Search

Background: There is currently no specific equation for estimating glomerular filtration rate (GFR) in Chinese children with chronic kidney disease (CKD). The commonly used equations are less robust than expected; we therefore sought to derive more appropriate equations for GFR estimation. Methods: A total of 751 Chinese children with CKD were divided into 2 groups, training group (n = 501) and validation group (n = 250). In the training group, a univariate linear regression model was used to calculate predictability of variables associated with GFR. Residuals were compared to determine multivariate predictability of GFR in the equation. Standard regression techniques for Gaussian data were used to determine coefficients of GFR-estimating equations after logarithmic transformation of measured GFR (iGFR), height/serum creatinine (height/Scr), cystatin C, blood urea nitrogen (BUN), and height. These were compared with other well-known equations using the validation group. Results: Median 99mTc-DTPA GFR was 90.1 (interquartile range: 67.3–108.6) mL/min/1.73 m2 in training dataset. Our CKD equation, eGFR (mL/min/1.73 m2) = 91.021 [height(m)/Scr(mg/dL)/2.7]0.443 [1.2/Cystatin C(mg/L)]0.335 [13.7/BUN (mg/dL)]−0.095 [ 0.991male] [height(m)/1.4]0.275, was derived. This was further tested in the validation group, with percentages of eGFR values within 30% and 15% of iGFR (P30 and P15) of 76.00% and 48.40%, respectively. For centres with no access to cystatin C, a creatinine-based equation, eGFR (mL/min/1.73 m2) = 89.674 [height(m)/Scr(mg/dL)/2.7]0.579 [ 1.007male] [height(m)/1.4]0.187, was derived, with P30 and P15 73.60% and 49.20%, respectively. These were significantly higher compared to other well-known equations (p < 0.05). Conclusion: We developed equations for GFR estimation in Chinese children with CKD based on Scr, BUN and cystatin C. These are more accurate than commonly used equations in this population. [ABSTRACT FROM AUTHOR]

On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources are repeatedly shared. To help users manage their knowledge, we wish to investigate the characteristics of the repeated external links in knowledge sharing on Stack Overflow. In this paper, we analyze the repeated external links on Stack Overflow. We observe that external links that point to the text resources (hosted in documentation websites, tutorial websites, etc.) are repeatedly shared the most. We observe that different users repeatedly share the same knowledge in the form of repeated external links, thus increasing the maintenance effort of knowledge (e.g., update invalid links in multiple posts). The repeated external links can bring risks to the software engineering process, as 1) the same users can repeatedly share the external links for the purpose of promotion, and 2) external links can point to webpages with an overload of information that makes it difficult for users to retrieve relevant information. Our findings provide insights to Stack Overflow moderators and researchers. For example, we encourage Stack Overflow to centrally manage the commonly occurring knowledge in the form of repeated external links in order to better maintain the crowdsourced knowledge on Stack Overflow. [ABSTRACT FROM AUTHOR]

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of different deep learning frameworks is the same, which enables us to find common behaviors on the removal of different types of technical debt across projects. By mining the file history of these frameworks, we find that design debt is introduced the most along the development process. As for the removal of technical debt, we find that requirement debt is removed the most, and design debt is removed the fastest. Most of test debt, design debt, and requirement debt are removed by the developers who introduced them. Based on the introduction and removal of different types of technical debt, we discuss the evolution of the frequencies of different types of technical debt to depict the unresolved sub-optimal trade-offs or decisions that are confronted by developers along the development process. We also discuss the removal patterns of different types of technical debt, highlight future research directions, and provide recommendations for practitioners. [ABSTRACT FROM AUTHOR]

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features—indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community. [ABSTRACT FROM AUTHOR]

