Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Fuzzy Inventory Model under Selling Price Dependent Demand and Variable Deterioration with Fully Backlogged Shortages

The objective is to develop a model considering demand dependent on selling price and deterioration occurs after a certain period of time, which follows two-parameter Weibull distribution. Shortages are allowed and fully backlogged. Fuzzy optimal solution is obtained by considering hexagonal fuzzy numbers and for defuzzification Graded Mean Integration Representation Method. A numerical example is provided for the illustration of crisp and fuzzy, both models. To observe the effect of changes in parameters, sensitivity analysis is carried out.

Inverse Spectral Problem for Sturm-Liouville Operator with Boundary and Jump Conditions Dependent on the Spectral Parameter

In this paper, the inverse spectral problem of Sturm-Liouville operator with boundary conditions and jump conditions dependent on the spectral parameter is investigated. Firstly, the self-adjointness of the problem and the eigenvalue properties are given, then the asymptotic formulas of eigenvalues and eigenfunctions are presented. Finally, the uniqueness theorems of the corresponding inverse problems are given by Weyl function theory and inverse spectral data approach.

Thermomechanical Dynamics (TMD) and Bifurcation-Integration Solutions in Nonlinear Differential Equations with Time-Dependent Coefficients

The new independent solutions of the nonlinear differential equation with time-dependent coefficients (NDE-TC) are discussed, for the first time, by employing experimental device called a drinking bird whose simple back-and-forth motion develops into water drinking motion. The solution to a drinking bird equation of motion manifests itself the transition from thermodynamic equilibrium to nonequilibrium irreversible states. The independent solution signifying a nonequilibrium thermal state seems to be constructed as if two independent bifurcation solutions are synthesized, and so, the solution is tentatively termed as the bifurcation-integration solution. The bifurcation-integration solution expresses the transition from mechanical and thermodynamic equilibrium to a nonequilibrium irreversible state, which is explicitly shown by the nonlinear differential equation with time-dependent coefficients (NDE-TC). The analysis established a new theoretical approach to nonequilibrium irreversible states, thermomechanical dynamics (TMD). The TMD method enables one to obtain thermodynamically consistent and time-dependent progresses of thermodynamic quantities, by employing the bifurcation-integration solutions of NDE-TC. We hope that the basic properties of bifurcation-integration solutions will be studied and investigated further in mathematics, physics, chemistry and nonlinear sciences in general.

水泥固化铅污染粘土的pH-Dependent试验研究

通过pH-dependent试验,研究水泥固化铅污染土的溶出特性.对滤出液pH的研究结果表明,滤出液pH随加酸量增加而下降,且试样C12Pb2较C12Pb0、试样C18Pb2较C18Pb0的碱性/酸缓冲能力弱.对滤出液中铅浓度([Pb])随滤出液pH、水泥掺量的变化特征进行分析,结果表明,[Pb]随滤出液pH增大而先减小后增加;且试样C18Pb2较C12Pb2,其溶出的[Pb]显著减小,铅最大溶出率小5个百分点,表明水泥掺量的增大使得水泥对铅的固定效果更好,使固化土中可溶出的铅减少.

