Identification of Pre-examination Errors in the Chemical Pathology Laboratory at the University Hospital of the West Indies

This study evaluated the types and frequencies of pre-examination errors recorded in the chemical pathology laboratory at the University Hospital of the West Indies, Jamaica. This was a retrospective analysis of errors recorded over a three year period. Data analysis was done on an average of 519,084 samples collected and tested per year. Samples included blood, urine, stool and other fluids. Pre-examination errors were identified and recorded following visual inspection of the samples and corresponding request forms by laboratory staff, then subsequently by the Senior Medical Technologist. Errors were generally classified as inappropriate sample (58 %), inappropriate form (23.4 %), inappropriate sample volume (9.3 %) and inappropriate sample tube (9.3 %). Over 90 % of recorded pre-examination errors were related to blood samples while urine samples accounted for 6.8 % error. Pre-examination errors were lower at this study location than elsewhere. Measures aimed at reducing instances of these errors are recommended for improved laboratory quality output.     

新型全自动高精度轴类零件尺寸误差测定装置

随着我国进入现代工业化发展阶段,无论是机械加工、汽车制造、冶金等重工业领域,或是纺织、电子、化工等轻工业领域,轴类零件无处不用,不管是在设备、工具、原料还是产品中都起着至关重要的作用。因此,各方对轴类零件的品质精度要求不断的提高,对于精度的检测也提出了更高的要求。本文提出并实现了一种新型的全自动轴类零件高精度尺寸误差测量系统,通过研制机电一体化的测量装置,编写配套程序,实现了对高速精确定位的测量点进行测定,并将结果通记录和显示的全自动测量装置。目前该装置已应用于打印机零件现场检测工序,并可测量多材质、表面复杂涂装、透明体等物件,应用前景非常广泛。     

基于最小二乘支持向量机算法的小地锚抗拔承载力研究

目的：基于最小二乘支持向量机算法预测小地锚的抗拔承载力。方法：最小二乘支持向量机算法中的输入参数包括等效地锚直径,地锚埋置深度,平均顶椎阻力,平均椎套摩擦力以及安装工艺。使用现场试验的119组数据中的83组数据进行最小二乘支持向量机回归模型分析,并使用剩余的36组数据测试模型的拟合良好性;同时用敏感度分析研究每个输入参数的作用。结论：通过与人工神经网络模型的对比,发现最小二乘支持向量机的性能表现优异。     

Positive error climate promotes learning outcomes through students’ adaptive reactions towards errors

Errors are an integral part of the learning process and an opportunity to increase skills and knowledge, but they are often discouraged, sanctioned and derided in the classroom. This study tests whether students' perceptions of being part of an error-friendly classroom context (i.e., a positive classroom error climate) is positively related to students' learning outcomes via students' adaptive reactions towards errors. A total of 563 Italian middle school students from 32 mathematics classes completed a questionnaire on their perceptions of classroom error climate and their reactions towards errors. Students' math grades were used as indicators of their level of learning outcomes. A multilevel model showed that perceived classroom error climate was positively related to math grades via increased adaptive reactions towards errors. Our findings revealed that an error-friendly classroom context is associated with students’ adaptive adjustment to errors and to better learning outcomes in mathematics.     

浅议课堂教学中教师的“错误”观

在课堂教学中,学生经常会出现各种各样的错误。面对学生的错误,教师所持的态度直接影响着教学的效果和学生错误潜在价值的充分发挥。教师的“错误”观是教师教育观、课程观、学生观的集中反映,也是其教学能力、评价能力的重要体现。教师在课堂教学中面对学生错误时,应该具有科学的“错误”观,如课程资源观、启发契机观、审视教学观、评价信息观。教师应该在这些“错误”观的指导下,采取正确的策略去帮助学生纠正错误。     

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, $F_2$-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.