Capacitated stochastic coloured Petri net-based approach for computing two-terminal reliability of multi-state network

Classical network reliability problems assume both net- works and components have only binary states, fully working or fully failed states. But many actual networks are multi-state, such as communication networks and transportation networks. The nodes and arcs in the networks may be in intermediate states which are not fully working either fully failed. A simulation ap- proach for computing the two-terminal reliability of a multi-state network is described. Two-terminal reliability is defined as the probability that d units of demand can be supplied from the source to sink nodes under the time threshold T. The capacities of arcs may be in a stochastic state following any discrete or continuous distribution. The transmission time of each arc is also not a fixed number but stochastic according to its current capacity and de- mand. To solve this problem, a capacitated stochastic coloured Petri net is proposed for modelling the system behaviour. Places and transitions respectively stand for the nodes and arcs of a net- work. Capacitated transition and self-modified token colour with route information are defined to describe the multi-state network. By the simulation, the two-terminal reliability and node importance can be estimated and the optimal route whose reliability is highest can also be given. Finally, two examples of different kinds of multi- state networks are given.

Development of diabetic complications and influencing factors among 32653 type 2 diabetes patients:retrospective cohort study using a multi-state Markov

Background:Patients with type 2 diabetes are at high risk for developing multiple chronic complications.However,there is a lack of studies of the cumulative number of diabetic complications in China.Methods:A retrospective cohort study was performed from 2009 to 2021.Type 2 diabetes patients who were first diagnosed after the age of 35 years between January 1,2009,and December 31,2017,were included.Five states were defined according to the number of chronic complications:no(S0),one(S1),two(S2),three(S3),and four or more complications(S4).A multi-state Markov model was constructed to estimate transition probability,transition intensity,mean sojourn time,and the possible factors for each state.Results:The study included 32653 type 2 diabetes patients(mean age,59.59 years;15929(48.8%)male),and mean follow-up time of 7.75 years.In all,4375 transitions were observed.The 12-year transition probability of from state S0 to S1 was the lowest at 16.4%,while that from S2 to S3 was the highest,at 45.6%.Higher fasting blood glucose,lower high-density lipoprotein cholesterol,higher total cholesterol,and an unhealthy diet were associated with higher risk of progression from S0 to S1.Being female,less than 60 years old,weekly physical activity,and vegetarian diet decreased this risk.Being female and less than 60 years old reduced the likelihood of transition from S1 to S2,whereas lower high-density lipoprotein cholesterol increased this likelihood.Conclusions:Following the occurrence of two complications in type 2 diabetes patients,the risk for accumulating a third complication within a short time is significantly increased.It is important to take advantage of the stable window period when patients have fewer than two complications,strengthen the monitoring of blood glucose and blood lipids,and encourage patients to maintain good living habits to prevent further deterioration.

AN IMPROVED ALGORITHM FOR SOLVING ALL D-MPS IN MULTI-STATE NETWORKS

Reliability is a desirable performance indicator of many real-world systems to measure the quality level. One general method for evaluating multi-state reliability is using d-minimal paths （d-MPs）. However, being an NP-hard problem, searching for all d-MPs is a rather challenging task. This paper proposes an improved algorithm to solve the d-MP problem. To reduce the search space of d-MPs, a concept of lower capacity bound is introduced into the d-MP problem, and an effective technique is developed to fred lower capacity bounds. Meanwhile, the fast enumeration method which is a recent improvement to the traditional enunaeration method is employed to solve d-MPs. In addition, by introducing the operation of transforming undirected edges into directed edges, the proposed algorithm is applicable to solving both directed networks and undirected networks. Through numerical experiments, it is found that the proposed algorithm holds a distinct advantage over the existing methods in solving all d-MPs.

