An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method

The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method.

An Intuitive Electric-field Contribution Decomposition Model for Chemical Processes and Its Applications on Diels-Alder Reactions

External electric field(EEF)has shown its advantages in tuning chemical reaction as an efficient and fea-sible-to-control tool.In this paper,we explored the mechanisms of three EEF-rerulated Diels-Alder reactions in-cluding two traditional-DA reactions to form two C-C single bonds and a hetero-DA reaction to form both a c and a CO bond,respectively,and introduced an EEF contribution decomposition(ECD)model to understand how the EEF coupled with the intrinsic nuclear and electronic redistributions so as to affect chemical reaction.The ECD model,by decomposing the overall EEF effects into geometry re-equilibrium and static induction parts,can give a clear and quantitative picture of a physical quantity change upon EEF,as demonstrated on relative energies,activa-tion barriers,charge distribution and dipole moments.The ECD analvses will shed light on the effective tuning of chemical reactions by the elestric field.

Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model

COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world.Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases.In this study,we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases,deaths,and recoveries in Pakistan for the upcoming month until the end of July.For the decomposition of data,the Ensemble Empirical Mode Decomposition(EEMD)technique is applied.EEMD decomposes the data into small components,called Intrinsic Mode Functions(IMFs).For individual IMFs modelling,we use the Autoregressive Integrated Moving Average(ARIMA)model.The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates.Our analyses reveal that the number of recoveries,new cases,and deaths are increasing in Pakistan exponentially.Based on the selected EEMD-ARIMA model,the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020,which is an increase of almost 1.46 times with a 95%prediction interval of 246,529 to 376,379.The 95%prediction interval for recovery is 162,414 to 224,579,with an increase of almost two times in total from 100802 to 193495 by 31 July 2020.On the other hand,the deaths are expected to increase from 4395 to 6751,which is almost 1.54 times,with a 95%prediction interval of 5617 to 7885.Thus,the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020.They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19,and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios.The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.

Research on Qualitative Model Decomposition

Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.

Temporal shifts in the explanatory power and relative importance of litter traits in regulating litter decomposition

Background:Litter traits critically affect litter decomposition from local to global scales.However,our understanding of the temporal dynamics of litter trait-decomposition linkages,especially their dependence on plant functional type remains limited.Methods:We decomposed the leaf litter of 203 tree species that belong to two different functional types(deciduous and evergreen)for 2 years in a subtropical forest in China.The Weibull residence model was used to describe the different stages of litter decomposition by calculating the time to 10%,25%and 50%mass loss(Weibull t_(1/10),t_(1/4),and t_(1/2)respectively)and litter mean residence time(Weibull MRT).The resulting model parameters were used to explore the control of litter traits(e.g.,N,P,condensed tannins and tensile strength)over leaf litter decomposition across different decomposition stages.Results:The litter traits we measured had lower explanatory power for the early stages(Weibull t_(1/10)and t_(1/4))than for the later stages(Weibull t_(1/2)and MRT)of decomposition.The relative importance of different types of litter traits in influencing decomposition changed dramatically during decomposition,with physical traits exerting predominant control for the stages of Weibull t_(1/10)and MRT and nutrient-related traits for the stages of Weibull t_(1/4),and t_(1/2).Moreover,we found that litter decomposition of the early three stages(Weibull t_(1/10),t_(1/4),and t_(1/2))of the two functional types was controlled by different types of litter traits;that is,the litter decomposition rates of deciduous species were predominately controlled by nutrient-related traits,while the litter decomposition rates of evergreen species were mainly controlled by carbon-related traits.Conclusions:This study suggests that litter trait-decomposition linkages vary with decomposition stages and are strongly mediated by plant functional type,highlighting the necessity to consider their temporal dynamics and plant functional types for improving predictions of litter decomposition.

Comparison of Two Time Series Decomposition Methods: Least Squares and Buys-Ballot Methods

This paper discusses comparison of two time series decomposition methods: The Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. As noted by Iwueze and Nwogu (2014), there exists a research gap for the choice of appropriate model for decomposition and detection of presence of seasonal effect in a series model. Estimates of trend parameters and seasonal indices are all that are needed to fill the research gap. However, these estimates are obtainable through the Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. Hence, there is need to compare estimates of the two methods and recommend. The comparison of the two methods is done using the Accuracy Measures (Mean Error (ME)), Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). The results from simulated series show that for the additive model;the summary statistics (ME, MSE and MAE) for the two estimation methods and for all the selected trending curves are equal in all the simulations both in magnitude and direction. For the multiplicative model, results show that when a series is dominated by trend, the estimates of the parameters by both methods become less precise and differ more widely from each other. However, if conditions for successful transformation (using the logarithmic transform in linearizing the multiplicative model to additive model) are met, both of them give similar results.

Identification of Model Parameters of Vaporized Hydrogen Peroxide Decomposi-tion Flux on Building Materials for Compu-tational Fluid Dynamics

To maintain healthy and sanitary indoor air quality, development of effective decontamination measures for the indoor environment is important and hydrogen peroxide is often used as decontamination agent in healthcare environment. In this study, we focused on the decomposition phenomena of vaporized hydrogen peroxide on wall surfaces in indoor environment and discussed a wall surface decomposition model for vaporized hydrogen peroxide using computational fluid dynamics to simulate the concentration distributions of vaporized hydrogen peroxide. A major drawback to using numerical simulations is the lack of sufficient data on boundary conditions for various types of building materials and hence. We also conducted the fundamental chamber experiment to identify the model parameters of wall surface decomposition model for targeting five types of building materials.

Improvement of the prediction accuracy of polar motion using empirical mode decomposition

Previous studies revealed that the error of pole coordinate prediction will significantly increase for a prediction period longer than 100 days, and this is mainly caused by short period oscillations. Empirical mode decomposition （EMD）, which is increasingly popular and has advantages over classical wavelet decomposition, can be used to remove short period variations from observed time series of pole co- ordinates. A hybrid model combing EMD and extreme learning machine （ELM）, where high frequency signals are removed and processed time series is then modeled and predicted, is summarized in this paper. The prediction performance of the hybrid model is compared with that of the ELM-only method created from original time series. The results show that the proposed hybrid model outperforms the pure ELM method for both short-term and long-term prediction of pole coordinates. The improvement of prediction accuracy up to 360 days in the future is found to be 24.91% and 26.79% on average in terms of mean absolute error （MAE） for the xp and yp components of pole coordinates, respectively.

A new approach for separating mixed model parameters:application to simultaneous inversion of earthquake source parameters

A method for simultaneous determination of mixed model parameters,which have different physical dimensions or different responses to data,is presented.Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system,the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the tradeoffs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history,source geometry,and hypocentral location from regional seismograms,although it is applicable to any geophysical inversion.

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

Carbon forestry is surprising

Background： Forestry offers possibilities to sequestrate carbon in living biomass, deadwood and forest soil, as we as in products prepared of wood. In addition, the use of wood may reduce carbon emissions from fossil fuels. However, harvesting decreases the carbon stocks of forests and increases emissions from decomposing harvest residues. Methods： This study used simulation and optimization to maximize carbon sequestration in a boreal forest estate consisting of nearly 600 stands. A reference management plan maximized net present value and the other plans maximized the total carbon balance of a 100-, 200- or 300-year planning horizon, taking into account the carbon balances of living forest biomass, dead organic matter, and wood-based products Results： Maximizing carbon balance led to low cutting level with all three planning horizons. Depending on the time span, the carbon balance of these schedules was 2 to 3.5 times higher than in the plan that maximized net present value. It was not optimal to commence cuttings when the carbon pool of living biomass and dead organic matter stopped increasing after 150-200 years. Conclusions： Letting many mature trees to die was a better strategy than harvesting them when the aim was to maximize the long-term carbon balance of boreal Fennoscandian forest. The reason for this conclusion was that large dead trees are better carbon stores than harvested trees. To alter this outcome, a higher proportion of harvested trees should be used for products in which carbon is stored for long time.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

DFT Calculations and NBO Analysis of 2-Chloroethylethyldichlorosilane Unimolecular Elimination Kinetics in the Gas Phase

Ab initio molecular orbital calculations have been used to investigate the thermal decomposition kinetics of 2-chloroethylethyldichlorosilane at the B3LYP/6-311＋G^＊＊,B3PW91/6-311＋G^＊＊,and MPW1PW91/6-311＋G^＊＊ levels of theory.Among these methods,the results（activation parameters） obtained using the B3LYP/6-311＋G＊＊ level are in good agreement with the available experimental data.The calculated data imply that in the unimolecular β-elimination reactions of the studied compound in the gas phase,the polarization of C（1）-Cl（3） and C（1）-H（4） bonds in the sense of C（1）^δ＋-Cl（3）^δ-and C（1）^δ＋-H（4）^δ-,respectively,is a determining factor in the gas phase elimination reactions 1,2 and 3.Analysis of bond order,natural bond orbital charges,bond indexes,synchro-nicity parameters,and IRC calculations suggest the elimination of 2-chloroethylethyldichlorosilane via reactions 1～3 can be described as concerted and slightly asynchronous.The transition state structures of these reactions are a four-membered cyclic structure.

Multi-User Collaborative Assembly Planning over the Internet

Due to the increasing complexity of products and for the distributed product development, more closely collaborative work among designers is required. A collaborative assembly planning approach is proposed to support assembly planning in a networked environment. The working procedure is depicted and the key techniques including collaborative-planning-oriented assembly decomposition modeling, assembly assignment modeling, and sub-plans merging are addressed. By incorporating visual models at client side with assembly application models at server side, a web-based supporting environment for collaborative assembly planning has been developed using VRML and Java-EAI techniques. A case study is given to illustrate the feasibility and validity of the idea.

