Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled situations.The use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging results.An innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work.By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network parameters.The cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection algorithms.On seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,respectively.The results demonstrate the advantage of the proposed work over cutting-edge techniques.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection

We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.

Evaluation of the famous classic formula Sanhua decoction based on network pharmacology and multi-component quantitative analysis

Background:Sanhua decoction has significant effects in the treatment of stroke.The study of the Sanhua decoction material benchmark was carried out to analyze the value transfer relationship between the Chinese herbal pieces and the substance benchmark.Methods:Network pharmacology was employed to investigate the potential active components and molecular mechanisms of Sanhua decoction in the treatment of stroke.15 batches of Sanhua decoction lyophilized powder were prepared using traditional formulas and subjected to high-performance liquid chromatography analysis to generate fingerprints of the Sanhua decoction substance benchmarks.Then,a multi-component quantitative analysis method was established,allowing for the simultaneous determination of ten components,to study the transfer of quantity values between pieces and substance benchmarks.Results:60 active ingredients were screened from Sanhua decoction by network pharmacology,of which gallic acid,magnolol honokiol,physcion,and aloe-emodin may have a greater effect than other active components.63 key targets and 134 pathways were predicted as the potential mechanism of Sanhua decoction in treating stroke.The fingerprint similarity of the Sanhua decoction substance benchmarks was found to be good among the 15 batches,confirming the 19 common peaks.The content of the 10 components was basically consistent.The components’transfer rates were within 30%of their respective means.Conclusions:This study provided a comprehensive and reliable strategy for the quality evaluation of Sanhua decoction substance benchmarks and held significant importance in improving its application value.

A Network Pharmacology Study on Active Components and Targets of Citri Reticulatae Pericarpium for Treating Keloids

[Objectives]To investigate the mechanisms and pharmacologic effects of Citri Reticulatae Pericarpium against keloids by network pharmacology systematically.[Methods]TCMSP,Uniprot and BATMAN-TCM databases were used to obtain the active constituents and targets of Citri Reticulatae Pericarpium."Keloid"was used as key word to search for related therapeutic targets from Drug Bank,OMIM,TTD,and GEO databases.The Chinese medicine compound-target network was constructed by Cytoscape software.Besides,gene ontology(GO)and Kyoto Encyclopedia of genes and genome enrichment analysis were also performed.Afterward,Discovery Studio software was used to assess the interaction of key components and genes.[Results]Five active components of Citri Reticulatae Pericarpium,773 compound targets and 676 keloid treatment targets were obtained in the databases.After the intersection,there are 47 targets of Citri Reticulatae Pericarpium for treating keloids.Hub genes were identified such as MMP9,IL6,TNF,TP53,and VEGFA,which were enriched in tumor necrosis factor-α,nuclear factor kappa-B,and other signaling pathways.The molecular docking stimulation confirmed the interaction between the MMP9 and three components of Citri Reticulatae Pericarpium.[Conclusions]Citri Reticulatae Pericarpium may play an important role in treating keloids through modulating genes and signaling pathways.The present study sheds light on the mechanisms of active compounds of Citri Reticulatae Pericarpium for the treatment of keloids.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

工业PON中确定性网络传输技术研究(特邀)

无源光网络(PON)凭借其大带宽、低成本和抗电磁干扰等优势,被认为是下一代工业互联网的重要组网技术之一。然而,以“带宽提升”为主要技术发展思路的常规PON,其传输控制机制难以满足以“时间敏感”为特征的高品质工业业务传输需求,对常规PON的网络传输能力提出了重要挑战,迫使其融入新的特性,即确定性。文章以时分复用(TDM)-PON为主要研究对象,首先阐述了工业互联网的业务特征及传输需求,分析了工业互联场景下常规TDM-PON面临的两大技术挑战:一是传统带宽分配方案引起的时延不确定性;二是队列调度机制僵化引起的时延不确定性。围绕上述挑战,文章介绍了提升TDM-PON确定性网络传输能力的关键技术,如协作传输接口、单帧多突发和确定性带宽分配(DetBA)等。其次,文章介绍了一种基于网络演算的时延边界建模思路作为确定性工业PON系统设计与性能评估的理论模型。最后,文章从业务层、媒质接入控制(MAC)层、物理层及控制管理平面等多个角度探讨了确定性工业PON的潜在技术及发展方向。

