The“notice-necessary measures”rule began with the“notice-and-takedown”rule,which originated from the United States safe harbors rule,established to limit the liability of Internet Service Providers(ISPs)for helpin...The“notice-necessary measures”rule began with the“notice-and-takedown”rule,which originated from the United States safe harbors rule,established to limit the liability of Internet Service Providers(ISPs)for helping infringement in network infringement.China’s Tort Liability Law and Civil Code amended the“notice-and-takedown”rule so that when the right holder sends an effective notice of infringement to the ISP,the ISP shall take necessary measures more than removal to be exempted from liability.This article will discuss the transformation of the rule and types of necessary measures,and reconstruct the relationship between effectiveness of notice and types of necessary measures.展开更多
Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in...Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy.In this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify intrusions.The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the environment.Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty.Also,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical IDS.Under extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional models.Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion detection.This research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.展开更多
This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independ...This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.展开更多
Against the backdrop of profound reshaping of the current international landscape and the unprecedentedly rapid iteration of digital technologies,the global digital economy landscape and international trade rule syste...Against the backdrop of profound reshaping of the current international landscape and the unprecedentedly rapid iteration of digital technologies,the global digital economy landscape and international trade rule system are entering a new era full of changes.Digital trade rules have emerged as a prominent focus in this field.Firstly,through a comprehensive review of sample data such as the WTO e-commerce proposals under the current multilateral framework,we can gain a profound insight into the negotiation practices of major economies regarding digital trade rules.In this context,China has demonstrated strong defensive interests in the“emerging”issues of digital trade,aiming to safeguard national data security and promote the healthy development of the digital economy.Therefore,when participating in the formulation of global digital trade rules,China needs to accurately strike a balance between offense and defense,contribute Chinese wisdom and solutions,and promote the construction of a fairer,more reasonable,and inclusive international digital trade governance system.展开更多
We propose the scaling rule of Morse oscillator,based on this rule and by virtue of the Her-mann-Feymann theorem,we respectively obtain the distribution of potential and kinetic ener-gy of the Morse Hamiltonian.Also,w...We propose the scaling rule of Morse oscillator,based on this rule and by virtue of the Her-mann-Feymann theorem,we respectively obtain the distribution of potential and kinetic ener-gy of the Morse Hamiltonian.Also,we derive the exact upper limit of physical energy level.Further,we derive some recursive relations for energy matrix elements of the potential and other similar operators in the context of Morse oscillator theory.展开更多
After the design of aerospace products is completed,a manufacturability assessment needs to be conducted based on 3D model's features in terms of modeling quality and process design,otherwise the cost of design ch...After the design of aerospace products is completed,a manufacturability assessment needs to be conducted based on 3D model's features in terms of modeling quality and process design,otherwise the cost of design changes will increase.Due to the poor structure and low reusability of product manufacturing feature information and assessment knowledge in the current aerospace product manufacturability assessment process,it is difficult to realize automated manufacturability assessment.To address these issues,a domain ontology model is established for aerospace product manufacturability assessment in this paper.On this basis,a structured representation method of manufacturability assessment knowledge and a knowledge graph data layer construction method are proposed.Based on the semantic information and association information expressed by the knowledge graph,a rule matching method based on subgraph matching is proposed to improve the precision and recall.Finally,applications and experiments based on the software platform verify the effectiveness of the proposed knowledge graph construction and rule matching method.展开更多
BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy,despite the growing recognition of the importance of bone health in individuals with epilepsy.Associations on...BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy,despite the growing recognition of the importance of bone health in individuals with epilepsy.Associations one statistical method for finding correlations between variables in big datasets is called association rule mining(ARM).This technique finds patterns of common items or events in the data set,including associations.Through the analysis of patient data,including demographics,genetic information,and reactions with previous treatments,ARM can identify harmful drug reactions,possible novel combinations of medicines,and trends which connect particular individual features to treatment outcomes.AIM To investigate the evidence on the effects of anti-epileptic drugs(AEDs)on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique.METHODS ARM technique was used to analyze patients’behavior on calcium metabolism,vitamin D and anti-epileptic medicines.Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study.There were three patient groups:Group 1 received one AED,group 2 received two AEDs,and group 3 received more than two AEDs.The researchers analyzed the alkaline phosphatase,ionized calcium,total calcium,phosphorus,vitamin D levels,or parathyroid hormone values.RESULTS A total of 150 patients,aged 12 years to 60 years,were studied,with 50 in each group(1,2,and 3).60%were men,this gender imbalance may affect the study’s findings,as women have different bone metabolism dynamics influenced by hormonal variations,including menopause.The results may not fully capture the distinct effects of AEDs on female patients.A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs.86 patients had generalized epilepsy,64 partial.42%of patients had AEDs for>5 years.Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy.Polytherapy elevated alkaline phosphatase and phosphorus levels.CONCLUSION ARM revealed the possible effects of variables like age,gender,and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.展开更多
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio...The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.展开更多
The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legisla...The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.展开更多
Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retro...Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retrospective analysis was conducted on the diagnosis and treatment data of 104 children with ASD complicated with sleep disorder admitted to Xi’an Traditional Chinese Medicine(TCM)Encephalopathy Hospital from January 2022 to December 2024.Cross-pattern main acupoints were screened via frequency statistics,chi-square test,and factor analysis;pattern-specific auxiliary acupoints were extracted by combining multiple correspondence analysis,cluster analysis,and association rule mining.Results:Ten cross-pattern main acupoints(Baihui,Sishenzhen,Language Area 1,Language Area 2,Neiguan,Shenmen,Yongquan,Xuanzhong)were identified,and acupoint combination schemes for four major TCM patterns(Hyperactivity of Liver and Heart Fire,Deficiency of Kidney Essence,Deficiency of Both Heart and Spleen,Hyperactivity of Liver with Spleen Deficiency)were established.Conclusion:Acupuncture treatment should follow the principle of“regulating spirit and calming the brain as the root,and dredging collaterals based on pattern differentiation as the branch”.The synergy between main and auxiliary acupoints can accurately regulate the disease,providing a basis for precise clinical treatment.展开更多
Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF...Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF inputs and outputs,neglecting the associations between smelting operations and RMC.Traditional methods of reducing RMC rely on manual experience and lack a standard operation guidance.A method based on association rules mining and metallurgical mechanism(ARM-MM)was proposed.ARM-MM proposed an improved evaluation indicator of RMC and the indicator independently showed the associations between smelting operations and RMC.On the basis,1265 heats of real EAF data were used to obtain the operation guidance for RMC reduction.According to the ratio of hot metal(HM)in charge metals,data were divided into all dataset,low HM ratio dataset,medium HM ratio dataset,and high HM ratio dataset.ARM algorithm was used in each dataset to obtain specific operation guidance.The real average RMC under all dataset,medium HM ratio dataset,and high HM ratio dataset was reduced by 279,486,and 252 kg/heat,respectively,when obtained operation guidance was applied.展开更多
Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,th...Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.展开更多
Bergmann's rule predicts that the larger of two homeotherm species differing only in size would occur at higher latitudes, or in cooler climates than the smaller, because of relative thermoregulatory costs in rela...Bergmann's rule predicts that the larger of two homeotherm species differing only in size would occur at higher latitudes, or in cooler climates than the smaller, because of relative thermoregulatory costs in relation to body mass/surface area ratio. Individual tracking data from two congeneric long-distance migratory northern nesting swan species, Tundra Cygnus columbianus (TS, n = 99) and Whooper Swans C. cygnus (WS, 61–71% larger mass than TS, n = 47) were used to determine their summering and wintering latitudes along similar migration routes and common staging areas along the same flyway. We hypothesised that throughout Arctic and Boreal breeding areas (10℃ in July), summer ambient temperatures mainly exceed the Lower Critical Temperatures (LCT, c. 1℃) for both swan species, so the duration of the snow-free summer period will favour smaller body size at highest latitudes, since this constrains the time available to lay, incubate eggs and raise cygnets to fledging. We hypothesised that in contrast, in winter, both species occur in temperatures near to freezing (−3℃ in January), below their respective LCT, so differential thermoregulation demands would constrain TS to winter south of WS. Tracking of individuals showed for the first time that while smaller TS summered significantly north of WS, WS wintered significantly north of TS, with limited overlap in both seasons. We conclude that differences in relative summer distribution of these two closely related migratory herbivores are not to do with latitude per se but are constrained by the time both species require to raise their young to fledging during the short northern summer, when thermoregulation costs are unlikely limiting. In winter, both swan species occur within a climate envelop at or below their respective LCT and smaller TS occurred consistently south of the range of the tracked WS, as predicted by Bergmann's rule.展开更多
This paper empirically studies the impact mechanism of the depth of digital trade rules on China’s digital service trade exports and explores the improvement paths for China accordingly.Based on the transaction cost ...This paper empirically studies the impact mechanism of the depth of digital trade rules on China’s digital service trade exports and explores the improvement paths for China accordingly.Based on the transaction cost theory and other foundations,this paper systematically classifies rule provisions into four categories:access and facilitation,cross-border data flow,digital intellectual property rights,and privacy protection and data security.It also uses the gravity model of trade to quantitatively analyze 22 Regional Trade Agreements texts involving China.The empirical results show that:the depth of digital trade rules as a whole significantly promotes digital service trade exports;the core driving factors include the gap in digital infrastructure,differences in higher education levels,urbanization levels,and GDP gaps;all four categories of provisions show a significant positive impact,among which access and facilitation provisions have the most prominent promotional effect.Heterogeneity analysis further reveals that the depth of rules has a significantly stronger promotional effect on trade partners in developed countries than in developing countries;sector-specific tests show that the financial services sector benefits the most,while the intellectual property sector is inhibited.Based on this,this paper proposes that China should actively participate in the construction of global rules,improve digital infrastructure,deepen the implementation of provisions,orderly expand opening-up in the digital field,strengthen intellectual property protection to balance innovation incentives and market expansion,and improve laws and regulations to ensure data security.展开更多
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...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).展开更多
Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional...Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.展开更多
To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on be...To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.展开更多
文摘The“notice-necessary measures”rule began with the“notice-and-takedown”rule,which originated from the United States safe harbors rule,established to limit the liability of Internet Service Providers(ISPs)for helping infringement in network infringement.China’s Tort Liability Law and Civil Code amended the“notice-and-takedown”rule so that when the right holder sends an effective notice of infringement to the ISP,the ISP shall take necessary measures more than removal to be exempted from liability.This article will discuss the transformation of the rule and types of necessary measures,and reconstruct the relationship between effectiveness of notice and types of necessary measures.
基金A research grant from the Multimedia University,Malaysia supports this work。
文摘Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy.In this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify intrusions.The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the environment.Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty.Also,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical IDS.Under extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional models.Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion detection.This research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.
文摘This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.
基金the achievements of the 2025 Guangxi Degree and Graduate Education Reform Project on Artificial Intelligence Empowering International Business Master’s Teaching Innovation Research(JGY2025036).
文摘Against the backdrop of profound reshaping of the current international landscape and the unprecedentedly rapid iteration of digital technologies,the global digital economy landscape and international trade rule system are entering a new era full of changes.Digital trade rules have emerged as a prominent focus in this field.Firstly,through a comprehensive review of sample data such as the WTO e-commerce proposals under the current multilateral framework,we can gain a profound insight into the negotiation practices of major economies regarding digital trade rules.In this context,China has demonstrated strong defensive interests in the“emerging”issues of digital trade,aiming to safeguard national data security and promote the healthy development of the digital economy.Therefore,when participating in the formulation of global digital trade rules,China needs to accurately strike a balance between offense and defense,contribute Chinese wisdom and solutions,and promote the construction of a fairer,more reasonable,and inclusive international digital trade governance system.
基金supported by the National Natural Science Foundation of China(No.10874174)。
文摘We propose the scaling rule of Morse oscillator,based on this rule and by virtue of the Her-mann-Feymann theorem,we respectively obtain the distribution of potential and kinetic ener-gy of the Morse Hamiltonian.Also,we derive the exact upper limit of physical energy level.Further,we derive some recursive relations for energy matrix elements of the potential and other similar operators in the context of Morse oscillator theory.
