Nitrite Accumulation by denitrifiers isolated from fluidized Bed Reactors operated in an Aquaculture Unit

In fluidized bed rnactors, applled for nitrate removal in an aquaculture unit, denitrificatlon of nitrate was found to result in an accumulation of nitrite. Isolation of denltrifylng bacteria from these reactors revealed that most of these isolates produced nitrite from nitrate under controlled laboratory. conditions. Two of the isolates, one with accumulated nitrite while respiring on nitrate, and the other not, were subject to further studies. It was found that nitrite accumulation in one of these isolates could be adequately explained by differences in the relative rates of nitrate and nitrite reduction. Inhibiting concentrations of nitrite were lower for the isolate which did not accumulate nitrite. Incubation of the nitrite-accumulating isolate with different carbon sources resulted ln differences in the relative nitrite accumulation (as compared with the runount of nitrate reduced

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

Dry season rice varieties for the Ord River Valley

PREVIOUS experimental work at the Kimberley Research Station (Langfield 1961) showed that, in general, indica varieties of rice are best adapted for wet-season sowing and japonica varieties for dry-season sowing. This article gives the results of three rice variety and time of planting experiments carried out at Kimberley Research Station in the I960, 1961, and 1962 dry seasons. The recommendation is to sow the variety Caloro during May

Phosphate requirements of rice in the Ord River Valley

A three-year experiment on the initial and maintenance phosphate requirements of wet-season rice was carried out at Kimberley Research Station between 1960 and 1963. Application of 2 cwt. per acre superphosphate as the first application on new land, followed by annual application of 1J cwt. per acre is recommended