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

24h pH-阻抗监测对胃食管反流病患者治疗反应性的预测价值

目的探讨24 h pH-阻抗监测对胃食管反流病(gastroesophageal reflux disease,GERD)患者治疗反应性的预测价值。方法选取2020年12月—2022年11月成都市第三人民医院接受治疗的286例具有典型反流症状的GERD患者作为研究对象,所有患者均接受质子泵抑制剂(proton pump inhibitors,PPI)治疗,2个月后根据治疗效果,将患者分为有效组和无效组。对比2组患者的日常生活习惯、高分辨率测压结果、24 h pH-阻抗监测结果、症状及量表评分。采用多因素logistic回归分析筛选治疗反应影响因素,构建预测模型,绘制ROC曲线分析其预测价值。结果治疗2个月后,有效组纳入184例,无效组纳入102例。有效组治疗前食管下括约肌(low esophageal sphincter,LES)长度以及LES静息压均低于无效组(P<0.10),远端潜伏期(distal latency,DL)及远端收缩积分(distal contractile integral,DCI)均高于无效组(P<0.10),有效组患者治疗前的酸暴露时间(acid exposure time,AET)≥6%、总反流发作次数、DeMeester评分均低于无效组(P<0.10);有效组反流后吞咽诱发的蠕动波(post-reflux swallow-induced peristaltic wave,PSPW)指数≥61%比例、平均夜间基线阻抗(mean nocturnal baseline impedance,MNBI)值≥2292Ω比例高于无效组(P<0.10)。多因素logistic回归分析显示,LES静息压(OR=0.738)、DL(OR=3.643)、DCI(OR=1.124)及24 h pH-阻抗监测综合预测(OR=1.940)水平均可影响GERD治疗反应性(P<0.10)。ROC曲线分析显示,24 h pH-阻抗监测的综合预测水平(AUC=0.969,特异度=90.2%,敏感度=91.3%)高于其他影响因素。结论24 h pH-阻抗监测的相关参数可高度预测典型的GERD患者对采用PPI后的治疗反应。

Heat transport properties within living biological tissues with temperature-dependent thermal properties

Understanding of the heat transport within living biological tissues is crucial to effective heat treatments. The heat transport properties of living biological tissues with temperature-dependent properties are explored in this paper. Taking into account of variable physical properties, the governing equation of temperature is first derived in the context of the dualphase-lags model(DPL). An effective method, according to the Laplace transform and a linearization technique, is then employed to solve this nonlinear governing equation. The temperature distribution of a biological tissue exposed to a pulsed heat flux on its exterior boundary, which frequently happens in various heat treatments, is predicted and analyzed. The results state that a lower temperature can be predicted when temperature dependence is considered in the heating process.The contributions of key thermal parameters are different and dependent on the ratio of phase lag and the amplitude of the exterior pulsed heat flux.

ON LOCAL CONTROLLABILITY FOR COMPRESSIBLE NAVIER-STOKES EQUATIONS WITH DENSITY DEPENDENT VISCOSITIES

In this paper,we study the controllability of compressible Navier-Stokes equations with density dependent viscosities.For when the shear viscosityμis a positive constant and the bulk viscosityλis a function of the density,it is proven that the system is exactly locally controllable to a constant target trajectory by using boundary control functions.

基于Context-dependent嵌入的省级碳排放权分层分配策略研究

考虑省际资源禀赋差异,在ZSG-DEA模型中嵌入Context-dependent,以分层的方式构建渐进式改进资源分配模型,对2030年全国30个省份碳排放权进行分配。结果表明:效率值方面,全国总体效率较好,呈现出东西中地区依次降低的状态,各省份间效率值差距较大,传统能源大省(区)碳排放效率低下;碳排放权调整方面,陕西、山西、内蒙古3个效率值较低的省份需要做出较大的缩减量,而山东、广东等处于第一层有效前沿面的省份获得了相较之前更多的碳排放量;模型对比方面,在保证2030年碳排放目标基础上,C-ZSG-DEA模型在效率值的改进上和碳排放调整上都比其他模型要小,符合渐进式改进的理念。

pH-时滞型新疆一枝蒿结肠定位片的制备及体外释放评价

目的:制备pH-时滞型新疆一枝蒿结肠定位片并对其体外释放性能进行评价。方法:采用压制包衣、喷雾包衣的方法制备结肠定位片,以总黄酮累积释放度为指标,分别考察压制包衣时滞材料处方比例、包衣用量、上下层填料比例,pH控释层处方比例与包衣增重。结果:经过考察,最优工艺处方为EC-PEG 6 000(4∶1),压制包衣0.1 g,压制包衣上下层填料(1.5∶1),Eudragit FS 30 D-Eudragit L30 D-55(2∶1),p H控释层包衣增重为7%时,制得结肠定位制剂在0.1 mol/L HCl溶液中2 h内不释药,在pH 6.8磷酸盐缓冲液中4 h累积释放度<10%,在p H 7.5磷酸盐缓冲液中6 h累积释放度>80%。结论:经过筛选后的结肠定位片能够实现结肠定位释放的目的且体外释放稳定。

