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18th Conference of the Associated Research centers for the Urban Underground Space, ACUUS 2023

作者： Liangcheng, Zhu Zhuo, Liu Qi, Chen School of Qilu Transportation Shandong University Jinan China Center of Science and Technology and Industrialization Development Ministry of Housing Urban-Rural Development Beijing China Urban and Rural Design and Development Branch China Construction Design and Research Institute Co. Ltd Shanghai China

ISBN: (纸本)9789819712564

From a worldwide perspective, China is the country with the fastest development and the most planning practices for urban underground space (UUS). Looking back over the past 40 years, the practices of UUS planning have gone through four development stages, firstly the exploratory stage of civil air defense engineering combining peacetime and wartime, secondly the rising stage led by a few pilot cities, thirdly the prosperous stage of standardized guidance and national promotion, and fourthly the innovative stage of coordination between ground and underground, and gradually forming UUS planning system with distinctive Chinese characteristics. This article analyzes the main characteristics and representative practical cases of the four stages, and deeply analyzes the problems and reasons that exist in current practice from five aspects: the differences in UUS planning between China and foreign countries, the necessity of UUS planning, reconstruction of UUS planning system, upgrading of UUS planning methods, and improving the efficiency of UUS planning implementation. Then it proposes targeted main ideas and suggestions. The future city will be a three-dimensional city with integrated development of ground and underground. China’s practical exploration will contribute more wisdom to the development and utilization of UUS in Global city. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban planning

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Aloin ameliorates cecal ligation and puncture-induced sepsis in mice by attenuating inflammation and modulating gut microbiota

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Food science and Human Wellness 2025年第2期14卷 550-568页

作者： Jingqian Su Jianbin Xiao Siyuan Chen Heng Zhao Xiaoni Zhang Zhihua Feng Kunsen Chen Biyun Guan Wenzhi Chen Youqiang Chen Duo Chen Qi Chen Fujian Key Laboratory of Innate Immune Biology Biomedical Research Center of South ChinaCollege of Life ScienceFujian Normal UniversityFuzhou 350117China The Public Service Platform for Industrialization Development Technology of Marine Biological Medicine and Products of the State Oceanic Administration Fujian Key Laboratory of Special Marine Bioresource Sustainable UtilizationSouthern Institute of OceanographyCollege of Life ScienceFujian Normal UniversityFuzhou 350117China

Most Aloe species are used as new food or functional food *** though widely known for its health benefits,the anti-inflammatory effects and underlying mechanisms of Aloin(Alo),an anthraquinone compound isolated from plant species of the genus Aloe,remain ***,we investigated the protective effects of Alo against cecal ligation and puncture(CLP)-induced sepsis and microflora in *** significantly improved CLP-induced sepsis and the survival rate of septic mice,downregulated the expression of proinflammatory factors,and decreased the infiltration of inflammatory cells in *** upregulated the proportion of peritoneal macrophages,reduced the number of peritoneal bacteria,decreased the content of short-chain fatty acids and bile acids in the abdominal cavity,and suppressed Toll-like receptor(TLR)-2/4/nuclear factor kappa-B(NF-κB)/NOD-like receptor thermal protein domain associated protein 3(NLRP3)/Caspase-1/3/8 ***,Alo altered the composition of the microbiome and promoted the growth of Lactobacillus,which showed a stronger anti-inflammatory ***-genome analysis identified the genes Saa3,Il10,Fpr1,and Eif4a1 associated with the protective effects of Alo in mice with CLP-induced ***,our results provide novel insights into the therapeutic potential and mechanism of action of Alo in the treatment of sepsis.

关键词： Aloin Gut microbiome Sepsis Inflammation Cecal ligation and puncture

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DeepTD: Diversity-Guided Deep Neural Network Test Generation 9th

DeepTD: Diversity-Guided Deep Neural Network Test Generation

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9th International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Zhu, Jin Tao, Chuanqi Guo, Hongjing Ju, Yue College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China State Key Laboratory for Novel Software Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9789819986637

Coverage-guided Fuzz Testing (CGF) techniques have been applied to deep neural network (DNN) testing in recent years, generating a significant number of test samples to uncover inherent defects in DNN models. However, the effectiveness of CGF techniques that utilize structured coverage metrics as coverage criteria is currently being questioned. A few unstructured coverage metrics, such as surprise adequacy, only take into account the diversity of the test samples against the training set, while ignoring the diversity of the test samples themselves. In addition to this, the existing surprise adequacy metrics have some limitations in their applications. Therefore, this paper proposes DeepTD, a diversity-guided deep neural networks test generation method. Firstly, DeepTD selects high-loss test samples from each class on average, ensuring these test seeds possess a strong ability to reveal model errors. Then, DeepTD transforms these test seeds to enhance the diversity of the generated samples. Finally, Cluster-based Surprise Adequacy is designed to guide the generation of test samples. To evaluate the effectiveness of DeepTD, six DNN models are selected as subjects, covering two well-known image datasets. Experimental results demonstrate that the Cluster-based Surprise Adequacy outperforms the two existing metrics not only in computational efficiency but also in discovering more model defects. What’s more, the test samples generated by DeepTD are on average 6.04% and 3.24% more effective for model retraining in MNIST and CIFAR10 compared to baseline methods, respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coverage-guided fuzz testing Deep neural network Test generation Test samples diversity

