Mechanical Behavior of Articular Cartilage After Osteochondral Autograft Transfer in an Ovine Model

BACKGROUND: Grafting of autologous hyaline cartilage and bone for articular cartilage repair is a well-accepted technique. Although encouraging midterm clinical results have been reported, no information on the mechanical competence of the transplanted joint surface is available. HYPOTHESIS: The mechanical competence of osteochondral autografts is maintained after transplantation. STUDY DESIGN: Controlled laboratory study. METHODS: Osteochondral defects were filled with autografts (7.45 mm in diameter) in one femoral condyle in 12 mature sheep. The ipsilateral femoral condyle served as the donor site, and the resulting defect (8.3 mm in diameter) was left empty. The repair response was examined after 3 and 6 months with mechanical and histologic assessment and histomorphometric techniques. RESULTS: Good surface congruity and plug placement was achieved. The Young modulus of the grafted cartilage significantly dropped to 57.5% of healthy tissue after 3 months (P < .05) but then recovered to 82.2% after 6 months. The aggregate and dynamic moduli behaved similarly. The graft edges showed fibrillation and, in some cases (4 of 6), hypercellularity and chondrocyte clustering. Subchondral bone sclerosis was observed in 8 of 12 cases, and the amount of mineralized bone in the graft area increased from 40% to 61%. CONCLUSIONS: The mechanical quality of transplanted cartilage varies considerably over a short period of time, potentially reflecting both degenerative and regenerative processes, while histologically signs of both cartilage and bone degeneration occur. CLINICAL RELEVANCE: Both the mechanically degenerative and restorative processes illustrate the complex progression of regeneration after osteochondral transplantation. The histologic evidence raises doubts as to the long-term durability of the osteochondral repair.

Three-dimensional printing of hierarchical and tough mesoporous bioactive glass scaffolds with a controllable pore architecture, excellent mechanical strength and mineralization ability

New-generation biomaterials for bone regenerations should be highly bioactive, resorbable and mechanically strong. Mesoporous bioactive glass (MBG), as a novel bioactive material, has been used for the study of bone regeneration due to its excellent bioactivity, degradation and drug-delivery ability; however, how to construct a 3D MBG scaffold (including other bioactive inorganic scaffolds) for bone regeneration still maintains a significant challenge due to its/their inherit brittleness and low strength. In this brief communication, we reported a new facile method to prepare hierarchical and multifunctional MBG scaffolds with controllable pore architecture, excellent mechanical strength and mineralization ability for bone regeneration application by a modified 3D-printing technique using polyvinylalcohol (PVA), as a binder. The method provides a new way to solve the commonly existing issues for inorganic scaffold materials, for example, uncontrollable pore architecture, low strength, high brittleness and the requirement for the second sintering at high temperature. The obtained 3D-printing MBG scaffolds possess a high mechanical strength which is about 200 times for that of traditional polyurethane foam template-resulted MBG scaffolds. They have highly controllable pore architecture, excellent apatite-mineralization ability and sustained drug-delivery property. Our study indicates that the 3D-printed MBG scaffolds may be an excellent candidate for bone regeneration.

A dynamic approach to forecast rental growth

Numerous econometric models have been proposed for forecasting property market performance, but limited success has been achieved in finding a reliable and consistent model to predict property market movements over a five to ten year timeframe. This research focuses on office rental growth forecasts and overviews many of the office rent models that have evolved over the past 20 years. A model by DiPasquale and Wheaton is selected for testing in the Brisbane, Australia office market. The adaptation of this study did not provide explanatory variables that could assist in developing a reliable, predictive model of office rental growth. In light of this result, the paper suggests a system dynamics framework that includes an econometric model based on historical data as well as user input guidance for the primary variables. The rent forecast outputs would be assessed having regard to market expectations and probability profiling undertaken for use in simulation exercises. The paper concludes with ideas for ongoing research.

Molecular dynamics study of dynamic buckling properties of nanowires with defects

Based on the embedded atom method (EAM) and molecular dynamics (MD) method, the deformation properties of Cu nanowires with different single defects under dynamic compression have been studied. The mechanical behaviours of the perfect nanowire are first studied, and the critical stress decreases with the increase of the nanowire’s length, which is well agreed with the modified Euler theory. We then consider the effects to the buckling phenomenon resulted from different defects. It is found that obvious decrease of the critical stress is resulted from different defects, and the largest decrease is found in nanowire with the surface vertical defect. Surface defects are found exerting larger influence than internal defects. The buckling duration is found shortened due to different defects except the nanowire with surface horizon defect, which is also found possessing the largest deflection. Different deflections are also observed for different defected nanowires. It is find that due to surface defects, only deflection in one direction is happened, but for internal defects, more complex deflection circumstances are observed.

Study of tissue damage during mechanical peeling of tough skinned vegetables

Peeling is an essential phase of post harvesting and processing industry; however the undesirable losses and waste rate that occur during peeling stage are always the main concern of food processing sector. There are three methods of peeling fruits and vegetables including mechanical, chemical and thermal, depending on the class and type of fruit. By comparison, the mechanical method is the most preferred; this method keeps edible portions of produce fresh and creates less damage. Obviously reducing material losses and increasing the quality of the process has a direct effect on the whole efficiency of food processing industry which needs more study on technological aspects of this industrial segment. In order to enhance the effectiveness of food industrial practices it is essential to have a clear understanding of material properties and behaviour of tissues under industrial processes. This paper presents the scheme of research that seeks to examine tissue damage of tough skinned vegetables under mechanical peeling process by developing a novel FE model of the process using explicit dynamic finite element analysis approach. In the proposed study a nonlinear model which will be capable of simulating the peeling process specifically, will be developed. It is expected that unavailable information such as cutting force, maximum shearing force, shear strength, tensile strength and rupture stress will be quantified using the new FEA model. The outcomes will be used to optimize and improve the current mechanical peeling methods of this class of vegetables and thereby enhance the overall effectiveness of processing operations. Presented paper aims to review available literature and previous works have been done in this area of research and identify current gap in modelling and simulation of food processes.

Static and dynamic strain sensing using a polymer : carbon nanotube film strain sensor

The search for new multipoint, multidirectional strain sensing devices has received a new impetus since the discovery of carbon nanotubes. The excellent electrical, mechanical, and electromechanical properties of carbon nanotubes make them ideal candidates as primary materials in the design of this new generation of sensing devices. Carbon nanotube based strain sensors proposed so far include those based on individual carbon nanotubes for integration in nano or micro elecromechanical systems (NEMS/MEMS) [1], or carbon nanotube films consisting of spatially connected carbon nanotubes [2], carbon nanotube - polymer composites [3,4] for macroscale strain sensing. Carbon nanotube films have good strain sensing response and offer the possibility of multidirectional and multipoint strain sensing, but have poor performance due to weak interaction between carbon nanotubes. In addition, the carbon nanotube film sensor is extremely fragile and difficult to handle and install. We report here the static and dynamic strain sensing characteristics as well as temperature effects of a sandwich carbon nanotube - polymer sensor fabricated by infiltrating carbon nanotube films with polymer.

Interplay effects during enhanced dynamic wedge deliveries

In this study the interplay effects for Enhanced Dynamic Wedge (EDW) treatments are experimentally investigated. Single and multiple field EDW plans for different wedge angles were delivered to a phantom and detector on a moving platform, with various periods, amplitudes for parallel and perpendicular motions. A four field 4D CT planned lung EDW treatment was delivered to a dummy tumor over four fractions. For the single field parallel case the amplitude and the period of motion both affect the interplay resulting in the appearance of a step function and penumbral cut off with the discrepancy worst where collimator-tumor speed is similar. For perpendicular motion the amplitude of tumor motion is the only dominant factor. For large wedge angle the dose discrepancy is more pronounced compared to the small wedge angle for the same field size and amplitude-period values. For a small field size i.e. 5 × 5 cm2 the loss of wedged distribution was observed for both 60º and 15º wedge angles for of parallel and perpendicular motions. Film results from 4D CT planned delivery displayed a mix of over and under dosages over 4 fractions, with the gamma pass rate of 40% for the averaged film image at 3%/1 mm DTA (Distance to Agreement). Amplitude and period of the tumor motion both affect the interplay for single and multi-field EDW treatments and for a limited (4 or 5) fraction delivery there is a possibility of non-averaging of the EDW interplay.

