Memory Effect Research Articles

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.

Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference.

Transcription is a stochastic process that involves several downstream operations which make it difficult to model and infer transcription kinetics from mature RNA numbers in individual cell. However, recent advances in single-cell technologies have enabled a more precise measurement of the fluctuations of nascent RNA that closely reflect transcription kinetics. In this paper we introduce a general stochastic model to mimic nascent RNA kinetics with complex promoter architecture. We derive the exact distribution and moments of nascent RNA using queuing theory techniques, which provide valuable insights into the effect of the molecular memory created by the multistep activation and deactivation on the stochastic kinetics of nascent RNA. Moreover, based on the analytical results, we develop a statistical method to infer the promoter memory from stationary nascent RNA distributions. Data analysis of synthetic data and a realistic example, the HIV-1 gene, verifies the validity of this inference method.

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.

Stress-induced microstructural evolution of martensite in additive manufactured Cu-13Al-4Mn shape memory alloy

Cu-13Al-4Mn (wt%) shape memory alloys were fabricated by additive manufacturing and exhibits good shape memory effect. Corresponding microstructural evolution was revealed under the compressive condition. Results indicate that stress-induced twin-related transformation played an important role in deformation process. At the initial deformation stage, the reorientation of (−1–1) twin was activated. With the pre strain increased, detwinning and crossed deformation twin occurred, which accompanied by massive stacking faults, Lomer-Cottrell (L-C) locks, resulted in the formation of irrecoverable strain.

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.

Specific and shared cognitive predictors of drawing and block building in typically developing children.

Spatial construction is a complex ability involving attention, global/local visual processing, mental representation, visuo-motor coordination and, to varying extent, working memory and executive functions, and verbal abilities. In developmental neuropsychology, little attention has been paid to comprehend whether and to what extent the above cognitive processes are involved in two main spatial construction tasks, that is drawing and block building. We used path analysis to test shared and specific effects of verbal and spatial working memory, spatial attention, inhibition, verbal abilities (vocabulary and naming), figure disembedding, mental rotation, and visual-motor coordination, as well as of demographics (sex, age and socio-economic status), on two classical drawing (Rey-Osterrieth Complex Figure; ROCF) and block building (Block design; BD) tasks in a sample of 195 typically developing children (age range: 7-11 years). Figure disembedding and visuo-motor coordination were the only shared predictors of both spatial construction tasks. Moreover, ROCF score was directly related with spatial attention and inhibition, while BD score was directly related with sex, vocabulary, mental rotation and backward spatial working memory. These findings distinguish between abilities involved in spatial construction regardless of the type of task and those specifically related to ROCF or Block Design, thus providing clues relevant to neuropsychological assessment and intervention in children with spatial construction disorders.

Transitions between equilibrium and nonequilibrium phenomena in the description of crystal growth

The close intertwining of equilibrium and nonequilibrium thermodynamic representations and transitions between the two limiting principles of thermodynamics: the second beginning and the principle of least coercion (minimum entropy production in the stationary regime) constitute the main content of phenomenological theories of crystal growth. The difference of basic postulates of two sections of thermodynamics forces to discuss problems of reversibility and irreversibility of time, scales of observed phenomena and rules of conjugation of thermodynamic forces and flows in theories of crystal growth. A variant of the solution of some conjugation problems is shown on the example of the fluctuation model of dislocation crystal growth, which is based on the stationary isothermal process of thermodynamic free energy fluctuations. In the case of the limiting mode of adsorption of impurities on the crystal face according to the Langmuir model, the free energy fluctuations possessing the absence of the memory effect allow us to identify three chemical potentials of building particles that determine the corresponding values of solution supersaturations realized at different scale levels at the growing crystal face containing a helical dislocation. The supersaturations control quasi-equilibrium and nonequilibrium thermodynamic processes that constitute a single dislocation mechanism of crystal growth.

Covalent Adaptable Network of Semicrystalline Polyolefin Blend with Triple-Shape Memory Effect

A covalent adaptable network (CAN) of semicrystalline polyolefin blends with triple-shape memory effects was fabricated by the reactive melt blending of maleated polypropylene (mPP) and maleated polyolefin elastomer (mPOE) (50 wt/50 wt) in the presence of a small amount of a tetrafunctional thiol (PETMP) and 1,5,7-triazabicyclo [4,4,0]dec-5-ene (TBD). The polymer blend formed a chemically crosslinked network via the reaction between the thiol group of PETMP and maleic anhydride of both polymers in the blend, which was confirmed by FTIR, the variation of torque during the melt mixing process, a solubility test, and DMA. DSC analysis revealed that the crosslinked polyolefin blends show two distinct crystalline melting transitions corresponding to each component polymer. Improved tensile strength as well as elongation at break were observed in the crosslinked blend as compared to the simple blend, and the mechanical properties were maintained after repeated melt processing. These results suggest that thermoplastic polyolefin blends can be transformed into a high-performance and value-added material with good recyclability and reprocessability.

