Finite element based evaluation of stress intensity factors for interactive semi-elliptic surface cracks

A finite thickness plate with two coplanar self-same shallow and deep semi-elliptical surface cracks subjected to remote tensile surface traction is considered for fracture analysis. Based on three-dimensional (3D) finite element solutions, stress intensity factors (SIFs) are evaluated along the entire crack front using a force method. The line spring model has also been used to evaluate crack depth point SIFs using shell finite element analysis. A wide range of geometric dimensions and crack configurations viz. crack shape aspect ratio (0.3≤a/c≤1.2), crack depth ratio (1.25≤t/a≤6), relative crack location (0.33≤2c/d≤0.9) and normalized location on the crack front (0≤2φ/π≤2) are considered for numerical estimation of crack interaction factors. SIFs evaluated at the depth point using the force method from the 3D finite element results are compared with SIFs evaluated using the line spring model. Finally, using finite element results, an empirical relation is proposed for the evaluation of crack interaction factors. For the ranges considered, the proposed empirical relation predicts crack interaction factors at critical locations within ±2% of the 3D finite element solutions.     

A novel nanostructured iron oxide-gold bioelectrode for hydrogen peroxide sensing

Fe(3)O(4) nanoparticles covalently linked to a gold electrode have been used for immobilizing catalase (CAT) enzyme to sense the presence of various concentrations of H(2)O(2). These nanoparticles ranging from 20 to 30 nm were synthesized by thermal co-precipitation of ferric and ferrous chlorides. SEM and XRD have been used for morphological and structural characterization of Fe(3)O(4) nanoparticles. CAT enzyme was linked covalently to the surface of iron oxide using carbodiimide in phosphate buffer (pH 7.4) at 4?°C. The enzyme-iron oxide link was confirmed by FT-IR spectroscopy. Sensing studies carried out using cyclic voltammetry showed a linear response of the CAT/nano Fe(3)O(4)/Au bioelectrode towards H(2)O(2) between 1.5 and 13.5 μM with a very sharp response time of 2 s.     

MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing

Forschung im Ingenieurwesen - Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments—especially offshore, where reliability and efficiency...     

Women's empowerment and domestic violence: the role of sociocultural determinants in maternal and child undernutrition in tribal and rural communities in South India

BACKGROUND: Moderate malnutrition continues to affect 46% of children under five years of age and 47% of rural women in India. Women's lack of empowerment is believed to be an important factor in the persistent prevalence of malnutrition. In India, women's empowerment often varies by community, with tribes sometimes being the most progressive. OBJECTIVE: To explore the relationship between women's empowerment, maternal nutritional status, and the nutritional status of their children aged 6 to 24 months in rural and tribal communities. METHODS: This study in rural Karnataka, India, included tribal and rural subjects and used both qualitative and quantitative methods of data collection. Structured interviews with mothers were performed and anthropometric measurements were obtained for 820 mother-child pairs. The data were analyzed by multivariate and logistic regression. RESULTS: Some degree of malnutrition was seen in 83.5% of children and 72.4% of mothers in the sample. Biological variables explained most of the variance in nutritional status, followed by health-care seeking and women's empowerment variables; socioeconomic variables explained the least amount of variance. Women's empowerment variables were significantly associated with child nutrition and explained 5.6% of the variance in the sample. Maternal experience of psychological abuse and sexual coercion increased the risk of malnutrition in mothers and children. Domestic violence was experienced by 34% of mothers in the sample. CONCLUSIONS: In addition to the known investments needed to reduce malnutrition, improving women's nutrition, promoting gender equality, empowering women, and ending violence against women could further reduce the prevalence of malnutrition in this segment of the Indian population.     

