In this paper, a Non-Ablative Thermal Protection System(NATPS) with the spiked body and the opposing jet combined configuration is proposed to reduce the aerodynamic heating of the hypersonic vehicle, and the coupled ...In this paper, a Non-Ablative Thermal Protection System(NATPS) with the spiked body and the opposing jet combined configuration is proposed to reduce the aerodynamic heating of the hypersonic vehicle, and the coupled fluid-thermal numerical analysis is performed to study the thermal control performance of the NATPS. The results show that the spiked body pushes the bow shock away from the protected structure and thus reduces the shock intensity and the wall heat flux. In addition, the low temperature gas of the opposing jet separates the high temperature gas behind the shock from the nose cone of the spiked body, ensuring the non-ablative property of the spiked body. Therefore, the NATPS reduces the aerodynamic heating by the reconfiguration of the flow field, and the thermal control efficiency of the system is better than the Thermal Protection System(TPS) with the single spiked body and the single opposing jet. The influencing factors of the NATPS are analyzed. Both increasing the length of the spiked body and reducing the total temperature of the opposing jet can improve the thermal control performance of the NATPS and the nonablative property of the spiked body. However, increasing the heat conductivity coefficient of the spiked body can enhance benefit the non-ablative property of the spiked body, but has little influence on the thermal control performance of the NATPS.展开更多
Background:Beverages play a positive role in a balanced diet.Beverages can provide an enjoyable and refreshing mind to reach a particular target.In beverages,date-rape drugs,such as Rohypnol,gamma-hydroxybutyric acid,...Background:Beverages play a positive role in a balanced diet.Beverages can provide an enjoyable and refreshing mind to reach a particular target.In beverages,date-rape drugs,such as Rohypnol,gamma-hydroxybutyric acid,and ketamine,were usually added to make victims to become weak,confused,unconscious,and vulnerable.Aims and Objectives:The aims and objectives of the research work are to analyze the beverages,viz.,Sprite,Coca-Cola and Coffee by analytical techniques and to degrade the date-rape drug present in the beverages by photocatalysis using activated carbon as the photocatalyst material.Materials and Methods:The drug(clonazepam)and beverages used in the research work were analyzed using FTIR,UV and HPLC techniques.Results:From the FTIR,in beverages(Sprite and Coca-Cola),the peaks corresponding to C-O and O-H functional groups confirmed the presence of CO_(2)and H_(2)O and in Coffee,the presence ofν_(as)(COC)andνs(COC)vibration bands is found out.The UV-visible analysis confirmed theλ_(max)value for activated carbon as 251 nm.Under visible light and activated carbon photocatalyst,53.57%of drug molecule was degraded from coca cola which was found to be highest than other beverages.The degradation of drug molecule was also confirmed by the reduction in the peak area for a particular retention time through HPLC analysis.Conclusion:Photocatalysis can be effectively used to remove any drug present in the spike drinks.展开更多
Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely us...Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.展开更多
Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantage...Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.展开更多
The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. Ho...The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. However, with the emergence of compressor instability starting from the stator region, the mechanism of various instability inceptions that occurs in different blade rows due to the change of IGV angles should be further examined. In this study, experiments were focused on three types of instability inceptions observed previously in a 1.5-stage axial flow compressor. To analyze the conversion of stall evolutions, the compressor rotating speed was set to 17 160 r/min, at which both the blade loading in the stator hub region and rotor tip region were close to the critical value before final compressor stall. Meanwhile, the dynamic test points with high-response were placed to monitor the pressures both at the stator trailing edges and rotor tips. The results indicate that the variation of reaction determines the region where initial instability occurs. Indeed, negative pre-rotation of the inlet guide vane leads to high-reaction, initiating stall disturbance from the rotor region. Positive pre-rotation results in low-reaction, initiating stall disturbance from the stator region. Furthermore, the type of instability evolution is affected by the radial loading distribution under different IGV angles. Specifically, a spike-type inception occurs at the rotor blade tip with a large angle of attack at the rotor inlet (−2°, −4° and −6°). Meanwhile, the critical total pressure ratio at the rotor tip is 1.40 near stall. As the angle of attack decreases, the stator blade loading reaches its critical boundary, with a value of approximately 1.35. At this moment, if the rotor tip maintains high blade loading similar to the stator hub, the partial surge occurs (0° and +2°);otherwise, the hub instability occurs (+4° and +6°).展开更多
