Adiponectin Deficiency Alters Placenta Function but Does Not Affect Fetal Growth in Mice

Adiponectin administration to pregnant mice decreases nutrient transport and fetal growth. An adiponectin deficiency, on the other hand, as seen in obese women during pregnancy, alters fetal growth; however, the mechanism is unclear. To determine the role of adiponectin on placenta function and fetal growth, we used adiponectin knockout, adiponectin heterozygote that displays reduced adiponectin levels, and wild-type mice on a control diet or high fat/high sucrose (HF/HS) diet. Triglycerides (TGs) in the serum, liver, and placenta were measured using colorimetric assays. Gene expression was measured using quantitative RT-PCR. Adiponectin levels did not affect fetal weight, but it reduced adiponectin levels, increased fetal serum and placenta TG content. Wildtype dams on a HF/HS diet protected the fetuses from fatty acid overload as judged by increased liver TGs in dams and normal serum and liver TG levels in fetuses, while low adiponectin was associated with increased fetal liver TGs. Low maternal adiponectin increased the expression of genes involved in fatty acid transport; Lpl and Cd36 in the placenta. Adiponectin deficiency does not affect fetal growth but induces placental dysfunction and increases fetal TG load, which is enhanced with obesity. This could lead to imprinting effects on the fetus and the development of metabolic dysfunction in the offspring.     

Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

Video recordings of earthmoving construction operations provide understandable data that can be used for benchmarking and analyzing their performance. These recordings further support project managers to take corrective actions on performance deviations and in turn improve operational efficiency. Despite these benefits, manual stopwatch studies of previously recorded videos can be labor-intensive, may suffer from biases of the observers, and are impractical after substantial period of observations. This paper presents a new computer vision based algorithm for recognizing single actions of earthmoving construction equipment. This is particularly a challenging task as equipment can be partially occluded in site video streams and usually come in wide variety of sizes and appearances. The scale and pose of the equipment actions can also significantly vary based on the camera configurations. In the proposed method, a video is initially represented as a collection of spatio-temporal visual features by extracting space–time interest points and describing each feature with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of the spatio-temporal features and action categories using a multi-class Support Vector Machine (SVM) classifier. This strategy handles noisy feature points arisen from typical dynamic backgrounds. Given a video sequence captured from a fixed camera, the multi-class SVM classifier recognizes and localizes equipment actions. For the purpose of evaluation, a new video dataset is introduced which contains 859 sequences from excavator and truck actions. This dataset contains large variations of equipment pose and scale, and has varied backgrounds and levels of occlusion. The experimental results with average accuracies of 86.33% and 98.33% show that our supervised method outperforms previous algorithms for excavator and truck action recognition. The results hold the promise for applicability of the proposed method for construction activity analysis.     

A Simple Rationally Integrated Drought Indicator for Rice–Wheat Productivity

Water is essential for growing food and in some areas without irrigation agricultural production would not possible. Crop specific agricultural drought assessment requires integrated information on meteorological parameters, soil characters and plant vegetative dynamics throughout the growing season. Meteorological, Crop Growth Simulation Model and Space based approaches have their own inherent limitations. The main objective of this study is to examine the possibility of developing a rationally integrated drought assessment indicator for rice–wheat productivity over Indo-Gangetic Plains in India (IGP-India). Rice(Oryza sativa)–Wheat(Triticum aestivum) (RW) system is the major cropping system of the IGP-India and occupies 10 million hectares and provides food security and livelihoods to the people of this region. There are four major reasons for droughts in IGP-delay in the onset of monsoon/ failure of monsoon, variability of monsoon rainfall, long break in monsoon and areal difference in the persistence of monsoon. The Rational Integrated Drought Assessment Index (RIDAI) developed here explains 72% variations in rice productivity while it explains 94% variations of wheat productivity. Thus, this combined drought index tool captures all the drought years in the selected study area and this tool can be useful for getting preliminary assessment of drought impact, so that the researchers and planners can use this integrated tool for their decisions.     

