This article shows the probabilistic modeling of hydrocarbon spills on the surface of the sea, using climatology data of oil spill trajectories yielded by applying the lagrangian model PETROMAR-3D. To achieve this goa...This article shows the probabilistic modeling of hydrocarbon spills on the surface of the sea, using climatology data of oil spill trajectories yielded by applying the lagrangian model PETROMAR-3D. To achieve this goal, several computing and statistical tools were used to develop the probabilistic modeling solution based in the methodology of Guo. Solution was implemented using a databases approach and SQL language. A case study is presented which is based on a hypothetical spill in a location inside the Exclusive Economic Zone of Cuba. Important outputs and products of probabilistic modeling were obtained, which are very useful for decision-makers and operators in charge to face oil spill accidents and prepare contingency plans to minimize its effects. In order to study the relationship between the initial trajectory and the arrival of hydrocarbons spills to the coast, a new approach is introduced as an incoming perspective for modeling. It consists in storage in databases the direction of movement of the oil slick at the first 24 hours. The probabilistic modeling solution presented is of great importance for hazard studies of oil spills in Cuban coastal areas.展开更多
This study statistically evaluated the characteristics of induced earthquakes by geothermal power plants(GPPs)and generated a probabilistic model for simulating stochastic seismic events.Four well-known power plant zo...This study statistically evaluated the characteristics of induced earthquakes by geothermal power plants(GPPs)and generated a probabilistic model for simulating stochastic seismic events.Four well-known power plant zones were selected worldwide from the United States,Germany,France,and New Zealand.The operational condition information,as well as the corresponding earthquake catalogs recorded in the vicinity of GPPs,were gathered from their commencement date.The statistical properties of events were studied elaborately.By using this proposed database,a probabilistic model was developed capable of generating the number of induced seismic events per month,their magnitude,focal depth,and distance from the epicenter to the power plant,randomly.All of these parameters are simulated as a function of power plant injection rate.Generally speaking,the model,introduced in this study,is a tool for engineers and scientists interested in the seismic risk assessment of built environments prone to induced seismicity produced by GPPs operation.展开更多
Integration of acquired immunity into microbial risk assessment for illness incidence is of no doubt essential for the study of susceptibility to illness.In this study,a probabilistic model was set up as dose response...Integration of acquired immunity into microbial risk assessment for illness incidence is of no doubt essential for the study of susceptibility to illness.In this study,a probabilistic model was set up as dose response for infection and a mathematical derivation was carried out by integrating immunity to obtain probability of illness models.Temporary acquire immunity from epidemiology studies which includes six different Norovirus transmission scenarios such as symptomatic individuals infectious,pre-and post-symptomatic infectiousness(low and high),innate genetic resistance,genogroup 2 type 4 and those with no immune boosting by asymptomatic infection were evaluated.Simulated results on illness inflation factor as a function of dose and exposure indicated that high frequency exposures had immense immunity build up even at high dose levels;hence minimized the probability of illness.Using Norovirus transmission dynamics data,results showed,and immunity included models had a reduction of 2e6 logs of magnitude difference in disease burden for both population and individual probable illness incidence.Additionally,the magnitude order of illness for each dose response remained largely the same for all transmission scenarios;symptomatic infectiousness and no immune boosting after asymptomatic infectiousness also remained the same throughout.With integration of epidemiological data on acquired immunity into the risk assessment,more realistic results were achieved signifying an overestimation of probable risk of illness when epidemiological immunity data are not included.This finding supported the call for rigorous integration of temporary acquired immunity in dose-response in all microbial risk assessments.展开更多
In this paper, we present the modeling and optimization of a Real-Time Protocol（RTP） used in Train Communication Networks（TCN）. In the proposed RTP, message arbitration is represented by a probabilistic model and ...In this paper, we present the modeling and optimization of a Real-Time Protocol（RTP） used in Train Communication Networks（TCN）. In the proposed RTP, message arbitration is represented by a probabilistic model and the number of arbitration checks is minimized by using the probability of device activity. Our optimized protocol is fully compatible with the original standard and can thus be implemented easily. The experimental results demonstrate that the proposed algorithm can reduce the number of checks by about 50%, thus significantly enhancing bandwidth.展开更多
The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 t...The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.展开更多
