Accuracy of students' self‐assessment and their beliefs about its utility

The purpose of the two studies presented here was to evaluate the accuracy of students' self‐assessment ability, to examine whether this ability improves over time and to investigate whether self‐assessment is more accurate if students believe that it contributes to improving learning. To that end, the accuracy of the self‐assessments of 3588 first‐year students enrolled in a post‐secondary institution was studied throughout a semester during which each student made approximately 80 self‐assessments about his or her own learning process. These self‐assessments were then compared with multiple judgements by peers and tutors. The overall correlations between the scores of self‐, peer and tutor assessments suggest weak to moderate accuracy of student self‐assessment ability. The findings also reveal an ability effect; students judged as more academically competent were able to self‐assess with higher accuracy than their less competent peers. Comparing the accuracy of student self‐assessment averaged over four consecutive periods indicates that the accuracy does not improve over time. In a second study, a questionnaire aimed at eliciting student's beliefs about the effects of self‐assessment on their learning was administered to 936 first‐year students. Based on their responses, sub‐groups of students were identified: those who either believed in the usefulness of self‐assessment or did not. Results suggest that there is no significant association between student beliefs about the utility of self‐assessment and the accuracy of their self‐assessments.     

Speed isn’t everything: a study of examination marking

The question of whether marking speed is related to marking accuracy is important for training examiners and planning realistic marking schedules. We explored marking speed in the context of a past examination for an international biology qualification for 14‐ to 16‐year‐olds. Forty‐two markers with differing backgrounds experimentally marked 23 diverse examination questions. All responded to questionnaires about times taken to mark two of four samples of candidate responses. We demonstrated a positive practice effect for inexperienced markers, who became significantly faster during the course of their marking whilst maintaining their accuracies; there was no clear trade‐off between speed and accuracy. The benefits of marking practice and background experience are distinct phenomena. To improve accuracy, longer term investments in education and experience are needed.     

Ultimate load capacity and reinforcement of circular-hollow-section N-joint

Experimental research and numerical analysis were applied to study the ultimate load capacity(ULC) and reinforcement of circular-hollow-section N-joint.Four specimens were tested under static load.The ULC of each specimen was obtained and the detailed failure conditions were discussed.Based on the results, both welding a plate on the chord member and filling concrete in the chord member are effective to reinforce the N-joint, but it is suggested that these two methods should not be applied simultaneously.Mo...     

高层建筑物沉降观测与预测

对北京工业职业技术学院综合楼沉降观测点和水准基点测量精度进行了方案设计与合理性研究。利用观测的原始资料,采用灰色GM(1,1)模型对封顶前、封顶后进行预测,并和精密水准测量的结果进行了对比,预测结果与实际结果吻合较好,所建预测模型精度高,在实际工程中有一定应用价值。并给出了完整的沉降观测的最终成果。     

数控机床定位精度的测试的探讨

首先介绍定位精度和重复定位精度的检测必要性,如何精确测试并计算出这两项指标,选用最合适的计算标准尤为重要;随后介绍了定位精度的先进测试仪器、测试方法、测试数据采集方法;接着介绍了评价定位精度指标的含义、特征值的统计计算法;最后介绍环境因素、环境温度、油系统、机械因素等主要因素对定位精度测试的影响。     

灰色预测理论在城镇居民可支配收入预测中的应用

通过综合适用于短期预测的GM（1,1）模型和适用于随机波动较大的数列预测的马尔科夫模型GMM（1,1）,对2000年-2006年十堰市城镇居民可支配收入进行建模计算预测,结果表明模型的预测精度较高。为制定新一轮的经济政策提供科学的决策依据。     

Optimizing neural networks by genetic algorithms for predicting particulate matter concentration in summer in Beijing

We developed and tested an improved neural network to predict the average concentration of PM10 （particulate matter with diameter smaller than 10 μm） several hours in advance in summer in Beijing. A genetic algorithm optimization procedure for optimizing initial weights and thresholds of the neural network was also evaluated. This research was based upon the PM10 data from seven monitoring sites in Beijing urban region and meteorological observation data, which were recorded every 3 h during summer of 2002. Two neural network models were developed. Model I was built for predicting PM10 concentrations 3 h in advance while Model II for one day in advance. The predictions of both models were found to be consistent with observations. Percent errors in forecasting the numerical value were about 20%. This brings us to the conclusion that short-term fluctuations of PM10 concentrations in Beijing urban region in summer are to a large extent driven by meteorological conditions. Moreover, the predicted results of Model II were compared with the ones provided by the Models-3 Community Multiscale Air Quality （CMAQ） modeling system. The mean relative errors of both models were 0.21 and 0.26, respectively. The performance of the neural network model was similar to numerical models, when applied to short-time prediction of PM10 concentration.     

Prediction of shelled shrimp weight by machine vision

The weight of shelled shrimp is an important parameter for grading process. The weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp thickness. In this paper, a multivariate prediction model containing area, perimeter, length, and width was established. A new calibration algorithm for extracting length of shelled shrimp was proposed, which contains binary image thinning, branch recognition and elimination, and length reconstruction, while its width was calculated during the process of length extracting. The model was further validated with another set of images from 30 shelled shrimps. For a comparison purpose, artificial neural network (ANN) was used for the shrimp weight predication. The ANN model resulted in a better prediction accuracy (with the average relative error at 2.67%), but took a tenfold increase in calculation time compared with the weight-area-perimeter (WAP) model (with the average relative error at 3.02%). We thus conclude that the WAP model is a better method for the prediction of the weight of shelled red shrimp.      

基于组合神经网络模型的新冠疫情传播预测分析

在全球抗击新型冠状病毒肺炎（COVID-19）疫情的过程中,合理的疫情传播预测对于疫情防控有重要参考意义。为了对病毒传播进行合理预测,针对传统疫情传播预测模型存在的不足,提出一种组合式神经网络的疫情传播预测模型,并将其应用于湖北省1月29日-3月15日每日新增确诊人数预测及湖北省每日累计确诊人数预测。预测结果分析显示,该神经网络预测模型预测结果可靠有效。模型性能分析结果表明,组合式神经网络预测模型平均相对误差（MRE）不超过0.16,均方误差（MSE）不超过0.1,均方根误差（RMSE）为0.262 9,性能明显优于其它几种神经网络预测模型。基于武汉市与广东省疫情传播预测的实证结果显示模型具有较好的适用性及准确性。     

傅立叶级数+机器学习法双联预测小行星分类

利用傅立叶级数对散点小行星光变数据进行曲线拟合,以获得小行星表示参数,选取傅立叶级数为6的拟合方式对单体或双体小行星光变曲线进行分类,并用机器学习算法中的SVM和决策树建立预测模型。检验结果表明,SVM模型对单体和双体小行星的预测正确率达到95%,相较于决策树正确率提高了10%,为从小行星实际观测数据直接推测双体小行星潜在相关应用提供了参考。