This paper deals with the identification of BIFs and associated sulphide mineralisation. An integrated approach, including the use of Landsat ETM-plus and Cartosat DEM data, GIS analysis, and geological data, is adopted for this purpose in the Nagavi area of Gadag Schist Belt (GSB), India. This integrated approach has enabled in identifying BIFs and structures. Band-7 of the ETM-plus sensor of Landsat-7 is used to identify BIFs and Band-5 for lineaments and shear zones. As a result of this study, the presence of gold mineralisation in sheared zones is noticed. BIFs are the economically prominent litho-units in the GSB hosting high-grade iron ore deposits along with sulphide mineralised shear zones. The strata bound ore is hosted primarily by BIF, consisting of chlorite, alternating chert and magnetite, sulphides and carbonate bands of a millimetre to centimetre scale.
Profitability of Turkish banking sector gained importance after national and international financial crisis happened in the last decade, which revealed the need to make a research on profitability and the factors determining profitability. In recent years, new techniques of soft computing (SC) like genetic algorithms (GAs), fuzzy logic (FL) and especially artificial neural networks (ANNs) have been applied into the financial domain to solve the domain issues because of their successful applications in nonlinear multivariate situations. An adaptive system was needed due to the fact that insufficient use of application software programs for SC and the fact that single software is only applicable for specific model. Furthermore, even though ANNs have been applied to many areas; little attention has been paid to estimation of bank profitability with ANNs. This article is intended to analyze and estimate the profitability of deposit banks in Turkey with an adaptive software model of ANNs which have not been previously applied for this context, comprehensively. The results from the software model, which processes the factors affecting profitability, indicate that all of the variables used have significant impacts in varying proportions on profitability and that obtained estimations achieved the targeted and acceptable performance of success. This software model is expected to provide easiness on estimating bank profitability, since giving successful estimations and not being affected by user differences. Additionally, it is aimed to construct a software model for being used in different fields of study and financial domain.
We give a short introduction to a method for the data-sparse approximation of matrices resulting from the discretisation of non-local operators occurring in boundary integral methods or as the inverses of partial differential operators. The result of the approximation will be the so-called hierarchical matrices (or short H-matrices). These matrices form a subset of the set of all matrices and have a data-sparse representation. The essential operations for these matrices (matrix-vector and matrixmatrix multiplication, addition and inversion) can be performed in, up to logarithmic factors, optimal complexity.