大学数学素质拓展课程的教学实践

本文结合昆明理工大学实际,探讨了在新的时代要求下为高校非数学专业学生开设数学拓展课程的必要性,并提出了相关的理论依据。同时,本文在已开设拓展课程的基础上总结了取得的成绩和存在的不足,为今后课程设置及教学的进一步完善提出了相关建议。     

专题微课生物化学教学新模式的探索与实践

专题微课教学是基于一门学科或课程的某个重要专题设计开发的教学模式。课题组在生物化学的课程中设计了专题微课。生物化学的专题微课教学包括确立教学目标、教学方法、微课录制与编辑加工等环节，应注意建立全新的网络教学平台、互动式教学、抓好指导小组资料整理等教学策略，其成功需要教师有较高的综合素质和教学技能。实践证明，生物化学的专题微课教学取得了良好的教学效果，学生的成绩有了一定提高。     

计算机相关学科的PDSIPE教学模式应用

随着国家应用型教学变革的推进,应用型教学途径与具体策略成为学校关注的焦点。借鉴已有的应用型研究成就并在课堂中直接运用,无疑会成为应用型教学改革成败的关键。PDSIPE应用型教学理论与技术的出现较好地解决了这一问题。燕京理工学院以计算机学科为试验对象,开展应用型课堂教学试验,验证PDSIPE教学理念与操作技术在计算机相关学科中的教学价值。     

基于工作过程的数控编程与操作课程学习情境设计

现代制造企业对高职院校学生的职业技能要求越来越高,开展课程教学改革,实施理实一体化教学非常重要。数控编程与加工课程是数控技术专业核心课程,开展基于工作过程的数控编程与操作课程学习情境设计,实施理实一体化教学,有利于提高学生职业技能的培养。     

The characteristic function of rough Heston models

It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with small Hurst parameter, provide very relevant dynamics in order to reproduce the behavior of both historical and implied volatilities. However, due to the non‐Markovian nature of the fractional Brownian motion, they raise new issues when it comes to derivatives pricing. Using an original link between nearly unstable Hawkes processes and fractional volatility models, we compute the characteristic function of the log‐price in rough Heston models. In the classical Heston model, the characteristic function is expressed in terms of the solution of a Riccati equation. Here, we show that rough Heston models exhibit quite a similar structure, the Riccati equation being replaced by a fractional Riccati equation.     

基于“三位一体”模式的大学生创新能力培养研究与实践

本文根据创造力三维模型理论,构建了以课堂教学为引导、大学生科技创新训练为平台、科研实践活动为载体的"三位一体"大学生创新能力培养模式,并通过教学实践环节锻炼学生的创新思维,有效提升大学生的创新能力,为培养具有良好综合素质的创新人才建立可行机制。     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

ASSESSING THE IMPACT OF DISTRIBUTIVE POLICIES ON THE BRAZILIAN ECONOMY USING AN SCGE MODEL

This study investigates the impact of macroeconomic policies on the Brazilian economy. We present a two-sector, open-economy, Structuralist Computable General Equilibrium model that distinguishes among three economic classes and assumes no financial sector. The Social Accounting Matrix for Brazil in 2006 serves as a benchmark for our model. We compare the medium-run effects of five experiments: an income transfer towards formal workers, a transfer to informal labour, an investment shock, an exchange rate depreciation, and a policy mix that combines (exchange rate) depreciation with income transfer towards modern (sector) workers. The policy measures reinforce each other in terms of their potential to enhance growth. Our findings underscore the importance of redistributive policies to foster economic expansion.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.     

A functional time series analysis of forward curves derived from commodity futures

We study forward curves formed from commodity futures prices listed on the Standard and Poor’s-Goldman Sachs Commodities Index (S&P GSCI) using recently developed tools in functional time series analysis. Functional tests for stationarity and serial correlation suggest that log-differenced forward curves may be generally considered as stationary and conditionally heteroscedastic sequences of functions. Several functional methods for forecasting forward curves that more accurately reflect the time to expiry of contracts are developed, and we found that these typically outperformed their multivariate counterparts, with the best among them using the method of predictive factors introduced by Kargin and Onatski (2008).