Multivariate data analyses of persistent organic pollutants in maternal adipose tissue in Singapore

Persistent organic pollutants (POPs) were detected in 88 maternal adipose tissue samples collected during year 2004 to 2006, in Singapore. Organochlorine pesticides (OCPs) were the most dominant followed by polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). Multivariate data analyses (MVA) including principal component analysis (PCA), partial least-squares regression (PLSR), and partial least-squares discriminant analysis (PLS-DA) were applied to elucidate the relationship between concentrations of POPs in adipose tissues and donors' characteristics. Food consumption played the most significant role in accounting for levels of POPs in adipose tissue. Fish and poultry consumption was the route of PCBs and PBDEs in mothers in Singapore, while beta-HCH came mainly from vegetables. An age-dependent accumulation of POPs was found for beta-HCH and PCB congeners, and lactation and gestation functioned as a decontamination processes for PCBs in adipose tissue. Gestational diabetes mellitus (GDM) may change the profile of POPs in adipose tissue, probably due to an alteration in lipid metabolism. POPs investigated here may not be the cause of antenatal complication in pregnant women, and baby gender was not related to the pattern of contaminants in maternal adipose tissue.     

Liquid yield from juniper and mesquite bio‐fuel gasification

Mesquite (Prosopis glandulosa) and redberry juniper (Juniperus pinchotii) are two woody species which dominate uncultivated lands in the south central Great Plains, USA (Texas, Oklahoma) and may have potential for bioenergy utilization. In this study, these two biomasses were gasified in an adiabatic, updraft fixed‐bed gasifier using air as medium. The products include low heating value gas and liquid tar yield. The effects of fuel moisture content and the equivalence ratio (ER) on liquid yields including heavy tar and light tar and higher heating value (HHV) of the producer gas were investigated. It was found that updraft gasifiers produce a large amount of tar, in a range of 100 g/Nm³ of gas produced. As the equivalence ratio (ER) increased (i.e. more rich char conditions), peak temperature (T_peak) within the bed decreased due to the lesser partial oxidization. The liquid yield showed peak value with change in moisture content between 6 and 24%. Heavy tar is a very high quality fuel with a higher heating value of over 29 MJ/kg on a dry, ash‐free (DAF) basis which is a slightly higher than that of ethanol. Copyright © 2015 John Wiley & Sons, Ltd.     

An efficient classification approach for detection of Alzheimer’s disease from biomedical imaging modalities

Multimedia Tools and Applications - The complex patterns of the neuroimaging data are analyzed successfully with bio-medical imaging applications. The patients with/without AD can be discriminated...     

An investigation on the effect of sintering mode on various properties of copper-graphene metal matrix composite

The present work investigates the effect of sintering mode and graphene addition on the microstructural, mechanical and electrical properties of copper–graphene metal matrix composites reinforced with varying amounts (0.9, 1.8, 2.7 and 3.6 vol%) of graphene particles fabricated through powder metallurgy route. Sintering was carried out at 900 °C in 95%N₂-5%H₂ (forming gas) atmosphere with a heating rate of 5°/min for conventional and 20 °C/min for microwave with a holding time of 60 min in both cases. All the composites were found to couple well with microwave field and had resulted in 63% reduction in the processing cycle time as compared to the conventional process. Micro-structural analysis revealed the homogeneous distribution of graphene in copper matrix. Copper-graphene composites exhibited excellent wear resistance due to higher hardness and excellent lubricating nature of graphene. It was observed that porosity has a significant effect on the electrical conductivity values.     

Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification

This article exploits a new brain tumor classification model that includes five steps like (a) denoising, (b) skull stripping, (c) segmentation, (d) feature extraction and (e) classification. Initially, the image is subjected under the denoising process, where the noise removal procedure is carried out by employing the entropy-based trilateral filter. Then, the denoised image is applied to the skull stripping process via Otsu thresholding and morphology segmentation. Subsequently, the next step is the segmentation, where the image is segmented by deploying the adaptive CLFAHE (contrast limited fuzzy adaptive histogram equalization) technique. Once the segmentation is completed, gray-level co-occurrence matrix (GLCM) based features are extracted. Finally, the extracted features are processed under hybrid classification model to attain enhanced classification rate. Here, hybrid classification hybrids two classifiers namely deep belief network (DBN) and Bayesian regularization classifier. The vital contribution of this research work exists in the optimal selection of hidden neurons in the DBN. Along with this, the membership function (bounding limits) of fuzzy logic is optimally selected. For this, a new lion exploration based whale optimization (LE-WO) algorithm is proposed in this article that hybrids the concept of (lion algorithm) LA and (whale optimization algorithm) WOA. Finally, the performance of proposed LE-WO is compared over the other methods in terms of accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1 _ score and Matthews correlation coefficient (MCC), False positive rate (FPR), False negative rate (FNR) and false discovery rate (FDR) and proves the betterments of proposed work. From the outcomes, the accuracy measure of proposed model at 60th population size is 1.98%, 1.81%, 1.32%, 3.46% and 0.75% better than PSO, FF, GWO, WOA and LA, respectively. Similarly, in 80th population size, the performance of the implemented model is 4.47%, 5.04%, 3.96%, 6.29% and 1.37% superior to PSO, FF, GWO, WOA and LA, respectively. Thus, the betterment of the adopted scheme is validated in an effective manner.     

Hydrothermally synthesized ZnO and Z-rGO nanorods: Effect of post-annealing temperature and rGO incorporation on hydrogen sensing

Journal of Materials Science: Materials in Electronics - In this work, hydrogen-sensing characteristics of zinc oxide nanorods and reduced graphene oxide-incorporated zinc oxide nanorods are...     

Co-firing of coal and cattle feedlot biomass (FB) Fuels, Part III: fouling results from a 500,000 BTU/h pilot plant scale boiler burner

Part I of the paper presented a methodology for fuel collection, fuel characteristics of the FB, its relation to ration fed, and the change in fuel characteristics and volatile oxides due to composting. The bench scale experiments with 30 kW_t (100,000 BTU/h) facility revealed better combustion for coal: FB blends (90:10) than for coal alone and the NO_x emission were slightly less with the blend (Part II). Part III concerns with larger-scale (pilot plant) experiments conducted at the 150 kW_t (150,0000 BTU/h) Combustion and Environmental Research Facility (CERF) of the National Energy Technology Laboratory (NETL). Only fouling part of the results are reported in part III. The 90:10 coal:FB blend resulted in almost twice the ash output compared to coal and ash deposits on heat exchanger tubes that were more difficult to remove than baseline coal ash deposits. The increased fouling behavior with blend is probably due to the higher ash loading and ash composition of FB.     

A three-phase inverter circuit using half-bridge cells and T-NPC for medium-voltage applications

Three-phase single DC-source based multilevel inverter topologies play a pivotal role in industrial applications due to the reduced number of components and higher efficiency. This paper emphasizes the inverter for medium-voltage applications that employ a conventional three-phase T-type structure (T-NPC). The primary circuit of the proposed configuration consists of a T-NPC structure connected to the half-bridge cells at the top and the bottom sides of each phase. The secondary circuit consists of DC-link capacitors whose voltage balancing is attained through a separate voltage balancing circuit (VBC). Using the proposed configuration, the number of components and independent DC supplies are reduced compared with the conventional topologies such as a neutral point clamped (NPC) inverter, a flying capacitor (FC) inverter, and a cascaded H-bridge (CHB) inverter for the same number of output voltage levels. Hence, the proposed topology results in the reduction of weight, volume, and power losses of the inverter. A sine-triangle comparison method is employed in the field programmable gate array (FPGA) platform to generate the firing pulses of the circuit switches. The effectiveness of the proposed topology is verified with simulation studies and is experimentally validated with a scaled-down prototype.