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BTM: Black-Box Testing for DNN Based on Meta-Learning 23

BTM: Black-Box Testing for DNN Based on Meta-Learning

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Zhang, Zhuangyu Zhang, Zhiyi Wang, Ziyuan Chen, Fang Huang, Zhiqiu Nanjing University of Aeronautics and Astronautics Collage of Computer Science and Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China

ISBN: (纸本)9798350319583

Deep learning is widely used in security fields like autonomous driving, but testing deep learning models poses challenges due to low generation efficiency and limited error detection. Current white-box test case generation methods rely on neuron coverage, but black-box testing is crucial when model details cannot be accessed. Prior methods also needed more consideration for error diversity under lower time resources. In this paper, we propose a novel approach based on the intuition that two models trained on the same classification task learn similar rules at a coarse-grained level. We employ meta-learning to train universal meta perturbation on a surrogate model, which increases neuron coverage on the surrogate model while inducing misclassifications in the original model (thus enhancing the adequacy of the initial tests). Subsequently, we apply data initialization to discover a wider range of errors faster. To accelerate test case generation and reduce resource consumption, we introduce a set of acceleration techniques based on image prediction probabilities, minimizing the time spent exploring irrelevant regions in images. Finally, we evaluate our approach on a well-known dataset and several well-known models, demonstrating its effectiveness. © 2023 IEEE.

关键词： Black-box testing

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Video Stereo Grid Construction Method for Accurate Forest Fire Location 4th

Video Stereo Grid Construction Method for Accurate Forest Fi...

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4th EAI International Conference on 6G for Future Wireless Networks, 6GN 2021

作者： Cao, Jichang Cao, Yichao Guo, Qing Ye, Liang Zhen, Jialing School of Electronic and Information Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology and Industrialization Development Center of Ministry of Housing and Urban-Rural Development Beijing100835 China School of Automation Southeast University Jiangsu Nanjing210096 China

ISBN: (纸本)9783031042447

Accurate forest fire location during the initial stages is vital for suppressing forest fires. However, in some mountainous areas, accurate automatic fire location is still a challenging task. There is a lack of a good association mechanism between video space and external geographic information. In this paper, we propose a method to measure the fire location and accurately map it to the stereo space coding, which uses the feedback parameters (yaw angle, pitch angle, geographic location and altitude where the camera installed) and digital elevation model. The position of the fire point from the image coordinate system is obtained by computer vision method, and then transform it to the camera coordinate system and the world coordinate system by coordinate transformation. And we establish the mapping from longitude, latitude and height coordinates into stereo grid code to achieve the construction of video stereo grid space. The proposed method has also been deployed and verified in actual forest video monitoring system. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Cameras

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BDGSE: A Symbolic Execution Technique for High MC/DC 23

BDGSE: A Symbolic Execution Technique for High MC/DC

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Cai, Huangli Zhang, Zhiyi Jian, Yifan Li, Dan Huang, Zhiqiu Nanjing University of Aeronautics and Astronautics Collage of Computer Science and Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China

ISBN: (纸本)9798350319583

Modified Condition/Decision Coverage (MC/DC) is a test coverage standard with excellent fault detection capability, which is widely used in testing safety-critical software. Symbolic execution generates test cases automatically to achieve high code coverage. However, since symbolic execution uses short-circuit evaluation to evaluate decisions, it fails to guarantee the consistency of other conditions in a decision required by the MC/DC criterion. As a result, it is incapable of generating MC/DC test cases to test safety-critical systems adequately. To solve this problem, we propose Branch Dependence Guided Symbolic Execution (BDGSE), a symbolic execution technique for high MC/DC. The approach utilizes static analysis to compute the branch dependencies, then guides symbolic execution to selectively explore paths and simplify test cases, and finally generates a small number of test cases to achieve high MC/DC coverage. Our experimental results show that BDGSE can generate high-quality test cases. Although BDGSE generates fewer test cases, it can achieve higher MC/DC coverage than SPF and random methods, and the fault detection capability of test cases generated by BDGSE is comparable to that of test cases generated by SPF. © 2023 IEEE.

关键词： Software testing

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Test Generation for Mutation Testing by Symbolic Execution 23

Test Generation for Mutation Testing by Symbolic Execution

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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Su, Rui Zhang, Zhiyi Zhou, Yuqian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Army Engineering of PLA Nanjing China

ISBN: (纸本)9798350359398

Mutation testing is a crucial technique to assess the effectiveness of test suites, but it can be computationally expensive. Symbolic execution is a formal technique that generates high-coverage test cases, but its scalability is limited. In this paper, we present a novel approach to mutation testing that utilizes symbolic execution, which explores only program regions impacted by mutations, prioritizes paths containing mutant branches, and generates test cases aimed at mutations. We implemented our approach in MutSym and evaluated it on real-world programs, demonstrating its effectiveness and efficiency. Our approach enhances the quality and reliability of software systems by detecting and eliminating faults early in the development process. © 2023 IEEE.

