Testing, characterization and modelling of mechanical behaviour of poly (lactic-acid) and poly (butylene succinate) blends

Background Significant amount of research, both experimental and numerical, has been conducted to study the mechanical behaviour of biodegradable polymer PL(L)A due to its wide range of applications. However, mechanical brittleness or poor elongation of PL(L)A has limited its applications considerably, particularly in the biomedical field. This study aims to study the potential in improving the ductility of PLA by blending with PBS in varied weight ratios. Methods The preparation of PLA and PBS blends, with various weight ratios, was achieved by melting and mixing technique at high temperature using HAAKE™ Rheomix OS Mixer. Differential Scanning Calorimetry (DSC) was applied to investigate the melting behaviour, crystallization and miscibility of the blends. Small dog-bone specimens, produced by compression moulding, were used to test mechanical properties under uniaxial tension. Moreover, an advanced viscoplastic model with nonlinear hardening variables was applied to simulate rate-dependent plastic deformation of PLA/PBS blends, with model parameters calibrated simultaneously against the tensile test data. Results Optical Microscopy showed that PBS composition aid with the crystallization of PLA. The elongation of PLA/PBS blends increased with the increase of PBS content, but with a compromise of tensile modulus and strength. An increase of strain rate led to enhanced stress response, demonstrating the time-dependent deformation nature of the material. Model simulations of time-dependent plastic deformation for PLA/PBS blends compared well with experimental results. Conclusions The crystallinity of PLA/PBS blends increased with the addition of PBS content. The brittleness of pure PLA can be improved by blending with ductile PBS using mechanical mixing technique, but with a loss of stiffness and strength. The tensile tests at different strain rates confirmed the time-dependent plastic deformation nature of the blends, i.e., viscoplasticity, which can be simulated by the Chaboche viscoplastic model with nonlinear hardening variables

A computational study of crimping and expansion of bioresorbable polymeric stents

This paper studied the mechanical performance of four bioresorbable PLLA stents, i.e., Absorb, Elixir, Igaki-Tamai and RevaMedical, during crimping and expansion using the finite element method. Abaqus CAE was used to create the geometrical models for the four stents. A tri-folded balloon was created using NX software. For the stents, elastic-plastic behaviour was used, with hardening implemented by considering the increase of yield stress with the plastic strain. The tri-folded balloon was treated as linear elastic. To simulate the crimping of stents, a set of 12 rigid plates were generated around the stents with a radially enforced displacement. During crimping, the stents were compressed from a diameter of 3 mm to 1.2 mm, with the maximum stress developed at both inner and outer sides of the U-bends. During expansion, the stent inner diameter increased to 3 mm at the peak pressure and then recoiled to different final diameters after balloon deflation due to different stent designs. The maximum stress was found again at the U-bends of stents. Diameter change, recoiling effect and radial strength/stiffness were also compared for the four stents to assess the effect of design variation on stent performance. The effect of loading rate on stent deformation was also simulated by considering the time-dependent plastic behaviour of polymeric material

Testing, characterization and modelling of mechanical behaviour of poly (lactic-acid) and poly (butylene succinate) blends

Background Significant amount of research, both experimental and numerical, has been conducted to study the mechanical behaviour of biodegradable polymer PL(L)A due to its wide range of applications. However, mechanical brittleness or poor elongation of PL(L)A has limited its applications considerably, particularly in the biomedical field. This study aims to study the potential in improving the ductility of PLA by blending with PBS in varied weight ratios. Methods The preparation of PLA and PBS blends, with various weight ratios, was achieved by melting and mixing technique at high temperature using HAAKE™ Rheomix OS Mixer. Differential Scanning Calorimetry (DSC) was applied to investigate the melting behaviour, crystallization and miscibility of the blends. Small dog-bone specimens, produced by compression moulding, were used to test mechanical properties under uniaxial tension. Moreover, an advanced viscoplastic model with nonlinear hardening variables was applied to simulate rate-dependent plastic deformation of PLA/PBS blends, with model parameters calibrated simultaneously against the tensile test data. Results Optical Microscopy showed that PBS composition aid with the crystallization of PLA. The elongation of PLA/PBS blends increased with the increase of PBS content, but with a compromise of tensile modulus and strength. An increase of strain rate led to enhanced stress response, demonstrating the time-dependent deformation nature of the material. Model simulations of time-dependent plastic deformation for PLA/PBS blends compared well with experimental results. Conclusions The crystallinity of PLA/PBS blends increased with the addition of PBS content. The brittleness of pure PLA can be improved by blending with ductile PBS using mechanical mixing technique, but with a loss of stiffness and strength. The tensile tests at different strain rates confirmed the time-dependent plastic deformation nature of the blends, i.e., viscoplasticity, which can be simulated by the Chaboche viscoplastic model with nonlinear hardening variables

