Three Polyoxometalate-Based Ag-Organic Compounds as Heterogeneous Catalysts for the Synthesis of Benzimidazoles.

Three new POM-based compounds, with formulae [Na0.63Ag3(Htba)2.37(tba)0.63(H2O)2(PMo12O40)]·4H2O (Ag3PMo), [Ag4(Htba)4(H2O)2(PMo12O40)](NO3)·H2O (Ag4PMo), and [Ag3(Htba)2(tba)(PW12O40)0.5](NO3)0.5·13H2O (Ag3PW), were prepared with a 3-(4H-1,2,4-triazol-4-yl)benzoic acid (Htba) ligand, Keggin-type anions ([PMo12O40]3-/[PW12O40]3-), and a silver ion (Ag+). The structural features of these compounds are particularly different from the multinuclear subunits, which are [Ag3(tba)3] clusters in Ag3PMo, [Ag4(tba)3] chains in Ag4PMo, and [Ag3(tba)3]2 clusters in Ag3PW, connected by multidonor atom tba ligands and Ag+ ions. Meanwhile, in these compounds, polyanions act as polydentate ligands to link adjacent Ag-tba metal-organic units and expand their spatial dimensions. These compounds, as heterogeneous catalysts, exhibit high stability and excellent catalytic activity to construct benzimidazoles. Ag3PMo could efficiently catalyze the condensation of benzene-1,2-diamines and benzaldehydes and produce benzimidazoles in good yields. In addition, Ag3PMo could be reused up to 7 times and was suitable for gram-scale reactions.

Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms.

Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.

The Relationship of Previous Breastfeeding Experiences and Factors Affecting Breastfeeding Rates: A Follow-Up Study.

Background/Objective: Many studies have focused on the effects of previous breastfeeding experience (PBE) on subsequent breastfeeding, but few have explored their specific relationships. To explain the relationship between PBE and subsequent breastfeeding behavior based on a follow-up study. Materials and Methods: After delivery, 394 participants who had no PBE completed a demographic questionnaire, breastfeeding knowledge questionnaire, the breastfeeding self-efficacy short-form scale (BSES-SF), and the Iowa infant feeding attitudes scale (IIFAS). Multiparas with PBE also completed the maternal breastfeeding evaluation scale (MBFES) in addition to the aforementioned four questionnaires. On the 42nd day after delivery, participants completed the breastfeeding experience scale (BES) through social networking platforms (QQ, WeChat: both are Chinese social medias). At 4 and 6 months postpartum, researchers contacted participants by phone or a social network regarding their exclusive and partial breastfeeding experiences. Results: In this study, exclusive breastfeeding rates were 58.6% and 30.5% at 4 and 6 months. PBE affected breastfeeding attitudes (p < 0.05), self-efficacy (p < 0.01), and difficulties (p < 0.05). Breastfeeding knowledge, attitude, self-efficacy, and difficulties were relevant to exclusive and partial breastfeeding at 4 and 6 months (p < 0.05). Multivariate logistic regression analysis showed that compared with women without PBE, the probability of exclusive breastfeeding of multiparas with PBE at 4 and 6 months increased by 275% and 369%, respectively. Conclusions: The rate of breastfeeding remains low among Chinese women, but PBE is associated with a higher probability of breastfeeding at 4 and 6 months postpartum. Multiparas, especially those having PBE were more likely to breastfeed for an extended period based on their knowledge, attitude, self-efficacy, and ability to manage difficulties.