Numerical analysis of ossicular chain lesion of human ear

Abstract Lesion of ossicular chain is a common ear disease impairing the sense of hearing. A comprehensive numerical model of human ear can provide better understanding of sound transmission. In this study, we propose a three-dimensional finite element model of human ear that incorporates the canal, tympanic membrane, ossicular bones, middle ear suspensory ligaments/muscles, middle ear cavity and inner ear fluid. Numerical analysis is conducted and employed to predict the effects of middle ear cavity, malleus handle defect, hypoplasia of the long process of incus, and stapedial crus defect on sound transmission. The present finite element model is shown to be reasonable in predicting the ossicular mechanics of human ear.

“Products-Knowledge” Two-Dimensional Increment Model of Fashion Industry Value Chain Based on Industry Analysis

Fashion industry has a complex characteristic for it spans the first, second, and third industries. In addition, the characteristic of creative industry has high value-added for its knowledge outputting, which makes the traditional value-added analysis based on supply chain not easy and good enough to interpret its industry value-added features. From the perspective of "products-knowledge" two-dimensional analysis,a fashion industry value chain increment model is built,by simulating the process of "product flow" and "information flow" value-added. The fashion industry value chain increment model provides an effective way for the enterprise strategy formulation and production strategy adjustment.

Conditional autoregressive negative binomial model for analysis of crash count using Bayesian methods

In order to improve crash occurrence models to account for the influence of various contributing factors, a conditional autoregressive negative binomial （CAR-NB） model is employed to allow for overdispersion （tackled by the NB component）, unobserved heterogeneity and spatial autocorrelation （captured by the CAR process）, using Markov chain Monte Carlo methods and the Gibbs sampler. Statistical tests suggest that the CAR-NB model is preferred over the CAR-Poisson, NB, zero-inflated Poisson, zero-inflated NB models, due to its lower prediction errors and more robust parameter inference. The study results show that crash frequency and fatalities are positively associated with the number of lanes, curve length, annual average daily traffic （AADT） per lane, as well as rainfall. Speed limit and the distances to the nearest hospitals have negative associations with segment-based crash counts but positive associations with fatality counts, presumably as a result of worsened collision impacts at higher speed and time loss during transporting crash victims.

A Micro Perspective of Research Dynamics Through“Citations of Citations”Topic Analysis

Purpose:Research dynamics have long been a research interest.It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject.A micro perspective of research dynamics,however,concerning a single researcher or a highly cited paper in terms of their citations and“citations of citations”(forward chaining)remains unexplored.Design/methodology/approach:In this paper,we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance,and topic innovation in each generation of forward chaining.Findings:For highly cited work,scientific influence exists in indirect citations.Topic modeling can reveal how long this influence exists in forward chaining,as well as its influence.Research limitations:This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations.Paraphrasing or semantically similar concept may be neglected in this research.Practical implications:This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining.This can serve as an inspiration on how to adequately evaluate research influence.Originality:The main contributions of this paper are the following three aspects.First,besides research dynamics of topic inheritance and topic innovation,we model topic disappearance by using a cross-collection topic model.Second,we explore the length and character of the research impact through“citations of citations”content analysis.Finally,we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton’s publications and the topic dynamics of forward chaining.

ICA Based Identification of Time-Varying Linear Causal Model

Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.

Tour Level Mode Choice Analysis of Madison Area in Wisconsin

This study is to investigate what factors and how they affect tours (trip chains) behavior. The key issue is the understanding and definition of tour and tour level mode. Also, these definitions should fit for the data. A semi-home based tour definition is stated, and a competing mode based tour mode is defined. Based on the definition, this study used Madison Area Data from National Household Survey to estimate a MNL structured model. It is found that travel distance could be a positive factor for car mode. Meanwhile, the number of trips is also a positive factor for choosing car.

