Right ventricle extraction by low level and model-based algorithm

In this paper we present an algorithm as the combination of a low level morphological operation and model based Global Circular Shortest Path scheme to explore the segmentation of the Right Ventricle. Traditional morphological operations were employed to obtain the region of interest, and adjust it to generate a mask. The image cropped by the mask is then partitioned into a few overlapping regions. Global Circular Shortest Path algorithm is then applied to extract the contour from each partition. The final step is to re-assemble the partitions to create the whole contour. The technique is deemed quite reliable and robust, as this is illustrated by a very good agreement between the extracted contour and the expert manual drawing output.

Mathematical modelling and control of agitated extraction columns

Liquid-liquid extraction has long been known as a unit operation that plays an important role in industry. This process is well known for its complexity and sensitivity to operation conditions. This thesis presents an attempt to explore the dynamics and control of this process using a systematic approach and state of the art control system design techniques. The process was studied first experimentally under carefully selected. operation conditions, which resembles the ranges employed practically under stable and efficient conditions. Data were collected at steady state conditions using adequate sampling techniques for the dispersed and continuous phases as well as during the transients of the column with the aid of a computer-based online data logging system and online concentration analysis. A stagewise single stage backflow model was improved to mimic the dynamic operation of the column. The developed model accounts for the variation in hydrodynamics, mass transfer, and physical properties throughout the length of the column. End effects were treated by addition of stages at the column entrances. Two parameters were incorporated in the model namely; mass transfer weight factor to correct for the assumption of no mass transfer in the. settling zones at each stage and the backmixing coefficients to handle the axial dispersion phenomena encountered in the course of column operation. The parameters were estimated by minimizing the differences between the experimental and the model predicted concentration profiles at steady state conditions using non-linear optimisation technique. The estimated values were then correlated as functions of operating parameters and were incorporated in·the model equations. The model equations comprise a stiff differential~algebraic system. This system was solved using the GEAR ODE solver. The calculated concentration profiles were compared to those experimentally measured. A very good agreement of the two profiles was achieved within a percent relative error of ±2.S%. The developed rigorous dynamic model of the extraction column was used to derive linear time-invariant reduced-order models that relate the input variables (agitator speed, solvent feed flowrate and concentration, feed concentration and flowrate) to the output variables (raffinate concentration and extract concentration) using the asymptotic method of system identification. The reduced-order models were shown to be accurate in capturing the dynamic behaviour of the process with a maximum modelling prediction error of I %. The simplicity and accuracy of the derived reduced-order models allow for control system design and analysis of such complicated processes. The extraction column is a typical multivariable process with agitator speed and solvent feed flowrate considered as manipulative variables; raffinate concentration and extract concentration as controlled variables and the feeds concentration and feed flowrate as disturbance variables. The control system design of the extraction process was tackled as multi-loop decentralised SISO (Single Input Single Output) as well as centralised MIMO (Multi-Input Multi-Output) system using both conventional and model-based control techniques such as IMC (Internal Model Control) and MPC (Model Predictive Control). Control performance of each control scheme was. studied in terms of stability, speed of response, sensitivity to modelling errors (robustness), setpoint tracking capabilities and load rejection. For decentralised control, multiple loops were assigned to pair.each manipulated variable with each controlled variable according to the interaction analysis and other pairing criteria such as relative gain array (RGA), singular value analysis (SVD). Loops namely Rotor speed-Raffinate concentration and Solvent flowrate Extract concentration showed weak interaction. Multivariable MPC has shown more effective performance compared to other conventional techniques since it accounts for loops interaction, time delays, and input-output variables constraints.

Nootropia : a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both short-term variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user.

A co-cultured skin model based on cell support membranes

Tissue engineering of skin based on collagen:PCL biocomposites using a designed co-culture system is reported. The collagen:PCL biocomposites having collagen:PCL (w/w) ratios of 1:4, 1:8, and 1:20 have been proven to be biocompatible materials to support both adult normal human epidermal Keratinocyte (NHEK) and mouse 3T3 fibroblast growth in cell culture, respectively, by Dai, Coombes, et al. in 2004. Films of collagen:PCL biocomposites were prepared using non-crosslinking method by impregnation of lyophilized collagen mats with PCL/dichloromethane solutions followed by solvent evaporation. To mimic the dermal/epidermal structure of skin, the 1:20 collagen:PCL biocomposites were selected for a feasibility study of a designed co-culture technique that would subsequently be used for preparing fibroblast/biocomposite/keratinocyte skin models. A 55.3% increase in cell number was measured in the designed co-culture system when fibroblasts were seeded on both sides of a biocomposite film compared with cell culture on one surface of the biocomposite in the feasibility study. The co-culture of human keratinocytes and 3T3 fibroblasts on each side of the membrane was therefore studied using the same co-culture system by growing keratinocytes on the top surface of membrane for 3 days and 3T3 fibroblasts underneath the membrane for 6 days. Scanning electron microscopy (SEM) and immunohistochemistry assay revealed good cell attachment and proliferation of both human keratinocytes and 3T3 fibroblasts with these two types of cells isolated well on each side of the membrane. Using a modified co-culture technique, a co-cultured skin model presenting a confluent epidermal sheet on one side of the biocomposite film and fibroblasts populated on the other side of the film was developed successfully in co-culture system for 28 days under investigations by SEM and immunohistochemistry assay. Thus, the design of a co-culture system based on 1:20 (w/w) collagen:PCL biocomposite membranes for preparation of a bi-layered skin model with differentiated epidermal sheet was proven in principle. The approach to skin modeling reported here may find application in tissue engineering and screening of new pharmaceuticals. © 2005 Elsevier Inc. All rights reserved.