Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Heavy oil component characterization with multi-dimensional unilateral NMR

Heavy oil is a complicated mixture and a potential resource and has attracted much attention since the end of last century. It is important to characterize the composition of heavy oil to enhance its recovery efficiency. A designed unilateral Nuclear Magnetic Resonance （NMR） sensor with a Larmor frequency of 20 MHz and a well-defined constant gradient of 23.25 T/m was employed to acquire three- dimensional （3D） data for three heavy oil samples. The highly-constant gradient is advantageous for diffusion coefficient measurement of heavy oil. A fast data-implementation procedure including specially designed 3D pulse sequence and Inversion Laplace Transform （ILT） algorithm was adopted to process the data and extract 3D T1-D-T2 probability function. It indicates that NMR relaxometry and diffusometry are useful to characterize the components of heavy oil samples. NMR results were compared with independent measurements of fractionation and gas chromatography analysis.

Laplace NMR谱图重建——从经典正则化到深度学习

拉普拉斯核磁共振(Laplace NMR)可以提供待测样品的扩散系数或弛豫时间等物理参数信息,是用于研究分子化学结构、动力学和相互作用的强大工具.Laplace NMR的适用性很大程度上取决于拉普拉斯逆变换相关的信号处理算法的性能.在本文中,我们首先讨论了Laplace NMR谱图重建问题的不适定性,接着回顾了经典的基于正则化约束的重建算法,并介绍了目前前沿的深度学习算法在处理Laplace反演问题方面的应用,最后总结了这些算法的优缺点,并对Laplace NMR信号处理方法未来改进方向进行了展望.

应用于太赫兹波驱动的DNP-NMR传输线

太赫兹波驱动的动态核极化增强核磁共振波谱系统可大幅提高信号灵敏度和缩短检测时间。该系统需采用传输线在驱动源回旋管与谱仪之间进行太赫兹波传输。本文针对400 MHz增强核磁共振系统设计了由准光模式变换器和波纹波导组成的太赫兹传输线,并进行了相关实验测试,测试结果表明准光模式变换器可将263 GHz回旋管TE 03模式转换成准高斯波束,其高斯成份为97%,整个传输线最终输出波束的高斯成份为96.3%。

烟草果胶和纤维素含量^(13)C MultiCP/MAS NMR同时测量

果胶和纤维素是烟草细胞壁的重要组成成分,其含量对烟草品质与安全性产生重要影响。传统分析方法难以实现果胶和纤维素的同时定量。本文在优化核磁共振波谱序列条件的基础上,采用固体^(13)C多重交叉极化/魔角旋转核磁共振波谱分析技术(^(13)C MultiCP/MAS NMR)建立了烟草果胶和纤维素含量的同时测量新方法。方法以聚半乳糖醛酸和微晶纤维素为标准物质,以3-(三甲基甲硅烷基)丙酸-d4钠盐(TMSP)做为内标物质,分别建立内标法标准曲线,相关系数R2为0.999 0和0.998 2。果胶测量的检出限和定量限分别为0.38和1.28 mg·g^(–1),精密度(RSD,n=5)小于3.05%。纤维素测量的检出限和定量限为1.01和3.32 mg·g^(–1),精密度(RSD,n=5)小于2.74%。应用本方法测量烟梗、烟草薄片和烟叶等不同类型样品中的果胶和纤维素含量,并对比烟草行业标准方法的测量结果,果胶含量的相对误差在-0.95%至4.51%之间,纤维素含量的相对误差在0.77%至2.46%之间。表明^(13)C MultiCP/MAS NMR方法快速,准确,适合批量样品的分析测量,为果胶和纤维素等细胞壁类大分子的同时定量分析提供重要技术支持。

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

NMR、GPC和MALDI-TOF应用于聚乙二醇单甲醚的教学实验

将核磁共振(NMR)、凝胶渗透色谱(GPC)和基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF)三种高级分析技术整合到本科《高分子材料研究方法》课程的实验教学中。通过分析聚乙二醇单甲醚样品,学生深入学习了高分子结构和分子量的表征方法,熟悉了这三种仪器的操作和原理。本实验显著提升了课程内容的深度和广度,激发了学生的学习兴趣,并且增强了学生的实践操作能力和创新思维。综合应用这些分析技术,使学生在高分子材料的研究中获得了更全面的理解和技能的提升。

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

基于小型化原子磁力计的零场NMR波谱仪搭建与测试

常规高场NMR波谱仪依托超导技术,其体积大、维护成本高,且存在样本磁化率不均匀导致的谱线展宽现象,零场或近零场NMR技术则可实现有效互补.本文自主搭建了一套基于小型原子磁力计的可移动零场NMR波谱仪,采用以多功能采集卡(National Instruments PCIe-6353)为核心的集成控制系统,仪器主磁场强度小于1nT,可实现高分辨率的J-耦合谱采集.为实现对自旋体系的有效操控,利用样品绝热初态的单脉冲激发实现对三轴脉冲线圈的精确标定.在此基础上,使用改进的组合脉冲序列实现了在^(13)C-^(1)H异核体系下的单自旋选择性操控,验证了零场NMR波谱仪的有效性.

