Elastic Memory Research Articles

Multifunctional Bone Regeneration Membrane with Flexibility, Electrical Stimulation Activity and Osteoinductive Activity.

The use of membrane-based guided bone regeneration techniques has great potential for single-stage reconstruction of critical-sized bone defects. Here, a multifunctional bone regeneration membrane combining flexible elasticity, electrical stimulation (ES) and osteoinductive activity is developed by in situ doping of MXene 2D nanomaterials with conductive functionality and β-TCP particles into a Poly(lactic acid-carbonate (PDT) composite nano-absorbable membrane (P/T/MXene) via electrostatic spinning technique. The composite membrane has good feasibility due to its temperature sensitivity, elastic memory capacity, coordinated degradation profile and easy preparation process. In vitro experiments showed the P/T/MXene membrane effectively promoted the recruitment and osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) under ES and enhanced the angiogenic capacity of endothelial cells, which synergistically promoted bone regeneration through neovascularization. In addition, an in vivo rat model of cranial bone defects further confirmed the bone regeneration efficacy of the P/T/MXene membrane. In conclusion, the developed P/T/MXene membrane can effectively promote bone regeneration through their synergistic multifunctional effects, suggesting the membranes have great potential for guiding tissue regeneration and providing guidance for the biomaterials design.

3D Printing Elastocaloric TiNiCu Thermoelectric Shape Memory Alloys.

The development of new thermoelectric conversion and cooling materials is an important means of addressing global climate and heat emissions in the future. While heavy and toxic elements like tellurium and lead are traditionally used to make thermoelectric materials with poor mechanical properties, recent decades have seen a gradual push towards greener and more sustainable alternatives. One such potential alternative material for thermoelectric and thermal management applications would be the Nitinol (TiNi) shape memory alloy, due to their superior mechanical properties. In this study, we have investigated the use of 3D melt printing techniques that can be used to achieve thermoelectric performance and efficiency of elastic memory alloys below 500 °C. The electrical and thermal properties of TiNiCu materials and their relation to morphology were investigated. All the alloys show similar effect sizes, their fatigue behavior is however different. By adjusting the composition of Ti and Ni elements and we have obtained memory alloys with high thermoelectric properties, with a 50% increase in power factor and a 100% increase in ZT values.

Equilibration dynamics of a dynamic covalent network diluted in a metallosupramolecular polymer matrix

We investigate the viscoelastic properties of double dynamic networks (DDNs) based on side-functionalized PnBA chains. One of these networks is highly crosslinked by metal-ligand junctions characterized by a fast association/dissociation dynamics, while the other network is sparsely crosslinked with slow dynamic covalent networks (DCNs). We first show that modulating the dynamics of the metallosupramolecular networks, by playing with the temperature, the density of reversible junctions, or the stress applied, has direct consequences on the local equilibration of the DCN. The latter takes place by a constraint release Rouse process at the rhythm of the association/dissociation of the metal-ligand junctions. Then, based on creep-recovery experiments, we investigate the ability of the DDNs to recover their initial shape after a creep test and show again the important role played by the metallosupramolecular network. In particular, the sample recovery strongly depends on the network connectivity, which is enhanced if a denser metallosupramolecular network is used as it reduces the possible creep of the double dynamic network and increases its elastic memory. The sample recovery also depends on the association-dissociation dynamics of the metallosupramolecular bonds as it fixes how fast the stretched DCN can come back to its equilibrium conformation and can recover its initial shape after a large deformation has been applied. Adjusting the dynamics of the weak network is thus a key process to govern the viscoelastic response of the slow network.

Dynamic properties of a flexible metal-organic framework exhibiting a unique “picture frame”-like crystal morphology

The precise control of the crystal morphology of metal-organic frameworks (MOFs) enables optimization of its adsorptive properties, as well as enables better integration within functional devices. However, the influence of such modifications on the dynamic properties of flexible MOFs is poorly understood. Here, we report the synthesis of a series of Cu2(bdc)2(bpy) (bdc2− = 1,4-benzenedicarboxylate; bpy = 4,4′-bipyridine) crystals having an unusual picture frame-like morphology that results from a restriction in the quantity of bpy pillars added to the reaction mixture during the intercalation of the Cu2(bdc)2(MeOH)2 layers. The width of the frames is found to correlate with the quantity of bpy, and importantly, causes the dynamic properties of the resulting Cu2(bdc)2(bpy) material to vary between rigid, elastic, and shape memory modes. In all, the results demonstrate the potential for the properties of MOFs to be optimized via subtle manipulations in the crystal morphology rather than changes in the overall material composition.