关键词： Software testing

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DeepRank: Test Case Prioritization for Deep Neural Networks 35

DeepRank: Test Case Prioritization for Deep Neural Networks

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Li, Wei Zhang, Zhiyi Jian, Yifan Liu, Chen Huang, Zhiqiu Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Marxism Yangzhou University Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China

Deep neural networks (DNNs) have been widely used in safety-critical fields such as autonomous driving and medical diagnosis. However, DNNs are easily disturbed to make wrong decisions, which may lead to loss of life or property. Therefore, it is vital to test DNN adequately. In practice, to reveal the incorrect behavior of DNN and improve its robustness, testers usually need massive labeled data to test and optimize DNN. However, labeling test inputs to detect the correctness of DNN predictions is an expensive and time-consuming task that even affects the efficiency of DNN testing. To relieve the labeling-cost problem, we propose DeepRank, a test case prioritization technique based on cross-entropy loss. The key idea of DeepRank is that the higher the loss value of a test case relative to the DNN, the more likely it is to be mispredicted and the more conducive it is to improve the robustness of the DNN through retraining. Therefore, the cross-entropy loss value can be used for test case prioritization. We experimentally validate our approach on two datasets and three DNNs models. The experimental results demonstrate that DeepRank is significantly better than existing test case prioritization methods regarding fault-revealing capability and retraining effectiveness. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep neural networks

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Software Defect Prediction via GCN based on Structural and Context Information 9

Software Defect Prediction via GCN based on Structural and C...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Tang, Lijin Tao, Chuanqi Guo, Hongjing Zhang, Jingxuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Software quality plays an important role in the software engineering. To improve software reliability, software defect prediction which predicts code region is buggy or not aims to assist developers find bugs and allocate their test resources reasonably. Traditional defect prediction studies focus on handcrafted features and train machine learning classifiers to identify the defective code area. Since the source code contains rich semantic and structural information, and defective code is closely related to its context, now many researchers try to utilize semantic information of programs and build more accurate defect prediction model. Existing methods mainly analyze Abstract Syntax Tree (AST) which is obtained from source code and transform it into token sequences. But these methods fail to capture rich information of structural and context information of code. At the same time, some traditional features(e.g. The number of methods in the class(WMC), Lines of code(LOC)) are also very valuable in defect prediction. Therefore in this paper, we propose to a method to build the graph by connecting parent and child nodes of the AST and connecting leaf nodes from left to right to preserve the context information that contained in source code. Then we use Graph Convolutional Network(GCN) to automatically generate semantic and structural features from source code. In addition, we combine GCN learned features with handcrafted features for more accurate prediction model. Based on the above model, our evaluation on 12 data sets shows that our proposed method is superior to the state-of-the-art software defect prediction method by 10.37 % in recall, 1.62 % in precision, 5.15% in F-measure, and 8.11 % in MCC. © 2022 IEEE.

关键词： Software reliability

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The cellular and molecular basis of the spur development in Impatiens uliginosa

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园艺研究（英文） 2024年第3期11卷 145-157页

作者： Yang Li Wu-lue Huang Xin-yi Li Ying-duo Zhang Dan-chen Meng Chun-mei Wei Mei-juan Huang Hai-quan Huang College of Landscape Architecture and Horticulture Sciences Southwest Research Center for Engineering Technology of Landscape Architecture(State Forestry and Grassland Administration)Yunnan Engineering Research Center for Functional Flower Resources and IndustrializationResearch and Development Center of Landscape Plants and Horticulture FlowersSouthwest Forestry UniversityKunmingYunnan650224China School of Art and Design Lanzhou Jiaotong UniversityLanzhou 730070China College of Landscape Architecture and Horticulture Sciences Southwest Research Center for Engineering Technology of Landscape Architecture(State Forestry and Grassland Administration)Yunnan Engineering Research Center for Functional Flower Resources and IndustrializationResearch and Development Center of Landscape Plants and Horticulture FlowersSouthwest Forestry UniversityKunmingYunnan650224China Department of Biodiversity Conservation Department of Life ScienceSouthwest Forestry UniversityKunming 650224China

The nectar spur is an important feature of pollination and ecological adaptation in flowering plants,and it is a key innovation to promote species diversity in certain plant *** development mechanism of spurs varies among different plant *** one of the largest angiosperm genera,we have little understanding of the mechanism of spur development in ***,we investigated the initiation and growth process of spurs of Impatiens uliginosa based on histology and hormone levels,and the roles of AUXIN BINDING PROTEIN(ABP)and extensin(EXT)in spur development were *** results indicate that the spur development of *** is composed of cell division and anisotropic cell *** in spur proximal-distal cell division lead to the formation of curved *** hormones,such as auxin and cytokinins,were enriched at different developmental stages of *** knockdown led to an increase in spur curves and distortion of *** knockdown resulted in reduced spur length and loss of curve and inner epidermal papillae *** study provides new insights into the mechanism of spur development in core eudicots.

关键词： development molecular cellular basis spur impatiens uliginosa

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