在线课程与翻转课堂相结合的大学化学混合式教学实践

介绍了自2017年以来天津大学大学化学课程使用在线课程和翻转课堂相结合的混合式教学实践,包括课程的组织模式、翻转课堂内容的选择、翻转课堂课时与课程总课时的比例、期末成绩分析和教学反思等。教学实践结果显示,选取学生在中学化学有一定基础的知识点作为教学内容,课时占课程总课时的30%–50%时进行翻转课堂教学,教学效果明显好于完全传统教学的平行对照班。     

“互联网+”背景下基础化学原理实验在线学习与考试系统的开发与实践

In recent years, the use of mobile terminals such as smart phones is increasingly popular with the rapid development of the mobile Internet. The online learning and examination system of basic chemistry laboratory is designed and developed under the background of "Internet + education". The system can achieve five functions: chemical laboratory safety education and examination, theoretical learning and examination of basic chemistry laboratory, test database management, online examination on mobile terminal, score management and statistical analysis. The development and construction of this online system conforms to the development trend of "Internet + education", which is beneficial to both students' independent learning and examination resource saving.     

UOOC平台与腾讯课堂相结合的“在线异步教学”

The COVID-19 broke out on a large scale, and online teaching became the first choice for teachers. Avoiding network congestion and ensuring learning effects are the dilemmas faced by online teaching. With the help of the UOOC platform and the Tencent Classroom, taking the "Polymer Structure and Performance" course as a sample, we suggested a new "online asynchronous teaching" model. Among them, the SPOC course is convenient for students to manage their learning time; "Chapter Quiz" and "Student-to-Student Mutual Evaluation" are helpful for students to obtain multi-angle feedback; project tasks are focused on case analysis so that students carry out inquiry-based learning. Overall, the "online asynchronous teaching" model breaks the time and space restrictions on teaching and learning, guarantees the quality of teaching during the epidemic, and lays the foundation for creating a "golden class".     

“课前六备、课中六讲、课后六辅”在线课程混合式教学模式研究与实践——以基于“雨课堂+钉钉直播”的中等无机化学课程为例

Taking the online teaching of "inorganic chemistry" course at Dezhou University as an example, the "six steps before class, six steps in class, six steps after class" online course teaching model based on "Rain Class + Dingding Live" and an online "short class" were constructed. The main content and implementation methods of teaching design, teaching process, teaching effect, teaching reflection, teaching evaluation in online course teaching were discussed. It has been proved in practice that this model can effectively improve students' learning interest and enhance teaching effects.     

液液萃取-高分辨气相色谱-高分辨双聚焦磁质谱法测定尿中羟基多环芳烃代谢物

建立了灵敏、准确的液液萃取-高分辨气相色谱-高分辨双聚焦磁质谱（LLC-HRGC-HRMS）同时测定人体尿样中8种多环芳烃（PAHs）代谢物的方法。将2 mL尿液与氘或¹³C标记的同位素内标物进行混合,在抗坏血酸的存在下酶解偶联目标物,再将游离化合物用甲苯-戊烷（1：4,v/v）萃取后浓缩至近干,再用甲苯复溶,衍生后经HRGC-HRMS分离和测定。1-羟基萘在0.14~41.6 μg/L、1-羟基菲在0.05~8.33 μg/L、2-羟基菲在0.04~8.33 μg/L、其他5种代谢产物在0.02~8.33 μg/L范围内线性关系良好,相关系数＞0.99,方法的检出限为0.006~0.042 μg/L,平均回收率为81.4%~127.0%,日内和日间精密度分别为2.7%~6.9%和5.1%~10.9%（n=6）。应用该方法测定330份人群尿液样本,3-羟基䓛和6-羟基䓛未检出,其他6种PAHs代谢物检出率为100%。该方法灵敏、准确、稳定,适用于人尿中8种PAHs代谢物的定量分析。     

化学类专业课程在线教学模式的探索与实践*——以“分析化学”为例

以分析化学为例,构建和实践了以学生为中心,实现课上与课下、理论与实践、现实与虚拟多种教学方式有机融合的教学模式。本教学模式为化学类专业课程完全线上教学的开展提供了可行途径,同时为线上线下混合式教学的开展提供了新的思路。     

带机器准备时间的平行机ordinal排序及近似算法

本文研究带机器准备时间的m台平行机ordinal在线排序问题。讨论了在极小化最大机器完工时间和极小化最大工件完工时间两种目标下的不同下界和相应的在线近似算法。对第一个目标，我们得到了3/2的下界和最坏情况界为2-1/m的近似算法。对第二个目标，我们得到了最坏情况为m的最好近似算法。我们还对一些特殊情况进行了分析。     

深部煤层采煤机关键部件磨损故障监测诊断的难点问题

正煤炭是我国的主体能源,煤炭产业是支撑国家经济发展的支柱之一.随着浅层煤矿接近枯竭,深部煤矿(500~1 000 m)开采已成必然.2013年我国煤炭产量达37亿吨,其中开采深度大于500 m的矿井超过40%,主体开采深度已达800 m以下,已有40多个矿井的开采深度超过千米.深部煤层的瓦斯爆炸及岩层垮塌、突水等地质灾害发生率高、偶然性强,严重威胁采掘矿工生命安全.国家安全监管局统计结果表     

一种惯性辅助卫星完好性监测多解分离法的优化方法

针对惯性辅助卫星完好性监测多解分离法计算量大的问题,给出了一种基于参数序贯复用的优化方法。在对多解分离法及其计算量分析基础上,采用序贯处理技术,推导了低阶顺序处理多解分离法。同时根据系统结构特点以及滤波器间量测信息高度相关性,提出滤波器参数复用方法,避免参数重复计算。通过仿真实验,从故障引入时间和可见星数目变化两个方面对优化前后多解分离法的计算量进行了对比。仿真结果表明该优化方法可以有效降低多解分离法的计算量,便于工程应用。     

Online multiple instance regression

The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method ＂OnlineMIR＂ is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets.