基于乘用车物流运输模型程序算法的分析

基于第十一届“华为杯”全国研究生数学建模竞赛E题，针对一类乘用车物流运输问题，首先建立了单目的地乘用车运输优化模型；其次通过在遗传算法中构造新的适应度函数对模型求解，得到较优的运载方案。该程序算法对直接利用软件或Java Web技术解决一般的乘用车物流运输计划问题提供了新的算法思想。     

金融市场的非线性:混沌与分形

传统的金融理论在有效市场假设的基础上,假定市场的收益率变化服从正态分布或对数正态分布, 但事实上,市场收益率分布明显偏离状态分布,呈现一种“胖尾特征”,由此根据非线性科学的混沌理 论和分形理论,对金融市场分形特征研究进行总结,并分析了国内外的研究现状。     

经济预测的混沌分析

未来经济发展状况一直是人们探讨的问题。由于经济现象纷繁复杂,现有的经济预测理论与方法还不能对此给予完全合理的解释和有效的预测,经济预测的实效往往不佳,为此,以混沌理论为基础,对经济现象的可预测性、影响经济预测精度的原因以及如何提高经济预测精度进行了探讨。     

现代企业活力分析

面对来自美国为首的世界经济衰退对中国的影响 ,来自全球化网络经济的冲击 ,以及 2 0世纪中国经济发展中的遗留问题 ,中国应继续执行和相应调整“两手政策” ,并着重采取发挥现代企业活力的政策。在阐述现代企业活力分析的目的和内容的基础上 ,建立了将现代企业活力的技术分析和经济分析相结合的分析理论体系、程序和方法。     

Managing complexity： A problem of chaos in fisheries policy

This work intends to present chaos theory （and dynamical systems such as the theories of complexity）, in terms of interpretation of ecological phenomena. The chaos theory applied in the context of ecological systems, especially in the context of fisheries has allowed the recognition of the relevance of this kind of theories to explain fishing phenomena and fisheries policies. It has permitted new advances in the study of marine systems, contributing to the preservation of fish stocks. This paper deals with the way how to manage fisheries taking chaos in account of the problem.     

Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation

In this paper, we perform a sparse filtering recursion for efficient changepoint detection for discrete-time observations. We attach auxiliary event times to the chronologically ordered observations and formulate multiple changepoint problems of discrete-time observations into continuous-time observations. Ideally, both the computational and memory costs of the proposed auxiliary uniformisation forward-filtering backward-sampling algorithm can be quadratically scaled down to the number of changepoints instead of the number of observations, which would otherwise be prohibitive for a long sequence of observations. To avoid model bias, a time-varying changepoint recurrence rate across different segments is assumed to characterise diverse scales of run lengths of the changepoints. We demonstrate the methods through simulation studies and real data analysis.     

Humanitarian demining for the clearance of landmine-affected areas

Humanitarian demining is the process of removing landmines from contaminated areas. Behind this arduous task lies a high risk of collateral damage to deminers and their equipment. The intrinsic operational cost often forces agents to abandon the task before its completion, making such areas a source of risk, especially for the civilian population and biodiversity. Most of the research to date has revolved around the technological development of devices capable of effectively detecting mines at a selected point within an area, while the search strategy itself to decide where to explore and which route to follow has been understudied. This paper proposes a decision support model (DSM) that produces highly safe and cost-effective mine detection search plans. Since the computational time of the solution increases along with the size of the area explored, exact linear methods are proposed for small and medium instances of the problem, whereas a genetic algorithm (GA) is proposed for large instances. Results show that the GA approach is capable of delivering solutions close to the optimum.     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.     

Predicting demand for air taxi urban aviation services using machine learning algorithms

This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms (MLAs). Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors for four popular MLAs, namely, logistic regression, artificial neural networks, random forests, and gradient boosting. Experimental results suggest gradient boosting to consistently provide higher prediction performance. Specific locations, certain time periods and weekdays consistently emerged as critical predictors.     

A data-driven approach to forecasting ground-level ozone concentration

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to forecasts of the day-ahead maximum value of ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use-case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighborhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions. Our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps to increase the accuracy of the forecasts for specific ranges of ozone’s daily peak values.