After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,co...After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.展开更多
The goal of this paper is to investigate the long-time dynamics of solutions to a Kirchhoff type suspension bridge equation with nonlinear damping and memory term.For this problem we establish the well-posedness and e...The goal of this paper is to investigate the long-time dynamics of solutions to a Kirchhoff type suspension bridge equation with nonlinear damping and memory term.For this problem we establish the well-posedness and existence of uniform attractor under some suitable assumptions on the nonlinear term g(u),the nonlinear damping f(u_(t))and the external force h(x,t).Specifically,the asymptotic compactness of the semigroup is verified by the energy reconstruction method.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability...Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.展开更多
剩余寿命(Remaining Useful Life, RUL)预测对于维护工业设备的可靠性和安全性至关重要,但现有的RUL预测方法在处理高维传感器数据以及捕捉时间退化模式方面仍然面临诸多挑战。为了解决上述问题,提出一种退化趋势平滑约束下基于双向长...剩余寿命(Remaining Useful Life, RUL)预测对于维护工业设备的可靠性和安全性至关重要,但现有的RUL预测方法在处理高维传感器数据以及捕捉时间退化模式方面仍然面临诸多挑战。为了解决上述问题,提出一种退化趋势平滑约束下基于双向长短时记忆网络-变分自编码器(Bidirectional Long Short Term-Memory-Variational Auto Encoder, BLSTM-VAE)的RUL预测方法。该方法首先进行数据预处理,包括数据降噪、滑动窗口分段和标签修正等步骤。然后设计基于BLSTM的VAE型特征提取器,以有效提取时间序列数据中的非线性关系和长距离依赖关系。最后提出一种基于流形学习的退化趋势平滑约束模块,通过局部不变性假设来增强模型的稳健性和泛化能力。通过航空发动机数据集数据集进行验证,结果表明所提出的RUL预测方法在数据集上的表现优于现有的多种RUL预测方法,具有更低的预测误差和更高的稳定性。展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rode...Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.展开更多
The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating t...The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400℃is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7].展开更多
BACKGROUND Fear-related disorders,such as post-traumatic stress disorder(PTSD),significantly impact patients and families.Exposure therapy is a common treatment,but imp-roving its effectiveness remains a key challenge...BACKGROUND Fear-related disorders,such as post-traumatic stress disorder(PTSD),significantly impact patients and families.Exposure therapy is a common treatment,but imp-roving its effectiveness remains a key challenge.Fear conditioning and extinction in animal models offer insights into its mechanisms.Our previous research indi-cates that DNA methyltransferases play a role in fear memory renewal.AIM To investigate the role of DNA methylation in the extinction of fear memory,with the goal of identifying potential strategies to enhance the efficacy of exposure therapy for fear-related disorders.METHODS This study investigated the role of DNA methylation in fear memory extinction in mice.DNA methylation was manipulated using N-phthalyl-L-tryptophan(RG108)to reduce methylation and L-methionine injections to enhance it.Neuronal activity,and dendritic spine density was measured following extinction training.RESULTS RG108 suppressed extinction,reduced spine density,and inhibited neuronal activity.Methionine injections facilitated extinction.CONCLUSION DNA methylation is crucial for fear memory extinction.Enhancing methylation may improve the efficacy of exposure therapy,offering a potential strategy to treat fear-related disorders.展开更多
Background:Over the past few decades,a threefold increase in obesity and type 2 diabetes(T2D)has placed a heavy burden on the health-care system and society.Previous studies have shown correlations between obesity,T2D...Background:Over the past few decades,a threefold increase in obesity and type 2 diabetes(T2D)has placed a heavy burden on the health-care system and society.Previous studies have shown correlations between obesity,T2D,and neurodegenera-tive diseases,including dementia.It is imperative to further understand the relation-ship between obesity,T2D,and cognitive deficits.Methods:This investigation tested and evaluated the cognitive impact of obesity and T2D induced by high-fat diet(HFD)and the effect of the host genetic background on the severity of cognitive decline caused by obesity and T2D in collaborative cross(CC)mice.The CC mice are a genetically diverse panel derived from eight inbred strains.Results:Our findings demonstrated significant variations in the recorded phenotypes across different CC lines compared to the reference mouse