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Automated software fault localization has become one of the hot spots on which researchers have focused in recent years. Existing studies have shown that learning-based techniques can effectively localize faults leveraging various information. However, there exist two problems in these techniques. The first is that they simply represent various information without caring the contribution of different information. The second is that the data imbalance problem is not considered in these techniques. Thus, their effectiveness is limited in practice. In this paper, we propose HetFL, a novel heterogeneous graph-based software fault localization technique to aggregate different information into a heterogeneous graph in which program entities and test cases are regarded as nodes, and coverage, change histories, and call relationships are viewed as edges. HetFL first extracts textual and structure information from source code as attributes of nodes and integrates them to form an attribute vector. Then, for a given node, HetFL finds its neighbor nodes based on the types of edges and aggregates corresponding neighbor nodes to form type vectors. After that, the attribute vector and all the type vectors of each node are aggregated to generate the final vector representation by an attention mechanism. Finally, we leverage a convolution neural network (CNN) to obtain the suspicious score of each method. To validate the effectiveness of HetFL, experiments are conducted on the widely used dataset Defects4J (v1.2.0). The experimental results show that HetFL can localize 217 faults within Top-1 that is 25 higher than the state-of-the-art technique DeepFL, and achieve 6.37 and 5.58 in terms of MAR and MFR which improve DeepFL by 9.0% and 5.6%, respectively. In addition, we also perform experiments on the latest version of Defects4J (v2.0.0). The experimental results show that HetFL has better performance than the baseline methods.

In Vacuum Induction Melting (VIM), reduction of defects such as porosity and cracking is critical to improve electrode integrity. Traditional simulation method ignores the temperature drop of liquid metal as it passes through the tundish, leading to inaccuracies in predicting the solidification process in the mold. Additionally, the crack of ingot may occur during demolding process, which is always overlooked. This paper presents an integrated simulation method that includes the flow model of liquid metal in the tundish, as well as the solidification model and the stress model of ingot in the mold. Based on these models, the criterion for demolding time is established based on First and Fourth Strength Theories. The entire VIM process from pouring to demolding can be analyzed comprehensively through this simulation method. The method is validated by 500 kg Inconel 718 VIM trial including the temperature measurement of outer mold wall, the height measurement of liquid metal in the tundish and longitudinal sectioned experiment of ingot. The suitable criteria for porosity of 500 kg Inconel 718 ingot, including shrinkage porosity and shrinkage cavity, have been found to be the Niyama criterion threshold of 15 K0.5s0.5cm−1and Classical porosity model. Finally, the impact of each process parameter on porosity and demolding time is studied through this method for 500 kg Inconel 718 ingot. This method is not only applied to 500 kg ingot but also to larger ingot, thereby providing a basis for the VIM process optimization.

A carbide strengthened wrought Ni-based superalloy is developed. The alloy depends on carbide dispersion strengthening. A high deformation plasticity in homogeneous alloy is shown in the 1100–1200 °C isothermal compression test and the improvement of microstructure can be achieved by recrystallization. After aging at 850–900 °C, the carbide strengthened wrought alloy appears excellent tensile strength. Carbide exists higher microstructure stability than γ′ in the same condition. The herein reported results reflect the potential of the economical wrought Ni-based superalloy to service above 800 °C.

Cobalt (Co) is a promising candidate to replace noble metals in the hydrogenation process, which is widely employed in the chemical industry. Although the catalytic performance for this reaction has been considered to be significantly dependent on the Co crystal phase, no satisfactory systematic studies have been conducted, because it is difficult to synthesize metal nanoparticles that have different crystalline structures with similar sizes. Here we report a new method for the synthesis of cobalt nanoparticles using hydrosilane as a reducing agent (hydrosilane-assisted method). This new method uses 1,3-butanediol and propylene glycol to successfully prepare fcc and hcp cobalt nanoparticles, respectively. These two types of Co nanoparticles have similar sizes and surface areas. The hcp Co nanoparticles exhibit higher catalytic performance than fcc nanoparticles for the hydrogenation of benzonitrile under mild conditions. The present hcp Co catalyst is also effective for highly selective benzyl amine production from benzonitrile without ammonia addition, whereas many catalytic systems require ammonia addition for selective benzyl amine production. Mechanistic studies revealed that the fast formation of the primary amine and the prevention of condensation and secondary amine hydrogenation promote selective benzonitrile hydrogenation for benzylamine over hcp Co nanoparticles.