A computational study of mechanical performance of bioresorbable polymeric stents with design variations

Purpose: The study compared the mechanical behavior of bioresorbable polymeric stents with various designs during deployment, and investigated their fatigue performance under pulsatile blood pressure loading. Methods: Finite element simulations have been carried out to compare the mechanical performance of four bioresorbable polymeric stents, i.e., Absorb, Elixir, Igaki-Tamai and RevaMedical, during deployment in diseased artery. Tri-folded balloon was modelled to expand the crimped stent onto the three-layered arterial wall with plaque. Cyclic diastolic-systolic pressure loading was applied to both stent and arterial wall to study fatigue behavior. Results: Stents with larger U-bend and longer axial strut demonstrate more flexibility but suffer from severe dogboning and recoiling effects. Stress concentrations in the stent, as well as in the plaque and artery, are higher for stents designed with increased rigidity such as those with smaller U-bends and shorter axial struts. Simulations of fatigue deformation for Elixir stent demonstrate that the U-bends, with high stress concentrations, have a potential risk of fatigue failure under pulsatile systolic-diastolic blood pressure as opposed to the counter metallic stents which are normally free of fatigue failure. Conclusion: The structural behaviour of bioresorbable polymeric stent is strongly affected by its design, in terms of expansion, dogboing, recoiling and stress distribution during the deployment process

Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS

In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastructure, including roadside units and road traffic clouds, the model parameters are efficiently disseminated by vehicular communications and hierarchically aggregated. Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i.e., aggregation in layers of roadside units and cloud. The experiment results indicate that our method can well balance the learning accuracy and stability according to the knowledge of heterogeneity in current communication networks. Compared to other baseline approaches, the evaluation on a Non-IID MNIST dataset shows that our framework is more general and capable especially in application scenarios with low communication quality. Even when 90% of the agents are timely disconnected, the pre-trained deep learning model can still be forced to converge stably, and its accuracy can be enhanced from 68% to over 90% after convergence

ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals

Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying federated learning requires a large communication bandwidth, which limits its deployment in wireless networks. To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training. In particular, we integrate two pairs of shared predictors for the model prediction in both server-to-client and client-to-server communication. By employing a common prediction rule, both locally and globally updated models are always fully recoverable in clients and the server. We highlight that the residuals only indicate the quasi-update of a model in a single inter-round, and hence contain more dense information and have a lower entropy than the model, comparing to model weights and gradients. Based on this property, we further conduct lossy compression of the residuals by sparsification and quantization and encode them for efficient communication. The experimental evaluation shows that our ResFed needs remarkably less communication costs and achieves better accuracy by leveraging less sensitive residuals, compared to standard federated learning. For instance, to train a 4.08 MB CNN model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, our approach achieves a compression ratio over 700X in each communication round with minimum impact on the accuracy. To reach an accuracy of 70%, it saves around 99% of the total communication volume from 587.61 Mb to 6.79 Mb in up-streaming and to 4.61 Mb in down-streaming on average for all clients

The hidden sterile neutrino and the (2+2) sum rule

We discuss oscillations of atmospheric and solar neutrinos into sterile neutrinos in the 2+2 scheme. A zeroth order sum rule requires equal probabilities for oscillation into nu_s and nu_tau in the solar+atmospheric data sample. Data does not favor this claim. Here we use scatter plots to assess corrections of the zeroth order sum rule when (i) the 4 x 4 neutrino mixing matrix assumes its full range of allowed values, and (ii) matter effects are included. We also introduce a related "product rule". We find that the sum rule is significantly relaxed, due to both the inclusion of the small mixing angles (which provide a short-baseline contribution) and to matter effects. The product rule is also dramatically altered. The observed relaxation of the sum rule weakens the case against the 2+2 model and the sterile neutrino. To invalidate the 2+2 model, a global fit to data with the small mixing angles included seems to be required.Comment: 43 pages, 11 figures (same as v2, accidental replacement