A pilot randomized controlled crossover study comparing regional heparinization to regional citrate anticoagulation for continuous venovenous hemofiltration

OBJECTIVE: To evaluate the efficacy and safety of a regional heparinization and a regional citrate method of anticoagulation in CVVH.
DESIGN: Randomized controlled cross-over study.
SUBJECTS: Ten critically ill patients with acute renal failure.
SETTING: ICU of tertiary hospital.
INTERVENTION: CVVH was performed with pre-filter fluid replacement at 2000 ml/h and a blood flow rate of 150 ml/min. Regional heparinization was by the administration of heparin pre-filter at 1500 IU/h and protamine post-filter at 15 mg/h. Regional citrate anticoagulation was by means of a citrate-based replacement fluid (14 mmol/L) administered pre-dilution.
RESULTS: We studied nine males and one female. The mean age and APACHE II score were 70.5 and 17 respectively. Median circuit life was 13 hours (IQR 9.28) for the regional heparinization method compared to 17 hours (IQR 12,19.5) for the regional citrate method (p=0.77). There were no episodes of bleeding in either group.
CONCLUSION: Regional heparinization and regional citrate anticoagulation achieve similar circuit life in critically ill patients receiving CVVH.

Development of a novel pulsatile bioreactor for tissue culture

The construction of tissue-engineered parts such as heart valves and arteries requires more than just the seeding of cells onto a biocompatible/biodegradable polymeric scaffold. It is essential that the functionality and mechanical integrity of the cell-seeded scaffold be investigated in vitro prior to in vivo implantation. The correct hemodynamic conditioning would lead to the development of tissues with enhanced mechanical strength and cell viability. Therefore, a bioreactor that can simulate physiological conditions would play an important role in the preparation of tissue-engineered constructs. In this article, we present and discuss the design concepts and criteria, as well as the development, of a multifunctional bioreactor for tissue culture in vitro. The system developed is compact and easily housed in an incubator to maintain sterility of the construct. Moreover, the proposed bioreactor, in addition to mimicking in vivo conditions, is highly flexible, allowing different types of constructs to be exposed to various physiological flow conditions. Initial verification of the hemodynamic parameters using Laser doppler anemometry indicated that the bioreactor performed well and produced the correct physiological conditions.

Transient fluid–structure coupling for simulation of a trileaflet heart valve using weak coupling

In this article, a three-dimensional transient numerical approach coupled with fluid–structure interaction for the modeling of an aortic trileaflet heart valve at the initial opening stage is presented. An arbitrary Lagrangian–Eulerian kinematical description together with an appropriate fluid grid was used for the coupling strategy with the structural domain. The fluid dynamics and the structure aspects of the problem were analyzed for various Reynolds numbers and times. The fluid flow predictions indicated that at the initial leaflet opening stage a circulation zone was formed immediately downstream of the leaflet tip and propagated outward as time increased. Moreover, the maximum wall shear stress in the vertical direction of the leaflet was found to be located near the bottom of the leaflet, and its value decreased sharply toward the tip. In the horizontal cross section of the leaflet, the maximum wall shear stresses were found to be located near the sides of the leaflet.

Biomimetic porous titanium scaffolds for orthopaedic and dental applications

The development of artificial organs and implants for replacement of injured and diseased hard tissues such as bones, teeth and joints is highly desired in orthopedic surgery. Orthopedic prostheses have shown an enormous success in restoring the function and offering high quality of life to millions of individuals each year. Therefore, it is pertinent for an engineer to set out new approaches to restore the normal function of impaired hard tissues.

Over the last few decades, a large number of metals and applied materials have been developed with significant improvement in various properties in a wide range of medical applications. However, the traditional metallic bone implants are dense and often suffer from the problems of adverse reaction, biomechanical mismatch and lack of adequate space for new bone tissue to grow into the implant. Scientific advancements have been made to fabricate porous scaffolds that mimic the architecture and mechanical properties of natural bone. The porous structure provides necessary framework for the bone cells to grow into the pores and integrate with host tissue, known as osteointegration. The appropriate mechanical properties, in particular, the low elastic modulus mimicking that of bone may minimize or eliminate the stress-shielding problem. Another important approach is to develop biocompatible and corrosion resistant metallic materials to diminish or avoid adverse body reaction. Although numerous types of materials can be involved in this fast developing field, some of them are more widely used in medical applications. Amongst them, titanium and some of its alloys provide many advantages such as excellent biocompatibility, high strength-to-weight ratio, lower elastic modulus, and superior corrosion resistance, required for dental and orthopedic implants. Alloying elements, i.e. Zr, Nb, Ta, Sn, Mo and Si, would lead to superior improvement in properties of titanium for biomedical applications.