Nonwetting droplet oscillation and displacement by viscoelastic fluids

Simulations and theory reveal oscillations of droplets when driven by viscoelastic fluids because of their elastic memory effect. The viscoelastic oscillation helps release trapped droplets from hard-to-displace positions.

Investigation of elastomeric materials for bolus using stereolithography printing technology in radiotherapy

Purpose: An investigation was conducted of an elastomeric material, VisiJet M2 (3D systems, USA) for use as 3D bolus within high energy photon beams for radiotherapy. Personalized conformal bolus material on complex structures like the nose can be challenging. This material was evaluated for its clinical feasibility due to its pliability and comfort compared to alternatives. Method: Regular slabs of bolus were created of various thicknesses for dosimetric and non-dosimetric characterization. Verification culminated with the creation of a custom nose bolus for an end to end verification using an anthropomorphic head phantom. In vivo dosimetry using Gafchromic EBT3 (Ashland, USA) film validated delivered doses from a 6 MV conformal field and a pair of 6 MV volumetric modulated arc therapy (VMAT) beams. Results & Conclusion: Non-dosimetric and dosimetric tests were conducted to assess clinical suitability. The bolus was precisely created using stereolithographic (SLA) methods and presented a compliant and uniform water equivalent material with elastic memory. Measurement yielded a physical density of 1.10 g cm−3 and 1.06 relative to water electron density, and the bolus to skin distance was measured to be a maximum of 3 mm. A maximum measured dose difference of <2% was observed for dynamic treatment. Based on the investigation conducted, and the benefits presented for patient comfort while being uniform and water equivalent, and correctly represented within the treatment planning system (TPS), this material has the potential for clinical use for patient specific custom bolus.

Modeling and Simulation of Thin Layered Composites Under Non-mechanical Stimuli

This study presents a nonlinear deformation analysis of a thin composite plate undergoing shape reconfigurations triggered by electric field inputs. The composite plate consists of electro-active and inactive (substrate) layers. Prescribing electric field inputs to the electro-active layer induces large curvatures in the composites, while the in-plane stretch is relatively small. This has a consequence that only shapes with zero (or nearly zero) Gaussian curvature can be attained by prescribing the electric field, which leads to snap-through shape reconfigurations in order to achieve developable surfaces. When the substrate is made of an elastic layer, the shape returns to its original planar configuration upon removal of the electric field. In order to fully or partially retain the reconfigured shape upon removing the electric field input, a substrate layer made of a shape memory polymer is considered. Shape reconfigurations and retentions of the composites having elastic substrate and shape memory substrate are simulated.

Akin osteotomy: Is the type of staple fixation relevant?

Although different fixation techniques for the Akin osteotomy have been described in the literature, there are no many studies trying to analyze the differences between the types of fixation available. The aim of this study is to analyze if there are any differences between three types of staple fixation available in the market. We present a retrospective study of 145 cases in which an Akin osteotomy was performed and fixed with three different kinds of implants staple A (28%), staple B (45%), and staple C (27%). Staple A is made out of stainless steel, and the surgeon mechanically controls the compression applied. Staple B increases the compression when heat is applied to it. Staple C has an intrinsic elastic memory that closes the osteotomy. In all cases, distal articular set angle, interphalangeal joint obliquity angle, and metatarsophalangeal angle were measured pre-operatively and 1.5months post-operatively on dorsoplantar weight-bearing radiographs. Other details such as post-operative complications, implant migration, osteolysis, or fracture of the lateral cortex during surgery were also recorded. Clinical and radiological results show no relevant differences between the three types of fixation. The mean angular corrections of DASA, interphalangeal joint obliquity angle, and metatarsophalangeal angle were 5, 12, and 21, respectively, for staple A; 4, 10, and 19, respectively, for staple B; and 7, 10, and 23, respectively, for staple C. The rates of intra-operative and post-operative complications were similar for all groups. There was one case of infection per group. We had five cases of delayed union two with staple A and three with staple C. In four cases, there was a loss of correction, two of them fixed with staple A and two with staple C. Seven cases developed a Südeck's syndrome, four of them fixed with staple A and three with staple C. Fifteen patients suffered an uncontrolled fracture of the lateral cortex of the phalanx when performing the osteotomy (3, 8, and 4 cases fixed with staples A, B, and C, respectively), and 87.5% of the patients that developed a plantar displacement of the osteotomy had an uncontrolled fracture of the lateral cortex (p < 0.05). All three staples achieved a rigid internal fixation and minimal periosteum damage and provided a good bone-bone contact. According to our results, the radiological differences are minimal, and although the thermal compression staple had less complication, clinical differences were also not statistically significant. This means the choice of implant could be left to the surgeon's preferences or made according to cost.

