Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

Heterogeneous Catalysis of Ozone Using Iron–Manganese Silicate for Degradation of Acrylic Acid

Iron–manganese silicate (IMS) was synthesized by chemical coprecipitation and used as a catalyst for ozonating acrylic acid (AA) in semicontinuous flow mode. The Fe-O-Mn bond, Fe-Si, and Mn-Si binary oxide were formed in IMS on the basis of the results of XRD, FTIR, and XPS analysis. The removal efficiency of AA was highest in the IMS catalytic ozonation processes (98.9% in 15 min) compared with ozonation alone (62.7%), iron silicate (IS) catalytic ozonation (95.6%), and manganese silicate catalytic ozonation (94.8%). Meanwhile, the removal efficiencies of total organic carbon (TOC) were also improved in the IMS catalytic ozonation processes. The IMS showed high stability and ozone utilization. Additionally, H₂O₂ was formed in the process of IMS catalytic ozonation. Electron paramagnetic resonance (EPR) analysis and radical scavenger experiments confirmed that hydroxyl radicals (•OH) were the dominant oxidants. Cl⁻, HCO₃⁻, PO₄³⁻, Ca²⁺, and Mg²⁺ in aqueous solution could adversely affect AA degradation. In the IMS catalytic ozonation of AA, the surface hydroxyl groups and Lewis acid sites played an important role.     

GlyNet: a multi-task neural network for predicting protein–glycan interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan–protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet''s output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein–glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.

GlyNet, a neural net model of glycan-protein binding strengths. Given a glycan it outputs binding to each of several protein samples. Reproducing glycan array data, it extrapolates the binding of untested glycans against the protein samples.     

用化学溶液方法在Ni-5at%W基底上制备La2Zr2O7过渡层的研究

La2Zr2O7(LZO)过渡层以其独特的物理化学性质越来越受到人们的关注。本文以乙酰丙酮镧和乙酰丙酮锆为前驱盐,丙酸为溶剂配置前驱液,用化学溶液方法(CSD)在具有立方织构的Ni-5at%W基底上制备了LZO过渡层薄膜。研究了前驱液成分、性质以及退火温度对LZO成相以及取向的影响。用常规XRD和X射线四环衍射仪分析了LZO薄膜的相成分和织构。结果显示,在1050℃下退火可以获得强立方织构的LZO薄膜,其中(222)峰的Phi扫描半高宽值为8.95°;(400)峰的Chi扫描半高宽值为6.8°。用高分辨扫描电子显微镜(FE-SEM)观察到LZO薄膜表面均匀致密,没有裂纹和空洞。     

Mesoporous sodium four-coordinate aluminosilicate nanoparticles modulate dendritic cell pyroptosis and activate innate and adaptive immunity

Pyroptosis is a programmed cell death widely studied in cancer cells for tumour inhibition, but rarely in dendritic cell (DC) activation for vaccine development. Here, we report the synthesis of sodium stabilized mesoporous aluminosilicate nanoparticles as DC pyroptosis modulators and antigen carriers. By surface modification of sodium-stabilized four-coordinate aluminium species on dendritic mesoporous silica nanoparticles, the resultant Na-^IVAl-DMSN significantly activated DC through caspase-1 dependent pyroptosis via pH responsive intracellular ion exchange. The released proinflammatory cellular contents further mediated DC hyperactivation with prolonged cytokine release. In vivo studies showed that Na-^IVAl-DMSN induced enhanced cellular immunity mediated by natural killer (NK) cells, cytotoxic T cells, and memory T cells as well as humoral immune response. Our results provide a new principle for the design of next-generation nanoadjuvants for vaccine applications.

Na-^IVAl-DMSN acts as both antigen carriers and modulators to “hyperactivate” dendritic cells (DCs) via potassium (K⁺) efflux dependent pyroptosis, eventually leading to enhanced adaptive and innate immunity.     

