A continuous time delay-difference type model(CTDDM) applied to stock assessment of the southern Atlantic albacore Thunnus alalunga

A continuous time delay-difference model(CTDDM) has been established that considers continuous time delays of biological processes.The southern Atlantic albacore(Thunnus alalunga) stock is the one of the commercially important tuna population in the marine world.The age structured production model(ASPM) and the surplus production model(SPM) have already been used to assess the albacore stock.However,the ASPM requires detailed biological information and the SPM lacks the biological realism.In this study,we focus on applying a CTDDM to the southern Atlantic albacore(T.alalunga) species,which provides an alternative method to assess this fishery.It is the first time that CTDDM has been provided for assessing the Atlantic albacore(T.alalunga) fishery.CTDDM obtained the 80%confidence interval of MSY(maximum sustainable yield) of(21 510 t,23 118 t).The catch in 2011(24 100 t) is higher than the MSY values and the relative fishing mortality ratio(F_(2011)/F_(MSY)) is higher than 1.0.The results of CTDDM were analyzed to verify the proposed methodology and provide reference information for the sustainable management of the southern Atlantic albacore stock.The CTDDM treats the recruitment,the growth,and the mortality rates as all varying continuously over time and fills gaps between ASPM and SPM in this stock assessment.

Almost sure, L1-and L2-growth behavior of supercritical multi-type continuous state and continuous time branching processes with immigration

Under a first order moment condition on the immigration mechanism,we show that an appropriately scaled supercritical and irreducible multi-type continuous state and continuous time branching process with immigration(CBI process)converges almost surely.If an x log(x)moment condition on the branching mechanism does not hold,then the limit is zero.If this x log(x)moment condition holds,then we prove L1 convergence as well.The projection of the limit on any left non-Perron eigenvector of the branching mean matrix is vanishing.If,in addition,a suitable extra power moment condition on the branching mechanism holds,then we provide the correct scaling for the projection of a CBI process on certain left non-Perron eigenvectors of the branching mean matrix in order to have almost sure and L1 limit.Moreover,under a second order moment condition on the branching and immigration mechanisms,we prove L2 convergence of an appropriately scaled process and the above-mentioned projections as well.A representation of the limits is also provided under the same moment conditions.

Chung-type Law of the Iterated Logarithm for Continuous Time Random Walk

A continuous time random walk is a random walk subordinated to a renewal process used in physics to model anomalous diffusion. In this paper, we establish a Chung-type law of the iterated logarithm for continuous time random walk with jumps and waiting times in the domains of attraction of stable laws.

Anomalous transport in fluid fleld with random waiting time depending on the preceding jump length

Anomalous(or non-Fickian) transport behaviors of particles have been widely observed in complex porous media.To capture the energy-dependent characteristics of non-Fickian transport of a particle in flow fields,in the present paper a generalized continuous time random walk model whose waiting time probability distribution depends on the preceding jump length is introduced,and the corresponding master equation in Fourier-Laplace space for the distribution of particles is derived.As examples,two generalized advection-dispersion equations for Gaussian distribution and levy flight with the probability density function of waiting time being quadratic dependent on the preceding jump length are obtained by applying the derived master equation.

Timing theory continuous nursing,resistance training:Rehabilitation and mental health of caregivers and stroke patients with traumatic fractures

