Assessing GCM Convergence for India Using the Variable Convergence Score

General circulation models (GCMs) use transient climate simulations to predict climate conditions in the future. Coarse-grid resolutions and process uncertainties necessitate the use of downscaling models to simulate precipitation. However, in the downscaling models, with multiple GCMs now available, selecting an atmospheric variable from a particular model which is representative of the ensemble mean becomes an important consideration. The variable convergence score (VCS) provides a simple yet meaningful approach to address this issue, providing a mechanism to evaluate variables against each other with respect to the stability they exhibit in future climate simulations. In this study, VCS methodology is applied to 10 atmospheric variables of particular interest in downscaling precipitation over India and also on a regional basis. The nested bias-correction methodology is used to remove the systematic biases in the GCMs simulations, and a single VCS curve is developed for the entire country. The generated VCS curve is expected to assist in quantifying the variable performance across different GCMs, thus reducing the uncertainty in climate impact-assessment studies. The results indicate higher consistency across GCMs for pressure and temperature, and lower consistency for precipitation and related variables. Regional assessments, while broadly consistent with the overall results, indicate low convergence in atmospheric attributes for the Northeastern parts of India.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

Extended liaison as an interface between product and process model in assembly

This paper describes the use of liaison to better integrate product model and assembly process model so as to enable sharing of design and assembly process information in a common integrated form and reason about them. Liaison can be viewed as a set, usually a pair, of features in proximity with which process information can be associated. A liaison is defined as a set of geometric entities on the parts being assembled and relations between these geometric entities. Liaisons have been defined for riveting, welding, bolt fastening, screw fastening, adhesive bonding (gluing) and blind fastening processes. The liaison captures process specific information through attributes associated with it. The attributes are associated with process details at varying levels of abstraction. A data structure for liaison has been developed to cluster the attributes of the liaison based on the level of abstraction. As information about the liaisons is not explicitly available in either the part model or the assembly model, algorithms have been developed for extracting liaisons from the assembly model. The use of liaison is proposed to enable both the construction of process model as the product model is fleshed out, as well as maintaining integrity of both product and process models as the inevitable changes happen to both design and the manufacturing environment during the product lifecycle. Results from aerospace and automotive domains have been provided to illustrate and validate the use of liaisons. (C) 2014 Elsevier Ltd. All rights reserved.

Isotropic finite volume discretization

Finite volume methods traditionally employ dimension by dimension extension of the one-dimensional reconstruction and averaging procedures to achieve spatial discretization of the governing partial differential equations on a structured Cartesian mesh in multiple dimensions. This simple approach based on tensor product stencils introduces an undesirable grid orientation dependence in the computed solution. The resulting anisotropic errors lead to a disparity in the calculations that is most prominent between directions parallel and diagonal to the grid lines. In this work we develop isotropic finite volume discretization schemes which minimize such grid orientation effects in multidimensional calculations by eliminating the directional bias in the lowest order term in the truncation error. Explicit isotropic expressions that relate the cell face averaged line and surface integrals of a function and its derivatives to the given cell area and volume averages are derived in two and three dimensions, respectively. It is found that a family of isotropic approximations with a free parameter can be derived by combining isotropic schemes based on next-nearest and next-next-nearest neighbors in three dimensions. Use of these isotropic expressions alone in a standard finite volume framework, however, is found to be insufficient in enforcing rotational invariance when the flux vector is nonlinear and/or spatially non-uniform. The rotationally invariant terms which lead to a loss of isotropy in such cases are explicitly identified and recast in a differential form. Various forms of flux correction terms which allow for a full recovery of rotational invariance in the lowest order truncation error terms, while preserving the formal order of accuracy and discrete conservation of the original finite volume method, are developed. Numerical tests in two and three dimensions attest the superior directional attributes of the proposed isotropic finite volume method. Prominent anisotropic errors, such as spurious asymmetric distortions on a circular reaction-diffusion wave that feature in the conventional finite volume implementation are effectively suppressed through isotropic finite volume discretization. Furthermore, for a given spatial resolution, a striking improvement in the prediction of kinetic energy decay rate corresponding to a general two-dimensional incompressible flow field is observed with the use of an isotropic finite volume method instead of the conventional discretization. (C) 2014 Elsevier Inc. All rights reserved.