基金Sponsored by the National Key Research and Development Program from Ministry of Science and Technology of the People's Republic of China (Grant No.2020YFB1711403)。
文摘After the design of aerospace products is completed,a manufacturability assessment needs to be conducted based on 3D model's features in terms of modeling quality and process design,otherwise the cost of design changes will increase.Due to the poor structure and low reusability of product manufacturing feature information and assessment knowledge in the current aerospace product manufacturability assessment process,it is difficult to realize automated manufacturability assessment.To address these issues,a domain ontology model is established for aerospace product manufacturability assessment in this paper.On this basis,a structured representation method of manufacturability assessment knowledge and a knowledge graph data layer construction method are proposed.Based on the semantic information and association information expressed by the knowledge graph,a rule matching method based on subgraph matching is proposed to improve the precision and recall.Finally,applications and experiments based on the software platform verify the effectiveness of the proposed knowledge graph construction and rule matching method.
文摘BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy,despite the growing recognition of the importance of bone health in individuals with epilepsy.Associations one statistical method for finding correlations between variables in big datasets is called association rule mining(ARM).This technique finds patterns of common items or events in the data set,including associations.Through the analysis of patient data,including demographics,genetic information,and reactions with previous treatments,ARM can identify harmful drug reactions,possible novel combinations of medicines,and trends which connect particular individual features to treatment outcomes.AIM To investigate the evidence on the effects of anti-epileptic drugs(AEDs)on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique.METHODS ARM technique was used to analyze patients’behavior on calcium metabolism,vitamin D and anti-epileptic medicines.Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study.There were three patient groups:Group 1 received one AED,group 2 received two AEDs,and group 3 received more than two AEDs.The researchers analyzed the alkaline phosphatase,ionized calcium,total calcium,phosphorus,vitamin D levels,or parathyroid hormone values.RESULTS A total of 150 patients,aged 12 years to 60 years,were studied,with 50 in each group(1,2,and 3).60%were men,this gender imbalance may affect the study’s findings,as women have different bone metabolism dynamics influenced by hormonal variations,including menopause.The results may not fully capture the distinct effects of AEDs on female patients.A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs.86 patients had generalized epilepsy,64 partial.42%of patients had AEDs for>5 years.Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy.Polytherapy elevated alkaline phosphatase and phosphorus levels.CONCLUSION ARM revealed the possible effects of variables like age,gender,and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.
基金supported by the Natural Science Foundation of China(Nos.U22A2099,62273113,62203461,62203365)the Innovation Project of Guangxi Graduate Education under Grant YCBZ2023130by the Guangxi Higher Education Undergraduate Teaching Reform Project Key Project,grant number 2022JGZ130.
文摘The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.
基金This paper is a phased result of the“Research on the Issue of China’s Data Export System”(24SFB3035)a research project of the Ministry of Justice of China on the construction of the rule of law and the study of legal theories at the ministerial level in 2024.
文摘The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.
基金Song Hujie’s Inheritance Studio of National Renowned Traditional Chinese Medicine Experts.
文摘Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retrospective analysis was conducted on the diagnosis and treatment data of 104 children with ASD complicated with sleep disorder admitted to Xi’an Traditional Chinese Medicine(TCM)Encephalopathy Hospital from January 2022 to December 2024.Cross-pattern main acupoints were screened via frequency statistics,chi-square test,and factor analysis;pattern-specific auxiliary acupoints were extracted by combining multiple correspondence analysis,cluster analysis,and association rule mining.Results:Ten cross-pattern main acupoints(Baihui,Sishenzhen,Language Area 1,Language Area 2,Neiguan,Shenmen,Yongquan,Xuanzhong)were identified,and acupoint combination schemes for four major TCM patterns(Hyperactivity of Liver and Heart Fire,Deficiency of Kidney Essence,Deficiency of Both Heart and Spleen,Hyperactivity of Liver with Spleen Deficiency)were established.Conclusion:Acupuncture treatment should follow the principle of“regulating spirit and calming the brain as the root,and dredging collaterals based on pattern differentiation as the branch”.The synergy between main and auxiliary acupoints can accurately regulate the disease,providing a basis for precise clinical treatment.
基金supported by National Natural Science Foundation of China(Nos.52174328 and 52474368)Fundamental Research Funds for Central Universities of Central South University(Nos.2022ZZTS0084 and 2024ZZTS0062).
文摘Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF inputs and outputs,neglecting the associations between smelting operations and RMC.Traditional methods of reducing RMC rely on manual experience and lack a standard operation guidance.A method based on association rules mining and metallurgical mechanism(ARM-MM)was proposed.ARM-MM proposed an improved evaluation indicator of RMC and the indicator independently showed the associations between smelting operations and RMC.On the basis,1265 heats of real EAF data were used to obtain the operation guidance for RMC reduction.According to the ratio of hot metal(HM)in charge metals,data were divided into all dataset,low HM ratio dataset,medium HM ratio dataset,and high HM ratio dataset.ARM algorithm was used in each dataset to obtain specific operation guidance.The real average RMC under all dataset,medium HM ratio dataset,and high HM ratio dataset was reduced by 279,486,and 252 kg/heat,respectively,when obtained operation guidance was applied.
基金supported in part by the National Natural Science Foundation of China(Nos.62202234,62401270)the China Postdoctoral Science Foundation(No.2023M741778)the Natural Science Foundation of Jiangsu Province(Nos.BK20240706,BK20240694).
文摘Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.
基金supported by the Joint Chinese Academy of Sciences(CAS)-Max Planck Society(MPG)Research Project(HZXM20225001MI)the China Biodiversity Observation Networks(Sino BON)+1 种基金the Ministry of the Environment of Japan(ME20080401,ME20090401)the US Geological Survey(Grant No.07WRAG0003,G09AC00046).
文摘Bergmann's rule predicts that the larger of two homeotherm species differing only in size would occur at higher latitudes, or in cooler climates than the smaller, because of relative thermoregulatory costs in relation to body mass/surface area ratio. Individual tracking data from two congeneric long-distance migratory northern nesting swan species, Tundra Cygnus columbianus (TS, n = 99) and Whooper Swans C. cygnus (WS, 61–71% larger mass than TS, n = 47) were used to determine their summering and wintering latitudes along similar migration routes and common staging areas along the same flyway. We hypothesised that throughout Arctic and Boreal breeding areas (10℃ in July), summer ambient temperatures mainly exceed the Lower Critical Temperatures (LCT, c. 1℃) for both swan species, so the duration of the snow-free summer period will favour smaller body size at highest latitudes, since this constrains the time available to lay, incubate eggs and raise cygnets to fledging. We hypothesised that in contrast, in winter, both species occur in temperatures near to freezing (−3℃ in January), below their respective LCT, so differential thermoregulation demands would constrain TS to winter south of WS. Tracking of individuals showed for the first time that while smaller TS summered significantly north of WS, WS wintered significantly north of TS, with limited overlap in both seasons. We conclude that differences in relative summer distribution of these two closely related migratory herbivores are not to do with latitude per se but are constrained by the time both species require to raise their young to fledging during the short northern summer, when thermoregulation costs are unlikely limiting. In winter, both swan species occur within a climate envelop at or below their respective LCT and smaller TS occurred consistently south of the range of the tracked WS, as predicted by Bergmann's rule.
文摘This paper empirically studies the impact mechanism of the depth of digital trade rules on China’s digital service trade exports and explores the improvement paths for China accordingly.Based on the transaction cost theory and other foundations,this paper systematically classifies rule provisions into four categories:access and facilitation,cross-border data flow,digital intellectual property rights,and privacy protection and data security.It also uses the gravity model of trade to quantitatively analyze 22 Regional Trade Agreements texts involving China.The empirical results show that:the depth of digital trade rules as a whole significantly promotes digital service trade exports;the core driving factors include the gap in digital infrastructure,differences in higher education levels,urbanization levels,and GDP gaps;all four categories of provisions show a significant positive impact,among which access and facilitation provisions have the most prominent promotional effect.Heterogeneity analysis further reveals that the depth of rules has a significantly stronger promotional effect on trade partners in developed countries than in developing countries;sector-specific tests show that the financial services sector benefits the most,while the intellectual property sector is inhibited.Based on this,this paper proposes that China should actively participate in the construction of global rules,improve digital infrastructure,deepen the implementation of provisions,orderly expand opening-up in the digital field,strengthen intellectual property protection to balance innovation incentives and market expansion,and improve laws and regulations to ensure data security.
文摘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).
基金National Natural Science Foundation of China Nos.61962054 and 62372353.
文摘Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.
基金This work was supported by the Youth Foundation of National Science Foundation of China(62001503)the Special Fund for Taishan Scholar Project(ts 201712072).
文摘To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.
