Influence of nanowire density on the shape and optical properties of ternary InGaAs nanowires

We have synthesized ternary InGaAs nanowires on (111)B GaAs surfaces by metal-organic chemical vapor deposition. Au colloidal nanoparticles were employed to catalyze nanowire growth. We observed the strong influence of nanowire density on nanowire height, tapering, and base shape specific to the nanowires with high In composition. This dependency was attributed to the large difference of diffusion length on (111)B surfaces between In and Ga reaction species, with In being the more mobile species. Energy dispersive X-ray spectroscopy analysis together with high-resolution electron microscopy study of individual InGaAs nanowires shows large In/Ga compositional variation along the nanowire supporting the present diffusion model. Photoluminescence spectra exhibit a red shift with decreasing nanowire density due to the higher degree of In incorporation in more sparsely distributed InGaAs nanowires.

Determination of the orientational distribution and orientation factor for transfer between membrane-bound fluorophores using a confocal microscope

Orientational fluorophores have been a useful tool in physical chemistry, biochemistry, and more recently structural biology due to the polarized nature of the light they emit and that fact that energy can be transferred between them. We present a practical scheme in which measurements of the intensity of emitted fluorescence can be used to determine limits on the mean and distribution of orientation of the absorption transition moment of membrane-bound. uorophores. We demonstrate how information about the orientation of. uorophores can be used to calculate the orientation factor k(2) required for use in FRET spectroscopy. We illustrate the method using images of AlexaFluor probes bound to MscL mechanosensitive transmembrane channel proteins in spherical liposomes.

A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

A graphical specification of model transformations with triple graph grammars

Models and model transformations are the core concepts of OMG's MDA (TM) approach. Within this approach, most models are derived from the MOF and have a graph-based nature. In contrast, most of the current model transformations are specified textually. To enable a graphical specification of model transformation rules, this paper proposes to use triple graph grammars as declarative specification formalism. These triple graph grammars can be specified within the FUJABA tool and we argue that these rules can be more easily specified and they become more understandable and maintainable. To show the practicability of our approach, we present how to generate Tefkat rules from triple graph grammar rules, which helps to integrate triple graph grammars with a state of a art model transformation tool and shows the expressiveness of the concept.

Termination of real-time programs: Definitely, definitely not, or maybe

Real-time control programs are often used in contexts where (conceptually) they run forever. Repetitions within such programs (or their specifications) may either (i) be guaranteed to terminate, (ii) be guaranteed to never terminate (loop forever), or (iii) may possibly terminate. In dealing with real-time programs and their specifications, we need to be able to represent these possibilities, and define suitable refinement orderings. A refinement ordering based on Dijkstra's weakest precondition only copes with the first alternative. Weakest liberal preconditions allow one to constrain behaviour provided the program terminates, which copes with the third alternative to some extent. However, neither of these handles the case when a program does not terminate. To handle this case a refinement ordering based on relational semantics can be used. In this paper we explore these issues and the definition of loops for real-time programs as well as corresponding refinement laws.

Compositional class refinement in object-Z

Object-Z allows coupling constraints between classes which, on the one hand, facilitate specification at a high level of abstraction, but, on the other hand, make class refinement non-compositional. The consequence of this is that refinement is not practical for large Systems. This paper overcomes this limitation by introducing a methodology for compositional class refinement in Object-Z. The key step is an equivalence transformation of an arbitrary Object-Z specification to one in which introduced constraints prohibit non-compositional refinements. The methodology also allows the constraints which couple classes to be refined yielding an unrestricted approach to compositional class refinement.

Reasoning algebraically about probabilistic loops

Back and von Wright have developed algebraic laws for reasoning about loops in the refinement calculus. We extend their work to reasoning about probabilistic loops in the probabilistic refinement calculus. We apply our algebraic reasoning to derive transformation rules for probabilistic action systems. In particular we focus on developing data refinement rules for probabilistic action systems. Our extension is interesting since some well known transformation rules that are applicable to standard programs are not applicable to probabilistic ones: we identify some of these important differences and we develop alternative rules where possible. In particular, our probabilistic action system data refinement rules are new.