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Flexible perovskite solar cells often experience constant or cyclic bending during their service life. Catastrophic failure of devices may occur due to the crack of polycrystalline perovskite films and delamination at the perovskite and the substrate interfaces, posing a significant stability concern. Here, a multiple dynamic hydrogen bonding polymer network is developed to enhance the mechanical strength of flexible perovskite solar cells in two ways. The main chain of poly(acrylic acid) decreases the mismatch of the coefficient of thermal expansion between the perovskite and the substrate by 16.7% through its flexibility and spatial occupation. The dopamine branch chains provide multiple dynamic hydrogen bonding sites, which contribute to increased energy dissipation upon stress deformation and reduce Young's modulus of perovskite by 54.3%. The inverted flexible perovskite solar cells achieve a champion power conversion efficiency of 23.02% and retain 81.3% of the initial PCE over 2000 h under continuous 1‐sun equivalent illumination. Moreover, devices show excellent mechanical stability by remaining 90.2% of the original value after 5000 bending cycles. [ABSTRACT FROM AUTHOR]

As software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data. [ABSTRACT FROM AUTHOR]

An automated task for finding the essential buggy files among software projects with the help of a given bug report is termed bug localization. The conventional approaches suffer from the challenges of performing lexical matching. Particularly, the terms utilized for describing the bugs in the bug reports are observed to be irrelevant to the terms used in the source code files. To resolve these problems, we propose an optimized and ensemble deep learning model for software bug localization. These features are reduced by the principle component analysis (PCA). Then, they are selected by the weighted convolutional neural network (CNN) model with the support of the Modified Scatter Probability‐based Coyote Optimization Algorithm (MSP‐COA). Finally, the optimal features are subjected to the ensemble deep neural network and long short‐term memory (DNN‐LSTM), with parameter tuning by the MSP‐COA. Experimental results show that the proposed approach can achieve higher bug localization accuracy than individual models. [ABSTRACT FROM AUTHOR]

Background: Serotonin (5‐hydroxytryptamine) is a multifunctional bioamine serving as a neurotransmitter, peripheral hormone and mitogen in the vertebrate system. It has pleiotropic activities in central nervous system and gastrointestinal function via an orchestrated action of serotonergic elements, particularly serotonin receptor‐mediated signalling cascades. The mitogenic properties of serotonin have garnered recognition for years and have been exploited for repurposing serotonergic‐targeted drugs in cancer therapy. However, emerging conflicting findings necessitate a more comprehensive elucidation of serotonin's role in cancer pathogenesis. Main body and conclusion: Here, we provide an overview of the biosynthesis, metabolism and action modes of serotonin. We summarise our current knowledge regarding the effects of the peripheral serotonergic system on tumourigenesis, with a specific emphasis on its immunomodulatory activities in human cancers. We also discuss the dual roles of serotonin in tumour pathogenesis and elucidate the potential of serotonergic drugs, some of which display favourable safety profiles and impressive efficacy in clinical trials, as a promising avenue in cancer treatment. Key points: Primary synthesis and metabolic routes of peripheral 5‐hydroxytryptamine in the gastrointestinal tract.Advanced research has established a strong association between the serotonergic components and carcinogenic mechanisms.The interplay between serotonergic signalling and the immune system within the tumour microenvironment orchestrates antitumour immune responses.Serotonergic‐targeted drugs offer valuable clinical options for cancer therapy. [ABSTRACT FROM AUTHOR]

Vulnerability classification is a significant activity in software development and software maintenance. Natural Language Processing (NLP) techniques, which utilize the descriptions in public repositories, are widely used in automatic software vulnerability classification. However, vulnerability descriptions are ordinarily short and contain many technical terms, making them difficult for machines to automatically comprehend. In this paper, we present an approach based on vulnerability triggers to automatically classify vulnerabilities. First, we extract vulnerability triggers with Bert Question and Answer (Bert Q&A). Then, we use Recurrent Convolutional Neural Networks for Text classification (TextRCNN) to classify vulnerabilities based on Common Weakness Enumeration (CWE). We statistically perform an analysis of vulnerability triggers and comprehensively evaluate the classification performance of our approach on a set of 4769 prelabeled vulnerability entries, as well as compare it with state‐of‐the‐art vulnerability classification approaches. Experiment results show that our approach can achieve a F1‐measure of 95% on extraction and 80.8% on classification. [ABSTRACT FROM AUTHOR]

Vulnerabilities in the source code of the software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects such as complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. This paper proposes an evaluation of vulnerability detection performance on source code representations and evaluates how machine learning (ML) strategies can improve them. The structure of our experiment consists of three deep neural networks (DNNs) in conjunction with five different source code representations: abstract syntax trees (ASTs), code gadgets (CGs), semantics‐based vulnerability candidates (SeVCs), lexed code representations (LCRs), and composite code representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML‐based techniques suffer from poor performance on class imbalance handling and dimensionality reduction when used in conjunction with source code representations. [ABSTRACT FROM AUTHOR]

Organic‐inorganic hybrid perovskites are promising candidates for direct X‐ray detection and imaging. The relatively high dark current in perovskite single crystals (SCs) is a major limiting factor hindering the pursuit of performance and stability enhancement. In this study, the contribution of dark current is disentangled from electronic (σe) and ionic conductivity (σi) and shows that the high σi dominates the dark current of MAPbBr3 SCs. A multilayer heterojunctions passivation strategy is developed that suppresses not only the σi by two orders of magnitude but also σe by a factor of 1.6. The multilayer heterojunctions passivate the halide vacancy defects and increase the electron and hole injection barrier by inducing surface p‐type doping of MAPbBr3. This enables the MAPbBr3 SC X‐ray detectors to obtain a high sensitivity of 19 370 µC Gyair−1 cm−2 under a high electric field of 100 V cm−1, a record high sensitivity for bromine self‐powered devices, and a low detection limit of 42.3 nGyair s−1. The unencapsulated detectors demonstrate a stable baseline after storage for 210 days and outstanding operational stability upon irradiation with an accumulated dose of up to 1944 mGyair. [ABSTRACT FROM AUTHOR]

The overuse and exploitation of fossil fuels has triggered the energy crisis and caused tremendous issues for the society. Lithium‐ion batteries (LIBs), as one of the most important renewable energy storage technologies, have experienced booming progress, especially with the drastic growth of electric vehicles. To avoid massive mineral mining and the opening of new mines, battery recycling to extract valuable species from spent LIBs is essential for the development of renewable energy. Therefore, LIBs recycling needs to be widely promoted/applied and the advanced recycling technology with low energy consumption, low emission, and green reagents needs to be highlighted. In this review, the necessity for battery recycling is first discussed from several different aspects. Second, the various LIBs recycling technologies that are currently used, such as pyrometallurgical and hydrometallurgical methods, are summarized and evaluated. Then, based on the challenges of the above recycling methods, the authors look further forward to some of the cutting‐edge recycling technologies, such as direct repair and regeneration. In addition, the authors also discuss the prospects of selected recycling strategies for next‐generation LIBs such as solid‐state Li‐metal batteries. Finally, overall conclusions and future perspectives for the sustainability of energy storage devices are presented in the last chapter. [ABSTRACT FROM AUTHOR]

Vulnerabilities can have devastating effects on information security, affecting the economy, social stability, and national security. The idea of automatic vulnerability detection has always attracted researchers. From traditional manual vulnerability mining techniques to static and dynamic detection, all rely on human experts for feature definition. The rapid development of machine learning and deep learning has alleviated the tedious task of manually defining features by human experts while reducing the lack of objectivity caused by human subjective awareness. However, it is still necessary to find an objective characterisation method to define the features of vulnerabilities. Therefore, the authors use code metrics for code characterisation, sequences of metrics representing code. To use code metrics for vulnerability detection, a deep learning‐based vulnerability detection approach that uses a composite neural network of convolutional neural network (CNN) with long short‐term memory (LSTM) is proposed. The authors conduct experiments independently using the proposed approach for CNN‐LSTM CNN, LSTM, gated recurrent units (GRU), and deep neural network (DNN). The authors' experimental results show that CNN‐LSTM has a high precision of 92%, a recall of 99%, and an accuracy of 91%. In terms of the F1‐score, it is 95%, compared to previous research results, which indicated an improvement of 18%. Compared to other deep learning‐based vulnerability detection models, the authors' proposed model produced a lower false‐positive rate, a lower miss rate, and improved accuracy. [ABSTRACT FROM AUTHOR]

Bug localization is an important field in software engineering research. The traditional bug localization approaches based on information retrieval separate words through lexical analysis. In this way, the comments of the source code are ignored or treated as plain text, which will lose some semantic information. In this paper, MBL_SHL, an automatic Method‐level Bug Localization approach, which utilises code Summarization, Historical fixed bugs and code Length, is presented. Based on the code summarization technology, this approach first supplements the comment for uncommented code, and then calculates the Word2vec vector and Term Frequency–Inverse Document Frequency vector for the bug report, methods and comments, respectively. After that the authors calculate separately the similarity between the bug report and each method, the bug report and each comment. The code length information and historical fix information are also considered as a weight and a part of the score, respectively, to calculate the final score of each method. Finally, the scores are sorted to determine the list of methods that may need to be modified when fixing the software bugs. We built a method‐granular bug localization dataset, which contains five open‐source projects. The experimental results show that the proposed approach significantly outperforms the existing approaches on the method level. [ABSTRACT FROM AUTHOR]

Vulnerabilities in software source code are one of the critical issues in the realm of software code auditing. Due to their high impact, several approaches have been studied in the past few years to mitigate the damages from such vulnerabilities. Among the approaches, deep learning has gained popularity throughout the years to address such issues. In this literature survey, the authors provide an extensive review of the many works in the field software vulnerability analysis that utilise deep learning‐based techniques. The reviewed works are systemised according to their objectives (i.e. the type of vulnerability analysis aspect), the area of focus (i.e. the focus area of the analysis), what information about source code is used (i.e. the features), and what deep learning techniques they employ (i.e. what algorithm is used to process the input and produce the output). They also study the limitations of the papers and topical trends concerning vulnerability analysis. [ABSTRACT FROM AUTHOR]

Teaching cases are crucial for computer science (e.g., software testing) teaching. With the fast development of computer science, old and outdated teaching cases cannot meet the requirements of teaching. Therefore, teachers need to update teaching case repository continually and timely. However, teaching case development is an extremely time‐consuming work. Given today's complex and fast‐moving environment of computer science, teachers often feel blind about about what types of cases should be added for teaching. This paper presents UTCPredictor—an automated approach of predicting novel teaching cases from real production by identifying the most uncertain data, which are always with new features that reflect the latest developments and trends in the field. The implementation of UTCPredictor is based on the idea of interactive machine learning as well as several text mining techniques. To evaluate the effectiveness of UTCPredictor, we take bug report case building in software testing teaching as an example, using UTCPredictor to perform 10‐fold cross‐validation on an existing teaching case set. The performance in terms of indicators; Recall, Precision, and F1‐score, achieved three very competitive values—0.91, 0.94, and 0.85, respectively. We further evaluate the effectiveness of UTCPredictor through a user study and a questionnaire. The results are very positive; the user study indicates that educators can build a teaching case set from latest bug report repository by spending only 8.16%–18.11% time costs compared with traditional manual approach; the responses from 2,000 students for the questionnaire show that the teaching cases built with UTCPredictor are very popular among students. [ABSTRACT FROM AUTHOR]

Test suite minimisation is a process that seeks to identify and then eliminate the obsolete orredundant test cases from the test suite. It is a trade‐off between cost andother value criteria and is appropriate to be described as a many‐objectiveoptimisation problem. This study introduces a mutation score (MS)‐guidedmany‐objective optimisation approach, which prioritises the fault detectionability of test cases and takes MS, cost and three standard code coveragecriteria as objectives for the test suite minimisation process. They use sixclassical evolutionary many‐objective optimisation algorithms to identifyefficient test suite, and select three small programs from the Software‐ArtefactInfrastructure Repository (SIR) and two larger program space and gzip forexperimental evaluation as well as statistical analysis. The experiment resultsof the three small programs show non‐dominated sorting genetic algorithm II(NSGA‐II) with tuning was the most effective approach. However, MOEA/D‐PBI andMOEA/D‐WS outperform NSGA‐II in the cases of two large programs. On the otherhand, the test cost of the optimal test suite obtained by their proposedMS‐guided many‐objective optimisation approach is much lower than the onewithout it in most situation for both small programs and large programs. [ABSTRACT FROM AUTHOR]

Abstract: Protein interactions with specific nucleic acid sequences are crucial in cell growth. Inspired by such binding events that often occur at nanoscale biointerface, here a trans‐scale functional interface capable of considerably enhancing in vitro DNA‐enzyme interaction is reported. Using a screen‐printed electrode with nanoroughened carbon surface, the high‐curvature gold nanostructures in a single electrodeposition step can be programmed. In this process, a synergistic effect is found between nanoroughened carbon and polyelectrolyte multilayer enabling the formation of high‐stability and high‐curvature nanostructures. More importantly, these fractal nanostructures effectively overcome neighboring probes aggregation at high density and allow the probes to be more freely accessed by target molecules. As compared to its planar counterparts, this nanostructuring interface demonstrates faster enzymatic dynamics that enables ultrasensitive detection of microRNA with a detection limit of 35 × 10−18 m. Such an efficient trans‐scale biosensing interface has also accurately differentiated the patients with rheumatic arthritis from the health ones, signifying its great potential in precision medicine. [ABSTRACT FROM AUTHOR]

BACKGROUND The epoxidation of propylene with H2O2 in liquid phase catalyzed by a TS-1/ SiO2 catalyst in a fixed-bed reactor has been studied. The effects of reaction temperature (313-328 K), methanol concentrations (55-70 wt%) and hydrogen peroxide concentrations (9-15 wt%) on the reaction are investigated. The fresh, deactivated and regenerated catalysts were characterized with XRD, FT-IR, UV-vis, N2 sorption and TG to study the reasons for catalyst deactivation. In addition, the kinetics of catalyst deactivation was studied by fitting the experimental data. RESULTS The rate of decrease of H2O2 conversion decreases with increasing reaction temperature and methanol concentration, but increases with increasing hydrogen peroxide concentration. The reason for catalyst deactivation is that the bulky organic matter covers the active centers. The study on deactivation kinetics shows that the deactivation reaction order is 2, and an expression for H2O2 conversion as a function of reaction time is obtained. CONCLUSION The operating conditions such as reaction temperature, methanol concentration and hydrogen peroxide concentration remarkably affect the reaction. The kinetic parameters including deactivation reaction order and activation energy are developed by fitting the experimental data based on the Wojciechowski model. © 2014 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

A novel and improved method for the quality assessment of Cinnamomi Ramulus was developed and completely validated. The method was established using fingerprint technology and simultaneous quantitative determination of six main marker compounds including coumarin, cinnamic alcohol, cinnamic acid, 2-methoxy cinnamic acid, cinnamaldehyde, and 2-methoxy cinnamaldehyde in the herbal medicine for the first time. A newly developed high-performance thin-layer chromatography method, which achieved simultaneous definition of five marker components by comparing the colors and retardation factor values of the bands in high-performance thin-layer chromatography, was first used for the authentication of Cinnamomi Ramulus. The fingerprints of 26 batches of herbal samples from different regions of China showed very similar chromatographic patterns that were evaluated by similarity analysis and hierarchical clustering analysis. In addition, six marker compounds were simultaneously determined using single standard to determine multiple components by the relative response factors. Compared with the external standard method, the new quantitative method was validated to determine multiple compounds in 26 batches of Cinnamomi Ramulus samples. All results demonstrated that the simple and rapid method could be effectively utilized for the quality control of Cinnamomi Ramulus. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs) have recently been identified as versatile sacrificing templates to construct functional nanomaterials for heterogeneous catalysis. Herein, we report a thermal transformation strategy to directly fabricate metal Pd nanoclusters inlaid within a ZrO2@nitrogen‐doped porous carbon (Pd/ZrO2@CN) composite using Pd@NH2‐UiO‐66(Zr) as a precursor that was pre‐synthesized by a one‐pot hydrothermal method. The developed Pd/ZrO2@CN as a robust catalyst delivered remarkable stability and activity to the catalytic hydrogenation of 2,3,5‐trimethylbenzoquinone (TMBQ) to 2,3,5‐trimethylhydroquinone (TMHQ), a key reaction involved in vitamin E production. The hydrogenation was carried out at 110 °C with 1.0 MPa H2, and it resulted in 98% TMHQ yield as the sole product over five consecutive cycles, outperforming the analogue Pd/ZrO2@C without nitrogen doping templated from Pd@UiO‐66(Zr). The excellent catalytic properties of Pd/ZrO2@CN likely originated from the highly stable ultrafine Pd nanoclusters inlaid within ZrO2@CN matrix on account of the strong interaction between N and Pd, as well as on the Lewis acidity of ZrO2, which was beneficial to the hydrogenation. [ABSTRACT FROM AUTHOR]