Phase relationships and magnetic properties of TbxDy1-xFe1.95 alloys

The effect of Tb/Dy ratio on the structural and magnetic properties of (Tb,Dy)Fe-2 class of alloys has been investigated using nine alloys of TbxDy1-xFe1.95 (x = 0-1) covering the entire range. Our results indicate that the three phases viz. (Tb,Dy)Fe-2 (major phase), (Tb,Dy)Fe-3 and(Tb,Dy)-solid solution (minor phases) coexist in all the alloys. The volume fraction of pro-peritectic (Tb,Dy)Fe-3 phase however, has a minimum at x = 0.4 and a maximum at x = 0.6 compositions. The volume fraction of this phase decreases upon heat treatment at 850 degrees C and 1000 degrees C. A Widmanstatten type precipitate of (Tb,Dy)Fe-3 was observed for Dy-rich compositions (0 <= x <= 0.5). The microstructural investigations indicate that the ternary phase equilibria of Tb-Dy-Fe are sensitive to Tb/Dy ratio including the expansion of (Tb,Dy)Fe-2 phase field which is in contrast to the pseudo-binary assumption that is followed in available literature to date. The lattice parameter, Curie temperature and coercivity are found to increase with Tb addition. Split of (440) peak of (Tb,Dy)Fe-2 observed in x >= 0.3 alloys indicate, a spin reorientation transition from 100] to 111] occurs with Tb addition. (C) 2012 Elsevier B. V. All rights reserved.

Phylogenetic relationships and classification of thiolases and thiolase-like proteins of Mycobacterium tuberculosis and Mycobacterium smegmatis

Thiolases are enzymes involved in lipid metabolism. Thiolases remove the acetyl-CoA moiety from 3-ketoacyl-CoAs in the degradative reaction. They can also catalyze the reverse Claisen condensation reaction, which is the first step of biosynthetic processes such as the biosynthesis of sterols and ketone bodies. In human, six distinct thiolases have been identified. Each of these thiolases is different from the other with respect to sequence, oligomeric state, substrate specificity and subcellular localization. Four sequence fingerprints, identifying catalytic loops of thiolases, have been described. In this study genome searches of two mycobacterial species (Mycobacterium tuberculosis and Mycobacterium smegmatis), were carried out, using the six human thiolase sequences as queries. Eight and thirteen different thiolase sequences were identified in M. tuberculosis and M. smegmatis, respectively. In addition, thiolase-like proteins (one encoded in the Mtb and two in the Msm genome) were found. The purpose of this study is to classify these mostly uncharacterized thiolases and thiolase-like proteins. Several other sequences obtained by searches of genome databases of bacteria, mammals and the parasitic protist family of the Trypanosomatidae were included in the analysis. Thiolase-like proteins were also found in the trypanosomatid genomes, but not in those of mammals. In order to study the phylogenetic relationships at a high confidence level, additional thiolase sequences were included such that a total of 130 thiolases and thiolase-like protein sequences were used for the multiple sequence alignment. The resulting phylogenetic tree identifies 12 classes of sequences, each possessing a characteristic set of sequence fingerprints for the catalytic loops. From this analysis it is now possible to assign the mycobacterial thiolases to corresponding homologues in other kingdoms of life. The results of this bioinformatics analysis also show interesting differences between the distributions of M. tuberculosis and M. smegmatis thiolases over the 12 different classes. (C) 2014 Elsevier Ltd. All rights reserved.

Differences in host species relationships and biogeographic influences produce contrasting patterns of prevalence, community composition and genetic structure in two genera of avian malaria parasites in southern Melanesia

1. Host-parasite interactions have the potential to influence broadscale ecological and evolutionary processes, levels of endemism, divergence patterns and distributions in host populations. Understanding the mechanisms involved requires identification of the factors that shape parasite distribution and prevalence. 2. A lack of comparative information on community-level host-parasite associations limits our understanding of the role of parasites in host population divergence processes. Avian malaria (haemosporidian) parasites in bird communities offer a tractable model system to examine the potential for pathogens to influence evolutionary processes in natural host populations. 3. Using cytochrome b variation, we characterized phylogenetic diversity and prevalence of two genera of avian haemosporidian parasites, Plasmodium and Haemoproteus, and analysed biogeographic patterns of lineages across islands and avian hosts, in southern Melanesian bird communities to identify factors that explain patterns of infection. 4. Plasmodium spp. displayed isolation-by-distance effects, a significant amount of genetic variation distributed among islands but insignificant amounts among host species and families, and strong local island effects with respect to prevalence. Haemoproteus spp. did not display isolation-by-distance patterns, showed marked structuring of genetic variation among avian host species and families, and significant host species prevalence patterns. 5. These differences suggest that Plasmodium spp. infection patterns were shaped by geography and the abiotic environment, whereas Haemoproteus spp. infection patterns were shaped predominantly by host associations. Heterogeneity in the complement and prevalence of parasite lineages infecting local bird communities likely exposes host species to a mosaic of spatially divergent disease selection pressures across their naturally fragmented distributions in southern Melanesia. Host associations for Haemoproteus spp. indicate a capacity for the formation of locally co-adapted host-parasite relationships, a feature that may limit intraspecific gene flow or range expansions of closely related host species.

