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。

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.

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.

Dual-functional poly(2-methyl-2-oxazoline)/poly(2-(dimethylamine)ethyl methacrylate)mixed brushes with switchable protein adsorption and antibacterial properties

Herein,binary mixed brushes consisting of poly(2-methyl-2-oxazoline)(PMOXA)and poly(2-(dimethylamine)ethyl methacrylate)(PDMAEMA)with different chain lengths were fabricated by successive grafting of NH_(2)-terminated PMOXA and SH-terminated PDMAEMA onto polydopamine-anchored substrates.The mixed-brush coating was characterized by variable-angle spectroscopic ellipsometry,X-ray photoelectron spectroscopy,Fourier transform infrared spectroscopy,zeta potential measurements,water contact angle,and atomic force microscopy.The mixed brushes showed tunable surface charge,wettability,and surface roughness,depending on the degree of PDMAEMA swelling under varying pH and ionic strength(Ⅰ).Then the adsorption behaviors of pepsin,bovine serum albumin(BSA),γ-globulin,and lysozyme,four very different proteins with regard to isoelectric point,on the mixed brushes coating were studied by using fluorescence microscopy and surface plasmon resonance.When the chain length of PDMAEMA was about twice as long as PMOXA,the mixed brushes not only had high adsorption capacity for pepsin,BSA,and y-globulin but also had a desorption efficiency of 86.9%,87.1%,and 93.5%,respectively.It is explained that electrostatic attraction between the protonated PDMAEMA and positively charged acidic proteins(pepsin and BSA,whose isoelectric points were below the pK_(a) of PDMAEMA)would drive the intensive adsorption(at pH 3,I=10^(-3)mol·L^(-1)for pepsin,and pH 5,I=10^(-5)mol·L^(-1)for BSA),while desorption was dominated by the hydrophilic PMOXA when PDMAEMA was shrinking(at pH 7,I=10^(-1)mol·L^(-1)for pepsin,and pH 9,I=10^(-1)mol·L^(-1)for BSA).Furthermore,the isoelectric precipitation led to the adsorption of neutral protein(γ-globulin,whose isoelectric point was near the pK_a of PDMAEMA)at pH 7,I=10^(-5)mol·L^(-1),while electrostatic repulsion and antifouling PMOXA triggered the desorption of y-globulin at pH 3,I-10^(-1)mol·L^(-1).However,alkaline protein(lysozyme,whose isoelectric point was higher than the pK_(a) of PDMAEMA)exhibited slight adsorption on PMOXA/PDMAEMA mixed brushes under test conditions,regardless of whether PMOXA or PDMAEMA occupied the outermost layer.The antibacterial property of the mixed brushes against Escherichia coli was investigated.PMOXA/PDMAEMA mixed brushes showed significant bactericidal activity at pH 3,I=10^(-3)mol·L^(-1),while the rinse of pH 9,I=10^(-1)mol·L^(-1)solution could remove most of the residual bacteria.This work not only enables controlled adsorption of proteins with different isoelectric points but also ensures that the surface of the coating is minimized from bacterial contamination.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

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.

基于Conformable分数阶导数的灰色Bernoulli模型

为增强灰色Bernoulli模型对各种实际数据序列的适应性,借助分数阶微积分在描述复杂系统中的优势,提出了一种基于Conformable分数阶导数的灰色Bernoulli模型。研究发现,可通过改变结构参数将模型转换为不同的经典灰色预测模型,体现了其统一性。此外,采用粒子群优化算法求解规划模型,获取了模型的最优超参数。最后,用所提模型和5个竞争模型对3个真实案例进行了预测建模,结果表明,所提模型的2项评估指标均优于5个竞争模型,验证了所提模型的有效性和可行性。

Momentum as Translations at Conformal Infinity

Although General Relativity is the classic example of a physical theory based on differential geometry, the momentum tensor is the only part of the field equation that is not derived from or interpreted with differential geometry. This work extends General Relativity and Einstein-Cartan theory by augmenting the Poincaré group with projective (special) conformal transformations, which are translations at conformal infinity. Momentum becomes a part of the differential geometry of spacetime. The Lie algebra of these transformations is represented by vectorfields on an associated Minkowski fiber space. Variation of projective conformal scalar curvature generates a 2-index tensor that serves as linear momentum in the field equations of General Relativity. The computation yields a constructive realization of Mach’s principle: local inertia is determined by local motion relative to mass at conformal infinity in each fiber. The vectorfields have a cellular structure that is similar to that of turbulent fluids.

