高等工程创新人才培养体系研究——以江苏大学流体机械专业为例

高素质创新型人才的培养是高校21世纪面临的重要使命。由此,应依据以“4C”能力为核心的一流人才培养理念和流体机械人才培养体系的创新改革思路,并在实践中注重构建课程新体系以及与之相适应的教材体系,创建立体化的工程实践教学体系,并在实践基础上完善一流人才培养体系的创新机制。     

基于Bass修正模型的产业集群技术创新扩散研究

产业集群技术创新扩散具有不同于一般技术创新扩散的多个特征,无法采用现有分析方法进行研究.本文在对技术创新扩散过程Bass模型研究的基础上,指出运用Bass模型研究产业集群技术创新扩散存在的不足,据此拓展、建立了产业集群技术创新扩散Bass修正模型,并进行了仿真、分析、比较.结果表明,本文提出的Bass修正模型更合理、有效,结论更符合实际情况,可为研究产业集群技术创新扩散问题提供理论与方法支持.     

多体系统动力学的算法研究现状及应用

本文分析了数值算法、遗传算法和人工神经网络算法等的研究现状、应用及对其发展趋势的展望,以期为研究多体系统动力学的算法研究提供有利的参考。     

基于系统动力学的区域林业经济发展战略仿真与调控研究

如何合理开发利用林业资源,优化林业产业,发展现代林业经济,实现人与自然的和谐发展,是中国林业面临的基本问题。利用系统动力学的方法对区域林业经济进行建模、仿真与调控研究。以黑龙江省南岔区（局）林业经济系统为例,在分析系统结构的基础上,构建了林业经济系统SD模型,并且对总体模型进行历史检验与仿真分析。通过调整对经济可持续发展系统有重要影响的政策性参数,并利用其参数的不同组合,规划模拟南岔区（局）经济发展的多种模式。研究结果表明,结合模型中政策变量的可行性变动影响,当“政府林业科技投入占财政支出比例”“林业企业科技投入占销售收入比例”和“投资比例”,同时增加1％时,系统达到相对最优状态,更符合可持续发展的要求。最后根据结果提出了提升林业经济发展水平的可行的政策建议。     

Inference for serological surveys investigating past exposures to infections resulting in long-lasting immunity – an approach using finite mixture models with concomitant information

This paper is concerned with developing a latent class mixture modelling technique which efficiently exploits data from serological surveys aiming to investigate past exposures to infections resulting in long-term or life-lasting immunity. Mixture components featured by antibody assays’ distribution are associated with the serological groups in the population, whilst the probability mixture that an individual belongs to the positive serological group is regarded as an age-dependent prevalence. The latter embeds a mechanistic model which explains the infection process, accounting for heterogeneities, contact patterns in the population and incorporating elements of study design. A Bayesian framework for statistical inference using Markov chain Monte Carlo estimation methods naturally accommodates missing responses in the data and allows straightforward assessement of uncertainties in nonlinear models. The applicability of the method is illustrated by investigating past exposure to varicella zoster virus infection in pre-school children, using data from a large scale UK cohort study which included a cross-sectional serological survey based on oral fluid samples.     

Bootstrapping Noncausal Autoregressions: With Applications to Explosive Bubble Modeling

In this article, we develop new bootstrap-based inference for noncausal autoregressions with heavy-tailed innovations. This class of models is widely used for modeling bubbles and explosive dynamics in economic and financial time series. In the noncausal, heavy-tail framework, a major drawback of asymptotic inference is that it is not feasible in practice as the relevant limiting distributions depend crucially on the (unknown) decay rate of the tails of the distribution of the innovations. In addition, even in the unrealistic case where the tail behavior is known, asymptotic inference may suffer from small-sample issues. To overcome these difficulties, we propose bootstrap inference procedures using parameter estimates obtained with the null hypothesis imposed (the so-called restricted bootstrap). We discuss three different choices of bootstrap innovations: wild bootstrap, based on Rademacher errors; permutation bootstrap; a combination of the two (“permutation wild bootstrap”). Crucially, implementation of these bootstraps do not require any a priori knowledge about the distribution of the innovations, such as the tail index or the convergence rates of the estimators. We establish sufficient conditions ensuring that, under the null hypothesis, the bootstrap statistics estimate consistently particular conditionaldistributions of the original statistics. In particular, we show that validity of the permutation bootstrap holds without any restrictions on the distribution of the innovations, while the permutation wild and the standard wild bootstraps require further assumptions such as symmetry of the innovation distribution. Extensive Monte Carlo simulations show that the finite sample performance of the proposed bootstrap tests is exceptionally good, both in terms of size and of empirical rejection probabilities under the alternative hypothesis. We conclude by applying the proposed bootstrap inference to Bitcoin/USD exchange rates and to crude oil price data. We find that indeed noncausal models with heavy-tailed innovations are able to fit the data, also in periods of bubble dynamics. Supplementary materials for this article are available online.     

