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The volatile compounds in the fruits of 24 apricot cultivars were quantitatively and qualitatively determined via headspace solid-phase microextraction and gas chromatography–mass spectrometry (HS-SPME-GC–MS). A total of 429 volatile compounds were detected in these fruits, and the greatest number of detected terpenoids was 77. Significant differences were found among the cultivars in terms of the total volatile compound content of the fruits, with variation from 112.76 (‘ZSHYX’) to 317.36 µg/g (‘JNL’). Using relative odor activity value (rOAV) analysis, 42 key aroma compounds were identified. The rOAVs of (2S,4R)-4-methyl-2-(2-methylprop-1-enyl)oxane, (E)-non-2-enal, (3-methyl-3-sulfanylbutyl) formate, and thiophen-2-ylmethanethiol were above 1000, and most had green, fruity, and woody odors; these results indicated that these substances were important contributors to the overall aroma of the apricot fruits. Our study provides a comprehensive analysis of the volatile compounds from 24 representative apricot cultivars and can aid in the further scientific understanding of the metabolites and aroma in apricots. These findings provide a reference for controlling fruit quality and for future apricot cultivar breeding.

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.