An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Integrin-functionalized artificial membranes as test platforms for monitoring small integrin ligand binding by surface plasmon-enhanced fluorescence spectroscopy

The design and synthesis of molecularly or supramolecularly defined interfacial architectures have seen in recent years a remarkable growth of interest and scientific research activities for various reasons. On the one hand, it is generally believed that the construction of an interactive interface between the living world of cells, tissue, or whole organisms and the (inorganic or organic) materials world of technical devices such as implants or medical parts requires proper construction and structural (and functional) control of this organism–machine interface. It is still the very beginning of generating a better understanding of what is needed to make an organism tolerate implants, to guarantee bidirectional communication between microelectronic devices and living tissue, or to simply construct interactive biocompatibility of surfaces in general. This exhaustive book lucidly describes the design, synthesis, assembly and characterization, and bio-(medical) applications of interfacial layers on solid substrates with molecularly or supramolecularly controlled architectures. Experts in the field share their contributions that have been developed in recent years.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

The development of community foundations in Australia - recreating the American dream

Community foundations have been described as the fastest growing form of philantrhopy. This report focuses on the development of community foundations in Australia as one especially important example of the growth of community foundations world-wide.

Binding of small mono- and oligomeric integrin ligands to membrane-embedded integrins monitored by surface plasmon-enhanced fluorescence spectroscopy

We recently developed a binding assay format by incorporating native transmembrane receptors into artificial phospholipid bilayers on biosensor devices for surface plasmon resonance spectroscopy. By extending the method to surface plasmon-enhanced fluorescence spectroscopy (SPFS), sensitive recording of the association of even very small ligands is enabled. Herewith, we monitored binding of synthetic mono- and oligomeric RGD-based peptides and peptidomimetics to integrins alphavbeta3 and alphavbeta5, after having confirmed correct orientation and functionality of membrane-embedded integrins. We evaluated integrin binding of RGD multimers linked together via aminohexanoic acid (Ahx) spacers and showed that the dimer revealed higher binding activity than the tetramer, followed by the RGD monomers. The peptidomimetic was also found to be highly active with a slightly higher selectivity toward alphavbeta3. The different compounds were also evaluated in in vitro cell adhesion tests for their capacity to interfere with alphavbeta3-mediated cell attachment to vitronectin. We hereby demonstrated that the different RGD monomers were similarly effective; the RGD dimer and tetramer showed comparable IC50 values, which were, however, significantly higher than those of the monomers. Best cell detachment from vitronectin was achieved by the peptidomimetic. The novel SPFS-binding assay platform proves to be a suitable, reliable, and sensitive method to monitor the binding capacity of small ligands to native transmembrane receptors, here demonstrated for integrins.

THE quest for alpha : can artificial neural networks help?

The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

Rational design of artificial cellular niches for tissue engineering

Tissue Engineering is a promising emerging field that studies the intrinsic regenerative potential of the human body and uses it to restore functionality of damaged organs or tissues unable of self-healing due to illness or ageing. In order to achieve regeneration using Tissue Engineering strategies, it is first necessary to study the properties of the native tissue and determine the cause of tissue failure; second, to identify an optimum population of cells capable of restoring its functionality; and third, to design and manufacture a cellular microenvironment in which those specific cells are directed towards the desired cellular functions. The design of the artificial cellular niche has a tremendous importance, because cells will feel and respond to both its biochemical and biophysical properties very differently. In particular, the artificial niche will act as a physical scaffold for the cells, allowing their three-dimensional spatial organization; also, it will provide mechanical stability to the artificial construct; and finally, it will supply biochemical and mechanical cues to control cellular growth, migration, differentiation and synthesis of natural extracellular matrix. During the last decades, many scientists have made great contributions to the field of Tissue Engineering. Even though this research has frequently been accompanied by vast investments during extended periods of time, yet too often these efforts have not been enough to translate the advances into new clinical therapies. More and more scientists in this field are aware of the need of rational experimental designs before carrying out complex, expensive and time-consuming in vitro and in vivo trials. This review highlights the importance of computer modeling and novel biofabrication techniques as critical key players for a rational design of artificial cellular niches in Tissue Engineering.

Visible and near infrared spectroscopy of hayabusa reentry using semi-autonomous tracking

Aground-based tracking camera and coaligned slitless spectrograph were used to measure the spectral signature of visible radiation emitted from the Hayabusa capsule as it entered into the Earth’s atmosphere in June 2010. Good quality spectra were obtained, which showed the presence of radiation from the heat shield of the vehicle and the shock-heated air in front of the vehicle. An analysis of the blackbody nature of the radiation concluded that the peak average temperature of the surface was about (3100± 100)K. Line spectra from oxygen and nitrogen atoms were used to infer a peak average shock-heated gas temperature of around((7000±400))K.

Can an Australian rap fan be a social actor in the African-American social movements?

I live in the Sydney North Shore suburb of Northbridge. In many ways it is a white middle class enclave, comparable to places like Cabramatta that are identified with a specifically represented ethnic group. Gated primarily by the inflated property prices, it is a location that marks a territory principally for the white middle class. It is not a place of African-American movements. Or is it? Radio, television, film and Internet increasingly constitute a large portion of the sonic and visual landscapes of our suburban lives. In our lounge rooms and in our cars we are presented texts that take us beyond our local environments, into the places of other nations. This paper will explore the position of a fan of rap music, physically located beyond the cultural and political circumstances that drive sustained action for the movements of African-Americans. It will analyze whether such a fandom can indicate membership, as a social actor, in this group and in doing so illuminate the boundaries of movement activity in an information society.

Differential regulation of growth and invasiveness of MCF-7 breast cancer cells by antiestrogens

Estrogen increases the ability of the estrogen-dependent MCF-7 human breast cancer cell line to both proliferate and invade through an artificial basement membrane. In studying the response of MCF-7 cells to various antiestrogens, we found that 4-hydroxytamoxifen and tamoxifen inhibited cell proliferation but increased their invasiveness. In contrast, the structurally unrelated benzothiophene antiestrogens, LY117018 and LY156758, were potent antiproliferative agents which did not stimulate invasiveness. The differential effects of these antiestrogenic agents on invasion correlated with changes in production of collagenase IV, while no significant change was seen in the chemotactic activity of the cells. Invasiveness was increased by 17β-estradiol or 4-hydroxytamoxifen after a few hours of treatment and was rapidly lost when 17β-estradiol was withdrawn. Stimulation of invasiveness with 17β-estradiol was blocked by the antiestrogen, LY117018. Cells from the MDA-MB-231 line which lacks estrogen receptors were not affected by estrogen or antiestrogen in terms of proliferation or invasion. These studies indicate that the invasiveness of MCF-7 cells is regulated by antiestrogens through the estrogen receptor and may be mediated by collagenase IV activity. Antiestrogens which reduce both the proliferation and invasiveness of these cells may be interesting new candidates for clinical application.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Determinants of physical activity in active and low-active, sixth grade African-American youth

This study compared the determinants of physical activity in active and low-active African-American sixth grade students (N=108, 57 F, 51 M). Objective assessments of physical activity over a seven-day period were obtained using the CSA 7164 accelerometer. Students were classified as active if they exhibited three pr more 20-minute bouts of moderate to vigorous physical activity over the seven-day period. Relative to low-actives, active boys reported significantly higher levels of self-efficacy, greater involvement in community physical activity organizations, and were significantly more likely to perceive their mother us active. Relative to low-actives, active girls reported significantly higher levels of physical activity self-efficacy, greater positive beliefs regarding physical activity outcomes, and were significantly less likely to watch television or play video games for greater than or equal to 3 hrs/day. These observations provide preliminary guidance as to the design of physical activity interventions targeted at African-American youth.

Psychosocial correlates of physical activity in white and African-American girls

Purpose To evaluate the relative utility of the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) in explaining intentions and physical activity behavior in white and African-American eighth-grade girls. Methods One-thousand-thirty white and 1114 African-American eighth-grade girls (mean age 13.6 ± 0.7 years) from 31 middle schools in South Carolina completed a 3-day physical activity recall and a questionnaire assessing attitudes, subjective norms, perceived behavioral control, self-efficacy, and intentions related to regular participation in moderate-to-vigorous physical activity (MVPA). Results Among Whites, 17% of the variance in intentions was contributed by subjective norms and attitude, with intentions accounting for 8% of the variance in MVPA. The addition of perceived behavioral control and self-efficacy to the TRA significantly improved the prediction of intentions and MVPA accounting for 40% and 10% of the variance, respectively. Among African-Americans, subjective norms and attitude accounted for 13% of the variance in intentions, with intentions accounting for only 3% of the variance in MVPA. The addition of perceived behavioral control and self-efficacy to the TRA significantly improved the prediction of intentions and MVPA accounting for 28% and 5% of the variance, respectively. Conclusions The results provided limited empirical support for the TPB among white adolescent girls; however, our findings suggest that the planned behavior framework has limited utility among African-American adolescent girls. The relatively weak link between intentions and MVPA observed in both population groups suggest that constructs external to the TPB may be more important mediators of physical activity behavior in adolescent girls.

Physical activity and physical fitness in African-American girls with and without obesity

Lack of physical activity and low levels of physical fitness are thought to be contributing factors to the high prevalence of obesity in African-American girls, To examine this hypothesis, we compared habitual physical activity and physical fitness in 54 African-American girls with obesity and 96 African-American girls without obesity residing in rural South Carolina, Participation in vigorous (greater than or equal to 6 METs) (VPA) or moderate and vigorous physical activity (greater than or equal to 4 METs) (MVPA) was assessed on three consecutive days using the Previous Day Physical Activity Recall, Cardiorespiratory fitness was assessed using the PWC 170 cycle ergometer test, Upper body strength was determined at two sites via isometric cable tensiometer tests, Relative to their counterparts without obesity, girls with obesity reported significantly fewer 30-minute blocks of VPA (0.90 +/- 0.14 vs. 1.3 +/- 0.14) and MVPA (1.2 +/- 0.18 vs. 1.7 +/- 0.16) (p<0.01), Within the entire sample, VPA and MVPA were inversely associated with body mass index (r=-0.17 and r=-0.19) and triceps skinfold thickness (r=-0.19 and r=-0.22) (p<0.05), In the PWC 170 test and isometric strength tests, girls with obesity demonstrated absolute scores that were similar to, or greater than, those of girls without obesity; however, when scores were expressed relative to bodyweight, girls with obesity demonstrated significantly lower values (p<0.05). The results support the hypothesis that lack of physical activity and low physical fitness are important contributing factors in the development and/or maintenance of obesity in African-American girls.

Physical activities and sedentary pursuits in African American and Caucasian girls

The purposes of this study were to describe and compare the specific physical activity choices and sedentary pursuits of African American and Caucasian American girls. Participants were 1,124 African American and 1,068 Caucasian American eighth grade students from 31 middle schools. The 3-Day Physical Activity Recall (3DPAR) was used to measure participation in physical activities and sedentary pursuits. The most frequently reported physical activities were walking, basketball, jogging or running, bicycling, and social dancing. Differences between groups were found in 11 physical activities and 3 sedentary pursuits. Participation rates were higher in African American girls (p<.001)for social dancing, basketball, watching television, and church attendance but lower in calisthenics, ballet and other dance, jogging or running, rollerblading, soccer, softball or baseball, using an exercise machine, swimming, and homework. Cultural differences of groups should be considered when planning interventions to promote physical activity.