宽带接入网10G PON技术发展及部署

随着数字化时代的到来,宽带接入网已成为支撑各行业发展和维持社会生活的重要基础设施。万兆无源光网络(10G PON)技术作为下一代宽带接入技术,具备高速、高带宽、低延迟等优势,能够给用户带来更加快速、稳定的网络体验,同时能满足未来应用对网络带宽需求的持续增长。基于此,对10G PON技术的发展现状、关键技术特点以及部署与应用进行深入探讨,以期为未来宽带接入网的发展提供有益启示与指导。

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

PON1基因在肝细胞癌中的表达及其对临床预后的意义和免疫浸润分析

目的探索人对氧磷酶-1(PON1)基因在肝细胞癌(HCC)的表达及其对临床预后的意义和免疫浸润分析。方法首先使用在线数据库TIMER、HCCDB和Kaplan-Meier Plotter,获得HCC患者PON1的基因表达水平及预后价值。并收集2011年4月至2015年6月在广西医科大学附属肿瘤医院接受手术治疗的90例HCC患者的癌及癌旁肝组织,检测PON1基因表达水平,结合临床相关参数,采用Cox回归分析,筛选与肝癌患者预后有意义的参数并构建预后模型。最后进行相关性分析,评估免疫细胞浸润特征。结果PON1的基因表达水平在HCC组织中均显著低于癌旁,且PON1低表达与HCC的预后不良相关(P<0.05)。PON1 mRNA水平、BCLC分期、CNLC分期、T分期、微血管侵犯(MVI)数量、血管侵犯、血清AFP水平与HCC预后显著相关(P<0.05)。BCLC分期、血清AFP、PON1 mRNA表达水平是HCC患者预后的独立危险因素。联合三个指标建立的1、2、3年总生存期预后列线图模型具有良好的预测性能及临床实用价值(C-index=0.757)。免疫浸润分析显示,PON1与HCC中多种免疫浸润细胞显著相关(P<0.05)。结论低表达的PON1可作为肝癌患者预后不良的潜在标志物,PON1联合血清AFP水平、BCLC分期构建的预测模型,可有效预测肝癌患者预后。

下一代相干PON关键技术研究进展与展望(特邀)

相比于传统的强度调制/直接检测(IM/DD)系统,相干系统具有更高的容量和功率预算,能更好地满足高容量无源光网络(PON)的需求。近年来,如何将相干应用于PON场景以更好地支撑未来高带宽业务已成为研究热点。文章从系统架构、相干简化、上行突发模式检测以及灵活PON 4个方面对相干PON关键技术研究现状进行了总结,并展望提出了激光共享上下行滤波器组多载波(FBMC)-PON和半导体光放大器(SOA)电流调控灵活PON方案。

基于PON的超低时延技术方案探讨

【目的】随着各种互联网新兴业务的崛起以及无源光网络(PON)在行业应用场景的延伸,作为第五代固定网络(F5G)代表技术的PON,需要不断演进升级才能满足未来网络的需求。其中,PON的时延是迫切要改善的关键网络性能指标,故文章对PON的低时延技术进行了深入研究。【方法】文章首先总结了当下PON在低时延方面所面临的挑战,分析了导致PON时延较高的主要原因有两个,分别是因光网络单元(ONU)带宽分配机制引入的上行固有时延和因ONU注册/测距机制引入的上行随机时延。因此,文章提出了基于单帧多突发技术和独立注册通道技术的两种超低时延技术方案,并进行实验验证分析。【结果】通过验证分析,对于单帧多突发技术的低时延技术方案,结合时延和带宽利用率两个性能指标综合来看,1/4调度周期表现最优,但会牺牲小部分带宽,比较适合传输对时延敏感而带宽不敏感的业务;对于独立注册通道技术的低时延技术方案,由于引入了额外的波长用于ONU注册/测距通道,可以完全消除正常业务通道因注册/测距的静默窗口引入的额外时延和抖动。【结论】综合而言,文章所探讨的基于PON的超低时延技术方案,可以有效降低PON的时延,有助于促进虚拟现实(VR)类等新兴业务的规模商用和工业互联网的快速发展。

