EUTELTRACS: The European experience on mobile satellite services

EUTELTRACS is Europe's first commercially operated Mobile Satellite Service. Under the overall network operation of EUTELSAT, the European Telecommunications Satellite Organization, EUTELTRACS provides an integrated message exchange and position reporting service. This paper describes the EUTELTRACS system architecture, the message exchange and the position reporting services, including the result of recent analysis of message delivery time and positioning accuracy. It also provides an overview of the commercial deployment, the regulatory situation for its operation within Europe and new applications outside its target market, the international road transportation

A comparison of the workload of rural and urban primary care physicians in Germany: analysis of a questionnaire survey

<p>Abstract</p> <p>Background</p> <p>Many western countries are facing an existing or imminent shortage of primary care physicians especially in rural areas. In Germany, working in rural areas is often thought to be associated with more working hours, a higher number of patients and a lower income than working in urban areas. These perceptions might be key reasons for the shortage. The aim of this analysis was to explore if working time, number of treated patients per week or proportion of privately insured patients vary between rural and urban areas in Germany using two different definitions of rurality within a sample of primary care physicians including general practitioners, general internists and paediatricians.</p> <p>Methods</p> <p>This is a secondary analysis of pre-collected data raised by a questionnaire that was sent to a representative random sample of 1500 primary care physicians chosen by data of the National Association of Statutory Health Insurance Physicians from all federal states in Germany. We employed two different methods of defining rurality; firstly, level of rurality as rated by physicians themselves (urban area, small town, rural area); secondly, rurality defined according to the Organisation for Economic Co-operation and Development.</p> <p>Results</p> <p>This analysis was based upon questionnaire data from 715 physicians. Primary care physicians in single-handed practices in rural areas worked on average four hours more per week than their urban counterparts (p < 0.05). Physicians' gender, the number of patients treated per week and the type of practice (single/group handed) were significantly related to the number of working hours. Neither the proportion of privately insured patients nor the number of patients seen per week differed significantly between rural and urban areas when applying the self-rated classification of rurality.</p> <p>Conclusion</p> <p>Overall this analysis identified few differences between urban and rural primary care physician working conditions. To counter future misdistribution of primary care, students should receive practical experience in rural areas to get more practical knowledge on working conditions.</p

Competence-based curriculum development for general practice in Germany: a stepwise peer-based approach instead of reinventing the wheel

BACKGROUND: Improving postgraduate medical training is one important step to attract more medical students into general practice. Keeping pace with international developments moving to competence-based curricula for general practice training, the aim of this project was to develop and implement such a curriculum in Germany. METHODS: A five-step, peer-based method was used for the curriculum development process including panel testing and a “test version” of the curriculum for the pilot implementation phase. The CanMEDS framework served as a basis for a new German competence-based curriculum in general practice training. Four curricula from European countries and Canada were reviewed and, following required cultural adaptions, key strengths from these were integrated. For the CanMEDS “medical expertise” element of the curriculum, the WONCA ICPC-2 classification of patient’s “reason for encounters” was also integrated. RESULTS: Altogether, 37 participants were involved in the development process representing 12 different federal states in Germany, and including an expert advisor from Denmark. An official “test version” of the curriculum consisting of three parts: medical expertise, additional competencies and medical procedures was established. A system of self-assessment for trainees was integrated into the curriculum using a traffic light scale. Since March 2012, the curriculum has been made freely available online as a “test version”. In 2014, an evaluation is planned using feedback from users of the test model as a further stage of the implementation process. CONCLUSIONS: The first German competence-based curriculum for general practice training has been developed using a pragmatic peer controlled approach and implementation is being trialed with a “test version” of the curriculum. This model project and its peer-based methodology may support competence-based curriculum development for other medical specialties both inside and outside Germany

Lagrangian Time Series Models for Ocean Surface Drifter Trajectories

This paper proposes stochastic models for the analysis of ocean surface trajectories obtained from freely-drifting satellite-tracked instruments. The proposed time series models are used to summarise large multivariate datasets and infer important physical parameters of inertial oscillations and other ocean processes. Nonstationary time series methods are employed to account for the spatiotemporal variability of each trajectory. Because the datasets are large, we construct computationally efficient methods through the use of frequency-domain modelling and estimation, with the data expressed as complex-valued time series. We detail how practical issues related to sampling and model misspecification may be addressed using semi-parametric techniques for time series, and we demonstrate the effectiveness of our stochastic models through application to both real-world data and to numerical model output.Comment: 21 pages, 10 figure

Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system

Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth's changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters. © 2011 IEEE

Questionnaire of chronic illness care in primary care-psychometric properties and test-retest reliability

<p>Abstract</p> <p>Background</p> <p>The Chronic Care Model (CCM) is an evidence-based approach to improving the structure of care for chronically ill patients with multimorbidity. The Assessment of Chronic Illness Care (ACIC), an instrument commonly used in international research, includes all aspects of the CCM, but cannot be easily extended to the German context. A new instrument called the "Questionnaire of Chronic Illness Care in Primary Care" (QCPC) was developed for use in Germany for this reason. Here, we present the results of the psychometric properties and test-retest reliability of QCPC.</p> <p>Methods</p> <p>A total of 109 family doctors from different German states participated in the validation study. Participating physicians completed the QCPC, which includes items concerning the CCM and practice structure, at baseline (T0) and 3 weeks later (T1). Internal consistency reliability and test-retest reliability were evaluated using Cronbach's alpha and Pearson's r, respectively.</p> <p>Results</p> <p>The QCPC contains five elements of the CCM (decision support, delivery system design, self-management support, clinical information systems, and community linkages). All subscales demonstrated moderate internal consistency and moderate test-retest reliability over a three-week interval.</p> <p>Conclusions</p> <p>The QCPC is an appropriate instrument to assess the structure of chronic illness care. Unlike the ACIC, the QCPC can be used by health care providers without CCM training. The QCPC can detect the actual state of care as well as areas for improvement of care according to the CCM.</p

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the Quantum-assisted Variational Autoencoder has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points