Microstructural evolution and phase transformation behavior of Ni-Ti alloys prepared by accumulative roll bonding-deformation diffusion process

Ni-Ti shape memory alloys were prepared by accumulative roll bonding-deformation diffusion (ARB-DD) process. Effects of deformation heat treatment on the microstructural evolution and phase transition behavior of ARB-DD Ni-Ti alloy were investigated by thermodynamic calculations, and the intrinsic mechanism was revealed. The results show that strong shear is induced by intensified plastic flow of the metal at the interface, resulting the interfaces engage with each other. The simultaneous opening and cross-tangling of multi-directional dislocations leads to the dislocation cells and Taylor lattice interact near the interface, and the compounds are preferentially formed. The ARB-DD treatment resulted in a one-step phase transition for the Ni-Ti alloy. After deformation heat treatment, R-phases appear and the phase transition is transformed into two-step. During heating and cooling the phase transitions are B19’→R→B2 and B2→R→B19’, respectively. The thermal hysteretic temperature increases and reaches 57.0 °C. The microstructure transforms from dislocations to nano-twin. A lattice distortion zone with a width of about 10 nm exists at the interface between precipitates and the matrix. It reduces the energy barrier required for phase transformation, leading to stabilization of the B2 phase and de-stabilization of the B19’ phase. Eventually, a wider temperature interval for the phase transformation is obtained. This process is an effective way to prepare Ni-Ti alloys with favorable shape memory effect.

On Self-Positioning and Self-Fixation of Parts Made of Alloys with Shape Memory Effect under Component Assembling

Introduction Violation of mutual positioning and fixation of parts worsens the operation of the equipment. Traditional approaches to solving the problem under consideration have been sufficiently studied: interchangeability of parts and the use of special equipment. Both methods involve a significant number of additional elements and assembly operations. Fixation is often provided by means of force fitting and welding. Disadvantages of these methods include assembly, residual and other stresses, engineering constraints, etc. To solve these problems, alloys with thermoelastic phase transformations are used, which provide shape memory effects (SME) to manifest themselves. This article describes, for the first time, self-positioning and self-fixation using the example of parts specially made from an alloy with SME. Materials and Methods. The pin element under pressing mandrels the blind hole of the cup and enters the seat. The alloy with SME was Ti-55.7wt%Ni. The temperature of the onset of its austenitic transformation was As = 95°C ± 5°C. The elemental composition was determined by a Shimadzu EDX-8000 X-ray fluorescence spectrometer, the phase composition — by a Shimadzu XRD-7000 diffractometer. The temperature was specified through differential scanning calorimetry. The range was 20–300°C, the heating rate was 5 deg/min. A Guide T120 thermal imager and a RangeVision DIY 3D scanner with structured illumination were used. After pressing the pin into the cup at different angles, the alignment and deviations between the axes of the cup and the pin were examined. Then, the cup was heated to 110–120°C, cooled, and control measurements were taken.Results. Values of the deflection angle after pressing were 0.2–11°. With a rigid structure and an installation angle of 0°, the pin deflected in the mounting hole by 0.2–0.5°. The axes shifted and did not intersect. The pin was not always completely pressed in. This indicated uneven deformation of the metal and different stress values around the hole. Such a unit would soon fail. The pin took the required position after heating the cup to 110–120°C (this temperature was higher than at the end of the reverse martensitic transformation). The angular deviation of the axes was noted to be 0.03–0.1°. The maximum misalignment (0.04 mm) corresponded to high positioning accuracy. Heating during the reverse martensitic transformation created internal stresses that returned the initial geometry of the cup. They also formed the forces that positioned and fixed the pin in the hole. That is, it is the parts that provide positioning and fixation (this is selfpositioning and self-fixation).Discussion and Conclusion. For self-positioning and self-fixation of parts due to the shape memory effect, it is necessary to avoid sharp transition lines between the surfaces of parts during design, select rounded corners or fillets, and get a clean surface without burrs. Self-fixation and self-positioning reduce defects and inaccuracies during assembly. The use of certain alloys increases the profitability of equipment production.