Impact of Inflation, Dollar Exchange Rate and Interest Rate on Red Meat Production in Turkey： Vector Autoregressive （VAR） Analysis

In this study, impact of inflation （WPI--Wholesale Price Index）, exchange rate, and interest rate on the production of red meat in Turkey was examined using the vector autoregressive （VAR） model. The model consisting of variables of dollar exchange rate, inflation rate, interest rate, beef, buffalo meat, mutton, and goat meat production amounts has been estimated for the period from 1981 to 2014. It has been detected that there is a tie among the dollar exchange rate, inflation rate, interest rate, and the amount of red meat production in Turkey. In order to determine the direction of this relation, Granger causality test was conducted. A one-way causal relation has been observed between： the goat meat production and dollar exchange rate; the buffalo meat production and the mutton production; and the beef production and the mutton production. To interpret VAR model, the impulse response function and variance decomposition analysis was used. As a result of variance decomposition, it has been detected that explanatory power of changes in the variance of dollar exchange rate, inflation rate, and interest rate in goat meat production amount is more than explanatory power of changes in the variances of mutton, beef, and buffalo meat variables.

Optimization of Manufacturing Supply Chain with Stochastic Demand and Planned Delivery Time

In this paper, a manufacturing supply chain system composed by a single-product machine, a buffer and a stochastic demand is considered. A stochastic fluid model is adopted to describe the system and to take into account stochastic delivery times. The objective of this paper is to evaluate the optimal buffer level used in hedging point policy taken into account planned delivery times, machine failures and random demands. This optimal buffer allows minimizing the sum of inventory, transportation, lost sales and late delivery costs. Infinitesimal perturbation analysis method is used for optimizing the proposed system. Using the stochastic fluid model, the trajectories of buffer level are studied and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm, which determines the optimal buffer level in the presence of planned delivery time. Also in this work, we discuss the advantage of the use of the infinitesimal perturbation analysis method comparing to classical simulation methods.

Local and global sensitivity analysis of PCR kinetic model

A mathematical model of polymerase chain reaction (PCR) containing uncertain and time-varying parameters has become important for model-based guidance experiment design. In this study, the local and global sensitivity analyses were conducted to identify that the responses of PCR process vary with their parameters of initial reactant concentrations and rate constants. Our results showed that the template concentration in initial reactant concentrations had the largest effect on DNA amplification yield. The rate constant characteristics showed that the local sensitivity basically determined the specific reactions; and the global sensitivity, the non-specific reactions. Our work should be helpful for optimizing PCR experimental conditions, and determining the PCR parameter sensitivities.

Observed communication between oncologists and patients:A causal model of communication competence

AIM： To investigate and test a causal model derivedfrom previous meta-analytic data of health provider be-haviors and patient satisfaction.METHODS： A literature search was conducted forrelevant manuscripts that met the following criteria：Reported an analysis of provider-patient interaction inthe context of an oncology interview; the study hadto measure at least two of the variables of interest tothe model （provider activity, provider patient-centeredcommunication, provider facilitative communication,patient activity, patient involvement, and patient satis-faction or reduced anxiety）; and the information had tobe reported in a manner that permitted the calculationof a zero-order correlation between at least two of thevariables under consideration. Data were transformedinto correlation coefficients and compiled to producethe correlation matrix used for data analysis. The test of the causal model is a comparison of the expected correlation matrix generated using an Ordinary Least Squares method of estimation. The expected matrix iscompared to the actual matrix of zero order correlation coeffcients. A model is considered a possible ft if the level of deviation is less than expected due to random sampling error as measured by a chi-square statistic. The signifcance of the path coeffcients was tested us-ing a z test. Lastly, the Sobel test provides a test of the level of mediation provided by a variable and provides an estimate of the level of mediation for each connec-tion. Such a test is warranted in models with multiple paths.RESULTS： A test of the original model indicated a lack of ft with the summary data. The largest discrepancy in the model was between the patient satisfaction and the provider patient-centered utterances. The observed correlation was far larger than expected given a medi-ated relationship. The test of a modifed model was un-dertaken to determine possible ft. The corrected model provides a fit to within tolerance as evaluated by the test statistic, χ2 （8, average n = 342） = 10.22. Each of the path coefficients for the model reveals that each one can be considered signifcant, P 〈 0.05. The Sobel test examining the impact of the mediating variables demonstrated that patient involvement is a signifcantmediator in the model, Sobel statistic = 3.56, P 〈 0.05. Patient active was also demonstrated to be a signifcant mediator in the model, Sobel statistic = 4.21, P 〈 0.05. The statistics indicate that patient behavior mediates the relationship between provider behavior and patient satisfaction with the interaction.CONCLUSION： The results demonstrate empirical support for the importance of patient-centered care and satisfy the need for empirical casual support of provider-patient behaviors on health outcomes.

