基于Rietveld精修方法解析Bi2O3的原子热振动

三氧化二铋(Bi₂O₃)是氧离子导电体,为了获得它的原子热振动各向同性温度因子,对该粉末晶体进行X射线衍射实验,建立了晶体结构模型,利用Rietveld 精修方法的RIETAN-2000 程序对所得实验结果进行了晶体结构精修,通过最大熵方法(MEM)解析得到了粉末晶体的等高电子密度分布三维(3D) 和二维(2D)可视化图谱。结果表明,各原子Bi₍₁₎、Bi₍₂₎、O₍₁₎、O₍₂₎和O₍₃₎的原子热振动各向同性温度因子分别为0.004 938 nm²、0.004 174 nm²、0.007 344 nm²、0.007 462 nm²、和0.007 857 nm²,等高电子密度分布的可视化,进一步验证了晶体结构模型和原子位置的准确性,这些参数对研究晶体材料的热性质具有一定参考意义。     

Semiconvergence analysis of the randomized row iterative method and its extended variants

The row iterative method is popular in solving the large‐scale ill‐posed problems due to its simplicity and efficiency. In this work we consider the randomized row iterative (RRI) method to tackle this issue. First, we present the semiconvergence analysis of RRI method for the overdetermined and inconsistent system, and derive upper bounds for the noise error propagation in the iteration vectors. To achieve a least squares solution, we then propose an extended version of the RRI (ERRI) method, which in fact can converge in expectation to the solution of the overdetermined or underdetermined, consistent or inconsistent systems. Finally, some numerical examples are given to demonstrate the convergence behaviors of the RRI and ERRI methods for these types of linear system.     

概率语言多属性群决策方法及其在新型智慧城市市民获得感评价中的应用

针对公众参与的语言信息多属性群决策问题，研究了考虑参与者满意度的概率语言多属性群决策方法。首先，根据参与者的语言评价信息确定并规范化概率语言决策矩阵。然后，对大群体进行共识分析，由最大化参与者群体的满意度构建线性规划模型，确定参与者群组的权重；构造正、负理想方案的评价向量，构建多目标规划模型，用拉格朗日乘子法求解属性权重；定义各方案的加权贴近度，并以此对方案进行排序和优选。最后，通过新型智慧城市市民获得感评价案例验证了模型的可行性和有效性。     

发动机机油滤清器虑层强度分析及优化

针对机油滤清器工作工况下进出口压差、机油滤层强度及导流桩高度等问题, 通过试验测试与仿真相结合, 对滤清器初步设计进行了评估及优化, 以确保滤清器在工作工况下进出口压降及滤层强度能满足要求. 首先进行滤层性能试验, 得到滤层的惯性阻力系数和黏性阻力系数; 再通过滤层多孔介质CFD分析, 对滤清器进出口压降进行分析计算. 结果表明: 在-18℃、25℃和70℃的工况下, 进出口压降都小于10kPa, 满足相关要求. 针对滤层的最大主应力超过其抗拉强度的问题, 通过CAE仿真分析, 优化滤层与导流桩间隙, 将滤层最大主应力由110.1MPa降至36.99MPa, 小于其抗拉强度42.8MPa.     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

Solvent Influence on Absorption Spectra and Tautomeric Equilibria of Symmetric Azomethine-Functionalized Derivatives: Structural Elucidation and Computational Studies