Recent development of nanomaterials-based PDT to improve immunogenic cell death.

Photodynamic therapy (PDT) is a clinically approved therapeutic modality for treating oncological and non-oncological disorders. PDT has proclaimed multiple benefits over further traditional cancer therapies including its minimal systemic toxicity and selective ability to eliminate irradiated tumors. In PDT, a photosensitizing substance localizes in tumor tissues and becomes active when exposed to a particular wavelength of laser light. This produces reactive oxygen species (ROS), which induce neoplastic cells to die and lead to the regression of tumors. The contributions of ROS to PDT-induced tumor destruction are described by three basic processes including direct or indirect cell death, vascular destruction,and immunogenic cell death. However, the efficiency of PDT is significantly limited by the inherent nature and tumor microenvironment. Combining immunotherapy with PDT has recently been shown to improve tumor immunogenicity while decreasing immunoregulatory repression, thereby gently modifying the anticancer immune response with long-term immunological memory effects. This review highlights the fundamental ideas, essential elements, and mechanisms of PDT as well as nanomaterial-based PDT to boost tumor immunogenicity. Moreover, the synergistic use of immunotherapy in combination with PDT to enhance immune responses against tumors is emphasized.

Three-Dimensional Phase-Field Simulation of Stress-Assisted Two-Way Shape Memory Effect and Its Cyclic Degradation of Single-Crystal NiTi Shape Memory Alloy

Three-Dimensional Phase-Field Simulation of Stress-Assisted Two-Way Shape Memory Effect and Its Cyclic Degradation of Single-Crystal NiTi Shape Memory Alloy

Single‐Shot Non‐Invasive Imaging Through Dynamic Scattering Media Beyond the Memory Effect via Virtual Reference‐Based Correlation Holography

AbstractNon‐invasive wide‐field imaging through dynamic random media is a sought‐after goal with important applications ranging from medical diagnosis to remote sensing. However, some existing methods, such as speckle correlation‐based techniques, are limited in field of view due to the memory effect; while some other methods, such as wavefront shaping and transmission matrix techniques, face considerable challenges when applied in dynamic scenarios because of the complexity involved in modulation and measurement. These limitations significantly impede the effectiveness and applicability of these approaches. Here, the concept of virtual reference light (VRL), which allows for the reconstruction of the original object with just a single‐shot detection of the speckle is proposed. Experimental results demonstrate that the imaging field achieves a 3.8‐fold memory effect range. In the experimental setup, the light source and detector are positioned on one side of the random medium, while the sample is placed on the opposite side, enabling non‐invasive detection. Imaging results with both static and dynamic scattering media are presented to verify the feasibility of the proposed method, offering an effective solution for real‐time target imaging and detection.

Higher memory effects and the post-Newtonian calculation of their gravitational-wave signals

Abstract A new hierarchy of lasting gravitational-wave effects (the higher memory effects) was recently identified in asymptotically flat spacetimes, with the better-known displacement, spin, and center-of-mass memory effects included as the lowest two orders in the set of these effects. These gravitational-wave observables are determined by a set of temporal moments of the news tensor, which describes gravitational radiation from an isolated source. The moments of the news can be expressed in terms of changes in charge-like expressions and integrals over retarded time of flux-like terms, some of which vanish in the absence of radiation. In this paper, we compute expressions for the flux-like contributions to the moments of the news in terms of a set of multipoles that characterize the gravitational-wave strain. We also identify a part of the strain that gives rise to these moments of the news. In the context of post-Newtonian theory, we show that the strain related to the moments of the news is responsible for the many nonlinear, instantaneous terms and "memory" terms that appear in the post-Newtonian expressions for the radiative multipole moments of the strain. We also apply our results to compute the leading post-Newtonian expressions for the moments of the news and the corresponding strains that are generated during the inspiral of compact binary sources. These results provide a new viewpoint on the waveforms computed from the multipolar post-Minkowski formalism, and they could be used to assess the detection prospects of this new class of higher memory effects.

Covariant phase space formalism for fluctuating boundaries

We reconsider formulating D dimensional gauge theories, with the focus on the case of gravity theories, in spacetimes with boundaries. We extend covariant phase space formalism to the cases in which boundaries are allowed to fluctuate. We analyze the symplectic form, the freedoms (ambiguities), and its conservation for this case. We show that boundary fluctuations render all the surface charges integrable. We study the algebra of charges and its central extensions, charge conservation, and fluxes. We briefly comment on memory effects and questions regarding semiclassical aspects of black holes in the fluctuating boundary setup.