Synthesis of Hierarchically Porous FAU/γ-Al2O3 Composites with Different Morphologies via Directing Agent Induced Method

Zeolite FAU composites with a macro/meso-microporous hierarchical structure were hydrothermally synthesized using macro-mesoporous γ-Al_2O_3 monolith as the substrate by means of the liquid crystallization directing agent(LCDA) induced method. No template was needed throughout the synthesis processes. The structure and porosity of zeolite composites were analyzed by means of X-ray powder diffraction(XRD), scanning electron microscopy(SEM) and N_2adsorption-desorption isotherms. The results showed that the supported zeolite composites with varied zeolitic crystalline phases and different morphologies can be obtained by adjusting the crystallization parameters, such as the crystallization temperature, the composition and the alkalinity of the precursor solution. The presence of LCDA was defined as a determinant for synthesizing the zeolite composites. The mechanisms for formation of the hierarchically porous FAU zeolite composites in the LCDA induced synthesis process were discussed. The resulting monolithic zeolite with a trimodal-porous hierarchical structure shows potential applicability where facile diffusion is required.

Constructing a hollow core-shell structure of RuO_(2) wrapped by hierarchical porous carbon shell with Ru NPs loading for supercapacitor

Hollow core-shell structure nanomaterials have been broadly used in energy storage, catalysis, reactor,and other fields due to their unique characteristics, including the synergy between different materials,a large specific surface area, small density, large charge carrying capacity and so on. However, their synthesis processes were mostly complicated, and few researches reported one-step encapsulation of different valence states of precious metals in carbon-based materials. Hence, a novel hollow core-shell nanostructure electrode material, RuO_(2)@Ru/HCs, with a lower mass of ruthenium to reduce costs was constructed by one-step hydrothermal method with hard template and co-assembled strategy, consisting of RuO_(2) core and ruthenium nanoparticles(Ru NPs) in carbon shell. The Ru NPs were uniformly assembled in the carbon layer, which not only improved the electronic conductivity but also provided more active centers to enhance the pseudocapacitance. The RuO_(2) core further enhanced the material’s energy storage capacity. Excellent capacitance storage(318.5 F·g^(-1)at 0.5 A·g^(-1)), rate performance(64.4%) from 0.5 A·g^(-1)to 20 A·g^(-1), and cycling stability(92.3% retention after 5000 cycles) were obtained by adjusting Ru loading to 0.92%(mass). It could be attributed to the wider pore size distribution in the micropores which increased the transfer of electrons and protons. The symmetrical supercapacitor device based on RuO_(2)@Ru/HCs could successfully light up the LED lamp. Therefore, our work verified that interfacial modification of RuO_(2) and carbon could bring attractive insights into energy density for nextgeneration supercapacitors.

THE ANALYSIS OF THIN WALLED COMPOSITE LAMINATED HELICOPTER ROTOR WITH HIERARCHICAL WARPING FUNCTIONS AND FINITE ELEMENT METHOD

In the present paper, a series of hierarchical warping functions is developed to analyze the static and dynamic problems of thin walled composite laminated helicopter rotors composed of several layers with single closed cell. This method is the development and extension of the traditional constrained warping theory of thin walled metallic beams, which had been proved very successful since 1940s. The warping distribution along the perimeter of each layer is expanded into a series of successively corrective warping functions with the traditional warping function caused by free torsion or free beading as the first term, and is assumed to be piecewise linear along the thickness direction of layers. The governing equations are derived based upon the variational principle of minimum potential energy for static analysis and Rayleigh Quotient for free vibration analysis. Then the hierarchical finite element method. is introduced to form a,. numerical algorithm. Both static and natural vibration problems of sample box beams axe analyzed with the present method to show the main mechanical behavior of the thin walled composite laminated helicopter rotor.

HIERARCHICAL ITERATION METHOD TO SOLVE DYNAMIC DIFFERENTIAL EQUATION

The dynamic differential equation of a multibody System can be presented inthe form of Aq= B. Calculating the inverse of matrix A is a simple way to solve this kindof differential equatbo. Matrix A will be in the ill condition if the system is configured asa main they with small mass appendages. A hierarchical iteration method is given in thispaper to avoid the problem of the inverse of an ill condition matrix calculatbo. It is pont-ed out that the stability of the system input and output is the suffcient condition of itera-tion convergence. The method omits a series formula expanding step. It is also useful toreduce the immuence of the stiff problem. The calculation progress i8 modular and structural.

