'A Breath of Fresh Air': The experiences of undergraduate nursing students' participating in a creative learning project:This paper presents the experiences of undergraduate nursing students who participated in a creative learning project to explore the cells, tissues and organs of the human body through felt making.

Aim of the study
This paper presents the experiences of undergraduate nursing students who participated in a creative learning project to explore the cells, tissues and organs of the human body through felt making.

Context and Background
This project was funded by a Teaching Innovation Award from the School of Nursing and Midwifery, Queen’s University Belfast to explore creative ways of engaging year one undergraduate nursing students in learning anatomy and physiology. The project was facilitated through collaboration between University Teaching staff and Arts Care, a unique arts and health charity in Northern Ireland.

Methodology
Twelve year one students participated in four workshops designed to explore the cells, tissues and organs of the human body through the medium of felt. Facilitated by an Arts Care artist, students translated their learning into striking felt images. The project culminated in the exhibition of this unique collection of work which has been viewed by fellow students, teaching staff, nurses from practice, and artists from Arts Care, friends, family and members of the public.

Key Findings and conclusions
The opportunity to learn in a more diverse way within a safe and non-judgmental environment was valued, with students’ reporting a greater confidence in life science knowledge. Self- reflection and group discussion revealed that the project was a unique creative learning experience for all involved – students, teaching staff and artist – resulting in individual and collective benefits far beyond knowledge acquisition. As individuals we each felt respected and recognised for our unique contribution to the project. Working in partnership with Arts Care enabled us to experience the benefits of creativity to well-being and reflect upon how engagement in creative activities can help healthcare professionals to focus on the individual patient’s needs and how this is fundamental to enhancing patient-centred care

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Ultrastructure of head organs (anterior adhesive apparatus) and posterior secretory systems of Caballeria liewi Lim, 1995 (Monogenea, Ancyrocephalidae)

Caballeria liewi Lim, 1995, uses adhesive secretions from the head organs and posterior secretory systems to assist in locomotion and attachment. Ultrastructural investigations show that the head organs of C. liewi consist of three pairs of antero-lateral pit-like openings bearing microvilli and ducts leading from two types of uninucleated gland cells (located lateral to the pharynx), one type producing rod-like (S1) bodies with an electron-dense matrix containing less electron-dense vesicles and the second type producing oval (S2) bodies with a homogeneous electron-dense matrix. Interlinking band-like structures are observed between S1 bodies and between S2 bodies. S1 body is synthesised in the granular endoplasmic reticulum, transported to a Golgi complex to be packaged into vesicles and routed into ducts for exudation. The synthesis of the S2 body is unresolved. Haptoral secretions manifested externally as net-like structures are derived from dual electron-dense (DED) secretory body produced in the peduncular gland cells. The DED body consists of a less electron-dense oval core in a homogeneous electron-dense matrix. On exocytosis into the pyriform haptoral reservoir, DED bodies are transformed into a secretion with two types of inclusions (less electron-dense oval and electron-dense spherical inclusions) in an electron-dense matrix. The secretions are further transformed (as small, oval, electron-dense bodies) when transported to the superficial anchor grooves, and on exudation into the gill tissues, the secretions become an electron-dense matrix. Secretory bodies associated with uniciliated structures, anchor sleeves and marginal hooks are also observed.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.