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Government-enterprise interaction is related to the synergistic development of the government and enterprises, and it is also the wind vane of regional economic development. This paper starts with the problems of the government-enterprise interaction mechanism, analyzes the causes of these problems, and constructs a model for government-enterprise interaction based on them. In order to verify the effectiveness of the government-enterprise interaction model, an evaluation index system is constructed to evaluate it, and the entropy weight method and TOPSIS method are utilized to calculate the weights and rank the comprehensive score. Based on the constructed government-enterprise interaction model, this paper utilizes examples to conduct government-enterprise interaction correlation analysis. The results show that the highest mean value of the first-level index in 2010-2020 after solving by entropy weighting method is 0.2829, and the weight of the government purchasing service in 2017-2020 is 0.2059. Among the 10 sample enterprises, the relative posting progress is the highest at 0.5662, and the highest composite score for the level of government-enterprise interaction is 0.52171 points. Under the background of informationization, it is necessary to further strengthen the government’s “delegating-regulation-service” reform, fully do a good job of macro-control in order to guide enterprises to better carry out government-enterprise interaction, innovate the mode of government-enterprise interaction, and fundamentally improve the interactive behavior of the government and enterprises.

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely employed in practice, as they have the unique advantages of high coverage and independence from the execution environment. However, existing tools for analyzing large codebases often produce a great deal of false warnings over genuine bug reports. As a result, developers are required to manually inspect and confirm each warning, a challenging, time-consuming, and automation-essential task. This article advocates a fast, general, and easily extensible approach called Llm4sa that automatically inspects a sheer volume of static warnings by harnessing (some of) the powers of Large Language Models (LLMs). Our key insight is that LLMs have advanced program understanding capabilities, enabling them to effectively act as human experts in conducting manual inspections on bug warnings with their relevant code snippets. In this spirit, we propose a static analysis to effectively extract the relevant code snippets via program dependence traversal guided by the bug warning reports themselves. Then, by formulating customized questions that are enriched with domain knowledge and representative cases to query LLMs, Llm4sa can remove a great deal of false warnings and facilitate bug discovery significantly. Our experiments demonstrate that Llm4sa is practical in automatically inspecting thousands of static warnings from Juliet benchmark programs and 11 real-world C/C++ projects, showcasing a high precision (81.13%) and a recall rate (94.64%) for a total of 9,547 bug warnings. Our research introduces new opportunities and methodologies for using the LLMs to reduce human labor costs, improve the precision of static analyzers, and ensure software trustworthiness [ABSTRACT FROM AUTHOR]

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this article, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools, Marabou and Planet, as the underlying verification engines, and evaluate on widely used benchmarks for three datasets ACAS, Xu, MNIST, and CIFAR-10. The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster. [ABSTRACT FROM AUTHOR]

The bonding of thick Cu and AlN is necessary in power modules with the development of high‐frequency communication technology. In this work, 2 mm‐thick Cu was successfully bonded with AlN by using Sn9Zn solder with the assistance of ultrasonication at 230°C. Results showed that an AlN/Sn9Zn/Cu joint without crack was manufactured with a cooling rate of 7°C/min. Nonreactive wetting was realized between AlN and Sn9Zn, forming an amorphous transition layer. CuZn2 and CuZn were formed at the Sn9Zn/Cu interface. A joint with the highest bonding strength of 30.07 MPa was obtained, in which the AlN/Sn9Zn interface was no longer the weakest location. This work provides a low‐temperature manufacturing method for thick Cu/AlN joints. [ABSTRACT FROM AUTHOR]