Condition deterioration prediction of bridge elements using Dynamic Bayesian Networks (DBNs)

The ability of bridge deterioration models to predict future condition provides significant advantages in improving the effectiveness of maintenance decisions. This paper proposes a novel model using Dynamic Bayesian Networks (DBNs) for predicting the condition of bridge elements. The proposed model improves prediction results by being able to handle, deterioration dependencies among different bridge elements, the lack of full inspection histories, and joint considerations of both maintenance actions and environmental effects. With Bayesian updating capability, different types of data and information can be utilised as inputs. Expert knowledge can be used to deal with insufficient data as a starting point. The proposed model established a flexible basis for bridge systems deterioration modelling so that other models and Bayesian approaches can be further developed in one platform. A steel bridge main girder was chosen to validate the proposed model.

Dynamic compression improves biosynthesis of human zonal chondrocytes from osteoarthritis patients

Objective: We hypothesize that chondrocytes from distinct zones of articular cartilage respond differently to compressive loading, and that zonal chondrocytes from osteoarthritis (OA) patients can benefit from optimized compressive stimulation. Therefore, we aimed to determine the transcriptional response of superficial (S) and middle/deep (MD) zone chondrocytes to varying dynamic compressive strain and loading duration. To confirm effects of compressive stimulation on overall matrix production, we subjected zonal chondrocytes to compression for 2 weeks. Design: Human S and MD chondrocytes from osteoarthritic joints were encapsulated in 2% alginate, pre-cultured, and subjected to compression with varying dynamic strain (5, 15, 50% at 1 Hz) and loading duration (1, 3, 12 h). Temporal changes in cartilage-specific, zonal, and dedifferentiation genes following compression were evaluated using quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR). The benefits of long-term compression (50% strain, 3 h/day, for 2 weeks) were assessed by measuring construct glycosaminoglycan (GAG) content and compressive moduli, as well as immunostaining. Results: Compressive stimulation significantly induced aggrecan (ACAN), COL2A1, COL1A1, proteoglycan 4 (PRG4), and COL10A1 gene expression after 2 h of unloading, in a zone-dependent manner (P < 0.05). ACAN and PRG4 mRNA levels depended on strain and load duration, with 50% and 3 h loading resulting in highest levels (P < 0.05). Long-term compression increased collagen type II and ACAN immunostaining and total GAG (P < 0.05), but only S constructs showed more PRG4 stain, retained more GAG (P < 0.01), and developed higher compressive moduli than non-loaded controls. Conclusions: The biosynthetic activity of zonal chondrocytes from osteoarthritis joints can be enhanced with selected compression regimes, indicating the potential for cartilage tissue engineering applications. © 2012 Osteoarthritis Research Society International.

Study of mechanical deformations on tough skinned vegetables during mechanical peeling process

Peeling is an essential phase of post harvesting and processing industry; however undesirable processing losses are unavoidable and always have been the main concern of food processing sector. There are three methods of peeling fruits and vegetables including mechanical, chemical and thermal, depending on the class and type of fruit. By comparison, the mechanical methods are the most preferred; mechanical peeling methods do not create any harmful effects on the tissue and they keep edible portions of produce fresh. The main disadvantage of mechanical peeling is the rate of material loss and deformations. Obviously reducing material losses and increasing the quality of the process has a direct effect on the whole efficiency of food processing industry, this needs more study on technological aspects of these operations. In order to enhance the effectiveness of food industrial practices it is essential to have a clear understanding of material properties and behaviour of tissues under industrial processes. This paper presents the scheme of research that seeks to examine tissue damage of tough skinned vegetables under mechanical peeling process by developing a novel FE model of the process using explicit dynamic finite element analysis approach. A computer model of mechanical peeling process will be developed in this study to stimulate the energy consumption and stress strain interactions of cutter and tissue. The available Finite Element softwares and methods will be applied to establish the model. Improving the knowledge of interactions and involves variables in food operation particularly in peeling process is the main objectives of the proposed study. Understanding of these interrelationships will help researchers and designer of food processing equipments to develop new and more efficient technologies. Presented work intends to review available literature and previous works has been done in this area of research and identify current gap in modelling and simulation of food processes.

