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.

基于改进Bayes信息量准则的锂电池自适应变阶AVO模型

提出了一种融合Bayes信息量准则和樽海鞘优化算法的自适应变阶(AVO)模型。该模型以端电压误差的Bayes信息量作为一阶和二阶RC模型的变阶依据,利用樽海鞘优化算法(SSA)搜索Bayes信息量序列的全局最优解,实现对模型最优变阶序列的求解;通过开展混合动力脉冲特性(HPPC)实验,选用含遗忘因子递推最小二乘法(FFRLS)获取精确的模型参数,完成AVO模型的电阻及电容参数辨识和精度在线验证。结果表明:所建立的锂电池自适应变阶AVO模型平均误差为0.011 9 V,精度及实用性较高。

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.

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.

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

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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 eigenvalue 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.

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.

基于小波分解与ARMA-BayesLSTM组合模型的风向短期预测

针对风向的随机性、不稳定性、强相关性,提出了1种基于小波分解的自回归滑动平均模型(Autoregressive moving average,ARMA)与Bayes-LSTM(Long short-term memory,LSTM)组合的风向预测算法。首先对风向序列进行小波分解;然后使用ARMA与Bayes-LSTM算法分别对各分量进行预测;最后对预测结果进行重构,得到预测风向。与ARMA、LSTM、Bayes-LSTM预测方法进行对比,结果表明,所提出的组合算法对风向具有更好的预测精度。

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.

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.

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.

Changes of coastline and tidal flat and its implication for ecological protection under human activities: Take China’s Bohai Bay as an example

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area,which is also of great significance for understanding coastal ecological environment changes and even global changes.Based on field measurements,combined with Linear Regression(LR)model and Inverse Distance Weighing(IDW)method,this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay.The shoreline faces a high erosion chance under the action of natural factors,while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities.The implication of change rule for ecological protection and recovery is also discussed.Measures should be taken to protect the coastal ecological environment.The models used in this paper show a high correlation coefficient between observed and modeling data,which means that this method can be used to predict the changing trend of shoreline and tidal flat.The research results of present study can provide scientific supports for future coastal protection and management.

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.

THE ASYMPTOTICALLY OPTIMAL EMPIRICAL BAYES ESTIMATION IN MULTIPLE LINEAR REGRESSION MODEL

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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.

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。