Memory Effect Research Articles

Evidence on the effect of priming-induced memory on the mechanisms underlying sugar transport and metabolism in rice plants under salinity

Evidence on the effect of priming-induced memory on the mechanisms underlying sugar transport and metabolism in rice plants under salinity

Quantumness of electron transport in quantum dot devices through Leggett–Garg inequalities: A non-equilibrium Green’s function approach

Although coherent manipulation of electronic states can be achieved in quantum dot (QD) devices by harnessing nanofabrication tools, it is often hard to fathom the extent to which these nanoelectronic devices can behave quantum mechanically. Witnessing their nonclassical nature would thus remain of paramount importance in the emerging world of quantum technologies, since the coherent dynamics of electronic states plays there a crucial role. Against this backdrop, we resort to the general framework of Leggett-Garg inequalities (LGI) as it allows for distinguishing the classical and quantum transport through nanostructures by way of various two-time correlation functions. Using the local charge detection at two different time, we investigate here theoretically whether any quantum violation of the original LGI exists with varying device configurations and parameters under both Markovian and non-Markovian dynamics. Two-time correlators within LGI are derived in terms of the non-equilibrium Green’s functions (NEGFs) by exactly solving the quantum Langevin equations. The present study of non-Markovian dynamics of quantum systems interacting with reservoirs is significant for understanding the relaxation phenomenon in the ultrafast transient regime to especially mimic what happens to high-speed quantum devices. We can potentially capture the effect of finite reservoir correlation time by accounting for level-broadening at the electrodes along with non-Markovian memory effects. Furthermore, the large bias restriction is no longer imposed in our calculations so that we can safely consider a finite bias between the electronic reservoirs. Our approach is likely to open up new possibilities of witnessing the quantumness for other quantum many-body systems as well that are driven out of the equilibrium.

Influence of Fractional Order on the Behavior of a Normalized Time-Fractional SIR Model

In this paper, we propose a novel normalized time-fractional susceptible–infected–removed (SIR) model that incorporates memory effects into epidemiological dynamics. The proposed model is based on a newly developed normalized time-fractional derivative, which is similar to the well-known Caputo fractional derivative but is characterized by the property that the sum of its weight function equals one. This unity property is crucial because it helps with evaluating how the fractional order influences the behavior of time-fractional differential equations over time. The normalized time-fractional derivative, with its unity property, provides an intuitive understanding of how fractional orders influence the SIR model’s dynamics and enables systematic exploration of how changes in the fractional order affect the model’s behavior. We numerically investigate how these variations impact the epidemiological dynamics of our normalized time-fractional SIR model and highlight the role of fractional order in improving the accuracy of infectious disease predictions. The appendix provides the program code for the model.

Experimental study on multiple self-healing and impact properties of 2D carbon fiber fabric-reinforced epoxy composites with shape memory properties

Fiber-reinforced thermoset polymers are widely used in aerospace as a material with excellent performance. However, for the low-velocity impact damage to which they are most susceptible, existing repair methods are difficult to maintain the aerodynamic performance of the components (back to its pre-damage shape) after repair. In this study, the multiple impact deformation recovery, internal damage healing, and post-repair impact properties of epoxy-PCL (ε-caprolactone) 2D carbon fiber fabric-reinforced polymers with shape memory and self-healing properties were investigated. The material is manufactured using a hot press tank-prepreg process, curing at 160 degrees for 3.5 hours at 6 atmospheres. The results show that the incorporation of thermoplastic PCL into the composite matrix can enhance the self-healing ability and impact resistance of the material. Composites after lower energy impacts retain their structural integrity and mechanical properties after healing. Materials can recover effectively from a single impact, but repeated impacts can lead to more extensive damage, which makes healing more difficult and causes a decrease in Healing efficiency. The shape memory effect of composites can restore plastic deformation caused by impact, which highlights the potential of shape memory smart composites for aerospace applications.

Использование имплантатов из пористого никелида титана в замещении тотальных дефектов скуловой кости и дуги

Summary. The aim of the study is to increase the efficiency of total replacement of the zygomatic bone and arch by developing a new medical technology using shape memory materials. Materials and methods of the study. Research materials – technologies using shape memory materials. Research method – development of an endoprosthesis of the zygomatic bone and arch. Research results and their analysis. Analysis of the research results showed: – the use of endoprostheses made of porous titanium nickelide in accordance with the anatomical features of the affected organ makes it possible to fully restore the lost anatomical and functional capabilities of the facial skull; – coating the endoprosthesis frame with textile titanium nickelide, in addition to improving the conditions for the interaction of the implant with the recipient tissues, allows to fully compensate for the insufficiency of the soft tissue component of the face; – fixing structures with a shape memory effect are easy to use, do not require significant time costs and provide stable fixation of the endoprosthesis with bone structures.

Biopolymer immune implants co-loaded with TMZ, R848 and IOX1 for perioperative therapy of glioblastoma

Glioblastoma (GBM), a prevalent and aggressive brain tumor, poses significant treatment challenges due to its rapid progression and the difficulty in achieving complete surgical resection. The current treatment regime, primarily surgery followed by radiotherapy and chemotherapy, offers limited success, with a five-year survival rate of less than 10 %. For addressing the challenges faced in the treatment of GBM, an approach using a biopolymer implant constructed with dynamic reversible covalent bonds, was designed to achieve controlled and constant-rate release of chemotherapy drug (Temozolomide, TMZ), immune adjuvant (Resiquimod, R848) and checkpoint inhibitor (5-carboxy-8-hydroxyquinoline, IOX1). The safety evaluation demonstrated the biocompatibility of the implants, with no significant inflammatory response or adverse effects on various systemic organs. In vivo antitumor study showed that the local delivery of drug combination via this implant significantly inhibited tumor recurrence of orthotopic GBM. Immune analysis revealed that the combination of the three drugs effectively activated systemic antitumor immune responses and induced memory effects. The synergistic mechanism of the drug combination was further validated by RNA whole sequencing. The innovative approach of combining chemotherapy and immunotherapy in biopolymer immune implants for GBM treatment showed promising and opens new avenues for treating GBM, particularly in addressing postoperative recurrence. Statement of significanceOur research introduces a pioneering approach in treating orthotopic brain glioblastoma (GBM), characterized by inevitable tumor recurrence, poor immune infiltration and the restrictive nature of the blood-brain barrier. To break the impasse of ineffective treatment for GBM, the innovative use of dynamically reversible covalent bonds in polymer matrix ensures the controlled, stable and sustained release of drug combinations of the chemotherapeutic agent temozolomide, immune adjuvants and checkpoint inhibitors, which maintains the optimal concentration in the tumor, overcoming problems associated with conventional chemotherapy such as systemic toxicity and low tumor targeting. Empirical evidence from in vivo experiments on the rat GBM model demonstrates significant outcomes: 90 % tumor size reduction and prolonged survival with over 70 % tumor cure rate.

Tailoring the microstructure, phase transition characteristics and one-way shape memory effect of Ni-Mn-Ga alloys by dual treatment of annealing and directional solidification

Tailoring the microstructure, phase transition characteristics and one-way shape memory effect of Ni-Mn-Ga alloys by dual treatment of annealing and directional solidification

Memory of shear flow in soft jammed materials.

Cessation of flow in yield stress fluids results in a stress relaxation process that eventually leads to a finite residual stress. Both the rate of stress relaxation and the magnitude of the residual stresses systematically depend on the preceding flow conditions. To assess the microscopic origin of this memory effect, we combine experiments with large-scale computer simulations, exploring the behavior of jammed suspensions of soft repulsive particles. A spatiotemporal analysis of particle motion reveals that memory formation during flow is primarily governed by the emergence of domains of spatially correlated nonaffine displacements. These domains imprint the configuration of stress imbalances that drive dynamics upon flow cessation, as evidenced by a striking equivalence of the spatial correlation patterns in particle displacements observed during flow and upon flow cessation. Additional contributions to stress relaxation result from the particle packing that reorganizes to minimize the resistance to flow by decreasing the number of locally stiffer configurations. Regaining rigidity upon flow cessation drives further relaxation and effectively sets the magnitude of the residual stress. Our findings highlight that flow in yield stress fluids can be seen as a training process during which the material stores information of the flowing state through the development of domains of correlated particle displacements and the reorganization of particle packings optimized to sustain the flow. This encoded memory can then be retrieved in flow cessation experiments.