line,C57BL/6J.CC037 line exhibited a substantial increase in body weight on HFD,whereas line CC005 ex-hibited differing responses based on sex.Glucose tolerance tests revealed significant variations,with some lines like CC005 showing a marked increase in area under the curve(AUC)values on HFD.Organ weights,including brain,spleen,liver,and kidney,varied significantly among the lines and sexes in response to HFD.Behavioral tests using the Morris water maze indicated that cognitive performance was differentially affected by diet and genetic background.Conclusions:Our study establishes a foundation for future quantitative trait loci map-ping using CC lines and identifying genes underlying the comorbidity of Alzheimer's disease(AD),caused by obesity and T2D.The genetic components may offer new tools for early prediction and prevention.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Background:Mitochondrial dysfunction plays a critical role in the pathogenesis of Alzheimer’s disease(AD).Resveratrol is a promising compound for the treatment of various neurodegenerative diseases,including AD.Aims:...Background:Mitochondrial dysfunction plays a critical role in the pathogenesis of Alzheimer’s disease(AD).Resveratrol is a promising compound for the treatment of various neurodegenerative diseases,including AD.Aims:To investigate mitochondrial damage and the effects of resveratrol on inflammation,cognitive function,and mitochondrial quality control in APP/PS1 mice.Methods:Comparative analysis of mitochondrial DNA(mtDNA)damage was conducted between 10-month-old APP/PS1 mice and age-matched C57BL/6 mice.Assessments included measurement of amyloid-βlevels,inflammatory markers,swimming distance in the Morris water maze,and gut microbiome composition.Resveratrol’s effects on cytokine expression,mtDNA levels in plasma,and activation of Nuclear factor erythroid 2-related factor 2/Antioxidant response element(Nrf2/ARE)and phosphoinositide 3-kinase/protein kinase B(also known as Akt)/mechanistic target of rapamycin complex 1(PI3K/Akt/mTORC1)signaling pathways were also evaluated.Results:APP/PS1 mice exhibited significantly increased mtDNA damage in the prefrontal cortex,midbrain,and cerebellum,alongside higher amyloid-βlevels and inflammatory markers.Resveratrol treatment led to reduced expression of pro-inflammatory cytokines,a decrease in Proteobacteria levels,and lower cell-free mtDNA in plasma.Partial improvement in long-term spatial memory was observed in APP/PS1 mice following resveratrol treatment,likely due to its anti-inflammatory properties.Activation of the Nrf2/ARE signaling pathway and markers of PI3K/Akt/mTORC1 axis activation were noted,with the latter regulating long-term potentiation.Conclusion:Resveratrol demonstrates potential in mitigating inflammation and improving mitochondrial quality control in APP/PS1 mice,but it does not reduce amyloid-βlevels,highlighting the complexity of AD pathology and the need for further research.展开更多
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 a...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.展开更多
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o...The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.展开更多
As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we emp...As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we employed high-quality crystalline TiN/Al_(2)O_(3)/BaTiO_(3)/Pt RRAM with an optimized thin Al_(2)O_(3) interlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the interlayer affects the memory window size. After insertion of the Al_(2)O_(3) interlayer, the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in performance. Moreover, the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magnitude, respectively, thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude, respectively. The device also exhibits multilevel resistive switching behavior when varying the applied voltage. Finally, we also developed a 6 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal variation. Hence, our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.展开更多
文摘After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11961059,1210502)the University Innovation Project of Gansu Province(Grant No.2023B-062)the Gansu Province Basic Research Innovation Group Project(Grant No.23JRRA684).
