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https://www.selleckchem.com/products/CX-3543.html Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework called DP-AGL, which uses attention-based GRU-LSTM for statement-level defect prediction. By using clang to build an abstract syntax tree (AST), we define a set of 32 statement-level metrics. We label each statement, then make a three-dimensional vector and apply it as an automatic learning model, and then use a gated recurrent unit (GRU) with a long short-term memory (LSTM). In addition, the Attention mechanism is used to generate important features and improve accuracy. To verify our experiments, we selected 119,989 C/C++ programs in Code4Bench. The benchmark tests cover various programs and variant sets written by thousands of programmers. As an evaluation standard, compared with the state evaluation method, the recall, precision, accuracy and F1 measurement of our well-trained DP-AGL under normal conditions have increased by 1%, 4%, 5%, and 2% respectively.The impacts of wavelengths in 500-600 nm on plant response and their underlying mechanisms remain elusive and required further investigation. Here, we investigated the effect of light quality on leaf area growth, biomass, pigments content, and net photosynthetic rate (Pn) across three Arabidopsis thaliana accessions, along with changes in transcription, photosynthates content, and antioxidative enzyme activity. Eleven-leaves plants were treated with BL; 450 nm, AL; 595 nm, RL; 650 nm, and FL; 400-700 nm as control. RL significantly increased leaf area growth, biomass, and promoted Pn. BL increased leaf area growth, carotenoid and anthocyanin content. AL significantly reduced leaf area growth and biomass, while Pn remained unaffected. Petiole elongation was further observe

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