"Last-Mile" preparation for a potential disaster

Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity

Endoscopic ultrasound—guided fine needle aspiration in the diagnosis of mediastinal masses of unknown origin

The ability of endosonography to diagnose a variety of gastrointestinal pathology has been significantly advanced with the introduction of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) biopsy. EUS-FNA technology can also be applied to the evaluation of non-GI disorders. The role of EUS-FNA to establish the diagnosis of unexplained mediastinal masses has not been previously described. The aim of this study was to determine the diagnostic accuracy, impact on subsequent workup, and role of EUS-FNA in treating mediastinal masses of unknown cause. METHODS : A total of 26 patients (15 men and 11 women, mean age 61 yr, range 39–77 yr) underwent EUS-FNA in patients presenting with unexplained mediastinal masses at four tertiary referral centers. Presenting symptoms included: chest pain (10 patients), dysphagia (eight), cough (seven), fever (six), night sweats (three), and no symptoms/abnormal x-ray (five patients). Five of 26 patients had prior history of cancer (three lung, one tracheal, and one esophageal). RESULTS : Final diagnosis using EUS-FNA, surgery, autopsy, other diagnostic study, or long-term follow-up was available in all patients. EUS-FNA results were classified under three disease categories: 1) infectious, 2) benign/inflammatory, and 3) malignant. Final diagnosis included infectious in five patents, benign/inflammatory in nine, and malignant in 12. EUS-FNA was successful in 21 of 26 patients (81%) for all disease categories (infectious 60%, benign/inflammatory 78%, and malignant 92%). EUS-FNA was successful in directing subsequent workup in 77% (20 of 26) and therapy in 73% (19 of 26). Mean EUS-FNA passes for adequate tissue sampling was lower of nonmalignant disease categories (3.0 and 3.4) versus malignant disease (4.4). No complications were seen during the course of this study. CONCLUSIONS : EUS-FNA in patients presenting with idiopathic mediastinal masses establishes the diagnosis in the vast majority of cases, particularly for those with malignant disease. The emergence of transesophageal EUS-FNA of the mediastinum provides the ability to alter subsequent workup and therapy, obviating the need for more invasive diagnostic studies such as thoracotomy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72588/1/j.1572-0241.2002.06023.x.pd

Contrasting Views of Physicians and Nurses about an Inpatient Computer-based Provider Order-entry System

Objective: Many hospitals are investing in computer-based provider order-entry (POE) systems, and providers’ evaluations have proved important for the success of the systems. The authors assessed how physicians and nurses viewed the effects of one modified commercial POE system on time spent patients, resource utilization, errors with orders, and overall quality of care. Design: Survey. Measurements: Opinions of 271 POE users on medicine wards of an urban teaching hospital: 96 medical house officers, 49 attending physicians, 19 clinical fellows with heavy inpatient loads, and 107 nurses. Results: Responses were received from 85 percent of the sample. Most physicians and nurses agreed that orders were executed faster under POE. About 30 percent of house officers and attendings or fellows, compared with 56 percent of nurses, reported improvement in overall quality of care with POE. Forty-four percent of house officers and 34 percent of attendings/fellows reported that their time with patients decreased, whereas 56 percent of nurses indicated that their time with patients increased (P \u3c 0.001). Sixty percent of house officers and 41 percent of attendings/fellows indicated that order errors increased, whereas 69 percent of nurses indicated a decrease or no change in errors. Although most nurses reported no change in the frequency of ordering tests and medications with POE, 61 percent of house officers reported an increased frequency. Conclusion: Physicians and nurses had markedly different views about effects of a POE system on patient care, highlighting the need to consider both perspectives when assessing the impact of POE. With this POE system, most nurses saw beneficial effects, whereas many physicians saw negative effects

Tunka-Rex: the Cost-Effective Radio Extension of the Tunka Air-Shower Observatory

Tunka-Rex is the radio extension of the Tunka cosmic-ray observatory in Siberia close to Lake Baikal. Since October 2012 Tunka-Rex measures the radio signal of air-showers in coincidence with the non-imaging air-Cherenkov array Tunka-133. Furthermore, this year additional antennas will go into operation triggered by the new scintillator array Tunka-Grande measuring the secondary electrons and muons of air showers. Tunka-Rex is a demonstrator for how economic an antenna array can be without losing significant performance: we have decided for simple and robust SALLA antennas, and we share the existing DAQ running in slave mode with the PMT detectors and the scintillators, respectively. This means that Tunka-Rex is triggered externally, and does not need its own infrastructure and DAQ for hybrid measurements. By this, the performance and the added value of the supplementary radio measurements can be studied, in particular, the precision for the reconstructed energy and the shower maximum in the energy range of approximately $10^{17}-10^{18}\,$eV. Here we show first results on the energy reconstruction indicating that radio measurements can compete with air-Cherenkov measurements in precision. Moreover, we discuss future plans for Tunka-Rex.Comment: Proceeding of UHECR 2014, Springdale, Utah, USA, accepted by JPS Conference Proceeding

Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures the radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km\textsuperscript{2}. In the present work we discuss the improvements of the signal reconstruction applied for the Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performance of matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised trace, i.e. removes all signal-unrelated amplitudes. We present the comparison between standard method of signal reconstruction, matched filtering and autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.Comment: ARENA2018 proceeding