舒肝宁注射液的^(1)H-NMR指纹图谱分析

目的对舒肝宁注射液的^(1)H-NMR指纹图谱进行分析,为其质量控制标准技术升级提供参考依据。方法使用Noesygppr1d脉冲序列[recycle delay(RD)-90°-t 1-90°-t_(m)-90°-ACQ]采集1H-NMR谱图,质子共振频率为600.13 MHz,实验温度为298 K,通过方法学考察验证分析方法可靠性,采用相关系数法和夹角余弦法评价不同批次舒肝宁注射液样品的相似度。结果对舒肝宁注射液的^(1)H-NMR指纹图谱进行分析,共鉴定出88种化学成分,且不同批次的舒肝宁注射液具有较高的相似度。结论建立了一种基于^(1)H-NMR的舒肝宁注射液质量控制方法,可为舒肝宁注射液技术升级提供参考依据。

Postmortem ^(7)Li NMR analysis for assessing the reversibility of lithium metal electrodes in lithium metal batteries

Despite the proficiency of lithium(Li)-7 NMR spectroscopy in delineating the physical and chemical states of Li metal electrodes,challenges in specimen preparation and interpretation impede its progress.In this study,we conducted a comprehensive postmortem analysis utilizing ^(7)Li NMR,employing a stan-dard magic angle spinning probe to examine protective-layer coated Li metal electrodes and LiAg alloy electrodes against bare Li metal electrodes within Li metal batteries(LMBs).Our investigation explores the effects of sample burrs,alignment with the magnetic field,the existence of liquid electrolytes,and precycling on the ^(7)Li NMR signals.Through contrasting NMR spectra before and after cycling,we identi-fied alterations in Li^(0) and Li^(+) signals attributable to the degradation of the Li metal electrode.Our NMR analyses decisively demonstrate the efficacy of the protective layer in mitigating dendrite and solid elec-trolyte interphase formation.Moreover,we noted that Li*ions near the Li metal surface exhibit magnetic susceptibility anisotropy,revealing a novel approach to studying diamagnetic species on Li metal elec-trodes in LMBs.This study provides valuable insights and practical guidelines for characterizing distinct lithium states within LMBs.

Operando NMR methods for studying electrocatalysis

The combination of electrochemical measurements with spectroscopic characterizations provides valuable insights into reaction mechanisms.Nuclear magnetic resonance(NMR)spectroscopy,as a powerful technique due to its atomic specificity and versatility in studying gas,liquid,and solid,allows the study of electrolyte solution,catalyst and catalyst-adsorbate interfaces.When applied in operando,NMR can offer molecular-level insights into various electrochemical processes.Operando NMR has been applied extensively in battery research,but relatively underexplored for electrocatalysis in the past two decades.In this mini review,we first introduce the operando electrochemical NMR setups,categorized by different probe designs.Then we review the applications of operando NMR for monitoring the electrolyte solution and the catalyst-adsorbate interface.Considering the high environmental impact of electrochemical conversion of CO_(2)into value-added products,we zoom in to the use of operando NMR in studying electrochemical CO_(2)reduction.Finally,we provide our perspective on further developing and applying operando NMR methods for understanding the complex reaction network of Cu-catalyzed electrochemical CO_(2)reduction.

Structure and ion transport properties of organic ionic compounds revealed by NMR

Organic ionic plastic crystals(OIPCs)are emerging as an important material family for solid-state electrolytes and many other applications.They have significant advantages over conventional electrolyte materials,such as high ionic conductivity,non-flammability,and plasticity.Various nuclear magnetic resonance(NMR)spectroscopy techniques including solid-state NMR,pulsed-field gradient(PFG)NMR,and magnetic resonance imaging(MRI)etc.,provide us a versatile toolkit to understand the fundamental level structures,molecular dynamics,and ionic interactions in these materials.This article reviews the commonly used NMR methods including solid-and solution-state NMR,PFG-NMR,dynamic nuclear polarization(DNP)and the application of these methods in revealing the microscopic level structures and ion-transport mechanisms in OIPC materials.

