Evaluation of direct light processing for the fabrication of bioactive ceramic scaffolds: Effect of pore/strut size on manufacturability and mechanical performance

Bioactive ceramic scaffolds for bone regeneration consisting of a three-dimensional mesh of interpenetrating struts with square section were fabricated via Digital Light Processing (DLP). The ability of the technique to manufacture 3D porous structures from β-tricalcium phosphate (β-TCP) powders with different dimensions of struts and pores was evaluated, identifying the possibilities and limitations of the manufacturing process. Small pore sizes were found to seriously complicate the elimination of excess slurry from the scaffold’s innermost pores. The effect of the strut/pore size on the mechanical performance of the scaffolds under compressive stresses was also evaluated, but no significant influence was found. Under compressive stresses, the structures resulted weaker when tested perpendicularly to the printing plane due to interlayer shear failure. Interlayer superficial grooves are proposed as potential failure-controlling defects, which could also explain the lack of a Weibull size effect on the mechanical strength of the fabricated DLP scaffolds.     

Phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters

Analog Integrated Circuits and Signal Processing - This paper presents the complete design of a phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters. The...     

Increasing the photocatalytic and fungicide activities of Ag3PO4 microcrystals under visible-light irradiation

The present study reports for the first time the performance of silver phosphate (Ag₃PO₄) microcrystals as photocatalyst (degradation of Rodamine B-RhB) and antifungal agent (against Candida albicans–C. albicans) under visible-light irradiation (455 nm). Ag₃PO₄ microcrystals were synthesized by a simple co-precipitation (CP) method at room temperature. The structural and electronic properties of the as-synthetized Ag₃PO₄ have been investigated before and after 4 cycles of RhB degradation under visible light using X-ray diffraction (XRD), micro-Raman spectroscopy, UV–Vis spectrophotometer and field emission scanning electron microscopy (FE-SEM) images. The antifungal activity was analyzed in planktonic cells and 48h-biofilm of C. albicans by colony forming units (CFU) counting, confocal laser and FE-SE microscopies. Statistical analysis was carried out using SPSS software. Morphological and structural modifications of Ag₃PO₄ were observed upon recycling. After 4 recycles, the material maintained its photodegradation property; an eightfold increase in the efficiency of Ag₃PO₄ was observed in planktonic cells and a two fold increase in biofilm when irradiated under visible light. Thus, higher antifungal effectiveness against C. albicans was obtained when associated with visible-light irradiation.     

QoS metrics-in-the-loop for endowing runtime self-adaptation to robotic software architectures

Multimedia Tools and Applications - The design of robots capable of operating autonomously in changing and unstructured environments, requires using complex software architectures in which,...     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.