Memory Effect Research Articles

Synthesis and nanoparticle formation of pyrazine, benzodithiophene (BDT) and fluorene containing conjugated polymer (P-PBF) and its biosensing performance toward catechol

The development of laccase-based biosensors, in particular, have attracted much interest due to their ability to detect highly toxic molecules in the environment. This article reports a novel biosensor for catechol detection that can be used to determine toxic molecules in the environment. For this purpose, a novel conjugated polymer (P-PBF) and conjugated polymer nanoparticles (P-PBFNPs) were synthesized and characterized, respectively. To our knowledge, research has yet to be done on conjugated polymer nanoparticle-based electrochemical biosensing platforms for catechol determination. The biosensor comprises a GE/P-PBF NPs/Lac electrode, constructed with P-PBF NPs deposited onto a bare graphite electrode followed by laccase (Lac) enzyme immobilization. While electrochemical characterization was performed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), surface characterization was performed by field emission scanning electron microscopy (FE-SEM). The biosensor exhibited high sensitivity (1.229 μA/μM.cm2), a good linear range (0.5–25 μM) and a low limit of detection (LOD) (0.49 μM). Moreover, the developed biosensor exhibited high selectivity in the presence of interfering analytes without any memory effects. The potential of the GE/P-PBF NPs/Lac electrode in tap water, green tea, synthetic urine and serum samples indicates the inspiring biosensing applications for monitoring environmental pollution.

Effect of Shape Memory on the Mechanical Properties of Concrete Reinforced with Short Shape Memory Alloy Fibers

Effect of Shape Memory on the Mechanical Properties of Concrete Reinforced with Short Shape Memory Alloy Fibers

Realization of the shape memory effect in a composite material PLA/Diopside with different supramolecular structures

This study explores the realization of the shape memory effect (SME) in composite materials composed of polylactide (PLA) filled with diopside particles exhibiting varied supramolecular structures, such as spherulites and amorphous lamellar structures. We investigated the influence of diopside filler on the thermomechanical properties and shape recovery behavior of PLA-based composites. Different supramolecular structures of PLA were achieved through controlled crystallization processes. Comprehensive characterization techniques, including differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and dynamic mechanical analysis (DMA), were employed to elucidate the structure-property relationships. The results indicate that the diopside enhances the SME of PLA composites, with the degree of improvement being dependent on the specific supramolecular structure of the polymer matrix. Our findings provide insights into the design of advanced SMPs with tailored properties for potential applications in medicine.

Effect of CsPbBr3 perovskite quantum dots on molecular alignment, dielectric and memory effect of a cyanobiphenyl based liquid crystal

Effect of CsPbBr3 perovskite quantum dots on molecular alignment, dielectric and memory effect of a cyanobiphenyl based liquid crystal

Study of atomic diffusion behavior in diffusion welding of NiTi alloy - based on molecular dynamics

NiTi alloy with nearly equiatomic ratios of Ni and Ti, has a unique shape memory effect and biocompatibility. Atomic diffusion is a determinant factor for the diffusion welding of NiTi alloys joining. In general, the atomic diffusion may be mediated by the temperature, time and pressure and so on. Despite the importance of technology, Ni and Ti atoms’ diffusion mechanism, however, still remains un-elucidated. Few works have investigated the movement rules of atoms from the atomic scale. Molecular dynamics simulations of diffusion welding were performed for 200-800ps to investigate the diffusion process of the NiTi alloy composite plate at temperatures of 1000, 1100, 1200, and 1300K. The results show that the holding temperature had the greatest effect on diffusion, with pressure having the least effect. The diffusion coefficients of NiTi, Ni, and Ti were calculated at temperatures of 1000, 1100, 1200, and 1300K, respectively. The diffusion degree of Ni atoms was higher than that of Ti atoms. The diffusion coefficients of Ni and Ti atoms satisfied the Arrhenius equation, and the diffusion activation energy of Ni atoms was lower than that of Ti atoms. Furthermore, this study demonstrates at the atomic scale that the growth of the diffusion layer at high temperatures follows the parabolic growth kinetics law.

Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor

Studying the influence of vegetation dynamics on water storage is fundamental for efficiently managing ecosystems in dryland areas. Therefore, this study was designed to investigate the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage (TWS) trends across the China-Pakistan Economic Corridor (CPEC). Exploring vegetation growth's effect on TWS utilizes remote sensing and statistical methods such as generalized additive model (GAM), random forest, Mann-Kendall test, Sen's slope, and Partial correlation coefficient. Our results revealed a consistent increase in vegetation cover in semi-arid and dry sub-humid regions, especially in croplands, from 1986 to 2020. However, a noticeable finding was that TWS has declined by approximately 39.65 % in the study area. On the other hand, forests have displayed resilience by mitigating the effects of climate change and human activities through hydraulic memory effects. Additionally, soil moisture has decreased in various land cover types, with croplands experiencing the most significant decrease. Similarly, increased vegetation growth in greening drylands significantly negatively impacts terrestrial water storage, with correlations of −0.31 for terrestrial water storage anomaly (TWSA), −0.26 for root zone soil moisture (RZSM), and −0.29 for surface soil moisture (SSM). Meanwhile, evapotranspiration (ET) also had negative correlations with TWSA, RZSM, and SSM, with standardized coefficients of −0.23, −0.16, and −0.11, respectively. These findings exposed the study area's vegetation interaction with land cover and hydrological dynamics. Addressing these hydrological imbalances will help ensure sustainable ecological management in the study area.

Effects of Memory on Inventory Control and Pricing Policy for Imperfect Production with Rework Process

Fractional calculus is a pertinent way to study the memory effect in an inventory model for investigating its dynamical behavior. Since inventory management is a memory-dependent process, fractional calculus approach may be employed to discover some fruitful insights and can help to gain more profit. In realistic scenarios, the manufacturing process cannot be perfect, and it delivers some faulty units due to many inevitable reasons. In literature, an imperfect production inventory problem under the memory effect has not been studied. Our study aims to investigate the memory effect on a production inventory system. In this article, a fractional order inventory control problem is formulated by considering an imperfect manufacturing process and price-sensitive demand. The faulty units are repaired through a rework process. Caputo fractional derivatives and integrals are used to consider the memory effect. Due to the nonlinear cost elements in the formulated problem, optimal pricing and production policies are investigated by using a quasi-Newton optimization algorithm and particle swarm optimization approach. The managerial implications of the proposed study are discussed with the help of numerical illustrations. The numerical outcomes suggest that consideration of memory in the inventory system boosts the profitability of the firm.

Identifying signals of memory from observations of animal movements

AbstractIncorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into step-selection analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data.

The lasting effect of childhood circus memories: nostalgia and guilt

ABSTRACT Traditional Big Top Circus has experienced a decline in popularity due to changing attitudes to its former elements and to the competition from other accessible forms of entertainment. Our study explores how childhood memories of circus experiences are retold and how these are revisited in the light of changing social values and life experiences. Data was generated through sixteen discussion groups with a total of eighty people over the age of 65 who had experienced circus in their childhood. Thematic analysis found that the circus experience still creates vivid memories decades later. These memories are based around emotions engendered by the sights, sounds and smells of the circus but also the fear of danger and the unknown. Our participants fondly remembered who they were with, highlighting the importance of the relational and social aspects in such leisure experiences. These factors induced a strong sense of nostalgia, but this was tainted by the recognition that what was enjoyed then may no longer be socially acceptable. We conclude that for traditional circus to remain relevant, it needs to harness the strength of nostalgia but separate this from guilt at what circus once included. In a highly competitive live experience, environment circus needs to create heightened sensory experiences (including fear), alongside family togetherness appealing to multiple generations with potentially different values.

Theory on New Fractional Operators Using Normalization and Probability Tools

We show how a rescaling of fractional operators with bounded kernels may help circumvent their documented deficiencies, for example, the inconsistency at zero or the lack of inverse integral operator. On the other hand, we build a novel class of linear operators with memory effects to extend the L-fractional and the ordinary derivatives, using probability tools. A Mittag–Leffler-type function is introduced to solve linear problems, and nonlinear equations are addressed with power series, illustrating the methods for the SIR epidemic model. The inverse operator is constructed, and a fundamental theorem of calculus and an existence-and-uniqueness result for differintegral equations are proven. A conjecture on deconvolution is raised, which would permit completing the proposed theory.

The salience of an agency experience influences its ability to improve memory.

Previous work reported that having a strong sense of agency can enhance memory for acted-upon items: Memory enhancement is evident when there is a strong sense of agency, but not when there is only a weak sense of agency. However, because of the way trials are distributed across conditions in typical studies, it is often the case that a strong sense of agency may also be very salient because it is experienced only infrequently within the context of the experiment. In this study, we examined the importance of salience in determining the memory enhancement potential of a sense of agency. We found that a weak sense of agency, provided it is made salient by infrequent occurrence, can also produce an improvement in memory. This finding indicates that salience can play an important role in determining whether an agency-related memory effect is ultimately observed. More generally, this highlights the importance of taking into consideration the salience of an agency experience when discussing its ability to affect cognition.

A new concept of structural smart fabric activated by shape memory alloys

Abstract This work presents the development, prototyping and validation of a new concept of structure called structural smart fabric (SSF). An SSF is a chainmail fabric composed by interconnected elements, called cells, which can be stiffened by reactive elements, such as those made by shape memory alloy (SMA). Exploiting the shape memory functionality, SSFs are capable of sensing and reacting to external stimuli. Based on this concept, in this study we propose an SSF that integrates SMA wires into a 3D-printed chainmail structure. The shape memory effect of these wires is used as an actuating mechanism that, at high temperature, tightens the adjacent cells together and provides increased structural stiffness. In this study, finite element simulations were initially conducted to enhance the comprehension of the SSF’s mechanical behaviour. The influence of the initial cell spacing on the SSF stiffness and the wire stress profiles was evaluated during bending loading, as well as the evolution of contact pressure profiles between adjacent cells. This numerical approach enabled to tune the design of the SSF prototypes which were successively manufactured using selective laser sintering additive manufacturing technology with PA12. After the integration with the SMA wires, the SSF prototypes were tested under a 3-point bending configuration at different temperatures. The results revealed a remarkable increase in structural stiffness at elevated temperatures compared to ambient conditions. This study set the basis for a deeper understanding of SSF’s unique capabilities and potential applications in fields where adaptive and responsive structures are required.

Transition Path Dynamics of Non-Markovian Systems across a Rough Potential Barrier.

Transition paths refer to rare events in physics, chemistry, and biology where the molecules cross barriers separating stable molecular conformations. The conventional analysis of the transition path times employs a diffusive and memoryless transition over a smooth potential barrier. However, it is widely acknowledged that the free energy profile between two minima in biomolecular processes is inherently not smooth. In this article, we discuss a theoretical model with a parabolic rough potential barrier and obtain analytical results of the transition path distribution and mean transition path times by incorporating absorbing boundary conditions across the boundaries under the driving of Gaussian white noise. Further, the influence of anomalous dynamics in rough potential driven by a power-law memory kernel is analyzed by deriving a time-dependent scaled diffusion coefficient that coarse-grains the effects of roughness, and the system's dynamics is reduced to a scaled diffusion on a smooth potential. Our theoretical results are tested and validated against numerical simulations. The findings of our study show the influence of the boundary conditions, barrier height, barrier roughness, and memory effect on the transition path time distributions in a rough potential, and the validity of the scaling diffusion coefficient has been discussed.

NCOG-31. NEUROCOGNITIVE OUTCOME FOLLOWING AWAKE CRANIOTOMY FOR INSULAR GLIOMA RESECTION: IMPACT OF SURGICAL APPROACH AND TUMOR CLASSIFICATION

