A Brief Review of the Relationship between Addiction and Memory Systems

This essay will reexamine research on the relationship between human memory and addiction. This paper will review several studies that discussed how memory systems in the human brain are involved in the acquisition of behavior that is learned and is associated with the development of drug addiction and drug relapse. Additional information reveals that when individuals make the transition from recreational drug or impulsive use to compulsive drug abuse, which may result in a neuroanatomical change in areas of the brain from cognitive control guided by the hippocampus/dorsomedial striatum towards conditioned control of behavior managed by the dorsolateral striatum (DLS) [1]. This review also looked at studies that involved experiments with humans and lower animals, which suggested that the hippocampus mediates a cognitive/spatial type of memory, while the dorsal striatum manages stimulus-response (S-R) habit memory, and the amygdala governs the classical conditioning form of learning and stimulus-affective-associative relationships [1]. Overall, these studies utilize the hypothesis of the memory systems view of addiction, and the involvement of learning and memory in the context of drug addiction, which was proposed by them [2]. This theory has been proposed in response to drug addiction research and includes alcohol, amphetamine, and cocaine [1]. The research also explains how stress and anxiety can play a role in how strong emotional excitement can lead to dependent habit memory in rodents and humans [1]. .

Fault-Tolerant Design of Spaceborne Mass Memory System

A fault-tolerant spaceborne mass memory architecture is presented based on entirely commercial-off-theshelf components.The highly modularized and scalable memory kernel supports the hierarchical design and is well suited to redundancy structure.Error correcting code(ECC) and periodical scrubbing are used to deal with bit errors induced by single event upset.For 8-bit wide devices, the parallel Reed Solomon(10, 8) can perform coder/decoder calculations in one clock cycle, achieving a data rate of several Gb/...

Diffusion induced by bounded noise in a two-dimensional coupled memory system

The diffusion behavior driven by bounded noise under the influence of a coupled harmonic potential is investigated in a two-dimensional coupled-damped model. With the help of the Laplace analysis we obtain exact descriptions for a particle’s two-time dynamics which is subjected to a coupled harmonic potential and a coupled damping. The time lag is used to describe the velocity autocorrelation function and mean square displacement of the diffusing particle. The diffusion behavior for the time lag is also discussed with respect to the coupled items and the amplitude of bounded noise.

An efficient labeled memory system for learned indexes

The appearance and wide use of memory hardware bring significant changes to the conventional vertical memory hierarchy that fails to handle contentions for shared hardware resources and expensive data movements.To deal with these problems,existing schemes have to rely on inefficient scheduling strategies that also cause extra temporal,spatial and bandwidth overheads.Based on the insights that the shared hardware resources trend to be uniformly and hierarchically offered to the requests for co-located applications in memory systems,we present an efficient abstraction of memory hierarchies,called Label,which is used to establish the connection between the application layer and underlying hardware layer.Based on labels,our paper proposes LaMem,a labeled,resource-isolated and cross-tiered memory system by leveraging the way-based partitioning technique for shared resources to guarantee QoS demands of applications,while supporting fast and low-overhead cache repartitioning technique.Besides,we customize LaMem for the learned index that fundamentally replaces storage structures with computation models as a case study to verify the applicability of LaMem.Experimental results demonstrate the efficiency and efficacy of LaMem.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

A multimode-fused sensory memory system based on a robust self-assembly nanoscaffolded BaTiO_(3):Eu_(2)O_(3) memristor

Biologically inspired neuromorphic sensory memory systems based on memristor have received a lot of attention in the booming artificial intelligence industry due to significant potential to effectively process multi-sensory signals from complex external environments.However,many memristors have significant switching parameters disperse,which is a great challenge for using memristors in bionic neuromorphic sensory memory systems.Herein,a stable ferroelectric memristor based on the Pd/BaTiO_(3):Eu2O_(3)/La0.67Sr0.33MnO_(3)grown on Silicon structure with SrTiO_(3)as buffer layer is presented.The device possesses low coercive field voltage(-1.3-2.1 V)and robust endurance characteristic(~10^(10)cycles)through optimizing the growth temperature.More importantly,an ultra-stable artificial multimodal sensory memory system with visual and tactile functions was reported for the first time by combining a pressure sensor,a photosensitive sensor,and a robotic arm.Utilizing the above system,the sensitivity value of the system is expressed by the conductance of the memristor to realize the gradual change of external stimulus,and multi signals inputs at the same time to this system have faithfully achieved sensory adaptation to multimodal sensors.This work paves the way for future development of memristor-based perception systems in efficient multisensory neural robots.

Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing

In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing applications.In particular,these non von Neumann computational elements and systems benefit from mass manufacturing of silicon photonic integrated circuits(PICs)on 8-inch wafers using a 130 nm complementary metal-oxide semiconductor line.Chip manufacturing based on deep-ultraviolet lithography and electron-beam lithography enables rapid prototyping of PICs,which can be integrated with high-quality PCMs based on the wafer-scale sputtering technique as a back-end-of-line process.In this article,we present an overview of recent advances in waveguide integrated PCM memory cells,functional devices,and neuromorphic systems,with an emphasis on fabrication and integration processes to attain state-of-the-art device performance.After a short overview of PCM based photonic devices,we discuss the materials properties of the functional layer as well as the progress on the light guiding layer,namely,the silicon and germanium waveguide platforms.Next,we discuss the cleanroom fabrication flow of waveguide devices integrated with thin films and nanowires,silicon waveguides and plasmonic microheaters for the electrothermal switching of PCMs and mixed-mode operation.Finally,the fabrication of photonic and photonic–electronic neuromorphic computing systems is reviewed.These systems consist of arrays of PCM memory elements for associative learning,matrix-vector multiplication,and pattern recognition.With large-scale integration,the neuromorphic photonic computing paradigm holds the promise to outperform digital electronic accelerators by taking the advantages of ultra-high bandwidth,high speed,and energy-efficient operation in running machine learning algorithms.

A Shape-Memory Deployable Subsystem with a Large Folding Ratio in China’s Tianwen-1 Mars Exploration Mission

Once China’s Tianwen-1 Mars probe arrived in a Mars orbit after a seven-month flight in the deep cold space environment,it would be urgently necessary to monitor its state and the surrounding environment.To address this issue,we developed a flexible deployable subsystem based on shape memory polymer composites(SMPC-FDS)with a large folding ratio,which incorporates a camera and two temperature telemetry points for monitoring the local state of the Mars orbiter and the deep space environment.Here,we report on the development,testing,and successful application of the SMPC-FDS.Before reaching its Mars remote-sensing orbit,the SMPC-FDS is designed to be in a folded state with high stiffness;after reaching orbit,it is in a deployed state with a large envelope.The transition from the folded state to the deployed state is achieved by electrically heating the shape memory polymer composites(SMPCs);during this process,the camera on the SMPC-FDS can capture the local state of the orbiter from multiple angles.Moreover,temperature telemetry points on the SMPC-FDS provide feedback on the environment temperature and the temperature change of the SMPCs during the energization process.By simulating a Mars on-orbit space environment,the engineering reliability of the SMPC-FDS was comprehensively verified in terms of the material properties,structural dynamic performance,and thermal vacuum deployment feasibility.Since the launch of Tianwen-1 on 23 July 2020,scientific data on the temperature environment around Tianwen-1 has been successfully acquired from the telemetry points on the SMPCFDS,and the local state of the orbiter has been photographed in orbit,showing the national flag of China fixed on the orbiter.

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

Between the City and Images:An Analysis of Mainstream Media’s Paths of Constructing the Cultural Memory of a City:Taking Chengdu Radio and Television’s“Hi Chengdu”as an Example

Mainstream media play a crucial role in constructing the cultural memory of a city.This study used 319 short videos released by“Hi Chengdu,”a new media product of Chengdu Radio and Television,as samples.Based on the grounded theory,a research framework encompassing“content,technology,and discourse”was established to explore the paths through which mainstream media construct the cultural memory.Regarding content,this paper emphasized temporal and spatial contexts and urban spaces,delving deep into the themes of the cultural memory and vehicles for it.In terms of technology,this paper discussed the practice of leveraging audio/visual-mode discourse to stitch together the impressions of a city and evoke emotional resonance to create a“flow”of memory.As for discourse,this paper looked at the performance of a communication ritual to frame concepts and shape urban identity.It is essential to break free from conventional thinking and leverage local culture as the primary driving force to further boost a city’s productivity,in order to excel in cultural communication.

Anticonvulsant Effects of Chrysanthellum americanum L. (Vatke) Aqueous Extract in Mice Pilocarpine Model of Epilepsy and Associated Memory Impairment: Role of Antioxidant Defense System and Cholinergic Transmission

Chrysanthellum americanum (L.) Vatke is a medicinal plant used by the traditional healers to treat epilepsy and associated memory impairment. This work aims at evaluating the anticonvulsant effects of Chrysanthellum americanum aqueous extract in mice pilocarpine model of epilepsy and associated memory loss. Mice were administered orally Chrysanthellum americanum aqueous extract (27.69, 69.22, 138.45, 276.9 mg/kg, prepared from the whole part) for test groups, intraperitoneally 300 mg/kg sodium valproate for the positive control group or orally 10 mL/kg distilled water for the negative control group, respectively, during a period of seven consecutive days. On the first day, temporal lobe epilepsy was induced by intraperitoneal injection of 360 mg/kg pilocarpine one hour after the administration of different treatment to mice, and the occurrence of status epilepticus was evaluated. On the second day, the anticonvulsant property was measured after the intraperitoneal injection of a sub-convulsive dose of picrotoxin (1 mg/kg). On the seventh day, the anti-amnesic properties of the extract were evaluated in the epileptic mice using the T-maze and open field paradigms. The results show that Chrysanthellum americanum extract significantly (p Chrysanthellum americanum (276.9 mg/kg) likewise sodium valproate (300 mg/kg) significantly (p Chrysanthellum americanum aqueous extract has anticonvulsant effects against pilocarpine induced-epileptic seizures and memory impairment. These properties could be mediated by the amelioration of antioxidant defense system and cholinergic neurotransmission in epileptic mice, which could partly justify the use of Chrysanthellum americanum in the traditional medicine for the treatment of epilepsy.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

The complex roles of m^(6)A modifications in neural stem cell proliferation, differentiation, and self-renewal and implications for memory and neurodegenerative diseases

N6-methyladenosine(m^(6)A), the most prevalent and conserved RNA modification in eukaryotic cells, profoundly influences virtually all aspects of mRNA metabolism. mRNA plays crucial roles in neural stem cell genesis and neural regeneration, where it is highly concentrated and actively involved in these processes. Changes in m^(6)A modification levels and the expression levels of related enzymatic proteins can lead to neurological dysfunction and contribute to the development of neurological diseases. Furthermore, the proliferation and differentiation of neural stem cells, as well as nerve regeneration, are intimately linked to memory function and neurodegenerative diseases. This paper presents a comprehensive review of the roles of m^(6)A in neural stem cell proliferation, differentiation, and self-renewal, as well as its implications in memory and neurodegenerative diseases. m^(6)A has demonstrated divergent effects on the proliferation and differentiation of neural stem cells. These observed contradictions may arise from the time-specific nature of m^(6)A and its differential impact on neural stem cells across various stages of development. Similarly, the diverse effects of m^(6)A on distinct types of memory could be attributed to the involvement of specific brain regions in memory formation and recall. Inconsistencies in m^(6)A levels across different models of neurodegenerative disease, particularly Alzheimer's disease and Parkinson's disease, suggest that these disparities are linked to variations in the affected brain regions. Notably, the opposing changes in m^(6)A levels observed in Parkinson's disease models exposed to manganese compared to normal Parkinson's disease models further underscore the complexity of m^(6)A's role in neurodegenerative processes. The roles of m^(6)A in neural stem cell proliferation, differentiation, and self-renewal, and its implications in memory and neurodegenerative diseases, appear contradictory. These inconsistencies may be attributed to the timespecific nature of m^(6)A and its varying effects on distinct brain regions and in different environments.

Design and additive manufacturing of bionic hybrid structure inspired by cuttlebone to achieve superior mechanical properties and shape memory function

