Static and dynamic mechanical testing of a polymer with potential use as heart valve material

Synthetic tri-leaflet heart valves generally fail in the long-term use (more than 10 years). Tearing and calcification of the leaflets usually cause failure of these valves as a consequence of high tensile and bending stresses borne on the material. The primary purpose of this study was to explore the possibilities of a new polymer composite to be used as synthetic tri-leaflet heart valve material. This composite was comprised of polystyrene-polyisobutylene-polystyrene (Quatromer), a proprietary polymer, embedded with continuous polypropylene (PP) fibers. Quatromer had been found to be less likely to degrade in vivo than polyurethane. Moreover, it was postulated that a decrease in tears and perforations might result from fiber-reinforced leaflets reducing high stresses on the leaflets. The static and dynamic mechanical properties of the Quatromer/PP composite were compared with those of an implant-approved polyurethane (PU) for cardiovascular applications. Results show that the reinforcement of Quatromer with PP fibers improves both its static and dynamic properties as compared to the PU. Hence, this composite has the potential to be a more suitable material for synthetic tri-leaflet heart valves.

A static traffic assignment model combined with an artificial neural network delay model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Design and performance assessment of aortic heart valve for tissue engineering

Current artificial heart valves are classified as mechanical and bioprosthetic. An appealing pathway that promises to overcome the shortcomings of commercially available heart valves is offered by the interdisciplinary approach of cardiovascular tissue engineering. However, the mechanical properties of the Tissue Engineering Heart Valves (TEHV) are limited and generally fail in the long-term use. To meet this performance challenge novel biodegradable triblock copolymer poly(ethylene oxide)-polypropylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO or F108) crosslinked to Silk Fibroin (F108-SilkC) to be used as tri-leaflet heart valve material was investigated. ^ Synthesis of ten polymers with varying concentration and thickness (55 µm, 75 µm and 100 µm) was achieved via a covalent crosslinking scheme using bifunctional polyethylene glycol diglycidyl ether (PEGDE). Static and fatigue testing were used to assess mechanical properties of films, and hydrodynamic testing was performed to determine performance under a simulated left ventricular flow regime. The crosslinked copolymer (F108-Silk C) showed greater flexibility and resilience, but inferior ultimate tensile strength, by increasing concentration of PEGDE. Concentration molar ratio of 80:1 (F108: Silk) and thickness of 75 µm showed longer fatigue life for both tension-tension and bending fatigue tests. Four valves out of twelve designed satisfactorily complied with minimum performance requirement ISO 5840, 2005. ^ In conclusion, it was demonstrated that the applicability of a degradable polymer in conjugation with silk fibroin for tissue engineering cardiovascular use, specifically for aortic valve leaflet design, met the performance demands. Thinner thicknesses (t<75 µm) in conjunction with stiffness lower than 320 MPa (80:1, F108: Silk) are essential for the correct functionality of proposed heart valve biomaterial F108-SilkC. Fatigue tests were demonstrated to be a useful tool to characterize biomaterials that undergo cyclic loading. ^

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.