Structure character in small-carbon-cluster deposition on diamond surface

Experimentally, hydrogen-free diamond-like carbon (DLC) films were assembled by means of pulsed laser deposition (PLD), where energetic small-carbon-clusters were deposited on the substrate. In this paper, the chemisorption of energetic C2 and C10 clusters on diamond (001)-( 2×1) surface was investigated by molecular dynamics simulation. The influence of cluster size and the impact energy on the structure character of the deposited clusters is mainly addressed. The impact energy was varied from a few tens eV to 100 eV. The chemisorption of C10 was found to occur only when its incident energy is above a threshold value ( E th). While, the C2 cluster was easily to adsorb on the surface even at much lower incident energy. With increasing the impact energy, the structures of the deposited C2 and C10 are different from the free clusters. Finally, the growth of films synthesized by energetic C2 and C10 clusters were simulated. The statistics indicate the C2 cluster has high probability of adsorption and films assembled of C2 present slightly higher SP3 fraction than that of C10-films, especially at higher impact energy and lower substrate temperature. Our result supports the experimental findings. Moreover, the simulation underlines the deposition mechanism at atomic scale.

An Analysis of the Character Animation in Disney’s Tangled

This paper offers an analysis of the character animation in Tangled to develop a deeper understanding of how Disney has approached the extension of their traditional aesthetic into the CG medium.

I’m friends with Louie the Fly, not Mortein : Conceptualising the new brand relationships on social media

Spokes-characters are ‘...animated beings or objects, created to promote a product, service or idea’ (Phillips 1996, p.155). They were first used in the late 1800s when they emerged as registered trademarks, but the use of spokes-characters for marketing communications has since grown, owing to their ability to remind consumers about a product, transfer positive associations to a brand, and give a corporate company a more ‘personal’ face (Callcott and Lee 1995). One example is the Michelin Man, who has served as spokes-character for Michelin tyres since 1898, after starting out in print advertising. Spokes-characters have become important brand representatives, no longer seen as simply entertaining cartoons featured in television and magazine advertisements. Corporations have now extended their use to interactive, social media platforms, where a consumer can be ‘friends’ with a spokes-character via Facebook, read their comments on the latest iPhone release through Twitter, and watch their family histories being documented on YouTube (see Figure 1). The interactions that consumers once had with two-dimensional spokes-characters have undergone significant transformation in the digital space. With spokes-character Facebook pages achieving significant numbers of ‘likes’ and interactions with consumers, one question concerns whether this strategy is creating characters that are more engaging than the brands they represent, and what impact this has on brand outcomes.

The inaccuracy of national character stereotypes

Consensual stereotypes of some groups are relatively accurate, whereas others are not. Previous work suggesting that national character stereotypes are inaccurate has been criticized on several grounds. In this article we (a) provide arguments for the validity of assessed national mean trait levels as criteria for evaluating stereotype accuracy and (b) report new data on national character in 26 cultures from descriptions (N= 3323) of the typical male or female adolescent, adult, or old person in each. The average ratings were internally consistent and converged with independent stereotypes of the typical culture member, but were weakly related to objective assessments of personality. We argue that this conclusion is consistent with the broader literature on the inaccuracy of national character stereotypes

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.

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.

Identification of Mendel's white flower character

Background The genetic regulation of flower color has been widely studied, notably as a character used by Mendel and his predecessors in the study of inheritance in pea. Methodology/Principal Findings We used the genome sequence of model legumes, together with their known synteny to the pea genome to identify candidate genes for the A and A2 loci in pea. We then used a combination of genetic mapping, fast neutron mutant analysis, allelic diversity, transcript quantification and transient expression complementation studies to confirm the identity of the candidates. Conclusions/Significance We have identified the pea genes A and A2. A is the factor determining anthocyanin pigmentation in pea that was used by Gregor Mendel 150 years ago in his study of inheritance. The A gene encodes a bHLH transcription factor. The white flowered mutant allele most likely used by Mendel is a simple G to A transition in a splice donor site that leads to a mis-spliced mRNA with a premature stop codon, and we have identified a second rare mutant allele. The A2 gene encodes a WD40 protein that is part of an evolutionarily conserved regulatory complex.

I’m friends with Louie the Fly, not Mortein : conceptualising the new brand relationships on social media [Reprint]

Social media offers many opportunities for building brand relationships. One method of fostering relationships is to create a social media presence for the company spokes-character. Anecdotal evidence suggests that such a strategy can be very effective, though empirical research is needed. This paper reviews what is known and highlights a concern, based on analysis of the case of Louie the Fly and Mortein, that consumers may interact with characters as if they were separate to their parent brand. Hence positive associations with the character may not translate into positive brand outcomes. Following discussion, a model is proposed for testing.

Accelerating taxonomic discovery through automated character extraction

This paper discusses the following key messages. Taxonomy is (and taxonomists are) more important than ever in times of global change. Taxonomic endeavour is not occurring fast enough: in 250 years since the creation of the Linnean Systema Naturae, only about 20% of Earth's species have been named. We need fundamental changes to the taxonomic process and paradigm to increase taxonomic productivity by orders of magnitude. Currently, taxonomic productivity is limited principally by the rate at which we capture and manage morphological information to enable species discovery. Many recent (and welcomed) initiatives in managing and delivering biodiversity information and accelerating the taxonomic process do not address this bottleneck. Development of computational image analysis and feature extraction methods is a crucial missing capacity needed to enable taxonomists to overcome the taxonomic impediment in a meaningful time frame. Copyright © 2009 Magnolia Press.

Cultural relativity as exchange : J.W. Lindt’s bush character and Aboriginal from The Grafton Album (circa 1872)

J.W.Lindt’s Colonial man and Aborigine image from the GRAFTON ALBUM: “On chemistry and optics all does not depend, art must with these in triple union blend” (text from J.W. Lindt’s photographic backing card)...

Impaction of spray droplets on leaves: Influence of formulation and leaf character on shatter, bounce and adhesion

This paper combines experimental data with simple mathematical models to investigate the influence of spray formulation type and leaf character (wettability) on shatter, bounce and adhesion of droplets impacting with cotton, rice and wheat leaves. Impaction criteria that allow for different angles of the leaf surface and the droplet impact trajectory are presented; their predictions are based on whether combinations of droplet size and velocity lie above or below bounce and shatter boundaries. In the experimental component, real leaves are used, with all their inherent natural variability. Further, commercial agricultural spray nozzles are employed, resulting in a range of droplet characteristics. Given this natural variability, there is broad agreement between the data and predictions. As predicted, the shatter of droplets was found to increase as droplet size and velocity increased, and the surface became harder to wet. Bouncing of droplets occurred most frequently on hard to wet surfaces with high surface tension mixtures. On the other hand, a number of small droplets with low impact velocity were observed to bounce when predicted to lie well within the adhering regime. We believe this discrepancy between the predictions and experimental data could be due to air layer effects that were not taken into account in the current bounce equations. Other discrepancies between experiment and theory are thought to be due to the current assumption of a dry impact surface, whereas, in practice, the leaf surfaces became increasingly covered with fluid throughout the spray test runs.

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.

Plantar pressure distribution character in young female with mild hallux valgus wearing high-heeled shoes

Young females with mild hallux valgus (HV) have been identified as having an increased risk of first ray deformation. Little is known, however, about the biomechanical changes that might contribute to this increased risk. The purpose of this study was to compare kinetics changes during walking for mild HV subjects with high-heel-height shoes. Twelve female subjects (six with mild HV and six controls) participated in this study with heel height varying from 0 cm (barefoot) to 4.5 cm. Compared to healthy controls, patients had significantly higher peak pressure on the big toe area during barefoot walking. When the heel height increased, loading was transferred to medial side of the forefoot, and the big toe area suffered more impact compared to barefoot in mild HV. This study also demonstrated that the center of pressure (COP) inclines to medial side alteration after high-heeled shoes wearing. These findings indicate that mild HV people should be discouraged from wearing high-heeled shoes.

Evolution of three-dimensional character across the Lan+1NinO3n+1 homologous series with increase in n1

Electrical and magnetic properties of La3Ni2O7 and La4Ni3O10 have been investigated in comparison with those of La2NiO4, LaNiO3, and LaSrNiO4. The results suggest an increasing 3-dimensional character across the homologous series Lan+1NinO3n+1 with increase in n. Accordingly, the electrical resistivity decreases in the order La3Ni2O7, La4Ni3O10, and LaNiO3 and this trend is suggested to be related to the percolation threshold. Magnetic properties of these oxides also show some interesting trends across the series.