Static job scheduling for environments with vertical elasticity

SummaryIn virtualized environments, such as Clouds, allocating a fixed amount of resources to a job a priori, may result in underutilization of the shared host. Meanwhile, vertical elasticity can be adopted to reduce the impact by resizing virtual machines (VMs) dynamically, in conjunction with suspension and/or migration before the host had been overloaded. In order to reduce the number of such events, but still avoid overloading the host, it is important to find an effective initial VM allocation. To achieve this, the scheduler must work in unison with the local host elasticity controllers to reduce interference. This work proposes and evaluates a framework for job scheduling in elastic memory managed virtualized environments. The memory elasticity management in clouds (MEMiC) framework is a two‐tier VM scheduler for batch jobs which attempts to predict the impact caused by competition for the memory of a host in shared Cloud‐like environments for harnessing VM allocation, suspension, and migration. Evaluations show that the MEMiC achieves a reduction of approximately 19% in the total time for jobs execution in comparison to other approaches, by reducing the degree of interference.

Learning elastic memory online for fast time series forecasting

It is well known that any kind of time series algorithm requires past information to model the inherent temporal relationship between past and future. This temporal dependency (i.e. number of past samples required for a good prediction) is generally addressed by feeding a number of past instances to the model in an empirical manner. Conventional approaches mostly rely on offline model, making them impractical to be adopted in the online or streaming context. Hence, a novel method of online temporality analysis is proposed in this paper. The estimated temporality is then employed to form an Adaptive Temporal Neural Network (ATNN) with an elastic memory capable of automatically selecting number of past samples to be used. Temporality change or drift can be a common occurrence in data streams. Hence a drift detection mechanism is also proposed. Once such drift is detected, a drift handling mechanism kicks in which utilizes the rate of drift, making our solution truly autonomous. The entire mechanism is termed as LEMON: Learning Elastic Memory Online. LEMON although not a time series model in itself, can work with any predictive models to improve their performance. Synthetic datasets are used here as proof of correct temporality estimation and drift detection whereas real world datasets are employed to demonstrate how LEMON improves the predictive performance and speed of an existing model with the knowledge of temporality and drift.

Assessing expandable Global Positioning System collars for moose neonates

ABSTRACTDeploying Global Positioning System (GPS) collars on ungulate neonates would offer notable advantages to examining their life history and influence on population performance. During 2013 and 2014, we deployed expandable GPS collars on 74 moose (Alces alces) neonates in Minnesota, USA, to estimate survival and cause‐specific mortality during their first year. Collars slipped from 10.5% and 62.5% of calves at 15.8 (±4.5 [SE]) and 27.9 (±8.1) days postcapture in 2013 and 2014, respectively, from premature deterioration of the breakaway mechanism or excessive band expansion. We conducted various controlled tests on the bands to quantify potential design flaws. We placed 8 bands (with GPS package) around a polyvinyl chloride (PVC) pipe outdoors (exposed to weather) with clear plastic tubing (sleeve) to prevent neck abrasions, 7 collars outdoors with no sleeve, and 7 collars indoors with no sleeve. We dropped each pipe 10 cm 50 times in the morning and in the afternoon daily for 4 weeks to simulate animal movement and test elastic memory. Circumference of bands from the 3 treatment groups increased 14.6 (±2.5), 8.5 (±2.9), and 3.9 (±2.4) cm, respectively, with 41.9% attributed to the sleeve, 26.9% to simulated animal movement, and 31.2% to weather exposure. Circumference of control group bands (indoors, not bounced) did not change. After design modifications were made to the collar, the band length increased only 1.5 ± 0.6 cm during a 4‐week trial. Subsequently, we placed 6 of these collars on confined and sheltered Holstein dairy calves; 5 retained their collar during an 8‐week test. After increasing the strength of the expandable loops via sewing, we placed 4 collars on pastured Angus beef calves. Three of 4 slipped their collars at 42.4 (±8.9) days. Our results indicate additional modifications of the band are needed before GPS‐collaring of moose neonates is resumed. © 2018 The Wildlife Society.

