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To forecast the path of tropic cyclones by using a non‐linear statistical forecasting technique – the method of successive analogy.

Non‐linear statistical forecasting models can describe the non‐linear relationship between the factors and the forecasting objects and the real atmospheric movement more accurately, so they usually have stronger forecasting capability. In practice, however, it is shown that the relationships between predictors and predictands sometimes are so complex that it is very difficult or even impossible to establish the kind of non‐linear mathematical model. Therefore, it is an important topic for atmospheric science to solve non‐linear prediction problem of atmospheric systems by using the non‐function model approach.

The objective and quantitative prediction of tropical cyclone moving path can be given by using the method of successive analogy, a non‐linear forecasting technique, and calculating the similarity parameters between the grayscale field and the height field.

Further experiments are needed to verify this technique.

A very useful technique for solving non‐linear problem.

Illustrates the new technique of solving non‐linear statistic problem and its application.

The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.

This study uses a full-flow field transient calculation method of mixed-flow pump based on a closed-loop model.

The findings show the hydraulic losses and internal flow characteristics of the piping system during the start-up process.

Large computational cost.

Improve the accuracy of current numerical simulation results in transient process of mixed-flow pump.

Simplify the setting of boundary conditions in the transient calculation.

The paper aims to evaluate the influence of atmospheric environment on the conductivity of nanoscale copper films sputtered on polyester substrates; process parameters of optimal conductivity were firstly analyzed by orthogonal test scheme design, and then the surface morphology, crystal structures and conductivity of samples were performed after samples were placed in the atmospheric conditions for some time according to the optimization of process parameters.

Nanoscale copper films was prepared by RF (radio frequency) magnetron sputtering and low-temperature plasma technology with polyester fabrics as substrates and metal copper as targets under the conditions of low temperature and high vacuum.

The experimental results showed that copper films were broken and the continuity of samples was destroyed after 60 days, while exposed in atmospheric environment for 90 days, cracks of copper films gradually expanded, there was no change in the atomic species for samples placed in the atmospheric conditions. However, the conductivity of the samples hardly had changed with the ambient temperature, humidity and degree of water washing, which is mainly decided by the internal structures of substrates.

This paper has some theoretical and applicable value to the functional textile.

This study aims to systematically review the literature in the fields of liquidity, informational efficiency and default risk. The authors outline the key research streams and provide possible pathways for future research.

The study adopts bibliographic mapping to identify the most influential studies in the research fields of liquidity, informational efficiency and default risk from 1984 to 2021.

The study identifies four key research themes that include efficiency and transparency of markets; corporate yield spreads; market interactions: bonds, stocks and cryptocurrencies; and corporate governance. By assessing publications published from 2018 to 2021, the authors also document seven key emerging research trends: cross markets, managerial learning and corporate governance, state ownership and government subsidies, international evidence, machine learning (FinTech approaches), environmental themes and financial crisis. Drawing on these emerging trends, the authors highlight the opportunities for future research.

Keyword searches have limitations since some studies might be overlooked if they do not match the specified search criteria, even though their relevance to the topic is under investigation. Adopt the R project to expand this review by incorporating more literature from other databases, such as the Scopus database could be a possible solution.

The four key research streams contribute to a comprehensive understanding of liquidity, informational efficiency and default risk. The emerging trends integrate existing knowledge and leave the chance for innovative research to expand the research frontier.

This study fulfills the systematic literature review streams in the fields of liquidity, informational efficiency and default risk, and provides fruitful opportunities for future research.

The purpose of this paper is to propose a new method based on nonlinear least squares (NLS) for solving the parameters of nonlinear grey Bernoulli model (NGBM(1,1)) and to verify the proposed model using the case of employee demand prediction of high-tech enterprises in China.

First of all, minimising the square sum of fitting error of grey differential equation of NGBM(1,1) is taken as the optimisation target and the parameters of classic grey model (GM(1,1)) are set as the initial value of parameter vector. Afterwards, the structural parameters and power exponents are solved by using the Gauss-Newton iteration algorithm so as to calculate the parameters of NGBM(1,1) under given rules for ceasing the algorithm. Finally, by taking the employee demand of high-tech enterprises in the state-level high-tech industrial development zone in China as examples, the validity of the new method is verified.

The results show that the parameter estimation algorithm based on the NLS method can effectively identify the power exponents of NGBM(1,1) and therefore can favourably adapt to the nonlinear fluctuations of sequences. In addition, the algorithm is superior to the GM(1,1) model, grey Verhulst model, and Quadratic-Exponential smoothing algorithm in terms of the simulation and prediction accuracy.

Under the framework of solving parameters based on NLS, various aspects of NGBM(1,1) remain to be further investigated including background value, initial condition and variable structural modelling methods.

The parameter estimation algorithm based on NLS can effectively identify the power exponent of NGBM(1,1) and therefore it can favourably adapt to the nonlinear fluctuation of sequences.

According to the basic principle of NLS, a new method for solving the parameters of NGBM(1,1) is proposed by using the Gauss-Newton iteration algorithm. Moreover, by conducting the modelling case about employees demand in high-tech enterprises in China, the effectiveness and superiority of the new method are verified.

