Strain hardening due to deformation twinning in α-titanium: Mechanisms

Novel experiments were conducted to elucidate the effect of deformation twinning on the mechanical response of high-purity α-titanium deformed at room temperature. Orientation-imaging microscopy (OIM), microhardness, and nanohardness evaluations were employed in conjunction with optical microscopy and quasi-static compression testing to obtain insight into the deformation mechanisms. Hardness measurements revealed that the newly formed deformation twins were harder than the matrix. This observation is perhaps the first experimental evidence for the Basinski mechanism for hardening associated with twinning, arising from the transition of glissile dislocations to a sessile configuration upon the lattice reorientation by twinning shear. This work also provided direct evidence for two competing effects of deformation twinning on the overall stress-strain response: (1) hardening via both a reduction of the effective slip length (Hall-Petch effect) and an increase in the hardness of twinned regions (Basinski mechanism) and (2) softening due to the lattice reorientation of the twinned regions.     

Study of the heart rate and energy expenditure of ponies during transport

The energy expenditure and heart rate of five Shetland ponies were measured during transport and compared with the values recorded while they were at rest and walking. Secondary aims of the study were to investigate whether there was any correlation between heart rate and energy expenditure and whether different orientations affected the values to different degrees. The measurements were recorded at one-minute intervals while the ponies were at rest, walking and being transported in four different orientations, on journeys lasting 30 minutes. There were significant increases (P < 0.001) in both heart rate and energy expenditure during transport which were similar to those associated with walking, and there was a strong correlation between the two measurements. There was no conclusive evidence that orientation affected the measurements, but it appeared that rear facing transport might have had the least effect.     

Automatic detection of mass-resolved ion conics

A processing algorithm for automatically detecting a specific type of ion distribution (called the ion conic distribution) in data obtained from a space-based mass spectrometer has been devised. Automation of this task is necessary due to the sparseness of conic events within the very large databases typical of space plasma instruments. The algorithm used to perform this automated analysis and the methods used to verify the algorithm are described. Initial results on the characterization of the near-Earth space plasma are summarized     

Predicting the shape of organic crystals grown from polar solvents

A method for predicting the shape of organic crystals grown from polar solvents is presented. The model is an improvement of the recent method developed by Winn and Doherty (A.I.Ch.E. Journal 44 (1998) 2501) for predicting the shape of organic crystals grown from solvents in which the energy of adhesion at the interfaces is dominated by dispersive forces (e.g., non-polar solvents). The principal characteristic of the new method is that it can account for the role of hydrogen donor and hydrogen acceptor atoms in forming strong bonds at the interface. This technique is a first step towards predicting the shapes of organic crystals grown from polar solvents, and has been applied successfully to predict the shape of adipic acid grown from water, and succinic acid grown from water and from propanol.     

Potent and selective bicyclic lactam inhibitors of thrombin: Part 2: P1 modifications

The synthesis and antithrombotic activity of a series of nonpeptide bicyclic thrombin inhibitors is described. We have explored the SAR with modifications to the P1 site. The introduction of arginine mimetics at the P1 site led to potent and selective thrombin inhibitors.     

A Modeling Framework for Troubleshooting Automotive Systems

This article presents a novel framework for modeling the troubleshooting process for automotive systems such as trucks and buses. We describe how a diagnostic model of the troubleshooting process can be created using event-driven, nonstationary, dynamic Bayesian networks. Exact inference in such a model is in general not practically possible. Therefore, we evaluate different approximate methods for inference based on the Boyen–Koller algorithm. We identify relevant model classes that have particular structure such that inference can be made with linear time complexity. We also show how models created using expert knowledge can be tuned using statistical data. The proposed learning mechanism can use data that is collected from a heterogeneous fleet of modular vehicles that can consist of different components. The proposed framework is evaluated both theoretically and experimentally on an application example of a fuel injection system.