浅析商业银行全面财务预算管理

目前我国商业银行的财务管理,普遍采取的是一种指标式的计划管理模式。这种较为粗放的管理模式存在许多缺陷,不能满足和适应银行进一步提高财务管理水平的需要。尽快探索、建立和实施更为行之有效的全面预算财务管理模式,提高财务资源配置效率,是我国银行提升竞争力的关键所在。全面财务预算管理,就是以盈利性目标为预算目标,通过对银行业务的发展规划、资金的运筹、营业收支及费用的测算,建立若干相互关联的子预算,以在业务发展、营业收支和代表效益的利润之间建立一个有机联系的作用与反应机制,实现对银行的经营活动进行有计划的过程动态管理。

L2(Rd)中Wey1——Heisenberg框架等式的刻画

根据判断一个Weyl-Heisenberg系统有有限上界的充分条件,然后推出了Weyl-Heisenberg等式成立的一个充要条件。

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

ERP环境下企业财务管理的实施及问题研究

ERP(企业资源计划,Enterprise Resources Planning)是指能够代表现代信息技术在企业应用中最新发展,其通过共享的数据流和信息流,成为对企业过程进行集成的综合软件包解决方案,是提升企业竞争力的有效工具。现阶段,ERP在我国的应用已经从概念走向普及。

A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

Proximal point algorithm for a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings

We introduced a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings. Using the resolvent operator method in Hilbert spaces, we suggested a new proximal point algorithm for finding approximate solutions, which strongly converge to the exact solution of a fuzzy set-valued variational inclusion with (H,η)-monotone. The results improved and generalized the general quasi-variational inclusions with fuzzy set-valued mappings proposed by Jin and Tian Jin MM, Perturbed proximal point algorithm for general quasi-variational inclusions with fuzzy set-valued mappings, OR Transactions, 2005, 9(3): 31-38, (In Chinese); Tian YX, Generalized nonlinear implicit quasi-variational inclusions with fuzzy mappings, Computers & Mathematics with Applications, 2001, 42: 101-108.

Subcarrier and power allocation in OFDM-based cognitive radio networks

We formulate the subcarrier and power allocation problem in cognitive radio networks employing orthogonal frequency division multiplexing （OFDM） as a non-linear optimization problem with the objective of maximizing sum capacity under constraints of available subcarriers, interference temperature, power budget, etc. A close-to-optimal solution with much reduced complexity is proposed to separate the problem into two steps, which also considers fairness among secondary users. A fair al- gorithm for subcarrier allocation （FA_SA） is firstly presented. Secondly, a fast iterative water-filling algorithm for power allocation （FIWFA_PA） is also proposed to maximize the sum capacity. Exten- sive simulation results show that sum capacity performance of our low-complexity solution is very close to the optimal one, while significantly improving fairness and reducing computation complexity compared with the existing solutions.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.

Identification of Potential Flavonoid Inhibitors of the SARS-CoV-2 Main Protease 6YNQ:A Molecular Docking Study

Objective Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the causative agent for coronavirus disease 2019(COVID-19),is responsible for the recent global pandemic.As there are no effective drugs or vaccines available for SARS-CoV-2,we investigated the potential of flavonoids against SARS-CoV-2 main protease 6YNQ.Methods In silico molecular simulation study against SARS-CoV-2 main protease 6YNQ.Results Among the 21 selected flavonoids,rutin demonstrated the highest binding energy(−8.7 kcal/mol)and displayed perfect binding with the catalytic sites.Conclusions Our study demonstrates the inhibitory potential of flavonoids against SARS-CoV-2 main protease 6YNQ.These computational simulation studies support the hypothesis that flavonoids might be helpful for the treatment of COVID-19.