Artificial intelligence and computer vision need methods for 2D （two-dimensional） shape retrieval having discrete set of boundary points. A novel method of MHR （Hurwitz-Radon Matrices） is used in shape modeling. P...Artificial intelligence and computer vision need methods for 2D （two-dimensional） shape retrieval having discrete set of boundary points. A novel method of MHR （Hurwitz-Radon Matrices） is used in shape modeling. Proposed method is based on the family of MHR which possess columns composed of orthogonal vectors. 2D curve is retrieved via different functions as probability distribution functions： sine, cosine, tangent, logarithm, exponent, arcsin, arccos, arctan and power function. Created from the family of N-1 MHR and completed with the identical matrix, system of matrices is orthogonal only for dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very significant for stability and high precision of calculations. MHR method is interpolating the function point by point without using any formula of function. Main features of MHR method are： accuracy of curve reconstruction depending on number of nodes and method of choosing nodes, interpolation of L points of the curve is connected with the computational cost of rank O（L）, MHR interpolation is not a linear interpolation.展开更多
New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, wh...New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.展开更多
Experimental study is performed on the probabilistic models for the long fatigue crack growth rates （da/dN） of LZ50 axle steel. An equation for crack growth rate was derived to consider the trend of stress intensity...Experimental study is performed on the probabilistic models for the long fatigue crack growth rates （da/dN） of LZ50 axle steel. An equation for crack growth rate was derived to consider the trend of stress intensity factor range going down to the threshold and the average stress effect. The probabilistic models were presented on the equation. They consist of the probabilistic da/dN-△K relations, the confidence-based da/dN-△K relations, and the probabilistic- and confidence-based da/dN-△K relations. Efforts were made respectively to characterize the effects of probabilistic assessments due to the scattering regularity of test data, the number of sampling, and both of them. These relations can provide wide selections for practice. Analysis on the test data of LZ50 steel indicates that the present models are available and feasible.展开更多
A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the...A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.展开更多
Background： With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified moni...Background： With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of ＂citizen scientists＂ can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. Methods： We conduct a systematic meta-analysis of 1015 publication abstracts addressing ＂forest monitoring＂ and ＂citizen science＂ in order to explore the combined topical landscape of these subjects. We employ ＇topic modelling an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. Results： We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on ＂forest monitoring＂ and ＂citizen science＂ and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. Conclusions： We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.展开更多
A simple probabilistic model for predicting crack growth behavior under random loading is presented. In the model, the parameters c and m in the Paris-Erdogan Equation are taken as random variables, and their stochast...A simple probabilistic model for predicting crack growth behavior under random loading is presented. In the model, the parameters c and m in the Paris-Erdogan Equation are taken as random variables, and their stochastic characteristic values are obtained through fatigue crack propagation tests on an offshore structural steel under constant amplitude loading. Furthermore, by using the Monte Carlo simulation technique, the fatigue crack propagation life to reach a given crack length is predicted. The tests are conducted to verify the applicability of the theoretical prediction of the fatigue crack propagation.展开更多
This paper is concerned with the modeling of randomness in multiscale analysis of heterogeneous materials. More specifically, a framework dedicated to the stochastic modeling of random properties is first introduced. ...This paper is concerned with the modeling of randomness in multiscale analysis of heterogeneous materials. More specifically, a framework dedicated to the stochastic modeling of random properties is first introduced. A probabilistic model for matrix-valued second-order random fields with symmetry propertries, recently proposed in the literature, is further reviewed. Algorithms adapted to the Monte Carlo simulation of the proposed representation are also provided. The derivations and calibration procedure are finally exemplified through the modeling of the apparent properties associated with an elastic porous microstructure containing stochastic interphases.展开更多
Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flo...Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flow.The carrying capacity of a deep-water foundation is influenced by the formation of a scour hole,which means that a severe scour can lead to a bridge failure without warning.Most of the current scour predictions are based on deterministic models,while other loads at bridges are usually provided as probabilistic values.To integrate scour factors with other loads in bridge design and research,a quantile regression model was utilized to estimate scour depth.Field data and experimental data from previous studies were collected to build the model.Moreover,scour estimations using the HEC-18 equation and the proposed method were compared.By using the“CCC(Calculate,Confirm,and Check)”procedure,the probabilistic concept could be used to calculate various scour depths with the targeted likelihood according to a specified chance of bridge failure.The study shows that with a sufficiently large and continuously updated database,the proposed model could present reasonable results and provide guidance for scour mitigation.展开更多
The growth and survival characteristic of Salmonella Enteritidis under acidic and osmotic conditions were studied.Meanwhile,a probabilistic model based on the theory of cell division and mortality was established to p...The growth and survival characteristic of Salmonella Enteritidis under acidic and osmotic conditions were studied.Meanwhile,a probabilistic model based on the theory of cell division and mortality was established to predict the growth or inactivation of S.Enteritidis.The experimental results demonstrated that the growth curves of planktonic and detached cells showed a significant difference(p<0.05)under four conditions,including pH5.0+0.0%NaCl,pH7.0+4.0%NaCl,pH6.0+4.0%NaCl,and pH5.0+4.0%NaCl.And the established primary and secondary models could describe the growth of S.enteritis well by estimating four mathematics evaluation indexes,including determination coefficient(R2),root mean square error(RMSE),accuracy factor(Af)and bias factor(Bf).Moreover,sequential treatment of 15%NaCl stress followed by pH 4.5 stress was the best condition to inactivate S.Enteritidis in 10 h at 25◦C.The probabilistic model with Logistical or Weibullian form could also predict the inactivation of S.Enteritidis well,thus realize the unification of predictive model to some extent or generalization of inactivation model.Furthermore,the primary 4-parameter probabilistic model or generalized inactivation model had slightly higher applicability and reliability to describe the growth or inactivation of S.Enteritidis than Baranyi model or exponential inactivation model within the experimental range in this study.展开更多
In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce...In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.展开更多
Current univariate approach to predict the probability of well construction time has limited accuracy due to the fact that it ignores key factors affecting the time.In this study,we propose a multivariate probabilisti...Current univariate approach to predict the probability of well construction time has limited accuracy due to the fact that it ignores key factors affecting the time.In this study,we propose a multivariate probabilistic approach to predict the risks of well construction time.It takes advantage of an extended multi-dimensional Bernacchia–Pigolotti kernel density estimation technique and combines probability distributions by means of Monte-Carlo simulations to establish a depth-dependent probabilistic model.This method is applied to predict the durations of drilling phases of 192 wells,most of which are located in the AustraliaAsia region.Despite the challenge of gappy records,our model shows an excellent statistical agreement with the observed data.Our results suggested that the total time is longer than the trouble-free time by at least 4 days,and at most 12 days within the 10%–90% confidence interval.This model allows us to derive the likelihoods of duration for each phase at a certain depth and to generate inputs for training data-driven models,facilitating evaluation and prediction of the risks of an entire drilling operation.展开更多
The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called M...The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.展开更多
Vegetation resilience(VR),providing an objective measure of ecosystem health,has received considerable attention,however,there is still limited understanding of whether the dominant factors differ across different cli...Vegetation resilience(VR),providing an objective measure of ecosystem health,has received considerable attention,however,there is still limited understanding of whether the dominant factors differ across different climate zones.We took the three national parks(Hainan Tropical Rainforest National Park,HTR;Wuyishan National Park,WYS;and Northeast Tiger and Leopard National Park,NTL)of China with less human interference as cases,which are distributed in different climatic zones,including tropical,subtropical and temperate monsoon climates,respectively.Then,we employed the probabilistic decay method to explore the spatio-temporal changes in the VR and their natural driving patterns using Geographically Weighted Regression(GWR)model as well.The results revealed that:(1)from 2000 to 2020,the Normalized Difference Vegetation Index(NDVI)of the three national parks fluctuated between 0.800 and 0.960,exhibiting an overall upward trend,with the mean NDVI of NTL(0.923)>HTR(0.899)>WYS(0.823);(2)the positive trend decay time of vegetation exceeded that of negative trend,indicating vegetation gradual recovery of the three national parks since 2012;(3)the VR of HTR was primarily influenced by elevation,aspect,average annual temperature change(AATC),and average annual precipitation change(AAPC);the WYS'VR was mainly affected by elevation,average annual precipitation(AAP),and AAPC;while the terrain factors(elevation and slope)were the main driving factors of VR in NTL;(4)among the main factors influencing the VR changes,the AAPC had the highest proportion in HTR(66.7%),and the AAP occupied the largest area proportion in WYS(80.4%).While in NTL,elevation served as the main driving factor for the VR,encompassing 64.2%of its area.Consequently,our findings indicated that precipitation factors were the main driving force for the VR changes in HTR and WYS national parks,while elevation was the main factors that drove the VR in NTL.Our research has promoted a deeper understanding of the driving mechanism behind the VR.展开更多
