Technological convergence is transforming business models, and new digital business paradigms are emerging.
Neha Choudhary, Chandrachur Ghosh, Varun Sharma, Partha Roy and Pradeep Kumar
The purpose of this paper is to fabricate the scaffolds with different pore architectures using additive manufacturing and analyze its mechanical and biological properties for…
Abstract
Purpose
The purpose of this paper is to fabricate the scaffolds with different pore architectures using additive manufacturing and analyze its mechanical and biological properties for bone tissue engineering applications.
Design/methodology/approach
The polylactic acid (PLA)/composite filament were fabricated through single screw extrusion and scaffolds were printed with four different pore architectures, i.e. circle, square, triangle and parallelogram with fused deposition modelling. Afterwards, scaffolds were coated with hydroxyapatite (HA) using dip coating technique. Various physical and thermo-mechanical tests have been conducted to confirm the feasibility. Furthermore, the biological tests were conducted with MG63 fibroblast cell lines to investigate the biocompatibility of the developed scaffolds.
Findings
The scaffolds were successfully printed with different pore architectures. The pore size of the scaffolds was found to be nearly 1,500 µm, and porosity varied between 53% and 63%. The fabricated circular pore architecture resulted in highest average compression strength of 13.7 MPa and modulus of 525 MPa. The characterizations showed the fidelity of the work. After seven days of cell culture, it was observed that the developed composites were non-toxic and supported cellular activities. The coating of HA made the scaffolds bioactive, showing higher wettability, degradation and high cellular responses.
Originality/value
The research attempts highlight the development of novel biodegradable and biocompatible polymer (PLA)/bioactive ceramic (Al2O3) composite for additive manufacturing with application in the tissue engineering field.
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Keywords
Subhamita Chakraborty, Prasun Das, Naveen Kumar Kaveti, Partha Protim Chattopadhyay and Shubhabrata Datta
The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of…
Abstract
Purpose
The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.
Design/methodology/approach
Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.
Findings
The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.
Originality/value
ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.
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Meng-Ting Chen and Richard J. Nugent
The authors evaluate financial stability and capital flows management objectives of capital controls in the context of four capital control events: removing or imposing controls…
Abstract
The authors evaluate financial stability and capital flows management objectives of capital controls in the context of four capital control events: removing or imposing controls on capital inflows and removing or imposing controls on capital outflows. The authors use synthetic control method to solve the endogeneity problem stemmed from the timing of capital control implementation. The authors find new evidence that capital controls are not consistently effective in reaching financial stability outcomes but are consistent in reaching capital flows management outcomes. The authors compare our results to estimates using difference-in-difference (DID) and carry out placebo analysis. Finally, we use synthetic DID to correct for the parallel trend bias and show that the results still hold.