Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances a...Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.展开更多
Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inh...Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.展开更多
The paper proposes a method for solving the problem of synthesis of an adaptive control system for unstable and deterministic chaotic processes in the class of‘dovetail’catastrophes for objects with m-inputs and n-o...The paper proposes a method for solving the problem of synthesis of an adaptive control system for unstable and deterministic chaotic processes in the class of‘dovetail’catastrophes for objects with m-inputs and n-outputs.The synthesis problem is solved by the gradient-velocity method of Lyapunov vector functions.From the conditions of the aperiodic robust stability of the etalon model with the desired dynamics of the main control loop and the generalised configurable object,the control goal is achieved.One of the most promising ways of solving the problem of managing unstable and deterministic chaotic processes is the synthesis of a control system in the class of‘swallowtail’catastrophes and the use of adaptation methods.Selecting and studying a reference model,and employing the gradient-velocity method for a generalised configurable control object ensure robust stability,utilise current information effectively,and achieve desired system quality and management goals.展开更多
Managing sensitive data in dynamic and high-stakes environments,such as healthcare,requires access control frameworks that offer real-time adaptability,scalability,and regulatory compliance.BIG-ABAC introduces a trans...Managing sensitive data in dynamic and high-stakes environments,such as healthcare,requires access control frameworks that offer real-time adaptability,scalability,and regulatory compliance.BIG-ABAC introduces a transformative approach to Attribute-Based Access Control(ABAC)by integrating real-time policy evaluation and contextual adaptation.Unlike traditional ABAC systems that rely on static policies,BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes,ensuring precise and efficient access control.Leveraging decision trees evaluated in real-time,BIG-ABAC overcomes the limitations of conventional access control models,enabling seamless adaptation to complex,high-demand scenarios.The framework adheres to the NIST ABAC standard while incorporating modern distributed streaming technologies to enhance scalability and traceability.Its flexible policy enforcement mechanisms facilitate the implementation of regulatory requirements such as HIPAA and GDPR,allowing organizations to align access control policies with compliance needs dynamically.Performance evaluations demonstrate that BIG-ABAC processes 95% of access requests within 50 ms and updates policies dynamically with a latency of 30 ms,significantly outperforming traditional ABAC models.These results establish BIG-ABAC as a benchmark for adaptive,scalable,and context-aware access control,making it an ideal solution for dynamic,high-risk domains such as healthcare,smart cities,and Industrial IoT(IIoT).展开更多
Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast res...Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.展开更多
Enzyme-like metal atomic site catalysts are promising alternatives of platinum group metals for oxygen reduction reaction(ORR)in fuel cell application.The local coordination structure at metal atomic sites plays a dom...Enzyme-like metal atomic site catalysts are promising alternatives of platinum group metals for oxygen reduction reaction(ORR)in fuel cell application.The local coordination structure at metal atomic sites plays a dominant role in optimizing the adsorption/desorption of oxygen intermediates to enhance ORR,but there is still a significant challenge in achieving.Herein,we report a type of stable and dynamically adjustable mono-oxygen-bridged asymmetric dual-atomic metal catalyst,in which the active Fe-oxo-Co motif demonstrates platinium-like ORR activity with a half-wave potential of 0.92 V vs.RHE in alkaline condition and a maximum power density of 228 mW·cm^(-2) in Zn-air batteries.Theoretical calculations reveal that the Fe-oxo ligands can act as electron regulators for neighboring Co sites,which optimize and promote the d-orbitals of Co metal shift towards lower energy levels,thereby weakening the adsorption of oxygen species,facilitating the progress of the ORR.More interestingly,the Fe-oxo-Co bond will dynamically change its strength to adaptively facilitate the intermediate steps during the ORR process.The design strategy towards enzyme-like adaptive behavior of active Fe-oxo-Co motifs brings significant hope for achieveing high performance fuel cell cathode materials.展开更多
Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose signifi...Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.展开更多
