基于Bayes时空模型分析HIV/AIDS晚发现的时空分布特征及其影响因素

【目的】旨在分析兰州市HIV/AIDS晚发现的时空聚集性特征及相关影响因素,明确兰州市HIV/AIDS晚发现高风险地区和时间趋势,为兰州市因地制宜地制定HIV/AIDS防治策略措施提供参考依据。【方法】选择兰州市2011-2018年间新报告的成年HIV/AIDS病例作为研究对象,研究中所需的数据资料来自兰州市疾病预防控制中心和兰州市统计年鉴。采用Bayes时空模型分析HIV/AIDS晚发现相对风险(RR)的时空分布特征及其影响因素。【结果】2011-2018年间兰州市新报告的HIV/AIDS病例共计1984例,其中HIV/AIDS晚发现者有982例(49.5%),平均年龄为39.67岁,男性占90.9%。老年人和女性HIV/AIDS病例中晚发现的比例更高;城关区(51.1%)、安宁区(50.3%)和榆中县(51.9%)具有高于平均水平的HIV/AIDS晚发现比例;2011-2018年间兰州市总体的晚发现比例呈波动上升趋势。Bayes时空模型分析结果显示,兰州市HIV/AIDS晚发现风险在2011-2015年间波动变化,而在2015年后迅速上升,其RR(95%CI)从1.01(0.84,1.23)上升到1.11(0.77,1.97);红古区和三个县的晚发现风险变化趋势与兰州市的总体变化趋势相似,而城关区和七里河区的晚发现风险呈下降趋势;晚发现相对风险大于1的区县包括:永登县(RR=1.07,95%CI:0.55,1.96)、西固区(RR=1.04,95%CI:0.67,1.49)、城关区(RR=2.41,95%CI:0.85,6.16)和七里河区(RR=2.03,95%CI:1.10,3.27)。冷热点分析结果显示城关区和七里河区为热点区。影响因素分析结果显示,随着人均GDP(RR=0.65,95%CI:0.35,0.90)和HIV/AIDS病例中的男性比例(RR=0.53,95%CI:0.19,0.92)的增高,HIV/AIDS晚发现的相对风险越低;而人口密度(RR=1.35,95%CI:1.01,1.81)越大,晚发现风险越高。【结论】兰州市的HIV/AIDS晚发现风险呈上升趋势,并且存在明显的地区差异特征;人均GDP、HIV/AIDS中男性比例和人口密度是HIV/AIDS晚发现的影响因素。因此,对于晚发现风险高和存在相关风险因素的区县,应重视并制定有针对性的HIV筛查和防治服务,降低HIV/AIDS晚发现比例和风险。

Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression(VAR)Model

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.

Influence of Spatial-Temporal and Environmental Factors on Larimichthys polyactis, Octopus variabilis, and Species Aggregated Set-Net CPUEs in Haizhou Bay, China

Set-nets are common alongshore fishing gear used in Haizhou Bay, which rely on flow to catch fish. The catch per unit effort(CPUE) of set-net is affected by spatial-temporal and environmental factors but no research has been conducted on this subject. In this study, we used generalized additive models(GAMs) to explore the influence of spatial-temporal and environmental factors on CPUEs of species aggregated, small yellow croaker(Larimichthys polyactis), and octopus(Octopus variabilis) based on logbooks investigations conducted at 4 stations in an alongshore area of Haizhou Bay from 2011 to 2012. The results showed that all CPUEs exhibited significant spatial-temporal differences at various scales. Aggregated CPUE was high when the sea surface temperature(SST) was 15-18℃ and 20-23℃, which was mainly determined by life history traits of the octopus and small yellow croaker(optimal SSTs 14-17℃ and 19-24℃, respectively). Chlorophyll-a concentration had significant influences on the aggregated, small yellow croaker and octopus CPUEs at optimal ranges of 3.8-6.2 mg m^(-3), 4.2-4.8 mg m^(-3) and 4.5-5.5 mg m^(-3), respectively. Flow through the net had positive relationships with CPUEs. The approximate logarithmic trends in regression curves had a critical point of 2.5 Mm^3 d^(-1), which was the dividing point that differentiated whether the major factor affecting CPUEs was the flow velocity or the fishery resource. Our results from this study will help guide fishery production and improve catch rate of set-net fishing in Haizhou Bay.