Deformation and stability regression models for soil nail walls

Lateral displacement and global stability are the two main stability criteria for soil nail walls. Conventional design methods do not adequately address the deformation behaviour of soil nail walls, owing to the complexity involved in handling a large number of influencing factors. Consequently, limited methods of deformation estimates based on empirical relationships and in situ performance monitoring are available in the literature. It is therefore desirable that numerical techniques and statistical methods are used in order to gain a better insight into the deformation behaviour of soil nail walls. In the present study numerical experiments are conducted using a 2 4 factorial design method. Based on analysis of the maximum lateral deformation and factor-of-safety observations from the numerical experiments, regression models for maximum lateral deformation and factor-of-safety prediction are developed and checked for adequacy. Selection of suitable design factors for the 2 4 factorial design of numerical experiments enabled the use of the proposed regression models over a practical range of soil nail wall heights and in situ soil variability. It is evident from the model adequacy analyses and illustrative example that the proposed regression models provided a reasonably good estimate of the lateral deformation and global factor of safety of the soil nail walls.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

A denotational semantics for the generalized ER model and a simple ER algebra

The recent spurt of research activities in Entity-Relationship Approach to databases calls for a close scrutiny of the semantics of the underlying Entity-Relationship models, data manipulation languages, data definition languages, etc. For reasons well known, it is very desirable and sometimes imperative to give formal description of the semantics. In this paper, we consider a specific ER model, the generalized Entity-Relationship model (without attributes on relationships) and give denotational semantics for the model as well as a simple ER algebra based on the model. Our formalism is based on the Vienna Development Method—the meta language (VDM). We also discuss the salient features of the given semantics in detail and suggest directions for further work.

Cement stabilised rammed earth. Part B: compressive strength and stress-strain characteristics

Strength and behaviour of cement stabilised rammed earth (CSRE) is a scantily explored area. The present study is focused on the strength and elastic properties of CSRE. Characteristics of CSRE are influenced by soil composition, density of rammed earth, cement and moisture content. The study is focused on examining (a) role of clay content of the soil on strength of CSRE and arriving at optimum clay fraction of the soil mix, (b) influence of moisture content, cement content and density on strength and (c) stress-strain relationships and elastic properties for CSRE. Major conclusions are (a) there is considerable difference between dry and wet compressive strength of CSRE and the wet to dry strength ratio depends upon the clay fraction of soil mix and cement content, (b) optimum clay fraction yielding maximum compressive strength for CSRE is about 16%, (c) strength of CSRE is highly sensitive to density and for a 20% increase in density the strength increases by 300-500% and (d) in dry state the ultimate strain at failure for CSRE is as high as 1.5%, which is unusual for brittle materials.

Interaction anisotropy and shear instability of aspirin polymorphs established by nanoindentation

Nanoindentation is applied to the two polymorphs of aspirin to examine and differentiate their interaction anisotropy and shear instability. Aspirin provides an excellent test system for the technique because: (i) polymorphs I and II exhibit structural similarity in two dimensions, thereby facilitating clear examination of the differences in mechanical response in relation to well-defined differences between the two crystal structures; (ii) single crystals of the metastable polymorph II have only recently become accessible; (iii) shear instability has been proposed for II. Different elastic moduli and hardness values determined for the two polymorphs are correlated with their crystal structures, and the interpretation is supported by measured thermal expansion coefficients. The stress-induced transformation of the metastable polymorph II to the stable polymorph I can be brought about rapidly by mechanical milling, and proceeds via a slip mechanism. This work establishes that nanoindentation provides ``signature'' responses for the two aspirin polymorphs, despite their very similar crystal structures. It also demonstrates the value of the technique to quantify stability relationships and phase transformations in molecular crystals, enabling a deeper understanding of polymorphism in the context of crystal engineering.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Reduced Hornbill Abundance Associated with Low Seed Arrival and Altered Recruitment in a Hunted and Logged Tropical Forest

Logging and hunting are two key direct threats to the survival of wildlife in the tropics, and also disrupt important ecosystem processes. We investigated the impacts of these two factors on the different stages of the seed dispersal cycle, including abundance of plants and their dispersers and dispersal of seeds and recruitment, in a tropical forest in north-east India. We focused on hornbills, which are important seed dispersers in these forests, and their food tree species. We compared abundances of hornbill food tree species in a site with high logging and hunting pressures (heavily disturbed) with a site that had no logging and relatively low levels of hunting (less disturbed) to understand logging impacts on hornbill food tree abundance. We compared hornbill abundances across these two sites. We, then, compared the scatter-dispersed seed arrival of five large-seeded tree species and the recruitment of four of those species. Abundances of hornbill food trees that are preferentially targeted by logging were two times higher in the less disturbed site as compared to the heavily disturbed site while that of hornbills was 22 times higher. The arrival of scatter-dispersed seeds was seven times higher in the less disturbed site. Abundances of recruits of two tree species were significantly higher in the less disturbed site. For another species, abundances of younger recruits were significantly lower while that of older recruits were higher in the heavily disturbed site. Our findings suggest that logging reduces food plant abundance for an important frugivore-seed disperser group, while hunting diminishes disperser abundances, with an associated reduction in seed arrival and altered recruitment of animal-dispersed tree species in the disturbed site. Based on our results, we present a conceptual model depicting the relationships and pathways between vertebrate-dispersed trees, their dispersers, and the impacts of hunting and logging on these pathways.