On the isotropy of temperature fluctuations in passive scalar turbulence

We use direct numerical simulation to investigate the scalar gradient skewness downstream of a square grid element. The passive scalar is generated by applying a constant heat flux on the grid element. We primarily focus on the far-downstream grid-element centerline, where the scalar turbulence is homogeneous, and there are no mean velocity or scalar gradients. In this region, some discrepancy exists in the literature concerning the isotropy of the scalar. To the best of our knowledge, this is the first study that measures scalar gradients in all three directions. We detect nonzero skewness for the lateral scalar gradients, while in the streamwise direction, the skewness is zero. We show, through statistical analysis, that the nonzero values are solely due to steep gradients called cliffs. The cliffs originate from the bars of the grid-element that generate sharp mean scalar gradients along the lateral directions in the near wake. These mean gradients decay as the flow develops downstream, but the convected cliffs remember the generating upstream conditions and, in particular, the direction of the mean scalar gradient. Due to this memory effect, the cliffs align preferentially along a specific direction despite the absence of a local mean scalar gradient, thus making the homogeneous scalar field anisotropic. We also report some hitherto undocumented properties of cliffs and ramps through conditional statistical analysis. Finally, we corroborate our conclusions with two additional simulations, one involving grid-element turbulence in the presence of a transverse mean scalar gradient and the other scalar turbulence due to a heated cylinder.

Reverse Shape Memory Effect and Toughness Recovery of Ti-10V-2Fe-3Al Alloy

Reverse Shape Memory Effect and Toughness Recovery of Ti-10V-2Fe-3Al Alloy

Investigating the Shape Memory Effect and Corrosion Resistance of the Fe-(17-2x) Mn-6Si-xNi-yCr-0.3C Alloys (x = 0, 1, 2, 3, 4; y = 0, 1, 3, 5)

In this study, low Mn content Fe-Mn-Si-based shape memory alloys [Fe-(17-2x) Mn-6Si-xNi-yCr-0.3C (x = 0, 1, 2, 3, 4; y = 0, 1, 3, 5)] were prepared via vacuum arc remelting. The alloys were hot-rolled and solid-solution-treated at 1150 °C for 1 h followed by aging at elevated temperatures. The effects of Cr and Ni addition on the shape memory performance and corrosion resistance of the alloys in 3.5 wt% NaCl solutions were investigated using bending test and potentiodynamic polarization, respectively. It was revealed that the recoverable strain of the alloys remains larger than 2% when 1Ni is replaced with 2Mn and Cr is added. However, it becomes less than 2% in 11Mn and 9Mn alloys because of the easy formation of the α’ martensite. The shape memory effect of alloys is highly improved due to the precipitation of fine carbides in the grains by the addition of Cr and after aging treatment at elevated temperatures (≧700 °C). The highest shape recovery ratios of 88.3% for 17Mn0Ni3Cr, 94.0% for 15Mn1Ni3Cr, 94.4% for 13Mn2Ni5Cr, 88.1% for 11Mn3Ni5Cr, and 86.8% for 9Mn4Ni7Cr, respectively, were achieved after 800 °C aging treatment. The strip-like second phase (carbides) forms at the grain boundaries in the Cr-free alloys after 600 °C aging treatment. There are lots of fine carbides (M23C6 and M7C3) precipitated in the interior of the grains at the aging treatments ≧ 700 °C. However, M7C3 is eliminated at 900 °C aging treatment. The corrosion resistance results showed that the corrosion resistance of the alloys is improved by adding Cr. The maximum corrosion potentials (−0.474 V) have been observed for 13Mn2Ni5Cr, and similar mechanisms have been analyzed in all series of alloys.

