Low-Energy Electron Attachment to Serine Conformers： Shape Resonances and Dissociation Dynamics

Shape resonances of electron-molecule system formed in the low-energy electron attachment to four low-lying conformers of serine （serine 1, serine 2, serine 3, and serine 4） in gas phase are investigated using the quantum scattering method with the non-empirical model potentials in single-center expansion. In the attachment energy range of 0-10 eV, three shape resonances for serine 1, serine 2, and serine 4 and four shape resonances for serine 3 are predicted. The one-dimensional potential energy curves of the temporary negative ions of electron-serine are calculated to explore the correlations between the shape resonance and the bond cleavage. The bond-cleavage selectivity of the different resonant states for a certain conformer is demonstrated, and the recent experimental results about the dissociative electron attachment to serine are interpreted on the basis of present calculations.

Theoretical study of γ-aminobutyric acid conformers:Intramolecular interactions and ionization energies

Allowing for all combinations of internal single-bond rotamers, 1,296 unique trial structures of γ-Aminobutyric acid （GABA） are obtained. All of these structures are optimized at the M06-2X level of theory and a total of 68 local minimal conformers are found. The nine low-lying conformers are used for further studies. According to the calculated relative Gibbs free energies at M06-2X level of theory, we find that the dispersion is important for the relative energy of GABA. The intramolecular hydrogen bonds and byperconjugative interaction and their effects on the conformational stability are studied. The results show that both of them have great influence on the conformers. The vertical ionization energies （VIE） are calculated and match the experimental data well. The results show that the neutral GABA in the gas phase is a multi-conformer system and at least four conformations exist.

High-level theoretical study of the evolution of abundances and interconversion of glycine conformers

The relative conformer energies of glycine are evaluated by using a focal point analysis expressed as（HF→MP2→MP3→CCSD→CCSD（T））.The conformer abundances at various temperatures（298–500 K） are calculated based on the relative energies and Boltzmann statistical thermostatistical analysis with and without considering internal hindered rotations.A comparison between the available Raman spectrum and the electron momentum spectrum confirms that the influence of rigid-rotor hindered rotation on the conformational proportions of glycine is considerable,especially for the IIIp structure.The conformational interconversions are discussed.It is found that with increasing temperature,the mole fraction of IIn keeps constant and Ip structure can convert into IVn and IIIp,leading to the decrease in the weight of Ip and the increase in the weights of IVn and IIIp conformers,which is in accordance with experimental observations.

Populations of Ethanol Conformers in Liquid CCI4 and CS2 by Raman Spectra in OH Stretching Region

Combining Raman spectroscopy with density functional theory, the populations of the trans- and gaucheethanol conformers are investigated in carbon tetrachloride （CC14） and carbon disulfide （CS2）. The spectral contributions of two ethanol conformers are identified in OH stretching region. The energy difference between both conformers is estimated with the aid of the calculated Raman cross sections. It can be seen that the trans- ethanol is more stable in CC14 and CS2 solutions. The spectra are also obtained at different temperatures, and it is found the van＇t Hoff analysis is invalid in these solutions. By taking accounts of the Boltzmann distribution and theoretical Raman cross section, the energy difference is found to be increased with temperature, which shows the weak intermolecular interactions can enhance the population of transethanol.

High Resolution Electron Momentum Spectroscopy Study on Ethanol： Orbital Electron Momentum Distributions for Individual Conformers

The outer-valence binding energy spectra of ethanol in the energy range of 9-21 eV are mea- sured by a high-resolution electron momentum spectrometer at an impact energy of 2.5 keV plus the binding energy. The electron momentum distributions for the ionization peaks cor- responding to the outer-valence orbitals are obtained by deconvoluting a series of azimuthal angular correlated binding energy spectra. Comparison is made with the theoretical calcu- lations for two conformers, trans and gauche, coexisting in the gas phase of ethanol at the level of B3LYP density functional theory with aug-cc-pVTZ basis sets. It is found that the measured electron momentum distributions for the peaks at 14.5 and 15.2 eV are in good agreement with the theoretical electron momentum distributions for the molecular orbitals of individual conformers （i.e., 8a＇ of trans and 9a of gauche）, but not in accordance with the thermally averaged ones. It demonstrates that the high-resolution electron momentum spectrometer, by inspecting the molecular electronic structure, is a promising technique to identify different conformers in a mixed sample.

基于改进Conformer的新闻领域端到端语音识别

目前,开源的中文语音识别数据集大多面向通用领域,缺少面向新闻领域的开源语音识别语料库,因此该文构建了面向新闻领域的中文语音识别数据集CH_NEWS_ASR,并使用ESPNET-0.9.6框架的RNN、Transformer和Conformer等模型对数据集的有效性进行了验证,实验表明,该文所构建的语料在最好的模型上CER为4.8%,SER为39.4%。由于新闻联播主持人说话语速相对较快,该文构建的数据集文本平均长度为28个字符,是Aishell_1数据集文本平均长度的2倍;且以往的研究中训练目标函数通常为基于字或词水平,缺乏明确的句子水平关系,因此该文提出了一个句子层级的一致性模块,与Conformer模型结合,直接减少源语音和目标文本的表示差异,在开源的Aishell_1数据集上其CER降低0.4%,SER降低2%;在CH_NEWS_ASR数据集上其CER降低0.9%,SER降低3%,实验结果表明,该方法在不增加模型参数量的前提下能有效提升语音识别的质量。

基于层次化Conformer的语音合成

语音合成需要将输入语句的文本转换为包含音素、单词和语句的语音信号。现有语音合成方法将语句看作一个整体,难以准确地合成出不同长度的语音信号。通过分析语音信号中蕴含的层次化关系,分别设计基于Conformer的层次化文本编码器和基于Conformer的层次化语音编码器,并提出了一种基于层次化文本-语音Conformer的语音合成模型。首先,该模型根据输入文本信号的长度,构建层次化文本编码器,包括音素级、单词级、语句级文本编码器3个层次,不同层次的文本编码器描述不同长度的文本信息;并使用Conformer的注意力机制来学习该长度信号中不同时间特征之间的关系。利用层次化的文本编码器,能够找出语句中不同长度需要强调的信息,有效实现不同长度的文本特征提取,缓解合成的语音信号持续时间长度不确定的问题。其次,层次化语音编码器包括音素级、单词级、语句级语音编码器3个层次。每个层次的语音编码器将文本特征作为Conformer的查询向量,将语音特征作为Conformer的关键字向量和值向量,来提取文本特征和语音特征的匹配关系。利用层次化的语音编码器和文本语音匹配关系,可以缓解不同长度语音信号合成不准确的问题。所提模型的层次化文本-语音编码器可以灵活地嵌入现有的多种解码器中,通过文本和语音之间的互补,提供更为可靠的语音合成结果。在LJSpeech和LibriTTS两个数据集上进行实验验证,实验结果表明,所提方法的梅尔倒谱失真小于现有语音合成方法。

基于特征提示的跨语种语音识别模型

跨语种语音识别是一种利用多种源语言的数据来训练一个能够识别目标语言的语音识别系统,它可以促进不同语言和文化之间的交流和理解。为解决跨语种语音识别存在着如何利用多语种数据来提高低资源语言的识别性能,源语言和目标语言之间的领域偏移或干扰,不同语言之间的任务权重和数据分布等问题,通过特征提示的方法研究跨语种语音识别模型;为简化传统需要专业人员对音素进行统一标注的过程,通过对原数据标识对应语种的方法研究跨语种语音数据标注方式,在2个公开数据集上进行实验。结果表明:所提模型相比于目前主流的语音识别模型Conformer模型平均错误率降低46.44%,相比于基线模型平均错误率降低2.1%,达到较高的识别准确率。研究成果为跨语种语音识别领域提供了新的思路和方法。

3D‑Printed Carbon‑Based Conformal Electromagnetic Interference Shielding Module for Integrated Electronics

Electromagnetic interference shielding(EMI SE)modules are the core com-ponent of modern electronics.However,the tra-ditional metal-based SE modules always take up indispensable three-dimensional space inside electronics,posing a major obstacle to the integra-tion of electronics.The innovation of integrating 3D-printed conformal shielding(c-SE)modules with packaging materials onto core electronics offers infinite possibilities to satisfy ideal SE func-tion without occupying additional space.Herein,the 3D printable carbon-based inks with various proportions of graphene and carbon nanotube nanoparticles are well-formulated by manipulating their rheological peculiarity.Accordingly,the free-constructed architectures with arbitrarily-customized structure and multifunctionality are created via 3D printing.In particular,the SE performance of 3D-printed frame is up to 61.4 dB,simultaneously accompanied with an ultralight architecture of 0.076 g cm^(-3) and a superhigh specific shielding of 802.4 dB cm3 g^(-1).Moreover,as a proof-of-concept,the 3D-printed c-SE module is in situ integrated into core electronics,successfully replacing the traditional metal-based module to afford multiple functions for electromagnetic compatibility and thermal dissipa-tion.Thus,this scientific innovation completely makes up the blank for assembling carbon-based c-SE modules and sheds a brilliant light on developing the next generation of high-performance shielding materials with arbitrarily-customized structure for integrated electronics.