Work function of organometallic halide perovskite (OHP) films is one of the most crucial photoelectric properties, which dominates the carrier dynamics in OHP-based devices. Despite surface treatments by additives being widely used to promote crystallization and passivate defects in OHP films, these chemical strategies for modulation of work functions face two trade-offs: homogeneity on the surface versus along the thickness; the range versus the accuracy of modulation. Herein, by using ferroelectric substrates of uniform polarization and subnanometer roughness, homogeneous CH3NH3PbI3films are fabricated with five states of work functions with large spanning (∼0.8 eV) and high precision (sd ∼ 0.01 eV). We reveal that the ferroelectric polarizations and the smooth surfaces regulate CH3NH3+orientations and suppress distortions of PbI6octahedrons. The wide-range and multistate work functions originate from the ordered CH3NH3+orientations and PbI6octahedrons, which result in intensity enhancements and wavelength shifts in photoluminescence with a 30-fold increase of photoexcited carrier lifetime.

The core task of neuromorphic devices is to effectively simulate the behavior of neurons and synapses. Based on the functionality of ferroelectric domains with the advantages of low power consumption and high-speed response, great progress has been made in realizing neuromimetic behaviors such as ferroelectric synaptic devices. However, the correlation between the ferroelectric domain dynamics and neuromimetic behavior remains unclear. Here, we reveal the correlation between domain/domain wall dynamics and neuromimetic behaviors from a microscopic perspective in real-time by using high temporal and spatial resolution in situ transmission electron microscopy. Furthermore, we propose utilizing ferroelectric microstructures for the simultaneous simulation of neuronal and synaptic plasticity, which is expected to improve the integration and performance of ferroelectric neuromorphic devices. We believe that this work to study neuromimetic behavior from the perspective of domain dynamics is instructive for the development of ferroelectric neuromorphic devices.

Few multi-agent reinforcement learning (MARL) researches on Google research football (GRF) focus on the 11-vs-11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this work, we fill the gap by providing a population-based MARL training pipeline and hyperparameter settings on multi-agent football scenario that outperforms the bot with difficulty 1.0 from scratch within 2 million steps. Our experiments serve as a reference for the expected performance of independent proximal policy optimization (IPPO), a state-of-the-art multi-agent reinforcement learning algorithm where each agent tries to maximize its own policy independently across various training configurations. Meanwhile, we release our training framework Light-MALib which extends the MALib codebase by distributed and asynchronous implementation with additional analytical tools for football games. Finally, we provide guidance for building strong football AI with population-based training and release diverse pretrained policies for benchmarking. The goal is to provide the community with a head start for whoever experiment their works on GRF and a simple-to-use population-based training framework for further improving their agents through self-play. The implementation is available at https://github.com/Shanghai-Digital-Brain-Laboratory/DB-Football.

The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method.

Fermented feed (FF) has several significant advantages in aquaculture. However, little is known about the characteristics of intestinal and environmental microbiota in the Chinese mitten crab Eriocheir sinensis. This study was conducted to elucidate the relationship between bacterial communities in the intestine of Chinese mitten crabs and the surrounding environment and to compare the bacterial communities in FF and non‐FF diet modes by using 16S ribosomal RNA high‐throughput sequencing technology. Fermented feed diets mainly influenced the alpha diversity of intestinal microbiomes but could affect microbial community structures of beta diversity in water and sediment. Compared to water and sediment, crab intestines had the lowest operational taxonomic unit (OTU) richness and bacterial community diversity. The OTU richness and Chao richness index values were lower in the intestines of crabs that received FF than in crabs that were given non‐FF. No significant differences were observed in the bacterial diversity and OTU richness of water and sediment between the two feeding modes. The bacterial community structures were significantly different among the three environments. Tenericutes, Proteobacteria, and Actinobacteria were the most abundant phyla in the crab intestine, sediment, and water, respectively. Bacterial functions were also different among the three environments. Proportions of nitrate reduction and fermentation in the crab intestine, proportions of nitrification and sulfate respiration in the sediment, and proportions of chemoheterotrophy and aerobic chemoheterotrophy in the water were higher for the FF feeding mode than for the non‐FF mode. The bacterial community structures were significantly different among the three environments. The microbiome of the crab intestine was partially shared with that of the ambient environment. Additionally, the FF mainly influenced the alpha diversity of intestinal microbiomes but also affected the community structures of beta diversity in water and sediment. These results will help us to devise dietary strategies to influence the crab intestinal microbiota and provide guidance for sustainable crab culture practices. Impact statementAs a new type of feed, fermented feed mainly influenced the alpha diversity of intestinal microbiomes but also affected the community structures of beta diversity in water and sediment. As a new type of feed, fermented feed mainly influenced the alpha diversity of intestinal microbiomes but also affected the community structures of beta diversity in water and sediment.