Background:Dengue fever,an acute insect-borne infectious disease caused by the dengue virus(DENV),poses a great challenge to global public health.Hepatic involve-ment is the most common complication of severe dengue a...Background:Dengue fever,an acute insect-borne infectious disease caused by the dengue virus(DENV),poses a great challenge to global public health.Hepatic involve-ment is the most common complication of severe dengue and is closely related to the occurrence and development of disease.However,the features of adaptive immune responses associated with liver injury in severe dengue are not clear.Methods:We used single-cell sequencing to examine the liver tissues of mild or se-vere dengue mice model to analyze the changes in immune response of T cells in the liver after dengue virus infection,and the immune interaction between macrophages and T cells.Flow cytometry was used to detect T cells and macrophages in mouse liver and blood to verify the single-cell sequencing results.Results:Our result showed CTLs were significantly activated in the severe liver injury group but the immune function-related signal pathway was down-regulated.The rea-son may be that the excessive immune response in the severe group at the late stage of DENV infection induces the polarization of macrophages into M2 type,and the macrophages then inhibit T cell immunity through the TGF-βsignaling pathway.In ad-dition,the increased proportion of Treg cells suggested that Th17/Treg homeostasis was disrupted in the livers of severe liver injury mice.Conclusions:In this study,single-cell sequencing and flow cytometry revealed the characteristic changes of T cell immune response and the role of macrophages in the liver of severe dengue fever mice.Our study provides a better understanding of the pathogenesis of liver injury in dengue fever patients.展开更多
With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing predictio...With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.展开更多
Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed ...Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.展开更多
The emergence of adaptive facades offers a new approach for buildings to enhance their resilience against external weather conditions while responding to occupants’demands,thereby improving both indoor environmental ...The emergence of adaptive facades offers a new approach for buildings to enhance their resilience against external weather conditions while responding to occupants’demands,thereby improving both indoor environmental quality and energy performance.Appropriate control methods are crucial to achieving these purposes.However,most existing studies for automatic control of blinds have focused on visual comfort,leaving potential for further energy savings by reducing cooling and artificial lighting demands.Additionally,current optimization methods for slat angles are mostly simplified as a discrete process,neglecting the impact of thermal mass in building envelopes.Therefore,this paper aims to explore the energy reduction potential of window blinds by developing an iterative optimization method for devising hourly adaptive control strategies.To this end,a co-simulation platform between EnergyPlus and Python was established for the optimization and a case study in a subtropical city was conducted.The proposed strategies effectively balanced lighting and cooling demands to achieve an overall energy reduction of 7.3%–12.5%compared to reference cases while also ensuring visual comfort by mitigating glare risk and excessive daylight.These advantages were also compared with several simpler control scenarios,with analyses tailored to various glazing types and orientations.Furthermore,the optimal window configurations with blind control strategies for different orientations were determined.The findings also indicated that glass properties markedly impact the performance of control strategies,underscoring the necessity of holistically considering shading components and glazing types in the optimization to achieve optimal performance.展开更多
Studies show that Graph Neural Networks(GNNs)are susceptible to minor perturbations.Therefore,analyzing adversarial attacks on GNNs is crucial in current research.Previous studies used Generative Adversarial Networks ...Studies show that Graph Neural Networks(GNNs)are susceptible to minor perturbations.Therefore,analyzing adversarial attacks on GNNs is crucial in current research.Previous studies used Generative Adversarial Networks to generate a set of fake nodes,injecting them into a clean GNNs to poison the graph structure and evaluate the robustness of GNNs.In the attack process,the computation of new node connections and the attack loss are independent,which affects the attack on the GNN.To improve this,a Fake Node Camouflage Attack based on Mutual Information(FNCAMI)algorithm is proposed.By incorporating Mutual Information(MI)loss,the distribution of nodes injected into the GNNs become more similar to the original nodes,achieving better attack results.Since the loss ratios of GNNs and MI affect performance,we also design an adaptive weighting method.By adjusting the loss weights in real-time through rate changes,larger loss values are obtained,eliminating local optima.The feasibility,effectiveness,and stealthiness of this algorithm are validated on four real datasets.Additionally,we use both global and targeted attacks to test the algorithm’s performance.Comparisons with baseline attack algorithms and ablation experiments demonstrate the efficiency of the FNCAMI algorithm.展开更多
