RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices by RealBench RealBench (22275393)

“…<br>│ └── level4/ # Level 4 projects (expert complexity).<br>├── src/ # Main source code for the RealBench framework.…”

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis by Alireza Enshaeian (20360253)

Mean values of different benchmark functions. by Jalal- ud-Din (21474307)

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Detail of benchmark functions comparison. by Jalal- ud-Din (21474307)

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Average execution time (in seconds) of different benchmark functions. by Jalal- ud-Din (21474307)

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Stander deviation values of different benchmark functions. by Jalal- ud-Din (21474307)

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Comparison of different crossover operators. by Jalal- ud-Din (21474307)

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The FEM mesh model for the three-layer case, where 7919 degrees of freedom are used. by Xiaogang Li (299205)

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The RMT diagram for an acoustic coating with three-layer periodic inclusions, in which the start (10 Hz, blue dots) and cut-off (10000 Hz, black asterisks) frequencies of sound abs... by Xiaogang Li (299205)

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The influence of different materials of scatterers on the sound absorption. by Xiaogang Li (299205)

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Data for Figs 3–9. by Xiaogang Li (299205)

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The solid material attributes. by Xiaogang Li (299205)

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Sound absorption spectrum calculated by TMM (dots) and FEM (solid lines). by Xiaogang Li (299205)

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Fig 6 - by Xiaogang Li (299205)

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Simulation sample. by Xiaogang Li (299205)

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Fig 7 - by Xiaogang Li (299205)

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The normalized acoustic resistance (blue lines) and reactance (red dotted lines) of the optimization case in Fig 7. by Xiaogang Li (299205)

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Cross section of the metagrating and the distribution of effective homogeneous layers. by Xiaogang Li (299205)

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Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Ablation study visualization results. by Xiaozhou Feng (2918222)

“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”