采用Spiked协方差模型与“相变”现象的电网不平衡扰动评估

为了提升随机矩阵理论在电网不平衡扰动场景中的适用性,提出了一种基于Spiked协方差模型与"相变"现象的电网扰动识别与定位方法。首先,通过含噪声三相数据源的采集,构造Spiked协方差模型与样本协方差矩阵。然后,利用特征值"相变"现象构建样本最大特征值评价指标及对应的动态阈值。当该指标越过其阈值,即判定电网中有扰动事件发生时,根据Spiked与样本最小特征向量元素改变量的网络位置,结合特征向量"相变"现象以实现电网不平衡扰动的快速定位。最后,借助DIgSILENT和MATLAB R2014a软件,案例分析在一个IEEE 118母线177支路系统和一个德国872母线1 840支路实际配电系统中展开,涉及负荷突变和线路故障等不平衡扰动事件,与传统随机矩阵理论方法的比较结果表明了所提方法的有效性和高效性。

基于Spiked模型的低信噪比环境电网异常状态检测

为发展基于大数据技术的电网态势感知理论与方法,提出了一种基于Spiked模型电网异常状态动态辨识方法,该方法源于随机矩阵理论。首先,通过数据源矩阵的构造,窗口数据矩阵及其标准矩阵的构建,进而形成其样本协方差矩阵,并计算该矩阵的最大特征值;然后,利用由Kaiser窗函数校正的经典谱估计法进行全局信噪比估计,进而得出对应的动态阈值,并与最大特征值比较来进行异常状态判别;最后,借助MATLAB软件,案例分析在一个IEEE50机标准系统展开,涉及负荷异常跃变及三相短路接地故障,与传统的平均谱半径分析法的计算结果比较表明该方法具有抗噪性能高、适应性强的优点,同时对于非完整性信息有一定的鲁棒性。

基于随机矩阵理论与Spiked模型的电力系统态势感知方法研究

从数据驱动角度,结合随机矩阵理论(RMT)和Spiked模型,研究了电力系统异常状态感知方法和关键节点辨识方法.提出了基于样本协方差矩阵最大特征值(MESCM)的电力系统异常状态感知方法;在此基础上引入Spiked模型对其进行改进,实现了电力系统异常状态的动态感知;以系统电压数据为原始数据,结合熵理论提取了数据有效信息,对电力系统网络的关键节点进行辨识.通过模拟分析和实际检测验证了该方法具有抗噪性能高、计算耗时少的优点,提高了电力系统异常状态感知模型的准确度和鲁棒性.该方法有效反映了电力系统异常状态的演化方向及其分布,可用于支持电力系统预防、检修和运维,辅助电力系统决策.

Coupled fluid-thermal investigation on non-ablative thermal protection system with spiked body and opposing jet combined configuration

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.

The wheat sucrose synthase gene TaSus1 is a determinant of grain number per spike

Some haplotypes of the sucrose synthase gene TaSus1 are associated with thousand-grain weight(TGW)in wheat(Triticum aestivum L.).However,no mutations have been identified within the gene to test this association.The effects of TaSus1 on grain number per spike(GNS)also are largely unknown.Our previous genome-wide association study identified TaSus-A1 as a candidate gene controlling fertile spikelet number per spike(FSN).In the present study,we generated two independent mutants for the three TaSus1 homoeologs by CRISPR/Cas9-mediated genome editing.The triple mutants displayed lower FSN,GNS,grain number per spikelet(GNST),and TGW than wild-type plants.In 306 hexaploid wheat accessions,two single-nucleotide polymorphisms in TaSus-A1 contributed differently to GNS.Introgression of the two alleles into a wheat genetic background confirmed their effects.The alleles differed in geographical distribution among the accessions.

Advances in memristor based artificial neuron fabrication-materials,models,and applications

Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

The first factor affecting dryland winter wheat grain yield under various mulching measures: Spike number

Water is the key factor limiting dryland wheat grain yield.Mulching affects crop yield and yield components by affecting soil moisture.Further research is needed to determine the relationships between yield components and soil moisture with yield,and to identify the most important factor affecting grain yield under various mulching measures.A long-term 9-yearifeld experiment in the Loess Plateau of Northwest China was carried out with three treatments:no mulch (CK),plastic mulch (M_(P)) and straw mulch (M_(S)).Yield factors and soil moisture were measured,and the relationships between them were explored by correlation analysis,structural equation modeling and significance analysis.The results showed that compared with CK,the average grain yields of M_(P) and M_(S) increased by 13.0and 10.6%,respectively.The average annual grain yield of the M_(P) treatment was 134 kg ha^(–1) higher than the M_(S) treatment.There were no significant differences in yield components among the three treatments (P<0.05).Soil water storage of the M_(S) treatment was greater than the M_(P) treatment,although the differences were not statistically signifiant.Soil water storage during the summer fallow period (SWSSF) and soil water storage before sowing (SWSS) of M_(S) were significantly higher than in CK,which increased by 38.5 and 13.6%,respectively.The relationship between M_(P) and CK was not statistically significant for SWSSF,but the SWSS in M_(P) was significantly higher than in CK.In terms of soil water storage after harvest (SWSH) and water consumption in the growth period(ET),there were no signi?cant differences among the three treatments.Based on the three analysis methods,we found that spike number and ET were positively correlated with grain yield.However,the relative importance of spike number to yield was the greatest in the M_(P )and M_(S) treatments,while that of ET was the greatest in CK.Suifcient SWSSF could indirectly increase spike number and ET in the three treatments.Based on these results,mulch can improve yield and soil water storage.The most important factor affecting the grain yield of dryland wheat was spike number under mulching,and ET with CK.These findings may help us to understand the main factors influencing dryland wheat grain yield under mulching conditions compared to CK.

Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Complementary memtransistors for neuromorphic computing: How, what and why

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit technology.Therefore,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges discussed.Then the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,etc.Through exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

Advances in neuromorphic computing:Expanding horizons for AI development through novel artificial neurons and in-sensor computing

AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.

A Review of Computing with Spiking Neural Networks

Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing.

The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.

Roles of host proteases in the entry of SARS-CoV-2

The spike protein(S)of SARS-CoV-2 is responsible for viral attachment and entry,thus a major factor for host suscep-tibility,tissue tropism,virulence and pathogenicity.The S is divided with S1 and S2 region,and the S1 contains the receptor-binding domain(RBD),while the S2 contains the hydrophobic fusion domain for the entry into the host cell.Numerous host proteases have been implicated in the activation of SARS-CoV-2 S through various c leavage sites.In this article,we review host proteases including furin,trypsin,transmembrane protease serine 2(TMPRSS2)and cathepsins in the activation of SARS-CoV-2 S.Many betacoronaviruses including SARS-CoV-2 have polybasic residues at the S1/S2 site which is subjected to the cleavage by furin.The S1/S2 cleavage facilitates more assessable RBD to the receptor ACE2,and the binding triggers further conformational changes and exposure of the S2'site to proteases such as type Il transmembrane serine proteases(TTPRs)including TMPRSS2.In the presence of TMPRSS2 on the target cells,SARS-CoV-2 can utilize a direct entry route by fusion of the viral envelope to the cellular membrane.In the absence of TMPRSS2,SARS-CoV-2 enter target cells via endosomes where multiple cathepsins cleave the S for the successful entry.Additional host proteases involved in the cleavage of the S were discussed.This article also includes roles of 3C-like protease inhibitors which have inhibitory activity against cathepsin L in the entry of SARS-CoV-2,and discussed the dual roles of such inhibitors in virus replication.

Translocation of a fish spike from the pharynx to the thyroid gland:A case report

BACKGROUND A fish spike stuck in the throat is a common ear,nose,and throat(ENT)emergency.However,it is very rare for a fish spike to reach the thyroid tissue through the throat,which is very dangerous and can lead to pharyngeal fistula,cervical abscess,mediastinal abscess,and thyroid abscess.Proper and timely management can help reduce complications,especially in elderly patients.CASE SUMMARY In the case presented here,the causative factor was dentures,but improper management aggravated the condition.In the case presented here,an elderly woman with a history of accidentally swallowing fish bones for 20 d had a sensation of foreign bodies in her throat.Eventually,computed tomography(CT)of the neck showed that the left side of the thyroid gland had a dense shadow in the form of a stripe.CONCLUSION If a fishbone foreign body is not visible during endoscopic examination but the patient has significant symptoms,the surgeon should be aware that the fishbone may be lodged in the thyroid.To avoid a misdiagnosis,ultrasound,CT,and other tests can be used to clarify the diagnosis.T The first step in treating a fish bone in the thyroid gland is to determine the position of the foreign body and the extent of the infection,and to develop a personalized surgical plan for its removal.At the same time,scientific information should be made available to the general public so that people know that if a fish bone is accidentally lodged,they should not force it to be swallowed or be spit out by inducing vomiting,which are incorrect methods and may aggravate the condition or even cause it to migrate outside the cavity,leading to serious complications,as in this reported case.

Innovative COVID-19 Screening: RT-LAMP Assay for Spike and NSP1 Proteins

Coronavirus disease(COVID-19)is a serious respiratory disease that spreads through the coronavirus globally.It soon became a pandemic after its appearance in 2019 and demanded new techniques for its identification and detection.Owing to this situation,RT-LAMP appears to be a novel method for the identification of COVID-19 because of its vast applications,including cost-effectiveness and time-saving.This research highlights the use of RT-LAMP,a more sensitive test than RT-PCR,for the assessment of SARS-CoV-2,the severe acute respiratory illness.To identify the spike(S)and NSP1 protein using RT-LAMP,170 total samples of coronavirus-suspected patients were served in this research.Health certifications and bioethical considerations were taken into consideration.After the sample was extracted from the patient's swabs,RNA was isolated,extracted,and purified.The response was then run on the RT-LAMP at the ideal temperature,and the outcomes could be observed with the unaided eye as they changed from pink to yellow.It is a simple method of determining if the test is positive or negative.For this purpose,both RT-LAMP and RT-PCR tests are used during these procedures.Genes linked with COVID-19 testing including S,nspl,and ORF are suited to coronavirus testing;they have 100%specificity and low sensitivity,but S has more specificity and sensitivity than nspl and ORF,respectively.Out of the 95 positive samples,89(93.68%)samples yielded favorable outcomes utilizing RT-LAMP,while 55 negative samples yielded 100%positive results.The present research demonstrates that RT-LAMP is less sensitive yet more selective for coronavirus detection.

