The task of issue resolving aims to modify a codebase to generate a patch that addresses a given issue. However, most existing benchmarks focus almost exclusively on Python, making them insufficient for evaluating Large Language Models (LLMs) across different programming languages. To bridge this gap, we introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench, covering 8 languages of Python, Java, TypeScript, JavaScript, Go, Rust, C, and C++. In particular, this benchmark includes a total of 2,132 high-quality instances, carefully curated by 68 expert annotators, ensuring a reliable and accurate evaluation of LLMs on the issue-resolving task. Based on human-annotated results, the issues are further classified into three difficulty levels. We evaluate a series of state-of-the-art models on Multi-SWE-bench, utilizing both procedural and agent-based frameworks for issue resolving. Our experiments reveal three key findings: (1) Limited generalization across languages: While existing LLMs perform well on Python issues, their ability to generalize across other languages remains limited; (2) Performance aligned with human-annotated difficulty: LLM-based agents' performance closely aligns with human-assigned difficulty, with resolution rates decreasing as issue complexity rises; and (3) Performance drop on cross-file issues: The performance of current methods significantly deteriorates when handling cross-file issues. These findings highlight the limitations of current LLMs and underscore the need for more robust models capable of handling a broader range of programming languages and complex issue scenarios.

Shunt Active Power Filters (APFs) are powerful in harmonic elimination and reactive power compensation in distribution network. The APF prototype discussed in this paper is based on conventional three-leg converter with an improved LCL filter in the ac side, which consists of a LC resonant branch and is tuned at switching frequency. It is helpful to suppress switching ripple injected to the grid and reduce the power loss in the damping resistor. What's more, a compound current control strategy with an outer loop of repetitive control and an inner one of Proportional-Integral (PI) control is adopted. The inner loop is used to guarantee the dynamic response, and the outer one is designed to redeem the inherent phase lag of inner loop. Consequently, excellent harmonic current tracking performance both in steady-state and dynamical-state are achieved. Experimental results of the laboratory prototype prove its feasibility and efficiency.