Abstract BACKGROUND Insular glioma resection harbors similar risk of neurocognitive function (NCF) decline as other tumor locations. However, differences in outcomes according to transsylvian versus transcortical surgical approach and tumor classification remain unknown. METHODS 53 patients with newly diagnosed insular glioma (47% high-grade; 23% GBM; 75% left hemisphere) underwent awake craniotomy for resection (transsylvian, 64%; transcortical, 36%). Patients completed neuropsychological testing preoperatively and <2 months postoperatively (M interval=23.7 days). Tumor location was described with Berger-Sinai zones collapsed into anterior (I and/or IV), posterior (II and/or III), superior (I and/or II), and inferior (III and/or IV) groups. Tumor extension was characterized with the Pitskhelauri system into insula only, insula with extension (lobar or medial), and predominantly (>50%) extra-insular groups. Result: Decline (z-score change<-1.0) on at least 1 test occurred in 85% of patients with largest effect (partial η2) in memory (.29–.44) and verbal fluency (.39). There was no interaction with approach on any NCF change score, though transcortical was associated with more frequent fluency decline (61% vs. 26%, p=.014). Left hemisphere tumor was associated with poorer outcome on 6/12 tests (all p<.01). Predominantly extra-insular tumors showed greater reductions in fluency (M=-1.21, SD=0.90) than insula only tumors (M=-0.10, SD=0.76; p=0.024). Number of Berger-Sanai quadrants involved was inversely associated with change in executive functioning (r=-0.34, p=.018). Poorer executive function outcome was also found in anterior (M=-0.61, SD=1.41) versus posterior tumors (M=0.86, SD=1.51; p=.014), and recognition memory outcome worse for inferior (M=-2.44, SD=1.81) than superior lesions (M=-0.31, SD=1.47; p=.021). CONCLUSIONS NCF decline is common in the early term following resection of insular glioma. While risk of verbal fluency decline may be greater for transcortical surgeries, outcomes were largely similar across approaches. In addition to hemisphere, tumor extension and insular zone localization may inform domain-specific risk of NCF decline following resection.

Modeling Zika Virus Disease Dynamics with Control Strategies

In this research, we formulated a fractional-order model for the transmission dynamics of Zika virus, incorporating three control strategies: health education campaigns, the use of insecticides, and preventive measures. We conducted a theoretical analysis of the model, obtaining the disease-free equilibrium and the basic reproduction number, and analyzing the existence and uniqueness of the model. Additionally, we performed model parameter estimation using real data on Zika virus cases reported in Colombia. We found that the fractional-order model provided a better fit to the real data compared to the classical integer-order model. A sensitivity analysis of the basic reproduction number was conducted using computed partial rank correlation coefficients to assess the impact of each parameter on Zika virus transmission. Furthermore, we performed numerical simulations to determine the effect of memory on the spread of Zika virus. The simulation results showed that the order of derivatives significantly impacts the dynamics of the disease. We also assessed the effect of the control strategies through simulations, concluding that the proposed interventions have the potential to significantly reduce the spread of Zika virus in the population.

STRUCTURAL FEATURES OF DRILLING EQUIPMENT COMPONENTS USING MATERIALS WITH THERMOELASTIC PHASE TRANSFORMATIONS

Alloys exhibiting thermoelastic phase transformations are increasingly being used in various industries. This is due to their shape memory effect and the phenomenon of pseudoelasticity (superelasticity), which can significantly improve the performance of various technical systems. Equipment for drilling oil and gas wells is one of the most demanding in terms of reliability and service life, in connection with which the gradual introduction of the above-described intelligent materials and the phenomena they manifest in the oil and gas industry began. This article analyzes the developed design and proposes methods of mounting/dismounting roller bit elements made according to the proposed design using an alloy with thermoelastic phase transformations, namely the shape memory effect, and compares them with existing designs. Computational studies and computational analysis of the design of the fastening of drilling equipment elements using the Solid Works program and the Solid Works Simulation module were carried out, which showed significant advantages of the proposed method when applying the shape memory effect as a method of geometric fixation during installation compared to interference fit. Tests were also carried out on the IR-200-0 breaking machine in order to verify and confirm the theoretical results obtained using special equipment, with the use of which the reliability of fixation due to the intermediate bushing with taper was determined. As an example, sleeves with an inclination angle to the axis of the cone of 3 degrees were made, fixed in the hole with a reverse taper with a response angle of inclination. The tests confirmed the assumptions made and the results obtained in the course of theoretical calculations and computer modeling, showing that even in the martensitic state this alloy fixes the teeth in special holes of the rolling cutter more reliably compared to the interference fit, which, in turn, makes it possible to conclude that it is advisable to use the proposed installation method, which gives obvious advantages over existing ones.