基于蜂群优化的Spiking神经网络模型研究与评估

为提高Spiking神经网络的训练能力,以多标签分类问题作为研究切入点,采用蜂群算法进行模型优化。基于Spiking理念的神经网络模型有多种,文中选择概率Spiking神经网络(Probabilistic Spiking Neural Network,PSNN)进行多标签分类。首先,建立概率Spiking神经网络分类模型,通过点火时间序列进行编码,触发脉冲响应实现数据传递;然后,利用Spiking神经网络的权重、动态阈值、遗忘参数等构建蜂群,并以多标签分类准确率作为人工蜂群(Artificial Bee Colony,ABC)算法的适应度函数,从而通过不断更新蜂群个体适应度值来获得最优个体;最后,以最优参数完成概率Spiking神经网络的多标签分类。实验结果表明,通过合理设置蜂群个体规模及蜜源搜索范围,ABC-PSNN算法能够获得较高的多标签分类准确率。相比其他Spiking神经网络模型和常用多标签分类算法,ABC-PSNN算法具备更高的分类准确率和稳定性。

贝叶斯支持向量回归及其应用

支持向量回归(SVR)是机器学习中重要的数据挖掘方法,当前关于SVR的研究大多基于二次规划理论,同时,利用交叉验证或一些智能算法选取模型中的超参数,然而,基于二次规划理论的SVR估计方法不仅计算量较大,而且不能进行后续的统计推断分析。文章基于贝叶斯方法研究SVR,通过引入两个潜在变量将SVR的ϵ不敏感损失函数表示为双重正态-尺度混合模型并构建似然函数,通过选取适当的先验分布获得兴趣参数和超参数的Gibbs抽样算法。为筛选重要变量和最优模型,引入0-1指示变量并选取回归参数的Spike and Slab先验来获得贝叶斯变量选择算法。数值模拟证明了所提算法的有效性,并在非正态误差下表现出很好的稳健性。最后将所提方法应用于房价数据分析,得到了有意义的结果。

Photonic integrated neuro-synaptic core for convolutional spiking neural network

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.

Introgression of chromosome 6PL terminal segment from Agropyron cristatum to increase both grain number and grain weight in wheat

Agropyron cristatum(2n=4x=28,PPPP),which harbours many high-yield and disease-resistance genes,is a promising donor for wheat improvement.Narrow genetic diversity and the trade-off between grain weight and grain number have become bottlenecks for increasing grain yield in wheat.In this study,a novel translocation line,WAT650l,was derived from the chromosome 6P addition line 4844–12,which can simultaneously increase both grain number per spike(GNS)and thousand-grain weight(TGW).Cytological analysis and molecular marker analysis revealed that WAT650l was a 5BL.5BS-6PL(bin 12–17)translocation line.Assessment of agronomic traits and analysis of the BC4F2 and BC5F2 populations suggested that the 6PL terminal chromosome segment in WAT650l resulted in increased grain number per spike(average increased by 14.07 grains),thousand-grain weight(average increased by 4.31 g),flag leaf length,plant height,spikelet number per spike and kernel number per spikelet during the two growing seasons of 2020–2021 and 2021–2022.Additionally,the increased GNS locus and high-TGW locus of WAT650l were mapped to the bins 16–17 and 12–13,respectively,on chromosome 6PL by genetic population analysis of three translocation lines.In summary,we provide a valuable germplasm resource for broadening the genetic base of wheat and overcoming the negative relationship between GNS and TGW in wheat breeding.

基于菌群优化Spiking神经网络的渲染时间估计

为了提高三维模型渲染时间估计的准确度,采用Spiking神经网络算法进行渲染时间预估。目前,基于Spiking理念的神经网络模型有多种,选择了其中的卷积Spiking神经网络(Convolutional Spike neural network,CSNN)来实现渲染时间计算。首先,建立了基于CSNN的渲染时间预估模型。通过点火时间序列完成编码,从而触发脉冲响应实现数据传递。其次,利用CSNN的权重、卷积核尺寸、偏置等参数来构建菌群优化(Bacterial foraging optimization,BFO)算法,并以渲染时间预估值和实际值的差值作为适应度函数。通过驱化、繁衍和迁徙操作不断更新菌群个体的适应度值来获得最优个体。最后,以最优参数进行CSNN的渲染时间预估。试验结果表明,通过合理设置BFO算法的引力系数、斥力系数和迁徙概率阈值等参数,BFO+CSNN算法能够获得较高的渲染时间预估准确率。相比于其他渲染时间预估算法,BFO+CSNN算法具备更高的渲染时间预估准鲁棒性。