This indicates that recharge for a heterogeneous profile cannot be estimated with an equivalent homogeneous profile. The value of μR was always smaller than μI and correlations were highly non-linear due to vadose zone storage. Knowledge of only infiltration volume can, therefore, lead to misinterpretation of recharge efficiency, especially at earlier times. The arrival time of the wetting front at the bottom boundary (60 m) ranged from 21-317 days, with earlier times occurring for increasing σ and Z. The corresponding first arrival location can be 0.1-44 m away from the bottom releasing point of a drywell in the horizontal direction, with greater distances occurring for increasing σ and X. This knowledge is important to accurately assess drywell recharged performance, water quantity, and water quality.In this work, a broadly-applicable and simple approach for building high accuracy viscosity correlations is demonstrated for propane. The approach is based on the combination of a number of recent insights related to the use of residual entropy scaling, especially a new way of scaling the viscosity for consistency with the dilute-gas limit. With three adjustable parameters in the dense phase, the primary viscosity data for propane are predicted with a mean absolute relative deviation of 1.38%, and 95% of the primary data are predicted within a relative error band of less than 5%. The dimensionality of the dense-phase contribution is reduced from the conventional two dimensional approach (temperature and density) to a one-dimensional correlation with residual entropy as the independent variable. The simplicity of the model formulation ensures smooth extrapolation behavior (barring errors in the equation of state itself). The approach proposed here should be applicable to a wide range of chemical species. The supporting information includes the relevant data in tabular form and a Python implementation of the model.Emissions generated from the combustion of coal have been a subject of regulation by the United States Environmental Protection Agency (U.S. EPA) and State agencies for years, as they have been associated with adverse effects on human health and the environment. Over the past several decades, regulations on these facility emissions have become more stringent and have therefore caused industry to look toward new pre- and post-combustion control technologies. In more recent years, there has been a "push" toward renewable and cleaner burning alternative fuels as replacements for traditional fossil fuels. Part of this "push" has been accomplished by States and Regions offering incentives and options for renewable portfolios, which over half of the states now have in some form. The current study investigates the potential changes in both gaseous and particulate emissions from the use of a variety of woody biomass materials as a drop-in replacement for coal as compared to use of 100% bituminous coal. Four different biomass materials are blended individually with coal at 20% and 40% by mass for testing on the U.S. https://www.selleckchem.com/products/mizagliflozin.html EPA's Multi-Pollutant Control Research Facility, a pilot-scale coal-fired facility located in Research Triangle Park, North Carolina. Emissions are calculated based on measurements from the flue gas to characterize gaseous species (CO, CO2, NOX, SO2, other acid gases, and several organic hazardous air pollutants) as well as fine and ultrafine particulate (mass, size distribution, number count, elemental carbon, organic carbon, and black carbon) and compared among each combination of fuels and 100% bituminous coal.Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents. However, there are many applications that require more specific summaries, such as supporting question answering or topic-based literature discovery. In this paper, we study the problem of conditional summarization in which content selection and surface realization are explicitly conditioned on an ad-hoc natural language question or topic description. Because of the difficulty in obtaining sufficient reference summaries to support arbitrary conditional summarization, we explore the use of multi-task fine-tuning (MTFT) on twenty-one natural language tasks to enable zero-shot conditional summarization on five tasks. We present four new summarization datasets, two novel "online" or adaptive task-mixing strategies, and report zero-shot performance using T5 and BART, demonstrating that MTFT can improve zero-shot summarization quality.Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge. In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training. We evaluated the performance of BERT pre-trained on Wikipedia with and without OSCR by measuring the performance when fine-tuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33.3 %, 18.6 %, and 4 % improved accuracy compared to pre-training BERT without OSCR.Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching discriminator that combines the translated images with task-relevant segmentation probability maps to further constrain and regularize image-to-image (I2I) translations. We performed extensive comparisons to multiple state-of-the-art I2I translation and segmentation methods.