ABSTRACT. Basal melt of ice shelves may lead to an accumulation of disc-shaped ice platelets underneath nearby sea ice, to form a sub-ice platelet layer. Here we present the seasonal cycle of sea ice attached to the Ekström Ice Shelf, Antarctica, and the underlying platelet layer in 2012. Ice platelets emerged from the cavity and interacted with the fast-ice cover of Atka Bay as early as June. Episodic accumulations throughout winter and spring led to an average platelet-layer thickness of 4 m by December 2012, with local maxima of up to 10 m. The additional buoyancy partly prevented surface flooding and snow-ice formation, despite a thick snow cover. Subsequent thinning of the platelet layer from December onwards was associated with an inflow of warm surface water. The combination of model studies with observed fast-ice thickness revealed an average ice-volume fraction in the platelet layer of 0.25 � 0.1. We found that nearly half of the combined solid sea-ice and ice-platelet volume in this area is generated by heat transfer to the ocean rather than to the atmosphere. The total ice-platelet volume underlying Atka Bay fast ice was equivalent to more than one-fifth of the annual basal melt volume under the Ekström Ice Shelf.

High-resolution ultrasonography is a noninvasive, readily applicable imaging modality, capable of depicting real-time static and dynamic morphological information concerning the peripheral nerves and their surrounding tissues. Continuous progress in ultrasonographic technology results in highly improved spatial and contrast resolution. Therefore, nerve imaging is possible to a fascicular level, and most peripheral nerves can now be depicted along their entire anatomical course. An increasing number of publications have evaluated the role of high-resolution ultrasonography in peripheral nerve diseases, especially in peripheral nerve entrapment. Ultrasonography has been shown to be a precious complementary tool for assessing peripheral nerve lesions with respect to their exact location, course, continuity, and extent in traumatic nerve lesions, and for assessing nerve entrapment and tumors. In this article, the authors discuss the basic technical considerations for using ultrasoniography in peripheral nerve assessment, and some of the clinical applications are illustrated.

BackgroundThe increasing prevalence and economic impact of chronic diseases challenge health care systems globally. Digital solutions can potentially improve efficiency and quality of care, but these initiatives struggle with nonusage attrition. Machine learning methods have been proven to predict dropouts in other settings but lack implementation in health care.ObjectiveThis study aimed to gain insight into the causes of attrition for patients in an electronic health (eHealth) intervention for chronic lifestyle diseases and evaluate if attrition can be predicted and consequently prevented. We aimed to build predictive models that can identify patients in a digital lifestyle intervention at high risk of dropout by analyzing several predictor variables applied in different models and to further assess the possibilities and impact of implementing such models into an eHealth platform.MethodsData from 2684 patients using an eHealth platform were iteratively analyzed using logistic regression, decision trees, and random forest models. The dataset was split into a 79.99% (2147/2684) training and cross-validation set and a 20.0% (537/2684) holdout test set. Trends in activity patterns were analyzed to assess engagement over time. Development and implementation were performed iteratively with health coaches.ResultsPatients in the test dataset were classified as dropouts with an 89% precision using a random forest model and 11 predictor variables. The most significant predictors were the provider of the intervention, 2 weeks inactivity, and the number of advices received from the health coach. Engagement in the platform dropped significantly leading up to the time of dropout.ConclusionsDropouts from eHealth lifestyle interventions can be predicted using various data mining methods. This can support health coaches in preventing attrition by receiving proactive warnings. The best performing predictive model was found to be the random forest.

Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts.In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios.Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

International audienceBecause of the cloud-induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward-facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial-sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all-sky cameras (“WobaS-4cam”). The WobaS-4cam system is operational at 2 solar energy research centers and at a commercial 50-MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS-4cam system is considered to be at least state of the art

BackgroundThe use of telemedicine technologies in health care has increased substantially, together with a growing interest in participatory design methods when developing telemedicine approaches.ObjectiveWe present lessons learned from a case study involving patients with heart disease and health care professionals in the development of a personalized Web-based health care intervention.MethodsWe used a participatory design approach inspired by the method for feasibility studies in software development. We collected qualitative data using multiple methods in 3 workshops and analyzed the data using thematic analysis. Participants were 7 patients with diagnosis of heart disease, 2 nurses, 1 physician, 2 systems architects, 3 moderators, and 3 observers.ResultsWe present findings in 2 parts. (1) Outcomes of the participatory design process: users gave valuable feedback on ease of use of the platforms’ tracking tools, platform design, terminology, and insights into patients’ monitoring needs, information and communication technologies skills, and preferences for self-management tools. (2) Experiences from the participatory design process: patients and health care professionals contributed different perspectives, with the patients using an experience-based approach and the health care professionals using a more attitude-based approach.ConclusionsThe essential lessons learned concern planning and organization of workshops, including the finding that patients engaged actively and willingly in a participatory design process, whereas it was more challenging to include and engage health care professionals.

The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 minutes. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.

With intraoperative ultrasound, the extent of traumatic peripheral nerve lesions can be examined morphologically for the first time. It is a promising, noninvasive method that seems capable of assessing the type (intraneural/perineural) and grade of nerve fibrosis. Therefore, in combination with intraoperative neurophysiological studies, intraoperative high-resolution ultrasound may represent a major tool for noninvasive assessment of the regenerative potential of a nerve lesion.