Noble metal ions in CeO2 and TiO2: synthesis, structure and catalytic properties

In the past four decades, CeO2 has been recognized as an attractive material in the area of auto exhaust catalysis because of its unique redox properties. In the presence of CeO2, the catalytic activity of noble metals supported on Al2O3 is enhanced due to higher dispersion of noble metals in their ionic form. In the last few years, we have been exploring an entirely new approach of dispersing noble metal ions on CeO2 and TiO2 matrices for redox catalysis. In this study, the dispersion of noble metal ions by solution combustion as well as other methods over CeO2 and TiO2 resulting mainly in Ce1-xMxO2-delta, Ce1-x-yTixMyO2-delta, Ce1-x-ySnxMyO2-delta, Ce1-x-yFexMyO2-delta, Ce1-x-yZrxMyO2-delta and Ti1-xMxO2-delta (M = Pd, Pt, Rh and Ru) catalysts, the structure of these materials, their catalytic properties toward different types of catalysis, structure-property relationships and mechanisms of catalytic reactions are reviewed. In these catalysts, noble metal ions are incorporated into a substrate matrix to a certain limit in a solid solution form. Lower valent noble metal-ion substitution in CeO2 and TiO2 creates noble metal ionic sites and oxide ion vacancies that act as adsorption sites for redox catalysis. It has been demonstrated that these new generation noble metal ionic catalysts (NMIC) have been found to be catalytically more active than conventional nanocrystalline noble metal catalysts dispersed on oxide supports.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Lectin Microarrays for Glycomic Analysis

Glycomics is the study of comprehensive structural elucidation and characterization of all glycoforms found in nature and their dynamic spatiotemporal changes that are associated with biological processes. Glycocalyx of mammalian cells actively participate in cell-cell, cell-matrix, and cell-pathogen interactions, which impact embryogenesis, growth and development, homeostasis, infection and immunity, signaling, malignancy, and metabolic disorders. Relative to genomics and proteomics, glycomics is just growing out of infancy with great potential in biomedicine for biomarker discovery, diagnosis, and treatment. However, the immense diversity and complexity of glycan structures and their multiple modes of interactions with proteins pose great challenges for development of analytical tools for delineating structure function relationships and understanding glycocode. Several tools are being developed for glycan profiling based on chromatography,m mass spectrometry, glycan microarrays, and glyco-informatics. Lectins, which have long been used in glyco-immunology, printed on a microarray provide a versatile platform for rapid high throughput analysis of glycoforms of biological samples. Herein, we summarize technological advances in lectin microarrays and critically review their impact on glycomics analysis. Challenges remain in terms of expansion to include nonplant derived lectins, standardization for routine clinical use, development of recombinant lectins, and exploration of plant kingdom for discovery of novel lectins.

Analysis of energy utilization in the grain mill sector in Karnataka

Energy is a major constituent of a small-scale industry such as grain mills. Based on a sample survey of several mills spread over Karnataka, a state in India, a number of energy analyses were conducted primarily to establish relationships and secondarily to look at them in more detail. Initially specific energy consumption (SEC) was computed for all industries so as to compare their efficiencies of energy use. A wide disparity exists in SEC among various grain mills. In order to understand the disparities better, regression analyses were performed on the variables energy and production, SEC and production, and energy/SEC with percentage production capacity utilization. The studies show that smaller range industries have lower capacity utilization. This paper also examines the energy savings possible by shifting industries from the lower production ranges to the next higher range (thereby utilizing installed production capacity optimally). This leads to an overall energy capacity saving of 23.12% for the foodgrain sector and 18.67% for the paddy dehusking subgroup. If this is extrapolated to the whole state, we obtain a saving of 55 million kWh.

Sequence-Structure Similarity: Do Sequentially Identical Peptide Fragments have Similar Three-Dimensional Structures?

The rapidly growing structure databases enhance the probability of finding identical sequences sharing structural similarity. Structure prediction methods are being used extensively to abridge the gap between known protein sequences and the solved structures which is essential to understand its specific biochemical and cellular functions. In this work, we plan to study the ambiguity between sequence-structure relationships and examine if sequentially identical peptide fragments adopt similar three-dimensional structures. Fragments of varying lengths (five to ten residues) were used to observe the behavior of sequence and its three-dimensional structures. The STAMP program was used to superpose the three-dimensional structures and the two parameters (Sequence Structure Similarity Score (Sc) and Root Mean Square Deviation value) were employed to classify them into three categories: similar, intermediate and dissimilar structures. Furthermore, the same approach was carried out on all the three-dimensional protein structures solved in the two organisms, Mycobacterium tuberculosis and Plasmodium falciparum to validate our results.