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The paper analyses afresh the data of Russell and Hastings on the changes in lattice parameter during post-irradiation annealing of copper-boron alloy in the temperature range 823-923 K. The changes in lattice parameter are brought about by the generation of helium during irradiation through (n, α) reaction and its subsequent behavior during annealing. The new analytical procedure adopted here follows chemical kinetics route to determine the appropriate activation energies associated with the rate processes controlling the changes in lattice parameter. During the first stage of annealing, the lattice parameter decreases to a value which is below the equilibrium one. The second stage involves the recovery of lattice parameter to the equilibrium value. The diffusion of monovacancy and the jump of helium atom to vacancy on its nearest neighbor site are identified as the unit processes for the first and second stages of annealing respectively. Finally, it is suggested that a helium–monovacancy complex may act as nucleus for the formation of helium gas bubbles.

Diffusion of water molecules in the human brain tissue has strong similarities with diffusion in porous media. It is affected by different factors such as restrictions and compartmentalization, interaction with membrane walls, strong anisotropy imposed by cellular microstructure, etc. However, multiple artefacts abound in in vivo measurements either from subject motions, such as cardiac pulsation, bulk head motion, respiratory motion, and involuntary tics and tremor, or hardware related problems, such as table vibrations, etc. All these artefacts can substantially degrade the resulting images and render postprocessing diffusion analysis difficult or even impossible. In order to overcome these problems, we have developed a robust and efficient approach based on the least trimmed squares algorithm that works well with severely degraded datasets with low signal-to-noise ratio. This approach has been compared with other diffusion imaging post-processing algorithms using simulations and in vivo experiments. We demonstrate that the least trimmed squares algorithm can be easily adopted for multiple non-Gaussian diffusion models such as the biexponential model. The developed approach is shown to exhibit a high efficiency and accuracy and can, in principle, be exploited in other diffusion studies where artefact/outlier suppression is demanded.

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