Hybrid finite element and laplace transform method for efficient numerical solutions of fractional PDEs on graphics processing units

Fractional Partial Differential equations (FPDEs) are essential for modeling complex systems across various scientific and engineering areas. However, efficiently solving FPDEs presents significant computational challenges due to their inherent memory effects, often leading to increased execution times for numerical solutions. This study proposes a highly parallelizable hybrid computational approach that combines the Finite Element Method (FEM) for spatial discretization with Numerical Inversion of the Laplace Transform (NILT) for time-domain solutions, optimized for execution on Graphics Processing Units (GPUs). The NILT method’s high parallelizability, stemming from the independence of its series terms, combined with the robust spatial discretization provided by FEM, enables the efficient and accurate solution of FPDEs on GPUs, demonstrating substantial performance improvements over traditional CPU-based implementations. We observe a generalized pattern in execution time behavior that accounts for both the number of nodes and the number of NILT terms. Specifically, execution time scales quadratically with the number of nodes, while showing only a logarithmic marginal increase with the number of NILT terms These behaviors not only enables consistent performance assessment but also highlights potential areas for algorithm optimization. Validation against exact solutions of fractional diffusion and wave equations, employing Caputo, modified Caputo-Fabrizio, and modified Atangana-Baleanu derivatives, demonstrates the accuracy and convergence of the hybrid FEM-NILT method. Notably, the exact solutions of wave equation are novel in literature. The results highlight the method’s potential for enabling high-precision, large-scale simulations in fractional calculus applications, thereby advancing computational capabilities and efficiency in the field.

Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Abstract Imaging objects behind a scattering medium beyond the optical memory effect (OME) range is vital in various imaging scenarios. Despite numerous proposed imaging methods aimed at separating objects, effectively tracking moving objects remains challenging. In this paper, we present a non-invasive scattering imaging method to reconstruct target beyond the OME range and track moving objects using a speckle separation and position estimation technique. A speckle autocorrelation separation method based on frequency intensity is proposed to eliminate the noise from the cross-correlation and to separate the objects from the mixed speckles. A position estimation technique is proposed to determine the positional relationships of the objects using speckle deconvolution using the point spread function of imaging regions. A non-invasive imaging system is designed to track the moving object without prior knowledge of the shape and distribution of objects. Experimental validation shows the efficiency of the method for separation and localization, allowing for the reconstruction of the target behind the scattering medium and tracking of the moving object behind the scattering medium. Moreover, the method is capable of separating and tracking a moving object when multiple objects are in the background.

OQuPy: A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is, however, a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems (OQS) in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows for an exact yet highly efficient description of non-Markovian OQS (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of OQS in fields such as quantum chemistry, quantum sensing, and quantum information.

A 3D finite deformation constitutive model for anisotropic shape memory polymer composites integrating viscoelasticity and phase transition concept

The phase transition theory of shape memory polymers (SMPs) often involves a phenomenological assumption that the reference configuration of the newly transformed phase deviates from that of the initial phase. This distinction serves as a crucial mechanism in the manifestation of the shape memory effect. However, elucidating the precise definition of the reference configuration of the transformed phase poses a significant challenge in the formulation of the constitutive model. To tackle this challenge, a three-dimensional (3D) finite deformation constitutive model incorporating effective phase evolution for SMPs has been developed. This model merges insights from the classical viscoelastic framework with the phase transition theory. The anisotropic thermo-viscoelastic constitutive model is further developed by introducing hyperelastic fibers, which integrate the anisotropy of the fibers into a continuous thermodynamic framework through structure tensors. Implemented within the ABAQUS software via a user material (UMAT) subroutine, the proposed model has been meticulously validated against experimental data, showcasing its prowess in simulating stress-strain responses and shape memory characteristics of SMPs and their composites (SMPCs). This innovative model stands as an invaluable instrument for the design and of sophisticated SMP and SMPC structures.

Towards a Generalized Bayesian Model of Reconstructive Memory

AbstractPrior knowledge has long been known to shape reconstruction from memory. An individual stimulus from a category is often remembered to be closer to the center of that category than its true location. This effect, together with more complex memory effects that involve prior knowledge at multiple levels of abstraction, has been successfully explained by the Category Adjustment Model (CAM; Huttenlocher et al. Journal of Experimental Psychology: General, 129(2), 220, 2000) and its extensions. However, recent experimental results diverge from CAM’s predictions showing that reconstructive memory for atypical category examples is influenced by the category center less than that of typical category examples. To unify these findings, we propose a generalized Bayesian model of reconstructive memory, called the generalized CAM model (g-CAM). We demonstrate through simulations that g-CAM can account for previously known effects of reconstructive memory, while additionally capturing recent empirical findings involving atypical category examples.