兼容50G PON的多模统一平台架构及实现方法

目前网络中多种PON模式共存,而传统的OLT软件处理多种PON模式时存在兼容能力差、开发繁琐、重复等问题。本文提出了一种兼容多模OLT的统一平台架构,支持现存多种PON模式,并具有优秀的向后兼容能力。该架构通过不同的速率组合对应SerDes通道实现了速率兼容,并对ONU进行统一管理和带宽分配,在软件上实现了多模协议栈兼容和多芯片适配。本文采用不同OLT业务板卡搭载统一平台,对注册和打流业务功能进行测试。结果表明,该平台架构兼容包括50G PON在内的6种PON模式,并支持在不同板卡上的模式切换。同时,能降低OLT产品的管理难度,缩短项目周期,降低研发成本,对50G PON和未来PON网络新技术的兼容和平滑升级具有重要意义。

Performance Analysis of a Heterogeneous Network Employing DAPSK Based OFDMA-PON

Recent mobile broadband networks require heterogeneous networks supporting high capacity on demand.A hybrid Fiber-Wireless(Fi-Wi)access network integrating Differential Amplitude and Phase Shift Keying(DAPSK)based-Orthogonal Frequency Division Multiple Access(OFDMA)Passive Optical Network(PON)and 4G wireless network using Radio over Fiber(Ro F)technique is proposed.The proposed heterogeneous network is simulated,and the performance is analyzed in terms of Bit Error Rate(BER),Error Vector Magnitude(EVM),Capacity,and Spectral efficiency.The proposed network is simulated considering a higher data rate of 10 and 25 Gbps,and the effect of system parameters like Laser Power,Fiber Length,and Number of users is analyzed.The results show that a higher network capacity of 720 Gbps with an average capacity of about 45 Gbps and a higher spectral efficiency of 4.85 bps/Hz is achieved for the multi-user heterogeneous network link with sixteen users.The minimum value of optical power sufficient to achieve the desired BER is found to be-6 dBm.The suitability of the proposed integrated architecture in supporting multiple services is analyzed by considering different services at each UE.The spectral efficiency of the multi-service link varies from 3 to 4 bps/Hz.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Corrigendum:Altered intra-and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis

Corrigendum:Altered intra-and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis https://doi.org/10.4103/1673-5374.373719 There is an error in the registration number of the published paper“Altered intra-and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis”(doi:10.4103/1673-5374.339496)(Bao et al.,2022).The correct registration number should be ChiCTR-IOR-17011413.

LncRNAs exert indispensable roles in orchestrating the interaction among diverse noncoding RNAs and enrich the regulatory network of plant growth and its adaptive environmental stress response