Circularly polarized luminescence (CPL) is of importance to advance science and technology with significant potential applications in information storage, spintronics and optoelectronic devices. Integrating CPL and the emission mechanism of thermally activated delayed fluorescence (TADF) has attracted considerable attention, because of their 100% internal quantum efficiency in theory and easily modified molecular structures for tunable photo-electrical properties. However, the typical donor-acceptor (D-A) architecture of CP-TADF emitters generally induces a broad emission profile with inferior color purity due to their large structural relaxation under the processes of excitation and emission. Recently, CP-multi-resonance (MR)-TADF emitters that concurrently display CPL activity, narrowband emission and high efficiency are emerging as the most suitable candidates for the next-generation organic light-emitting diode (OLED) display. In this review, the progress of CP-MR-TADF materials is reviewed in terms of the molecular design strategy, the common relationship between the molecular structures and optoelectronic properties, and their OLEDs performance. Finally, the opportunities and challenges in this research topic are discussed.

Sporopollenin, a critical innovation in the evolution of terrestrial plants, is the core building brick for the outer wall of land-plant spores and pollen. Despite its significance, the basic structure of sporopollenin remains elusive due to its extreme chemical inertness. In this study, we used ethanolamine to completely dissolve rape sporopollenin and successfully identified a total of 22 components, including fatty acids, p-coumaric acid, sterols and polymeric phenylpropanoid derivatives. After that, using NaOH treatment and partial dissolution, alongside Arabidopsismutants analysis and spectroscopic methods, we determined that polymeric phenylpropanoid derivatives crosslinked by hydroxyl fatty acids serve as the core structure of sporopollenin. The free hydroxyl groups and carboxyl groups of the polymeric phenylpropanoid derivatives can be modified by other fatty acids (C16:0, C18:0 and C18:3) as well as alcohols/phenols (for example, naringenin, β-sitosterol), resulting in a structure that protects pollen from terrestrial stresses. This discovery provides a basis for further exploration of sporopollenin’s role in plant reproduction and evolution.

Printed circuit board (PCB) design software is an important category of electronic design automation (EDA) tool which aims to simplify PCB design through graphical interaction. As a type of software, PCB design software is also susceptible to software bugs. Bugs in PCB design software may cause huge losses since they could directly affect the correctness of the final PCBs, which are often used in safety-critical fields. Unfortunately, the lack of understanding of bugs in PCB design software impedes the effective handling of these bugs. To this end, we present the first empirical study to analyze the characteristics of PCB design software bugs. Specifically, we collect 349 bugs from two popular open-source PCB design software projects (i.e., KiCAD and LibrePCB) and investigate the root causes, symptoms, relationships between root causes and symptoms, bug-prone components, and bug fix characteristics. Based on the analysis, we get 13 findings. For example, the majority of bugs are caused by implementation logic errors, resulting from lacking/misunderstanding domain knowledge, such as electrical engineering principles, component placement and arrangement, hierarchical layout techniques, and signal transmission considerations. From these findings, we draw the guidelines for PCB design software development and future research in bug detection, localization, and repair.

Simulink compiler testing is important since all cyber-physical system (CPS) models are required to be compiled by Simulink compiler. Current testing processes use CPS models generated by CPS model generators for testing. Since the effectiveness of CPS model generators heavily relies on suitable generator configurations, existing approaches randomize configurations or infer configurations with historical bug information to generate diverse bug-triggering CPS models. However, these approaches are designed for general-purpose compilers (e.g., GCC), which have two challenges when testing Simulink compiler, namely, the CPS model representation challenge on representing CPS models for diversity measurement and the configuration learning challenge on learning configurations to generate diverse CPS models. To address these challenges, we propose Reinforcement lEarning-based COnfiguRation Diversification (RECORD), a new configuration diversification approach. RECORD has a feature vectorization component, which addresses the first challenge by representing CPS models as feature vectors to capture the local and global characteristics of CPS models for diversity measurement. RECORD then uses a reinforcement learning component to generate diverse CPS models based on the learned relationship between configuration updates and diversity changes, thus addressing the second challenge. Experiments demonstrate that within three months, RECORD reported 11 confirmed Simulink compiler bugs, significantly outperforming the state-of-the-art configuration diversification approaches. RECORD can also facilitate different testing strategies to find more bugs.

Familiar analysis for malware plays an important role in comprehending the diversity of malicious behaviors and identifying the emerging security threats. Existing studies mainly focus on classifying malware into known families by supervised learning. However, these methods face two main challenges, 1) the lack of a large amount of labeled data and 2) the poor effectiveness in identifying unknown families of malware. To overcome these challenges, we propose a new method called multiple features (MulFC) based on unsupervised learning. In the method, we first leverage a decompiling tool to extract multiple features, including manifest features, application programming interface (API) features, and opcode features. Then, the opcode features are preprocessed to filter out the redundant ones to reduce the calculation cost. After that, we adopt the Jaccard index to calculate the similarities between malware and construct a malware network. Finally, InfoMap is applied to perform the clustering on the basis of the malware network. Overall, MulFC does not require the use of labeled data and can identify unknown families of malware. Experiments are conducted on two datasets for the performance evaluation of MulFC. The experimental results show that MulFC achieves 0.810 in terms of normalized mutual information, 0.576 in terms of adjusted rand index, 0.620 in terms of the Fowlkes–Mallows index, and 0.805 in terms of V-measure on average, and outperforms the state-of-the-art baseline method by 0.060, 0.054, 0.038, and 0.065, respectively.

Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitrotime series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.

The establishment of a long-term and accurate forecasting approach of urban air pollution is conducive to the implementation of pollution prevention and control policies. However, existing research has not fully taken into account the spatial-temporal pattern characteristics of long-distance air pollution transport. Multistep ahead forecasting faces the challenge of aliasing long-term spatial-temporal correlation and accumulating errors. In this study, a long-term spatial–temporal pattern forecasting model (ASTemCN) of PM$_{2.5}$ air pollutants in Chinese cities was established based on the monitoring sites of the Internet of Things. The model skillfully designs a novel spatial–temporal fusion mechanism to integrate the temporal and spatial characteristics under the spatial–temporal pattern of PM$_{2.5}$. Compared with other learning paradigms, ASTemCN is more suitable for learning long-term spatial–temporal patterns, has the highest forecasting accuracy and stronger generalization ability, and provides a research direction for the spatial–temporal pattern analysis of air pollution.

High-Level Synthesis (HLS) tools convert C/C++ design code into Hardware Description Language (HDL) code automatically, which are often used for Field Programmable Gate Array (FPGA) design. HLS tools provide many pragmas, which are a kind of directive to be inserted into C/C++ code, for designers to efficiently control the synthesis of code components (e.g., arrays and loops) to generate FPGA implementations with varying performances and costs. However, the use of some pragmas may trigger HLS tool bugs (e.g., tool crashes). Although many formal methods have been proposed to verify the correctness of various HLS phases, no relevant work addresses the problem on detecting HLS tool pragma-related bugs. To resolve this problem, two challenges need to be addressed, namely the selection of testing programs and the acquisition of pragma combinations, due to the enormous number of testing programs and pragma combinations. In this paper, we propose TEPACS, a TEsting Program and prAgma Combination Selection-based framework, to construct diverse testing programs with pragmas for effectively detecting HLS tool pragma-related bugs. TEPACS follows the idea of fuzzing, which is a widely used technique in software testing. First, TEPACS selects the representative testing program according to the cosine distance between the code component vectors of testing programs. Then, for a selected program, TEPACS generates its golden output and uses the pragma combination selection method based on combinatorial testing to generate a set of programs with different pragmas. TEPACS uses the HLS tool under test to convert these testing programs into HDL codes and obtains the simulation results of the HDL code. Finally, based on differential testing, TEPACS identifies HLS tool bugs triggered if the simulation result and golden output are inconsistent. We evaluate TEPACS and its five variants on Vitis HLS, a widely used FPGA HLS tool. Experimental results show that TEPACS outperforms the baselines by at least 11.17% in terms of the bug-finding capability. In one month, TEPACS detected 34 bugs on the latest version of Vitis HLS, of which 9 bugs have been confirmed.

Compiler bugs pose a significant threat to safety-critical applications, and promptly as well as effectively isolating these bugs is crucial for assuring the quality of compilers. However, the limited availability of debugging information on reported bugs complicates the compiler bug isolation task. Existing compiler bug isolation approaches convert the problem into a test program mutation problem, but they are still limited by ineffective mutation strategies or high human effort requirements. Drawing inspiration from the recent progress of pre-trained Large Language Models (LLMs), such as ChatGPT, in code generation, we propose a new approach named LLM4CBI to utilize LLMs to generate effective test programs for compiler bug isolation. However, using LLMs directly for test program mutation may not yield the desired results due to the challenges associated with formulating precise prompts and selecting specialized prompts. To overcome the challenges, three new components are designed in LLM4CBI. First, LLM4CBI utilizes a program complexity-guided prompt production component, which leverages data and control flow analysis to identify the most valuable variables and locations in programs for mutation. Second, LLM4CBI employs a memorized prompt selection component, which adopts reinforcement learning to select specialized prompts for mutating test programs continuously. Third, a test program validation component is proposed to select specialized feedback prompts to avoid repeating the same mistakes during the mutation process. Compared with the state-of-the-art approaches (DiWi and RecBi) over 120 real bugs from the two most popular compilers, namely GCC and LLVM, our evaluation demonstrates the advantages of LLM4CBI: It can isolate 69.70%/21.74% and 24.44%/8.92% more bugs than DiWi and RecBi within Top-1/Top-5 ranked results. Additionally, we demonstrate that the LLMs component (i.e., GPT-3.5) used in LLM4CBI can be easily replaced by other LLMs while still achieving reasonable results in comparison to related studies.

Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can have catastrophic consequences. Symbolic execution-based approaches have been used to detect such memory errors, benefiting from their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines still suffer from fundamental limitations in modeling dynamic memory layouts; they either represent the locations of memory objects as concrete addresses and thus limit their analyses only to specific address layouts and miss errors that may only occur when the objects are located at special addresses, or represent the locations as simple symbolic variables without sufficient constraints and thus suffer from memory state explosion when they execute read/write operations involving symbolic addresses. Such limitations hinder the existing symbolic execution engines from effectively detecting certain memory errors. In this study, we propose SymLoc, a symbolic execution-based approach that uses concretely mapped symbolic memory locations to alleviate the limitations mentioned above. Specifically, a new integration of three techniques is designed in SymLoc: (1) the symbolization of addresses and encoding of symbolic addresses into path constraints, (2) the symbolic memory read/write operations using a symbolic-concrete memory map, and (3) the automatic tracking of the uses of symbolic memory locations. We build SymLoc on top of the well-known symbolic execution engine KLEE and demonstrate its benefits in terms of memory error detection and code coverage capabilities. Our evaluation results show that: for address-specific spatial memory errors, SymLoc can detect 23 more errors in GNU Coreutils, Make, and m4 programs that are difficult for other approaches to detect, and cover 15% and 48% more unique lines of code in the programs than two baseline approaches; for temporal memory errors, SymLoc can detect 8%-64% more errors in the Juliet Test Suite than various existing state-of-the-art memory error detectors. We also present two case studies to show sample memory errors detected by SymLoc along with their root causes and implications.

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics, and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

Low-code development platforms (LCDPs) are increasingly being introduced and leveraged by major IT enterprises to lower the threshold and promote the efficiency of software development. Like other software systems, LCDPs are also inevitable to have bugs. The bugs in LCDPs may cause unpredictable consequences as they pose risks to all the downstream software products. However, to the best of our knowledge, there exist no studies that ever consider the bugs caused by LCDPs. To handle the LCDP bugs better, in this article, we conduct an empirical study of the characteristics of LCDP bugs by examining 974 confirmed bugs of four dominant LCDPs (i.e., OutSystems, Mendix, Appsmith, and Budibase) from both commercial and open-source domains. These bugs are analyzed from three perspectives, including bug root causes, bug symptoms, and the affected stages of LCDPs. Based on the analysis, we obtain a series of valuable findings. For example, around 60% of the bugs reside in the stage of designing and specifying the developed applications. Over 37% of the bugs lead LCDPs to behave unexpectedly but without showing explicit signs. Moreover, the bugs relevant to the incorrect graphics of user interfaces are significant due to the characteristics of LCDPs. These findings point out the guidelines, challenges, and future directions to address LCDP bugs.

Spinning reserve without accurate load forecasting can lead to automatic disconnection of critical loads by under-frequency load shedding devices. Such a predicament poses a grave threat to the economic and social welfare of the affected region, and in extreme scenarios, can result in a debilitating grid collapse. However, existing models lack the deep feature extraction capability and cannot accurately predict the uncertainty of electricity demand. Thus, a multitask integrated deep-learning probabilistic prediction with multidimensional feature extraction based on granularity information and quantile regression is explored in this study to solve the insufficient feature extraction problem in probabilistic prediction. In the proposed scheme, we designed a multi-layer feature extraction framework, which information granularity based on fuzzy rough sets to extract time-domain features, multilayer Laplace operators to extract features globally, and quantile regression recurrent neural network variants to extract time context information. In addition, the Pareto integration method is extended to capture better individuals. The experimental results demonstrate that the proposed system can effectively improve deterministic forecasting and uncertainty analysis of electricity demand and manage daily fluctuations.

Code comments are a crucial source of software documentation that captures various aspects of the code. Such comments play a vital role in understanding the source code and facilitating communication between developers. However, with the iterative release of software, software projects become larger and more complex, leading to a corresponding increase in issues such as mismatched, incomplete, or outdated code comments. These inconsistencies in code comments can misguide developers and result in potential bugs, and there has been a steady rise in reports of such inconsistencies over time. Despite numerous methods being proposed for detecting code comment inconsistencies, their learning effect remains limited due to a lack of consideration for issues such as characterization noise and labeling errors in datasets. To overcome these limitations, we propose a novel approach called MCCL that first removes noise from the dataset and then detects inconsistent code comments in a timely manner, thereby enhancing the model's learning ability. Our proposed model facilitates better matching between code and comments, leading to improved development of software engineering projects. MCCL comprises two components, namely method comment detection and confidence learning denoising. The method comment detection component captures the intricate relationships between code and comments by learning their syntactic and semantic structures. It correlates the code and comments through an attention mechanism to identify how changes in the code affect the comments. Furthermore, confidence learning denoising component of MCCL identifies and removes characterization noises and labeling errors to enhance the quality of the datasets. This is achieved by implementing principles such as pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. By effectively eliminating noise from the dataset, our model is able to more accurately learn inconsistencies between comments and source code. Our experiments on 1,518 open-source projects demonstrate that MCCL can accurately detect inconsistencies, achieving an average F1-score of 82.6%. This result outperforms state-of-the-art methods by 2.4% to 28.0%. Therefore, MCCL is more effective in identifying inconsistent comments based on code changes compared to existing approaches.

