REGIONAL IMPACT OF URBAN WATER USE ON IRRIGATED AGRICULTURE

Linear programming and regional input-output models were applied to estimate the impacts of increased pumping costs for irrigated agriculture due to groundwater depletion principally caused by the expanding urban area of San Antonio, Texas. A biophysical simulator was use to estimate linear programming coefficients of crop yield by irrigation level and timing. The results indicated significant local (county) economic impacts from groundwater mining but insignificant regional impacts. A major improvement on irrigation efficiency would be required to offset the increased pumping costs and reduce water availability associated with increased lifts due to urban expansion.Resource /Energy Economics and Policy,

¹H NMR spectra dataset and solid-state NMR data of cowpea (Vigna unguiculata)

In this article the NMR data from chemical shifts, coupling constants, and structures of all the characterized compounds were provided, beyond a complementary PCA evaluation for the corresponding manuscript (E.G. Alves Filho, L.M.A. Silva, E.M. Teofilo, F.H. Larsen, E.S. de Brito, 2017) [3]. In addition, a complementary assessment from solid-state NMR data was provided. For further chemometric analysis, numerical matrices from the raw 1H NMR data were made available in Microsoft Excel workbook format (.xls)

Genotype evaluation of cowpea seeds (Vigna unguiculata) using 1H qNMR combined with exploratory tools and solid-state NMR.

Made available in DSpace on 2018-05-10T01:04:42Z (GMT). No. of bitstreams: 1 ART17019.pdf: 861613 bytes, checksum: 6d1095f78d2e48befce3607f88e9eef8 (MD5) Previous issue date: 2017-08-03bitstream/item/167028/1/ART17019.pd

Rate Dependent Performance Related to Crystal Structure Evolution of Na0.67Mn0.8Mg0.2O2 in a Sodium-Ion Battery

Sodium-ion batteries are considered as a favorable alternative to the widely used lithium-ion batteries for applications such as grid-scale energy storage. However, to meet the energy density and reliability that is necessary, electrodes that are structurally stable and well characterized during electrochemical cycling need to be developed. Here, we report on how the applied discharge current rate influences the structural evolution of Na0.67Mn0.8Mg0.2O2 electrode materials. A combination of ex situ and in situ X-ray diffraction (XRD) data were used to probe the structural transitions at the discharged state and during charge/discharge. Ex situ data shows a two-phase electrode at the discharged state comprised of phases that adopt Cmcm and P63/mmc symmetries at the 100 mA/g rate but a predominantly P63/mmc electrode at 200 and 400 mA/g rates. In situ synchrotron XRD data at 100 mA/g shows a solely P63/mmc electrode when 12 mA/g charge and 100 mA/g discharge is used even though ex situ XRD data shows the p..

Sequence-aware multimodal page classification of Brazilian legal documents

The Brazilian Supreme Court receives tens of thousands of cases each semester. Court employees spend thousands of hours to execute the initial analysis and classification of those cases -- which takes effort away from posterior, more complex stages of the case management workflow. In this paper, we explore multimodal classification of documents from Brazil's Supreme Court. We train and evaluate our methods on a novel multimodal dataset of 6,510 lawsuits (339,478 pages) with manual annotation assigning each page to one of six classes. Each lawsuit is an ordered sequence of pages, which are stored both as an image and as a corresponding text extracted through optical character recognition. We first train two unimodal classifiers: a ResNet pre-trained on ImageNet is fine-tuned on the images, and a convolutional network with filters of multiple kernel sizes is trained from scratch on document texts. We use them as extractors of visual and textual features, which are then combined through our proposed Fusion Module. Our Fusion Module can handle missing textual or visual input by using learned embeddings for missing data. Moreover, we experiment with bi-directional Long Short-Term Memory (biLSTM) networks and linear-chain conditional random fields to model the sequential nature of the pages. The multimodal approaches outperform both textual and visual classifiers, especially when leveraging the sequential nature of the pages.Comment: 11 pages, 6 figures. This preprint, which was originally written on 8 April 2021, has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in the International Journal on Document Analysis and Recognition, and is available online at https://doi.org/10.1007/s10032-022-00406-7 and https://rdcu.be/cRvv