The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environme...The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment.However,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory requirements.This restricts its application to large-scale and complex problems.To address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory consumption.Furthermore,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence speed.To verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test sets.The results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization capabilities.Finally,the eGO algorithm was applied to image fusion.Through a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.展开更多
To study the ground motion intensity measures(IMs)suitable for the design of seismic performance with a focus on longitudinal resistance in tunnel structures,21 different seismic intensity parameters are selected for ...To study the ground motion intensity measures(IMs)suitable for the design of seismic performance with a focus on longitudinal resistance in tunnel structures,21 different seismic intensity parameters are selected for nonlinear calculation and analysis of tunnel structures,in order to determine the optimal IM for the longitudinal seismic performance of tunnel structures under different site conditions.An improved nonlinear beam-spring model is developed to calculate the longitudinal seismic response of tunnels.The PQ-Fiber model is used to simulate the longitudinal nonlinear behavior of tunnel structures and the tangential interactions between the tunnel and the soil is realized by load in the form of moment.Five different site types are considered and 21 IMs is evaluated against four criteria:effectiveness,practicality,usefulness,and sufficiency.The results indicate that the optimal IMs are significantly influenced by the site conditions.Specifically,sustained maximum velocity(V_(SM))emerges as the optimal IM for circular tunnels in soft soil conditions(CaseⅠsites),peak ground velocity(V PG)is best suited for CaseⅡsites,sustained maximum acceleration(A_(SM))is ideal for both CaseⅢand CaseⅤsites,and peak ground acceleration(A PG)for CaseⅣsites.As site conditions transition from CaseⅠto CaseⅤ,from soft to hard,the applicability of acceleration-type intensity parameters gradually decreases,while the applicability of velocity-type intensity parameters gradually increases.展开更多
文摘This article shows the probabilistic modeling of hydrocarbon spills on the surface of the sea, using climatology data of oil spill trajectories yielded by applying the lagrangian model PETROMAR-3D. To achieve this goal, several computing and statistical tools were used to develop the probabilistic modeling solution based in the methodology of Guo. Solution was implemented using a databases approach and SQL language. A case study is presented which is based on a hypothetical spill in a location inside the Exclusive Economic Zone of Cuba. Important outputs and products of probabilistic modeling were obtained, which are very useful for decision-makers and operators in charge to face oil spill accidents and prepare contingency plans to minimize its effects. In order to study the relationship between the initial trajectory and the arrival of hydrocarbons spills to the coast, a new approach is introduced as an incoming perspective for modeling. It consists in storage in databases the direction of movement of the oil slick at the first 24 hours. The probabilistic modeling solution presented is of great importance for hazard studies of oil spills in Cuban coastal areas.
基金TUM Talent Factory division of the Technical University of München for its support by providing a TUM University Foundation Fellowship for Dr.Ali Khansefid
文摘This study statistically evaluated the characteristics of induced earthquakes by geothermal power plants(GPPs)and generated a probabilistic model for simulating stochastic seismic events.Four well-known power plant zones were selected worldwide from the United States,Germany,France,and New Zealand.The operational condition information,as well as the corresponding earthquake catalogs recorded in the vicinity of GPPs,were gathered from their commencement date.The statistical properties of events were studied elaborately.By using this proposed database,a probabilistic model was developed capable of generating the number of induced seismic events per month,their magnitude,focal depth,and distance from the epicenter to the power plant,randomly.All of these parameters are simulated as a function of power plant injection rate.Generally speaking,the model,introduced in this study,is a tool for engineers and scientists interested in the seismic risk assessment of built environments prone to induced seismicity produced by GPPs operation.
基金This work was supported by DANIDA SaWaFo project with grant number 11-058DHI.
文摘Integration of acquired immunity into microbial risk assessment for illness incidence is of no doubt essential for the study of susceptibility to illness.In this study,a probabilistic model was set up as dose response for infection and a mathematical derivation was carried out by integrating immunity to obtain probability of illness models.Temporary acquire immunity from epidemiology studies which includes six different Norovirus transmission scenarios such as symptomatic individuals infectious,pre-and post-symptomatic infectiousness(low and high),innate genetic resistance,genogroup 2 type 4 and those with no immune boosting by asymptomatic infection were evaluated.Simulated results on illness inflation factor as a function of dose and exposure indicated that high frequency exposures had immense immunity build up even at high dose levels;hence minimized the probability of illness.Using Norovirus transmission dynamics data,results showed,and immunity included models had a reduction of 2e6 logs of magnitude difference in disease burden for both population and individual probable illness incidence.Additionally,the magnitude order of illness for each dose response remained largely the same for all transmission scenarios;symptomatic infectiousness and no immune boosting after asymptomatic infectiousness also remained the same throughout.With integration of epidemiological data on acquired immunity into the risk assessment,more realistic results were achieved signifying an overestimation of probable risk of illness when epidemiological immunity data are not included.This finding supported the call for rigorous integration of temporary acquired immunity in dose-response in all microbial risk assessments.
基金supported by the National Natural Science Foundation of China (Nos. U1201251 and 61402248)the National Key Technologies Research and Development Program of China (No. 2015BAG14B01-02)MIIT IT funds (Research and application of TCN key technologies) of China
文摘In this paper, we present the modeling and optimization of a Real-Time Protocol（RTP） used in Train Communication Networks（TCN）. In the proposed RTP, message arbitration is represented by a probabilistic model and the number of arbitration checks is minimized by using the probability of device activity. Our optimized protocol is fully compatible with the original standard and can thus be implemented easily. The experimental results demonstrate that the proposed algorithm can reduce the number of checks by about 50%, thus significantly enhancing bandwidth.
文摘The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.
文摘Artificial intelligence and computer vision need methods for 2D （two-dimensional） shape retrieval having discrete set of boundary points. A novel method of MHR （Hurwitz-Radon Matrices） is used in shape modeling. Proposed method is based on the family of MHR which possess columns composed of orthogonal vectors. 2D curve is retrieved via different functions as probability distribution functions： sine, cosine, tangent, logarithm, exponent, arcsin, arccos, arctan and power function. Created from the family of N-1 MHR and completed with the identical matrix, system of matrices is orthogonal only for dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very significant for stability and high precision of calculations. MHR method is interpolating the function point by point without using any formula of function. Main features of MHR method are： accuracy of curve reconstruction depending on number of nodes and method of choosing nodes, interpolation of L points of the curve is connected with the computational cost of rank O（L）, MHR interpolation is not a linear interpolation.
文摘New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.
基金国家自然科学基金，Special Foundation of National Excellent Ph.D.Thesis，Outstanding Young Teachers of Ministry of Education of China
文摘Experimental study is performed on the probabilistic models for the long fatigue crack growth rates （da/dN） of LZ50 axle steel. An equation for crack growth rate was derived to consider the trend of stress intensity factor range going down to the threshold and the average stress effect. The probabilistic models were presented on the equation. They consist of the probabilistic da/dN-△K relations, the confidence-based da/dN-△K relations, and the probabilistic- and confidence-based da/dN-△K relations. Efforts were made respectively to characterize the effects of probabilistic assessments due to the scattering regularity of test data, the number of sampling, and both of them. These relations can provide wide selections for practice. Analysis on the test data of LZ50 steel indicates that the present models are available and feasible.
基金Supported by the National Natural Science Foundation of China(61374140)Shanghai Pujiang Program(12PJ1402200)
文摘A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.
文摘Background： With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of ＂citizen scientists＂ can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. Methods： We conduct a systematic meta-analysis of 1015 publication abstracts addressing ＂forest monitoring＂ and ＂citizen science＂ in order to explore the combined topical landscape of these subjects. We employ ＇topic modelling an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. Results： We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on ＂forest monitoring＂ and ＂citizen science＂ and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. Conclusions： We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.