Elastic memory learning for fuzzy inference models

In this paper we present a novel approach for solving the consequent part of neuro-fuzzy modeling with an emphasis on the forgetting factor in the multi-class learning problem. Our solution is based on recursive least squares (RLS) for online and incremental learning applications, where the data stream is not necessarily uniformly distributed over time. Such a setup can lead to forgetting of specific classes that have not been used for a period of time. In this work we present a reasoned and detailed description of elastic memory learning (EML) and EML with the use of confidence interval (EML+) to avoid unnecessary treatment of the forgetting factor. We present the experimental results and evaluation of our methods in order to show their usefulness not only against forgetting of unused classes, but also for dealing with the lowered recognition rate after all classes have been learned. We note that by using EML, forgetting is significantly eliminated and the recognition rate is slightly affected as well, while EML+ puts more emphasis on keeping the recognition rate higher than the forgetting. Thus, this paper presents two methods that significantly eliminate the forgetting factor for incremental learning with a different focus on its importance, i.e. high recognition rate vs high immunity to forgetting; both of these methods perform significantly better than RLS for these aspects.

Revisiting the micro-buckling of carbon fibers in elastic memory composite plates under pure bending

Elastic memory composites (EMCs) consisting of carbon fibers embedded in a shape memory polymer (SMP) matrix are receiving substantial interests in deployable space structures. Their favourable ability to be packaged to a much high folding curvature is due to that the soft SMP matrix allows the fibers to micro-buckle without breaking. This paper studies the buckling mechanics of fibers embedded in a thin EMC plate at finite strain under pure bending. The analytical expressions of three key parameters, namely the locations of neutral strain surface and critical buckling surface, the half-wavelength of buckling fibers, are determined by energy method. The fiber is modeled as 3D linear elastic solid, and its size effect on the shear strain of matrix is considered. Detailed discussions and comparisons are provided between the results obtained from this work and the ones without considering the fiber size effect. It is shown that considering the size effect of fibers has an influence on the half-wavelength and could reasonably predict the scope of the model, but it produces little differences on the variation trend of locations of neutral strain surface and critical buckling surface.

Efficacy of elastic memory chains versus nickel–titanium coil springs in canine retraction: A two-center split-mouth randomized clinical trial

The use of newly-introduced elastic memory chains (EMCs) in space closure is increasingly gaining popularity. However, no clinical studies have evaluated their efficacy. Therefore, this study was conducted. In this two-center split-mouth single-blind randomized controlled trial, 21 jaws were divided into 42 quadrants. The two treatments [canine retraction using EMCs versus nickel-titanium (NiTi) coil springs (as control)] were randomly assigned to two quadrants of each jaw. The premolar space was measured at the baseline, and in the 1st, 2nd, and 3rd months of canine retraction, by a blinded orthodontist. Space closure rates were compared using a paired t-test. The rates of space closure using NiTi springs were 1.93±0.62, 1.71±0.75, and 1.36±0.51mm/month, during the 1st, 2nd, and 3rd months of treatment, respectively. The 3-month average rates of space closure were 1.67±0.39 and 1.89±0.36mm/month in the NiTi and elastic groups, respectively (faster in the elastic group, P=0.022). The application of elastic memory chains is as effective as NiTi springs.

EMI: A Rack-scale Memory Interconnection Network Architecture

Big data processing applications more and more demand for memory, especially data-intensive applications. The emergence of large-scale storage media supplies for the expansion of memory scale. As a consequence, the interconnection of memories is a key factor to be considered. This paper presents a memory interconnection network architecture for rack-scale supporting for dynamic allocation and expansion of memory, EMI (Elastic Memory Interconnection). EMI’s topology belongs to a hierarchical design approach to extend the network to rack-scale. We also explore the interconnection within every layer, as far as possible to do low latency communications and improve local performance.

Flagellar swimming in viscoelastic fluids: role of fluid elastic stress revealed by simulations based on experimental data

Many important biological functions depend on microorganisms' ability to move in viscoelastic fluids such as mucus and wet soil. The effects of fluid elasticity on motility remain poorly understood, partly because the swimmer strokes depend on the properties of the fluid medium, which obfuscates the mechanisms responsible for observed behavioural changes. In this study, we use experimental data on the gaits of Chlamydomonas reinhardtii swimming in Newtonian and viscoelastic fluids as inputs to numerical simulations that decouple the swimmer gait and fluid type in order to isolate the effect of fluid elasticity on swimming. In viscoelastic fluids, cells employing the Newtonian gait swim faster but generate larger stresses and use more power, and as a result the viscoelastic gait is more efficient. Furthermore, we show that fundamental principles of swimming based on viscous fluid theory miss important flow dynamics: fluid elasticity provides an elastic memory effect that increases both the forward and backward speeds, and (unlike purely viscous fluids) larger fluid stress accumulates around flagella moving tangent to the swimming direction, compared with the normal direction.