Psychiatric Services: Organizing Impossibility

This paper explores the proposition thatelements of the work of psychiatric services areimpossible. The notion of impossibility is consideredfrom three perspectives: societal, interpersonal, andorganizational.Organizational elements of impossibility areevidenced in attempts to work with madness, a phenomenonthat defies clear definition. Resultant confusion inrole and task boundaries is exacerbated by madinterpersonal interactions. The situation iscompounded by societal conflicts between the mad and thesane. Attempts to house and treat the mad over the past200 years are reviewed, and recent illustrativecase material is presented from two communitytreatment teams. It is argued that primarilysolution-focused approaches to madness have limitedsuccess. To acknowledge impossible elements of the workmay enable psychiatric institutions to be designedin ways that help make individual and collective madnessmore bearable rather than something to be controlled andor denied.     

The roles of caregivers and friends in adolescent daily emotion dynamics

Daily emotion dynamics provide valuable information about individuals’ emotion processes as they go about their lives. Emotion dynamics such as emotion levels (mean), emotion variability (degree of fluctuation), and emotion network density (strength of temporal connections among emotions) are associated with risks for various psychopathology in youth and adults. Prior work has shown that caregivers and friends play crucial socializing roles in adolescent emotional well-being, but less is known about their roles in daily emotion dynamics. This study examined whether caregiver emotion coaching, caregiver-adolescent closeness, and friendship quality were associated with adolescents’ emotion levels, emotion variability, and emotion network density. Further, we examined whether caregiver-adolescent closeness moderated the associations between coaching and emotion dynamics. Participants were 150 adolescents (61% girls; M_age = 14.75) and one of their caregivers (95% female; M_age = 43.35) who completed a baseline survey and 21 daily surveys. Results showed that caregiver emotion coaching interacted with caregiver-adolescent closeness in predicting emotion levels and variability. Specifically, when closeness was higher, emotion coaching was significantly associated with lower sadness and anger levels, higher happiness levels, and lower happiness variability. Caregiver emotion coaching, independent of closeness, was also associated with lower anxiety levels, lower sadness variability, and lower emotion network density. Friendship quality was significantly associated with lower levels of sadness, anxiety, and anger, higher levels of happiness, and lower variability in anxiety and anger. These findings suggest that caregivers and friends are central to everyday emotion levels and variability and a more flexible emotion system in adolescents.     

P2P网贷市场演化：基于系统动力学的仿真研究

准确把握P2P网贷市场的演化规律,对其发展至关重要。采用系统动力学方法,综合考虑市场内外因素,建立P2P网贷市场演化模型并对P2P市场在2014年7月至2017年4月间的演化情况进行仿真。研究表明：未来的P2P网贷余额数量将逐步攀升,但受到竞争压力与监管力度变化的影响,其平台数量会不断减少,出现强者愈强的局面;网贷市场对政府监管力度的敏感程度较高,受政策的影响程度较大;提高P2P网贷市场的征信能力是增强其活力的重要途径。     

重大疫情下基于系统动力学的网络舆情应急响应研究

SARS和新型冠状肺炎等重大疫情催生的负面网络舆情容易引发物价飞涨、民众恐慌等次生效应,造成巨大社会危害。洞悉舆情传播规律,利用政务新媒体及时引导与控制其演化趋势,对控制疫情、稳定经济社会秩序具有重要意义。文章基于“情景—回应”视角,利用COVID-19期间的十五次舆情事件数据建立系统动力学仿真模型。基于“逆向回归法”,进一步提高模型的仿真效果。以“接子回荆”事件为例,文章通过建模、仿真、分析,验证了模型的有效性。对政务新媒体回应策略进行模拟分析,结果表明：政务新媒体的回应频率和媒体的影响力程度会对舆情热度产生非线性影响;政务新媒体的回应速度会对舆情的峰值、谷值以及生命周期产生较大影响。