Hyers–Ulam stability of Nipah virus model using Atangana–Baleanu–Caputo fractional derivative with fixed point method

In this study, we present a novel investigation into the dynamics of the Nipah virus through the lens of fractional differential equations (FDEs), employing the Atangana–Baleanu–Caputo fractional derivative (ABCFD) and the fixed-point approach (FPA). The core contribution of this work lies in establishing the existence and uniqueness of solutions to the proposed FDEs, a critical step for validating the model. Furthermore, we explore the Hyers–Ulam (HU) stability of these generalized FDEs, providing a rigorous mathematical foundation for the stability analysis within the context of viral dynamics. By leveraging the ABCFD, our work extends the classical stability criteria, offering new insights into the role of memory effects in disease modeling. Additionally, we present approximate solutions across various compartments and fractional orders, highlighting the sensitivity of the system to key parameters. Numerical simulations, conducted using the Cullis method, illustrate the impact of fractional orders and validate the theoretical findings, positioning this work as a significant advancement in the application of fractional calculus to epidemiological models.

Molecular switching by proton-coupled electron transport drives giant negative differential resistance

To develop new types of dynamic molecular devices with atomic-scale control over electronic function, new types of molecular switches are needed with time-dependent switching probabilities. We report such a molecular switch based on proton-coupled electron transfer (PCET) reaction with giant hysteric negative differential resistance (NDR) with peak-to-valley ratios of 120 ± 6.6 and memory on/off ratios of (2.4 ± 0.6) × 103. The switching dynamics probabilities are modulated by bias voltage sweep rate and can also be controlled by pH and relative humidity, confirmed by kinetic isotope effect measurements. The demonstrated dynamical and environment-specific modulation of giant NDR and memory effects provide new opportunities for bioelectronics and artificial neural networks.

Simulation and time series analysis of responsive active Brownian particles (rABPs) with memory

To realise the goals of active matter at the micro- and nano-scale, the next generation of microrobots must be capable of autonomously sensing and responding to their environment to carry out pre-programmed tasks. Memory effects are proposed to have a significant effect on the dynamics of responsive robotic systems, drawing parallels to strategies used in nature across all length-scales. Inspired by the integral feedback control mechanism by which Escherichia coli (E. coli) are proposed to sense their environment, we develop a numerical model for responsive active Brownian particles (rABP) in which the rABPs continuously react to changes in the physical parameters dictated by their local environment. The resulting time series, extracted from their dynamic diffusion coefficients, velocity or from their fluctuating position with time, are then used to classify and characterise their response, leading to the identification of conditional heteroscedasticity in their physics. We then train recurrent neural networks (RNNs) capable of quantitatively describing the responsiveness of rABPs using their 2D trajectories. We believe that our proposed strategy to determine the parameters governing the dynamics of rABPs can be applied to guide the design of microrobots with physical intelligence encoded during their fabrication.

Flow memory effects on the nonlinear hydrodynamic load of an ROV in translational maneuvering

This paper describes experimental and numerical investigations of the flow memory effect on the nonlinear hydrodynamic load response of a work-class remotely operated vehicle (ROV) under surge, sway, and heave impulse motions. The hysteresis loops of the dynamic drag coefficients during impulse motion tests are analyzed by comparing them with those obtained in steady drag tests. The areas of the loops and differences between the maximum and minimum dynamic and steady coefficients are used to quantify the flow memory effect. A novel unsteady hydrodynamic model for ROVs in translational maneuvering is established. This model considers the flow memory effect in terms of the unsteady hydrodynamic load response obtained from impulse motion response tests. The results given by the unsteady model are found to be in good agreement with those from CFD simulations. The unsteady hydrodynamic characteristics (including the asymmetric forces, force magnitude, and phase delay), motion amplitude, and frequency effect on the hydrodynamic forces of the ROV are analyzed by comparing the unsteady model results with those obtained from the steady model, revealing the rules governing the flow memory effect.