Spike length(SL)is an important factor affecting yield in wheat(Triticum aestivum L.).Here,a recombinant inbred line(RIL)population derived from a cross between Shannong 4155(SN4155)and Shimai 12(SM12)was used to map ...Spike length(SL)is an important factor affecting yield in wheat(Triticum aestivum L.).Here,a recombinant inbred line(RIL)population derived from a cross between Shannong 4155(SN4155)and Shimai 12(SM12)was used to map quantitative trait loci(QTL)controlling SL.A QTL,q SL2B,on chromosome 2B was identified in all experiments and explained 9.92%–12.71%of the phenotypic variation.Through transcriptome and gene expression analysis,we identified a gene encoding Elongation Factor 1-alpha(Tae EF1A)as the candidate gene for q SL2B.Genome editing of Tae EF1A demonstrated that Tae EF1A positively regulates SL,spikelet number per spike(SNS),and grain number per spike(GN).Transcriptome analysis showed that Tae EF1A may affect the protein translation process and photosynthesis to regulate spike development.We used haplotype analysis of wheat germplasm to identify seven types of genetic variations in Tae EF1A,with TypeⅠ,TypeⅡ,and TypeⅢbeing the major haplotypes.Screening of 428 cultivars and breeding lines identified 225 and 203 accessions as TypeⅠand TypeⅡhaplotypes,respectively,with TypeⅢnot detected.Comparison of SL,SNS,and GN between the TypeⅠand TypeⅡhaplotypes revealed that the TypeⅠallele can increase SL,SNS,and GN simultaneously,and is thus preferred for use in wheat molecular breeding efforts to increase SL,SNS,and GN.展开更多
Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and...Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.展开更多
Wheat(Triticum aestivum L.)is an important staple food crop in the world and supplies about 20%of human caloric and protein consumption(Giraldo et al.,2019;Xiao et al.,2022).Wheat production accounts for~30%of global ...Wheat(Triticum aestivum L.)is an important staple food crop in the world and supplies about 20%of human caloric and protein consumption(Giraldo et al.,2019;Xiao et al.,2022).Wheat production accounts for~30%of global cereal crops(Li et al.,2019).With the global population increasing and the frequency of natural disasters rising,enhancing wheat yield is crucial to meet food demand.Spike traits such as increased grain number per spike are key determinants of wheat yield.Pre-harvest sprouting(PHS)is a significant natural disaster that severely impacts grain yield and end-use quality of wheat(Tai et al.,2021,2024).展开更多
Deep reinforcement learning(DRL)achieves success through the representational capabilities of deep neural networks(DNNs).Compared to DNNs,spiking neural networks(SNNs),known for their binary spike information processi...Deep reinforcement learning(DRL)achieves success through the representational capabilities of deep neural networks(DNNs).Compared to DNNs,spiking neural networks(SNNs),known for their binary spike information processing,exhibit more biological characteristics.However,the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains,directly related to the information integration and transmission in SNNs.Inspired by the advanced computational power of dendrites in biological neurons,we propose a multi-dendrite spiking neuron(MDSN)model based on Multi-compartment spiking neurons(MCN),expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane potential.We apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks.The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources.Our model uses a bioinspired event-enhanced dendrite structure to emphasize features.Meanwhile,by utilizing dynamic membrane potential thresholds,it adaptively maintains the homeostasis of MDSN.Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.展开更多
This paper addresses the computational problem of fixed-interval smoothing state estimation in linear time-varying Gaussian stochastic systems.A new fixed-interval Kalman smoothing algorithm is proposed,and the corres...This paper addresses the computational problem of fixed-interval smoothing state estimation in linear time-varying Gaussian stochastic systems.A new fixed-interval Kalman smoothing algorithm is proposed,and the corresponding form of the smoother is derived.The method is able to accommodate situations where process and measurement noises are correlated,a limitation often encountered in conventional approaches.The Kalman smoothing problem discussed in this paper can be reformulated as an equivalent constrained optimization problem,where the solution corresponds to a set of linear equations defined by a specific co-efficient matrix.Through multiple permutations,the co-efficient matrix of linear equations is transformed into a block tridiagonal form,and then both sides of the linear system are multiplied by the inverse of the co-efficient matrix.This approach is based on the transformation of linear systems described in the SPIKE algorithm and is particularly well-suited for large-scale sparse block tridiagonal matrix structures.It enables efficient,parallel,and flexible solutions while maintaining a certain degree of block diagonal dominance.Compared to directly solving block tridiagonal co-efficient matrices,this method demonstrates appreciable advantages in terms of numerical stability and computational efficiency.Consequently,the new smoothing algorithm yields a new smoother that features fewer constraints and broader applicability than traditional methods.The estimates,such as smoothed state,covariance,and cross-covariance,are essential for fields,such as system identification,navigation,guidance,and control.Finally,the effectiveness of the proposed smoothing algorithm and smoother is validated through numerical simulations.展开更多
