We have previously shown that surface lesions and acute necrosis of chondrocytes are produced by severe levels of blunt mechanical load, generating contact pressures greater than 25 MPa, on chondral and osteochondral explants. We have also found surface lesions and chronic degradation of retro-patellar cartilage within 3 years following a 6J impact intensity with an associated average pressure of 25 MPa in the rabbit patello-femoral joint. We now hypothesized that cellular necrosis is produced acutely in the retro-patellar cartilage in this model as a result of a 6J impact and that an early injection of the non-ionic surfactant, poloxamer 188 (P188), would significantly reduce the percentage of necrotic cells in the traumatized cartilage. Eighteen rabbits were equally divided into a 'time zero' group and two other groups carried out for 4 days. One '4 day' group was administered a 1.5 ml injection of P188 into the impacted joint immediately after trauma, while the other was injected with a placebo solution. Impact trauma produced surface lesions on retro-patellar cartilage in all groups. Approximately 15% of retro-patellar chondrocytes suffered acute necrosis in the 'time zero' and '4-day no poloxamer' groups. In contrast, significantly fewer cells (7%) suffered necrosis in the poloxamer group, most markedly in the superficial cartilage layer. The use of P188 surfactant early after severe trauma to articular cartilage may allow sufficient time for damaged cells to heal, which may in turn mitigate the potential for post-traumatic osteoarthritis. Additional studies are needed to improve the efficacy of this surfactant and to determine the long-term health of joint cartilage after P188 intervention.

Owing to the rich processing, multi-modal sensing, and versatile networking capabilities, smartphones are increasingly used to build data-intensive embedded sensing applications. However, various challenges must be systematically addressed before smartphones can be used as a generic embedded sensing platform, including high power consumption, lack of real-time functionality and user-friendly embedded programming support. This paper presents ORBIT, a smartphone-based platform for data-intensive embedded sensing applications. ORBIT features a tiered architecture, in which a smartphone can interface to an energy-efficient peripheral board and/or a cloud service. ORBIT as a platform addresses the shortcomings of current smartphones while utilizing their strengths. ORBIT provides a profile-based task partitioning that allows it to intelligently dispatch the processing tasks among the tiers to minimize the system power consumption. ORBIT also provides a data processing library that includes two mechanisms namely adaptive delay-quality trade-off and data partitioning via multi-threading to optimize resource usage. Moreover, ORBIT supplies an annotation-based programming API for developers that significantly simplifies the application development and provides programming flexibility. Extensive microbenchmark evaluation and three case studies including seismic sensing, visual tracking using an ORBIT robot, and multi-camera 3D reconstruction, validate the generic design of ORBIT.

No abstract

No abstract

Appliance-level power usage monitoring may help conserve electricity in homes. Several existing systems achieve this goal by exploiting appliances’ power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in a high installation cost. This article presents Supero —the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, nonprofessional users can quickly deploy Supero with considerable flexibility.