Geothermo-mechanical energy conversion using shape memory alloy heat engine

AbstractThe shift towards renewable energy sources like geothermal energy has become desirable due to the recurrent energy crisis and global warming challenges influenced by fossil fuels. Geothermo-mechanical energy conversion using shape memory alloy (SMA) heat engines presents a novel and sustainable approach for harnessing geothermal energy. Shape memory alloys, known for their ability to undergo reversible phase transformations driven by temperature changes, are ideal for thermal-to-mechanical energy conversion. This paper explores the design and performance of an SMA heat engine that utilizes geothermal heat sources to drive mechanical work. The engine operates by cycling between the high-temperature geothermal environment and a cooler sink, exploiting the shape memory effect to generate mechanical motion. By integrating geothermal energy and SMA technology, this system offers a potential solution for renewable energy generation, with applications in remote or off-grid locations. The paper also investigates output power and the thermodynamic efficiency. A model is formulated and the engine behavior is simulated. A series of experiments are conducted for engine output power and efficiency. The model is compared to the experimental data for validation. The engine developed a maximum power of 3.5, 8.5, and 11.5 watts at 60, 80, and 90 °C respectively. The proposed SMA-based geothermo-mechanical energy conversion system offers a promising solution for efficient, reliable, and scalable geothermal energy harvesting. This research contributes to the development of innovative, efficient geothermal energy conversion technologies, supporting global renewable energy goals and reducing greenhouse gas emissions. This innovative energy conversion mechanism could play a key role in the future of sustainable power generation.

Quantum Coherence Control at Temperatures up to 1400 K.

Coherent quantum control at high temperatures is important for expanding the quantum world and is useful for applying quantum technologies to realistic environments. Quantum control of spins in diamond has been demonstrated near 1000 K, with the spins polarized and read out at room temperature and controlled at elevated temperatures by rapid heating and cooling. Further increase of the working temperature is challenging due to fast spin relaxation in comparison with the heating and cooling rates. Here we significantly improve the heating and cooling rates by using reduced graphene oxide as the laser absorber and heat drain and hence realize coherent quantum operation at up to 1400 K, which is higher than the Curie temperatures of all known materials. This work facilitates the use of diamond sensors to study a wide range of magnetic effects in the high-temperature regime, such as thermoremanent magnetism and magnetic shape memory effects.

Dynamics of a single population model with memory and nonlinear boundary condition

In this paper, a heterogeneous single population model with memory effect and nonlinear boundary condition is investigated. By virtue of the implicit function theorem and Lyapunov-Schmidt reduction, spatially nonconstant positive steady state solutions appear from two trivial solutions, respectively. Through the distribution of the eigenvalues and bifurcation analysis, sufficient conditions for the occurrence of the Hopf bifurcation associated with one spatially nonconstant positive steady state are obtained. Results about the stability associated with the other one are also yielded. It is found that with the interaction of memory delay, spatial heterogeneity and nonlinear boundary condition, the Hopf bifurcation will happen in such diffusive model. To be specific, the memory delay could induce a single stability switch from stability to instability. As contrast to the diffusive model only with memory effect, the stability switch is independent of memory delay. The obtained results are applied to some specific models, from which we can find that the theoretical analysis is verified and the results enrich the existing ones.

Decoding lake water dynamics to optimize watershed agriculture through isotopic analyses of memory effects and hydrological connectivity

Understanding the complex spatiotemporal dynamics of lake water systems is critical in the context of intensifying global environmental changes. In this study, a novel stable isotope analysis method, combined with Bayesian mixing models, is applied to investigate hydrological connections and water source contributions in a specific area on the southwest shore of Dianchi Lake, China, at a monthly scale. The study reveals a significant “memory effect,” where 56.76% of the lake water volume in the current month is associated with the lake water volume in the prior month, with notable seasonal variations. The relative contributions of precipitation, surface water, soil water for different agricultural land uses, and groundwater to the lake water balance are assessed. The hydrological processes in Dianchi Lake are significantly influenced by agricultural land use, with greenhouse soils contributing less water than open field soils. Water outflow, rather than evaporation, is the primary factor reducing the nearshore lake volume, highlighting the impact of human activities. The dependency of water source contributions on meteorological factors is also examined, with seasonal and weather effects on lake water dynamics and agricultural water availability observed. By integrating isotope data with meteorological records and advanced modeling techniques, a quantitative framework for evaluating hydrological changes in lake catchments is developed. The developed approach enhances our understanding of lake water system dynamics and can enhance agricultural water management strategies, water resource allocation, irrigation planning, and climate change adaptation in agricultural watersheds.