Ionizing radiation presents an important solution for virus inactivation.However,its efficacy for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)inactivation and the underlying mechanisms remain unclear.Th...Ionizing radiation presents an important solution for virus inactivation.However,its efficacy for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)inactivation and the underlying mechanisms remain unclear.This study demonstrates radiosensitivity and radiation-induced biological changes in SARS-CoV-2 using 20 wild-type and mutant strains.The results show that 1.2 kGy of electron beam(E-beam)or 0.9 kGy of X-ray irradiation can eliminate 99.99%of SARS-CoV-2 particles.The Delta and various Omicron variants exhibit heightened sensitivity to radiation compared to the wild-type,showing nearly 99.99%inactivation efficiency at 1.0 and 0.8 kGy.The relationship between irradiation dose and the logarithmic reduction in virus load adheres to a dose-response model,characterized by extremely narrow windows.Spike(S)protein disruption,rather than the commonly accepted nucleic acid cleavage,is identified as the primary inactivation mechanism(triggering a conformation transition of S protein from pre-fusion to post-fusion with minimal impact on nucleic acid integrity).This study introduces the concept of targeting critical proteins in coronavirus inactivation,offering valuable insight for infectious coronavirus disease control and vaccine development.展开更多
Photonic hardware implementation of spiking neural networks,regarded as a viable potential paradigm for ultra-high speed and energy efficiency computing,leverages spatiotemporal spike encoding and event-driven dynamic...Photonic hardware implementation of spiking neural networks,regarded as a viable potential paradigm for ultra-high speed and energy efficiency computing,leverages spatiotemporal spike encoding and event-driven dynamics to simulate brain-like parallel information processing.Silicon-based microring resonators(MRRs)offer a power efficiency and ultrahigh flexibility scheme to mimic biological neuron,however,their substantial potential for integrated neuromorphic systems remains limited by insufficient exploration of MRR-based spiking digital and analog computation.Here,an all-optical neural dynamics framework,encompassing both excitatory and inhibitory behaviors based on multi-wavelength auxiliary and competition mechanism in an MRR,is proposed numerically.Leveraging multi-wavelength resonance characteristics and wavelength division multiplexing(WDM)technology,a single MRR implements the five fundamental optical digital logic gates:AND,OR,NOT,XNOR and XOR.Besides,the cascading capabilities of MRR-based spiking neurons are demonstrated through multi-level digital logic gates including NAND,NOR,4-input AND,8-input AND,and a full adder,emphasizing their promise for large-scale digital logic networks.Furthermore,an exemplary binary convolution has been achieved by utilizing the proposed MRR-based digital logic operation,illustrating the potential of all-optical binary convolution to compute image gradient magnitudes for edge detection.Such passive photonic neurons and networks promise access to the high transmission speed and low power consumption inherent to optical systems,thus enabling direct hardware-algorithm co-computation and accelerating artificial intelligence.展开更多
Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on trai...Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on training methodologies,hardware implementations,and practical applications.We analyze four major training paradigms:ANN-to-SNN conversion,direct gradient-based training,spike-timing-dependent plasticity(STDP),and hybrid approaches.Our review encompasses major specialized hardware platforms:Intel Loihi,IBM TrueNorth,SpiNNaker,and BrainScaleS,analyzing their capabilities and constraints.We survey applications spanning computer vision,robotics,edge computing,and brain-computer interfaces,identifying where SNN provide compelling advantages.Our comparative analysis reveals SNN offer significant energy efficiency improvements(1000-10000×reduction)and natural temporal processing,while facing challenges in scalability and training complexity.We identify critical research directions including improved gradient estimation,standardized benchmarking protocols,and hardware-software co-design approaches.This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities,limitations,and future prospects.展开更多
