Current Trends in Improving of Artificial Joints Design and Technologies for Their Arthroplasty

There is a global tendency to rejuvenate joint diseases, and serious diseases such as arthrosis and arthritis develop in 90% of people over 55 years of age. They are accompanied by degradation of cartilage, joint deformities and persistent pain, which leads to limited mobility and a significant deterioration in the quality of life of patients. For the treatment of these diseases in the late stages, depending on the indications, various methods are used, the most radical of which are methods of joint arthroplasty and, in particular, total arthroplasty. Currently, total arthroplasty is one of the most effective and high-quality surgical operations at the relevant medical indications. However, complications may also arise after it, leading, inter alia, to the need for repeated surgical intervention. In order to minimize the likelihood of complications, the artificial joints used in total arthroplasty and the technology of their fabrication are constantly being improved, which leads to the emergence of new designs and methods for their integration with living tissues. At the same time, at the moment, the improvement of traditional designs and production technologies has almost reached the top of their art, and their further improvements can be insignificantly or are associated with the use of the most up-to-day technologies, allowing for friction couples with low tribological properties to provide for them high ones, for example, gradient increase hardness in the couple titanium alloy on titanium alloy. This paper presents the current state of traditional technical means and technologies for joint arthroplasty. The main attention is paid to the analysis of the latest technologies in the field of joint arthroplasty, such as osseointegration of artificial joints, the improvement of materials with the property of osteoimmunomodulation, the improvement of joint arthroplasty technologies based on the modeling of dynamic osteosynthesis, as well as the identification of possible unconventional designs of artificial joints that contribute to these technologies, predictive assessment of areas for technologies improvement.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Iterative Application of the aiNET Algorithm in the Construction of a Radial Basis Function Neural Network

This paper presents some of the procedures adopted in the construction of a Radial Basis Function Neural Network by iteratively applying the aiNET, an Artificial Immune Systems Algorithm. These procedures have shown to be effective in terms of i) the free determination of centroids inspired by an immune heuristics; and ii) the achievement of appropriate minimal square errors after a number of iterations. Experimental and empirical results are compared aiming at confirming (or not) some hypotheses

Bidirectional optimization of the melting spinning process

This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex