The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic "fingerprint". In general, in each fingerprint, the more elements, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.

Wi-Fi positioning has found favour in environments which are traditionally challenging for GPS. The currently used method of Wi-Fi fingerprinting assumes that the devices used for training and locating perform identically. We have undertaken an experiment to determine how different devices behave in an empirical controlled test to identify the challenges and limitations which Wi-Fi fingerprinting positioning systems will face when deployed across many devices. We found that they performed significantly differently in respect to the mean reported signal strength -even those which came from the same vendor. We also found that multiple samples of the same device do not perform identically. Furthermore, it was found that certain devices were entirely unsuitable for positioning as they reported signal strength values uncorrelated with distance from the transmitter. Some other devices behaved in a way that made them poor candidates for use in fingerprinting. Temporal patterns were found in some wireless cards which suggest that filtering should be used. The tests also found that the use of 5GHz band signals had the potential to improve the accuracy of Wi-Fi location due to its higher stability compared to 2.4GHz. Ultimately however, the accuracy of Wi-Fi fingerprinting is limited due to many factors in the hardware and software design of Wi-Fi devices which affect the reported signal strength.

Purpose: Patients at high risk for developing breast cancer can be identified using a validated predictive tool: the Gail model. Patients thus identified can undergo careful breast cancer screening and be considered for preventive measures, such as chemoprevention with tamoxifen or raloxifene. An organized health system can create a screening and high-risk intervention program for breast cancer and potentially save lives and resources. Multiple components of the health system must work together in a multidisciplinary manner to successfully implement such a program.Methods: Aurora Health Care is a large health system in Wisconsin. In 2007, a medical center within Aurora initiated a program to identify patients at high risk for developing breast cancer and intervene with screening and prevention. The program used the Gail model, which was administered to patients presenting for comprehensive physical examination at the women's center. Results:During the first year, 5,718 Gail model scores were calculated, and 15.2% of patients were deemed high risk. Most were counseled by their primary care providers, and few underwent screening with magnetic resonance imaging, genetics consultation, or chemoprevention. Primary care providers expressed concerns regarding the accuracy of the Gail model, the additional time necessary for patient counseling, how few patients underwent chemoprevention, and perceived medicolegal risk. The program was altered to address these concerns. Conclusion:Success of a breast cancer risk and intervention program in a large health system is more likely if concerns of participating disciplines are acknowledged and addressed.

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