Spiking neural networks(SNNs)represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence,offering unique advantages in temporal data processing,energy efficiency,and...Spiking neural networks(SNNs)represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence,offering unique advantages in temporal data processing,energy efficiency,and real-time decision-making.This paper explores the evolution of SNN technologies,emphasizing their integration with advanced learning mechanisms such as spike-timing-dependent plasticity(STDP)and hybridization with deep learning architectures.Leveraging memristors as nanoscale synaptic devices,we demonstrate significant enhancements in energy efficiency,adaptability,and scalability,addressing key challenges in neuromorphic computing.Through phase portraits and nonlinear dynamics analysis,we validate the system’s stability and robustness under diverse workloads.These advancements position SNNs as a transformative technology for applications in robotics,IoT,and adaptive low-power AI systems,paving the way for future innovations in neuromorphic hardware and hybrid learning paradigms.展开更多
Grain yield variation has been associated to variation in grain number per unit area(GN).It has been shown in the last about 40 years that GN is linearly associated to the spike dry weight(SDW)at anthesis in wheat,fac...Grain yield variation has been associated to variation in grain number per unit area(GN).It has been shown in the last about 40 years that GN is linearly associated to the spike dry weight(SDW)at anthesis in wheat,fact that has been useful to understand mechanistically potential grain yield.Fruiting efficiency(FE,grains per gram of spike dry weight),the slope between GN and SDW relationship,has been proposed as a possible trait to improve wheat yield potential.The linear relationship between GN and SDW implies a constant increase in GN per unit increase in spike growth and,then a constant FE.However,there are empirical and theoretical elements suggesting that this relationship would not be linear.In this study,we hypothesised and showed that the linearity of the relationship between GN and SDW would be non-linear for extreme values of SDW,implying that the FE would be noticeably reduced at these extreme cases of dry matter allocation to the juvenile spikes.These results have implications for both,genetic and management improvements in grain yield.展开更多
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘In this paper, a Non-Ablative Thermal Protection System(NATPS) with the spiked body and the opposing jet combined configuration is proposed to reduce the aerodynamic heating of the hypersonic vehicle, and the coupled fluid-thermal numerical analysis is performed to study the thermal control performance of the NATPS. The results show that the spiked body pushes the bow shock away from the protected structure and thus reduces the shock intensity and the wall heat flux. In addition, the low temperature gas of the opposing jet separates the high temperature gas behind the shock from the nose cone of the spiked body, ensuring the non-ablative property of the spiked body. Therefore, the NATPS reduces the aerodynamic heating by the reconfiguration of the flow field, and the thermal control efficiency of the system is better than the Thermal Protection System(TPS) with the single spiked body and the single opposing jet. The influencing factors of the NATPS are analyzed. Both increasing the length of the spiked body and reducing the total temperature of the opposing jet can improve the thermal control performance of the NATPS and the nonablative property of the spiked body. However, increasing the heat conductivity coefficient of the spiked body can enhance benefit the non-ablative property of the spiked body, but has little influence on the thermal control performance of the NATPS.
文摘Background:Beverages play a positive role in a balanced diet.Beverages can provide an enjoyable and refreshing mind to reach a particular target.In beverages,date-rape drugs,such as Rohypnol,gamma-hydroxybutyric acid,and ketamine,were usually added to make victims to become weak,confused,unconscious,and vulnerable.Aims and Objectives:The aims and objectives of the research work are to analyze the beverages,viz.,Sprite,Coca-Cola and Coffee by analytical techniques and to degrade the date-rape drug present in the beverages by photocatalysis using activated carbon as the photocatalyst material.Materials and Methods:The drug(clonazepam)and beverages used in the research work were analyzed using FTIR,UV and HPLC techniques.Results:From the FTIR,in beverages(Sprite and Coca-Cola),the peaks corresponding to C-O and O-H functional groups confirmed the presence of CO_(2)and H_(2)O and in Coffee,the presence ofν_(as)(COC)andνs(COC)vibration bands is found out.The UV-visible analysis confirmed theλ_(max)value for activated carbon as 251 nm.Under visible light and activated carbon photocatalyst,53.57%of drug molecule was degraded from coca cola which was found to be highest than other beverages.The degradation of drug molecule was also confirmed by the reduction in the peak area for a particular retention time through HPLC analysis.Conclusion:Photocatalysis can be effectively used to remove any drug present in the spike drinks.