We report two subjects with tracheobronchopathia osteochondroplastica (TO), including the clinical manifestations, histological findings, and clinical treatments, which were analyzed retrospectively. One patient with TO was a 60-year-old woman, and the other was a 47-year-old man. The main clinical manifestations were cough, chest pain, and dyspnea. Computed tomography (CT) images showed that TO mainly occurred in the trachea and main bronchus. Histological analysis showed inflammatory exudation, squamous metaplasia, submucosal cartilaginous, and ossification. We present the two cases to increase physician and patient awareness of this benign disease and to improve their understanding of the disease manifestations and potential complications.

AbstractObjective:This study aimed to determine the ideal pacing site in children by comparing the postoperative ventricular synchrony in children with left bundle branch area pacing and those with right ventricular septal pacing.Methods:This retrospective study included children with complete atrioventricular block who underwent permanent pacemaker implantation from March 2019 to August 2021. Patients were grouped according to their ventricular pacing site, the left bundle branch area pacing group and the right ventricular septal pacing group. Two-dimensional speckle tracking echocardiography was used to evaluate the ventricular synchrony.Results:Forty-eight children (median age, 2.7 years; interquartile range, 1.7–4.6 years) were included. The paced QRS duration in the left bundle branch area pacing group was significantly narrower than that in the right ventricular septal pacing group (100.2 ± 9.3 versus 115.4 ± 15.1 ms, p = 0.001). The median follow-up duration was 1.5 years (interquartile range, 1–2 years). At the last follow-up, the average capture threshold of the ventricular electrode in the left bundle branch area pacing group was lower than that in the right ventricular septal pacing group (0.79 ± 0.18 versus 1.20 ± 0.56 V, p = 0.008). The left ventricular intraventricular synchrony parameters in the left bundle branch area pacing group were better than those in the right ventricular septal pacing group (e.g. standard deviation of the time to peak longitudinal strain, 37.4 ± 4.3 versus 46.6 ± 8.2 ms, p = 0.000). The average interventricular mechanical delay time in the left bundle branch area pacing group was significantly shorter than that in the right ventricular septal pacing group (36.4 ± 14.2 versus 52.5 ± 22.7 ms, p = 0.016).Conclusion:Compared with right ventricular septal pacing, left bundle branch area pacing in children produces a narrower QRS duration and better pacing and ventricular synchrony parameters postoperatively.

Application review event inference aims to assess the effectiveness of application problems in response to user actions, which enables application developers to promptly discover and address potential issues in various applications, thereby improving their development and maintenance efficiency. Despite the development of event inference models for app reviews, which extract them as user action and app problem events and establish a relationship model between events and inference labels, the accuracy of these models is constrained due to limitations in labeling and characterizing noise and the lack of robustness and generalization. To address this challenge, we propose a model called Easy Data Augmentation for Application Reviews Event Inference (short for EDA-AREI), which comprises a denoising component, data augmentation component, and event inference prediction component. Specifically, the denoising component identifies labels and characterizes noisy data to enhance dataset quality, the data augmentation component replaces non-stop words with synonyms to increase textual diversity, and the event inference and prediction component reconstructs the classifier using denoised and augmented data. Experimental results on six datasets of one-star app reviews in the Apple App Store demonstrate that the EDA-AREI method achieves an Accuracy of 71.19%, 79.14%, 69.05%, 69.02%, 68.24% and 68.48%, respectively, representing an improvement of 0.83%–2.09% compared to state-of-the-art models. Regarding the F1-score, EDA-AREI achieves values of 71.30%, 69.93%, and 68.76% on the threshold_0.5, k-means_2, and random datasets, respectively, outperforming state-of-the-art models by 1.89%–4.02%. Furthermore, EDA-AREI achieves AUC values of 75.66% and 73.37% on the threshold_0.5 and k-means_2 datasets, respectively. As a result, EDA-AREI demonstrates substantial improvements in Accuracy, as well as enhanced F1-score and AUC across most datasets, thereby enhancing the model's accuracy and robustness in identifying related action-problem pairs.

