Co?N graphene encapsulated cobalt catalyst for H2O2 decomposition under acidic conditions

Hydrogen peroxide (H₂O₂) has been listed as one of the 100 most important chemicals in the world. However, huge amount of residual H₂O₂ is hard to timely decomposed into O₂ and H₂O under acidic condition, easily resulting in explosion hazard. Here, we reported a core–shell structure catalyst, that is graphene with Co N structure encapsulated Co nanoparticles. Co N graphene shell serves as the active site for the H₂O₂ decomposition, and Co core further enhance this decomposition. Benefiting from it, the H₂O₂ decomposition were close to 100% after 6 cycles without pH adjustment, which increased 6 orders of magnitude compared with no catalyst. At the same time, the O₂ generation reached 99.67% in 2 h with little metal leaching, and ·OH has been greatly inhibited to only 0.08%. This work can cleanly remove H₂O₂ with little deep oxidation and protect the process of H₂O₂ utilization to achieve a safer world.     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

The Pythagorean fuzzy set (PFS) is characterized by two functions expressing the degree of membership and the degree of nonmembership, which square sum of them is equal or less than 1. It was proposed as a generalization of a fuzzy set to deal with indeterminate and inconsistent information. In this study, we shall present some novel Dice similarity measures of PFSs and the generalized Dice similarity measures of PFSs and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute group decision-making models with Pythagorean fuzzy information. Then, we apply the generalized Dice similarity measures between PFSs to multiple attribute group decision making. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

基于张量分解融合RGB-D图像的物体识别

为了充分利用RGB-D图像的深度图像信息，提出了基于张量分解的物体识别方法。首先将RGB-D图像构造成一个四阶张量，然后将该四阶张量分解为一个核心张量和四个因子矩阵，再利用相应的因子矩阵将原张量进行投影，获得融合后的RGB-D数据，最后输入到卷积神经网络中进行识别。RGB-D数据集中三组相似物体的识别结果表明，利用张量分解融合RGB-D图像的物体识别准确率高于未采用张量分解的物体识别准确率，并且单一错分实例的准确率最高可提升99%。     

On global Hessenberg based methods for solving Sylvester matrix equations

In the first part of this paper, we investigate the use of Hessenberg-based methods for solving the Sylvester matrix equation $AX+XB=C$. To achieve this goal, the Sylvester form of the global generalized Hessenberg process is presented. Using this process, different methods based on a Petrov–Galerkin or on a minimal norm condition are derived. In the second part, we focus on the SGl-CMRH method which is based on the Sylvester form of the Hessenberg process with pivoting strategy combined with a minimal norm condition. In order to accelerate the SGl-CMRH method, a preconditioned framework of this method is also considered. It includes both fixed and flexible variants of the SGl-CMRH method. Moreover, the connection between the flexible preconditioned SGl-CMRH method and the fixed one is studied and some upper bounds for the residual norm are obtained. In particular, application of the obtained theoretical results is investigated for the special case of solving linear systems of equations with several right-hand sides. Finally, some numerical experiments are given in order to evaluate the effectiveness of the proposed methods.     

Oxidation‐led decomposition of hexagonal boron nitride coatings on alloy substrates at 900 °C: Ti‐6Al‐4V

Hexagonal boron nitride (h‐BN) coatings on Ti‐6Al‐4V substrates undergo complete decomposition in air at 900 °C. This fate is similar to that of this ceramic material on chromia‐former alloys, and unlike that of a mass of powder treated in isolation. As the ceramic and alloy oxidize concurrently, outwardly diffusing aluminum (III) ions but not the predominant titanium (IV) ions react with the boron trioxide that forms around the h‐BN basal plane peripheries. Resultant aluminum borate is incorporated into the growing scale and the boron trioxide diffusion barrier is depleted. By this mechanism, the oxidation of h‐BN is maintained at an enhanced rate, until both this material and its oxide completely decompose. Liberated nitrogen from the oxidation of h‐BN can enter the underlying scale as a randomly distributed solute in rutile solid solution. The post‐coating oxide‐atmosphere interface comprises elongated aluminum borate crystallites protruding through at the boundaries between 3–5 at% nitrogen‐doped rutile grains. It differs significantly from that of oxidized, uncoated Ti‐6Al‐4V, which is occupied by a thin α‐alumina layer atop rutile. This interface does not change with an additional 72 h of heat‐treatment.     

POD-assisted strategies for structural topology optimization

We propose a new numerical tool for structural optimization design. To cut down the computational burden typical of the Solid Isotropic Material with Penalization (SIMP) method, we apply Proper Orthogonal Decomposition on SIMP snapshots computed on a fixed grid to construct a rough structure (predictor) which becomes the input of a SIMP procedure performed on an anisotropic adapted mesh (corrector). The benefit of the proposed design tool is to deliver smooth and sharp layouts which require a contained computational effort before moving to the 3D printing production phase.     

不同粒度纳米碳酸钙的可控制备

不同粒度的纳米碳酸钙具有不同的特性和应用范围,目前不同粒度纳米碳酸钙的可控制备方法还未见报道。以氯化钙、碳酸铵为原料,以乙醇、柠檬酸和焦磷酸钠为分散剂,采用复分解法研究了不同制备条件对纳米碳酸钙粒径的影响规律,在此基础上制备出平均粒径为17~71 nm的碳酸钙,并对其进行了表征。实验结果表明,所制备的纳米碳酸钙均为球霰石型,纯度很高,而且形貌近似球形。制备条件对纳米碳酸钙的粒径有显著的影响：随着反应温度升高,纳米碳酸钙的粒径减小;随着氯化钙滴加时间的增加,纳米碳酸钙的粒径增大;随着反应物浓度的提高,纳米碳酸钙的粒径先减小后增大;当分散剂为柠檬酸和焦磷酸钠的混合溶液时所得纳米碳酸钙的粒径最小,当分散剂为乙醇和焦磷酸钠的混合溶液时所得纳米碳酸钙的粒径最大。利用这些影响规律通过控制制备条件可以实现所需粒径纳米碳酸钙的可控制备。     

Adomian decomposition method for the MHD flow of a viscous fluid with the influence of dissipative heat energy

An investigation is carried out on the effect of dissipative heat energy on the flow of an electrically conducting viscous fluid past a shrinking sheet. Both viscous and Joule dissipation effects are considered along with heat generation/absorption for the enhancement of heat transfer properties. The governing nonlinear coupled partial differential equations are transformed into nonlinear ordinary differential equations by a suitable choice of similarity transformations. However, the complex transformed equations are solved by an approximate analytical method known as the Adomian decomposition method with a suitable initial guess solution assumed from the known initial conditions. Moreover, the behavior of several parameters characterizing the flow phenomena are studied via graphs and the numerical computations for the engineering coefficients are obtained and presented through tables. However, the major outcomes of the results are that a higher suction is required to resist the fluid temperature and sinks as well as the dissipative heat energy favors enhancing the fluid temperature at all points in the flow domain.