基金support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009)National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159)from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).
文摘Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grants No.2021B0909060002)National Natural Science Foundation of China(Grants No.62204219,62204140)Major Program of Natural Science Foundation of Zhejiang Province(Grants No.LDT23F0401).
文摘Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.
基金support of the National Natural Science Foundation of China(No.52322603)the Science Center for Gas Turbine Project of China(Nos.P2022-B-II-004-001 and P2023-B-II-001-001)+1 种基金the Fundamental Research Funds for the Central Universities,Chinathe Beijing Nova Program of China(Nos.20220484074 and 20230484479).
文摘The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. However, with the emergence of compressor instability starting from the stator region, the mechanism of various instability inceptions that occurs in different blade rows due to the change of IGV angles should be further examined. In this study, experiments were focused on three types of instability inceptions observed previously in a 1.5-stage axial flow compressor. To analyze the conversion of stall evolutions, the compressor rotating speed was set to 17 160 r/min, at which both the blade loading in the stator hub region and rotor tip region were close to the critical value before final compressor stall. Meanwhile, the dynamic test points with high-response were placed to monitor the pressures both at the stator trailing edges and rotor tips. The results indicate that the variation of reaction determines the region where initial instability occurs. Indeed, negative pre-rotation of the inlet guide vane leads to high-reaction, initiating stall disturbance from the rotor region. Positive pre-rotation results in low-reaction, initiating stall disturbance from the stator region. Furthermore, the type of instability evolution is affected by the radial loading distribution under different IGV angles. Specifically, a spike-type inception occurs at the rotor blade tip with a large angle of attack at the rotor inlet (−2°, −4° and −6°). Meanwhile, the critical total pressure ratio at the rotor tip is 1.40 near stall. As the angle of attack decreases, the stator blade loading reaches its critical boundary, with a value of approximately 1.35. At this moment, if the rotor tip maintains high blade loading similar to the stator hub, the partial surge occurs (0° and +2°);otherwise, the hub instability occurs (+4° and +6°).
基金supported by the Key R&D Program of Shandong province(2022LZGC001,2024CXPT072)the National Natural Science Foundation of China(32201863)the Tai’shan Scholars Program。
文摘Spike length(SL)is an important factor affecting yield in wheat(Triticum aestivum L.).Here,a recombinant inbred line(RIL)population derived from a cross between Shannong 4155(SN4155)and Shimai 12(SM12)was used to map quantitative trait loci(QTL)controlling SL.A QTL,q SL2B,on chromosome 2B was identified in all experiments and explained 9.92%–12.71%of the phenotypic variation.Through transcriptome and gene expression analysis,we identified a gene encoding Elongation Factor 1-alpha(Tae EF1A)as the candidate gene for q SL2B.Genome editing of Tae EF1A demonstrated that Tae EF1A positively regulates SL,spikelet number per spike(SNS),and grain number per spike(GN).Transcriptome analysis showed that Tae EF1A may affect the protein translation process and photosynthesis to regulate spike development.We used haplotype analysis of wheat germplasm to identify seven types of genetic variations in Tae EF1A,with TypeⅠ,TypeⅡ,and TypeⅢbeing the major haplotypes.Screening of 428 cultivars and breeding lines identified 225 and 203 accessions as TypeⅠand TypeⅡhaplotypes,respectively,with TypeⅢnot detected.Comparison of SL,SNS,and GN between the TypeⅠand TypeⅡhaplotypes revealed that the TypeⅠallele can increase SL,SNS,and GN simultaneously,and is thus preferred for use in wheat molecular breeding efforts to increase SL,SNS,and GN.
基金funded by Jilin Province Innovation and Entrepreneurship Talent Project,grant number 2023QN15funded by Science and Technology Development Plan Project of Jilin Province,grant number 20220202035NC.
文摘Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.
基金supported by the National Key Research and Development Program of China(2023YFD1200403 and 2023YFF1000600)the Innovation Program of Chinese Academy of Agricultural Sciences。
文摘Wheat(Triticum aestivum L.)is an important staple food crop in the world and supplies about 20%of human caloric and protein consumption(Giraldo et al.,2019;Xiao et al.,2022).Wheat production accounts for~30%of global cereal crops(Li et al.,2019).With the global population increasing and the frequency of natural disasters rising,enhancing wheat yield is crucial to meet food demand.Spike traits such as increased grain number per spike are key determinants of wheat yield.Pre-harvest sprouting(PHS)is a significant natural disaster that severely impacts grain yield and end-use quality of wheat(Tai et al.,2021,2024).
基金supported by the National Natural Science Foundation of China(62236002,62303009,62206001,52305001,62102387,62206005)the University Synergy Innovation Program of Anhui Province(GXXT-2022-041)the China Postdoctoral Science Foundation(2023M740013)
文摘Deep reinforcement learning(DRL)achieves success through the representational capabilities of deep neural networks(DNNs).Compared to DNNs,spiking neural networks(SNNs),known for their binary spike information processing,exhibit more biological characteristics.However,the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains,directly related to the information integration and transmission in SNNs.Inspired by the advanced computational power of dendrites in biological neurons,we propose a multi-dendrite spiking neuron(MDSN)model based on Multi-compartment spiking neurons(MCN),expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane potential.We apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks.The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources.Our model uses a bioinspired event-enhanced dendrite structure to emphasize features.Meanwhile,by utilizing dynamic membrane potential thresholds,it adaptively maintains the homeostasis of MDSN.Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.
文摘This paper addresses the computational problem of fixed-interval smoothing state estimation in linear time-varying Gaussian stochastic systems.A new fixed-interval Kalman smoothing algorithm is proposed,and the corresponding form of the smoother is derived.The method is able to accommodate situations where process and measurement noises are correlated,a limitation often encountered in conventional approaches.The Kalman smoothing problem discussed in this paper can be reformulated as an equivalent constrained optimization problem,where the solution corresponds to a set of linear equations defined by a specific co-efficient matrix.Through multiple permutations,the co-efficient matrix of linear equations is transformed into a block tridiagonal form,and then both sides of the linear system are multiplied by the inverse of the co-efficient matrix.This approach is based on the transformation of linear systems described in the SPIKE algorithm and is particularly well-suited for large-scale sparse block tridiagonal matrix structures.It enables efficient,parallel,and flexible solutions while maintaining a certain degree of block diagonal dominance.Compared to directly solving block tridiagonal co-efficient matrices,this method demonstrates appreciable advantages in terms of numerical stability and computational efficiency.Consequently,the new smoothing algorithm yields a new smoother that features fewer constraints and broader applicability than traditional methods.The estimates,such as smoothed state,covariance,and cross-covariance,are essential for fields,such as system identification,navigation,guidance,and control.Finally,the effectiveness of the proposed smoothing algorithm and smoother is validated through numerical simulations.
基金supported by the National Key Research and Development Program of China(2024YFC2309900 and 2021YFC2301200)the Zhejiang Plan for the Special Support for Top-notch Talents in China(2022R52029)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH003)the Hangzhou Global Scientific and Technological Innovation Center of Zhejiang University(KC2021ZY0B0002).
文摘Ionizing radiation presents an important solution for virus inactivation.However,its efficacy for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)inactivation and the underlying mechanisms remain unclear.This study demonstrates radiosensitivity and radiation-induced biological changes in SARS-CoV-2 using 20 wild-type and mutant strains.The results show that 1.2 kGy of electron beam(E-beam)or 0.9 kGy of X-ray irradiation can eliminate 99.99%of SARS-CoV-2 particles.The Delta and various Omicron variants exhibit heightened sensitivity to radiation compared to the wild-type,showing nearly 99.99%inactivation efficiency at 1.0 and 0.8 kGy.The relationship between irradiation dose and the logarithmic reduction in virus load adheres to a dose-response model,characterized by extremely narrow windows.Spike(S)protein disruption,rather than the commonly accepted nucleic acid cleavage,is identified as the primary inactivation mechanism(triggering a conformation transition of S protein from pre-fusion to post-fusion with minimal impact on nucleic acid integrity).This study introduces the concept of targeting critical proteins in coronavirus inactivation,offering valuable insight for infectious coronavirus disease control and vaccine development.
基金supports from National Natural Science Foundation of China(62171087,62475036).
