Extraskeletal mesenchymal chondrosarcomas (EMCs) are relatively uncommon, and a location in the upper extremity, especially in the shoulder or axillary region, is rare. Furthermore, the radiographic findings of EMCs do not show any features that distinguish them from other neoplasms, and therefore, definitive diagnoses are made based on histological features. EMC is an aggressive tumor with a poor prognosis, and requires wide surgical excision. However, its treatment may involve peculiarities such as a difficulty in obtaining a proper surgical margin in the axillary region or shoulder. In this report, the authors present two rare cases of EMCs in the axillary region.

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This paper discusses a data management infrastructure framework for bridge monitoring applications. As sensor technologies mature and become economically affordable, their deployment for bridge monitoring will continue to grow. Data management becomes a critical issue not only for storing the sensor data but also for integrating with the bridge model to support other functions, such as management, maintenance and inspection. The focus of this study is on the effective data management of bridge information and sensor data, which is crucial to structural health monitoring and life cycle management of bridge structures. We review the state-of-the-art of bridge information modeling and sensor data management, and propose a data management framework for bridge monitoring based on NoSQL database technologies that have been shown useful in handling high volume, time-series data and to flexibly deal with unstructured data schema. Specifically, Apache Cassandra and Mongo DB are deployed for the prototype implementation of the framework. This paper describes the database design for an XML-based Bridge Information Modeling (BrIM) schema, and the representation of sensor data using Sensor Model Language (SensorML). The proposed prototype data management framework is validated using data collected from the Yeongjong Bridge in Incheon, Korea.

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