Inference for accelerated bivariate dependent competing risks model based on Archimedean copulas under progressive censoring

Dependent competing risks model is a practical model in the analysis of lifetime and failure modes.The dependence can be captured using a statistical tool to explore the re-lationship among failure causes.In this paper,an Archimedean copula is chosen to describe the dependence in a constant-stress accelerated life test.We study the Archimedean copula based dependent competing risks model using parametric and nonparametric methods.The parametric likelihood inference is presented by deriving the general expression of likelihood function based on assumed survival Archimedean copula associated with the model parameter estimation.Combining the nonparametric estimation with progressive censoring and the non-parametric copula estimation,we introduce a nonparametric reliability estimation method given competing risks data.A simulation study and a real data analysis are conducted to show the performance of the estimation methods.

Goodness-of-Fit Test for Non-Stationary and Strongly Dependent Samples

In this article we improve a goodness-of-fit test, of the Kolmogorov-Smirnov type, for equally distributed- but not stationary-strongly dependent data. The test is based on the asymptotic behavior of the empirical process, which is much more complex than in the classical case. Applications to simulated data and discussion of the obtained results are provided. This is, to the best of our knowledge, the first result providing a general goodness of fit test for non-weakly dependent data.

Percolation transitions in edge-coupled interdependent networks with directed dependency links

We propose a model of edge-coupled interdependent networks with directed dependency links(EINDDLs)and develop the theoretical analysis framework of this model based on the self-consistent probabilities method.The phase transition behaviors and parameter thresholds of this model under random attacks are analyzed theoretically on both random regular(RR)networks and Erd¨os-Renyi(ER)networks,and computer simulations are performed to verify the results.In this EINDDL model,a fractionβof connectivity links within network B depends on network A and a fraction(1-β)of connectivity links within network A depends on network B.It is found that randomly removing a fraction(1-p)of connectivity links in network A at the initial state,network A exhibits different types of phase transitions(first order,second order and hybrid).Network B is rarely affected by cascading failure whenβis small,and network B will gradually converge from the first-order to the second-order phase transition asβincreases.We present the critical values ofβfor the phase change process of networks A and B,and give the critical values of p andβfor network B at the critical point of collapse.Furthermore,a cascading prevention strategy is proposed.The findings are of great significance for understanding the robustness of EINDDLs.

Nonuniform Dependence on the Initial Data for Solutions of Conservation Laws

In this paper,we study systems of conservation laws in one space dimension.We prove that for classical solutions in Sobolev spaces H^(s),with s>3/2,the data-to-solution map is not uniformly continuous.Our results apply to all nonlinear scalar conservation laws and to nonlinear hyperbolic systems of two equations.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Urban Accessibility in Motorcycle Dependent Cities - Case Study in Ho Chi Minh City, Vietnam

Motorcycle dependent cities have specific characteristics in terms of urban accessibility.A rapid increase in the number of motorcycles and other private motorized modes make transport problems more serious and cause severe capacity problems for the infrastructure systems in these cities.Therefore,it is necessary to optimize the development of different modes to meet travel demand and to ensure accessibility in all urban areas.This paper aims to explore accessibility conditions in Ho Chi Minh City,a typical motorcycle dependent city in Vietnam.Understanding of accessibility could be the key element for urban planning in Ho Chi Minh City in particular and motorcycle dependent cities in general.Then,management measures for motorcycles and competitive modes will be proposed to improve the accessibility conditions and thus support sustainable urban transport development for motorcycle dependent cities.

基于Graph-LSTMs的双重位置感知方面级情感分类

目前针对用户评论中方面词项情感分类任务的研究大多忽略了依存句法信息,或并未建立依存句法结构与单词之间的联系。为此,提出一种基于Graph-LSTMs的双重位置感知方面级情感分类方法。通过Graph-LSTMs学习词项的上下文语境特征;在双向GRU的输入中拼接具有双重位置信息的位置向量,优化句子情感编码;利用注意力机制捕获关键的情感特征,实现分类。在SemEval2014的两个数据集上的实验结果表明,该模型相比几种基线模型在准确率和Macro-F1这两个指标上提升明显。