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【Objective】 Jujube is one of the cash crops in northern Shaanxi. Understanding the suitable areas and conditions for its growth is important to its sustainable production but is currently lacking. This paper is to fill this knowledge gap. 【Method】 The analysis is based on GIS; the suitability for jujube growth in different areas is analyzed using the geographical detector model. 【Result】 ①The suitability for jujube growth decreases progressively from the east and south to the northwest of the region, with the optimal area and sub-optimal area concentrated in Fu county. Suitable areas with large jujube planting are mainly in the districts and counties along the western region of the Yellow River. ②Climatic and topographic factors combine to greatly influence the suitability of jujube, and their effects are nonlinearly confounded. ③Annual precipitation, elevation and annual accumulative temperature (≥10 ℃) are the main factors controlling suitability for jujube growth, and the suitability index is negatively correlated with annual precipitation and annual accumulated temperature, and positively with elevation. ④Annual precipitation, elevation and annual accumulated temperature explain 22%, 15% and 11% of the suitability index, respectively, and the comprehensive interpretation rate reaches 78%. 【Conclusion】 The suitability for jujube growth in northern Shaanxi province decreases from the southeast to the northwest; developing jujube industry or forest ecosystem in this region should consider the jointed effects of annual precipitation, elevation and annual accumulated temperature.

This paper describes a concise, asymmetric and stereodivergent total synthesis of tacaman alkaloids. A key step in this synthesis is the biocatalytic Baeyer–Villiger oxidation of cyclohexanone, which was developed to produce seven‐membered lactones and establish the required stereochemistry at the C14 position (92 % yield, 99 % ee, 500 mg scale). Cis‐ and trans‐tetracyclic indoloquinolizidine scaffolds were rapidly synthesized through an acid‐triggered, tunable acyl‐Pictet–Spengler type cyclization cascade, serving as the pivotal reaction for building the alkaloid skeleton. Computational results revealed that hydrogen bonding was crucial in stabilizing intermediates and inducing different addition reactions during the acyl‐Pictet–Spengler cyclization cascade. By strategically using these two reactions and the late‐stage diversification of the functionalized indoloquinolizidine core, the asymmetric total syntheses of eight tacaman alkaloids were achieved. This study may potentially advance research related to the medicinal chemistry of tacaman alkaloids. [ABSTRACT FROM AUTHOR]

With the development of the social economy, research on stock market prediction is in full swing. However, the fluctuations in stock price and returns are influenced by many factors, including political policies, market environment, investor psychology, and so on. The traditional analysis method, based on subjective experience, requires significant time and effort, and its prediction accuracy is often poor. Now, the application of machine learning algorithms to predict stock returns has become a hot topic among scholars. This article comprehensively analyzes the advantages and disadvantages of support vector machine (SVM), tree-based algorithms, and neural network algorithms in processing tabular data and time series data. It proposes a hybrid model based on the deep neural network (DNN) and TabNet models, combining the strengths of the DNN and tree-based models. In the model training stage, two neural networks are established to accept the inputs of ID features and numerical features, respectively, and multiple fully connected layers are used to complete the construction of the DNN model. The TabNet is implemented based on the attention transformer and feature transformer, and the prediction results of the two models are fused. The proposed model has a best Pearson correlation coefficient (PCC) value and a lowest root mean square error (RMSE) value at the same time, because the hybrid algorithm performs particularly well on large data sets with the least feature engineering and has strong interpretability, such as quantifying the contribution of different features in the model, it has certain theoretical significance and wide application value. [ABSTRACT FROM AUTHOR]