A Local Macroscopic Conservative(LoMaC)Low Rank Tensor Method with the Discontinuous Galerkin Method for the Vlasov Dynamics

In this paper,we propose a novel Local Macroscopic Conservative(LoMaC)low rank tensor method with discontinuous Galerkin(DG)discretization for the physical and phase spaces for simulating the Vlasov-Poisson(VP)system.The LoMaC property refers to the exact local conservation of macroscopic mass,momentum,and energy at the discrete level.The recently developed LoMaC low rank tensor algorithm(arXiv:2207.00518)simultaneously evolves the macroscopic conservation laws of mass,momentum,and energy using the kinetic flux vector splitting;then the LoMaC property is realized by projecting the low rank kinetic solution onto a subspace that shares the same macroscopic observables.This paper is a generalization of our previous work,but with DG discretization to take advantage of its compactness and flexibility in handling boundary conditions and its superior accuracy in the long term.The algorithm is developed in a similar fashion as that for a finite difference scheme,by observing that the DG method can be viewed equivalently in a nodal fashion.With the nodal DG method,assuming a tensorized computational grid,one will be able to(i)derive differentiation matrices for different nodal points based on a DG upwind discretization of transport terms,and(ii)define a weighted inner product space based on the nodal DG grid points.The algorithm can be extended to the high dimensional problems by hierarchical Tucker(HT)decomposition of solution tensors and a corresponding conservative projection algorithm.In a similar spirit,the algorithm can be extended to DG methods on nodal points of an unstructured mesh,or to other types of discretization,e.g.,the spectral method in velocity direction.Extensive numerical results are performed to showcase the efficacy of the method.

SPECTRAL/HP ELEMENT METHOD WITH HIERARCHICAL RECONSTRUCTION FOR SOLVING NONLINEAR HYPERBOLIC CONSERVATION LAWS

The hierarchical reconstruction （HR） [Liu, Shu, Tadmor and Zhang, SINUM ＇07] has been successfully applied to prevent oscillations in solutions computed by finite volume, Runge-Kutta discontinuous Galerkin, spectral volume schemes for solving hyperbolic conservation laws. In this paper, we demonstrate that HR can also be combined with spectral/hp element method for solving hyperbolic conservation laws. An orthogonal spectral basis written in terms of Jacobi polynomials is applied. High computational efficiency is obtained due to such matrix-free algorithm. The formulation is conservative, and essential nomoscillation is enforced by the HR limiter. We show that HR preserves the order of accuracy of the spectral/hp element method for smooth solution problems and generate essentially non-oscillatory solutions profiles for capturing discontinuous solutions without local characteristic decomposition. In addition, we introduce a postprocessing technique to improve HR for limiting high degree numerical solutions.

Coarse-Graining Method Based on Hierarchical Clustering on Complex Networks

With the rapid development of big data, the scale of realistic networks is increasing continually. In order to reduce the network scale, some coarse-graining methods are proposed to transform large-scale networks into mesoscale networks. In this paper, a new coarse-graining method based on hierarchical clustering (HCCG) on complex networks is proposed. The network nodes are grouped by using the hierarchical clustering method, then updating the weights of edges between clusters extract the coarse-grained networks. A large number of simulation experiments on several typical complex networks show that the HCCG method can effectively reduce the network scale, meanwhile maintaining the synchronizability of the original network well. Furthermore, this method is more suitable for these networks with obvious clustering structure, and we can choose freely the size of the coarse-grained networks in the proposed method.

HIERARCHICAL BASIS METHOD FOR COVOLUME METHOD FOR NONSYMMETRIC AND INDEFINITE ELLIPTIC PROBLEM

In this paper,hierarchical basis method for second order nonsymmetric andindefinite elliptic problem on a polygonal domain(possibly nonconvex)discreted by avertex-centered covolume method is constructed.

Method for the classification of tea diseases via weighted sampling and hierarchical classification learning

This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs,low accuracy,and time-consuming traditional tea disease recognition methods.This method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model,effectively alleviating the impact of high similarity between tea diseases on the model’s classification performance.To better solve the problem of few and unevenly distributed tea disease samples,this study introduced a weighted sampling scheme to optimize data processing,which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification data.The experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea(tea algal spot disease,tea white spot disease,tea anthracnose disease,and tea leaf blight disease).After applying the“weighted sampling hierarchical classification learning method”to train 7 different efficient backbone networks,most of their accuracies have improved.The EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21%after adopting this learning method,which is higher than EfficientNet-b2(98.82%)and MobileNet-V3(98.43%).In addition,to better apply the results of identifying tea diseases,this study developed a mini-program that operates on WeChat.Users can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple operations.This intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers,consumers,and related scientific researchers and has certain practical value.

Preparation of Hierarchically Trimodal-Porous ZSM-5 Composites through Steam-assisted Conversion of Macroporous Aluminosilica Gel with Two Different Quaternary Ammonium Hydroxides

This article presents a detailed structural study of a new spherical Mg Cl2-supported Ti Cl4 Ziegler-Natta catalyst for isotactic propylene polymerization, and researches on the relationship between catalyst structure and polymer properties. The spherical support with the chemical composition of CH3CH2 OMg OCH(CH2Cl)2 has been synthesized from a new dispersion system and is used as the supporting material to prepare Ziegler-Natta catalyst. The XRD analysis indicates that the catalyst is fully activated with δ-Mg Cl2 in the active catalyst. The far-IR spectrometric results confirm again the presence of δ-Mg Cl2 in the active catalyst. Textural property of the active catalyst exhibits high surface area coupled with high porosity. The high activity in propylene polymerization is mainly ascribed to the full activation and the porous structure of the catalyst. Scanning electron microscopy/energy dispersive spectrometer mapping results indicate a uniform titanium distribution throughout the catalyst particles. Particle size analysis shows that the catalyst has a narrow particle size distribution. The perfect spherical shape, uniform titanium distribution and narrow particle size distribution of the catalyst confirm the advantage of polymer particles production with less fines. The solid state 13 C NMR and mid-IR spectroscopic analyses indicate that there exists strong complexation between diisobutyl phthalate and Mg Cl2, which leads to the high isotacticity of polypropylene.

Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.

Status and perspectives of hierarchical porous carbon materials in terms of high-performance lithium-sulfur batteries

Lithium-sulfur(Li-S)batteries,although a promising candidate of next-generation energy storage devices,are hindered by some bottlenecks in their roadmap toward commercialization.The key challenges include solving the issues such as low utilization of active materials,poor cyclic stability,poor rate performance,and unsatisfactory Coulombic efficiency due to the inherent poor electrical and ionic conductivity of sulfur and its discharged products(e.g.,Li2S2 and Li_(2)S),dissolution and migration of polysulfide ions in the electrolyte,unstable solid electrolyte interphase and dendritic growth on an odes,and volume change in both cathodes and anodes.Owing to the high specific surface area,pore volume,low density,good chemical stability,and particularly multimodal pore sizes,hierarchical porous carbon(HPC)mate rials have received considerable attention for circumventing the above pro blems in Li-S batteries.Herein,recent progress made in the synthetic methods and deployment of HPC materials for various components including sulfur cathodes,separators and interlayers,and lithium anodes in Li-S batteries is presented and summarized.More importantly,the correlation between the structures(pore volume,specific surface area,degree of pores,and heteroatom-doping)of HPC and the electrochemical performances of Li-S batteries is elaborated.Finally,a discussion on the challenges and future perspectives associated with HPCs for Li-S batteries is provided.

Hierarchically skeletal multi-layered Pt-Ni nanocrystals for highly efficient oxygen reduction and methanol oxidation reactions