With the advent of advanced sequencing technologies,non-coding RNAs(ncRNAs)are increasingly pivotal and play highly regulated roles in the modulation of diverse aspects of plant growth and stress response.This includes a spectrum of ncRNA classes,ranging from small RNAs to long non-coding RNAs(lncRNAs).Notably,among these,lncRNAs emerge as significant and intricate components within the broader ncRNA regulatory networks.Here,we categorize ncRNAs based on their length and structure into small RNAs,medium-sized ncRNAs,lncRNAs,and circle RNAs.Furthermore,the review delves into the detailed biosynthesis and origin of these ncRNAs.Subsequently,we emphasize the diverse regulatory mechanisms employed by lncRNAs that are located at various gene regions of coding genes,embodying promoters,5’UTRs,introns,exons,and 3’UTR regions.Furthermore,we elucidate these regulatory modes through one or two concrete examples.Besides,lncRNAs have emerged as novel central components that participate in phase separation processes.Moreover,we illustrate the coordinated regulatory mechanisms among lncRNAs,miRNAs,and siRNAs with a particular emphasis on the central role of lncRNAs in serving as sponges,precursors,spliceosome,stabilization,scaffolds,or interaction factors to bridge interactions with other ncRNAs.The review also sheds light on the intriguing possibility that some ncRNAs may encode functional micropeptides.Therefore,the review underscores the emergent roles of ncRNAs as potent regulatory factors that significantly enrich the regulatory network governing plant growth,development,and responses to environmental stimuli.There are yet-to-be-discovered roles of ncRNAs waiting for us to explore.

Restoration of the focal parameters for an extreme-power laser pulse with ponderomotively scattered proton spectra by using a neural network algorithm

A neural network-based approach is proposed both for reconstructing the focal spot intensity profile and for estimating the peak intensity of a high-power tightly focused laser pulse using the angular energy distributions of protons accelerated by the pulse from rarefied gases.For these purposes,we use a convolutional neural network architecture.Training and testing datasets are calculated using the test particle method,with the laser description in the form of Stratton-Chu integrals,which model laser pulses focused by an off-axis parabolic mirror down to the diffraction limit.To demonstrate the power and robustness of this method,we discuss the reconstruction of axially symmetric intensity profiles for laser pulses with intensities and focal diameters in the ranges of 10^(21)-10^(23) W cm^(−2) and ~(1-4)λ,respectively.This approach has prospects for implementation at higher intensities and with asymmetric laser beams,and it can provide a valuable diagnostic method for emerging extremely intense laser facilities.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

超100 Gbit/s相干PON系统关键技术研究

50 Gbit/s无源光网络(PON)标准已趋于完善,后50 Gbit/s PON时代的技术标准尚属空白,亟待开展相关研究以推动整个产业链从系统、模块和芯片等方面提前进行布局。文章判断单波长200 Gbit/s速率和相干技术将会是继50 Gbit/s PON之后下一代PON系统的两大关键特征。单波长200 Gbit/s速率对运营商具备更大的吸引力,而强度调制/直接检测(IM/DD)技术难以持续满足200 Gbit/s速率下系统对Class C+等级功率预算的要求,需要采用灵敏度更高的相干技术。然而PON系统是一种典型的点对多点(P2MP)拓扑架构,将相干技术下沉到PON还有许多关键技术需要攻克,其中,涉及PON系统设备和媒体访问控制(MAC)芯片的架构重构、相干PON光模块的单纤双向(Bi-Di)技术改造、突发模式的相干发送与接收技术以及相干PON系统的波长管控技术。时分复用(TDM)仍然是实现P2MP传输的推荐方式,在TDM基础之上可以叠加新的复用维度,例如子载波复用(SCM)。新复用维度的引入给PON系统带来了灵活性,但同时也增加了设计的复杂度,将会颠覆当前的PON系统架构。叠加了SCM的相干PON系统将不再采用数字化接口方式与光模块进行连接,光模块本身需要具备高度线性驱动和调制能力。此外,用户侧光模块需要具备瞬时开关能力,以避免对其他用户造成干扰,因此需要开发新型的支持突发控制功能的相干光芯片。考虑到上行P2MP的突发相干接收环境,需要从系统层面实现对多个用户激光器的波长管控,避免上行方向因多用户波长快速切换造成的局端频偏估算偏差问题。综上,将相干技术应用于PON将会是一个全新的复杂系统工程,难以直接继承现有相干系统架构,需要匹配P2MP的应用需求,从芯片、模块和设备多个方面实现技术创新。