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  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Atmosphere Vol. 11, No. 2 ( 2020-02-07), p. 170-
    In: Atmosphere, MDPI AG, Vol. 11, No. 2 ( 2020-02-07), p. 170-
    Abstract: During intense heat episodes, the human population suffers from an increased morbidity and mortality. In order to minimize such negative health impacts, the general public and the public health authorities are informed and warned by means of an advanced procedure known as a “heat health warning system” (HHWS). It is aimed at triggering interventions and at taking preventive measures. The HHWS in Germany has been in operation since 2005. The present work is aimed at showing the updated structure of an advanced HHWS that has been developed further several times during its 15 years of operation. This is to impart knowledge to practitioners about the concept of the system. In Germany, dangerous heat episodes are predicted on the basis of the numerical weather forecast. The perceived temperature as an appropriate thermal index is calculated and used to assess the levels of heat stress. The thermo-physiologically based procedure contains variable thresholds taking into account the short time acclimatization of the people. The forecast system further comprises the nocturnal indoor conditions, the specific characteristics of the elderly population, and the elevation of a region. The heat warnings are automatically generated, but they are published with possible adjustments and a compulsory confirmation by the biometeorology forecaster. Preliminary studies indicate a reduction in the heat related outcomes. In addition, the extensive duration of the strongest heat wave in summer 2018, which lasted three weeks, highlights the necessity of the HHWS to protect human health and life.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2017
    In:  Atmosphere Vol. 8, No. 11 ( 2017-11-15), p. 224-
    In: Atmosphere, MDPI AG, Vol. 8, No. 11 ( 2017-11-15), p. 224-
    Abstract: After 2003, another hot summer took place in Western and Central Europe in 2015. In this study, we compare the characteristics of the two major heat waves of these two summers and their effect on the heat related mortality. The analysis is performed with focus on South-West Germany (Baden–Württemberg). With an additional mean summer mortality of +7.9% (2003) and +5.8% (2015) both years mark the top-two records of the summer mortality in the period 1968–2015. In each summer, one major heat wave contributed strongly to the excess summer mortality: In August 2003, daily mortality reached anomalies of +70% and in July 2015 maximum deviations of +56% were observed. The August 2003 heat wave was very long-lasting and characterized by exceptional high maximum and minimum temperatures. In July 2015, temperatures were slightly lower than in 2003, however, the high air humidity during the day and night, lead to comparable heat loads. Furthermore, the heat wave occurred earlier during the summer, when the population was less acclimated to heat stress. Using regional climate models we project an increasing probability for future 2003- and 2015-like heat waves already in the near future (2021–2050), with a 2015-like event occurring about every second summer. In the far future (2070–2099) pronounced increases with more than two 2015-like heat waves per summer are possible.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2605928-9
    SSG: 23
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    In: Current Directions in Biomedical Engineering, Walter de Gruyter GmbH, Vol. 6, No. 1 ( 2020-09-17)
    Abstract: Semantic segmentation of organs and tissue types is an important sub-problem in image based scene understanding for laparoscopic surgery and is a prerequisite for context-aware assistance and cognitive robotics. Deep Learning (DL) approaches are prominently applied to segmentation and tracking of laparoscopic instruments. This work compares different combinations of neural networks, loss functions, and training strategies in their application to semantic segmentation of different organs and tissue types in human laparoscopic images in order to investigate their applicability as components in cognitive systems. TernausNet-11 trained on Soft-Jaccard loss with a pretrained, trainable encoder performs best in regard to segmentation quality (78.31% mean Intersection over Union [IoU]) and inference time (28.07 ms) on a single GTX 1070 GPU.
    Type of Medium: Online Resource
    ISSN: 2364-5504
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2835398-5
    Library Location Call Number Volume/Issue/Year Availability
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