Beadex Function in the Motor Neurons Is Essential for Female Reproduction in Drosophila melanogaster

Drosophila melanogaster has served as an excellent model system for understanding the neuronal circuits and molecular mechanisms regulating complex behaviors. The Drosophila female reproductive circuits, in particular, are well studied and can be used as a tool to understand the role of novel genes in neuronal function in general and female reproduction in particular. In the present study, the role of Beadex, a transcription co-activator, in Drosophila female reproduction was assessed by generation of mutant and knock down studies. Null allele of Beadex was generated by transposase induced excision of P-element present within an intron of Beadex gene. The mutant showed highly compromised reproductive abilities as evaluated by reduced fecundity and fertility, abnormal oviposition and more importantly, the failure of sperm release from storage organs. However, no defect was found in the overall ovariole development. Tissue specific, targeted knock down of Beadex indicated that its function in neurons is important for efficient female reproduction, since its neuronal knock down led to compromised female reproductive abilities, similar to Beadex null females. Further, different neuronal class specific knock down studies revealed that Beadex function is required in motor neurons for normal fecundity and fertility of females. Thus, the present study attributes a novel and essential role for Beadex in female reproduction through neurons.

Microstructure and Crystallographic Texture Evolution During the Friction-Stir Processing of a Precipitation-Hardenable Aluminum Alloy

Friction-stir processing (FSP) has been proven as a successful method for the grain refinement of high-strength aluminum alloys. The most important attributes of this process are the fine-grain microstructure and characteristic texture, which impart suitable properties in the as-processed material. In the current work, FSP of the precipitation-hardenable aluminum alloy 2219 has been carried out and the consequent evolution of microstructure and texture has been studied. The as-processed materials were characterized using electron back-scattered diffraction, x-ray diffraction, and electron probe microanalysis. Onion-ring formation was observed in the nugget zone, which has been found to be related to the precipitation response and crystallographic texture of the alloy. Texture development in the alloy has been attributed to the combined effect of shear deformation and dynamic recrystallization. The texture was found heterogeneous even within the nugget zone. A microtexture analysis revealed the dominance of shear texture components, with C component at the top of nugget zone and the B and A(2)* components in the middle and bottom. The bulk texture measurement in the nugget zone revealed a dominant C component. The development of a weaker texture along with the presence of some large particles in the nugget zone indicates particle-stimulated nucleation as the dominant nucleation mechanism during FSP. Grain growth follows the Burke and Turnbull mechanism and geometrical coalescence.

Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land- cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed. (C) 2015 The Authors. Published by Elsevier B.V.

Nest site characterization of sympatric hornbills in a tropical dry forest

Hornbills, among the largest and most threatened tropical frugivores, provide important seed dispersal services. Hornbill nest site characteristics are known primarily from wet tropical forests. Nests of the Indian grey hornbill Ocyceros birostris and Oriental pied hornbill Anthracoceros albirostris were characterized in a tropical dry forest. Despite A. albirostris being twice the size of O. birostris, few of the nest cavity attributes were different. A. albirostris nests were surrounded by higher proportion of mixed forest and lower sal forest compared to O. birostris. In this landscape, the larger A. albirostris may prefer to nest in sites with more food plants compared to the smaller O. birostris.