No-Load Iron Loss Model for a Fractional-Slot Surface-Mounted Permanent Magnet Motor Based on Magnetic Field Analytical Calculation

The common analytical models for the no-load iron loss of permanent magnet(PM)motors usually neglect the iron loss caused by the rotating magnetic field in the tooth tips and the harmonics of the magnetic fields in the teeth and yokes.This paper presents an analytical model for no-load iron loss of a fractional-slot surface-mounted permanent magnet motor.According to the existing analytical model of the magnetic field distribution in the slotted air gap,the magnetic flux densities considering the harmonics of the stator tooth and yoke are both derived based on the continuity of magnetic flux.Due to the complexity of the magnetic field in the tooth tip,the tangential flux density of the tooth tip is approximated by an equivalent sine wave and the radial component is regarded to be the same as that of the corresponding tooth.After obtaining the magnetic fields in stator different regions,the analytical iron loss is calculated by using the Bertotti model and the orthogonal decomposition model.A 20-pole/24-slot PM synchronous motor is taken as an example.The maximum error between the analytical model and finite element model(FEM)is 5.46%,which verifies the validity of the proposed method.

An ADMM-based parallel algorithm for solving traffic assignment problem with elastic demand

Efficiently solving the user equilibrium traffic assignment problem with elastic demand(UE-TAPED)for transportation networks is a critical problem for transportation studies.Most existing UE-TAPED algorithms are designed using a sequential computing scheme,which cannot take advantage of advanced parallel computing power.Therefore,this study focuses on model decomposition and parallelization,proposing an origin-based formulation for UE-TAPED and proving an equivalent reformulation of the original problem.Furthermore,the alternative direction method of multipliers(ADMM)is employed to decompose the original problem into independent link-based subproblems,which can solve large-scale problems with small storage space.In addition,to enhance the efficiency of our algorithm,the parallel computing technology with optimal parallel computing schedule is implemented to solve the link-based subproblems.Numerical experiments are performed to validate the computation efficiency of the proposed parallel algorithm.

The driving effects of urbanization on economic growth and water use change in China： A provincial-level analysis in 1997-2011

As one of the key issues in China＇s sustainable development, rapid urbanization and continuous economic growth are accompanied by a steady increase of water consump- tion and a severe urban water crisis. A better understanding of the relationship among ur- banization, economic growth and water use change is necessary for Chinese decision mak- ers at various levels to address the positive and negative effects of urbanization. Thus, we established a complete decomposition model to quantify the driving effects of urbanization on economic growth and water use change for China and its 31 provincial administrative regions during the period of 1997-2011. The results show that, （1） China＇s urbanization only contrib- uted about 30% of the economic growth. Therefore, such idea as urbanization is the major driving force of economic growth may be weakened. （2） China＇s urbanization increased 2352×10^8 m3 of water use by increasing the economic aggregate. However, it decreased 4530×10^8 m3 of water use by optimizing the industrial structure and improving the water use efficiency. Therefore, such idea as urbanization is the major driving force of water demand growth may be reacquainted. （3） Urbanization usually made greater contribution to economic and water use growth in the provincial administrative regions in east and central China, which had larger population and economic aggregate and stepped into the accelerating period of urbanization. However, it also made greater contribution to industrial structure optimization and water use efficiency improvement, and then largely decreased total water use. In total, urbanization had negative effects on water use growth in most provincial administrative re- gions in China, and the spatiotemporal differences among them were lessened on the whole. （4） Though urbanization helps to decrease water use for China and most provincial adminis- trative regions, it may cause water crisis in urban built-up areas or urban agglomerations. Therefore, China should construct the water transfer and compensation mechanisms be- tween urban and rural areas, or low and high density urban areas as soon as possible.

Impact of Embeddedness in Global Value Chains on the Differentiation of Technological Sophistication of Exports

This paper adopts the production decomposition developed by Wang et al.(2017)and data from the World Input-Output Database(WIOD)to estimate the degrees of forward and backward participation in global value chains(GVCs)in 2000-2014 by the world’s major economies including China,and to do an empirical examination on the impact that heterogeneous forms of participation in GVCs have on the improvement of GVCs.The results show that forward participation in GVCs helps increase the sophistication of exports,while backward participation in GVCs exerts different infl uence on the sophistication of exports.While a lower level of backward participation by a country is constrained by the country’s current position in the international division of labor and thus does not help increase the sophistication of its exports,a higher level of backward participation helps break through the bottleneck of low-end locking in GVCs and increase the sophistication of exports.