Clothianidin served as the model pollutant to investigate the performance and mechanism of pollutant removal by dielectric barrier discharge plasma (DBD) combined with the titanium dioxide-reduced graphene oxide (rGO-TiO2) composite catalyst. In this study, different ratios of titanium dioxide-graphene catalysts were loaded onto honeycomb ceramic plates via the sol-gel method, and the modified catalytic ceramic plates were characterized by XRD, SEM, FTIR, DRS, and energy dispersive X-ray. The results suggested that the rGO-TiO2 was well loaded on the surface of the honeycomb ceramic plates. According to the results of the characterization experiments and the degradation of the clothianidin solution with different proportions of the catalyst, 8 wt% rGO-TiO2 was selected as the optimum ratio for degradation. Clothianidin degradation efficiency was significantly influenced by input power, clothianidin concentration, pH value, liquid conductivity, free radical quencher. Finally, six degradation products of clothianidin were identified by HPLC-MS, and the possible transformation pathways of clothianidin degradation were identified. [ABSTRACT FROM AUTHOR]

In practice, a popular and coarse-grained approach for recovering from a problematic commit is to revert it (i.e., undoing the change). However, reverted commits could induce some issues for software development, such as impeding the development progress and increasing the difficulty for maintenance. In order to mitigate these issues, we set out to explore the following central question: can we characterize and identify which commits will be reverted? In this paper, we characterize commits using 27 commit features and build an identification model to identify commits that will be reverted. We first identify reverted commits by analyzing commit messages and comparing the changed content, and extract 27 commit features that can be divided into three dimensions, namely change, developer and message, respectively. Then, we build an identification model (e.g., random forest) based on the extracted features. To evaluate the effectiveness of our proposed model, we perform an empirical study on ten open source projects including a total of 125,241 commits. Our experimental results show that our model outperforms two baselines in terms of AUC-ROC and cost-effectiveness (i.e., percentage of detected reverted commits when inspecting 20% of total changed LOC). In terms of the average performance across the ten studied projects, our model achieves an AUC-ROC of 0.756 and a cost-effectiveness of 0.746, significantly improving the baselines by substantial margins. In addition, we found that "developer" is the most discriminative dimension among the three dimensions of features for the identification of reverted commits. However, using all the three dimensions of commit features leads to better performance. [ABSTRACT FROM AUTHOR]

Modern Code Review (MCR) has been widely used by open source and proprietary software projects. Inspecting code changes consumes reviewers much time and effort since they need to comprehend patches, and many reviewers are often assigned to review many code changes. Note that a code change might be eventually abandoned, which causes waste of time and effort. Thus, a tool that predicts early on whether a code change will be merged can help developers prioritize changes to inspect, accomplish more things given tight schedule, and not waste reviewing effort on low quality changes. In this paper, motivated by the above needs, we build a merged code change prediction tool. Our approach first extracts 34 features from code changes, which are grouped into 5 dimensions: code, file history, owner experience, collaboration network, and text. And then we leverage machine learning techniques such as random forest to build a prediction model. To evaluate the performance of our approach, we conduct experiments on three open source projects (i.e., Eclipse, LibreOffice, and OpenStack), containing a total of 166,215 code changes. Across three datasets, our approach statistically significantly improves random guess classifiers and two prediction models proposed by Jeong et al. (2009) and Gousios et al. (2014) in terms of several evaluation metrics. Besides, we also study the important features which distinguish merged code changes from abandoned ones. [ABSTRACT FROM AUTHOR]

Chinese developers often cannot effectively search questions in English, because they may have difficulties in translating technical words from Chinese to English and formulating proper English queries. For the purpose of helping Chinese developers take advantage of the rich knowledge base of Stack Overflow and simplify the question retrieval process, we propose an automated cross-language relevant question retrieval (CLRQR) system to retrieve relevant English questions for a given Chinese question. CLRQR first extracts essential information (both Chinese and English) from the title and description of the input Chinese question, then performs domain-specific translation of the essential Chinese information into English, and finally formulates an English query for retrieving relevant questions in a repository of English questions from Stack Overflow. We propose three different retrieval algorithms (word-embedding, word-matching, and vector-space-model based methods) that exploit different document representations and similarity metrics for question retrieval. To evaluate the performance of our approach and investigate the effectiveness of different retrieval algorithms, we propose four baseline approaches based on the combination of different sources of query words, query formulation mechanisms and search engines. We randomly select 80 Java, 20 Python and 20.NET questions in SegmentFault and V2EX (two Chinese Q&A websites for computer programming) as the query Chinese questions. We conduct a user study to evaluate the relevance of the retrieved English questions using CLRQR with different retrieval algorithms and the four baseline approaches. The experiment results show that CLRQR with word-embedding based retrieval achieves the best performance. [ABSTRACT FROM AUTHOR]

