Evaluation of teaching quality is essential for teachers’ promotion, students’ course selection, and institutes’ standing. A multilevel evaluation framework for teaching quality in higher education was investigated by combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the single-valued neutrosophic set (SVNS). An indicator system was constructed, including the teaching performance and the students’ learning outcomes. For the qualitative indicator values, an SVNS representation method was proposed, aiming to describe the uncertainty and improve the credibility and validity of the evaluation. Then, both the qualitative data and quantitative data were applied to the TOPSIS-based multilevel evaluation framework, which consisted of an overall assessment and five specific evaluations. The former assessment would provide a final rank and determine the best lecturers to be given priority in awards and promotion. The latter would focus on identifying areas where the lecturers could do better and would give them tips to overcome their challenges. Finally, a descriptive example was provided to verify the proposed framework and demonstrate its practicality.

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The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms. The noise formulation existing in HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study proposes a robust hyperspectral unmixing method with practical learning-based hyperspectral image denoising. We formulated a close-to-reality noise model for hyperspectral data and provide a calibration approach for the noise parameters. On the basis of the calibrated noise model, synthetic data were generated and used for training a KST-based denoising network. The noisy hyperspectral data were firstly denoised by the trained denoising network and were then used to perform the unmixing process. A variety of unmixing algorithms can be integrated into our method to improve the accuracy of unmixing in noisy situations. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively.