充满多孔介质的方腔内双扩散自然对流格子Boltzmann模拟

采用格子Boltzmann方法,对4个壁面均为低温、低浓度,内置高浓度发热圆的充满均匀多孔介质的方腔内双扩散自然对流现象进行了数值模拟研究.分析了Darcy(达西)数Da(10-4≤D_a≤10~(-2))和浮升力比B(-5.0≤B≤5.0)对内部发热圆表面平均Nusselt(努赛尔)数Nuav和平均Sherwood(舍伍德)数Shav的影响.模拟结果表明:除B=-1.0时,Nu_(av)和Sh_(av)随D_a的增加而增大;当-5.0B5.0,在D_a=10~(-4)时,Nu_(av)和Sh_(av)几乎不受B变化的影响;在Da=10~(-3)和Da=10~(-2)时,Nu_(av)和Sh_(av)随B的增加先减小后增大,在B=-1.0时取得最小值.     

基于决策树与多元支持向量机的齿轮箱早期故障诊断方法

针对齿轮箱部件故障形式多样的特点和典型故障训练样本数量有限的难点,提出了基于决策树与多元支持向量机的齿轮箱早期故障诊断方法。利用决策树分类速度快、效率高的优点和支持向量机在小样本二元分类方面突出的特点构建多元分类识别模型,在不同故障情形下提取齿轮箱振动信号典型特征参数作为故障特征向量训练模型,并对样本进行测试。实验结果表明,该方法在小样本情况下识别效果明显优于神经网络方法,同时在识别效率方面比常规多元支持向量机方法有了较大的提高。     

三角波脉动流通栓的晶格玻尔兹曼方法模型

血液栓塞作为心血管疾病的一大诱因, 其形成机理及外部因素一直是医学、生物物理等领域专家关心的问题. 血栓的形成及其结构复杂多样, 大大增加了治愈血栓的难度. 脉动对于疏通血液栓塞有良好的作用, 而由于血液的黏滞作用以及红细胞的惯性, 脉动流的波形、振幅和频率都会影响通栓的效果. 本文主要基于晶格玻尔兹曼方法, 在栓塞的锥形管中, 用三角波脉动流进行通栓计算, 探索三角波脉动流的波形、压差、频率对血管通栓效果的影响. 计算发现, 低频低压条件下三角波脉动流通栓效果不明显, 而高频条件下通栓效果良好; 适当增加压差, 可以提高能通栓的三角波脉动流的频率.     

液滴撞击液膜过程的格子Boltzmann方法模拟

本文采用格子Boltzmann方法对液滴撞击液膜过程进行了研究, 主要考察了雷诺数(Re)、韦伯数(We)、相对液膜厚度 (h) 以及表面张力 (σ) 等物理参数对界面运动过程的影响. 首先, 随着Re数和We数的增加, 可以明显观察到液滴撞击液膜过程中形成的皇冠状水花以及卷吸现象; 当Re数较大时, 液体会发生飞溅, 由液体飞溅形成的小液滴则会继续下落, 并与液膜再次发生碰撞. 其次, 当相对液膜厚度较小时, 液滴撞击液膜并最终导致液膜断裂; 然而随着相对液膜厚度的增大, 尽管撞击过程溅起的液体会越来越多, 但是液膜并不会发生断裂. 再次, 随着表面张力的增大, 界面变形阻力增大, 撞击过程中产生的界面形变也逐渐减弱. 最后还发现皇冠(由液滴溅起形成)半径r 随时间满足r/(2R) ≈ α√Ut/(2R), 这一结果与已有结论是一致的.     

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine,biomedical text mining and network analysis

In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.     

Multi-instance multi-label distance metric learning for genome-wide protein function prediction

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms.     

Understanding Alkaline Hydrogen Oxidation Reaction on PdNiRuIrRh High-Entropy-Alloy by Machine Learning Potential

High-entropy alloy (HEA) catalysts have been widely studied in electrocatalysis. However, identifying atomic structure of HEA with complex atomic arrangement is challenging, which seriously hinders the fundamental understanding of catalytic mechanism. Here, we report a HEA-PdNiRuIrRh catalyst with remarkable mass activity of 3.25 mA μg⁻¹ for alkaline hydrogen oxidation reaction (HOR), which is 8-fold enhancement compared to that of commercial Pt/C. Through machine learning potential-based Monte Carlo simulation, we reveal that the dominant Pd−Pd−Ni/Pd−Pd−Pd bonding environments and Ni/Ru oxophilic sites on HEA surface are beneficial to the optimized adsorption/desorption of *H and enhanced *OH adsorption, contributing to the excellent HOR activity and stability. This work provides significant insights into atomic structure and catalytic mechanism for HEA and offers novel prospects for developing advanced HOR electrocatalysts.     

Multiplex Identification of Post-Translational Modifications at Point-of-Care by Deep Learning-Assisted Hydrogel Sensors

Multiplex detection of protein post-translational modifications (PTMs), especially at point-of-care, is of great significance in cancer diagnosis. Herein, we report a machine learning-assisted photonic crystal hydrogel (PCH) sensor for multiplex detection of PTMs. With closely-related PCH sensors microfabricated on a single chip, our design achieved not only rapid screening of PTMs at specific protein sites by using only naked eyes/cellphone, but also the feasibility of real-time monitoring of phosphorylation reactions. By taking advantage of multiplex sensor chips and a neural network algorithm, accurate prediction of PTMs by both their types and concentrations was enabled. This approach was ultimately used to detect and differentiate up/down regulation of different phosphorylation sites within the same protein in live mammalian cells. Our developed method thus holds potential for POC identification of various PTMs in early-stage diagnosis of protein-related diseases.     

Rational Atom Substitution to Obtain Efficient,Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO₂ reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs₂AgBiBr₆ perovskites. Machine learning models determined the importance of d¹⁰ orbitals, highlighting how substituent electron configuration affects electronic structure of Cs₂AgBiBr₆. Conspicuously, d¹⁰-configured Zn²⁺ boosted the photoactivity of Cs₂AgBiBr₆. Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d¹⁰ configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure–property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.     

Herz spaces and restricted summability of Fourier transforms and Fourier series

A general summability method, the so-called θ-summability is considered for multi-dimensional Fourier transforms and Fourier series. A new inequality for the Hardy-Littlewood maximal function is verified. It is proved that if the Fourier transform of θ is in a Herz space, then the restricted maximal operator of the θ-means of a distribution is of weak type (1,1), provided that the supremum in the maximal operator is taken over a cone-like set. From this it follows that over a cone-like set a.e. for all f∈L₁(Rd). Moreover, converges to f(x) over a cone-like set at each Lebesgue point of f∈L₁(Rd) if and only if the Fourier transform of θ is in a suitable Herz space. These theorems are extended to Wiener amalgam spaces as well. The Riesz and Weierstrass summations are investigated as special cases of the θ-summation.