Effects of hibernation on bone in yellow-bellied marmots

Disuse osteoporosis is a problem for people with spinal cord injury or stroke, patients confined to bed rest, and astronauts exposed to microgravity. Unlike most mammals however, bears have been shown to prevent bone loss during hibernation, a seasonal period of disuse. Similarly, studies in ground squirrels indicate preservation of whole bone strength during hibernation, though evidence suggests there may be some increased osteocytic osteolysis. Uncovering the mechanism by which these animals prevent bone loss during hibernation could lead to an improved treatment for osteoporosis in humans. Marmots are a good animal model for these studies because they are small enough to easily house in an animal facility yet still utilize intracortical remodeling like humans and bears, and unlike smaller rodents like squirrels. Marmots preserve bone mechanical and microstructural properties during hibernation. Bone mechanical and geometrical properties are not diminished in post-hibernation samples compared to pre-hibernation samples. Mineral content, measured by ash fraction, was higher in post-hibernation samples (p = 0.0003). Haversian porosity as well as remodeling cavity density were not different (p > 0.38) between pre- and post-hibernation samples. Similarly, average lacunar area, lacunar density, and lacunar porosity were all lower (p < 0.0001) in post-hibernation samples. Trabecular thickness was larger in posthibernation samples (p = 0.0058). Bone volume fraction was not different between groups, but approached significance (p = 0.0725). Further studies in marmots and other hibernators could help uncover the mechanism that allows hibernators to prevent disuse osteoporosis during hibernation.

Stochastic knock detection model for spark ignited engines

This report presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. Knock Signal Simulator (KSS) was developed as the plant model for the engine. The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies. The SKD method is implemented in Knock Detection Module (KDM) which processes the knock intensities generated by KSS with a stochastic distribution estimation algorithm and outputs estimates of high and low knock intensity levels which characterize knock and reference level respectively. These estimates are then used to determine a knock factor which provides quantitative measure of knock level and can be used as a feedback signal to control engine knock. The knock factor is analyzed and compared with a traditional knock detection method to detect engine knock under various engine operating conditions. To verify the effectiveness of the SKD method, a knock controller was also developed and tested in a model-in-loop (MIL) system. The objective of the knock controller is to allow the engine to operate as close as possible to its border-line spark-timing without significant engine knock. The controller parameters were tuned to minimize the cycle-to-cycle variation in spark timing and the settling time of the controller in responding to step increase in spark advance resulting in the onset of engine knock. The simulation results showed that the combined system can be used adequately to model engine knock and evaluated knock control strategies for a wide range of engine operating conditions.