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...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.展开更多
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...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.展开更多
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 be...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.展开更多
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 ex...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.展开更多
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 estima...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.展开更多
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 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.展开更多
Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one ...Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data,we first perform a few denoising steps.Then,human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques,including diverse feature selection.Next,we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)dataset.The second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.展开更多
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 re...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.展开更多
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 n...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.展开更多
Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection.It is also a popular biometric authentication technology in forensic and com...Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection.It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages,including noninvasiveness,user-friendliness,and social and legal acceptability.According to the literature,extensive research has been conducted on signature verification systems in a variety of languages,including English,Hindi,Bangla,and Chinese.However,the Arabic Offline Signature Verification(OSV)system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,diacritics,ligatures,and overlapping,making verification more difficult.Recently,signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries;however,performance on skilled forgery detection is still unsatisfactory.Most existing methods require many learning samples to improve verification accuracy,which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems.This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm(GA).In contrast to existing methods,which use multiclass learning approaches,this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification system.The proposed approach is tested on three signature databases(SID)-Arabic handwriting signatures,CEDAR(Center of Excellence for Document Analysis and Recognition),and UTSIG(University of Tehran Persian Signature),and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate(FAR),False Rejection Rate(FRR),and Equal Error Rate(ERR).The proposed system achieved 5%improvement.展开更多
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 ...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.展开更多
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)h...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.展开更多
Background:Bupleuri Radix is a common Chinese medicinal material in traditional Chinese medicine.Currently,the therapeutic effect of treating schizophrenia is relatively well understood.However,there are fewer studies...Background:Bupleuri Radix is a common Chinese medicinal material in traditional Chinese medicine.Currently,the therapeutic effect of treating schizophrenia is relatively well understood.However,there are fewer studies examining the underlying mechanisms of its treatment.The objective of the study was to investigate the primary mechanisms of Bupleuri Radix in treating schizophrenia through network pharmacology and clinical validation.Method:Network pharmacology revealed possible molecular mechanisms,followed by clinical verification.Sixty-seven schizophrenia patients undergoing treatment at the Hunan Brain Hospital between October and November 2022 were recruited and randomly divided into the olanzapine group and the olanzapine+Bupleuri Radix group.Additionally,32 healthy people undergoing physical examinations during the same period were included as the control group.The patient’s positive and negative symptom scale scores were compared.qPCR was used to detect the mRNA expression levels of ESR1,mTOR,EIF4E,and SMAD4 in peripheral blood.Results:Through network pharmacological analysis,it was concluded in this study that Bupleuri Radix might regulate the mTOR,PI3K-Akt,and HIF-1 signaling pathways.Clinical experiments indicated that compared with before treatment,the positive and negative symptom scale scores and total scores of the two treatment groups were significantly decreased after treatment(P<0.01).In addition,the positive and negative symptom scale scores and total scores in the olanzapine+Bupleuri Radix group were significantly decreased(P<0.01)compared to the olanzapine group after treatment.Before treatment,ESR1 mRNA expression levels in peripheral blood were significantly higher in the two treatment groups than in the control group,whereas the mRNA expression levels of mTOR,EIF4E,and SMAD4 in peripheral blood were significantly lower(P<0.01).The mRNA expression levels of mTOR,EIF4E,and SMAD4 in peripheral blood were significantly higher after therapy than before treatment,whereas the mRNA expression levels of ESR1 in peripheral blood were significantly lower(P<0.01).After therapy,the olanzapine+Bupleuri Radix group’s mRNA expression levels of mTOR,EIF4E,and SMAD4 were significantly higher than those of the olanzapine group,whereas the mRNA expression levels of ESR1 were significantly lower(P<0.01).Conclusion:The mechanism of Bupleuri Radix’s therapeutic efficacy in schizophrenia may involve the up-regulation of mTOR,EIF4E,and SMAD4 mRNA expression and the down-regulation of ESR1 mRNA expression in peripheral blood.展开更多
Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent years.These models seem a natural fit to handle the rising number of biometric recogn...Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent years.These models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security systems.DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems.Iris recognition was considered the more reliable and accurate biometric detection method accessible.Iris recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border control.This article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)model.The presented PODTL-BIR technique recognizes the iris for biometric security.In the presented PODTL-BIR model,an initial stage of pre-processing is carried out.In addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature vectors.The PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric verification.Finally,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition efficiency.Awide-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR system.The experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.展开更多
In order to realize a general-purpose automatic formal verification platform based on WebAssembly technology as a web service(FVPS),which aims to provide an automated report of vulnerability detections,this work build...In order to realize a general-purpose automatic formal verification platform based on WebAssembly technology as a web service(FVPS),which aims to provide an automated report of vulnerability detections,this work builds a Hyperledger Fabric blockchain runtime model.It proposes an optimized methodology of the functional equivalent translation from source program languages to formal languages.This methodology utilizes an external application programming interface(API)table to replace the source codes in compilation,thereby pruning the part of housekeeping codes to ease code inflation.Code inflation is a significant metric in formal language translation.Namely,minor code inflation enhances verification scale and performance efficiency.It determines the efficiency of formal verification,involving launching,running,and memory usage.For instance,path explosion increases exponentially,resulting in out-of-memory.The experimental results conclude that program languages like golang severely impact code inflation.FVPS reduces the wasm code size by over 90%,achieving two orders of optimization magnitude,from 2000 kilobyte(KB)to 90 KB.That means we can cope with golang applications up to 20 times larger than the original in scale.This work eliminates the gap between Hyperledger Fabric smart contracts and WebAssembly.Our approach is pragmatic,adaptable,extendable,and flexible.Nowadays,FVPS is successfully applied in a Railway-Port-Aviation blockchain transportation system.展开更多
Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other...Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.展开更多
[Objectives]To study the potential molecular mechanism of ginseng in treating nephrotic syndrome(NS)by using network pharmacology,molecular docking and experimental verification methods.[Methods]The active components ...[Objectives]To study the potential molecular mechanism of ginseng in treating nephrotic syndrome(NS)by using network pharmacology,molecular docking and experimental verification methods.[Methods]The active components and targets of ginseng were obtained through the network pharmacology database,and the potential targets for the treatment of NS were predicted.The STRING data platform and Cytoscape software were used to construct protein interaction network,and carry out GO and KEGG enrichment analysis.Molecular docking of active components of ginseng and core targets was performed.The in vitro experiment verified the improvement effect of kaempferol,a key active ingredient of ginseng,on podocyte injury.[Results]After screening,17 active components of ginseng and 38 key targets for treating NS were obtained.GO and KEGG enrichment analysis showed that NF-κB,MAPK and other inflammatory pathways were involved.Molecular docking results show that the core components had good binding activity to key targets.The results of in vitro experiments show that kaempferol can reduce the phosphorylation level of AKT1,down-regulate the expression levels of NF-κB p65 and p-NF-κB p65,play an anti-inflammatory effect by inhibiting the activation of NF-κB pathway,and improve podocyte injury.[Conclusions]Ginseng may play a role in the treatment of NS by regulating multiple targets and pathways such as inflammatory response,substance metabolism,and signal transduction.展开更多
This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provid...This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.展开更多
Identity verification using authenticity evaluation of handwritten signatures is an important issue.There have been several approaches for the verification of signatures using dynamics of the signing process.Most of t...Identity verification using authenticity evaluation of handwritten signatures is an important issue.There have been several approaches for the verification of signatures using dynamics of the signing process.Most of these approaches extract only global characteristics.With the aim of capturing both dynamic global and local features,this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system(NRVS)and Genetic NRVS(GNRVS)models.The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values:truth,indeterminacy,and falsity.These three values are determined by neutrosophic membership functions.The proposed model also is able to deal with all features without the need to select from them.In the GNRVS model,the neutrosophic rules are automatically chosen by Genetic Algorithms.The performance of the proposed system is tested on the MCYT-Signature-100 dataset.In terms of the accuracy,average error rate,false acceptance rate,and false rejection rate,the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.展开更多
文摘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.
基金funded by the National Natural Science Foundation of China(62072056,62172058)the Researchers Supporting Project Number(RSP2023R102)King Saud University,Riyadh,Saudi Arabia+4 种基金funded by the Hunan Provincial Key Research and Development Program(2022SK2107,2022GK2019)the Natural Science Foundation of Hunan Province(2023JJ30054)the Foundation of State Key Laboratory of Public Big Data(PBD2021-15)the Young Doctor Innovation Program of Zhejiang Shuren University(2019QC30)Postgraduate Scientific Research Innovation Project of Hunan Province(CX20220940,CX20220941).
文摘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.
基金supported by National Natural Science Foundation of China(62032003).
文摘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.
