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  • 1
    ISSN: 1573-3521
    Keywords: back pain ; acute ; behavioral ; prevention
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Psychology
    Notes: Abstract Back-pain patients with onset in the preceding 1–10 days and comparable on a back examination were randomly assigned to traditional management (A regimen) and behavioral treatment methods (B regimen). Patients were compared at 6 weeks and 9–12 months on a set of “Sick/Well” scores derived from patient reported vocational status (V), health-care utilization (HCU), claimed impairment (CI), and pain drawings (D) and on two measures of activity level. No differences were found at 6 weeks, but at 9–12 months, A-group S's were more “sick.” No A/B differences were found on activity-level measures. Group A S's showed significant increases in claimed impairment from preonset to follow-up, whereas Group B S's had returned at follow-up to preonset levels
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2023-07-06
    Description: In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space.
    Language: English
    Type: article , doc-type:article
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