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Three neodymium compounds, Nd(4DNP)3(C2H5OH)3 (1), Nd(4DNPO)3 · 7H2O (2), and {[Nd(μ-ox)(4DNPO)(H2O)4] · 4H2O}n (3) (4HDNP, 3,5-dinitropyrid-4-one; 4HDNPO, 3,5-dinitropyrid-4-one-N-hydroxide; ox, oxalate), were synthesized and characterized by FT-IR, elemental analysis, thermogravimetry-differential scanning calorimetry (TG-DSC), and two of them by single-crystal X-ray diffraction analyses. The results revealed that in 1, the Nd(III) is surrounded by three chelating ligands and three ethanol solvent molecules. The structure of 3 could be described as 1-D chains in which Nd is bis-bidentately bridged by oxalates, together with a monodentate 3,5-dinitropyrid-4-onate N-oxide 4DNPO ligand and four water molecules as well as four uncoordinated water molecules in the crystal lattice. The origin of the oxalate anions in 3 is probably due to decomposition of the 4DNPO or its oxidation of other species. The TG-DTG (DTG, derivative thermogravimetry) and DSC analyses showed that 1 underwent a three-stage decomposition process. A kinetic equation for decomposition reaction of 2 was also obtained. The apparent activation energy (Ea) and pre-exponential factor (A) of the main decomposition reaction are 157.70 kJ mol-1 and 1011.97 s-1, respectively. The kinetic equation can thus be expressed as dα/dt = 1011.97(1 - α)e-1.90 × 104/T. [ABSTRACT FROM AUTHOR]

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.