时序数据的因果关系交互式可视分析

针对时序数据中因果关系检测算法效率低、错误率高、可解释性低的问题,本文提出一种新颖的用于时序数据的因果关系检测模型。该模型整合了泛函贪婪等价搜索(F-GES)模型与格兰杰(Granger)因果关系模型,展开因果关系的抽取和推断,并提出了因果关系可视分析方法,以交互式地分析时序数据中变量间的因果关系。可视分析方法形成了参数视图用于提高因果关系探索效率、因果关系树图用于直观有效地展示变量之间的因果关系、时间视图用于比较原始时序数据、堆叠流图用于帮助用户探索时序数据的层次演变以及平行坐标图用于进行相关性分析。基于真实数据形成的原型系统交互式地验证和总结时序数据中的因果关系,从而更高效地挖掘和理解时序数据中变量之间蕴含的因果规律以帮助决策。

郑州“7·20”地铁水淹事件STAMP致因分析

为探讨河南郑州“7·20”地铁5号线伤亡事件致因,综合运用系统理论事件模型与过程(STAMP)模型,分析5号线伤亡事件安全控制结构,据此逐级辨识事件致因;基于复杂网络理论将事件致因及其联系抽象为网络的节点和边,建立地铁水灾事件网络;计算聚类系数、度数等拓扑参数定量分析事件致因性质,确定关键致因。结果表明:地方党委政府面对汛情的应对部署不紧不实,缺少有效的组织动员和有关部门对工程建设存在失管失察是事件的深层根源;建设单位、设计单位等五方主体单位及运维单位的主体责任失职是事件的主观关键致因;应从政府及有关部门、五方主体单位和运维单位方面采取多元协同的风险防控措施。

融合异质层次因果图的尾矿堆积坝安全状态分析

为探究尾矿堆积坝内外部因素对堆积坝体安全状态的影响,提出基于异质层次图的尾矿堆积坝安全状态分析方法。该方法通过构建异质层次因果图,将环境、渗流场和应力场等关键因素与堆积坝安全状态建立联系,并结合异质节点属性特征,形成环-渗-应安全状态分析指标体系;通过提出定量指标动态等级区间计算方法和安全状态等级计算模型,将坝体安全状态定性问题的模糊性转化为定量化表达,实现对尾矿堆积坝安全等级评价;以洛阳某尾矿坝为例验证模型的科学性。结果表明:该模型能合理定量分析因素与状态之间联系,并找出堆积坝负向演化的敏感性因素,给后续筑坝过程的安全管理提供决策依据。

粤港澳大湾区“四链”融合发展评价探索性研究:基于耦合协调度与灰色关联度的分析

面对当前要素驱动向创新驱动转型的现实发展需求,粤港澳大湾区已初步构筑起创新链、产业链、资金链、人才链“四链”深度融合的基础生态,但融合的状态、水平和层次都存在可以优化提升的空间。文章采用耦合协调度与灰色关联度对粤港澳大湾区“四链”融合发展水平进行实证分析,研究发现:“创新—产业—资金—人才”四个子链已逐渐从低水平均衡向高水平均衡过渡,从极度不协调发展向高度协调状态过渡发展。“创新链—产业链”子系统耦合对整体“四链”融合水平贡献度最高。

普惠金融对经济韧性和产业结构升级的影响研究

普惠金融作为我国金融发展战略“五篇大文章”之一,是未来经济增长的动力所在。在经济因素、政治因素、环境因素不断波动的当下,国家经济体系的发展质量至关重要。本文探究普惠金融与经济韧性和产业结构升级之间的因果关系和动态影响,对于增强我国经济系统应对外部冲击、实现经济的自我造血有着重要意义。结果表明,普惠金融通过多渠道对经济韧性和产业结构升级产生促进作用,但普惠金融可能因为金融结构不合理和过度金融问题对产业结构升级产生抑制作用。建议加大普惠金融渗透度和使用度、优化对创新创业项目的支持、多样化加强普惠金融供给、优化金融供给侧结构、警惕“金融过度”。

油气管道事故原因分类模型及其社会网络分析

为提高油气管道事故预防策略的有效性,首先,基于系统理论事故建模与过程(STAMP)模型及人为因素分析与分类系统(HFACS)模型,建立油气管道事故预防控制结构,分析国内外35起油气管道事故原因,并依据扎根理论对分析结果进行统计编码,得出油气管道事故原因分类模型;其次,应用社会网络分析法构建油气管道事故原因关系网络,通过核心—边缘分析、中心性分析和关联方向指数分析,识别油气管道事故原因中核心及具有高关联性和强影响力因素。研究结果表明:油气管道事故原因分类模型包含6个层次:政府及监管部门因素、第三方因素、运营商组织因素、运营商不安全监督、现场人员不安全行为的前提条件、现场人员的不安全行为,并可将其细分为22个最底层原因因素。其中,政府及监管部门因素、运营商组织因素、运营商不安全监督和第三方因素均为核心因素;制度缺陷、监督不充分、运行计划不当、第三方破坏行为、管材与焊缝缺陷、施工/维修/配件问题、技能失误均为具有高关联性和强影响力因素。

信息计量学的创新之道:近年优秀国际期刊论文的启示

[目的/意义]在智联网时代,信息计量学的研究环境、研究对象、研究内容和研究方法都发生了深刻变化。揭示近年信息计量学研究的创新之道,有助于了解信息计量学领域的前沿知识,为信息计量学的高质量发展提供参考。[方法/过程]文章系统梳理了2019—2022年的国际信息计量学研究,归纳出三类创新之道。[结果/结论]一是通过引入计算机科学、数学、经济学和社会学等领域的先进知识,实现交叉融合创新;二是辩证思考经典的计量方法、计量指标和计量模型,实现分析方法创新;三是顺应智能预测和因果推断的时代潮流,实现研究范式创新。结论表明,为适应新时代的发展需要,信息计量学研究者必须积极汲取多学科知识,拓展分析方法的适用范围,不断突破面向国家需求的战略问题。

数字经济发展对中国制造业升级转型的影响研究

利用数字经济发展带来的机遇来促进我国制造业转型升级,并促使其在全球价值链中移动到中高端位置,是实现我国经济高质量发展的必经之路.基于此,通过研究数字经济发展对我国制造业转型升级的影响,采用2013—2022年我国31个省份的面板数据,分析数字经济发展对我国制造业的影响.研究结果表明:数字经济发展能够通过完善数字基础设施建设来直接推动中国制造业转型升级,即在价值链上向研发设计和销售服务两端转移;数字经济发展通过人力资本积累来间接推动中国制造业转型升级,即在价值链上聚集更多的高素质人才来发展中国制造业.

政策视角下光伏全产业链的地区差异性分析

[目的]“双碳”战略为中国能源发展提出了一条新路径,限制对传统化石能源的使用,就促使中国政府加大对新能源产业发展的支持。光伏作为一种覆盖面最广、最易获取的新能源,成为解决当前能源和环境问题的首选。但如果想要增大其市场规模、实现光伏产业的蓬勃发展,就必须要找出并解决当前阶段光伏的痛点与难点。[方法]文章对中国的光伏全产业链政策进行梳理分析,探究光伏产业激励政策存在的不足之处;并利用2000-2021年A股市场上33家样本企业的数据,建立DEA模型探究政策引导下的中国光伏产业综合创新效益和规模效益;再利用面板回归分析法探究不同地区政策对光伏产业上、中、下游企业创新能力的不同影响。[结果]研究表明:政策补贴对光伏上游企业的创新效益影响更为显著,与东部地区的企业发展成正相关关系,西部地区则相反,存在一定的地区差异性。[结论]应该提高在光伏技术创新层面的政策优惠力度,并结合地区优势进行合理的产业链规划布局。