A reactivity index study to rationalize the effect of dopants on Brönsted and Lewis acidity occurring in MeAlPOs

The influence of both bivalent and trivalent metal substituents from a range of metal cation (Co, Mn, Mg, Fe and Cr) on the acidic property (both Brönsted and Lewis) of metal substituted aluminum phosphate MeAlPOs is monitored. The influence of the environment of the acid site is studied both by localized cluster and periodic calculations to propose that the acidity of AlPOs can be predictable with accuracy so that AlPO material with desired acidity can be designed. A semi-quantitative reactivity scale within the domain of hard soft acid–base (HSAB) principle is proposed in terms of the metal substitutions using density functional theory (DFT). It is observed that for the bivalent metal cations Lewis acidity linearly increases with ionic size, where as the Brönsted acidity is solely dependent on the nearest oxygen environment. Intramolecular and intermolecular interactions show that once active site of the interacting species is identified, the influence of the environment can be prescribed. Mg(II)-doped AlPO-34 shows highest Brönsted acidity and whereas Cr(III)-doped species shows lowest acidity. Fe(II)/Fe(III)-doped AlPO-34 shows highest Lewis acidity, whereas Mn(III), Mg (II) shows lowest acidity.     

Deep learning techniques for observing the impact of the global warming from satellite images of water-bodies

Multimedia Tools and Applications - Global warming is a threat to modern human civilization. There are different reasons for speed up the global average temperature. The consequences are...     

Numerical simulation of flow past row of square cylinders for various separation ratios

A systematic study for the flow around a row of five square cylinders placed in a side-by-side arrangement and normal to the oncoming flow at a Reynolds number of 150 is carried out through the numerical solution of the two-dimensional unsteady incompressible Navier-Stokes equations. Special attention is paid to investigate the effect of the spacing between the five cylinders on the wake structure and vortex shedding mechanism. The simulations are performed for the separation ratios (spacing to size ratio) of 1.2, 2, 3 and 4. Depending on the separation ratio the following flow patterns are observed: a flip-flopping pattern, in-phase and anti-phase synchronized pattern and non-synchronized pattern. These flow patterns are supposed to be a consequence of the interaction between two types of frequencies viz. the vortex shedding (primary) and the cylinder interaction (secondary) frequencies. At small separation ratio the flow is predominantly characterized by the jet in the gaps between successive cylinders and the secondary frequencies play a role in the resulting chaotic flow. On the contrary, at higher separation ratio the secondary frequencies almost disappear and the resulting flow becomes more synchronized dominated by the primary frequency.     

A profit maximizing mathematical model for pricing and selecting optimal product line

Variety among products is manifested in terms of different attributes or features present in a product. Each attribute in its turn, may be built-in in the product at different levels, giving rise to an increased choice for the customer. However, higher level of an attribute, yielding higher utility for the customers, typically requires higher costs and commands higher price. Increasing price results in higher profit margin but lowers the attractiveness of the product to the customers. In this regard, product line optimization is concerned with the offering of a set of product variants to a large customer base, such that, certain objectives like maximization of buyers’ utility, seller’s return can be met. In this paper, a mathematical programming model has been developed, to determine the optimal product combination that fetches the maximum profit from a potential targeted market segment. Contrary to the existing formulations on product line optimization, where the pricing decision is determined exogenously, we consider simultaneous decision on pricing and product line optimization. Price is considered as a decision variable along with other attributes. Another major departure from the past models is the incorporation of costs associated with each level of attributes. These costs, used as input data, are justified, as the manufacturers typically use combinations of different modules to create different levels of the attributes. The resulting model became computationally complex and hence a greedy heuristic is developed for the purpose. An example is provided to illustrate the working of the heuristic. The same example has been solved for optimality, yielding identical solution to the heuristic results. Finally, a proposition has been presented to show the condition under which the heuristic will give the optimal solution.     

Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area,India

ABSTRACT

The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAM_tan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAM_tan. The RSDPW and CM illustrate that the SIDSAM_tan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (?) of SAM, SID, and SIDSAM_tan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (?), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.     

Unsupervised and supervised classification of hyperspectral imaging data using projection pursuit and Markov random field segmentation

This work presents a classification technique for hyperspectral image analysis when concurrent ground truth is either unavailable or available. The method adopts a principal component analysis (PCA)-based projection pursuit (PP) procedure with an entropy index for dimensionality reduction, followed by a Markov random field (MRF) model-based segmentation. An ordinal optimization approach to PP determines a set of ‘good enough projections’ with high probability, the best among which is chosen with the help of MRF model-based segmentation. When ground-truth is absent, the segmented output obtained is labelled with the desired number of classes so that it resembles the natural scene closely. When the land-cover classes are in detailed level, some special reflectance characteristics based on the classes of the study area are determined and incorporated in the segmentation stage. Segments are evaluated with training samples so as to yield a classified image with respect to the type of ground-truth data. Two illustrations are presented: (i) an AVIRIS-92AV3C image with concurrent ground truth – for both supervised and unsupervised cases and (ii) an EO-1 Hyperion sensor image with concurrent ground-truth at detailed level classes. Provided with the illustrations are comparisons of classification accuracies and computational times of other approaches with those of the proposed methodology. Experimental results demonstrate that the proposed method provides high classification accuracy and is not computationally intensive.     

Fixed latency on-chip interconnect for hardware spiking neural network architectures

Information in a Spiking Neural Network (SNN) is encoded as the relative timing between spikes. Distortion in spike timings can impact the accuracy of SNN operation by modifying the precise firing time of neurons within the SNN. Maintaining the integrity of spike timings is crucial for reliable operation of SNN applications. A packet switched Network on Chip (NoC) infrastructure offers scalable connectivity for spike communication in hardware SNN architectures. However, shared resources in NoC architectures can result in unwanted variation in spike packet transfer latency. This packet latency jitter distorts the timing information conveyed on the synaptic connections in the SNN, resulting in unreliable application behaviour.     

Study of the potential of alternative crops by integration of multisource data using a neuro‐fuzzy technique

This work proposes a neuro‐fuzzy method for suggesting alternative crop production over a region using integrated data obtained from land‐survey maps as well as satellite imagery. The methodology proposed here uses an artificial neural network (multilayer perceptron, MLP) to predict alternative crop production. For each pixel, the MLP takes vector input comprising elevation, rainfall and goodness values of different existing crops. The first two components of the aforementioned input, that is, elevation and rainfall, are determined from contour information of land‐survey maps. The other components, such as goodness values of different existing crops, are based on the productivity estimates of soil determined by fuzzyfication and expert opinion (on soil) along with production quality by the Normalized Difference Vegetation Index (NDVI) obtained from satellite imagery. The methodology attempts to ensure that the suggested crop will also be a high productivity crop for that region.     

Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO)

The present paper proposes a new stochastic optimization algorithm as a hybridization of a relatively recent stochastic optimization algorithm, called biogeography-based optimization (BBO) with the differential evolution (DE) algorithm. This combination incorporates DE algorithm into the optimization procedure of BBO with an attempt to incorporate diversity to overcome stagnation at local optima. We also propose to implement an additional selection procedure for BBO, which preserves fitter habitats for subsequent generations. The proposed variation of BBO, named DBBO, is tested for several benchmark function optimization problems. The results show that DBBO can significantly outperform the basic BBO algorithm and can mostly emerge as the best solution providing algorithm among competing BBO and DE algorithms.