Forgetting of unused classes in missing data environment using automatically generated data: Application to on-line handwritten gesture command recognition

In this paper we exploit the use of synthetic data for on-line handwritten gesture commands recognition with an emphasis on the problem of forgetting unused classes. For on-line learning, one of the most crucial moments of the processing is the initialization. In some applications the data is available and these can be fed to the learning model. However, in applications such as user-friendly handwritten gesture recognition, this scenario is not possible. Since from the user perspective it is better to let the user define his own symbols, the learning model is lacking in the amount of data at the initialization. Some strategies have been proposed to acquire synthetic handwritten gesture commands and use these for on-line learning. In this paper we exploit this technique further and focus on the forgetting of unused classes by applying a random buffer and Elastic Memory Learning (EML) to avoid this from happening. In the experiments we search for the proper amount of synthetic data produced for each sample as well as exploit the most appropriate time to stop the generation of synthetic data for learning purposes. We also investigate the influence of synthetic data on forgetting when using the proposed EML. We base the generation of synthetic gesture commands on Kinematic Theory.

Stress growth and relaxation of dendritically branched macromolecules in shear and uniaxial extension

We present unique nonlinear rheological data of well-defined symmetric Cayley-tree poly(methyl methacrylates) in shear and uniaxial extension. Earlier work has shown that their linear viscoelasticity is governed by the hierarchical relaxation of different generations, whereas the segments between branch points are responsible for their substantial strain hardening as compared to linear homopolymers of the same total molar mass at the same value of imposed stretch rate. Here, we extend that work in order to obtain further experimental evidence that will help understanding the molecular origin of the remarkable properties of these highly branched macromolecules. In particular, we address three questions pertinent to the specific molecular structure: (i) Is steady state attainable during uniaxial extension? (ii) What is the respective transient response in simple shear? and (iii) How does stress relax upon cessation of extension or shear? To accomplish our goal, we utilize state-of-the-art instrumentation, i.e., filament stretching rheometry and cone-partitioned plate shear rheometry for polymers with 3 and 4 generations, and complement it with state-of-the-art modeling predictions using the branch-on-branch (BoB) algorithm. The data indicate that the extensional viscosity reaches a steady state value, whose dependence on extensional rate is identical to that of entangled linear and other branched polymer melts. Nonlinear shear is characterized by transient stress overshoots and the validity of the Cox–Merz rule. Remarkably, nonlinear stress relaxation is much broader and slower in extension compared to shear. It is also slower at higher generation, and rate-independent for rates below the Rouse rate of the outer segment. For extension, the relaxation time is longer than that of the linear stress relaxation, suggesting a strong “elastic memory” of the material. These results are described by BoB semiquantitatively, both in linear and nonlinear shear and extensional regimes. Given the fact that the segments between branch points are less than three entanglements long, this is a very promising outcome that gives confidence in using BoB for understanding the key features. Moreover, the response of the segments between generations controls the rheology of the Cayley trees. Their substantial stretching in uniaxial extension appears responsible for strain hardening, whereas coupling of stretches of different parts of the polymer appears to be the origin of the slower subsequent relaxation of extensional stress. Concerning the latter effect, for which predictions are not available, it is hoped that the present experimental findings and proposed framework of analysis will motivate further developments in the direction of molecular constitutive models for branched and hyperbranched polymers.

Transition Curvature of Soft Matrix Composite Laminates during Bending

As an Elastic memory composite (EMC) laminate is bent, the prebuckled plate theory is valid until the transition curvature at which point the postbuckled solution becomes effective. This is the point at which microbuckling is predicted to occur. From the point onward, EMC laminate follows the postbuckled solution. In this paper, a solution of the transition curvature is proposed by combining matrix shear energy with Timoshenko’s elastic foundation energy. By comparing with the intersection point of the straight line given by the prebuckled plate theory and the curve given by the postbuckled solution, the solution can be used to predict the point at which microbuckling is predicted to occur.

Polytriazole-based shape memory composite by Huisgen 1, 3 dipolar polycycloaddition reaction

A new shape memory polymer was synthesized by Huisgen 1, 3 dipolar polycycloaddition in the absence of solvents. The monomers, namely, 2,2′ (4,4′bispropargylbiphenol) perfluropropane and bisphenol A (bisazidohydroxy propyl) ether, were synthesized and characterized by FTIR, 1H and 13C NMR spectroscopies. The polyaddition resulting in polytriazole was performed at 140°C. The elastic memory carbon composite of the resultant linear polymer possessed transition temperature of 113°C and exhibited good shape recovery (95%) and shape retention (99%) properties. The polymer is thermally stable up to 275°C.