文摘The goal of this paper is to investigate the long-time dynamics of solutions to a Kirchhoff type suspension bridge equation with nonlinear damping and memory term.For this problem we establish the well-posedness and existence of uniform attractor under some suitable assumptions on the nonlinear term g(u),the nonlinear damping f(u_(t))and the external force h(x,t).Specifically,the asymptotic compactness of the semigroup is verified by the energy reconstruction method.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Natural Science Foundation of China(Nos.52473080,52403167 and 52173079)the Fundamental Research Funds for the Central Universities(Nos.xtr052023001 and xzy012023037)+1 种基金the Postdoctoral Research Project of Shaanxi Province(No.2024BSHSDZZ054)the Shaanxi Laboratory of Advanced Materials(No.2024ZY-JCYJ-04-12).
文摘Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.
文摘剩余寿命(Remaining Useful Life, RUL)预测对于维护工业设备的可靠性和安全性至关重要,但现有的RUL预测方法在处理高维传感器数据以及捕捉时间退化模式方面仍然面临诸多挑战。为了解决上述问题,提出一种退化趋势平滑约束下基于双向长短时记忆网络-变分自编码器(Bidirectional Long Short Term-Memory-Variational Auto Encoder, BLSTM-VAE)的RUL预测方法。该方法首先进行数据预处理,包括数据降噪、滑动窗口分段和标签修正等步骤。然后设计基于BLSTM的VAE型特征提取器,以有效提取时间序列数据中的非线性关系和长距离依赖关系。最后提出一种基于流形学习的退化趋势平滑约束模块,通过局部不变性假设来增强模型的稳健性和泛化能力。通过航空发动机数据集数据集进行验证,结果表明所提出的RUL预测方法在数据集上的表现优于现有的多种RUL预测方法,具有更低的预测误差和更高的稳定性。
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
基金supported by the National Institutes of Health,Nos.AA025919,AA025919-03S1,and AA025919-05S1(all to RAF).
文摘Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.
基金supported by the National Natural Science Foundation of China(Nos.52201207 and 52271169)the Fundamental Research Funds for the Central University(No.3072024LJ1002).
文摘The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400℃is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7].
基金Supported by National Natural Science Foundation of China,No.82360231Yunnan Basic Research Program General Project,No.202401AT070075+1 种基金Dali Basic Research Program Key Project,No.202301A020021Youth Special Project for Basic Research of Local Universities in Yunnan Province,No.202301BA070001-127.
文摘BACKGROUND Fear-related disorders,such as post-traumatic stress disorder(PTSD),significantly impact patients and families.Exposure therapy is a common treatment,but imp-roving its effectiveness remains a key challenge.Fear conditioning and extinction in animal models offer insights into its mechanisms.Our previous research indi-cates that DNA methyltransferases play a role in fear memory renewal.AIM To investigate the role of DNA methylation in the extinction of fear memory,with the goal of identifying potential strategies to enhance the efficacy of exposure therapy for fear-related disorders.METHODS This study investigated the role of DNA methylation in fear memory extinction in mice.DNA methylation was manipulated using N-phthalyl-L-tryptophan(RG108)to reduce methylation and L-methionine injections to enhance it.Neuronal activity,and dendritic spine density was measured following extinction training.RESULTS RG108 suppressed extinction,reduced spine density,and inhibited neuronal activity.Methionine injections facilitated extinction.CONCLUSION DNA methylation is crucial for fear memory extinction.Enhancing methylation may improve the efficacy of exposure therapy,offering a potential strategy to treat fear-related disorders.