Natural biological microtubular networks have undergone adaptive evolutionary selection and may offer viable solutions to design challenges in artificial microtubular networks.The plasmodium of the slime mold Physarum...Natural biological microtubular networks have undergone adaptive evolutionary selection and may offer viable solutions to design challenges in artificial microtubular networks.The plasmodium of the slime mold Physarum polycephalum(P.polycephalum)extends continuously to form a protoplasmic microtubular network structure;directly connecting food sources.Computational simulations revealed that the formation of this adaptive P.polycephalum microtubular network could be captured by a mathematical algorithm.Inspired by the P.polycephalum microtubular networks;we propose an adaptive optimization design method for artificial microtubular networks.Specifically;we utilized hydrogels with biodegradable and tissue-adhesive properties to replicate the P.polycephalum microtubular networks via photomask.In Rhodamine B diffusion and glucosecatalyzed reaction experiments;we found that the P.polycephalum microtubular networks exhibited significantly enhanced efficiency compared to vascular and artificial networks.Furthermore;we demonstrated the potential for uric acid(UA)degradation of the hydrogels with a real P.polycephalum microtubular network loaded with urate oxidase(UOx)in a rodent model of hyperuricemia.And this network achieved more than double the effect of the artificial network.This underscores the potential of natural microtubular networks to replace artificial microtubular networks.展开更多
Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wil...Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.展开更多
Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncerta...Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.展开更多
Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approac...Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.展开更多
A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
AIM:To assess the variations in photoreceptor cell packing density(PCPD)across the retina among young healthy individuals with emmetropia,low and moderate myopia.METHODS:High-resolution adaptive optics scanning laser ...AIM:To assess the variations in photoreceptor cell packing density(PCPD)across the retina among young healthy individuals with emmetropia,low and moderate myopia.METHODS:High-resolution adaptive optics scanning laser ophthalmoscopy(AOSLO)systems were utilized for retinal imaging with a large sampling window of 700μm×700μm.The study cohort included 14 emmetropic[spherical equivalent(SE)ranged+0.5 to-0.5 D],15 low myopic(SE ranged-0.5 to-3 D)and 21 moderate myopic(SE ranged-3 to-6 D)healthy young adults.Photoreceptors at 3°temporal,6°superior and inferior 6°were captured.Statistical analysis was then performed to obtain PCPD and cell spacing.RESULTS:The average age of participants was 22.54±2.86(ranged 20–30y)with no difference among 3 groups.At 3°temporal,the emmetropic group exhibited the highest PCPD of 15186.16±2050.54 cells/mm2,while the low and moderate myopic groups had PCPD of 14009.15±1073.01 and 13466.92±1121.71 cells/mm2,respectively.At 3°temporal,the emmetropic group also had the smallest cell spacing at 6.66±0.26 mm,compared to 6.85±0.26 and 6.91±0.28 mm for the low and moderate myopic groups,respectively.Compared to the emmetropic group,at 3°temporal,the myopic groups showed significantly reduced PCPD(low myopia:P=0.032;moderate myopia:P=0.001).At 6°inferior,the moderate myopic group exhibited a significant decrease in PCPD(P=0.013),while at 6°superior,there were no significant statistical differences in PCPD for the low and moderate myopic groups(P>0.05).In comparison to the emmetropic group,only the moderate myopic group showed significantly increased cell spacing at all three positions(temporal 3°:P=0.011,superior 6°:P=0.046,inferior 6°:P=0.013).Correlation analysis revealed a positive correlation between PCPD and axial length changes(P<0.05).CONCLUSION:Reduced PCPD and increased cell spacing strongly correlated with refractive error in mild to moderate myopic eyes,especially at 6°inferior to the fovea and the decreased PCPD in the macular region of myopic patients may be associated with increased axial lengthinduced retinal stretching.展开更多
基金the National Key Research and Development Program of China(2021YFA0717900)National Natural Science Foundation of China(62471251,62405144,62288102,22275098,and 62174089)+1 种基金Basic Research Program of Jiangsu(BK20240033,BK20243057)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB402).
文摘Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.
文摘Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.