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。

基于多尺度阶梯时频Conformer GAN的语音增强算法

针对频率域语音增强算法中因相位混乱产生人工伪影,导致去噪性能受限、语音质量不高的问题,提出一种基于多尺度阶梯型时频Conformer生成对抗网络(MSLTF-CMGAN)的语音增强算法。将语音语谱图的实部、虚部和振幅谱作为输入,生成器首先在多个尺度上利用时间-频率Conformer学习时域和频域的全局及局部特征依赖;其次,利用Mask Decoder分支学习振幅掩码,而Complex Decoder分支则直接学习干净的语谱图,融合这两个Decoder分支的输出可得到重建后的语音;最后,利用指标判别器判别语音的评价指标得分,通过极大极小训练使生成器生成高质量的语音。采用主观评价平均意见得分(MOS)和客观评价指标在公开数据集VoiceBank+Demand上与各类语音增强模型进行对比,结果显示,所提算法的MOS信号失真(CSIG)和MOS噪声失真(CBAK)比目前最先进的方法CMGAN(基于Conformer的指标生成对抗网络语音增强模型)分别提高了0.04和0.07,尽管它的MOS整体语音质量(COVL)和语音质量的感知评估(PESQ)略低于CMGAN,但与其他对比模型相比在多项主客观语音质量评估方面的评分均处于领先水平。

基于Conformer的时域多通道语音分离方法

多通道语音中的空间特征信息为说话人分离提供了重要的线索,为了更好地提取通道间信息并有效降低网络的处理时延,提出一种多通道时域语音分离方法。利用多层编码器实现语音特征提取并挖掘通道间信息,在逐层编码过程中获得不同时间分辨率的语音特征并降低特征时间维度;引入Conformer结构对语音全局时间关系进行建模,在解码阶段使用特征加权跳跃连接融合对应编码层的输出特征进行解码,并将高维语音特征恢复为时域信号。在基于LibriSpeech仿真的多通道混响带噪语音数据集中进行实验,实验结果表明,所提方法通过多层编解码机制充分利用了多通道语音信息并降低了网络处理时延,通过Conformer实现并行数据处理和全局时间关系建模,在推理速度、分离语音质量和语音感知质量方面均优于基线单通道和多通道时域语音分离算法。

使用Conformer增强的混合CTC/Attention端到端中文语音识别

最近,基于自注意力的Transformer结构在不同领域的一系列任务上表现出非常好的性能。探索了基于Transformer编码器和LAS(listen,attend and spell)解码器的Transformer-LAS语音识别模型的效果,并针对Transformer不善于捕捉局部信息的问题,使用Conformer代替Transformer,提出Conformer-LAS模型。由于Attention过于灵活的对齐方式,使得在嘈杂环境中的效果急剧下降,采用连接时序分类(connectionist temporal classification,CTC)辅助训练以加快收敛,并加入音素级别的中间CTC损失联合优化,提出了效果更好的Conformer-LAS-CTC语音识别模型。在开源中文普通话Aishell-1数据集上对提出来的模型进行验证,实验结果表明,Conformer-LAS-CTC相对于采用的基线BLSTM-LAS和Transformer-LAS模型在测试集上的字错率分别相对降低了22.58%和48.76%,模型最终字错误率为4.54%。

基于多任务损失附加语言模型的语音识别方法

针对Attention过于灵活的对齐方式在复杂环境中适应性差、简单端到端模型对语言特征利用不充分的问题,研究了基于多任务损失附加语言模型的语音识别方法.通过分析语音信号特征,训练中选用包含更多信息的特征.以基于Attention的Conformer端到端模型为基础,采用CTC损失辅助纯Conformer(Attention)的多任务损失训练模型,得到Conformer-CTC语音识别模型.在Conformer-CTC模型基础上,通过分析对比部分语言模型的特点与效果,将Transformer语言模型通过重打分机制附加至上述模型的训练中,最终得到Conformer-CTC-Transformer语音识别模型.在AISHELL-1数据集上对上述模型进行了试验.结果表明:Conformer-CTC模型相对于纯Conformer(Attention)模型在测试集上的字错率(character error rate,CER)降低了0.49%,而Conformer-CTC-Transformer模型相对于Conformer-CTC模型在测试集上的CER又降低了0.79%.CTC损失可以改善Attention对齐方式在复杂环境中的适应性,并且对Conformer-CTC模型附加Transformer语言模型重打分后能再次提升0.30%的识别准确率.相较于现有的部分端到端模型,Conformer-CTC-Transformer模型识别效果较好,说明该模型具有一定的有效性.