Soft,body conformable electronics for thermoregulation enabled by kirigami

The application of thermoelectric devices(TEDs)for personalized thermoregulation is attractive for saving energy while balancing the quality of life.TEDs that directly attach to human skin remarkably minimized the energy wasted for cooling the entire environment.However,facing the extreme dynamic geometry change and strain of human skin,conventional TEDs cannot align with the contour of our bodies for the best thermoregulation effect.Hence,we designed a kirigami-based wearable TED with excellent water vapor permeability,flexibility,and conformability.Numerical analysis and experimental results reveal that our product can withstand various types of large mechanical deformation without circuit rupture.The stated outcome and proposed facile approach not only reinforce the development of wearable TEDs but also offer an innovative opportunity for different electronics that require high conformability.

Gelatin-Based Metamaterial Hydrogel Films with High Conformality for Ultra-Soft Tissue Monitoring

Implantable hydrogel-based bioelectronics(IHB)can precisely monitor human health and diagnose diseases.However,achieving biodegradability,biocompatibility,and high conformality with soft tissues poses significant challenges for IHB.Gelatin is the most suitable candidate for IHB since it is a collagen hydrolysate and a substantial part of the extracellular matrix found naturally in most tissues.This study used 3D printing ultrafine fiber networks with metamaterial design to embed into ultra-low elastic modulus hydrogel to create a novel gelatin-based conductive film(GCF)with mechanical programmability.The regulation of GCF nearly covers soft tissue mechanics,an elastic modulus from 20 to 420 kPa,and a Poisson’s ratio from-0.25 to 0.52.The negative Poisson’s ratio promotes conformality with soft tissues to improve the efficiency of biological interfaces.The GCF can monitor heartbeat signals and respiratory rate by determining cardiac deformation due to its high conformability.Notably,the gelatin characteristics of the biodegradable GCF enable the sensor to monitor and support tissue restoration.The GCF metamaterial design offers a unique idea for bioelectronics to develop implantable sensors that integrate monitoring and tissue repair and a customized method for endowing implanted sensors to be highly conformal with soft tissues.

基于Conformer的端到端语音识别方法

针对Conformer编码器的声学输入网络对FBank语音信息提取不足和通道特征信息缺失问题,提出一种RepVGG-SE-Conformer的端到端语音识别方法。首先,利用RepVGG的多分支结构,增强模型的语音信息提取能力,而在模型推理时通过结构重参数化将多分支融合为单分支,以降低计算复杂度、加快模型推理速度。然后,利用基于压缩和激励网络的通道注意力机制弥补缺失的通道特征信息,以提高语音识别准确率。最后,在公开数据集Aishell-1上的实验结果表明:相较于Conformer,所提出方法的字错误率降低了10.67%,验证了方法的先进性。此外,RepVGG-SE声学输入网络能够有效提高多种Transformer变体的端到端语音识别模型的整体性能,具有很好的泛化能力。

Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

Monaco计划系统中Conformality函数对宫颈癌容积旋转调强计划的影响

目的 探讨Monaco计划系统中Conformality函数的不同参数值对计划质量的影响。方法 选择15例宫颈癌患者,年龄46~74岁,中位年龄51岁。分别采用Conformality函数参数值为0.25、0.50、0.75、1.00,以及不加函数设计容积旋转调强放射治疗(VMAT)计划,评估靶区及危及器官(OAR)剂量学参数和20 Gy、30 Gy、40 Gy等剂量线的体积和计划执行效率。结果 不同Conformality函数参数值计划的靶区剂量学差异均有统计学意义(P <0.05)。Conformality函数参数值为0.25时,靶区的Dmax、Dmin、Dmean与不加函数的增减幅度分别为2.45%、-0.96%、1.48%,适形度指数(CI)、均匀性指数(HI)增加0.05、0.04。当Conformality函数参数值> 0.50时,CI、HI基本无改变。小肠、结肠、左右股骨头的剂量学差异无统计学意义(P> 0.05)。当Conformality函数参数值为0.25时,直肠的V30、V40[(81.80±7.74)%、(63.44±10.23)%]、膀胱V30、V40[(78.82±5.69)%、(56.40±9.33)%]、正常组织V_(20)、V_(30)、V_(40)的体积[(6 137.64±622.88) cm^(3)、(2 903.55±221.08) cm^(3)、(1 862.93±112.81) cm^(3)]最低。Conformality函数在不同参数之下,膀胱、直肠V_(30)、V_(40)差异均具有统计学意义(P <0.05)。随着Conformality函数参数值增大机器参数值随之减小,机器跳数(MU)、控制点个数(CP)、执行时间(DT)在各Conformality函数参数值之间差异均有统计学意义(P <0.05)。结论 对于宫颈癌在Monaco计划系统中恰当使用Conformality函数设计计划可以提高计划质量,参数值设置在0.25~0.75为宜。综合剂量学参数及计划执行效率,推荐使用0.50。

