Standardized maximim D-optimal designs for enzyme kineticinhibition models

Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters.An alternative is the maximin approach that allows the user to specify a range of values for each parameter ofinterest. However, the maximin approach is difficult because we first have to determine the locally optimal designfor each set of nominal values before maximin types of optimal designs can be found via a nested optimizationprocess. We show that particle swarm optimization (PSO) techniques can solve such complex optimizationproblems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae forstandardized maximin D-optimal designs for nonlinear model with 3 or 4 parameters on the compact andnonnegative design space. Additionally, we show locally and standardized maximin D-optimal designs for inhibitionmodels are not necessarily supported at a minimum number of points. To facilitate use of such designs, wecreate a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs

Linkages Between Multiscale Global Sea Surface Temperature Change and Precipitation Variabilities in the US

A growing number of evidence indicates that there are coherent patterns of variability in sea surface temperature (SST) anomaly not only at interannual timescales, but also at decadal-to-inter-decadal timescale and beyond. The multi-scale variabilities of SST anomaly have shown great impacts on climate. In this work, we analyze multiple timescales contained in the globally averaged SST anomaly with and their possible relationship with the summer and winter rainfall in the United States over the past four decades

Acquiring Knowledge from Pre-trained Model to Neural Machine Translation

Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled monolingual data. Then, fine-tuning the pre-trained model with labeled data from downstream tasks. However, in neural machine translation (NMT), we address the problem that the training objective of the bilingual task is far different from the monolingual pre-trained model. This gap leads that only using fine-tuning in NMT can not fully utilize prior language knowledge. In this paper, we propose an APT framework for acquiring knowledge from the pre-trained model to NMT. The proposed approach includes two modules: 1). a dynamic fusion mechanism to fuse task-specific features adapted from general knowledge into NMT network, 2). a knowledge distillation paradigm to learn language knowledge continuously during the NMT training process. The proposed approach could integrate suitable knowledge from pre-trained models to improve the NMT. Experimental results on WMT English to German, German to English and Chinese to English machine translation tasks show that our model outperforms strong baselines and the fine-tuning counterparts

catena-Poly[[[bis­[4-(1H-1,3,7,8-tetra­azacyclo­penta­[l]phenanthren-2-yl)phenol-κ2 N 7,N 8]manganese(II)]-μ-naphthalene-1,4-dicarboxyl­ato-κ2 O 1:O 4] naphthalene-1,4-dicarboxylic acid hemisolvate monohydrate]

The 1,4-dicarboxyl­ate dianions in the title compound, [Mn(C12H6O4)(C19H12N4O)2]·0.5C12H8O4·H2O, bond to two 4-(1H-1,3,7,8-tetra­azacyclo­penta­[l]phenanthren-2-yl)phenol-chelated Mn atoms to form a chain that features the metal atom in an octa­hedral coordination geometry. Adjacent chains inter­act with the uncoordinated water mol­ecules to form a three-dimensional network. The naphthalene-1,4-dicarboxylic acid solvent mol­ecule, which is disordered about a centre of inversion, occupies the space within the network but is not bonded to the network. One NH group is disordered equally over two positions

Effect of hydroxypropylmethyl cellulose (HPMC) coating on flavour, moisture and oil content in chicken nugget

This study was carried out to determine the effects of hydroxy propyl methyl cellulose (HPMC) on the flavour compounds (eugenol and limonene), moisture and oil content in chicken nuggets during frying. Chicken nugget added with 500 ppm eugenol and limonene were coated with HPMC solution (0, 0.75 and 1.5%) and then with a commercial coating (ADABI, Malaysia). Chicken nuggets were fried at 180oC for 4 min. Quantity of eugenol and limonene in the substrate (chicken meat) and coating were measured alongwith the moisture and oil content. The results showed that 0.75 and 1.5% HPMC were not able to retain either eugenol or limonene in both substrate and coating portion of the nuggets when compared to control except for eugenol in the substrate portion when using 1.5% HPMC. Application of HPMC also resulted in reduced moisture loss and oil absorption. The reduced moisture loss and oil absorption in the coating and substrate of the chicken nuggets showed that HPMC was able to form a barrier that restricted the migration of moisture from the nuggets and absorption of oil into the nuggets. However, only the 1.5% HPMC barrier formed was able to reduce the loss of eugenol in the nugget substrate. Both 0.75 and 1.5% HPMC was not able to significantly reduce the loss of limonene during frying