BACKGROUND Stroke is the leading cause of adult lifelong disability worldwide.A stroke is an acute cerebrovascular disease with a variety of causes and corresponding clinical symptoms.Around 75%of surviving stroke patients experience impaired nerve function,and some suffer from traumatic fractures,which can lead to special care needs.AIM To determine the effect of timing theory continuous care,with resistance training,on the rehabilitation and mental health of caregivers and stroke patients with traumatic fractures.METHODS Between January 2017 to March 2021,we selected 100 hospital admissions with post-stroke hemiplegia complicated with a traumatic fracture.Two participant groups were created:(1)Control group:given resistance training;and(2)Observation group:given timing theory continuous care combined with resistance training.The degree of satisfaction and differences in bone and phosphorus metabolism indexes between the two groups were compared.The self-perceived burden scale(SPBS)and caregiver burden questionnaire were used to evaluate the psychological health of patients and caregivers.The Harris hip function score,ability of daily living(ADL)scale,and global quality of life questionnaire(GQOL-74)were used to evaluate hip function,ability of daily living,and quality of life.RESULTS Data were collected prior to and after intervention.Alkaline phosphatase(ALP),osteocalcin,and vitamin D3 in the observation group and control group increased after intervention(P<0.05),and carboxy-terminal peptide of type I collagenβSpecial sequence(β-CTX)decreased(P<0.05).ALP and osteocalcin in the observation group were higher than in the control group(P<0.05).There was no significant difference inβ-CTX and vitamin D3 between the two groups(P>0.05).The SPBS score of the observation group was lower and the ADL score was higher than the control group.The burden score was lower and the Harris hip function and GQOL-74 scores were higher than that of the control group(P<0.05).The observation group’s satisfaction rating was 94.00%,which was higher than the rating from the control group(P<0.05).CONCLUSION Timing theory continuous nursing with resistance training can reduce hip dysfunction in stroke patients with a traumatic fracture and enhance quality of life and mental health of patients and caregivers.

Formal management-specifying approach for model-based safety assessment

In the field of model-based system assessment,mathematical models are used to interpret the system behaviors.However,the industrial systems in this intelligent era will be more manageable.Various management operations will be dynamically set,and the system will be no longer static as it is initially designed.Thus,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the dependability.There mainly exists three problems.Complex:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same.Motivated by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be re-suable.Finally,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Reliability analysis for wireless communication networks via dynamic Bayesian network

The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks.As one of the most popular modeling methodologies,the dynamic Bayesian network(DBN)is proposed.However,it is insufficient for the wireless communication network which contains temporal and non-temporal events.To this end,we present a modeling methodology for a generalized continuous time Bayesian network(CTBN)with a 2-state conditional probability table(CPT).Moreover,a comprehensive reliability analysis method for communication devices and radio propagation is suggested.The proposed methodology is verified by a reliability analysis of a real wireless communication network.

Reliability Analysis of Satellite Turntable System under Multiple Operation Modes Based on Multi-Valued Decision Diagrams

As a payload support system deployed on satellites,the turntable system is often switched among different working modes during the on-orbit operation,which can experience great state changes.In each mode,the missions to be completed are different,consecutive and non-over-lapping,from which the turntable system can be considered to be a phased-mission system(PMS).Reliability analysis for PMS has been widely studied.However,the system mode cycle characteristic has not been taken into account before.In this paper,reliability analysis method of the satellite turntable system is proposed considering its multiple operation modes and mode cycle characteristic.Firstly,the multi-valued decision diagrams(MDD)manipulation rules between two adjacent mission cycles are proposed.On this basis,MDD models for the turntable system in different states are established and the reliability is calculated using the continuous time Markov chains(CTMC)method.Finally,the comparative study is carried out to show the effectiveness of our proposed method.

天气衍生品气温预测模型对比研究

气温衍生品是天气衍生品交易中最活跃的合约之一,确定合理预测气温动态变化的模型,是气温衍生品开发设计的基础。考虑到气温在时间变化上具有趋势性、季节性和周期性等特点,文中使用了以O-U均值回复过程为基础的Continuous Time Autoregressive Model(CAR)模型、Seasonal Autoregressive Integrated Moving Average(SARIMA)模型和小波神经网络算法,并选择漠河、北京、乌鲁木齐、芜湖、昆明和海口具有地域性代表的城市气温进行拟合,使用无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差检验指标检验了模型的预测精度。研究结果表明,小波神经网络算法在预测6个城市的无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差的值最小;同时,相比CAR模型、SARIMA模型,其预测效果最优。因此,小波神经网络算法能够很好地拟合气温数据的变化,可以为我国气温天气衍生品的定价提供一定的指导。

