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Two new discrete anionic species, [Cd2Cl7]3–and [Cd2Br7]3–, have been isolated in the form of cobalt(III) complex salts, [Co(phen)3][Cd2Cl7]·3H2O (1) and [Co(phen)3][Cd2Br7]·2H2O (2). The complex salts have been characterized by spectroscopic techniques and other investigations like TGA, solubility product measurements, conductance measurements, luminiscent study, DFT calculations and single crystal structure determinations. The new discrete [Cd2X7]3–anions reside in cavities that are formed by complex cations and are stabilized by different noncovalent interactions (hydrogen bonding, π–π stacking, C–H···π and anion···π). With the help of DFT calculations, an in depth investigation has been conducted into what happens to the conformation of these anions in crystal, solution and gas phase, and the nature of the Cd–halogen bond has been elucidated.

El Niño-Southern Oscillation (ENSO), as a global climate event with cyclical characteristics, often causes global climate anomalies and produces non-negligible economic and social impacts. Therefore, the prediction and research of ENSO events are important for understanding and solving global climate change issues. It has important scientific and practical significance. Previous research on ENSO events mainly used traditional statistical analysis and numerical simulation methods. This study explores the use of deep learning to improve the accuracy of El Niño-Southern Oscillation (ENSO) prediction. Based on long-term and short-term memory neural networks, the time series of meteorological and marine elements were analyzed. In the meantime, the sea surface temperatures (SST) and sea level pressure were predicted to calculate the Southern Oscillation Index (SOI) to reflect the ENSO phenomenon. Finally, this article takes the Niño3.4 regional data from the National Centers for Environmental Prediction (NCEP) dataset as an example, and uses the model proposed in this paper to compare with traditional statistical regression methods. The results show that the Long Short-Term Memory (LSTM) has a good effect in the prediction of ENSO events, and has certain scientific significance and practical value for the prediction of ENSO events.

In this paper, the numerical solution method based on L1 norm for inverse problem is studied. First, according to the theory of the L1 norm and the characteristics of the problem to be solved, a cost function is constructed. Further, for complex parameter estimation problems and derivative discontinuities, the regularization method is used to construct the cost function and the Huber function is used in the derivative discontinuity. Finally, the problem is solved numerically by the semi-smooth Newton method. The experimental results show that the method based on L1 norm is an effective method under the interference of non-Gaussian noises. For the parameter estimation of complex models, the results based on the L1 norm method are very closer to the real parameters when there is impulse noise. For parameter estimation under non-Gaussian noise, the L1 norm estimation method has significant advantages over the L2 norm method.