文摘The paper proposes a method for solving the problem of synthesis of an adaptive control system for unstable and deterministic chaotic processes in the class of‘dovetail’catastrophes for objects with m-inputs and n-outputs.The synthesis problem is solved by the gradient-velocity method of Lyapunov vector functions.From the conditions of the aperiodic robust stability of the etalon model with the desired dynamics of the main control loop and the generalised configurable object,the control goal is achieved.One of the most promising ways of solving the problem of managing unstable and deterministic chaotic processes is the synthesis of a control system in the class of‘swallowtail’catastrophes and the use of adaptation methods.Selecting and studying a reference model,and employing the gradient-velocity method for a generalised configurable control object ensure robust stability,utilise current information effectively,and achieve desired system quality and management goals.
文摘Managing sensitive data in dynamic and high-stakes environments,such as healthcare,requires access control frameworks that offer real-time adaptability,scalability,and regulatory compliance.BIG-ABAC introduces a transformative approach to Attribute-Based Access Control(ABAC)by integrating real-time policy evaluation and contextual adaptation.Unlike traditional ABAC systems that rely on static policies,BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes,ensuring precise and efficient access control.Leveraging decision trees evaluated in real-time,BIG-ABAC overcomes the limitations of conventional access control models,enabling seamless adaptation to complex,high-demand scenarios.The framework adheres to the NIST ABAC standard while incorporating modern distributed streaming technologies to enhance scalability and traceability.Its flexible policy enforcement mechanisms facilitate the implementation of regulatory requirements such as HIPAA and GDPR,allowing organizations to align access control policies with compliance needs dynamically.Performance evaluations demonstrate that BIG-ABAC processes 95% of access requests within 50 ms and updates policies dynamically with a latency of 30 ms,significantly outperforming traditional ABAC models.These results establish BIG-ABAC as a benchmark for adaptive,scalable,and context-aware access control,making it an ideal solution for dynamic,high-risk domains such as healthcare,smart cities,and Industrial IoT(IIoT).
基金supported by the National Natural Science Foundation of China(Grant Nos.42130608 and 42075142)the National Key Research and Development Program of China(Grant No.2020YFA0608000)the CUIT Science and Technology Innovation Capacity Enhancement Program Project(Grant No.KYTD202330)。
文摘Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.
基金the National Key Research and Development Program of China(Nos.2022YFA1204500 and 2022YFA1204502)the National Natural Science Foundation of China(Nos.22293043,51932001,52372170,and 92163209)+1 种基金the Beijing-Tianjin-Hebei Basic Research Cooperation Special Project(No.B2024204027)IPE Project for Frontier Basic Research(No.QYJC-2023-08).
文摘Enzyme-like metal atomic site catalysts are promising alternatives of platinum group metals for oxygen reduction reaction(ORR)in fuel cell application.The local coordination structure at metal atomic sites plays a dominant role in optimizing the adsorption/desorption of oxygen intermediates to enhance ORR,but there is still a significant challenge in achieving.Herein,we report a type of stable and dynamically adjustable mono-oxygen-bridged asymmetric dual-atomic metal catalyst,in which the active Fe-oxo-Co motif demonstrates platinium-like ORR activity with a half-wave potential of 0.92 V vs.RHE in alkaline condition and a maximum power density of 228 mW·cm^(-2) in Zn-air batteries.Theoretical calculations reveal that the Fe-oxo ligands can act as electron regulators for neighboring Co sites,which optimize and promote the d-orbitals of Co metal shift towards lower energy levels,thereby weakening the adsorption of oxygen species,facilitating the progress of the ORR.More interestingly,the Fe-oxo-Co bond will dynamically change its strength to adaptively facilitate the intermediate steps during the ORR process.The design strategy towards enzyme-like adaptive behavior of active Fe-oxo-Co motifs brings significant hope for achieveing high performance fuel cell cathode materials.
基金supported by the National Natural Science Foundation of China(Nos.62002100,61902237)Key Research and Promotion Projects of Henan Province(Nos.232102240023,232102210063,222102210040).
文摘Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.