AVAILABILITY MODEL FOR SELF TEST AND REPAIR IN FAULT TOLERANT FPGA-BASED SYSTEMS

Dynamically reconfigurable Field Programmable Gate Array(dr-FPGA) based electronic systems on board mission-critical systems are highly susceptible to radiation induced hazards that may lead to faults in the logic or in the configuration memory. The aim of our research is to characterize self-test and repair processes in Fault Tolerant(FT) dr-FPGA systems in the presence of environmental faults and explore their interrelationships. We develop a Continuous Time Markov Chain(CTMC) model that captures the high level fail-repair processes on a dr-FPGA with periodic online Built-In Self-Test(BIST) and scrubbing to detect and repair faults with minimum latency. Simulation results reveal that given an average fault interval of 36 s, an optimum self-test interval of 48.3 s drives the system to spend 13% of its time in self-tests, remain in safe working states for 76% of its time and face risky fault-prone states for only 7% of its time. Further, we demonstrate that a well-tuned repair strategy boosts overall system availability, minimizes the occurrence of unsafe states, and accommodates a larger range of fault rates within which the system availability remains stable within 10% of its maximum level.

Study on Temporal and Spatial Psychological Transformation Mechanism of Landscape Perception

From the perspective of phenomenism,people pay more attention to the causal relationship of the place,the overall atmosphere of space and the experience of time process,and the interaction between the body and the environment in the perception of landscape.Timeliness has become an important dimension of landscape perception.Based on the basic principles of landscape phenomenology,the temporal characteristics of landscape perception were studied,and the different ways and categories of time perception were summarized.The coupling mechanism of the senses of time and space was analyzed,and the temporal and spatial psychological conversion mechanism was further revealed.

A Stochastic SIVS Epidemic Model Based on Birth and Death Process

A new stochastic epidemic model, that is, a general continuous time birth and death chain model, is formulated based on a deterministic model including vaccination. We use continuous time Markov chain to construct the birth and death process. Through the Kolmogorov forward equation and the theory of moment generating function, the corresponding population expectations are studied. The theoretical result of the stochastic model and deterministic version is also given. Finally, numerical simulations are carried out to substantiate the theoretical results of random walk.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

We propose a probabilistic approach to modelling the propagation of the coronavirus disease 2019 (COVID-19) in Madagascar, with all its specificities. With the strategy of the Malagasy state, which consists of isolating all suspected cases and hospitalized confirmed case, we get an epidemic model with seven compartments: susceptible (S), Exposed (E), Infected (I), Asymptomatic (A), Hospitalized (H), Cured (C) and Death (D). In addition to the classical deterministic models used in epidemiology, the stochastic model offers a natural representation of the evolution of the COVID-19 epidemic. We inferred the models with the official data provided by the COVID-19 Command Center (CCO) of Madagascar, between March and August 2020. The basic reproduction number R0 and the other parameters were estimated with a Bayesian approach. We developed an algorithm that allows having a temporal estimate of this number with confidence intervals. The estimated values are slightly lower than the international references. Generally, we were able to obtain a simple but effective model to describe the spread of the disease.

A 28/56 Gb/s NRZ/PAM-4 dual-mode transceiver with 1/4 rate reconfigurable 4-tap FFE and half-rate slicer in a 28-nm CMOS

A 28/56 Gb/s NRZ/PAM-4 dual-mode transceiver(TRx)designed in a 28-nm complementary metal-oxide-semiconduc-tor(CMOS)process is presented in this article.A voltage-mode(VM)driver featuring a 4-tap reconfigurable feed-forward equal-izer(FFE)is employed in the quarter-rate transmitter(TX).The half-rate receiver(RX)incorporates a continuous-time linear equal-izer(CTLE),a 3-stage high-speed slicer with multi-clock-phase sampling,and a clock and data recovery(CDR).The experimen-tal results show that the TRx operates at a maximum speed of 56 Gb/s with chip-on board(COB)assembly.The 28 Gb/s NRZ eye diagram shows a far-end vertical eye opening of 210 mV with an output amplitude of 351 mV single-ended and the 56 Gb/s PAM-4 eye diagram exhibits far-end eye opening of 33 mV(upper-eye),31 mV(mid-eye),and 28 mV(lower-eye)with an output amplitude of 353 mV single-ended.The recovered 14 GHz clock from the RX exhibits random jitter(RJ)of 469 fs and deterministic jitter(DJ)of 8.76 ps.The 875 Mb/s de-multiplexed data features 593 ps horizontal eye opening with 32.02 ps RJ,at bit-error rate(BER)of 10-5(0.53 UI).The power dissipation of TX and RX are 125 and 181.4 mW,respectively,from a 0.9-V sup-ply.

