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Abstract: The fabrication of biomaterials to be used in segmental bone defects, mimicking the bone's organic-inorganic architecture and mechanical properties to induce osteogenesis, persists as a key challenge. The purpose of this study was to elucidate the effect of a lightweight, morphologically graded, and multiphase self-standing scaffold structure prepared from a combination of gelatine (Gel), collagen type 1 (Col) and/or hydroxyapatite (HAP) nanoparticles by a unidirectional freeze-casting process at different temperatures (−20, −40, −60 °C), followed by carbodiimide induced cross-linking, on their in-vitro mechanical stability and bioactive properties. In addition, the rheological study of differently formulated Gel solutions has been performed to determine the effect of Col and HAP content on their microstructural arrangement, which, together with the freezing kinetic, affects Gel/Col orientation and cross-linking, and, thus, the scaffold's mechanical strength and stability. A bone-like anisotropic, interconnected, and graded porosity (from 120 to a few μm) scaffold structure with up to 30% total porosity and ~61 μm average pores' diameter is obtained by using a higher Col content (Col: Gel = 2:5) and freezing temperature (−20 °C) while forming a few μm thick close-to-parallel lamellae, separated with a 10–100 μm space when prepared at −60 °C. Such a structure influenced in-vitro stability strongly (lower swelling without weight loss), being accompanied with a ~76% increase of compression strength (to 37 kPa) and ~67% decrease of elastic modulus (to 17 kPa) when prepared with HAP and incubated in HBSS for 7 days. On the other hand, a significant reduction of both strength (~78%, to 15 kPa) and elasticity (~95%, to 5 kPa) was noted for a scaffold prepared with HAP at −60 °C, being related to faster degradation and the formation of a highly opened structure on the bottom, required to stimulate the bone ingrowth, while a more closed network structure on the top to adhere with the surrounding soft tissue. None of the scaffolds induced cytotoxicity to human bone-derived osteoblasts, even after 19 days of incubation, but rather improved their viability while promoting cells' adhesions, proliferation, and differentiation, being supported with an increased alkaline phosphatase activity and rod-like CaP formation.
Keywords: biomimetic scaffolds, rheology, unidirectional freeze-casting, morphology, compression properties, bioactivity
Published in DKUM: 10.03.2025; Views: 0; Downloads: 3
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Abstract: After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight. In this article, we try to critically evaluate the most prominent of these trends and to explore their parallels, complementarities, and differences. Digital data restoration mimics the human ability to omit memorising information that is satisfactorily retrievable from the context. Feature-based data compression introduces a two-level data representation with higher-level semantic features and with residuals that correct the feature-restored (predicted) data. The integration of the advantages of individual domain-specific data compression methods into a general approach is also challenging. To the best of our knowledge, a method that addresses all these trends does not exist yet. Our methodology, COMPROMISE, has been developed exactly to make as many solutions to these challenges as possible inter-operable. It incorporates features and digital restoration. Furthermore, it is largely domain-independent (general), asymmetric, and universal. The latter refers to the ability to compress data in a common framework in a lossy, lossless, and near-lossless mode. COMPROMISE may also be considered an umbrella that links many existing domain-dependent and independent methods, supports hybrid lossless–lossy techniques, and encourages the development of new data compression algorithms
Keywords: data compression, data resoration, universal algorithm, feature, residual
Published in DKUM: 04.02.2025; Views: 0; Downloads: 9
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Abstract: The complexity in the styles of 1200 Byzantine icons painted between 13th and 16th from Greece, Russia and Romania was investigated through the Kolmogorov algorithmic information theory. The aim was to identify specific quantitative patterns which define the key characteristics of the three different painting schools. Our novel approach using the artificial surface images generated with Inverse FFT and the Midpoint Displacement (MD) algorithms, was validated by comparison of results with eight fractal and non-fractal indices. From the analyzes performed, normalized Kolmogorov compression complexity (KC) proved to be the best solution because it had the best complexity pattern differentiations, is not sensitive to the image size and the least affected by noise. We conclude that normalized KC methodology does offer capability to differentiate the icons within a School and amongst the three Schools.
Keywords: iconography, complexity, Kolmogorov compression, art, physics
Published in DKUM: 15.07.2024; Views: 117; Downloads: 16
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Keywords: 3D printing, gelatin, collagen, hydroxyapatite, microstructure, compression
Published in DKUM: 09.04.2024; Views: 201; Downloads: 18
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Abstract: The compression index is one of the important soil parameters that are essential for geotechnical designs. Because laboratory and in-situ tests for determining the compression index (Cc) value are laborious, time consuming and costly, empirical formulas based on soil parameters are commonly used. Over the years a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, the liquid limit, the plasticity index, the specific gravity. These empirical formulas provide good results for a specific test set, but cannot accurately or reliably predict the compression index from various test sets. The other disadvantage is that they tend to use a single parameter to estimate the compression index (Cc), even though Cc exhibits spatial characteristics depending on several soil parameters. This study presents the potential for Genetic Expression Programming (GEP) and the Adaptive Neuro-Fuzzy (ANFIS) computing paradigm to predict the compression index from soil parameters such as the natural water content, the liquid limit, the plastic index, the specific gravity and the void ratio. A total of 299 data sets collected from the literature were used to develop the models. The performance of the models was comprehensively evaluated using several statistical verification tools. The predicted results showed that the GEP and ANFIS models provided fairly promising approaches to the prediction of the compression index of soils and could provide a better performance than the empirical formulas.
Keywords: compression index, statistical analysis, genetic expression programming, adaptive neuro-fuzzy, empirical equations
Published in DKUM: 14.06.2018; Views: 1499; Downloads: 91
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