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Herein, we report the first example of an effective and green approach for the oxidative cleavage of olefins to carboxylic acids using a 1,2-dibutoxyethane/O2system under clean conditions. This novel oxidation system also has excellent functional-group tolerance and is applicable for large-scale synthesis. The target products were prepared in good to excellent yields by a one-pot sequential transformation without an external initiator, catalyst, and additive.

As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing (MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking (SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything (V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others' algorithms.

Machine learning has exhibited high efficiency in designing concrete. However, collecting the dataset for training machine learning models is challenging. To address this challenge, this paper develops an approach to collect concrete design data automatically based on information extraction techniques. The approach enables machine learning models to automatically track, extract, and learn knowledge embedded in data from relevant publications. The approach has been incorporated into AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) via integrating the capabilities of automatically collecting and processing data, predicting UHPC properties, and optimizing UHPC properties regarding the material cost, carbon footprint, and compressive strength. A self-updating mechanism is imparted to continuously learn available data. Such a mechanism enables the discovery of low-carbon cost-effective UHPC. The results showed increasing prediction accuracy and optimization performance of the proposed approach over time when more knowledge was learned from new data, therefore accelerating the design of UHPC.