文摘A simple probabilistic model for predicting crack growth behavior under random loading is presented. In the model, the parameters c and m in the Paris-Erdogan Equation are taken as random variables, and their stochastic characteristic values are obtained through fatigue crack propagation tests on an offshore structural steel under constant amplitude loading. Furthermore, by using the Monte Carlo simulation technique, the fatigue crack propagation life to reach a given crack length is predicted. The tests are conducted to verify the applicability of the theoretical prediction of the fatigue crack propagation.
基金supported by the French National Research Agency(ANR)(ANR-12-JS09-0001-01)
文摘This paper is concerned with the modeling of randomness in multiscale analysis of heterogeneous materials. More specifically, a framework dedicated to the stochastic modeling of random properties is first introduced. A probabilistic model for matrix-valued second-order random fields with symmetry propertries, recently proposed in the literature, is further reviewed. Algorithms adapted to the Monte Carlo simulation of the proposed representation are also provided. The derivations and calibration procedure are finally exemplified through the modeling of the apparent properties associated with an elastic porous microstructure containing stochastic interphases.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.51908421 and 41172246).
文摘Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flow.The carrying capacity of a deep-water foundation is influenced by the formation of a scour hole,which means that a severe scour can lead to a bridge failure without warning.Most of the current scour predictions are based on deterministic models,while other loads at bridges are usually provided as probabilistic values.To integrate scour factors with other loads in bridge design and research,a quantile regression model was utilized to estimate scour depth.Field data and experimental data from previous studies were collected to build the model.Moreover,scour estimations using the HEC-18 equation and the proposed method were compared.By using the“CCC(Calculate,Confirm,and Check)”procedure,the probabilistic concept could be used to calculate various scour depths with the targeted likelihood according to a specified chance of bridge failure.The study shows that with a sufficiently large and continuously updated database,the proposed model could present reasonable results and provide guidance for scour mitigation.
基金This work has been financially supported by the National Natural Science Foundation of China(NSFC 31271896 and 31371776)the project in the National Science&Technology Pillar Program during the Twelfth Five-year Plan Period(2015BAK36B04)and the project of Science and Technology Commission of Shanghai Municipality(15395810900).
文摘The growth and survival characteristic of Salmonella Enteritidis under acidic and osmotic conditions were studied.Meanwhile,a probabilistic model based on the theory of cell division and mortality was established to predict the growth or inactivation of S.Enteritidis.The experimental results demonstrated that the growth curves of planktonic and detached cells showed a significant difference(p<0.05)under four conditions,including pH5.0+0.0%NaCl,pH7.0+4.0%NaCl,pH6.0+4.0%NaCl,and pH5.0+4.0%NaCl.And the established primary and secondary models could describe the growth of S.enteritis well by estimating four mathematics evaluation indexes,including determination coefficient(R2),root mean square error(RMSE),accuracy factor(Af)and bias factor(Bf).Moreover,sequential treatment of 15%NaCl stress followed by pH 4.5 stress was the best condition to inactivate S.Enteritidis in 10 h at 25◦C.The probabilistic model with Logistical or Weibullian form could also predict the inactivation of S.Enteritidis well,thus realize the unification of predictive model to some extent or generalization of inactivation model.Furthermore,the primary 4-parameter probabilistic model or generalized inactivation model had slightly higher applicability and reliability to describe the growth or inactivation of S.Enteritidis than Baranyi model or exponential inactivation model within the experimental range in this study.
基金Supported by the National High Technology Research and Development Program of China (No. 2007AA11Z227)the Natural Science Foundation of Jiangsu Province of China(No. BK2009352)the Fundamental Research Funds for the Central Universities of China (No. 2010B16414)
文摘In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.
文摘Current univariate approach to predict the probability of well construction time has limited accuracy due to the fact that it ignores key factors affecting the time.In this study,we propose a multivariate probabilistic approach to predict the risks of well construction time.It takes advantage of an extended multi-dimensional Bernacchia–Pigolotti kernel density estimation technique and combines probability distributions by means of Monte-Carlo simulations to establish a depth-dependent probabilistic model.This method is applied to predict the durations of drilling phases of 192 wells,most of which are located in the AustraliaAsia region.Despite the challenge of gappy records,our model shows an excellent statistical agreement with the observed data.Our results suggested that the total time is longer than the trouble-free time by at least 4 days,and at most 12 days within the 10%–90% confidence interval.This model allows us to derive the likelihoods of duration for each phase at a certain depth and to generate inputs for training data-driven models,facilitating evaluation and prediction of the risks of an entire drilling operation.