基金Chinese Academy of Medical Sciences Initiative for Innovative Medicine,Grant/Award Number:2021-I2M-1-035 and 2022-I2M-1-011。
文摘Background:Dengue fever,an acute insect-borne infectious disease caused by the dengue virus(DENV),poses a great challenge to global public health.Hepatic involve-ment is the most common complication of severe dengue and is closely related to the occurrence and development of disease.However,the features of adaptive immune responses associated with liver injury in severe dengue are not clear.Methods:We used single-cell sequencing to examine the liver tissues of mild or se-vere dengue mice model to analyze the changes in immune response of T cells in the liver after dengue virus infection,and the immune interaction between macrophages and T cells.Flow cytometry was used to detect T cells and macrophages in mouse liver and blood to verify the single-cell sequencing results.Results:Our result showed CTLs were significantly activated in the severe liver injury group but the immune function-related signal pathway was down-regulated.The rea-son may be that the excessive immune response in the severe group at the late stage of DENV infection induces the polarization of macrophages into M2 type,and the macrophages then inhibit T cell immunity through the TGF-βsignaling pathway.In ad-dition,the increased proportion of Treg cells suggested that Th17/Treg homeostasis was disrupted in the livers of severe liver injury mice.Conclusions:In this study,single-cell sequencing and flow cytometry revealed the characteristic changes of T cell immune response and the role of macrophages in the liver of severe dengue fever mice.Our study provides a better understanding of the pathogenesis of liver injury in dengue fever patients.
基金supported by the National Natural Science Foundation of China under Grant Nos.T2293773,72371145,and 12288201the Special Funds for Taishan Scholars Project of Shandong Province,China under Grant No.tsqn202211004National Key Research and Development Program under Grant No.2022YFC3303000.
文摘With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.
基金supported by the National Natural Science Foundation of China under Grant U21A20449in part by Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2。
文摘Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.
基金supported by the National Key Research and Development Project of China(No.2019YFE0124500).
文摘The emergence of adaptive facades offers a new approach for buildings to enhance their resilience against external weather conditions while responding to occupants’demands,thereby improving both indoor environmental quality and energy performance.Appropriate control methods are crucial to achieving these purposes.However,most existing studies for automatic control of blinds have focused on visual comfort,leaving potential for further energy savings by reducing cooling and artificial lighting demands.Additionally,current optimization methods for slat angles are mostly simplified as a discrete process,neglecting the impact of thermal mass in building envelopes.Therefore,this paper aims to explore the energy reduction potential of window blinds by developing an iterative optimization method for devising hourly adaptive control strategies.To this end,a co-simulation platform between EnergyPlus and Python was established for the optimization and a case study in a subtropical city was conducted.The proposed strategies effectively balanced lighting and cooling demands to achieve an overall energy reduction of 7.3%–12.5%compared to reference cases while also ensuring visual comfort by mitigating glare risk and excessive daylight.These advantages were also compared with several simpler control scenarios,with analyses tailored to various glazing types and orientations.Furthermore,the optimal window configurations with blind control strategies for different orientations were determined.The findings also indicated that glass properties markedly impact the performance of control strategies,underscoring the necessity of holistically considering shading components and glazing types in the optimization to achieve optimal performance.
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2022JM-381,2017JQ6070)National Natural Science Foundation of China(Grant No.61703256),Foundation of State Key Laboratory of Public Big Data(No.PBD2022-08)the Fundamental Research Funds for the Central Universities,China(Program No.GK202201014,GK202202003,GK201803020).
文摘Studies show that Graph Neural Networks(GNNs)are susceptible to minor perturbations.Therefore,analyzing adversarial attacks on GNNs is crucial in current research.Previous studies used Generative Adversarial Networks to generate a set of fake nodes,injecting them into a clean GNNs to poison the graph structure and evaluate the robustness of GNNs.In the attack process,the computation of new node connections and the attack loss are independent,which affects the attack on the GNN.To improve this,a Fake Node Camouflage Attack based on Mutual Information(FNCAMI)algorithm is proposed.By incorporating Mutual Information(MI)loss,the distribution of nodes injected into the GNNs become more similar to the original nodes,achieving better attack results.Since the loss ratios of GNNs and MI affect performance,we also design an adaptive weighting method.By adjusting the loss weights in real-time through rate changes,larger loss values are obtained,eliminating local optima.The feasibility,effectiveness,and stealthiness of this algorithm are validated on four real datasets.Additionally,we use both global and targeted attacks to test the algorithm’s performance.Comparisons with baseline attack algorithms and ablation experiments demonstrate the efficiency of the FNCAMI algorithm.