A new series of azomethine-functionalized compounds was synthesized from the condensation of 2-hydroxy-1,3-propanediamine and 2-thienylcarboxaldehydes in the presence of a drying agent. The derivatives were spectroscopically characterized by NMR, LC-MS, UV/Vis, IR and elemental analysis. Variable temperature ¹H-NMR (−60 to +60 °C) was performed to investigate the effect of solvent polarity; the capability of solvent to form H-bond was found to dramatically influencing the tautomerization process of the desired structures. The calculated thermochemical parameters (ΔH₂₉₈, ΔG₂₉₈ and ΔS₂₉₈) at DFT and MP2 levels of theory explained that 3 b exists in equilibrium with two tautomers. The basis of the electronic absorptions was pursued through Time-Dependent Density-Functional Theory (TD-DFT). Analysis of the structural surfaces was inspected and the molecular electrostatic potential (MEP) demonstrated that the three functionalized compounds were relatively analogous in the electronic distributions. Furthermore, the electrophilic and nucleophilic centers lying on the molecular surfaces were probably playing a key-role in stabilizing the compounds through the nonclassical C−H⋅⋅⋅π interactions and hydrogen bonding. The impact of solvent polarity on absorption spectra were investigated via solvatochromic shifts. For instance, compound 3 c displayed a gradual shift of the maximum absorption to the red area when the solvent polarity was increased, recording a 21 nm of bathochromic shift. In contrast, no significant solvent-effect on 3 a and 3 b was observed. The solvation relation was pursued between Gutmann's donicity numbers the experimental λ_max; exhibited almost positive linear performance with a minor oscillation, that ascribe to the possible weak interface between the molecules of solute and designated solvents. The bandgap energy of all products were assessed experimentally using optical absorption spectra following Tauc approach, giving −4.050 ( 3 a ), −3.900 ( 3 b ) and −3.210 ( 3 c ) eV. However, the ΔE were computationally figured out from TD-DFT simulation to be −4.258 ( 3 a ), −4.022 ( 3 b ) and −3.390 ( 3 c ) eV.     

LC-MS-based multi-omics analysis of brain tissue for the evaluation of the anti-ischemic stroke potential of Tribulus terrestris L. fruit extract in MCAO rats

This study aimed to investigate the chemical composition of Tribulus terrestris L. fruit (TT) extract named TT15 and its protective effect against ischemic stroke (IS) as well as corresponding mechanisms. The chemical composition of TT15 was analyzed by liquid chromatography-mass spectrometry (LC-MS), and the compound identification was conducted via searching the in-house database. The LC-MS-based multi-omics approach was applied to search the differential metabolites and differential proteins in rat brain tissue and to explore the biomarker and molecular mechanism of TT15 against middle cerebral artery occlusion (MCAO). A total of 20 compounds were identified from TT15, mainly including alkaloids, flavonoids, phenols, quinones, and esters. These 20 compounds significantly affected the metabolism of 44 metabolites and the expression of 51 proteins. Joint pathway analysis showed that these metabolites and proteins were mainly involved in the response to elevated platelet cytosolic Ca²⁺ and platelet activation, which inferred that TT15 may exert a protective effect against cerebral ischemic injury via regulating platelet function. This study provides useful information for further exploration of the mechanisms of TT extract against IS.     

Validation of a robust and rapid liquid chromatography tandem mass spectrometric method for the quantitative analysis of navitoclax

The Bcl-2 family small molecule inhibitor navitoclax is being clinically evaluated to treat multiple cancers including lymphoid malignancies and small cell lung cancer. A sensitive and reliable method was developed to quantitate navitoclax in human plasma using liquid chromatography with tandem mass spectrometry with which to perform detailed pharmacokinetic studies. Sample preparation involved protein precipitation using acetonitrile. Separation of navitoclax and the internal standard, navitoclax-d8, was achieved with a Waters Acquity UPLC BEH C₁₈ column using isocratic flow over a 3 min total analytical run time. A SCIEX 4500 triple quadrupole mass spectrometer operated in positive electrospray ionization mode was used for the detection of navitoclax. The assay range was 5–5,000 ng/ml and proved to be accurate (89.5–104.9%) and precise (CV ≤ 11%). Long-term frozen plasma stability for navitoclax at −70°C was at least 43 months. The method was applied for the measurement of total plasma concentration of navitoclax in a patient receiving a 250 mg daily oral dose.     

Bifurcation analysis of visual angle model with anticipated time and stabilizing driving behavior

In the light of the visual angle model (VAM), an improved car-following model considering driver's visual angle, anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow. Based on the model, linear stability analysis is performed together with bifurcation analysis, whose corresponding stability condition is highly fit to the results of the linear analysis. Furthermore, the time-dependent Ginzburg-Landau (TDGL) equation and the modified Korteweg-de Vries (mKdV) equation are derived by nonlinear analysis, and we obtain the relationship of the two equations through the comparison. Finally, parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis, whose results are highly consistent with the theoretical analysis.