Reliability Modeling and Evaluation of Complex Multi-State System Based on Bayesian Networks Considering Fuzzy Dynamic of Faults

In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods,this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness.The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function.Based on the solving characteristics of the dynamic fuzzy set and Bayesian network,the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes are solved.Finally,through the dynamic fuzzy reliability analysis of CNC machine tool hydraulic system balance circuit,the application of this method in system reliability evaluation is verified,which provides support for fault diagnosis of CNC machine tools.

A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data:An Application to COVID-19

Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.

Optimal transmission lines assignment with maximal reliabilities in multi-source multi-sink multi-state computer network

The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

A voltage support control strategy based on three-port flexible multi-state switch in distribution networks

Voltage sags in power system may lead to serious problems such as the off-grid of distributed generation and electrical equipment failures.As a novel type of power electronic equipment,a flexible multi-state switch(FMSS)is capable to support the voltage during the grid faults.In this paper,a voltage control strategy to support the voltage in a distribution network is proposed by introducing three-port FMSS.The positive-negative-sequence compensation(PNSC)scheme is adopted to control the active and reactive current.This control scheme eliminates active power oscillations at the port of voltage sags and reduces coupling oscillations of other ports.Based on the characteristics of the voltage support under PNSC scheme,two voltage support strategies are proposed.A proportional-integral controller is introduced to provide the reactive power references,which eliminates the errors when estimating the grid voltage and impedance.A current limiting scheme is adopted to keep the port current in a safe range by adjusting the active and reactive power references.The voltage support strategies in two different voltage sags are simulated,and results show the feasibility and effectiveness of the proposed control strategies.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Reliability analysis of monotone coherent multi-state systems based on Bayesian networks

The Bayesian networks (BNs) provide a robust probabilistic method of reasoning under uncertainty and have been successfully applied to a variety of real-world tasks. Aiming to explore the capabilities of the BN formalism in reliability analysis of monotone coherent multi-state systems, the BNs are compared with a popular tool for reliability analysis of monotone coherent multi-state systems, namely the multi-state fault trees (MFTs). It is shown that any MFT can be directly mapped into BN and the basic inference techniques on the latter may be used to obtain classical parameters computed from the former (i.e. probability distribution of top variable, minimal upper vectors and maximum lower vectors for any performance level, importance measures of components). Furthermore, some additional information can be obtained by using BN, both at the modeling and analysis level. At the modeling level, several restrictive assumptions implicit in the MFT methodology can be removed and various kinds of dependencies among components can be accommodated. At the analysis level, a general diagnostic analysis can be performed. The comparison of these methods is illustrated by an example of the water supply system. ? 2016 Beijing Institute of Aerospace Information.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Multi-style Chord Music Generation Based on Artificial Neural Network

With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Geometric Error Identification of Gantry-Type CNC Machine Tool Based on Multi-Station Synchronization Laser Tracers

Laser tracers are a three-dimensional coordinate measurement system that are widely used in industrial measurement.We propose a geometric error identification method based on multi-station synchronization laser tracers to enable the rapid and high-precision measurement of geometric errors for gantry-type computer numerical control(CNC)machine tools.This method also improves on the existing measurement efficiency issues in the single-base station measurement method and multi-base station time-sharing measurement method.We consider a three-axis gantry-type CNC machine tool,and the geometric error mathematical model is derived and established based on the combination of screw theory and a topological analysis of the machine kinematic chain.The four-station laser tracers position and measurement points are realized based on the multi-point positioning principle.A self-calibration algorithm is proposed for the coordinate calibration process of a laser tracer using the Levenberg-Marquardt nonlinear least squares method,and the geometric error is solved using Taylor’s first-order linearization iteration.The experimental results show that the geometric error calculated based on this modeling method is comparable to the results from the Etalon laser tracer.For a volume of 800 mm×1000 mm×350 mm,the maximum differences of the linear,angular,and spatial position errors were 2.0μm,2.7μrad,and 12.0μm,respectively,which verifies the accuracy of the proposed algorithm.This research proposes a modeling method for the precise measurement of errors in machine tools,and the applied nature of this study also makes it relevant both to researchers and those in the industrial sector.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.