Implicit Variational Inclusions and Algorithms Involving （A, η）-monotone Operators in 2-uniformly Smooth Banach Spaces

This paper deals with a new class of nonlinear set valued implicit variational inclusion problems involving （A, η）-monotone mappings in 2-uniformly smooth Banach spaces. Semi-inner product structure has been used to study the （A, η）-monotonicity. Using the generalized resolvent operator technique and the semi-inner product structure, the approximation solvability of the proposed problem is investigated. An iterative algorithm is constructed to approximate the solution of the problem. Convergence analysis of the proposed algorithm is investigated. Similar results are also investigated for variational inclusion problems involving （H, η）-monotone mappings.

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.

PV Power Short-Term Forecasting Model Based on the Data Gathered from Monitoring Network

The degree of accuracy in predicting the photovoltaic power generation plays an important role in appropriate allocations and economic operations of the power plants based on the generating capacity data gathered from the geographically separated photovoltaic plants through network. In this paper, a forecasting model is designed with an optimization algorithm which is developed with the combination of PSO （Particle Swarm Optimization） and BP （Back Propagation） neural network. The proposed model is further validated and the experiment results show that the predication model assures the prediction accuracy regardless the day type transitions and other relevant factors, in the proposed model, the prediction error rate is worth less than 20% in all different climatic conditions and most of the prediction error accuracy is less than 10% in sunny day, and whose precision satisfies the management requirements of the power grid companies, reflecting the significance of the proposed model in engineering applications.

Pseudo-Bonding Interaction between Boron-doped Heterofullerene and Zinc Porphine Predicted by DFT Calculation

Theoretical study on the supramolecular complexes formed between boron-doped het- erofullerene （C59B） and zinc porphine （ZnF）, namely C59B-ZnP and its anion species C59B-ZnP, was performed by density functional theory calculation at wB97XD/6-31G（d） level. Strong interaction between porphyrin and heterofullerene moiety was predicted for these complexes based on geometry and electronic structure analysis. Especially, pseudobonding interaction occurring between the B atom of fullerene and the N atom of porphyrin was predicted to occur in C59B-ZnP complex, but be broken in C59B-ZnP complex. Time-dependent density functional theory calculation manifests the redshift of electron absorption for ZnP upon the interaction with heterofullerene.

Positive combinations of projections in von Neumann algebras and purely infinite simple C*-algebras

We give an overview of the question： which positive elements in an operator algebra can be written as a linear combination of projections with positive coefficients. A special case of independent interest is the question of which positive elements can be written as a sum of finitely many projections. We focus on von Neumann Mgebras, on purely infinite simple C^＊-algebras, and on their associated multiplier algebras.

The global oxygen budget and its future projection

Atmospheric oxygen （02） is the most crucial element on earth for the aerobic organisms that depend on it to release energy from carbon-based macromolecules. This is the first study to systematically analyze the global O2 budget and its changes over the past 100 years. It is found that anthropogenic fossil fuel combustion is the largest contributor to the current O2 deficit, which consumed 2.0 Gt/a in 1900 and has increased to 38.2 Gt/a by 2015. Under the Representative Concentration Pathways （RCPs） RCP8,5 scenario, approximately 100Gt （gigatonnes） of O2 would be removed from the atmosphere per year until 2100, and the O2 concentration will decrease from its current level of 20.946% to 20.825%. Human activities have caused irreversible decline of atmospheric O2. It is time to take actions to promote O2 production and reduce O2 consumption.

A TRANSFER FORECASTING MODEL FOR CONTAINER THROUGHPUT GUIDED BY DISCRETE PSO

Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.

A Jacobi-collocation method for solving second kind Fredholm integral equations with weakly singular kernels

In this work,we propose a Jacobi-collocation method to solve the second kind linear Fredholm integral equations with weakly singular kernels.Particularly,we consider the case when the underlying solutions are sufficiently smooth.In this case,the proposed method leads to a fully discrete linear system.We show that the fully discrete integral operator is stable in both infinite and weighted square norms.Furthermore,we establish that the approximate solution arrives at an optimal convergence order under the two norms.Finally,we give some numerical examples,which confirm the theoretical prediction of the exponential rate of convergence.