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Error messages provided by the programming environments are often cryptic and confusing to learners. This study explored the effectiveness of enhanced programming error messages (EPEMs) in a Python-based introductory programming course. Participants were two groups of middle school students. The control group only received raw programming error messages (RPEMs) and had 35 students. The treatment group received EPEMs and had 33 students. During the class, students used an automated assessment tool called Mulberry to practice their programming skill. Mulberry automatically collected all the solutions students submitted when solving programming problems. Data analysis was based on 6339 student solutions collected by Mulberry. Our results showed that EPEMs did not help to reduce student errors or improve students’ performance in debugging. The ineffectiveness of EPEMs may result from reasons such as the inaccuracy of the interpreter’s error messages or students not reading the EPEMs. However, the viewpoint of productive failure may provide a better explanation of the ineffectiveness of EPEMs. The failures in coding and difficulties in debugging can be resources for learning. We recommend that researchers reconsider the role of errors in code and investigate whether and how failures and debugging contribute to the learning of programming.

<b><i>Background:</i></b> Most data about the trachea are collected during deep inspiration breath holding (DIBH) using multi-detector computed tomography (MDCT). Images of the physiological changes in the central airway are lacking. <b><i>Objective:</i></b> The aim of this study was to explore the physiological changes in the central airway on MDCT during DIBH and deep expiration breath holding (DEBH). <b><i>Method:</i></b> The data from 62 patients (38 men and 24 women) who underwent enhanced computed tomography in our hospital were collected. Patients were grouped according to sex and age (18–45, 46–60, and &#x3e;61 years). Anteroposterior diameter (APD) and transverse diameter (TD) at 3 levels (cricoid, intrathoracic inlet, and 2 cm above the carina), tracheal length, bronchial length, and subcarina angle (SCA) were measured. <b><i>Results:</i></b> The average length of the trachea from the cricoid cartilage to the carina was 103.91 ± 10.37 mm at DEBH and 108.63 ± 11.31 mm at DIBH (<i>p &#x3c;</i> 0.001). The APD of the trachea at the level of the cricoid, intrathoracic inlet, and 2 cm above the carina showed no differences between DEBH and DIBH. The TD of the trachea at the level of the cricoid, intrathoracic inlet, and 2 cm above the carina showed no differences between DEBH and DIBH. The average length of the right main bronchus during DEBH and DIBH was measured as 13.21 ± 3.60 and 13.24 ± 3.49 mm, respectively (<i>p =</i> 0.956). The average length of the left main bronchus at DEBH and DIBH was measured as 44.19 ± 5.50 and 44.27 ± 5.11 mm, respectively (<i>p</i> = 0.929). The average SCA was 81.74 ± 14.56 at DIBH, while it was 80.53 ± 14.38 at DEBH. The change in SCA between DIBH and DEBH showed no significant difference (<i>p</i> = 0.642). <b><i>Conclusions:</i></b> The APD at the level of the intrathoracic inlet is larger than that at the cricoid and 2 cm above the carina, while the TD is the opposite. These findings about the trachea and bronchus in our study may contribute to bronchoscopy examinations, tube applications, stent design, and stenting.

The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets.