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 prac...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.展开更多
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.I...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.展开更多
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...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.展开更多
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 ...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.展开更多
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...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.展开更多
The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these be...The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.展开更多
Background and Purpose: Opioids, used for centuries to alleviate pain, have become a double-edged sword. While effective, they come with a host of adverse effects, including memory and cognition impairment. This revie...Background and Purpose: Opioids, used for centuries to alleviate pain, have become a double-edged sword. While effective, they come with a host of adverse effects, including memory and cognition impairment. This review delves into the impact of opioid drugs on cognitive functions, explores underlying mechanisms, and investigates their prevalence in both medical care and illicit drug use. The ultimate goal is to find ways to mitigate their potential harm and address the ongoing opioid crisis. Methods: We sourced data from PubMed and Google Scholar, employing search combinations like “opioids,” “memory,” “cognition,” “amnesia,” “cognitive function,” “executive function,” and “inhibition.” Our focus was on English-language articles spanning from the inception of these databases up to the present. Results: The literature consistently reveals that opioid use, particularly at high doses, adversely affects memory and other cognitive functions. Longer deliberation times, impaired decision-making, impulsivity, and behavioral disorders are common consequences. Chronic high-dose opioid use is associated with conditions such as amnesiac syndrome (OAS), post-operative cognitive dysfunction (POCD), neonatal abstinence syndrome (NAS), depression, anxiety, sedation, and addiction. Alarming trends show increased opioid use over recent decades, amplifying the risk of these outcomes. Conclusion: Opioids cast a shadow over memory and cognitive function. These effects range from amnesiac effects, lessened cognitive function, depression, and more. Contributing factors include over-prescription, misuse, misinformation, and prohibition policies. Focusing on correct informational campaigns, removing punitive policies, and focusing on harm reduction strategies have been shown to lessen the abuse and use of opioids and thus helping to mitigate the adverse effects of these drugs. Further research into the impacts of opioids on cognitive abilities is also needed as they are well demonstrated in the literature, but the mechanism is not often completely understood.展开更多
The adoption of Docker containers has revolutionized software deployment by providing a lightweight and efficient way to isolate applications in data centers. However, securing these containers, especially when handli...The adoption of Docker containers has revolutionized software deployment by providing a lightweight and efficient way to isolate applications in data centers. However, securing these containers, especially when handling sensitive data, poses significant challenges. Traditional Linux Security Modules (LSMs) such as SELinux and AppArmor have limitations in providing fine-grained access control to files within containers. This paper presents a novel approach using eBPF (extended Berkeley Packet Filter) to implement a LSM that focuses on file-oriented access control within Docker containers. The module allows the specification of policies that determine which programs can access sensitive files, providing enhanced security without relying solely on the host operating system’s major LSM.展开更多
With the rapid development of deep learning algorithms,the computational complexity and functional diversity are increasing rapidly.However,the gap between high computational density and insufficient memory bandwidth ...With the rapid development of deep learning algorithms,the computational complexity and functional diversity are increasing rapidly.However,the gap between high computational density and insufficient memory bandwidth under the traditional von Neumann architecture is getting worse.Analyzing the algorithmic characteristics of convolutional neural network(CNN),it is found that the access characteristics of convolution(CONV)and fully connected(FC)operations are very different.Based on this feature,a dual-mode reronfigurable distributed memory architecture for CNN accelerator is designed.It can be configured in Bank mode or first input first output(FIFO)mode to accommodate the access needs of different operations.At the same time,a programmable memory control unit is designed,which can effectively control the dual-mode configurable distributed memory architecture by using customized special accessing instructions and reduce the data accessing delay.The proposed architecture is verified and tested by parallel implementation of some CNN algorithms.The experimental results show that the peak bandwidth can reach 13.44 GB·s^(-1)at an operating frequency of 120 MHz.This work can achieve 1.40,1.12,2.80 and 4.70 times the peak bandwidth compared with the existing work.展开更多
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 attra...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.展开更多
Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,...Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM devices.In this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically investigated.The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments.The read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(<10%).The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%,whereas it reduces RRAM read resistance by 4×,resulting in a higher power and area consumption.As such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.展开更多
Bisphenol A (BPA), a toxicant which can leach into food from plastic containers, is reported to induce neurotoxicity among others via oxidative mechanisms. However, antioxidant compounds have been suggested to mitigat...Bisphenol A (BPA), a toxicant which can leach into food from plastic containers, is reported to induce neurotoxicity among others via oxidative mechanisms. However, antioxidant compounds have been suggested to mitigate BPA-induced toxicities. Garcinia kola (GK) and its bioactive compound, kolaviron, are well-established natural antioxidants, which can exert protective effects against BPA-induced toxicities. This study was designed to investigate the likely mitigating effect of GK and kolaviron on BPA-induced memory impairment and hippocampal neuroinflammation in male Wistar rats. Thirty-five rats were equally grouped and treated as follows: I and II received distilled water and corn oil, respectively at 0.2 mL, while III - VII received BPA (50 mg/kg), BPA + GK (200 mg/kg), BPA + kolaviron (200 mg/kg), GK and kolaviron, respectively for 28 days p.o. Thereafter, behavioral studies were done using the Novel Object Recognition and Y maze tests. Subsequently under anaesthesia, the hippocampus in each animal was dissected out, homogenized and analysed for malondialdehyde, superoxide dismutase, catalase, reduced glutathione, glutathione transferase, nitrites, interleukin-6, tumour necrosis factor-α, acetylcholinesterase, glutamate acid decarboxylase, and arginase activity. Data were analyzed by ANOVA and Tukey Post-hoc test at p p Garcinia kola and Kolaviron mitigate bisphenol A-induced memory impairment and neuroinflammation via antioxidant potentiation and neurotransmitter balance.展开更多
Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections an...Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.展开更多
A significant obstacle impeding the advancement of the time fractional Schrodinger equation lies in the challenge of determining its precise mathematical formulation.In order to address this,we undertake an exploratio...A significant obstacle impeding the advancement of the time fractional Schrodinger equation lies in the challenge of determining its precise mathematical formulation.In order to address this,we undertake an exploration of the time fractional Schrodinger equation within the context of a non-Markovian environment.By leveraging a two-level atom as an illustrative case,we find that the choice to raise i to the order of the time derivative is inappropriate.In contrast to the conventional approach used to depict the dynamic evolution of quantum states in a non-Markovian environment,the time fractional Schrodinger equation,when devoid of fractional-order operations on the imaginary unit i,emerges as a more intuitively comprehensible framework in physics and offers greater simplicity in computational aspects.Meanwhile,we also prove that it is meaningless to study the memory of time fractional Schrodinger equation with time derivative 1<α≤2.It should be noted that we have not yet constructed an open system that can be fully described by the time fractional Schrodinger equation.This will be the focus of future research.Our study might provide a new perspective on the role of time fractional Schrodinger equation.展开更多
In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state throu...In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown,and the state is permanently preserved.The memory unit features a current ratio of more than 10^(3),a read voltage window of 6 V,a programming time of less than 10^(−4)s,a stability of more than 108 read cycles,and a lifetime of far more than 10 years.Besides,the fabrication of the device is fully compatible with commercial Si-based GaN process platforms,which is of great significance for the realization of low-cost read-only memory in all-GaN integration.展开更多
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.%)shap...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.展开更多
In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies...In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.展开更多
Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a lear...Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.展开更多
Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to en...Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.展开更多
基金financially supported by the National Natural Science Foundation of China(No.51974028)。
文摘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.
基金the support of the National Natural Science Foundation of China(Grant No.62204201)。
文摘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.
基金financially supported by the National Natural Science Foundation of China,No.81303115,81774042 (both to XC)the Pearl River S&T Nova Program of Guangzhou,No.201806010025 (to XC)+3 种基金the Specialty Program of Guangdong Province Hospital of Chinese Medicine of China,No.YN2018ZD07 (to XC)the Natural Science Foundatior of Guangdong Province of China,No.2023A1515012174 (to JL)the Science and Technology Program of Guangzhou of China,No.20210201 0268 (to XC),20210201 0339 (to JS)Guangdong Provincial Key Laboratory of Research on Emergency in TCM,Nos.2018-75,2019-140 (to JS)
文摘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.
基金supported by grants from the Ministerio de Economia y Competitividad(BFU2013-43458-R)Junta de Andalucia(P12-CTS-1694 and Proyexcel-00422)to ZUK。
文摘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.
文摘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.
文摘The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.