基金supported by the National Key Research and Development Program of China(No.2016YFB0800601)the Key Program of NSFC-Tongyong Union Foundation(No.U1636209)+1 种基金the National Natural Science Foundation of China(61602358)the Key Research and Development Programs of Shaanxi(No.2019ZDLGY13-04,No.2019ZDLGY13-07)。
文摘The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.
基金the National Natural Science Foundation of China(grant no.31971639)the Natural Science Foundation of Fujian Province(grant no.2023J01477)the Special Investigation on Science and Technology Infrastructure Resources(grant no.2019FY202108)for their support of this research。
文摘Vegetation resilience(VR),providing an objective measure of ecosystem health,has received considerable attention,however,there is still limited understanding of whether the dominant factors differ across different climate zones.We took the three national parks(Hainan Tropical Rainforest National Park,HTR;Wuyishan National Park,WYS;and Northeast Tiger and Leopard National Park,NTL)of China with less human interference as cases,which are distributed in different climatic zones,including tropical,subtropical and temperate monsoon climates,respectively.Then,we employed the probabilistic decay method to explore the spatio-temporal changes in the VR and their natural driving patterns using Geographically Weighted Regression(GWR)model as well.The results revealed that:(1)from 2000 to 2020,the Normalized Difference Vegetation Index(NDVI)of the three national parks fluctuated between 0.800 and 0.960,exhibiting an overall upward trend,with the mean NDVI of NTL(0.923)>HTR(0.899)>WYS(0.823);(2)the positive trend decay time of vegetation exceeded that of negative trend,indicating vegetation gradual recovery of the three national parks since 2012;(3)the VR of HTR was primarily influenced by elevation,aspect,average annual temperature change(AATC),and average annual precipitation change(AAPC);the WYS'VR was mainly affected by elevation,average annual precipitation(AAP),and AAPC;while the terrain factors(elevation and slope)were the main driving factors of VR in NTL;(4)among the main factors influencing the VR changes,the AAPC had the highest proportion in HTR(66.7%),and the AAP occupied the largest area proportion in WYS(80.4%).While in NTL,elevation served as the main driving factor for the VR,encompassing 64.2%of its area.Consequently,our findings indicated that precipitation factors were the main driving force for the VR changes in HTR and WYS national parks,while elevation was the main factors that drove the VR in NTL.Our research has promoted a deeper understanding of the driving mechanism behind the VR.
文摘The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment.However,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory requirements.This restricts its application to large-scale and complex problems.To address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory consumption.Furthermore,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence speed.To verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test sets.The results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization capabilities.Finally,the eGO algorithm was applied to image fusion.Through a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.
基金National Key Research and Development Program of China(No.2022YFC3004300)the National Natural Science Foundation of China(No.52378475).
文摘To study the ground motion intensity measures(IMs)suitable for the design of seismic performance with a focus on longitudinal resistance in tunnel structures,21 different seismic intensity parameters are selected for nonlinear calculation and analysis of tunnel structures,in order to determine the optimal IM for the longitudinal seismic performance of tunnel structures under different site conditions.An improved nonlinear beam-spring model is developed to calculate the longitudinal seismic response of tunnels.The PQ-Fiber model is used to simulate the longitudinal nonlinear behavior of tunnel structures and the tangential interactions between the tunnel and the soil is realized by load in the form of moment.Five different site types are considered and 21 IMs is evaluated against four criteria:effectiveness,practicality,usefulness,and sufficiency.The results indicate that the optimal IMs are significantly influenced by the site conditions.Specifically,sustained maximum velocity(V_(SM))emerges as the optimal IM for circular tunnels in soft soil conditions(CaseⅠsites),peak ground velocity(V PG)is best suited for CaseⅡsites,sustained maximum acceleration(A_(SM))is ideal for both CaseⅢand CaseⅤsites,and peak ground acceleration(A PG)for CaseⅣsites.As site conditions transition from CaseⅠto CaseⅤ,from soft to hard,the applicability of acceleration-type intensity parameters gradually decreases,while the applicability of velocity-type intensity parameters gradually increases.
