in the cases exposed to hydrogen.

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.

Full-Scale Numerical Simulation of the Local Scour Under Combined Current and Wave Conditions Based on Field Data

The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of wind turbine foundations.In this paper,a full-scale numerical model was developed and validated based on field data from Rudong,China.The scour development around monopiles was investigated,and the effects of waves and the Reynolds number Re were analyzed.Several formulas for predicting the scour depth in the literature have been evaluated.It is found that waves can accelerate scour development even if the KC number is small(0.78<KC<1.57).The formula obtained from small-scale model tests may be unsafe or wasteful when it is applied in practical design due to the scale effect.A new equation for predicting the scour depth based on the average pile Reynolds number(Rea)is proposed and validated with field data.The equilibrium scour depth predicted using the proposed equation is evaluated and compared with those from nine equations in the literature.It is demonstrated that the values predicted from the proposed equation and from the S/M(Sheppard/Melville)equation are closer to the field data.

A measurable refinement method of design and verification for micro-kernel operating systems in communication network

A secure operating system in the communication network can provide the stable working environment,which ensures that the user information is not stolen.The micro-kernel operating system in the communication network retains the core functions in the kernel,and unnecessary tasks are implemented by calling external processes.Due to the small amount of code,the micro-kernel architecture has high reliability and scalability.Taking the microkernel operating system in the communication network prototype VSOS as an example,we employ the objdump tool to disassemble the system source code and get the assembly layer code.On this basis,we apply the Isabelle/HOL,a formal verification tool,to model the system prototype.By referring to the mathematical model of finite automata and taking the process scheduling module as an example,the security verification based on the assembly language layer is developed.Based on the Hoare logic theory,each assembly statement of the module is verified in turn.The verification results show that the scheduling module of VSOS has good functional security,and also show the feasibility of the refinement framework.

A Full-Scale Optimization of a Crop Spatial Planting Structure and its Associated Effects

Driven by the concept of agricultural sustainable development,crop planting structure optimization(CPSO)has become an effective measure to reduce regional crop water demand,ensure food security,and protect the environment.However,traditional optimization of crop planting structures often ignores the impact on regional food supply–demand relations and interprovincial food trading.Therefore,using a system analysis concept and taking virtual water output as the connecting point,this study proposes a theoretical CPSO framework based on a multi-aspect and full-scale evaluation index system.To this end,a water footprint(WF)simulation module denoted as soil and water assessment tool–water footprint(SWAT-WF)is constructed to simulate the amount and components of regional crop WFs.A multi-objective spatial CPSO model with the objectives of maximizing the regional economic water productivity(EWP),minimizing the blue water dependency(BWFrate),and minimizing the grey water footprint(GWFgrey)is established to achieve an optimal planting layout.Considering various benefits,a fullscale evaluation index system based on region,province,and country scales is constructed.Through an entropy weight technique for order preference by similarity to an ideal solution(TOPSIS)comprehensive evaluation model,the optimal plan is selected from a variety of CPSO plans.The proposed framework is then verified through a case study of the upper–middle reaches of the Heihe River Basin in Gansu province,China.By combining the theory of virtual water trading with system analysis,the optimal planting structure is found.While sacrificing reasonable regional economic benefits,the optimization of the planting structure significantly improves the regional water resource benefits and ecological benefits at different scales.

Numerical simulation and experimental verification of plasma jet development in gas gap switch

Plasma jet triggered gas gap switch has obvious advantages in fast control switch.The development of the plasma in the ambient medium is the key factor affecting the triggering conduction of the gas switch.However,the plasma jet process and its characteristic parameters are complicated and the existing test methods cannot fully characterize its development laws.In this work,a two-dimensional transient fluid calculation model of the plasma jet process of the gas gap switch is established based on the renormalization-group k-εturbulence equation.The results show that the characteristic parameters and morphological evolution of the plasma jet are basically consistent with the experimental results,which verifies the accuracy of the simulation model calculation.The plasma jet is a long strip with an initial velocity of 1.0 km·s-1and develops in both axial and radial directions.The jet velocity fluctuates significantly with axial height.As the plasma jet enters the main gap,the pressure inside the trigger cavity drops by80%,resulting in a rapid drop in the jet velocity.When the plasma jet head interacts with the atmosphere,the two-phase fluid compresses each other,generating a forward-propelled pressure wave.The plasma jet heads flow at high velocity,a negative pressure zone is formed in the middle part of the jet,and the pressure peak decreases gradually with height.As the value of the inlet pressure increases,the characteristic parameters of the plasma jet increase.The entrainment phenomenon is evident,which leads to an increase in the pressure imbalance of the atmospheric gas medium,leading to a significant Coanda effect.Compared with air,the characteristic parameters of a plasma jet in SF6are lower,and the morphological evolution is significantly suppressed.The results of this study can provide some insight into the mechanism of action of the switch jet plasma development process.

Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Detailed analysis and simulation verification for reconstruction of plasma optical boundary with spectrometric technique on HL-2M tokamak

The plasma optical boundary reconstruction technique based on Hommen's theory is promising for future tokamaks with high parameters. In this work, we conduct detailed analysis and simulation verification to estimate the ‘logic loophole' of this technique. The finite-width effect and unpredictable errors reduce the technique's reliability, which leads to this loophole. Based on imaging theory, the photos of a virtual camera are simulated by integrating the assumed luminous intensity of plasma. Based on Hommen's theory, the plasma optical boundary is reconstructed from the photos. Comparing the reconstructed boundary with the one assumed, the logic loophole and its two effects are quantitatively estimated. The finite-width effect is related to the equivalent thickness of the luminous layer, which is generally about 2-4 cm but sometimes larger. The level of unpredictable errors is around 0.65 cm. The technique based on Hommen's theory is generally reliable, but finite-width effect and unpredictable errors have to be taken into consideration in some scenarios. The parameters of HL-2M are applied in this work.