文摘Background and Purpose: Opioids, used for centuries to alleviate pain, have become a double-edged sword. While effective, they come with a host of adverse effects, including memory and cognition impairment. This review delves into the impact of opioid drugs on cognitive functions, explores underlying mechanisms, and investigates their prevalence in both medical care and illicit drug use. The ultimate goal is to find ways to mitigate their potential harm and address the ongoing opioid crisis. Methods: We sourced data from PubMed and Google Scholar, employing search combinations like “opioids,” “memory,” “cognition,” “amnesia,” “cognitive function,” “executive function,” and “inhibition.” Our focus was on English-language articles spanning from the inception of these databases up to the present. Results: The literature consistently reveals that opioid use, particularly at high doses, adversely affects memory and other cognitive functions. Longer deliberation times, impaired decision-making, impulsivity, and behavioral disorders are common consequences. Chronic high-dose opioid use is associated with conditions such as amnesiac syndrome (OAS), post-operative cognitive dysfunction (POCD), neonatal abstinence syndrome (NAS), depression, anxiety, sedation, and addiction. Alarming trends show increased opioid use over recent decades, amplifying the risk of these outcomes. Conclusion: Opioids cast a shadow over memory and cognitive function. These effects range from amnesiac effects, lessened cognitive function, depression, and more. Contributing factors include over-prescription, misuse, misinformation, and prohibition policies. Focusing on correct informational campaigns, removing punitive policies, and focusing on harm reduction strategies have been shown to lessen the abuse and use of opioids and thus helping to mitigate the adverse effects of these drugs. Further research into the impacts of opioids on cognitive abilities is also needed as they are well demonstrated in the literature, but the mechanism is not often completely understood.
文摘The adoption of Docker containers has revolutionized software deployment by providing a lightweight and efficient way to isolate applications in data centers. However, securing these containers, especially when handling sensitive data, poses significant challenges. Traditional Linux Security Modules (LSMs) such as SELinux and AppArmor have limitations in providing fine-grained access control to files within containers. This paper presents a novel approach using eBPF (extended Berkeley Packet Filter) to implement a LSM that focuses on file-oriented access control within Docker containers. The module allows the specification of policies that determine which programs can access sensitive files, providing enhanced security without relying solely on the host operating system’s major LSM.
基金Supported by the National Key R&D Program of China(No.2022ZD0119001)the National Natural Science Foundation of China(No.61834005,61802304)+1 种基金the Education Department of Shaanxi Province(No.22JY060)the Shaanxi Provincial Key Research and Devel-opment Plan(No.2024GX-YBXM-100)。
文摘With the rapid development of deep learning algorithms,the computational complexity and functional diversity are increasing rapidly.However,the gap between high computational density and insufficient memory bandwidth under the traditional von Neumann architecture is getting worse.Analyzing the algorithmic characteristics of convolutional neural network(CNN),it is found that the access characteristics of convolution(CONV)and fully connected(FC)operations are very different.Based on this feature,a dual-mode reronfigurable distributed memory architecture for CNN accelerator is designed.It can be configured in Bank mode or first input first output(FIFO)mode to accommodate the access needs of different operations.At the same time,a programmable memory control unit is designed,which can effectively control the dual-mode configurable distributed memory architecture by using customized special accessing instructions and reduce the data accessing delay.The proposed architecture is verified and tested by parallel implementation of some CNN algorithms.The experimental results show that the peak bandwidth can reach 13.44 GB·s^(-1)at an operating frequency of 120 MHz.This work can achieve 1.40,1.12,2.80 and 4.70 times the peak bandwidth compared with the existing work.
基金This work was supported by the National Natural Science Foundation of China(Nos.62034006,92264201,and 91964105)the Natural Science Foundation of Shandong Province(Nos.ZR2020JQ28 and ZR2020KF016)the Program of Qilu Young Scholars of Shandong University.
文摘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.
基金supported in part by the Open Fund of State Key Laboratory of Integrated Chips and Systems,Fudan Universityin part by the National Science Foundation of China under Grant No.62304133 and No.62350610271.
文摘Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM devices.In this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically investigated.The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments.The read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(<10%).The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%,whereas it reduces RRAM read resistance by 4×,resulting in a higher power and area consumption.As such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.
