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Production development for asparagus has become an important research subject due to its low shelf life. In order to determine the content of flavonoids in asparagus tips and shoots, LC-MS-based method was performed for a targeted analysis of flavonoids in asparagus, and 34 peaks attributed to the targeted flavonoids were characterised. Twelve peaks corresponding to rutin, isoquercitrin, quercetin, naringin, taxifolin, vitexin, genistin, daidzein, luteolin, chrysin, and kaempferide were identified and quantified from the asparagus tips and shoots by the LC-MS-based detection with monitoring of parent/daughter ions. The results showed that rutin (&gt; 99%) was the main flavonoid present in the asparagus tips and shoots. Although the tips and shoots contained almost similar compounds, the content of the major compounds, especially rutin, was significantly different. Therefore, the method established through this study could be used for quantitative analysis of flavonoids, especially rutins, in asparagus. The result will provide a theoretical basis for food development in asparagus.<br /><br />

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Fe–Co-based soft magnetic alloys with excellent magnetic properties were prepared by metal injection moulding and hot isostatic pressing. The effect of processing parameters on microstructure and magnetic properties of the alloy were investigated. Good magnetic performance of Fe-50 %Co alloy with a maximum permeability of 9550, saturation induction of 2.42 T, and coercive force of 68.14 A m–1 was achieved. Hot isostatic pressing processing resulted in a near-full density and the achieved saturation induction exceeded the reported value (1.99 to 2.26 T) of Fe-50 %Co produced by metal injection moulding.

Accurate traffic prediction is critical for industry practitioners and researchers in intervening and dredging future traffic in advance to avoid traffic congestion. Considering that most prediction models fail to effectively capture the complex nonlinearity of traffic data and thus cannot obtain satisfactory prediction results, we propose a novel deep-learning architecture for traffic flow prediction, called AC-BLSTM (attention-based convolutional bidirectional long short-term memory). The proposed model captures traffic information through multilayer network architectures composed of convolutional bidirectional long short-term memory (conv-BiLSTM) network and attention mechanism. The spatiotemporal features of traffic flow are extracted by convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) network. Then attention mechanism combines the outputs of CNN and BiLSTM to assign corresponding weights to the features extracted at different times. In addition, we employ a parallel sub-module structure to model three temporal properties of traffic flow, that is, weekly, daily, and recent dependencies. Finally, the results of these three parts are fused to predict the traffic flow values through the fully connected (FC) layers. Experiment results using a real-world urban road traffic dataset demonstrate that compared with other competing models, the proposed model has better prediction performance.