金红石TiO_2中本征缺陷扩散性质的第一性原理计算

基于密度泛函理论的爬坡弹性带方法,对金红石相二氧化钛晶体中钛间隙、钛空位、氧间隙、氧空位4种本征缺陷的扩散特征进行了研究.对比4种本征缺陷在晶格内部沿不同扩散路径的过渡态势垒后发现,缺陷扩散过程呈现出明显的各向异性.其中,钛间隙和氧间隙沿[001]方向具有最小的扩散势垒路径,激活能分别为0.505 eV和0.859 eV;氧空位和钛空位的势垒最小的扩散路径分别沿[110]方向和[111]方向,激活能分别为0.735 eV和2.375 eV.     

Performance of La2O3/InAlN/GaN metal—oxide—semiconductor high electron mobility transistors

We report on the performance of La2O3/InAlN/GaN metal-oxide-semiconductor high electron mobility transistors(MOSHEMTs) and InAlN/GaN high electron mobility transistors(HEMTs).The MOSHEMT presents a maximum drain current of 961 mA/mm at Vgs = 4 V and a maximum transconductance of 130 mS/mm compared with 710 mA/mm at Vgs = 1 V and 131 mS/mm for the HEMT device,while the gate leakage current in the reverse direction could be reduced by four orders of magnitude.Compared with the HEMT device of a similar geometry,MOSHEMT presents a large gate voltage swing and negligible current collapse.     

单向悬挂屋盖结构的风致气弹耦合效应数值模拟

综合运用计算流体力学和计算结构力学技术,建立了适用于索膜结构流固耦合风振分析的CFD数值模拟方法,并编制了相应的有限元计算程序。应用该程序对具有不同参数的大跨度单向屋盖结构进行了风振响应分析,探讨了来流风速、屋面质量和初始预张力等参数对结构流固耦合特性的影响。研究表明,由于屋盖前缘周期脱落的大尺度旋涡是诱导结构振动的主要原因,随着大尺度旋涡沿屋面向下游移动,其动能逐渐转化为结构变形能,使结构振动形态呈现明显的涡激振动特征。屋面质量、来流风速和初始预张力是影响结构流固耦合性能的主要参数。对于索膜结构,仅通过刚性模型来确定结构风压并进行风振响应分析,其结果是不可靠的。     

Computational Insight into Biotransformation Profiles of Organophosphorus Flame Retardants to Their Diester Metabolites by Cytochrome P450

Biotransformation of organophosphorus flame retardants (OPFRs) mediated by cytochrome P450 enzymes (CYPs) has a potential correlation with their toxicological effects on humans. In this work, we employed five typical OPFRs including tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), tris(1-chloro-2-propyl) phosphate (TCIPP), tri(2-chloroethyl) phosphate (TCEP), triethyl phosphate (TEP), and 2-ethylhexyl diphenyl phosphate (EHDPHP), and performed density functional theory (DFT) calculations to clarify the CYP-catalyzed biotransformation of five OPFRs to their diester metabolites. The DFT results show that the reaction mechanism consists of Cα-hydroxylation and O-dealkylation steps, and the biotransformation activities of five OPFRs may follow the order of TCEP ≈ TEP ≈ EHDPHP > TCIPP > TDCIPP. We further performed molecular dynamics (MD) simulations to unravel the binding interactions of five OPFRs in the CYP3A4 isoform. Binding mode analyses demonstrate that CYP3A4-mediated metabolism of TDCIPP, TCIPP, TCEP, and TEP can produce the diester metabolites, while EHDPHP metabolism may generate para-hydroxyEHDPHP as the primary metabolite. Moreover, the EHDPHP and TDCIPP have higher binding potential to CYP3A4 than TCIPP, TCEP, and TEP. This work reports the biotransformation profiles and binding features of five OPFRs in CYP, which can provide meaningful clues for the further studies of the metabolic fates of OPFRs and toxicological effects associated with the relevant metabolites.