基于时频感知双路径Conformer的语音增强

近年来,Conformer在语音领域的应用表现较为突出。该模块通过结合多头自注意力机制和卷积神经网络,能够同时关注短时和长时序列信息,从而在语音处理任务中表现出卓越的性能。在此基础上提出了一种基于时频感知双路径Conformer的语音增强网络(TFDPCNet)。首先,该网络将改进的Conformer结构作为核心,采用双路径结构,构成时频感知的双路径Conformer模块(TFDP-Conformer),增强了整体网络的时频提取能力;同时,为了减小时频特征融合的难度,提出了注意力门控交叉融合模块(AGCF),通过额外的注意力门进一步增强了网络训练过程中时频特征的交互,提高了时频特征的利用率;最后,引用度量鉴别器,并对其进行适当剪枝,使得增强后的音频和原始音频在量化评价指标上保持更高的一致性。实验结果表明,相比于TSTNN算法,TFDPCNet在主观和客观指标上都有明显提高。

低资源非自回归壮语语音合成

基于FastSpeech2模型,文章提出了非自回归的壮语语音合成模型Zhuang-TTS。为了提升模型合成壮语语音的韵律,根据壮语特点及实地调查提出了一套新的壮语音系(声调、声母或辅音、韵母或元音),同时针对壮语声学特点进行了改进:(1)使用壮语音素序列表征壮语发音信息;(2)使用音素级的声学调节器(与FastPitch类似),使合成结果更加稳定;(3)使用Conformer代替FastSpeech2模型中的Transformer,同时构建了一个壮语语音合成语料库。实验结果表明,Zhuang-TTS在韵律方面的意见评分(Mean Opinion Score, MOS)达到3.90,合成实时率达8.65×10^(-2)。该模型在合成壮语语音的质量和速度方面获得了较大提升,优于Tacotron2和FastSpeech2基线模型,研究推动了壮语语音合成领域的发展。

基于深度学习的构音障碍人群语音事件识别模型

构音障碍是许多患有特殊疾病的患者所要面对的问题,会造成说话人发音不清晰。为更好地理解构音障碍患者所表达的语音事件,文章提出了一个新的基于深度学习的语音事件识别模型。模型以语音片段作为输入,运用格拉姆角场保留了时间序列的原始特征,利用Conformer对序列的局部特征和全局特征进行提取,最后用Res Net作为分类模型。在EasyCall corpus数据集上的实验结果表明,文章提出的模型具有良好的识别效果。

Laccase/caffeic acid-catalyzed crosslinking coupled with galactomannan alters the conformational structure of ovalbumin and alleviates Th2-mediated allergic asthma

Ovalbumin(OVA)is the major allergenic protein that can induce T helper 2(Th2)-allergic reactions,for which current treatment options are inadequate.In this study,we developed a polymerized hypoallergenic OVA product via laccase/caffeic acid(Lac/CA)-catalyzed crosslinking in conjunction with galactomannan(Man).The formation of high molecular weight crosslinked polymers and the Ig G-binding were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis(SDS-PAGE)and Western blotting.The study indicated that Lac/CA-catalyzed crosslinking plus Man conjugation substantially altered secondary and tertiary structures of OVA along with the variation in surface hydrophobicity.Gastrointestinal digestion stability assay indicated that crosslinked OVA exhibited less resistance in simulated gastric fluid(SGF)and simulated intestinal fluid(SIF).Mouse model study indicated that Lac-Man/OVA ameliorated eosinophilic airway inflammatory response and efficiently downregulated the expression of Th2-related cytokines(interleukin(IL)-4,IL-5,and IL-13),and upregulated IFN-γand IL-10 expression.Stimulation of bone marrow-derived dendritic cells with Lac-Man/OVA suppressed the expression of phenotypic maturation markers(CD80 and CD86)and MHC class II molecules,and suppressed the expression levels of proinflammatory cytokines.The knowledge obtained in the present study offers an effective way to acquire a hypoallergenic OVA product that can have a therapeutic effect in alleviating OVA-induced allergic asthma.

Piezoelectric fibers based on silk fibroin with excellent output performance

The self-powered tissue engineering scaffold with good biocompatibility is of great significance for stimulating nerve cell growth.In this study,silk fibroin(SF)-based fibers with regulatable structure and piezoelectric performance are fabricated by dry-spinning and post-treatment.The concentration of SF and calcium ion in spinning dope and the post-treatment affect the conformation transition and crystallinity of SF.As a result,the SF fibers exhibit high piezoelectric coefficient d_(33)(3.24 pm/V)and output voltage(~27 V).Furthermore,these piezoelectric fibers promote the growth of PC-12 cells,demonstrating the promising potential for nerve repair and other energy harvester.

Classifying rockburst with confidence:A novel conformal prediction approach

The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.