Activity-travel behaviour of non-workers from Bangalore City in India

This paper presents exploratory and statistical analyses of the activity-travel behaviour of non-workers in Bangalore city in India. The study summarises the socio-demographic characteristics as well as the activity-travel behaviour of non-workers using a primary activity-travel survey data collected by the authors. Where possible, the research also compares the analysis findings with the case studies on activity-travel behaviour of non-workers, carried out in developed and developing countries. This gives an opportunity to understand the differences/similarities in the activity-travel behaviour of non-workers across diverse socio-cultural settings. The preliminary exploratory analysis shed light on the differences in activity participation, trip chaining, time-of-day preference for trip departure, and mode use behaviour of non-workers in Bangalore city. Statistical models were developed for investigating the effects of individual and household socio-demographics, land use parameters, and travel context attributes on activity participation, trip chaining, time-of-day choice, and mode choice decisions of non-workers. A few important results of the analysis are the influence of viewing television at home on out-of-home activity participation and trip-chaining behaviour, and the impact of in-home maintenance activity duration on time-of-day choice. Further, based on the findings of the initial analyses, an attempt has been made in this study to develop an integrated model that links time allocation, time-of-day choice, and trip chaining behaviour of non-workers. The study also discusses the implications of the research findings for transportation planning and policy for Bangalore city. (C) 2015 Elsevier Ltd. All rights reserved.

Draft genome of a commonly misdiagnosed multidrug resistant pathogen Candida auris

Background: Candida auris is a multidrug resistant, emerging agent of fungemia in humans. Its actual global distribution remains obscure as the current commercial methods of clinical diagnosis misidentify it as C. haemulonii. Here we report the first draft genome of C. auris to explore the genomic basis of virulence and unique differences that could be employed for differential diagnosis. Results: More than 99.5 % of the C. auris genomic reads did not align to the current whole (or draft) genome sequences of Candida albicans, Candida lusitaniae, Candida glabrata and Saccharomyces cerevisiae; thereby indicating its divergence from the active Candida clade. The genome spans around 12.49 Mb with 8527 predicted genes. Functional annotation revealed that among the sequenced Candida species, it is closest to the hemiascomycete species Clavispora lusitaniae. Comparison with the well-studied species Candida albicans showed that it shares significant virulence attributes with other pathogenic Candida species such as oligopeptide transporters, mannosyl transfersases, secreted proteases and genes involved in biofilm formation. We also identified a plethora of transporters belonging to the ABC and major facilitator superfamily along with known MDR transcription factors which explained its high tolerance to antifungal drugs. Conclusions: Our study emphasizes an urgent need for accurate fungal screening methods such as PCR and electrophoretic karyotyping to ensure proper management of fungemia. Our work highlights the potential genetic mechanisms involved in virulence and pathogenicity of an important emerging human pathogen namely C. auris. Owing to its diversity at the genomic scale; we expect the genome sequence to be a useful resource to map species specific differences that will help develop accurate diagnostic markers and better drug targets.

Mg/BN nanocomposites: Nano-BN addition for enhanced room temperature tensile and compressive response

The present study elucidates the effects of nanoscale boron nitride particles addition on the microstructural and mechanical characteristics of monolithic magnesium. Novel light-weight Mg nanocomposites containing 0.3, 0.6 and 1.2vol% nano-size boron nitride particulates were synthesized using the disintegrated melt deposition method followed by hot extrusion. Microstructural characterization of developed Mg/x-boron nitride composites revealed significant grain refinement due to the uniform distribution of nano-boron nitride particulates. Texture analysis of selected Mg-1.2 boron nitride nanocomposite showed an increase in the intensity of fiber texture alongside enhanced localized recrystallization when compared to monolithic Mg. Mechanical properties evaluation under indentation, tension and compression loading indicated superior response of Mg/x-boron nitride composites in comparison to pure Mg. The uniform distribution of nanoscale boron nitride particles and the modified crystallographic texture achieved due to the nano-boron nitride addition attributes to the superior mechanical characteristics of Mg/boron nitride nanocomposites.

