THE ARTIFICIAL URINARY SPHINCTER - A NEW SOLUTION FOR INCONTINENT PATIENTS

Treatment of urinary incontinence with the artificial urinary sphincter has been available in centres such as London and Liverpool for a number of years. This service is now available in the department of urology of the Belfast City Hospital. Twelve patients have had successful implantation of an artificial urinary sphincter for urinary incontinence, and ten are now fully continent. One patient with Wegener's granulomatosis developed active disease in his urethra which has precluded activation of the device. One patient has had the device removed because of erosion into the urethra.

The Application of Computational Chemistry and Chemometrics to Developing a Method for Online Monitoring of Polymer Degradation in the Manufacture of Bioresorbable Medical Implants

Bioresorbable polymers such as PLA have an important role to play in the development of temporary implantable medical devices with significant benefits over traditional therapies. However, development of new devices is hindered by high manufacturing costs associated with difficulties in processing the material. A major problem is the lack of insight on material degradation during processing. In this work, a method of quantifying degradation of PLA using IR spectroscopy coupled with computational chemistry and chemometric modeling is examined. It is shown that the method can predict the quantity of degradation products in solid-state samples with reasonably good accuracy, indicating the potential to adapt the method to developing an on-line sensor for monitoring PLA degradation in real-time during processing.

Nanoscale reinforced orthopaedic bone cement with graphene oxide - a possible solution to aseptic loosening of cemented hip implants? Preliminary results of an ongoing study

Hip replacement surgery is amongst the most common orthopaedic operations performed in the UK. Aseptic loosening is responsible for 40% of hip revision procedures. Aseptic loosening is a result of cement mantle fatigue. The aim of the current study is to analyse the effect of nanoscale Graphene Oxide (GO) on the mechanical properties of orthopaedic bone cement. Study Design A experimental thermal and mechanical analysis was conducted in a laboratory set up conforming to international standards for bone cement testing according to ISO 5583. Testing was performed on control cement samples of Colacryl bone cement, and additional samples reinforced with variable wt% of Graphene Oxide containing composites – 0.1%, 0.25%, 0.5% and 1.0% GO loading. Pilot Data Porosity demonstrated a linear relationship with increasing wt% loading compared to control (p<0.001). Thermal characterisation demonstrated maximal temperature during polymerization, and generated exotherm were inversely proportional to w%t loading (p<0.05) Fatigue strength performed on the control and 0.1 and 0.25%wt loadings of GO demonstrate increased average cycles to failure compared to control specimens. A right shift of the Weibull curve was demonstrated for both wt% available currently. Logistic regression analysis for failure demonstrated significant increases in number of cycles to failure for both specimens compared to a control (p<0.001). Forward Plan Early results convey positive benefits at low wt% loadings of GO containing bone cement. Study completion and further analysis is required in order to elude to the optimum w%t of GO which conveys the greatest mechanical advantage.

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.

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.

Improving antimicrobial release kinetics from hydrophobic polymer implants via ion pairing to combat biomedical-device related infection

Purpose The aim of this study is to improve the drug release properties of antimicrobial agents from hydrophobic biomaterials using using an ion pairing strategy. In so doing antimicrobial agents may be eluted and maintained over a sufficient time period thereby preventing bacterial colonisation and subsequent biofilm formation on medical devices. Methods The model antimicrobial agent was chlorhexidine and the selected fatty acid counter ions were capric acid, myristic acid and stearic acid. The polymethyl methacrylate films were loaded with 2% of fatty acid:antimicrobial agent at the following molar ratios; 0.5:1M, 1:1M and 2:1M and thermally polymerized using azobisisobutyronitrile initiator. Drug release experiments were subsequently performed over a 3-month period and the mass of drug released under sink conditions (pH 7.0, 37oC) quantified using a validated HPLC-UV method. Results In all platforms, a burst of chlorhexidine release was observed over the initial 24-hour period. Similar release kinetics were observed between the formulations during the initial 28 days. However, as time progressed, the chlorhexidine baseline plateaued after 56 days whereas formulations containing the counterions appeared to continuously elute linearly with time. As can be observed in figure 1, the rank order of total chlorhexidine release in the presence of 0.5M fatty acid was myristic acid (40%) > capric acid (35%) > stearic acid (30%)> chlorhexidine baseline (15%). Conclusion The incorporation of fatty acids within the formulation significantly improved chlorhexidine solubility within both the monomer and the polymer and enhanced the drug release kinetics over the period of study. This is attributed to the greater diffusivity of chlorhexidine through PMMA in the presence of fatty acids. In th absence of fatty acids, chlorhexidine release was facilitated by dissolution of surface associated drug particles. This study has illustrated the ability of fatty acids to modulate chlorhexidine release from a model biomaterial through enhanced diffusivity. This strategy may prove advantageous for improved medical devices with enhanced resistance to infection.

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.