基金funding from the Basic Research Project of the Education Department of Shaanxi Province(21JC010,21JP035)the Young and Middle-Aged Scientific Research and Innovation Team of the Shaanxi Provincial Administration of Traditional Chinese Medicine(2022SLRHLJ001)the 2023 Central Financial Transfer Payment Local Project“Innovation and Improvement of Five Types of Hospital Preparations,Such as Roumudan Granules”.
文摘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.
基金supported by the Tsinghua University 2021 Doctoral Summer Projectsupported by the National Key R&D Program of China (No. 2018YFE0301102)National Natural Science Foundation of China (Nos. 11875020 and 11875023)。
文摘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.
基金supported by National Natural Science Foundation of China(No.52107142)。
文摘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.
文摘Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data,we first perform a few denoising steps.Then,human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques,including diverse feature selection.Next,we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)dataset.The second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.
基金This work was supported in part by the Natural Science Foundation of Jiangsu Province under grant No.BK20191475the fifth phase of“333 Project”scientific research funding project of Jiangsu Province in China under grant No.BRA2020306the Qing Lan Project of Jiangsu Province in China under grant No.2019.
文摘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.
文摘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.
文摘Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection.It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages,including noninvasiveness,user-friendliness,and social and legal acceptability.According to the literature,extensive research has been conducted on signature verification systems in a variety of languages,including English,Hindi,Bangla,and Chinese.However,the Arabic Offline Signature Verification(OSV)system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,diacritics,ligatures,and overlapping,making verification more difficult.Recently,signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries;however,performance on skilled forgery detection is still unsatisfactory.Most existing methods require many learning samples to improve verification accuracy,which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems.This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm(GA).In contrast to existing methods,which use multiclass learning approaches,this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification system.The proposed approach is tested on three signature databases(SID)-Arabic handwriting signatures,CEDAR(Center of Excellence for Document Analysis and Recognition),and UTSIG(University of Tehran Persian Signature),and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate(FAR),False Rejection Rate(FRR),and Equal Error Rate(ERR).The proposed system achieved 5%improvement.
基金supported by the Fundamental Research Funds for the Central Universities(2021RC239)the Postdoctoral Science Foundation of China(2021 M690338)+3 种基金theHainan Provincial Natural Science Foundation of China(620RC562,2019RC096,620RC560)the Scientific Research Setup Fund of Hainan University(KYQD(ZR)1877)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(61802092,62162021).
文摘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.
基金Prince Sultan University for funding this publication’s Article Process Charges(APC).
文摘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.
基金funded by the Key Research and Development Program of Hunan Province(No.2022SK2163)Research Project of Hunan Provincial Health Commission(No.D202319017874,202214052635)+2 种基金Chinese Medicine Science&Research Project of Hunan Province(No.2021045)Natural Science Foundation of Hunan Province,China(No.2023JJ30339,2023JJ60292)grateful for the support by the Institute of Diagnostics of TCM,Hunan University of Chinese Medicine,Changsha,China.
文摘Background:Bupleuri Radix is a common Chinese medicinal material in traditional Chinese medicine.Currently,the therapeutic effect of treating schizophrenia is relatively well understood.However,there are fewer studies examining the underlying mechanisms of its treatment.The objective of the study was to investigate the primary mechanisms of Bupleuri Radix in treating schizophrenia through network pharmacology and clinical validation.Method:Network pharmacology revealed possible molecular mechanisms,followed by clinical verification.Sixty-seven schizophrenia patients undergoing treatment at the Hunan Brain Hospital between October and November 2022 were recruited and randomly divided into the olanzapine group and the olanzapine+Bupleuri Radix group.Additionally,32 healthy people undergoing physical examinations during the same period were included as the control group.The patient’s positive and negative symptom scale scores were compared.qPCR was used to detect the mRNA expression levels of ESR1,mTOR,EIF4E,and SMAD4 in peripheral blood.Results:Through network pharmacological analysis,it was concluded in this study that Bupleuri Radix might regulate the mTOR,PI3K-Akt,and HIF-1 signaling pathways.Clinical experiments indicated that compared with before treatment,the positive and negative symptom scale scores and total scores of the two treatment groups were significantly decreased after treatment(P<0.01).In addition,the positive and negative symptom scale scores and total scores in the olanzapine+Bupleuri Radix group were significantly decreased(P<0.01)compared to the olanzapine group after treatment.Before treatment,ESR1 mRNA expression levels in peripheral blood were significantly higher in the two treatment groups than in the control group,whereas the mRNA expression levels of mTOR,EIF4E,and SMAD4 in peripheral blood were significantly lower(P<0.01).The mRNA expression levels of mTOR,EIF4E,and SMAD4 in peripheral blood were significantly higher after therapy than before treatment,whereas the mRNA expression levels of ESR1 in peripheral blood were significantly lower(P<0.01).After therapy,the olanzapine+Bupleuri Radix group’s mRNA expression levels of mTOR,EIF4E,and SMAD4 were significantly higher than those of the olanzapine group,whereas the mRNA expression levels of ESR1 were significantly lower(P<0.01).Conclusion:The mechanism of Bupleuri Radix’s therapeutic efficacy in schizophrenia may involve the up-regulation of mTOR,EIF4E,and SMAD4 mRNA expression and the down-regulation of ESR1 mRNA expression in peripheral blood.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project,under grant no.KEP-3-120-42.