Isotopic and geochemical characterization of invader tilapia fishes from water bodies of West Bengal and Karnataka, India

The otoliths (N = 12) of freshwater invasive species tilapia (Tilapia mossambicus) collected from two water bodies located at Kolkata and Bangalore, India, were analyzed for stable isotopes (delta 18O, delta 14C) and major and trace elements in order to assess the suitability of using otoliths as a tracer of aquatic environmental changes. The stable isotope analysis was done using the dual inlet system of a Finnigan-MAT 253 isotope ratio mass spectrometer (Thermo-Fisher, Bremen, Germany). Concentrations of major and trace elements were determined using a Thermo X-Series II quadrupole mass spectrometer. The stable isotope composition in tilapia otolith samples from Bangalore and Kolkata water bodies are quite good agreeing with that of the respective lake/pond and rain water. Elemental composition revealed in a pattern of Ca > Fe > Na > Sr > K > Ba > Cr > Mg > As > Mn > Zn > Co > Cu > Cd > Pb. The otoliths from Kolkata pond water are more enriched in Ba, Zn, Pb, Mn, Se, Cu, Zn, Cd, and Ni whereas Cr and As were found to be higher in otolith samples from Bangalore lake. The enrichment factor (EF) values of Cr were higher for both the sampling location in comparison with other metals, although all the studied metals exhibited EF values >1. The PCA shows clustering of metals in the otolith which are related either with the metabolic and physiological attributes or waterborne source. The study demonstrated the potential of stable isotope techniques to distinguish otolith specimens from varied climatic zone, while elemental composition recorded the quality of water at both the locations. The role of climate driving the quality of water can be understood by detailed and continuous monitoring of otolith specimens in the future. Future method allows reconstruction of climate and water quality from old specimens from field exposures or museum collection.

Structure-mechanical property correlations in mechanochromic luminescent crystals of boron difluoride dibenzoylmethane derivatives

The structure and mechanical properties of crystalline materials of three boron difluoride dibenzoylmethane (BF(2)dbm) derivatives were investigated to examine the correlation, if any, among mechanochromic luminescence (ML) behaviour, solid-state structure, and the mechanical behaviour of single crystals. Qualitative mechanical deformation tests show that the crystals of BF(2)dbm(Bu-t)(2) can be bent permanently, whereas those of BF(2)dbm(OMe)(2) exhibit an inhomogeneous shearing mode of deformation, and finally BF(2)dbmOMe crystals are brittle. Quantitative mechanical analysis by nanoindentation on the major facets of the crystals shows that BF(2)dbm(Bu-t)(2) is soft and compliant with low values of elastic modulus, E, and hardness, H, confirming its superior suceptibility for plastic deformation, which is attributed to the presence of a multitude of slip systems in the crystal structure. In contrast, both BF(2)dbm(OMe)(2) and BF(2)dbmOMe are considerably stiffer and harder with comparable E and H, which are rationalized through analysis of the structural attributes such as the intermolecular interactions, slip systems and their relative orientation with respect to the indentation direction. As expected from the qualitative mechanical behaviour, prominent ML was observed in BF(2)dbm(Bu-t)(2), whereas BF(2)dbm(OMe)(2) exhibits only a moderate ML and BF(2)dbmOMe shows no detectable ML, all examined under identical conditions. These results confirm that the extent of ML in crystalline organic solid-state fluorophore materials can be correlated positively with the extent of plasticity (low recovery). In turn, they offer opportunities to design new and improved efficient ML materials using crystal engineering principles.

A recursive multi-scaling approach to regional flood frequency analysis

Scaling approaches are widely used by hydrologists for Regional Frequency Analysis (RFA) of floods at ungauged/sparsely gauged site(s) in river basins. This paper proposes a Recursive Multi-scaling (RMS) approach to RFA that overcomes limitations of conventional simple- and multi-scaling approaches. The approach involves identification of a separate set of attributes corresponding to each of the sites (being considered in the study area/region) in a recursive manner according to their importance, and utilizing those attributes to construct effective regional regression relationships to estimate statistical raw moments (SMs) of peak flows. The SMs are then utilized to arrive at parameters of flood frequency distribution and quantile estimate(s) corresponding to target return period(s). Effectiveness of the RMS approach in arriving at flood quantile estimates for ungauged sites is demonstrated through leave-one-out cross-validation experiment on watersheds in Indiana State, USA. Results indicate that the approach outperforms index-flood based Region-of-Influence approach, simple- and multi-scaling approaches and a multiple linear regression method. (C) 2015 Elsevier B.V. All rights reserved.