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This paper aims to present an inexact 4:2 compressor cell using carbon nanotube filed effect transistors (CNFETs).

To design this cell, the capacitive threshold logic (CTL) has been used.

To evaluate the proposed cell, comprehensive simulations are carried out at two levels of the circuit and image processing. At the circuit level, the HSPICE software has been used and the power consumption, delay, and power-delay product are calculated. Also, the power-delaytransistor count product (PDAP) is used to make a compromise between all metrics. On the other hand, the Monte Carlo analysis has been used to scrutinize the robustness of the proposed cell against the variations in the manufacturing process. The results of simulations at this level of abstraction indicate the superiority of the proposed cell to other circuits. At the application level, the MATLAB software is also used to evaluate the peak signal-to-noise ratio (PSNR) figure of merit. At this level, the two primary images are multiplied by a multiplier circuit consisting of 4:2 compressors. The results of this simulation also show the superiority of the proposed cell to others.

This cell significantly reduces the number of transistors and only consists of NOT gates.

Artificial Intelligence (AI) technology, having powerful capabilities and rapid development, has been able to move the structures of businesses and organizational processes towards intelligent automation. The role of digital transformation in universities and educational institutions has an increasing trend. New business structures and the digitization of processes, other than the advantages they bring about, might have different effects on the environment and sustainability. This study aims to identify the effective factors on AI adoption and the effect of using this technology in educational institutions and universities on their sustainable performance.

This research is applied using a quantitative approach. Universities selected for the study were ranked by Quacquarelli Symonds (QS). Of the 111 QS listed universities in the Middle East in 2023, 30 universities were randomly selected, and the research questionnaire was emailed to 50 people (administrative, educational and research staff) from each university. Information related to the level of AI technology acceptance and use was collected using a questionnaire among the university staff and faculty members; moreover, their relationship with universities’ sustainable performance scores was assessed. Path analysis and Smart PLS software have been used for data analysis.

The research findings showed that factors of technology performance, enjoyment, trust, social influence and organizational capabilities all have positive effect on AI adoption at universities. Also, the adoption of AI is considered as an effective factor in improving university sustainable performance. Therefore, based on exact data analysis using AI, universities can manage their activities and better control their environmental performance. Also, the use of AI can be effective in the availability to sustainable education in universities and the establishment of social justice in society. Accordingly, to facilitate executive processes and decision-making, policymakers in the field of science and university principals can improve administrative, educational and research processes via investing on AI, in addition to improving environmental activities and sustainable development.

The theoretical contribution of this research, other than designing an AI acceptance model for universities includes evaluating the relationship between using AI and university sustainable performance.