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Introduction: The U.S. Army medics are often the first responders in the care of sick and injured soldiers on the battlefield, sick call in a Role 2 aid station and garrison clinics. Sick call medics are required to utilize the Algorithm Directed Troop Medical Care (ADTMC) to care for and then render a disposition for these soldiers. The current ADTMC manual is a thick, heavy paper manual. A desktop and smartphone application has been developed that contains the entire ADTMC manual algorithm-based content. Our goal is to enhance the medics' clinical learning and critical thinking skills while improving their evaluation, disposition, and documentation during patient encounters.Materials and Methods: The application was field-tested with the 173rd IBCT (A) while attending a field exercise at Grafenwoehr, Germany. At the unit's Role 2 tent setup, the use of the paper manual to the utilization of the same material via the ADTMC application was compared by observing the medics' workflows directly while they were caring for ill and injured soldiers.Results: Medics, while examining patients, would demonstrate 50% faster exam time using the application compared to medics using only the manual. Moreover, a 50% decrease in document processing time as a result of digitization of the paper documentation process was confirmed. The application ("app") enabled the medics to continue their screening assessments and patient disposition duties on a desktop computer or a smartphone without the need to refer to the paper manual.Conclusion: Medics, by adopting this tool, will become quicker, more efficient, and develop critical thinking skills. In other words, the ability to objectively evaluate patients in order to form a proper disposition of sick and injured soldiers during training, in the field, as well as in garrison. When utilized properly, the ADTMC application ensures that soldiers reporting to sick call are expeditiously routed to the appropriate level of care, and is a vehicle for further training for medics in the care of soldiers. [ABSTRACT FROM AUTHOR]

One major criticism against the use of synthetic data has been that the efforts necessary to generate useful synthetic data are so intense that many statistical agencies cannot afford them. We argue many lessons in this evolving field have been learned in the early years of synthetic data generation, and can be used in the development of new synthetic data products, considerably reducing the required investments. The final goal of the project described in this paper will be to evaluate whether synthetic data algorithms developed in the U.S. to generate a synthetic version of the Longitudinal Business Database (LBD) can easily be transferred to generate a similar data product for other countries. We construct a German data product with information comparable to the LBD - the German Longitudinal Business Database (GLBD) - that is generated from different administrative sources at the Institute for Employment Research, Germany. In a future step, the algorithms developed for the synthesis of the LBD will be applied to the GLBD. Extensive evaluations will illustrate whether the algorithms provide useful synthetic data without further adjustment. The ultimate goal of the project is to provide access to multiple synthetic datasets similar to the SynLBD at Cornell to enable comparative studies between countries. The Synthetic GLBD is a first step towards that goal. [ABSTRACT FROM AUTHOR]

In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4-5 points/m². Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data. [ABSTRACT FROM AUTHOR]