Protein-biomolecule interactions play pivotal roles in almost all biological processes. For a biomolecule of interest, the identification of the interacting protein(s) is essential. For this need, although many assays are available, highly robust and reliable methods are always desired. By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosisand the streptavidin (SA)-biotin system, we developed the Specific Pupylation as IDEntity Reporter (SPIDER) method for identifying protein-biomolecule interactions. Using SPIDER, we validated the interactions between the known binding proteins of protein, DNA, RNA, and small molecule. We successfully applied SPIDER to construct the global protein interactome for m6A and mRNA, identified a variety of uncharacterized m6A binding proteins, and validated SRSF7 as a potential m6A reader. We globally identified the binding proteins for lenalidomide and CobB. Moreover, we identified SARS-CoV-2-specific receptors on the cell membrane. Overall, SPIDER is powerful and highly accessible for the study of protein-biomolecule interactions.

The excited-state manipulation of the phosphorescent iridium(III) complexes plays a vital role in their photofunctional applications. The development of the molecular design strategy promotes the creative findings of novel iridium(III) complexes. The current molecular design strategies for iridium(III) complexes mainly depend on the selective cyclometalation of the ligands with the iridium(III) ion, which is governed by the steric hindrance of the ligand during the cyclometalation. Herein, a new molecular design strategy (i.e., random cyclometalation strategy) is proposed for the effective excited-state manipulation of phosphorescent cyclometalated iridium(III) complexes. Two series of new and separable methoxyl-functionalized isomeric iridium(III) complexes are accessed by a one-pot synthesis via random cyclometalation, resulting in a dramatic tuning of the phosphorescence peak wavelength (∼57 nm) and electrochemical properties attributed to the high sensitivity of their excited states to the position of the methoxyl group. These iridium(III) complexes show intense phosphorescence ranging from the yellow (567 nm) to the deep-red (634 nm) color with high photoluminescence quantum yields of up to 0.99. Two deep-red emissive iridium(III) complexes with short decay lifetimes are further utilized as triplet emitters to afford efficient solution-processed electroluminescence with reduced efficiency roll-offs.

C++ is a widely used programming language and the C++ front-end is a critical part of a C++ compiler. Although many techniques have been proposed to test compilers, few studies are devoted to detecting bugs in C++ compiler. In this study, we take the first step to detect bugs in C++ compiler front-ends. To do so, two main challenges need to be addressed, namely, the acquisition of test programs that are more likely to trigger bugs in compiler front-ends and the bug identification from complicated compiler outputs. In this article, we propose a novel framework named Ccoft to detect bugs in C++ compiler front-ends. To address the first challenge, Ccoft implements a practical program generator. The generator first transforms C++ grammars into a flexible structured format and then utilizes an equal-chance selection (ECS) strategy to conduct structure-aware grammar mutation to generate diverse C++ programs. Next, Ccoft employs a set of differential testing strategies to identify various kinds of bugs in C++ compiler front-ends by comparing complex outputs emitted by C++ compilers, thus tackling the second challenge. Empirical evaluation results over two mainstream compilers (i.e., GCC and Clang) show that Ccoft greatly improves two state-of-the-art approaches (i.e., Dharma and Grammarinator) by 135% and 111% in terms of the numbers of detected bugs, respectively. By running Ccoft for three months, we have successfully reported 136 bugs for two C++ compilers, of which 78 (57 confirmed, assigned, or fixed) for GCC and 58 (10 confirmed or fixed) for Clang.

As one of the most important resources to express the semantics of source code, identifiers are usually composed of several common or domain-specific terms and abbreviations, thus heavily hindering developers from analyzing and comprehending source code. Hence, it is very necessary to normalize identifiers, which aims to align the vocabulary found in identifiers with natural language words found in other software artifacts. Even though researchers have proposed several identifier normalization approaches in the literature, these approaches only rely on the lexical information in identifiers and related source code entities to normalize identifiers, suffering from the lack of deep semantic understanding of identifiers. In this paper, we propose an effective and efficient identifier normalization approach BEQAIN to split identifiers into their composing words and expand the enclosed abbreviations. Specifically, BEQAIN employs a deep learning model, which is mainly composed of a Bidirectional Encoder Representation from Transformers (BERT) layer and a Conditional Random Fields (CRF) layer to embed identifiers into low-level vectors and learn the identifier splitting patterns. The BERT-CRF network is also combined with a pre-processing component and a post-processing component to resolve the problems of over-splitting and under-splitting so as to improve the identifier splitting performance. Furthermore, BEQAIN also employs a Question Answering (Q&A) system to learn the abbreviation expansion mappings and leverages the current programming context to determine the exactly correct expansion when there are multiple expansions for specific abbreviations. After BEQAIN is fully trained, it can be used to normalize identifiers. We conduct extensive experiments to validate the effectiveness and efficiency of BEQAIN over two publicly available datasets with nine projects. Experimental results show that BEQAIN achieves the overall average Accuracy of 80.20% and outperforms the existing state-of-the-art approach by 9.88% in normalizing identifiers. The pre-processing and post-processing components could improve the Accuracy of BEQAIN in identifier splitting by 11.70%. Employing the programming context information could improve the Accuracy of BEQAIN in abbreviation expansion by 11.15% on average. In addition, the average normalization time of BEQAIN is less than one second. Finally, we also discuss some observations for the road ahead for identifier normalization to inspire other researchers.

