Synthesis and characterization of ceramic - polymer composites containing bioactive synthetic hydroxyapatite for biomedical applications

Presented research involved preparation of hydroxyapatite and synthesis of composites based on gelatin, albumin and polyvinylpyrrolidone (PVP) modified with the obtained compound. Hydroxyapatite was attained as a product of two-stage processing of pig bones. Applied procedure involved hydrolysis of the raw material in acidic environment and double calcination. Molar ratio Ca/P of hydroxyapatite has been determined and its chemical structure has been characterized using X-ray diffraction and FT-IR spectroscopy. Ratio Ca/P calculated on the basis of conducted research was 1.50?±?0.05. Thus prepared material met the ISO requirements, which assume that the Ca/P ratio should be in the range 1.5–2.0, which qualifies the material for further studies. Next, series of polymer matrix on the basis of gelatin, albumin and polyvinylpyrrolidone (PVP) has been synthesized and subjected to some analyzes. On the basis of the conducted studies, matrixes with the most favorable features such as desirable strength, flexibility and crosslinking degree were modified with previously prepared hydroxyapatite. Surface morphology and elemental composition of the composites have been analyzed using SEM-EDS method. Additionally, sorption capacity of modified composites and their behavior in simulated body fluids have been determined. Based on the conducted research it can be concluded that pig bones represent a good material for preparation of hydroxyapatite. Furthermore, composites based on proteins of natural origin modified with attained hydroxyapatite constitute a promising material that can be used for biomedical purposes.     

A sequential ensemble model for software fault prediction

Innovations in Systems and Software Engineering - Unlike several other engineering disciplines, software engineering lacks well-defined research strategies. However, with the exponential rise in...     

jSIPRO – Analysis tool for magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) involves a huge number of spectra to be processed and analyzed. Several tools enabling MRSI data processing have been developed and widely used. However, the processing programs primarily focus on sophisticated spectra processing and offer limited support for the analysis of the calculated spectroscopic maps. In this paper the jSIPRO (java Spectroscopic Imaging PROcessing) program is presented, which is a java-based graphical interface enabling post-processing, viewing, analysis and result reporting of MRSI data. Interactive graphical processing as well as protocol controlled batch processing are available in jSIPRO. jSIPRO does not contain a built-in fitting program. Instead, it makes use of fitting programs from third parties and manages the data flows. Currently, automatic spectra processing using LCModel, TARQUIN and jMRUI programs are supported. Concentration and error values, fitted spectra, metabolite images and various parametric maps can be viewed for each calculated dataset. Metabolite images can be exported in the DICOM format either for archiving purposes or for the use in neurosurgery navigation systems.     

Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?

Streamwater temperature may be severely affected by the global warming. Different types of models could be used to evaluate the regime of water temperatures in future climatic conditions, including artificial neural networks. As neural networks have no physical background, they require calibration of large number of parameters. This is typically done by gradient-based algorithms, however there is an ongoing debate on usefulness of metaheuristics for this task. In this paper more than ten Swarm Intelligence and Evolutionary Algorithms, including one developed especially for this study, are tested to train four kinds of artificial neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) for daily water temperature prediction in a natural river located in temperate climate zone. The results are compared with the ones obtained when the classical Levenberg-Marquardt algorithm is used. Finally, the ensemble aggregating approach is tested. Although the research confirms that most metaheuristics do not suite well for training any kind of neural networks, there are exceptions that include the newly proposed heuristic. However, the gain achieved when using even the best metaheuristics is low, comparing to the effort (computational time and complexity of such algorithms). Using ensemble aggregation approach seems to have higher impact on the model performance than seeking for new training methods.