Dimethyl 4-(4-formyl­phen­yl)-2,6-di­methyl-1,4-dihydro­pyridine-3,5-dicar­boxyl­ate

The title compound, C18H19NO5, is a product of the Hantzsch reaction of p-phthalaldehyde, methyl acetoacetate, and ammonium acetate. The 1,4-dihydro­pyridine ring of the mol­ecule adopts a flattened boat conformation. The benzene ring is almost perpendicular to the 1,4-dihydro­pyridine ring; the plane through the six C atoms of the benzene ring and the plane through the four C atoms that form the base of the boat-shaped 1,4-dihydro­pyridine ring (excluding the ring N atom and the opposite ring C atom) make a dihedral angle of 87.60 (3)°. Inter­molecular N—H⋯O hydrogen bonds result in the formation of extended chains along the a axis

A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets

The evaluation of ecosystem health based on hybrid TODIM method for Chinese case

The health evaluation of urban ecosystem is the need of urban sustainable development and the construction of urban ecological civilization, in order to scientifically evaluate the ecosystem health, in this paper, we establish the mathematical model based on hybrid multiple attributes decision-making. Firstly, we introduce the original city ecosystem health evaluation indexes which reflect on Vitality, Composition Structure, Recovery Capacity, The Ecological System Continually Offering Service Function, Population Health and Ecosystem Cognition ecosystem health. Then in order to obtain the reasonable weights, we integrate the subjective weights by linguistic AHP method and objective weights by deviation maximization method, and get the combined weights for city ecosystem health evaluation indexes. Further, according to the characteristics of the different indexes, we propose an extended TODIM method to evaluate the city ecosystem health in which the indexes take the form of real number, interval number, and probabilistic linguistic term set. Moreover, with respect to the evaluation values of city ecosystem health in Jinan from 2011 to 2015, this paper evaluates the health status of Jinan ecological system, and analyzes the role of various indicators in the process of city ecological development. Result shows that: (1) Jinan ecosystem health status remained at the sub-health state from 2011 to 2015, and the ecological situation is not optimistic. (2) Prominent problems restricting are lack of investment in environmental protection efforts, increasing pollutant emissions, and imperfect industrial structure. To solve the problems in the healthy development of Jinan urban ecosystem, this paper puts forward corresponding countermeasures and suggestions to improve the healthy development of urban ecosystem.  First published online 17 April 201