The femoral bone fracture is one of the most common fractures that orthopedic surgeons deal with. These fractures are associated with a significant percentage of death due to non‐orthopedic complications. For this reason, it is necessary that the surgeon be well‐aware of the condition of the patient and also of the biomechanical conditions of the bone and implant before surgery, in order to use the best surgical technique. Nowadays, the use of implants is a popular technique among the available methods for the treatment of femoral fractures. In the present study, two patients with different ages, three types of femoral bone fractures, that is, oblique, reverse oblique, and neck fracture, and two types of implants, namely, the dynamic hip screw (DHS) and the Gamma nail have been investigated. The behavior of the implants has been investigated at the two stages of treatment, that is, before and after bone union. The analysis of implants was based on the amount of stress and displacement induced in different parts of the bone and the implant. From the viewpoint of the stresses induced in the bone, all models are quite similar and in terms of the implant stresses, the Gamma nail is more reliable than the DHS. Additionally, the relative displacement of the fractured bone segments at the fracture planes was calculated. According to the obtained results, it can be concluded that the relative displacement of the fracture planes with the use of Gamma nail is somewhat less than the DHS, but this difference is not significant.
As modern software systems grow in size and complexity so do their configuration possibilities. Users are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. We propose a method to construct adaptive configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in the form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Process (MDP). Association rules are used to inform the model about configuration decisions that can be automatically inferred from knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. The method is applied to the privacy configuration of Facebook.
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