Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at th...

Lignin is possibly one new product from the paper pulp mill. Lignin of relatively high purity can be separated from black liquor by using the so called “LignoBoost” process. The first step of the LignoBoost process is the precipitation of lignin by lowering black liquor pH. The efficiency of this step depends on the properties of black liquor and the process conditions in the precipitation step...

We separate and quantify the sources of uncertainty in projections of regional (∼ 2500 km) precipitation changes for the 21st century using the CMIP3 multi-model ensemble, allowing a direct comparison with a similar analysis for regional temperature changes. For decadal means of seasonal mean precipitation, internal variability is the dominant uncertainty for predictions of the first decade eve...

A diverse collection of correctness statements have been proposed and used in microprocessor verification efforts. Correctness statements have evolved from criteria that match a single step of the implementation against the specification to seemingly looser, multi-step, criteria. In this paper, we formally verify conditions under which two categories of multi-step correctness statements logical...

A new method of numerical integration is presented here, the variable-step Störmer-Cowell method. The method uses error control to regulate the step size, so larger step sizes can be taken when possible, and is double-integration, so only one evaluation per step is necessary when integrating second-order differential equations. The method is not variable-order, because variable-order algorithms...

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to single large model learned end-to-end without sub-tasks, presented. Pre-optimized ML models are better performance obtained by re-optimizing the one. selection an from several small candidates for each sub-task has been performed...