A study on the temperature sensitivity of NMR porosity in porous media based on the intensity of magnetization Dedicated to the special issue “Magnetic Resonance in Porous Media”

The measurement of nuclear magnetic resonance(NMR)porosity is affected by temperature.Without considering the impact of NMR logging tools,this phenomenon is mainly caused by variations in magnetization intensity of the measured system due to temperature fluctuations and difference between the temperature of the porous medium and calibration sample.In this study,the effect of temperature was explained based on the thermodynamic theory,and the rules of NMR porosity responses to temperature changes were identified through core physics experiments.In addition,a method for correcting the influence of temperature on NMR porosity measurement was proposed,and the possible factors that may affect its application were also discussed.

Structural Elucidation of the Polymeric Condensed Tannins of Acacia nilotica Subspecies by ^(13)C NMR, MALDI-TOF and TMA as Sources of Bioadhesives

Tannin was extracted from different subspecies of Acacia nilotica,Acacia nilotica nilotica(Ann),Acacia nilotica tomentosa(Ant)and Acacia nilotica adansonii(Ana).The aim was to elucidate their structure and evaluate their reactivity as bioadhesives in the wood industry.The extracts were prepared by hot water extraction(90°C tem-perature).Their gel time with paraformaldehyde was used atfirst to compare their reactivity.The tannin contents and the percentage of total polyphenolic materials in different solutions of the extracts spray dried powder were determined by the hide powder method.Concentrated solutions(47%)were tested by both MALDI ToF,13CNMR.The thermomechanical analysis(TMA)was performed to evaluate their modulus of elasticity(MOE)at different pHs.The gel times of all the three tannin extracts showed that their reactivity and it was com-parable to other known procyanidin/prodelphinidin tannin extract types.Ana,Ann and Ant showed highest per-cent of total polyphenolic materials at 70%,64%,and 57%,respectively.The 13CNMR spectra showed that the three subspecies of condensed tannins were mainly constituted of procyanidins(PC)and prodelphinidins(PD)in slightly different ratios.Ann(56.5%PC and 43.4%PD),Ant(57%PC and 43%PD)and Ana(58%PC and 42%PD).MALDI–TOF spectra showed the presence offlavonoid monomers,and oligomers some of which linked to short carbohydrates monomers or dimers.TMA revealed that the three types of tannins had high MOE at their initial pH(5).

Matrix Assisted Laser Desorption Ionization Time of Flight(MALDI-TOF)-Mass Spectrometry and^(13)C-NMR-Identified New Compounds in Paraberlinia bifoliolata(Ekop-Beli)Bark Tannins

Extracts of plant origin,particularly tannins,are attracting growing interest for the sustainable development of materials in the industrial sector.The discovery of new tannins is therefore necessary.The aim of this work was to contribute to the understanding of the properties of Paraberlinia bifoliolata tannin by Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectroscopy MALDI-TOF/MS and Carbon 13 Nuclear Magnetic Resonance(13C NMR).The chemical composition of tannin extracted from Paraberlinia bifoliolata bark was determined,as was the mechanical strength of the resin hardened with Acacia nilotica extracts.Yield by successive water extraction was 35%.MALDI-TOF/MS analysis revealed the presence of three new compounds in this tannin,previously unknown in this family of extracts.These are 3-hydroxyproline acid,N-methyl-4-hydroxypipecolic acid and N-methyl-5-dihydroxypipecolic acid.The identification of the above molecules means that this tannin can be used for industrial applications,as a resin in the manufacture of particleboard and in the formulation of green corrosion inhibitors.This information is reinforced by 13C NMR spectrometry,which indicates the presence of several polyflavonoid units,confirming the condensed nature of the tannin.Thermomechanical analysis of the resin formed by the purified tannin of Paraberlinia bifoliolata to which a vegetal biohardener has been added provided a Modulus of Elasticity(MOE)value of 4840 MPa at 150℃,confirming its possible use as a binder resin in the manufacture of wood panels as well as for the formulation of a corrosion inhibitor.

HdeA在细菌外膜囊泡环境下的原位NMR研究

HdeA是一种定位于细菌周质的分子伴侣,在维持蛋白质稳态中起着重要的作用.以往对HdeA的研究主要是在体外条件下进行,限制了人们对HdeA在天然环境下发挥作用机制的理解.细菌外膜囊泡是细菌自发分泌到胞外环境的外膜囊泡,其内容物与周质环境相似.本研究将HdeA富集到细菌外膜囊泡(OMVs)中,通过核磁共振波谱研究HdeA在OMVs中的构象变化.结果表明,HdeA在其原位环境中表现出酸依赖性的构象变化.在低pH条件下HdeA主要通过S15、W16、T17、S27、T32、E36、G54、T57、C66、Q71、F74及D83等残基启动其分子伴侣功能.此外本研究也为原位研究其它周质分子伴侣的功能提供了新方法.