基于GAN的全时间尺度语音增强方法

现有语音增强方法在时域端到端语音增强中无法学习全面时间尺度特征信息且中间层序列建模不充分。提出从全面时间尺度进行语音降噪的方法。通过线性插值方式对输入特征序列进行扩充,获得比原输入数据具有更高分辨率的时间特征,使得模型能够从更细时间尺度上进行特征提取,并利用间隔抽样方法对每一层编码后的特征进行下采样,在较粗时间尺度上计算越来越多的高维特征,使网络模型能够捕获深层次的有用信息。同时,在网络模型中引入ConformerBlock作为中间层,其中,多头注意力机制和卷积模块能够增强中间层网络的序列建模能力,突出中间向量的表征信息,根据语音和噪声线性叠加的原理,采用联合噪声训练生成对抗网络的方法使网络从目标语音和噪声2个角度获取有用信息,进一步提升模型降噪能力。在公开语音增强测试数据集上的实验结果表明,该方法降噪后语音的各项指标均得到显著提升,相比Wave-U-net模型,在PESQ、STOI和SSNR这3项主要指标上分别提升了2.75%、1.06%、6.34%。

On Nonlinear Conformable Fractional Order Dynamical System via Differential Transform Method

This article studies a nonlinear fractional order Lotka-Volterra prey-predator type dynamical system.For the proposed study,we consider the model under the conformable fractional order derivative(CFOD).We investigate the mentioned dynamical system for the existence and uniqueness of at least one solution.Indeed,Schauder and Banach fixed point theorems are utilized to prove our claim.Further,an algorithm for the approximate analytical solution to the proposed problem has been established.In this regard,the conformable fractional differential transform(CFDT)technique is used to compute the required results in the form of a series.Using Matlab-16,we simulate the series solution to illustrate our results graphically.Finally,a comparison of our solution to that obtained for the Caputo fractional order derivative via the perturbation method is given.

语音识别中的Conformer模型压缩研究

针对使用Conformer模型的语音识别算法在实际应用时设备算力不足及资源缺乏的问题,提出一种基于Conformer模型间隔剪枝和参数量化相结合的模型压缩方法。实验显示,使用该方法压缩后,模型的实时率(real time factor, RTF)达到0.107614,较基线模型的推理速度提升了16.2%,而识别准确率只下降了1.79%,并且模型大小也由原来的207.91MB下降到72.69MB。该方法在模型准确率损失很小的情况下,较大程度地提升了模型的适用性。

基于CNN和Transformer的双路径语音分离

使用深度学习技术进行语音分离已经取得了优异的成果。当前主流的语音分离模型主要基于注意力模块或卷积神经网络,它们通过许多中间状态传递信息,难以对较长的语音序列建模导致分离性能不佳。首先提出了一种端到端的双路径语音分离网络(DPCFNet),该网络通过引入改进的密集连接块,使编码器能提取到丰富的语音特征。然后使用卷积增强Transformer(Conformer)作为分离层的主要组成部分,使语音序列中的元素可以直接交互,不再通过中间状态传递信息。最后将Conformer与双路径结构相结合使得该模型能够有效地进行长语音序列建模。实验结果表明,相比于当前主流的Conv-Tasnet算法及DPTNet算法,所提出的模型在信噪失真比(Signal to noise Distortion Ratio,SDR)和尺度不变信噪失真比(Scale-Invariant Signal to noise Distortion Ratio,SI-SDR)上有明显提高,分离性能更好。

In situ kinetics and flocs conformation studies of kaolinite flocculated by Chi-g-CPAM

This study presents a comparative study of the flocculation behavior of kaolinite induced by chitosan-graft-poly(acrylamide-dimethyl diallyl ammonium chloride)(Chi-g-CPAM) and a commercial cationic polyacrylamide(CPAM).The flocculation behaviour was characterised in terms of both flocculation kinetics and the corresponding morphology changes during flocculation.Both Chi-g-CPAM and CPAM were grafted from silica wafers by means of atom transfer radical polymerization(ATRP).The quartz crystal microbalance with dissipation(QCM-D) tests were conducted.The equilibrium time flocculated by Chi-g-CPAM was found to be 0.46 times as that of CPAM,together with a larger total mass of kaolinite layer.The flocculation behaviour by Chi-g-CPAM can be well captured by a pseudo-first-order model.In contrast,the presence of CPAM leads to a more complex kinetics.A relatively larger fitting slope(0.4663) was obtained at the initial stage but the fitting slope droped to 0.2026 after 800 min,indicating a densification process caused by CPAM.The flocculation kinetics of CPAM can be captured by the Elovich model for the inital stage but the combination of pseudo-first-order and pseudo-second-order models for the latter stages,which can be attributed to the long chain of CPAM.With a dosage of 75 g/t,the settling test with Chi-g-CPAM exhibits the same turbidity in the supernatant but a smaller layer thickness of the settlement compared to CPAM.The study enables a better understanding of the flocculation behavior and contributes to the development of efficient flocculants in mineral processing and tailings treatment.