Room temperature ethyl formate fuel cells for consumer electronics

published_or_final_versio

Study on Composition Distribution and Ferromagnetism of Monodisperse FePt Nanoparticles

Monodisperse FePt nanoparticles with size of 4.5 and 6.0 nm were prepared by simultaneous reduction of platinum acetylacetonate and thermal decomposition of iron pentacarbonyl in benzylether. The crystallography structure, size, and composition of the FePt nanoparticles were examined by X-ray diffraction and transmission electron microscopy. Energy dispersive X-ray spectrometry measurements of individual particles indicate a broad compositional distribution in both the 4.5 and 6 nm FePt nanoparticles. The effects of compositional distribution on the phase-transition and magnetic properties of the FePt nanoparticles were investigated

Bioavailability in soils

The consumption of locally-produced vegetables by humans may be an important exposure pathway for soil contaminants in many urban settings and for agricultural land use. Hence, prediction of metal and metalloid uptake by vegetables from contaminated soils is an important part of the Human Health Risk Assessment procedure. The behaviour of metals (cadmium, chromium, cobalt, copper, mercury, molybdenum, nickel, lead and zinc) and metalloids (arsenic, boron and selenium) in contaminated soils depends to a large extent on the intrinsic charge, valence and speciation of the contaminant ion, and soil properties such as pH, redox status and contents of clay and/or organic matter. However, chemistry and behaviour of the contaminant in soil alone cannot predict soil-to-plant transfer. Root uptake, root selectivity, ion interactions, rhizosphere processes, leaf uptake from the atmosphere, and plant partitioning are important processes that ultimately govern the accumulation ofmetals and metalloids in edible vegetable tissues. Mechanistic models to accurately describe all these processes have not yet been developed, let alone validated under field conditions. Hence, to estimate risks by vegetable consumption, empirical models have been used to correlate concentrations of metals and metalloids in contaminated soils, soil physico-chemical characteristics, and concentrations of elements in vegetable tissues. These models should only be used within the bounds of their calibration, and often need to be re-calibrated or validated using local soil and environmental conditions on a regional or site-specific basis.Mike J. McLaughlin, Erik Smolders, Fien Degryse, and Rene Rietr

Pre-pregnancy body mass index and gestational weight gain and their effects on pregnancy and birth outcomes: a cohort study in West Sumatra, Indonesia

Background: Indonesia has a considerably high incidence of maternal and infant mortality. The country has however been experiencing a social and economic transition, influencing its general population demographics and nutritional status including the state of health and nutrition of pregnant women. This study aimed to explore body mass index (BMI) and gestational weight gain (GWG), and their relationship with pregnancy outcomes in a sample of Indonesian pregnant women. Methods: This observational cohort study included a total of 607 pregnant women who were recruited in 2010 from maternity clinics in Western Sumatra, Indonesia. Multiple logistic and regression analyses were undertaken to compare pregnancy and birth outcomes for different BMI and GWG, using normal weight women and women with a recommended weight gain as the referent groups. Results: The prevalence of underweight (BMI < 18.5 kg/m2) in pregnancy was high at 20.1%; while 21.7% of women were overweight (BMI: 23.0–27.4 kg/m2) and 5.3% obese (BMI ≥ 27.5 kg/m2) using the Asian BMI classifications. The incidence of overweight (BMI: 25.0–29.9 kg/m2) and obese (BMI ≥ 30.0 kg/m2) according to the international BMI classifications were 13.5% and 1.1% respectively. The majority of women gained inadequate weight in pregnancy compared to the Institute of Medicine (IOM)recommendations, especially those who had a normal BMI. Birthweight adjusted mean difference aMD (95% confidence interval) 205 (46,365) and the odds of macrosomia adjusted odds ratio aOR 13.46 (2.32–77.99) significantly increased in obese women compared to those with a normal BMI. Birthweight aMD -139 (−215, −64) significantly decreased in women with inadequate GWG compared to those with recommended GWG, while SGA aOR 5.44 (1.36, 21.77) and prematurity aOR 3.55 (1.23, 10.21) increased. Conclusions: Low nutritional status and inadequate GWG remain a cause for concern in these women. The higher odds of macrosomia with increasing maternal BMI and higher odds of prematurity and small for gestational age infants with inadequate weight gain also require attention. Research and practice recommendations: Urgent attention is required by researchers, policy makers and decision makers to facilitate development of culturally sensitive interventions to enhance nutritional status and health of mothers and babies, in an area known for its high incidence of maternal and neonatal mortality. Keywords: Maternal BMI, Gestational weight gain, Pregnancy outcomes, Birthweight, Indonesia, Cohort stud

The Nutrition Transition: New Trends in the Global Diet

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72461/1/j.1753-4887.1997.tb01593.x.pd

Methods of estimation of mitral valve regurgitation for the cardiac surgeon

Mitral valve regurgitation is a relatively common and important heart valve lesion in clinical practice and adequate assessment is fundamental to decision on management, repair or replacement. Disease localised to the posterior mitral valve leaflet or focal involvement of the anterior mitral valve leaflet is most amenable to mitral valve repair, whereas patients with extensive involvement of the anterior leaflet or incomplete closure of the valve are more suitable for valve replacement. Echocardiography is the recognized investigation of choice for heart valve disease evaluation and assessment. However, the technique is depended on operator experience and on patient's hemodynamic profile, and may not always give optimal diagnostic views of mitral valve dysfunction. Cardiac catheterization is related to common complications of an interventional procedure and needs a hemodynamic laboratory. Cardiac magnetic resonance (MRI) seems to be a useful tool which gives details about mitral valve anatomy, precise point of valve damage, as well as the quantity of regurgitation. Finally, despite of its higher cost, cardiac MRI using cine images with optimized spatial and temporal resolution can also resolve mitral valve leaflet structural motion, and can reliably estimate the grade of regurgitation

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/