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Graphene composite conductive inks are promising for the scalable production of printable electronics, and multifunctional coatings, owing to their cost-effectiveness, high conductivity, and remarkable flexibility. However, the restacking of graphene (Gr) sheets and agglomeration of carbon black (CB) significantly reduce the conductivity of aqueous graphene composite inks, limiting the exploration of high-performance devices. Herein, a highly conductive and dispersed graphene composite ink, based on a composite system of two- and zero-dimensional carbon materials was developed by a scalable sand-milling method. Carboxymethyl cellulose (CMC) can effectively address the aggregation issues of Gr and CB through steric hindrance. Functionalized Gr and CB can synergistically form an interpenetrating conductive network structure. The synthesis of a highly dispersed Gr/CB composite was facilitated by an in situ exfoliation process, which resulted in a high electrical conductivity of 2.12 × 104 S m−1 (i.e., a sheet resistance of 2.16 Ω sq−1). A screen-printed Gr/CB @ thermoplastic polyurethane fabric based on the Gr/CB composite ink exhibited an excellent electrical conductivity (7.08 Ω sq−1). The resulting composite fabric demonstrated promising potential for application in low-voltage driven wearable heaters and ultra-sensitive flexible strain sensors, while being waterproof, stable, and comfortable, suggesting significant potential for use in flexible wearable electronics. [ABSTRACT FROM AUTHOR]

With the fast development of large software projects, automatic code summarization techniques, which summarize the main functionalities of a piece of code using natural languages as comments, play essential roles in helping developers understand and maintain large software projects. Many research efforts have been devoted to building automatic code summarization approaches. Typical code summarization approaches are based on deep learning models. They transform the task into a sequence-to-sequence task, which inputs source code and outputs summarizations in natural languages. All code summarization models impose different input size limits, such as 50 to 10,000, for the input source code. However, how the input size limit affects the performance of code summarization models still remains under-explored. In this article, we first conduct an empirical study to investigate the impacts of different input size limits on the quality of generated code comments. To our surprise, experiments on multiple models and datasets reveal that setting a low input size limit, such as 20, does not necessarily reduce the quality of generated comments. Based on this finding, we further propose to use function signatures instead of full source code to summarize the main functionalities first and then input the function signatures into code summarization models. Experiments and statistical results show that inputs with signatures are, on average, more than 2 percentage points better than inputs without signatures and thus demonstrate the effectiveness of involving function signatures in code summarization. We also invite programmers to do a questionnaire to evaluate the quality of code summaries generated by two inputs with different truncation levels. The results show that function signatures generate, on average, 9.2% more high-quality comments than full code. [ABSTRACT FROM AUTHOR]

This paper observed the effect of ear-tip bloodletting therapy combined with external application of Traditional Chinese Medicine (TCM) in treating hordeolum at the early stage in a child, and summarized related nursing measures during the treatment. Key issues of ear-tip bloodletting therapy, TCM external application emotional care and diet intervention were mentioned. The ear-tip bloodletting therapy combined with TCM external application is effective to relieve the hordeolum associated symptoms and improve the rehabilitation of the patient. (本文总结耳尖放血联合中药外敷治疗1例小儿早期麦粒肿的护理经验, 包括耳尖放血的操作方法、中药局部外敷以及情志、饮食等常规护理等。通过经络刺激和中药湿敷, 耳尖放血联合中药外敷能有效缓解患儿麦粒肿症状, 促进疾病康复。)

A smart contract is a kind of code deployed on the blockchain that executes automatically once an event triggers a clause in the contract. Since smart contracts involve businesses such as asset transfer, they are more vulnerable to attacks, so it is crucial to ensure the security of smart contracts. Because a smart contract cannot be tampered with once deployed on the blockchain, for smart contract developers, it is necessary to fix vulnerabilities before deployment. Compared with many vulnerability detection tools for smart contracts, the amount of automatic fix approaches for smart contracts is relatively limited. These approaches mainly use defined pattern-based methods or heuristic search algorithms for vulnerability repairs. In this article, we propose RLRep, a reinforcement learning-based approach to provide smart contract repair recommendations for smart contract developers automatically. This approach adopts an agent to provide repair action suggestions based on the vulnerable smart contract without any supervision, which can solve the problem of missing labeled data in machine learning-based repair methods. We evaluate our approach on a dataset containing 853 smart contract programs (programming language: Solidity) with different kinds of vulnerabilities. We split them into training and test sets. The result shows that our approach can provide 54.97% correct repair recommendations for smart contracts. [ABSTRACT FROM AUTHOR]

In software evolution, keeping the test code co‐change with the production code is important, because the outdated test code may not work and is ineffective in revealing faults in the production code. However, due to the tight development time, the production and test code may not be co‐changed immediately by developers. For example, we analysed the top 1003 popular Java projects on GitHub and found that nearly 9.3% of cases (i.e., 464,417) did not update their production and test code at the same time, that is, the production code is updated first, and then the test code is updated at intervals. The result indicates that much test code will not be updated in time. In this paper, we propose a novel approach, Jtup, to remind developers to co‐change the production code and test code in time. Specifically, we first define the co‐changed production and test code as a positive instance, while unchanged test code (i.e., production code changed and test code unchanged) as a negative instance. Then, we extract multidimensional features from the production code to characterize the possibility of their co‐change, including code change features, code complexity features, and code semantic features. Finally, several machine learning‐based methods are employed to identify the co‐changed production and test code. We conduct comprehensive experiments on 20 datasets, and the results show that the Accuracy, Precision, and Recall achieved by Jtup are 76.7%, 78.1%, and 77.4%, which outperforms the state‐of‐the‐art method. [ABSTRACT FROM AUTHOR]

Code commenting plays an important role in program comprehension. Automatic comment generation helps improve software maintenance efficiency. The code comments to annotate a method mainly include header comments and snippet comments. The header comment aims to describe the functionality of the entire method, thereby providing a general comment at the beginning of the method. The snippet comment appears at multiple code segments in the body of a method, where a code segment is called a code snippet. Both of them help developers quickly understand code semantics, thereby improving code readability and code maintainability. However, existing automatic comment generation models mainly focus more on header comments, because there are public datasets to validate the performance. By contrast, it is challenging to collect datasets for snippet comments, because it is difficult to determine their scope. Even worse, code snippets are often too short to capture complete syntax and semantic information. To address this challenge, we propose a novel Snippet Comment Generation approach called SCGen. First, we utilize the context of the code snippet to expand the syntax and semantic information. Specifically, 600,243 snippet code-comment pairs are collected from 959 Java projects. Then, we capture variables from code snippets and extract variable-related statements from the context. After that, we devise an algorithm to parse and traverse abstract syntax tree (AST) information of code snippets and corresponding context. Finally, SCGen generates snippet comments after inputting the source code snippet and corresponding AST information into a sequence-to-sequence-based model. We conducted extensive experiments on the dataset we collected to evaluate our SCGen. Our approach obtains 18.23 in BLEU-4 metrics, 18.83 in METEOR, and 23.65 in ROUGE-L, which outperforms state-of-the-art comment generation models. [ABSTRACT FROM AUTHOR]