Pt based materials are the most efficient electrocatalysts for the oxygen reduction reaction(ORR)and methanol oxidation reaction(MOR)in fuel cells.Maximizing the utilization of Pt based materials by modulating their morphologies to expose more active sites is a fundamental objective for the practical application of fuel cells.Herein,we report a new class of hierarchically skeletal Pt-Ni nanocrystals(HSNs)with a multi-layered structure,prepared by an inorganic acid-induced solvothermal method.The addition of H_(2)SO_(4)to the synthetic protocol provides a critical trigger for the successful growth of Pt-Ni nanocrystals with the desired structure.The Pt-Ni HSNs synthesized by this method exhibit enhanced mass activity of 1.25 A mgpt−1 at 0.9 V(versus the reversible hydrogen electrode)towards ORR in 0.1-M HClO_(4),which is superior to that of Pt-Ni multi-branched nanocrystals obtained by the same method in the absence of inorganic acid;it is additionally 8.9-fold higher than that of the commercial Pt/C catalyst.Meanwhile,it displays enhanced stability,with only 21.6%mass activity loss after 10,000 cycles(0.6–1.0 V)for ORR.Furthermore,the Pt-Ni HSNs show enhanced activity and anti-toxic ability in CO for MOR.The superb activity of the Pt-Ni HSNs for ORR and MOR is fully attributed to an extensively exposed electrochemical surface area and high intrinsic activity,induced by strain effects,provided by the unique hierarchically skeletal alloy structure.The novel open and hierarchical structure of Pt-Ni alloy provides a promising approach for significant improvements of the activity of Pt based alloy electrocatalysts.

Local and regional flood frequency analysis based on hierarchical Bayesian model in Dongting Lake Basin,China

This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.

Analyzing the Urban Hierarchical Structure Based on Multiple Indicators of Economy and Industry: An Econometric Study in China

For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail.In this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is investigated.The indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure.The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China.According to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using K-means clustering algorithm.Then,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are proposed.The results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing countries.The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.

A multidimensional and hierarchical model of library mobile service quality

Purpose:Based on the understanding of the characteristics of mobile services(m-services)provided by libraries,we attempt to develop a multidimensional and hierarchical model and a specifically designed measurement scale for the assessment of libraries' m-service quality.Design/methodology/approach:The research has been conducted sequentially in two stages.At the first stage,a multidimensional and hierarchical model of library m-service quality and a measurement scale are developed through literature review and focus group interviews.At the second stage the model is tested using partial disaggregation technique for an analysis of the data collected from over 400 library users in 3 Chinese universities.Findings:The proposed model is shown to fit the data well.The empirical analysis provides strong support for the model,which includes 4 dimensions(service environment,service interaction,information control and service effect) and 12 corresponding sub-dimensions.Research limitations:The sample size needs to be enlarged and the user types need to be expanded to make the sample more representative.Practical implications:The study provides a conceptual framework and measurement tool that can help library managers understand the users' needs in the context of mobile network environment,and boost their libraries' competitiveness by carrying out better services to meet the user demands.Originality/value:The proposed model and measurement scale specifically designed for the assessment of libraries' m-service quality fully reflect the hierarchical structure of libraries' m-service quality.In addition,by using partial disaggregation technique for model testing,we believe our research can serve as a practical reference to those engaged in the development and examination of a multidimensional and hierarchical model.

Multivariable Adaptive Decoupling Controller Using Hierarchical Multiple Models

To solve the problem such as too many models, long computing time and so on, a hierarchical multiple models direct adaptive decoupling controller is designed. It consists of multiple levels. In the upper level, the best model is chosen according to the switching index. Then multiple fixed models are constructed on line to cover the region which the above chosen fixed model lies in.In the last level, one free-running and one re-initialized adaptive model are added to guarantee the stability and improve the transient response. By selection of the weighting polynomial matrix, it not only eliminates the steady output error and places the poles of the closed loop system arbitrarily, but also decouples the system dynamically. At last, for this multiple models switching system, global convergence is obtained under common assumptions. Compared with the conventional multiple models adaptive controller, it reduces the number of the fixed models greatly. If the same number of the fixed models is used, the system transient response and decoupling result are improved. The simulation example illustrates the power of the derived controller.

A HIERARCHICAL IDENTIFICATION ALGORITHM FOR THE EXTENDED EXPONENTIAL MODEL

A new hierarchical identification algorithm is proposed for solving the identi-fication problem of the extended exponential model,which is used frequently in ecological,social and economical systems.By using the zero character of the optimal Lagrangianmultipliers of the equivalent identification problem,a two-level structure of the algorithmis derived first.Then,the convergence and the correspondence with the conventionalnonlinear approaches of the algorithm are proved.The results of simulation and applica-tion show that its convergent rate is greatly higher than that of the L-Mmethod.