文摘Background:Over the past few decades,a threefold increase in obesity and type 2 diabetes(T2D)has placed a heavy burden on the health-care system and society.Previous studies have shown correlations between obesity,T2D,and neurodegenera-tive diseases,including dementia.It is imperative to further understand the relation-ship between obesity,T2D,and cognitive deficits.Methods:This investigation tested and evaluated the cognitive impact of obesity and T2D induced by high-fat diet(HFD)and the effect of the host genetic background on the severity of cognitive decline caused by obesity and T2D in collaborative cross(CC)mice.The CC mice are a genetically diverse panel derived from eight inbred strains.Results:Our findings demonstrated significant variations in the recorded phenotypes across different CC lines compared to the reference mouse line,C57BL/6J.CC037 line exhibited a substantial increase in body weight on HFD,whereas line CC005 ex-hibited differing responses based on sex.Glucose tolerance tests revealed significant variations,with some lines like CC005 showing a marked increase in area under the curve(AUC)values on HFD.Organ weights,including brain,spleen,liver,and kidney,varied significantly among the lines and sexes in response to HFD.Behavioral tests using the Morris water maze indicated that cognitive performance was differentially affected by diet and genetic background.Conclusions:Our study establishes a foundation for future quantitative trait loci map-ping using CC lines and identifying genes underlying the comorbidity of Alzheimer's disease(AD),caused by obesity and T2D.The genetic components may offer new tools for early prediction and prevention.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
基金supported by the Russian science foundation(grant#22-74-00115 to A.P.G.).
文摘Background:Mitochondrial dysfunction plays a critical role in the pathogenesis of Alzheimer’s disease(AD).Resveratrol is a promising compound for the treatment of various neurodegenerative diseases,including AD.Aims:To investigate mitochondrial damage and the effects of resveratrol on inflammation,cognitive function,and mitochondrial quality control in APP/PS1 mice.Methods:Comparative analysis of mitochondrial DNA(mtDNA)damage was conducted between 10-month-old APP/PS1 mice and age-matched C57BL/6 mice.Assessments included measurement of amyloid-βlevels,inflammatory markers,swimming distance in the Morris water maze,and gut microbiome composition.Resveratrol’s effects on cytokine expression,mtDNA levels in plasma,and activation of Nuclear factor erythroid 2-related factor 2/Antioxidant response element(Nrf2/ARE)and phosphoinositide 3-kinase/protein kinase B(also known as Akt)/mechanistic target of rapamycin complex 1(PI3K/Akt/mTORC1)signaling pathways were also evaluated.Results:APP/PS1 mice exhibited significantly increased mtDNA damage in the prefrontal cortex,midbrain,and cerebellum,alongside higher amyloid-βlevels and inflammatory markers.Resveratrol treatment led to reduced expression of pro-inflammatory cytokines,a decrease in Proteobacteria levels,and lower cell-free mtDNA in plasma.Partial improvement in long-term spatial memory was observed in APP/PS1 mice following resveratrol treatment,likely due to its anti-inflammatory properties.Activation of the Nrf2/ARE signaling pathway and markers of PI3K/Akt/mTORC1 axis activation were noted,with the latter regulating long-term potentiation.Conclusion:Resveratrol demonstrates potential in mitigating inflammation and improving mitochondrial quality control in APP/PS1 mice,but it does not reduce amyloid-βlevels,highlighting the complexity of AD pathology and the need for further research.
基金supported by the Natural Science Foundation of Heilongjiang Province of China,Outstanding Youth Foundation,No.YQ2022H003 (to DW)。
文摘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.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFF0901300in part by the National Natural Science Foundation of China under Grant Nos.62173076 and 72271048.
文摘The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.
基金supported by the National Research Foundation of Korea funded by the Korean Government(grant No.RS-2023-00208801).
文摘As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we employed high-quality crystalline TiN/Al_(2)O_(3)/BaTiO_(3)/Pt RRAM with an optimized thin Al_(2)O_(3) interlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the interlayer affects the memory window size. After insertion of the Al_(2)O_(3) interlayer, the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in performance. Moreover, the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magnitude, respectively, thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude, respectively. The device also exhibits multilevel resistive switching behavior when varying the applied voltage. Finally, we also developed a 6 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal variation. Hence, our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.