EMPIRICAL BAYES TEST PROBLEMS OF VARIANCE COMPONENTS IN RANDOM EFFECTS MODEL

Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.

THE SUPERIORITY OF EMPIRICAL BAYES ESTIMATION OF PARAMETERS IN PARTITIONED NORMAL LINEAR MODEL

In this article,the empirical Bayes（EB）estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares（LS）estimator are investigated under mean square error matrix（MSEM）criterion.

A Spatial-Temporal Attention Model for Human Trajectory Prediction

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

Bayesian zero-failure reliability modeling and assessment method for multiple numerical control(NC) machine tools

A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.

PARAMETER IDENTIFICATION OF DYNAMIC MODELS USING A BAYES APPROACH

The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized evaluate problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.

EMPIRICAL BAYES ESTIMATION FOR ESTIMABLE FUNCTION OF REGRESSION COEFFICIENT IN A MULTIPLE LINEAR REGRESSION MODEL

In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard normal distribution. We get the EB estimators by using kernel estimation of multivariate density function and its first order partial derivatives. It is shown that the convergence rates of the EB estimators are under the condition where an integer k > 1 . is an arbitrary small number and m is the dimension of the vector Y.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

New model of linkage evolution for the transtensional fault systems in the Nanpu Sag of Bohai Bay Basin:Insight from seismic interpretation and analogue modelling

The evolution of faults within the same stress field is frequently influenced by numerous factors,involving the reactivation of pre-existing structures,stress transmission through ductile detachment layers,and the growth,interaction,as well as linkage of new fault segments.This study analyses a complex multi-phase oblique extension fault system in the Nanpu Sag(NPS)of the Bohai Bay Basin(BBB),China.High-resolution three-dimensional(3D)seismic data and analogue modelling indicate that the oblique extensional reactivation of pre-existing structures governs the sequential arrangement of fault segments in the caprock,and they dip synthetically to the reactivated fault at depth.During the NW-SE extension in the Eocene,the predominant movement of the pre-existing fault is strike-slip.Subsequently,during the N-S extension since the Oligocene,inclined at 20.to the pre-existing fault,forming splay fault segments and ultimately creating large en-echelon arcuate faults linked by relay ramps.Using fault throw-distance(T-D)and laser scanning,we reconstructed the fault evolution model of oblique extension reactivation in the presence of a ductile detachment basement.Our study illustrates that the arcuate faults can be categorized into linear master fault segments controlled by pre-existing structures,bending splay faults in the termination zone,and normal fault segments responding to the regional stress field.The interaction between faults occurs among normal faults and strike-slip faults,and the kinematic unification of the two fault systems is accomplished in the intersection zone.As the faults continue to evolve,the new fault segments tend to relinquish the control of pre-existing structures and concentrate more on the development of planar and continuous major faults.The ductile detachment layer significantly contributes to the uniform distribution of strain,resulting in narrow shear zones and discontinuous normal faults in its absence.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

基于改进GA-Bayes模型的办公系统网络安全检测

为保证办公系统网络安全,避免重要资料被窃取或篡改,设计了一种基于改进GA-Bayes网络安全检测模型.先借助DEMATEL法选取办公系统网络指标体系,再利用遗传算法取代传统禁忌搜索方法,同步融合贝叶斯网络结构的学习功能.以评估指标为基础,通过GA-Bayes改进模型推理得出每一个网络安全等级类别对应的后验概率,选取后验概率最大值对应的等级作为最终的检测结果.结果表明:时间段1网络安全等级高,时间段2与时间段1相同,时间段3网络安全等级低,时间段4网络安全等级一般,时间段5网络安全等级高,证明提出模型的杰卡德系数应用效果和检测准确性要优于传统方法.