基金supported by the National Key Research and Development Program of China(2019YFA0905603)the National Natural Science Foundation of China(51988102,52333004,22135005)。
文摘Natural biological microtubular networks have undergone adaptive evolutionary selection and may offer viable solutions to design challenges in artificial microtubular networks.The plasmodium of the slime mold Physarum polycephalum(P.polycephalum)extends continuously to form a protoplasmic microtubular network structure;directly connecting food sources.Computational simulations revealed that the formation of this adaptive P.polycephalum microtubular network could be captured by a mathematical algorithm.Inspired by the P.polycephalum microtubular networks;we propose an adaptive optimization design method for artificial microtubular networks.Specifically;we utilized hydrogels with biodegradable and tissue-adhesive properties to replicate the P.polycephalum microtubular networks via photomask.In Rhodamine B diffusion and glucosecatalyzed reaction experiments;we found that the P.polycephalum microtubular networks exhibited significantly enhanced efficiency compared to vascular and artificial networks.Furthermore;we demonstrated the potential for uric acid(UA)degradation of the hydrogels with a real P.polycephalum microtubular network loaded with urate oxidase(UOx)in a rodent model of hyperuricemia.And this network achieved more than double the effect of the artificial network.This underscores the potential of natural microtubular networks to replace artificial microtubular networks.
基金funded by the National Natural Science Foundation of China(grant no.32270238 and 31870311).
文摘Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.
基金National Natural Science Foundation of China(62373102)Jiangsu Natural Science Foundation(BK20221455)Anhui Provincial Key Research and Development Project(2022i01020013)。
文摘Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.
基金Graduate Student Innovation Projects of Beijing University of Civil Engineering and Architecture(No.PG2024121)。
文摘Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
基金Supported by National Natural Science Foundation of China(No.82271107).
文摘AIM:To assess the variations in photoreceptor cell packing density(PCPD)across the retina among young healthy individuals with emmetropia,low and moderate myopia.METHODS:High-resolution adaptive optics scanning laser ophthalmoscopy(AOSLO)systems were utilized for retinal imaging with a large sampling window of 700μm×700μm.The study cohort included 14 emmetropic[spherical equivalent(SE)ranged+0.5 to-0.5 D],15 low myopic(SE ranged-0.5 to-3 D)and 21 moderate myopic(SE ranged-3 to-6 D)healthy young adults.Photoreceptors at 3°temporal,6°superior and inferior 6°were captured.Statistical analysis was then performed to obtain PCPD and cell spacing.RESULTS:The average age of participants was 22.54±2.86(ranged 20–30y)with no difference among 3 groups.At 3°temporal,the emmetropic group exhibited the highest PCPD of 15186.16±2050.54 cells/mm2,while the low and moderate myopic groups had PCPD of 14009.15±1073.01 and 13466.92±1121.71 cells/mm2,respectively.At 3°temporal,the emmetropic group also had the smallest cell spacing at 6.66±0.26 mm,compared to 6.85±0.26 and 6.91±0.28 mm for the low and moderate myopic groups,respectively.Compared to the emmetropic group,at 3°temporal,the myopic groups showed significantly reduced PCPD(low myopia:P=0.032;moderate myopia:P=0.001).At 6°inferior,the moderate myopic group exhibited a significant decrease in PCPD(P=0.013),while at 6°superior,there were no significant statistical differences in PCPD for the low and moderate myopic groups(P>0.05).In comparison to the emmetropic group,only the moderate myopic group showed significantly increased cell spacing at all three positions(temporal 3°:P=0.011,superior 6°:P=0.046,inferior 6°:P=0.013).Correlation analysis revealed a positive correlation between PCPD and axial length changes(P<0.05).CONCLUSION:Reduced PCPD and increased cell spacing strongly correlated with refractive error in mild to moderate myopic eyes,especially at 6°inferior to the fovea and the decreased PCPD in the macular region of myopic patients may be associated with increased axial lengthinduced retinal stretching.