An investigation of the electrical transport properties of graphene-oxide thin films

The electrical transport properties of graphene-oxide (GO) thin films were investigated. The GO was synthesized by a modified Hummers method and was characterized by X-ray diffraction and UV-visible spectroscopy. The thin film of GO was made on a Si/SiO₂ substrate by drop-casting. The surface morphology of the GO film was analyzed by using scanning electron microscopy and atomic force microscopy techniques. Temperature dependent resistance and current-voltage measurements were studied using four-terminal method at various temperatures (120, 150, 175, 200, 250 and 300 K) and their charge transport followed the 3D variable range hopping mechanism which was well supported by Raman spectra analysis. The presence of various functional groups in GO were identified by using high resolution X-ray photo electron (XPS) and Fourier transform infra red (FT-IR) spectroscopic techniques. Graphene-oxide thin film field effect transistor devices show p-type semiconducting behavior with a hole mobility of 0.25 cm² V⁻¹ s⁻¹ and 0.59 cm² V⁻¹ s⁻¹ when measured in air and vacuum respectively.     

Multiple-band large-signal characterization of millimeter-wave double avalanche region transit time diode

A large-signal method based on non-sinusoidal voltage excitation model is used to study the DC and RF characteristics of Double Avalanche Region (DAR) Silicon Transit Time diode. A large-signal simulation program based on drift-diffusion model is developed for this study. The simulation results show the existence of several distinct negative conductance bands in the admittance characteristics separated by positive conductance. Thus the DAR device is capable of delivering RF power not only at the design frequency but also at several frequency bands higher than the design frequency band in the mm-wave regime. A comparative study with DDR Si device designed to deliver RF power at a particular mm-wave frequency band shows that DAR Si device is capable of delivering significantly higher RF power not only at the designed mm-wave frequency band, but also at higher frequency bands.     

Removal of Lissamine Red From Wastewater Using Waste Materials

Feasibility of a waste material, flyash, as a material for purification of wastewater containing Lissamine Red has been studied. Effects of time and concentration, temperature and pH on the removal of the dye have been studied. Lower concentrations favour the uptake of dye from water and the maximum removal was observed at a dye concentration of 20 mgL m 1 , 30°C, pH of 7 and adsorbent particle size of 53 µm. Dynamics of the uptake was studied using Lagergren's equation. The mass transfer coefficient was found to be 0.05 cmmin m 1 at a concentration of 20 mgL m 1 , 30°C and 53 µm particle size.     

Application of image processing and radial basis neural network techniques for ore sorting and ore classification

Ore sorting is a useful tool to remove gangue material from the ore of bigger size ranges. The radical development in the area of artificial intelligence allows speedy processing the full color digital images for the preferred investigations. In this paper a novel approach to classify the ores for ferromanganese metallurgical plant feed has been proposed based on the visual texture of the ore particles (Mn, Fe, and Al₂O₃ rich) and radial basis neural network. The visual texture of ore particles vary with the mineral contents. This information can be quantified by using image processing technique in RGB color space and, first and second-order statistical analysis. Commonly used Hartlic’s textural features was calculated and presented as neural network inputs along with red, green and blue color values for 5 × 5 pixel size windowpanes extracted from three separate images. Results obtained show encouraging accuracy to apply the approach to develop an expert system for on line ore quality monitoring to control the ore blending in the feed ore circuits as well as separating gangue minerals present in the feed ores. Matlab^® 7.0 was used for visual textural analysis and neural network classification.     

Tensile and Fracture Properties of Aluminium Alloy AA2219-T87 Friction Stir Weld Joints for Aerospace Applications

Metallurgical and Materials Transactions A - The purpose of the present work is to study the mechanical behaviour of AA2219-T87 friction stir weld (FSW) joints at different temperatures (Room...     

Mechanical,morphological, and thermogravimetric analysis of alkali-treated Cordia-Dichotoma natural fiber composites

ABSTRACT

Usage of composites with natural fiber reinforcement is drastically increasing in recent times because of their low density, biodegradable nature, and low cost. However, natural fibers have certain core problems such as poor adhesion between the fiber and matrix and a relatively high degree of moisture absorption. Alkaline treatment of natural fibers is aimed at improving the adhesive strength so that effective stress transferability takes place in the composite. In the present work, Cordia-Dichotoma fibers were treated with sodium hydroxide (NaOH) and composites were prepared with different weight ratios of these fibers reinforced with epoxy. The prepared composites were tested for their tensile and flexural strengths (mechanical properties). Besides, for a comprehensive material characterization, IR spectroscopy (FT-IR), scanning electron microscope, and thermogravimetric analysis were carried out. This work investigates the influence of aforementioned NaOH treatment on thermal, mechanical, and morphological properties of the composite material.