Fabrication using a rapid prototyping system and in vitro characterization of PEG-PCL-PLA scaffolds for tissue engineering

n the field of tissue engineering new polymers are needed to fabricate scaffolds with specific properties depending on the targeted tissue. This work aimed at designing and developing a 3D scaffold with variable mechanical strength, fully interconnected porous network, controllable hydrophilicity and degradability. For this, a desktop-robot-based melt-extrusion rapid prototyping technique was applied to a novel tri-block co-polymer, namely poly(ethylene glycol)-block-poly(epsi-caprolactone)-block-poly(DL-lactide), PEG-PCL-P(DL)LA. This co-polymer was melted by electrical heating and directly extruded out using computer-controlled rapid prototyping by means of compressed purified air to build porous scaffolds. Various lay-down patterns (0/30/60/90/120/150°, 0/45/90/135°, 0/60/120° and 0/90°) were produced by using appropriate positioning of the robotic control system. Scanning electron microscopy and micro-computed tomography were used to show that 3D scaffold architectures were honeycomb-like with completely interconnected and controlled channel characteristics. Compression tests were performed and the data obtained agreed well with the typical behavior of a porous material undergoing deformation. Preliminary cell response to the as-fabricated scaffolds has been studied with primary human fibroblasts. The results demonstrated the suitability of the process and the cell biocompatibility of the polymer, two important properties among the many required for effective clinical use and efficient tissue-engineering scaffolding.

Dwell-time and run-time control for DC mass rapid transit railways

Dwell times at stations and inter-station run times are the two major operational parameters to maintain train schedule in railway service. Current practices on dwell-time and run-time control are that they are only optimal with respect to certain nominal traffic conditions, but not necessarily the current service demand. The advantages of dwell-time and run-time control on trains are therefore not fully considered. The application of a dynamic programming approach, with the aid of an event-based model, to devise an optimal set of dwell times and run times for trains under given operational constraints over a regional level is presented. Since train operation is interactive and of multi-attributes, dwell-time and run-time coordination among trains is a multi-dimensional problem. The computational demand on devising trains' instructions, a prime concern in real-time applications, is excessively high. To properly reduce the computational demand in the provision of appropriate dwell times and run times for trains, a DC railway line is divided into a number of regions and each region is controlled by a dwell- time and run-time controller. The performance and feasibility of the controller in formulating the dwell-time and run-time solutions for real-time applications are demonstrated through simulations.

Coast control for mass rapid transit railways with searching methods

With daily commercial and social activity in cities, regulation of train service in mass rapid transit railways is necessary to maintain service and passenger flow. Dwell-time adjustment at stations is one commonly used approach to regulation of train service, but its control space is very limited. Coasting control is a viable means of meeting the specific run-time in an inter-station run. The current practice is to start coasting at a fixed distance from the departed station. Hence, it is only optimal with respect to a nominal operational condition of the train schedule, but not the current service demand. The advantage of coasting can only be fully secured when coasting points are determined in real-time. However, identifying the necessary starting point(s) for coasting under the constraints of current service conditions is no simple task as train movement is governed by a large number of factors. The feasibility and performance of classical and heuristic searching measures in locating coasting point(s) is studied with the aid of a single train simulator, according to specified inter-station run times.

Generation evolution in GA application on railway traffic control

This paper presents a Genetic Algorithms (GA) approach to resolve traffic conflicts at a railway junction. The formulation of the problem for the suitable application of GA will be discussed and three neighborhoods have been proposed for generation evolution. The performance of the GA is evaluated by computer simulation. This study paves the way for more applications of artificial intelligence techniques on a rather conservative industry.

A review of rapid prototyping techniques for tissue engineering purposes

Rapid prototyping (RP) is a common name for several techniques, which read in data from computer-aided design (CAD) drawings and manufacture automatically threedimensional objects layer-by-layer according to the virtual design. The utilization of RP in tissue engineering enables the production of three-dimensional scaffolds with complex geometries and very fine structures. Adding micro- and nanometer details into the scaffolds improves the mechanical properties of the scaffold and ensures better cell adhesion to the scaffold surface. Thus, tissue engineering constructs can be customized according to the data acquired from the medical scans to match the each patient’s individual needs. In addition RP enables the control of the scaffold porosity making it possible to fabricate applications with desired structural integrity. Unfortunately, every RP process has its own unique disadvantages in building tissue engineering scaffolds. Hence, the future research should be focused into the development of RP machines designed specifically for fabrication of tissue engineering scaffolds, although RP methods already can serve as a link between tissue and engineering.

Estimation of parameters for macroparasite population evolution using approximate Bayesian computation

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.