Research on the logic monitoring method for the cloud computing based safety computer

With the development of railway construction in China,the computing demand of the train control system is increasing day by day.The application of cloud computing technology on the rail transit signal system has become a research hotspot in recent years.How to improve the safety and availability of the safety computer platform in the cloud computing environment is the key problem when applying cloud computing to the train operation control system.Since the cloud platform is in an open network environment,fac-ing many security vulnerabilities and malicious network attacks,it is necessary to monitor the operation of computer programmes through edge safety nodes.Firstly,this paper encrypts the logical monitoring method,and then proposes a secure computer de fence model based on the dynamic heterogeneous redundancy structure.Then the continuous time Markov chain(CTMC)is used to quantitatively solve the stable probability of the system,and the influence of different logical monitoring methods on the anti-attack performance of the system is analysed.Finally,the experiment proves that the dynamic heterogeneous redundancy structure composed of encryption logic monitoring can guarantee the safe and stable operation of the safety computer more effectively.

Research on anti-attack performance of a private cloud safety computer based on the Markov-Percopy dynamic heterogeneous redundancy structure

With the increasing computing demand of train operation control systems,the application of cloud computing technology on safety computer platforms of train control system has become a research hotspot in recent years.How to improve the safety and availability of private cloud safety computers is the key problem when applying cloud computing to train operation control systems.Because the cloud computing platform is in an open network environment,it can face many security loopholes and malicious network at-tacks.Therefore,it is necessary to change the existing safety computer platform structure to improve the attack resistance of the private cloud safety computer platform,thereby enhancing its safety and reliability.Firstly,a private cloud safety computer platform architecture based on dynamic heterogeneous redundant(DHR)structure is proposed,and a dynamic migration mechanism for heterogeneous executives is designed.Then,a generalized stochastic Petri net(GSPN)model of a private cloud safety computer platform based on DHR is established,and its steady-state probability is solved by using its isomorphism with the continuous-time Markov model(CTMC)to analyse the impact of different system structures and executive migration mechanisms on the system's anti-attack performance.Finally,through experimental verifcation,the system structure proposed in this paper can improve the anti-attack capability of the private cloud safety computer platform,thereby improving its safety and reliability.

Simplified Markov Model for Reliability Analysis of Phased-Mission System Using States Merging Method

This paper presents a simplified Markov model to evaluate the reliability of phased-mission system(PMS). The time cost and storage requirement are very huge for traditional Markov model to analyze the PMS reliability as the number of components increases to a large scale. The states merging method proposed in this paper can account for the PMS with subsystems consisting of identical components, and similar PMSs are common in real-world systems. The simplified Markov model by states merging has smaller number of system states, compared with the traditional one. Furthermore, for the above subsystems, the size of our model increases only linearly as the number of components increases, while the size of the traditional model exponentially increases.Finally, the effectiveness and correctness of our approach are analyzed by comparing with the traditional Markov method.

Disjoint-Path Routing Mechanism in Mobile Opportunistic Networks

The prevalent multi-copy routing algorithms in mobile opportunistic networks(MONs)easily cause network congestion.This paper introduces a disjoint-path(DP)routing algorithm,where each node can only transmit packets once except the source node,to effectively control the number of packet copies in the network.The discrete continuous time Markov chain(CTMC)was utilized to analyze the state transition between nodes,and the copy numbers of packets with the DP routing algorithm were calculated.Simulation results indicate that DP has a great improvement in terms of packet delivery ratio,average delivery delay,average network overhead,energy and average hop count.