Computational fluid dynamic analysis of intracranial aneurysm bleb Formation (NS26P)

Purpose: The management of unruptured aneurysms remains controversial as treatment infers potential significant risk to the currently well patient. The decision to treat is based upon aneurysm location, size and abnormal morphology (e.g. bleb formation). A method to predict bleb formation would thus help stratify patient treatment. Our study aims to investigate possible associations between intra-aneurysmal flow dynamics and bleb formation within intracranial aneurysms. Competing theories on aetiology appear in the literature. Our purpose is to further clarify this issue. Methodology: We recruited data from 3D rotational angiograms (3DRA) of 30 patients with cerebral aneurysms and bleb formation. Models representing aneurysms pre-bleb formation were reconstructed by digitally removing the bleb, then computational fluid dynamics simulations were run on both pre and post bleb models. Pulsatile flow conditions and standard boundary conditions were imposed. Results: Aneurysmal flow structure, impingement regions, wall shear stress magnitude and gradients were produced for all models. Correlation of these parameters with bleb formation was sought. Certain CFD parameters show significant inter patient variability, making statistically significant correlation difficult on the partial data subset obtained currently. Conclusion: CFD models are readily producible from 3DRA data. Preliminary results indicate bleb formation appears to be related to regions of high wall shear stress and direct impingement regions of the aneurysm wall.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Impact and energy absorption of road safety barriers by coupled SPH/FEM

Road safety barriers are used to minimise the severity of road accidents and protect lives and property. There are several types of barrier in use today. This paper reports the initial phase of research carried out to study the impact response of portable water-filled barrier (PWFB) which has the potential to absorb impact energy and hence provide crash mitigation under low to moderate speeds. Current research on the impact and energy absorption capacity of water-filled road safety barriers is limited due to the complexity of fluid-structure interaction under dynamic impact. In this paper, a novel fluid-structure interaction method is developed based on the combination of Smooth Particle Hydrodynamics (SPH) and Finite Element Method (FEM). The sloshing phenomenon of water inside a PWFB is investigated to explore the energy absorption capacity of water under dynamic impact. It was found that water plays an important role in energy absorption. The coupling analysis developed in this paper will provide a platform to further the research in optimising the behaviour of the PWFB. The effect of the amount of water on its energy absorption capacity is investigated and the results have practical applications in the design of PWFBs.

Beat phenomena in metal nanowires, and their implications for resonance-based elastic property measurements

The elastic properties of 1D nanostructures such as nanowires are often measured experimentally through actuation of the nanowire at its resonance frequency, and then relating the resonance frequency to the elastic stiffness using elementary beam theory. In the present work, we utilize large scale molecular dynamics simulations to report a novel beat phenomenon in [110]oriented Ag nanowires. The beat phenomenon is found to arise from the asymmetry of the lattice spacing in the orthogonal elementary directions of the [110] nanowire, i.e. the [-110] and [001] directions, which results in two different principal moments of inertia. Because of this, actuations imposed along any other direction are found to decompose into two orthogonal vibrational components based on the actuation angle relative to these two elementary directions, with this phenomenon being generalizable to <110> FCC nanowires of different materials (Cu, Au, Ni, Pd and Pt). The beat phenomenon is explained using a discrete moment of inertia model based on the hard sphere assumption, the model is utilized to show that surface effects enhance the beat phenomenon, while the effect is reduced with increasing nanowires cross-sectional size or aspect ratio. Most importantly, due to the existence of the beat phenomena, we demonstrate that in resonance experiments only a single frequency component is expected to be observed, particularly when the damping ratio is relatively large or very small. Furthermore, for a large range of actuation angles, the lower frequency is more likely to be detected than the higher one, which implies that experimental predictions of Young’s modulus obtained from resonance may in fact be under predictions. The present study therefore has significant implications for experimental interpretations of Young’s modulus as obtained via resonance testing.

Model development and dynamic load-sharing analysis of longitudinal-connected air suspensions

The objective of this research was to investigate the effect of suspension parameters on dynamic load-sharing of longitudinal-connected air suspensions of a tri-axle semi-trailer. A novel nonlinear model of a multi-axle semi-trailer with longitudinal-connected air suspension was formulated based on fluid mechanics and thermodynamics and was validated through test results. The effects of suspension parameters on dynamic load-sharing and road-friendliness of the semi-trailer were analyzed. Simulation results indicate that the road-friendliness metric DLC (Dynamic Load Coefficient), is generally in accordance with the load-sharing metric - DLSC (Dynamic Load Sharing Coefficient). When the static height or static pressure increases, the DLSC optimization ratio declines monotonically. The effect of employing larger air lines and connectors on the DLSC optimization ratio gives varying results as road roughness increases and as driving speed increases. The results also indicate that if the air line diameter is always assumed to be larger than the connector diameter, the influence of air line diameter on load-sharing is more significant than that of the connector.