The emergence of numerous fork-based development platforms facilitates the development of Open-Source Software (OSS) projects. Developers across the world can fork software projects and submit their Pull Requests (PRs) to the projects. However, as the number of forks increases, numerous duplicate PRs might be submitted. These duplicate PRs may cause extra code review workload and frustrate developers working on the projects. To detect duplicate PRs, many approaches have been proposed, which analyze the similarity of different elements in PRs. However, previous approaches still suffer from unsatisfied detection accuracy due to two challenges. That is, they ignore the syntactic structural information of text elements in PRs and lack the joint reasoning between different elements of two PRs. In this study, we propose an automated duplicate PRs detector named DupHunter (Duplicate PRs Hunter), which includes a graph embedding component and a duplicate PRs detection component to address the above challenges. The graph embedding component uses a feature graph to represent a PR. It encodes the syntactic structure and semantics of text elements (e.g., the title and the description), as well as the knowledge of non-text elements (e.g., the submission time), to address the syntactic structural information challenge. The duplicate PRs detection component tackles the joint reasoning challenge using a graph matching network, which enables the information exchange and matching across different elements of two feature graphs with an attention coefficient mechanism. Experiments on 26 open-source projects show that DupHunter achieves an average F1-score@1 value of 0.650, significantly outperforming the state-of-the-art approaches by 3.2% to 48.1%. DupHunter can accurately detect duplicate PRs, with an average Precision@1 value of 0.922 and an average Recall@1 value of 0.502.

The development of highly efficient cyclometalated phosphorescent iridium(III) complexes is greatly promoted by their rational molecular design. Manipulating the excited states of iridophosphors could endow them with appealing photophysical properties, which play vital roles in triplet state-related photofunctional applications (e.g., electroluminescence, photodynamic therapy, etc.). In general, the most effective approach for decreasing the emission energies of iridophosphors is to extend the π-skeleton of ligands. However, the π-extension strategy often results in decreased solubility, lower synthetic yield, decreased photoluminescence quantum yield, and so forth. In this work, a simple yet efficient strategy is proposed for the effective excited-state manipulation of 2-phenyllepidine-based iridophosphors. Surprisingly, dramatic tuning of phosphorescence wavelength (∼70 nm) is achieved by simply controlling the position of a single methoxyl substituent on these iridophosphors. An oxygen-responsive iridophosphor featuring far-red emission (660 nm), long emission lifetime (1.60 μs), and high singlet oxygen quantum yield (0.73) is employed to realize accurate oxygen sensing in vitroand in vivo, and it also shows efficient photodynamic therapy in cancer cells, making it a promising candidate for the efficient image-guided photodynamic therapeutic agent. This molecular design strategy clearly demonstrates the advantages of designing novel long-wavelength emissive iridophosphors without increasing the π-conjugation of the ligand.

Compiler diagnostic warnings help developers identify potential programming mistakes during program compilation. However, these warnings could be erroneous due to the defects of compiler warning diagnostics. Although the existing technique (i.e., Epiphron) can automatically generate test programs for compiler warning defect detection, the effectiveness of Epiphron on defect-finding is still limited, due to the limitation for generating warning-sensitive test program structures. Therefore, in this paper, we propose a DIversity-guided PROgram Mutation approach, called DIPROM, to construct diverse warning-sensitive programs for effective compiler warning defect detection. Given a seed test program, DIPROM first removes its dead code to reduce false positive warning defects. Then, the abstract syntax tree (AST) of the test program is constructed; DIPROM iteratively mutates the structures of the AST to generate warning-sensitive program variants. To effectively construct diverse warning-sensitive structures, DIPROM applies a novel diversity-guided strategy to generate program variants in each iteration. With the generated program variants, differential testing is conducted to detect warning defects in different compilers. In the experiments, we evaluate DIPROM with two popular C compilers (i.e., GCC and Clang). Experimental results show that DIPROM significantly outperforms three state-of-the-art approaches (i.e., HiCOND, Epiphron, and Hermes) by up to 18.93%$\sim$∼76.74% in terms of the bug-finding capability on average. Meanwhile, DIPROM is efficient, which spends less time on finding the same average number of warning defects. We at last applied DIPROM to the latest development versions of GCC and Clang. After two months’ running, we reported 8 new warning defects; 5 of them have been confirmed/fixed by developers.