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By studying the ammoniation of the silicoaluminophosphate-type zeolites H-SAPO-34 and H-SAPO-37 by in situ 1 H and 27 Al MAS NMR under continuous-flow (CF) conditions, a two-step adsorption process was determined. The first ammoniation step consists of an adsorption of ammonia exclusively at Brønsted acidic bridging OH groups (SiOHAl), leading to the formation of ammonium ions (NH 4 -form). The second ammoniation step, which occurs at a higher ammonia coverage, consists of a coordination of ammonia molecules to framework Al atoms in ≡P-O-Al≡ bridges. This second adsorption step causes a change of the aluminum coordination from a tetrahedral coordination to an octahedral coordination. The ammonia coordination to Al atoms is reversible when the material is purged at 413 K. The hydration of NH 4 -form silicoaluminophosphates (ammoniated bridging OH groups) causes a coordination of water molecules exclusively to Al atoms in ≡P-O-Al≡ bridges, leading to a hydrolysis of the framework. Therefore, a hydrolysis of the silicoaluminophosphate framework is hindered if the bridging OH groups (SiOHAl), as well as the aluminophosphate framework (≡P-O-Al≡), is covered by ammonia. The latter may support the stabilizing effect of preloaded ammonia on H-form silicoaluminophosphates toward hydration and weak hydrothermal treatments, as recently observed for H-SAPO-34.

Task parallelism and data parallelism are often seen as mutually exclusive approaches to parallel programming. Yet there are important classes of application, f o r example in niultidisciplanary simulaiion and command and control, that would benefit from an integration of the two approaches. In this paper, we describe a programming system that we are developing to explore this sort of integration. This system builds on previous work on task-parallel and data-parallel Fortran compilers to provide an environment in which the task-parallel language Fortran M can be used to coordinate data-parallel High Performance Fordran tasks. We use an image-processing problem to illustrate the issues that arise when building an integrated compilation system of this sort.

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In deep-sea source localization, some of the existing methods only estimate the source range, while the others produce large errors in distance estimation when estimating both the range and depth. Here, a convolutional neural network-based method with high accuracy is introduced, in which the source localization problem is solved as a regression problem. The proposed neural network is trained by a normalized acoustic matrix and used to predict the source position. Experimental data from the western Pacific indicate that this method performs satisfactorily: the mean absolute percentage error of the range is 2.10%, while that of the depth is 3.08%.