文摘Bisphenol A (BPA), a toxicant which can leach into food from plastic containers, is reported to induce neurotoxicity among others via oxidative mechanisms. However, antioxidant compounds have been suggested to mitigate BPA-induced toxicities. Garcinia kola (GK) and its bioactive compound, kolaviron, are well-established natural antioxidants, which can exert protective effects against BPA-induced toxicities. This study was designed to investigate the likely mitigating effect of GK and kolaviron on BPA-induced memory impairment and hippocampal neuroinflammation in male Wistar rats. Thirty-five rats were equally grouped and treated as follows: I and II received distilled water and corn oil, respectively at 0.2 mL, while III - VII received BPA (50 mg/kg), BPA + GK (200 mg/kg), BPA + kolaviron (200 mg/kg), GK and kolaviron, respectively for 28 days p.o. Thereafter, behavioral studies were done using the Novel Object Recognition and Y maze tests. Subsequently under anaesthesia, the hippocampus in each animal was dissected out, homogenized and analysed for malondialdehyde, superoxide dismutase, catalase, reduced glutathione, glutathione transferase, nitrites, interleukin-6, tumour necrosis factor-α, acetylcholinesterase, glutamate acid decarboxylase, and arginase activity. Data were analyzed by ANOVA and Tukey Post-hoc test at p p Garcinia kola and Kolaviron mitigate bisphenol A-induced memory impairment and neuroinflammation via antioxidant potentiation and neurotransmitter balance.
基金supported by the US Department of Energy (DOE),the Office of Nuclear Energy,Spent Fuel and Waste Science and Technology Campaign,under Contract Number DE-AC02-05CH11231the National Energy Technology Laboratory under the award number FP00013650 at Lawrence Berkeley National Laboratory.
文摘Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.
基金Project supported by the National Natural Science Foun dation of China(Grant No.11274398).
文摘A significant obstacle impeding the advancement of the time fractional Schrodinger equation lies in the challenge of determining its precise mathematical formulation.In order to address this,we undertake an exploration of the time fractional Schrodinger equation within the context of a non-Markovian environment.By leveraging a two-level atom as an illustrative case,we find that the choice to raise i to the order of the time derivative is inappropriate.In contrast to the conventional approach used to depict the dynamic evolution of quantum states in a non-Markovian environment,the time fractional Schrodinger equation,when devoid of fractional-order operations on the imaginary unit i,emerges as a more intuitively comprehensible framework in physics and offers greater simplicity in computational aspects.Meanwhile,we also prove that it is meaningless to study the memory of time fractional Schrodinger equation with time derivative 1<α≤2.It should be noted that we have not yet constructed an open system that can be fully described by the time fractional Schrodinger equation.This will be the focus of future research.Our study might provide a new perspective on the role of time fractional Schrodinger equation.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB3604400in part by the Youth Innovation Promotion Association of Chinese Academy Sciences (CAS)+4 种基金in part by the CAS-Croucher Funding Scheme under Grant CAS22801in part by National Natural Science Foundation of China under Grant 62334012, Grant 62074161, Grant 62004213, Grant U20A20208, and Grant 62304252in part by the Beijing Municipal Science and Technology Commission project under Grant Z201100008420009 and Grant Z211100007921018in part by the University of CASin part by the IMECAS-HKUST-Joint Laboratory of Microelectronics
文摘In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown,and the state is permanently preserved.The memory unit features a current ratio of more than 10^(3),a read voltage window of 6 V,a programming time of less than 10^(−4)s,a stability of more than 108 read cycles,and a lifetime of far more than 10 years.Besides,the fabrication of the device is fully compatible with commercial Si-based GaN process platforms,which is of great significance for the realization of low-cost read-only memory in all-GaN integration.
基金supported by the Natural Science Foundation of Shandong Province (ZR2020YQ39, ZR2020ZD05)Taishan Scholar Foundation of Shandong Province (tsqn202211002)the Young Scholars Program of Shandong University (Grant Number 2018WLJH24)
文摘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.
基金supported by the CRRC Zhuzhou Institute Company Ltd.and in part by Key R&D projects in Hunan+1 种基金ChinaNo.2022GK2062。
文摘In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.
基金supported in part by the National Natural Science Foundation of China (62225309,62073222,U21A20480,62361166632)。
文摘Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grant No.2021B0909060002)National Natural Science Foundation of China(Grant Nos.62204219,62204140)+1 种基金Major Program of Natural Science Foundation of Zhejiang Province(Grant No.LDT23F0401)Thanks to Professor Zhang Yishu from Zhejiang University,Professor Gao Xu from Soochow University,and Professor Zhong Shuai from Guangdong Institute of Intelligence Science and Technology for their support。
文摘Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.