文摘Photonic hardware implementation of spiking neural networks,regarded as a viable potential paradigm for ultra-high speed and energy efficiency computing,leverages spatiotemporal spike encoding and event-driven dynamics to simulate brain-like parallel information processing.Silicon-based microring resonators(MRRs)offer a power efficiency and ultrahigh flexibility scheme to mimic biological neuron,however,their substantial potential for integrated neuromorphic systems remains limited by insufficient exploration of MRR-based spiking digital and analog computation.Here,an all-optical neural dynamics framework,encompassing both excitatory and inhibitory behaviors based on multi-wavelength auxiliary and competition mechanism in an MRR,is proposed numerically.Leveraging multi-wavelength resonance characteristics and wavelength division multiplexing(WDM)technology,a single MRR implements the five fundamental optical digital logic gates:AND,OR,NOT,XNOR and XOR.Besides,the cascading capabilities of MRR-based spiking neurons are demonstrated through multi-level digital logic gates including NAND,NOR,4-input AND,8-input AND,and a full adder,emphasizing their promise for large-scale digital logic networks.Furthermore,an exemplary binary convolution has been achieved by utilizing the proposed MRR-based digital logic operation,illustrating the potential of all-optical binary convolution to compute image gradient magnitudes for edge detection.Such passive photonic neurons and networks promise access to the high transmission speed and low power consumption inherent to optical systems,thus enabling direct hardware-algorithm co-computation and accelerating artificial intelligence.
文摘Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on training methodologies,hardware implementations,and practical applications.We analyze four major training paradigms:ANN-to-SNN conversion,direct gradient-based training,spike-timing-dependent plasticity(STDP),and hybrid approaches.Our review encompasses major specialized hardware platforms:Intel Loihi,IBM TrueNorth,SpiNNaker,and BrainScaleS,analyzing their capabilities and constraints.We survey applications spanning computer vision,robotics,edge computing,and brain-computer interfaces,identifying where SNN provide compelling advantages.Our comparative analysis reveals SNN offer significant energy efficiency improvements(1000-10000×reduction)and natural temporal processing,while facing challenges in scalability and training complexity.We identify critical research directions including improved gradient estimation,standardized benchmarking protocols,and hardware-software co-design approaches.This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities,limitations,and future prospects.
基金Supported by CUP(J53C22003010006,J43C24000230007)ICREA2019.
文摘Spiking neural networks(SNNs)represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence,offering unique advantages in temporal data processing,energy efficiency,and real-time decision-making.This paper explores the evolution of SNN technologies,emphasizing their integration with advanced learning mechanisms such as spike-timing-dependent plasticity(STDP)and hybridization with deep learning architectures.Leveraging memristors as nanoscale synaptic devices,we demonstrate significant enhancements in energy efficiency,adaptability,and scalability,addressing key challenges in neuromorphic computing.Through phase portraits and nonlinear dynamics analysis,we validate the system’s stability and robustness under diverse workloads.These advancements position SNNs as a transformative technology for applications in robotics,IoT,and adaptive low-power AI systems,paving the way for future innovations in neuromorphic hardware and hybrid learning paradigms.
基金mainly funded by the State Research Agency of Spain through the Competitive Project PID2021-127415OB-I00 on "Spike fertility in wheat" with some contribution from an AGROTECNIO Seed-funding on "Analysing the physiology of spike density to provide support to selection in breeding programs"RAS did part of the work in this project during a research stay at the Crop Physiology Lab of the University of Lleida co-funded by AUIP (Postgraduate Iberoamerican University Association) grants+1 种基金core funds Crop Physiology Lab of the Ud L. CSC held a Maria Zambrano’s fellowship from the University of Lleida funded by the Spanish Ministry of Universities and the European Social Fund and is a member of CONICET (the Scientific Research Council of Argentina)INTA (the National Institute of Agriculture Technology of Argentina)
文摘Grain yield variation has been associated to variation in grain number per unit area(GN).It has been shown in the last about 40 years that GN is linearly associated to the spike dry weight(SDW)at anthesis in wheat,fact that has been useful to understand mechanistically potential grain yield.Fruiting efficiency(FE,grains per gram of spike dry weight),the slope between GN and SDW relationship,has been proposed as a possible trait to improve wheat yield potential.The linear relationship between GN and SDW implies a constant increase in GN per unit increase in spike growth and,then a constant FE.However,there are empirical and theoretical elements suggesting that this relationship would not be linear.In this study,we hypothesised and showed that the linearity of the relationship between GN and SDW would be non-linear for extreme values of SDW,implying that the FE would be noticeably reduced at these extreme cases of dry matter allocation to the juvenile spikes.These results have implications for both,genetic and management improvements in grain yield.
