The acid-catalyzed intramolecular oxa-Michael addition of (E)-1-aryl-4-hydroxy-4-methyl--pent-1-en-3-ones under solvent-free and microwave irradiation conditions has been investigated. The results showed that Bronsted acids are more efficient than Lewis acids in this reaction. Up to 90% conversion and 81% yield were obtained using trifluoromethanesulfonic acid (triflic acid) as the catalyst, with short reaction times and an environmentally benign procedure.

With the rapid development of deep learning, researchers have gradually applied it to motor imagery brain computer interface (MI-BCI) and initially demonstrated its advantages over traditional machine learning. However, its application still faces many challenges, and the recognition rate of electroencephalogram (EEG) is still the bottleneck restricting the development of MI-BCI. In order to improve the accuracy of EEG classification, a DSC-ConvLSTM model based on the attention mechanism is proposed for the multi-classification of motor imagery EEG signals. To address the problem of the small sample size of well-labeled and accurate EEG data, the preprocessing uses sliding windows for data augmentation, and the average prediction loss of each sliding window is used as the final prediction loss for that trial. This not only increases the training sample size and is beneficial to train complex neural network models, but also the network no longer extracts the global features of the whole trial so as to avoid learning the difference features among trials, which can effectively eliminate the influence of individual specificity. In the aspect of feature extraction and classification, the overall network structure is designed according to the characteristics of the EEG signals in this paper. Firstly, depth separable convolution (DSC) is used to extract spatial features of EEG signals. On the one hand, this reduces the number of parameters and improves the response speed of the system. On the other hand, the network structure we designed is more conducive to extract directly the direct extraction of spatial features of EEG signals. Secondly, the internal structure of the Long Short-Term Memory (LSTM) unit is improved by using convolution and attention mechanism, and a novel bidirectional convolution LSTM (ConvLSTM) structure is proposed by comparing the effects of embedding convolution and attention mechanism in the input and different gates, respectively. In the ConvLSTM module, the convolutional structure is only introduced into the input-to-state transition, while the gates still remain the original fully connected mechanism, and the attention mechanism is introduced into the input to further improve the overall decoding performance of the model. This bidirectional ConvLSTM extracts the time-domain features of EEG signals and integrates the feature extraction capability of the CNN and the sequence processing capability of LSTM. The experimental results show that the average classification accuracy of the model reaches 73.7% and 92.6% on two datasets, BCI Competition IV Dataset 2a and High Gamma Dataset, respectively, which proves the robustness and effectiveness of the model we proposed. It can be seen that the model in this paper can deeply excavate significant EEG features from the original EEG signals, show good performance in different subjects and different datasets, and improve the influence of individual variability on the classification performance, which is of practical significance for promoting the development of brain-computer interface technology towards a practical and marketable direction.

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An efficient large-scale preparation of 2-chlorotetrahydroquinoline with cyclohexanone and benzylamine as starting materials was developed and well optimized, in which benzyl-protected enamide was successfully cyclized and benzyl group was directly removed under Vilsmeier conditions. Azeotropic distillation provided 264 g of 2-chlorotetrahydroquinoline (79%) on a 2 mol scale of reaction without intermediate isolation. The downstream product 2-chlorotetrahydroquinolin-8-one was acquired through Boekelheide rearrangement, hydrolysis of acetate via NaBH4 reduction, and Anelli oxidation. With the developed procedure, the intermediates were not necessary to be isolated and 2-chlorotetrahydroquinolin-8-one was conveniently obtained with solvent slurry in 65% overall isolated yield in a four-step sequence.

This study presents a method of high precision stable Sr isotope measurement for barite using the Na2CO3 exchange reaction and MC-ICP-MS. The reliability of this method was strictly tested by...