New processes have recently been developed to synthesize biomimetic porous titanium scaffolds for bone replacement through powder metallurgy. In particular, the space holder sintering method is capable of adjusting the pore shape, the porosity, and the pore size distribution, notably within the range of 200 to 500 m as required for osteoconductive applications. The present chapter provides a review on the characteristics of porous metal scaffolds used as bone replacement as well as fabrication processes of porous titanium (Ti) scaffolds through a space holder sintering method. Finally, surface modification of the resultant porous Ti scaffolds through a biomimetic chemical technique is reviewed, in order to ensure that the surfaces of the scaffolds fulfill the requirements for biomedical applications.

Intelligent animal fiber classification with artificial neural networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.

Predicting the pilling propensity of fabrics through artificial neural network modeling

Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.

Knowledge extraction from a mixed transfer function artificial neural network

One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Knowledge extraction from a mixed transfer function artificial neural network

One of the big problems with Artificial Neural Networks (ANN) is that their results are not intuitively clear. For example, if we use the traditional neurons, with a sigmoid activation function, we can approximate any function, including linear functions, but the coefficients (weights) in this approximation will be rather meaningless. To resolve this problem, this paper presents a novel kind of ANN with different transfer functions mixed together. The aim of such a network is to i) obtain a better generalization than current networks ii) to obtain knowledge from the networks without a sophisticated knowledge extraction algorithm iii) to increase the understanding and acceptance of ANNs. Transfer Complexity Ratio is defined to make a sense of the weights associated with the network. The paper begins with a review of the knowledge extraction from ANNs and then presents a Mixed Transfer Function Artificial Neural Network (MTFANN). A MTFANN contains different transfer functions mixed together rather than mono-transfer functions. This mixed presence has helped to obtain high level knowledge and similar generalization comparatively to monotransfer function nets in a global optimization context.

Artificial formation forces for stable aggregation of multi-agent system

This paper introduces, a robust and stable algorithm based on artificial formation forces, for multi-agent system (MAS) aggregation in 2D space. The MAS model with artificial forces; consists of inter-member collision avoidance element, formation generation element and a velocity based damping element; is analysed for stability and convergence. Computer simulations are used to illustrate stability and convergence, and to demonstrate effectiveness of the algorithm.

Geometric formations in swarm aggregation: an artificial formation force based approach

Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc.

Learning to detect texture objects by artificial immune approaches

This paper introduces a novel method to detect texture objects from satellite images. First, a hierarchical strategy is developed to extract texture objects according to their roughness. Then, an artificial immune approach is presented to automatically generate segmentation thresholds and texture filters, which are used in the hierarchical strategy. In this approach, texture objects are regarded as antigens, and texture object filters and segmentation thresholds are regarded as antibodies. The clonal selection algorithm inspired by human immune system is employed to evolve antibodies. The population of antibodies is iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into the optimal antibody using the evolution principles of the clonal selection. Experimental results of texture object detection on satellite images are presented to illustrate the merit and feasibility of the proposed method.

The effect of artificial defects on the compressive properties of hollow sphere cellular aluminium (HSCA)

Hollow sphere cellular aluminium (HSCA) samples were fabricated by bonding together two kinds of single aluminium hollow spheres with the same outside diameter of 4 mm but different wall thicknesses of 0.1 mm and 0.3 mm, in which the hollow spheres with the thinner sphere wall thickness were used as artificial defects. Four types of HSCA samples with the same relative density but various distributions of artificial defects were prepared by simple cubic packing. For comparing, HSCA sample without defective hollow spheres inside was also prepared. The effects of the distribution of the artificial defects on the deformation behaviours and mechanical properties were investigated by compressive tests. Results indicated that the nominal stress - nominal strain curve and the deformation behavior of the HSCA samples varied with the distribution of the artificial defects in spite of the same relative density. It is therefore suggested that the deformation behavior and mechanical property of cellular materials were also significantly affected by the distribution of defects. In particular, the plateau stress of the HSCA samples increased with the decrease in number of contact points between the normal hollow spheres and the defective hollow spheres in the loading direction during deformation.

Improving the quality of die castings by using artificial neural networks for porosity defect modelling

The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence techniques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.