Lightweight porous materials with high load-bearing,damage tolerance and energy absorption(EA)as well as intelligence of shape recovery after material deformation are beneficial and critical for many applications,e.g.aerospace,automobiles,electronics,etc.Cuttlebone produced in the cuttlefish has evolved vertical walls with the optimal corrugation gradient,enabling stress homogenization,significant load bearing,and damage tolerance to protect the organism from high external pressures in the deep sea.This work illustrated that the complex hybrid wave shape in cuttlebone walls,becoming more tortuous from bottom to top,creates a lightweight,load-bearing structure with progressive failure.By mimicking the cuttlebone,a novel bionic hybrid structure(BHS)was proposed,and as a comparison,a regular corrugated structure and a straight wall structure were designed.Three types of designed structures have been successfully manufactured by laser powder bed fusion(LPBF)with NiTi powder.The LPBF-processed BHS exhibited a total porosity of 0.042% and a good dimensional accuracy with a peak deviation of 17.4μm.Microstructural analysis indicated that the LPBF-processed BHS had a strong(001)crystallographic orientation and an average size of 9.85μm.Mechanical analysis revealed the LPBF-processed BHS could withstand over 25000 times its weight without significant deformation and had the highest specific EA value(5.32 J·g^(−1))due to the absence of stress concentration and progressive wall failure during compression.Cyclic compression testing showed that LPBF-processed BHS possessed superior viscoelastic and elasticity energy dissipation capacity.Importantly,the uniform reversible phase transition from martensite to austenite in the walls enables the structure to largely recover its pre-deformation shape when heated(over 99% recovery rate).These design strategies can serve as valuable references for the development of intelligent components that possess high mechanical efficiency and shape memory capabilities.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

Effect of solution treatment on the microstructure,phase transformation behavior and functional properties of NiTiNb ternary shape memory alloys fabricated via laser powder bed fusion in-situ alloying

Post-heat treatment is commonly employed to improve the microstructural homogeneity and enhance the mechanical performances of the additively manufactured metallic materials.In this work,a ternary(NiTi)91Nb9(at.%)shape memory alloy was produced by laser powder bed fusion(L-PBF)using pre-alloyed NiTi and elemental Nb powders.The effect of solution treatment on the microstructure,phase transformation behavior and mechanical/functional performances was investigated.The in-situ alloyed(NiTi)91Nb9 alloy exhibits a submicron cellular-dendritic structure surrounding the supersaturated B2-NiTi matrix.Upon high-temperature(1273 K)solution treatment,Nb-rich precipitates were precipitated from the supersaturated matrix.The fragmentation and spheroidization of the NiTi/Nb eutectics occurred during solution treatment,leading to a morphological transition from mesh-like into rod-like and sphere-like.Coarsening of theβ-Nb phases occurred with increasing holding time.The martensite transformation temperature increases after solution treatment,mainly attributed to:(i)reduced lattice distortion due to the Nb expulsion from the supersaturated B2-NiTi,and(ii)the Ti expulsion from theβ-Nb phases that lowers the ratio Ni/Ti in the B2-NiTi matrix,which resulted from the microstructure changes from non-equilibrium to equilibrium state.The thermal hysteresis of the solutionized alloys is around 145 K after 20%pre-deformation,which is comparable to the conventional NiTiNb alloys.A short-term solution treatment(i.e.at 1273 K for 30 min)enhances the ductility and strength of the as-printed specimen,with the increase of fracture stress from(613±19)MPa to(781±20)MPa and the increase of fracture strain from(7.6±0.1)%to(9.5±0.4)%.Both the as-printed and solutionized samples exhibit good tensile shape memory effects with recovery rates>90%.This work suggests that post-process heat treatment is essential to optimize the microstructure and improve the mechanical performances of the L-PBF in-situ alloyed parts.

Exercise preconditioning alleviates ischemia-induced memory deficits by increasing circulating adiponectin

Cerebral ischemia is a major health risk that requires preventive approaches in addition to drug therapy.Physical exercise enhances neurogenesis and synaptogenesis,and has been widely used for functional rehabilitation after stroke.In this study,we determined whether exercise training before disease onset can alleviate the severity of cerebral ischemia.We also examined the role of exercise-induced circulating factors in these effects.Adult mice were subjected to 14 days of treadmill exercise training before surgery for middle cerebral artery occlusion.We found that this exercise pre-conditioning strategy effectively attenuated brain infarct area,inhibited gliogenesis,protected synaptic proteins,and improved novel object and spatial memory function.Further analysis showed that circulating adiponectin plays a critical role in these preventive effects of exercise.Agonist activation of adiponectin receptors by Adipo Ron mimicked the effects of exercise,while inhibiting receptor activation abolished the exercise effects.In summary,our results suggest a crucial role of circulating adiponectin in the effects of exercise pre-conditioning in protecting against cerebral ischemia and supporting the health benefits of exercise.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.