Surface topography of polylactic acid nanofibrous mats: influence on blood compatibility

Fabricating nanofibrous scaffolds with robust blood compatibility remains an unmet challenge for cardiovascular applications since anti-thrombogenic surface coatings did not withstand physiological shear force. Hence, the present study envisages the influence of smooth and porous topographies of poly(lactic acid) (PLA) nanofibers on hemocompatibility as it could offer time-independent blood compatibility. Further, recent studies have evolved to integrate various contrasting agents for augmenting the prognostic properties of tissue engineered scaffolds; an attempt was also made to synthesize Curcumin–superparamagnetic iron oxide nanoparticle complex (Cur–SPION) as a contrasting agent and impregnated into PLA nanofibers for evaluating the blood compatibility. Herein, electrospun nanofibers of PLA with different topographies (smooth and porous) were fabricated and characterized for surface morphology, zeta potential, fluorescence, and crystallinity. The scaffolds with smooth, porous and rough surface topographies were thoroughly investigated for its hemocompatibility by evaluating hemolysis percentage, platelet adhesion, in vitro kinetic clotting time, serum protein adsorption, plasma recalcification time (PRT), capture and release of erythrocytes. Although the nanofibers of all three groups showed acceptable hemolytic percentage (HP?<?5%), the adhered RBCs on Cur–SPION based fibers undergo morphological transformation from biconcave discocytes to echinocytes with cube-like protrusions. On the contrary, no morphological changes were observed in RBCs cultured on smooth and porous nanofibers. Porous fibers exhibited excellent anti-thrombogenic property and adhered lesser platelets and maintained the discoidal morphology of native platelets. Cur–SPION integrated PLA nanofibers showed inactivated platelets with anti-thrombogenic activity compared to smooth nanofibers. In conclusion, PLA nanofibers porous topography did not affect the RBC membrane integrity and maintained discoidal morphology of platelets with superior anti-thrombogenic activity. However, smooth and Cur–SPION integrated PLA nanofibers were found to activate the platelets and deform the RBC membrane integrity, respectively. Hence, the nanofibers with porous structures provide an ideal topography for time-independent hemocompatibility.

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Saccharomyces cerevisiae S288C genome annotation: a working hypothesis

The S. cerevisiae genome is the most well-characterized eukaryotic genome and one of the simplest in terms of identifying open reading frames (ORFs), yet its primary annotation has been updated continually in the decade since its initial release in 1996 (Goffeau et al., 1996). The Saccharomyces Genome Database (SGD; www.yeastgenome.org) (Hirschman et al., 2006), the community-designated repository for this reference genome, strives to ensure that the S. cerevisiae annotation is as accurate and useful as possible. At SGD, the S. cerevisiae genome sequence and annotation are treated as a working hypothesis, which must be repeatedly tested and refined. In this paper, in celebration of the tenth anniversary of the completion of the S. cerevisiae genome sequence, we discuss the ways in which the S. cerevisiae sequence and annotation have changed, consider the multiple sources of experimental and comparative data on which these changes are based, and describe our methods for evaluating, incorporating and documenting these new data.     

Tissue-level segmentation and tracking of cells in growing plant roots

With the spread of systems approaches to biological research, there is increasing demand for methods and tools capable of extracting quantitative measurements of biological samples from individual and time-based sequences of microscope images. To this end, we have developed a software tool for tissue level segmentation and automatic tracking of a network of cells in confocal images of the roots of the model plant Arabidopsis thaliana. The tool implements a novel hybrid technique, which is a combination of the recently developed Network Snakes technique and MCMC-based particle filters and incorporates automatic initialisation of the network snakes. A novel method of evaluation of network-structured multi-target tracking is also presented, and is used to evaluate the developed tracking framework for accuracy and robustness against several timelapse sequences of Arabidopsis roots. Evaluation results are presented, along with a comparison between the results of the component techniques and the hybrid approach. The results show that the hybrid approach performed consistently well at all levels of complexity and better than the component methods alone.     

Carbon black–polybenzoxazine nanocomposites for high K dielectric applications

This work involves the development of surface functionalized carbon black (FCB) reinforced polybenzoxazine (PBZ) nanocomposites, using a benzoxazine monomer and varying weight percentages of functionalized carbon black (0.5, 1.0, and 1.5 wt%) through ring opening polymerization via thermal cure. Data from the morphological studies indicate that the nanosized FCB particles are homogeneously distributed in the polybenzoxazine matrix. The incorporation of FCB improves the dielectric constant, electrical conductivity, and reduction in resistivity of the resulting FCB–PBZ nanocomposites when compared to neat PBZ. A significant improvement observed in glass transition temperature and thermal stability of resulting FCB–PBZ nanocomposites, when compared to neat PBZ. POLYM. COMPOS., 35:2121–2128, 2014. © 2014 Society of Plastics Engineers     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.