文摘Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent years.These models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security systems.DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems.Iris recognition was considered the more reliable and accurate biometric detection method accessible.Iris recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border control.This article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)model.The presented PODTL-BIR technique recognizes the iris for biometric security.In the presented PODTL-BIR model,an initial stage of pre-processing is carried out.In addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature vectors.The PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric verification.Finally,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition efficiency.Awide-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR system.The experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.
基金This work was supported by the National Key R&D Program of China,Grant No.2018YFA0306703.
文摘In order to realize a general-purpose automatic formal verification platform based on WebAssembly technology as a web service(FVPS),which aims to provide an automated report of vulnerability detections,this work builds a Hyperledger Fabric blockchain runtime model.It proposes an optimized methodology of the functional equivalent translation from source program languages to formal languages.This methodology utilizes an external application programming interface(API)table to replace the source codes in compilation,thereby pruning the part of housekeeping codes to ease code inflation.Code inflation is a significant metric in formal language translation.Namely,minor code inflation enhances verification scale and performance efficiency.It determines the efficiency of formal verification,involving launching,running,and memory usage.For instance,path explosion increases exponentially,resulting in out-of-memory.The experimental results conclude that program languages like golang severely impact code inflation.FVPS reduces the wasm code size by over 90%,achieving two orders of optimization magnitude,from 2000 kilobyte(KB)to 90 KB.That means we can cope with golang applications up to 20 times larger than the original in scale.This work eliminates the gap between Hyperledger Fabric smart contracts and WebAssembly.Our approach is pragmatic,adaptable,extendable,and flexible.Nowadays,FVPS is successfully applied in a Railway-Port-Aviation blockchain transportation system.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.
基金Supported by College Students'Innovation Entrepreneurship and Training Program of Yantai University(X202211066143)。
文摘[Objectives]To study the potential molecular mechanism of ginseng in treating nephrotic syndrome(NS)by using network pharmacology,molecular docking and experimental verification methods.[Methods]The active components and targets of ginseng were obtained through the network pharmacology database,and the potential targets for the treatment of NS were predicted.The STRING data platform and Cytoscape software were used to construct protein interaction network,and carry out GO and KEGG enrichment analysis.Molecular docking of active components of ginseng and core targets was performed.The in vitro experiment verified the improvement effect of kaempferol,a key active ingredient of ginseng,on podocyte injury.[Results]After screening,17 active components of ginseng and 38 key targets for treating NS were obtained.GO and KEGG enrichment analysis showed that NF-κB,MAPK and other inflammatory pathways were involved.Molecular docking results show that the core components had good binding activity to key targets.The results of in vitro experiments show that kaempferol can reduce the phosphorylation level of AKT1,down-regulate the expression levels of NF-κB p65 and p-NF-κB p65,play an anti-inflammatory effect by inhibiting the activation of NF-κB pathway,and improve podocyte injury.[Conclusions]Ginseng may play a role in the treatment of NS by regulating multiple targets and pathways such as inflammatory response,substance metabolism,and signal transduction.
文摘This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.
文摘Identity verification using authenticity evaluation of handwritten signatures is an important issue.There have been several approaches for the verification of signatures using dynamics of the signing process.Most of these approaches extract only global characteristics.With the aim of capturing both dynamic global and local features,this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system(NRVS)and Genetic NRVS(GNRVS)models.The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values:truth,indeterminacy,and falsity.These three values are determined by neutrosophic membership functions.The proposed model also is able to deal with all features without the need to select from them.In the GNRVS model,the neutrosophic rules are automatically chosen by Genetic Algorithms.The performance of the proposed system is tested on the MCYT-Signature-100 dataset.In terms of the accuracy,average error rate,false acceptance rate,and false rejection rate,the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.