Linking Structural Equation Modeling with Bayesian Network and Its Application to Coastal Phytoplankton Dynamics in the Bohai Bay

Bayesian networks （BN） have many advantages over other methods in ecological modeling, and have become an increasingly popular modeling tool. However, BN are flawed in regard to building models based on inadequate existing knowledge. To overcome this limitation, we propose a new method that links BN with structural equation modeling （SEM）. In this method, SEM is used to improve the model structure for BN. This method was used to simulate coastal phytoplankton dynamics in the Bohai Bay. We demonstrate that this hybrid approach minimizes the need for expert elicitation, generates more reasonable structures for BN models, and increases the BN model＇s accuracy and reliability. These results suggest that the inclusion of SEM for testing and verifying the theoretical structure during the initial construction stage improves the effectiveness of BN models, especially for complex eco-environment systems. The results also demonstrate that in the Bohai Bay, while phytoplankton biomass has the greatest influence on phytoplankton dynamics, the impact of nutrients on phytoplankton dynamics is larger than the influence of the physical environment in summer. Furthermore, although the Redfield ratio indicates that phosphorus should be the primary nutrient limiting factor, our results show that silicate plays the most important role in regulating phytoplankton dynamics in the Bohai Bay.

Bayesian Estimation of Population Size via Capture-Recapture Model with Time Variation and Behavioral Response

We consider the problem of population estimation using capture-recapture data, where capture probabilities can vary between sampling occasions and behavioural responses. The original model is not identifiable without further restrictions. The novelty of this article is to expand the current research practice by developing a hierarchical Bayesian approach with the assumption that the odds of recapture bears a constant relationship to the odds of initial capture. A real-data example of deer mice population is given to illustrate the proposed method. Three simulation studies are developed to inspect the performance of the proposed Bayesian estimates. Compared with the maximum likelihood estimates discussed in Chao et al. (2000), the hierarchical Bayesian estimate provides reasonably better population estimation with less mean square error;moreover, it is sturdy to underline relationship between the initial and re-capture probabilities. The sensitivity study shows that the proposed Bayesian approach is robust to the choice of hyper-parameters. The third simulation study reveals that both relative bias and relative RMSE approach zero as population size increases. A R-package is developed and used in both data example and simulation.

Minimum Description Length Methods in Bayesian Model Selection: Some Applications

Computations involved in Bayesian approach to practical model selection problems are usually very difficult. Computational simplifications are sometimes possible, but are not generally applicable. There is a large literature available on a methodology based on information theory called Minimum Description Length (MDL). It is described here how many of these techniques are either directly Bayesian in nature, or are very good objective approximations to Bayesian solutions. First, connections between the Bayesian approach and MDL are theoretically explored;thereafter a few illustrations are provided to describe how MDL can give useful computational simplifications.

Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.

Application of the Sediment Mathematical Modelling on Planned Project of Lingdingyang Bay

Based on the combined hydraulic calculation for the eastern network region at the Pearl River estuary and several outlets to the Lingdingyang Bay, the sediment calculation modelling was introduced in the establishment of the sediment mathematical model for Lingdingyang Bay and the eastern region with one and two dimensional flow calculation. Model adjustment and verification were performed in conjunction with field data. The simulated results coincide well with measured data.In addition the model is applied to predict the shore-line planning scheme of Lingdingyang Bay.The theoretical criterion is provided for the shore line plan in the model.And a new mathematical simulated method is put out to research the planning engineering concerned with one-dimensional net rivers and two-dimensional estuary.

Bayes方法在矿井突水水源判别中的应用

快速有效地判别突水水源是矿井安全生产的重要保障。选取各含水层多项水质指标,应用Bayes方法建立适用于不同水质类型的矿井突水水源快速判别模型。结合SPSS软件,以淮南顾桥矿为例,并与模糊综合评判模型、神经网络模型进行分析比较。结果表明:贝叶斯多类线性判别模型能够有效地判别突水水源,比模糊综合评判有更高的准确性,与神经网络模型的判别准确率相同。Bayes多类线性判别模型又以其计算过程简单、模型结构稳定而优于神经网络模型。既提高判别准确率又提高判别速度,实现对突水水源快速有效判别。

基于Bayes信任模型的Web服务组合优化方法

为提高组合服务的可信性,提出了一种将Bayes信任模型与遗传算法相结合的服务组合优化方法。首先利用Bayes方法对Web服务的信任度进行估计;然后根据Web服务的基本组合结构,给出组合服务的执行成功概率的计算模型;最后采用遗传算法对服务组合进行优化。仿真实验表明,该方法可为Web服务组合提供一种优化方案,极大地提高组合后服务的执行成功概率。