Technical debt is a metaphor to describe the situation in which long-term code quality is traded for short-term goals in software projects. Recently, the concept of self-admitted technical debt (SATD) was proposed, which considers debt that is intentionally introduced, e.g., in the form of quick or temporary fixes. Prior work on SATD has shown that source code comments can be used to successfully detect SATD, however, most current state-of-the-art classification approaches of SATD rely on manual inspection of the source code comments. In this paper, we proposed an automated approach to detect SATD in source code comments using text mining. In our approach, we utilize feature selection to select useful features for classifier training, and we combine multiple classifiers from different source projects to build a composite classifier that identifies SATD comments in a target project. We investigate the performance of our approach on 8 open source projects that contain 212,413 comments. Our experimental results show that, on every target project, our approach outperforms the state-of-the-art and the baselines approaches in terms of F1-score. The F1-score achieved by our approach ranges between 0.518 - 0.841, with an average of 0.737, which improves over the state-of-the-art approach proposed by Potdar and Shihab by 499.19%. When compared with the text mining-based baseline approaches, our approach significantly improves the average F1-score by at least 58.49%. When compared with a natural language processing-based baseline, our approach also significantly improves its F1-score by 27.95%. Our proposed approach can be used by project personnel to effectively identify SATD with minimal manual effort. [ABSTRACT FROM AUTHOR]

A synergistic system of dielectric barrier discharge (DBD) combined with La/Ce-TiO was developed to investigate the decomposition performance of the environmentally persistent perfluorooctanoic acid (PFOA). The La/Ce-TiO was modified by sol-gel method and characterized by XRD, SEM, and energy dispersive X-ray. The effects of PFOA concentration, applied voltage, initial pH, liquid conductivity, and additives on the removal rate of PFOA were explored. The results showed that the La/Ce-TiO exhibited excellent catalytic effects on PFOA degradation in DBD system. When the applied voltage, PFOA concentration, pH value, and solution volume were 75 V, 100 mg/L, 3.63, and 1000 mL, respectively, the removal efficiency of PFOA was up to 97.5% by adding LaCe-TiO in DBD. The corresponding defluorination ratio, TOC removal, and decomposition yield were 62.2%, 57.3%, and 37 g/kWh, respectively. Furthermore, five main intermediates including CF(CF)H, CF(CF)COOH, CF(CF)COH, CF(CF)COOH, and CFCFCF were identified with LC-MS, and the degradation pathways of PFOA were proposed. The degradation mechanisms revealed that hydroxyl radicals play a significant role in the degradation of PFOA in the synergistic system. [ABSTRACT FROM AUTHOR]

Wastewater treatment is an important source of nitrous oxide (NO), which is a strong greenhouse gas and dominate ozone-depleting substance. The purpose of this study was to evaluate the effect of carbon source on NO emission from anoxic/oxic biological nitrogen removal process. The mechanisms of NO emission were also studied. Long-term experiments were operated to evaluate the effect of three different carbon sources (i.e., glucose, sodium acetate, and soluble starch) on NO emission characteristics. And batch experiments, in the presence or absence of specific inhibitors, were carried out to identify the sources of NO emission. The ammonia-oxidizing bacteria (AOB) and denitrifiers community compositions under different circumstances were also analyzed based on which the underlying mechanisms of NO emission were elucidated. The conversion ratios of NO in reactors with glucose, sodium acetate, and soluble starch were 5.3 %, 8.8 %, and 2.8 %, respectively. The primary process responsible for NO emission was nitrifier denitrification by Nitrosomonas-like AOB, while denitrification by heterotrophic denitrifiers acted as the sink. Reactor with sodium acetate showed the highest NO emission, together with the highest nitrogen and phosphate removal ratios. Carbon source has a significant impact on NO emission quantity and relatively minor effect on its production mechanism. [ABSTRACT FROM AUTHOR]