Recognizing the state of the universities and disrupting their functions by performance evaluation helps them adopt more appropriate educational, research and institutional policies to conduct a university system. In this paper, the importance of the services provided and the activities of the university are determined by means of the balanced scorecard (BSC) approach, and the performance assessment structure is implemented based on an integrated fuzzy multi-criteria decision making (MCDM) approach. For this purpose, interdependencies between BSC aspects and effective indicators weight are determined by Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) and Fuzzy Analytic Network Process (FANP) methods, respectively. Accordingly, the final weight of the effective indexes on the performance evaluation of university is presented and the educational income is recognized as one of the most important indicators. Finally, the priorities of universities are specified in order to improve the performance and policy making by the importance-performance analysis (IPA). Therefore, the growth of the number of students should be considered as one of the most important stages in improving university performance in the future in order to achieve educational income. Moreover, the guidelines for universities and higher education institutions are presented to identify key factors in implementing and improving performance.

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Every second counts for patients with life-threatening injuries, and trauma centers deliver timely emergency care to patients with traumatic injuries. Quality assessment and improvement are some of the most fundamental concerns in trauma centers. In this study, a comprehensive organizational resilience approach is proposed to evaluate performance in trauma centers using the European Foundation for Quality Management as a fundamental and strategic approach. We propose a unique intelligent algorithm composed of parametric and non-parametric statistical methods to determine the type and the extent of influence within the organizational resilience and quality management perspectives. We use structural equation modeling to examine the reliability and validity of the input data. The efficiency of each trauma center is then measured using a machine learning method with genetic programming, support vector regression, and Gaussian process regression. The mean absolute percentage error is used to determine the optimal model, and a fuzzy data envelopment analysis model is used to verify and validate the results obtained from the optimal model. The results show that customer results, human capital results, and key performance results have the highest importance weights and positive influence on quality management. Cognitive resources, roles and responsibilities, and self-organization have the highest importance weights and positive influence on organizational resilience.

ABSTRACT:Having a high spatial resolution of Thermal InfraRed (TIR) Sensors is a challenge in remote sensing applications. Airborne high spatial resolution TIR is a novel source of data that became available lately. Recent developments in spatial resolution of the TIR sensors have been an interesting topic for scientists. TIR sensors are very sensitive to the energies emitted from objects. Past researches have been shown that increasing the spatial resolution of an airborne image will decrease the spectral content of the data and will reduce the Signal to Noise Ratio (SNR). Therefore, in this paper a comprehensive assessment is adapted to estimate an appropriate spatial resolution of the TIR data (TELOPS TIR data), in consideration of the SNR. So, firstly, a low-pass filter is applied on TIR data and the achieved products fed to a classification method for analysing of the accuracy improvement. The obtained results show that, there is no significant change in classification accuracy by applying low-pass filter. Furthermore, estimation of the appropriate spatial resolution of the TIR data is evaluated for obtaining higher spectral content and SNR. For this purpose, different resolutions of the TIR data are created and fed to the maximum likelihood classification method separately. The results illustrated in the case of using images with ground pixel size four times greater than the original image, the classification accuracy is not reduced. Also, SNR and spectral contents are improved. But the corners sharpening is declined.