The phosphate mineral arrojadite-(KFe) and its spectroscopic characterization

The arrojadite-(KFe) mineral has been analyzed using a combination of scanning electron microscopy and a combination of Raman and infrared spectroscopy. The origin of the mineral is Rapid Creek sedimentary phosphatic iron formation, northern Yukon. The formula of the mineral was determined as K2.06Na2Ca0.89Na3.23(Fe7.82Mg4.40Mn0.78)Σ13.00Al1.44(PO4)10.85(PO3OH0.23)(OH)2. The complexity of the mineral formula is reflected in the spectroscopy. Raman bands at 975, 991 and 1005 cm−1 with shoulder bands at 951 and 1024 cm−1 are assigned to the View the MathML source ν1 symmetric stretching modes. The Raman bands at 1024, 1066, 1092, 1123, 1148 and 1187 cm−1 are assigned to the View the MathML source ν3 antisymmetric stretching modes. A series of Raman bands observed at 540, 548, 557, 583, 604, 615 and 638 cm−1 are attributed to the ν4 out of plane bending modes of the PO4 and H2PO4 units. The ν2 PO4 and H2PO4 bending modes are observed at 403, 424, 449, 463, 479 and 513 cm−1. Hydroxyl and water stretching bands are readily observed. Vibrational spectroscopy enables new information about the complex phosphate mineral arrojadite-(KFe) to be obtained.

Extending services delivery with lightweight composition

The tertiary sector is an important employer and its growth is well above average. The Texo project’s aim is to support this development by making services tradable. The composition of new or value-added services is a cornerstone of the proposed architecture. It is, however, intended to cater for build-time. Yet, at run-time unforseen exceptions may occur and user’s requirements may change. Varying circumstances require immediate sensemaking of the situation’s context and call for prompt extensions of existing services. Lightweight composition technology provided by the RoofTop project enables domain experts to create simple widget-like applications, also termed enterprise mashups, without extensive methodological skills. In this way RoofTop can assist and extend the idea of service delivery through the Texo platform and is a further step towards a next generation internet of services.

Benefiting white noise in developing feedforward active noise control systems

In many applications of active noise control (ANC), an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to adjust the secondary path estimation. Comparative simulation results shown in this paper indicate effectiveness of the proposed method.