Constructing a Basefile for Simulating Kunming’s Medical Insurance Scheme of Urban Employees

Focusing on China’s medical insurance scheme which covers all employers and employees in urban areas, this research aims to assess the distributional impacts of medical insurance policies and to predict medical expenses by using microsimulation techniques. As an important part of the project, this article provides a brief overview of China’s medical insurance reform of urban employees and detail the techniques and processes to construct a basefile in 2005 for projecting the medical expenditures for urban employees over the period of 2006-2010. The main data used are administrative medical records of medical insurance participants provided by the Bureau of Labour and Social Security of Kunming, Yunnan Province. Along with the initial analysis for the raw datasets and age processing and adjustment for the individual records, monthly income information was imputed and personal savings accounts were established for each individual record. Important modelling parameters such as death rates and income adjustment factors were constructed. Furthermore, this article identifies medical insurance for government officials by using the combination of logarithm curve fitting and binary discriminant analysis. Based on this basefile, a static microsimulation model can be built to assess the implementation effects of the medical insurance policy and analyse the impact of the medical insurance scheme on urban employees.Urban medical insurance, China, microsimulation, basefile, Policy Research

Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes

Engaging Hmong Learners With Oral Storytelling And Flipgrid

Since the start of the covid-19 pandemic, Minnesota had to make the decision to temporarily close schools. Many school districts resorted to online distance learning in order to protect the community from covid-19 spread. There are so many teachers that have not been prepared to teach from an online model. Moreover, teachers were losing engagement from students. Hmong students face a unique challenge where engagement is highly absent due to the switch of an online learning setting. This capstone explored a curriculum design that supports the cultural tradition of oral storytelling using Flipgrid to enhance engagement in the online classroom for Hmong students at the 7th grade level. Pre and post surveys, oral storytelling elements, writing, and Flipgrid are the main resources to support engagement among Hmong students along with rubrics to evaluate the level of success from the curriculum. Research concluded that incorporating more than one online learning media platform and using culturally relevant practices in the classroom can support the engagement of Hmong students, such as oral storytelling and Flipgrid. This provides the occasion to further explore the possibilities of using culture and technology together for online education