Shrinkage of nanocavities in silicon during electron beam irradiation

An internal shrinkage of nanocavity in silicon was in situ observed under irradiation of energetic electron on electron transmission microscopy. Because there is no addition of any external materials to cavity site, a predicted nanosize effect on the shrinkage was observed. At the same time, because there is no ion cascade effect as encountered in the previous ion irradiation-induced nanocavity shrinkage experiment, the electron irradiation-induced instability of nanocavity also provides a further more convincing evidence to demonstrate the predicted irradiation-induced athermal activation effect. (c) 2006 American Institute of Physics.

THE COMPOUND EYE AS AN INDICATOR OF AGE AND SHRINKAGE IN ANTARCTIC KRILL

Laboratory studies have shown that Antarctic krill (Euphausia superba) shrink if maintained in conditions of low food availability. Recent studies have also demonstrated that E. superba individuals may be shrinking in the field during winter. If krill shrink during the winter, conclusions reached by length-frequency analysis may be unreliable because smaller animals may not necessarily be younger animals. In this study, the correlation between the body-length and the crystalline cone number of the compound eye was examined. Samples collected in the late summer show an apparent linear relationship between crystalline cone number and body-length. From a laboratory population, it appears that when krill shrink the crystalline cone number remains relatively unchanged. If crystalline cone number is little affected by shrinking, then the crystalline cone number may be a more reliable indicator of age than body-length alone. The ratio of crystalline cone number to body-length offers a method for detecting the effect of shrinking in natural populations of krill. On the basis of the crystalline cone number count, it appears from a field collection in early spring that E. superba do shrink during winter.

Does an incremental filling technique reduce polymerization shrinkage stresses

It is widely accepted that volumetric contraction and solidification during the polymerization process of restorative composites in combination with bonding to the hard tissue result in stress transfer and inward deformation of the cavity walls of the restored tooth. Deformation of the walls decreases the size of the cavity during the filling process. This fact has a profound influence on the assumption-raised and discussed in this paper-that an incremental filling technique reduces the stress effect of composite shrinkage on the tooth. Developing stress fields for different incremental filling techniques are simulated in a numerical analysis. The analysis shows that, in a restoration with a well-established bond to the tooth-as is generally desired-incremental filling techniques increase the deformation of the restored tooth. The increase is caused by the incremental deformation of the preparation, which effectively decreases the total amount of composite needed to fill the cavity. This leads to a higher-stressed tooth-composite structure. The study also shows that the assessment of intercuspal distance measurements as well as simplifications based on generalization of the shrinkage stress state cannot be sufficient to characterize the effect of polymerization shrinkage in a tooth-restoration complex. Incremental filling methods may need to be retained for reasons such as densification, adaptation, thoroughness of cure, and bond formation. However, it is very difficult to prove that incrementalization needs to be retained because of the abatement of shrinkage effects.

A novel shrinkage technique based on the normal inverse Gaussian density model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm proposed by us. This algorithm can converge to feature basis vectors, which behave in the locality and orientation in spatial and frequency domain. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In the denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive Gaussian noise, the noise can be reduced successfully. This shrinkage technique, also referred to as the NNSC shrinkage technique, is self-adaptive to the statistical properties of image data. This denoising method is evaluated by values of the normalized signal to noise rate (SNR). Experimental results show that the NNSC shrinkage approach is indeed efficient and effective in denoising. Otherwise, we also compare the effectiveness of the NNSC shrinkage method with methods of standard sparse coding shrinkage, wavelet-based shrinkage and the Wiener filter. The simulation results show that our method outperforms the three kinds of denoising approaches mentioned above.