Hide-n-Sense: Preserving Privacy Efficiently in Wireless mHealth

As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Furthermore, individuals can become better engaged in monitoring and managing their own health. Although some work on mHealth sensing has addressed security, achieving strong privacy for low-power sensors remains a challenge. We make three contributions. First, we propose an mHealth sensing protocol that provides strong security and privacy properties at the link layer, with low energy overhead, suitable for low-power sensors. The protocol uses three novel techniques: adaptive security, to dynamically modify transmission overhead; MAC striping, to make forgery difficult even for small-sized Message Authentication Codes; and asymmetric resource requirements, in recognition of the limited resources in tiny mHealth sensors. Second, we demonstrate its feasibility by implementing a prototype on a Chronos wrist device, and evaluating it experimentally. Third, we provide a security, privacy, and energy analysis of our system.     

The economics of visual disamenity reductions of offshore wind farms—Review and suggestions from an emerging field

The offshore wind power generation market is currently experiencing large growth rates on a global scale and investments exceeding several billion euro are being made. From a welfare economic point of view there is a non-trivial economic trade-off between offshore wind generation costs and the visual impacts from offshore wind farms. Offshore wind farms close to the shore generate cheaper electricity, but also cause higher levels of visual impacts compared to locations at larger distances. In the present paper we carry out a review of the stated preference studies that have elicited the demand for visual disamenity reduction from offshore wind farms. The review has three objectives: (a) to present the results of the different surveys; (b) to explore the more technical parts of the different surveys; and (c) to present the frontiers in the assessment of the demand for visual disamenity reductions associated with offshore wind farm locations. The paper is based on the results from five different studies. The review indicates that locations of offshore wind farms which are close to the shore generate significant welfare losses and that these can be reduced by locating the wind farms at more distant locations. The results also show that the welfare economic costs vary in terms of a range of socio demographic characteristics, experience with wind turbines and recreational activities. Finally, the review suggests that the welfare impacts related to the spatial distribution of the wind farms, intergenerational effects and experience with wind turbines are potential areas that would be beneficial to explore in future studies.     

Use of secondary data to estimate instantaneous model parameters of diabetic heart disease: Lemonade Method

With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single “best” study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where (1) the theoretical model may include an instantaneous state of distinct interest to the researchers and (2) the study design may be such that study data cannot be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of “If life gives you lemon, make lemonade”, we call this method “Lemonade Method”. Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.     

Transversely compressed bonded joints

The load capacity of bonded joints can be increased if transverse pressure is applied at the interface. The transverse pressure is assumed to introduce a Coulomb-friction contribution to the cohesive law for the interface. Response and load capacity for a bonded single-lap joint was derived using non-linear fracture mechanics. The results indicated a good correlation between theory and tests. Furthermore, the model is suggested as theoretical base for determining load capacity of bonded anchorages with transverse pressure, in externally reinforced concrete structures.