The purpose of this paper is to deal with the economic requirements of power system loading dispatch and reduce the fuel cost of generation units. In order to optimize the scheduling of power load, an improved chicken swarm optimization (ICSO) is proposed to be adopted, for solving economic load dispatch (ELD) problem.

The ICSO increased the self-foraging factor to the chicks whose activities were the highest. And the evolutionary operations of chicks capturing the rooster food were increased. Therefore, these helped the ICSO to jump out of the local extreme traps and obtain the global optimal solution. In this study, the generation capacity of the generation unit is regarded as a variable, and the fuel cost is regarded as the objective function. The particle swarm optimization (PSO), chicken swarm optimization (CSO), and ICSO were used to optimize the fuel cost of three different test systems.

The result showed that the convergence speed, global search ability, and total fuel cost of the ICSO were better than those of PSO and CSO under different test systems. The non-linearity of the input and output of the generating unit satisfied the equality constraints; the average ratio of the optimal solution obtained by PSO, CSO, and ICSO was 1:0.999994:0.999988. The result also presented the equality and inequality constraints; the average ratio of the optimal solution was 1:0.997200:0.996033. The third test system took the non-linearity of the input and output of the generating unit that satisfied both equality and inequality constraints; the average ratio was 1:0.995968:0.993564.

This study realizes the whole fuel cost minimization in which various types of intelligent algorithms have been applied to the field of load economic scheduling. With the continuous evolution of intelligent algorithms, they save a lot of fuel cost for the ELD problem.

The ICSO is applied to solve the ELD problem. The quality of the optimal solution and the convergence speed of ICSO are better than that of CSO and PSO. Compared with PSO and CSO, ICSO can dispatch the generator more reasonably, thus saving the fuel cost. This will help the power sector to achieve greater economic benefits. Hence, the ICSO has good performance and significant effectiveness in solving the ELD problem.

The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system.

A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets.

The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance.

Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

This paper aims to present a survey concerning the accuracy of thermoplastic polymeric parts fabricated by additive manufacturing (AM). Based on the scientific literature, the aim is to provide an updated map of trends and gaps in this relevant research field. Several technologies and investigation methods are examined, thus giving an overview and analysis of the growing body of research.

Permutations of keywords, which concern materials, technologies and the accuracy of thermoplastic polymeric parts fabricated by AM, are used for a systematic search in peer-review databases. The selected articles are screened and ranked to identify those that are more relevant. A bibliometric analysis is performed based on investigated materials and applied technologies of published papers. Finally, each paper is categorised and discussed by considering the implemented research methods.

The interest in the accuracy of additively manufactured thermoplastics is increasing. The principal sources of inaccuracies are those shrinkages occurring during part solidification. The analysis of the research methods shows a predominance of empirical approaches. Due to the experimental context, those achievements have consequently limited applicability. Analytical and numerical models, which generally require huge computational costs when applied to complex products, are also numerous and are investigated in detail. Several articles deal with artificial intelligence tools and are gaining more and more attention.

The cross-technology survey on the accuracy issue highlights the common critical aspects of thermoplastics transformed by AM. An updated map of the recent research literature is achieved. The analysis shows the advantages and limitations of different research methods in this field, providing an overview of research trends and gaps.

The purpose of this paper is to develop a methodology to analyze the total sintering energy (TSE) required for manufacturing a part in metal powder-based additive manufacturing (AM) processes and optimize AM processes for minimizing total energy and form errors of AM parts while maximizing part strength.

The paper uses a computational geometry approach to determine the TSE expended for manufacturing a metal AM part. The stereolithography (STL) file of a part is converted into a voxel data structure and the total sintering volume (TSV) is computed from the voxel representation. The TSE is then calculated from the TSV using the material property information of the metal powder.

The TSE of an AM part is calculated for different slice thickness and part orientations, and the correlation of the total energy to these parameters is calculated. Using these correlations, the AM process is optimized to calculate the optimal values of slice thickness and part orientation which would result in lower process energy, lower part form errors and higher part strength.

The methodology presented in this paper provides AM users a roadmap to predict the energy required for manufacturing a part. In addition, the optimization model will allow engineers to manufacture precision parts which satisfy their design specifications with minimal energy expenditure.

The purpose of this paper is to provide a better understanding of process parameters that have a significant effect on the shrinkage behaviour of laser-sintered PA 3200GF specimens.

A five-factor, three-level and face-centred central composite design was used to collect data, and two methods, namely, response surface methodology (RSM) and artificial neural network (ANN) were used for predicting shrinkage. Sensitivity analysis based on the developed empirical equations has been carried out to determine the most significant parameter, which contributes the most to control shrinkage. In addition, a comparative analysis has also been performed for the results obtained by RSM and ANN.

The results revealed that part bed temperature, scan speed and scan spacing are the three dominant parameters, which have a great influence on shrinkage. Strong interactions between laser power-scan spacing, laser power-scan length and scan speed-scan spacing have been observed. Through sensitive analysis, it is observed that shrinkage is more sensitive to the scan speed variations than other four process parameters.

This study can be used as a guide, and the demonstrated results will provide a good technical database to the different additive manufacturing users of various industries such as automobile, aerospace and medical.

To the best of the authors’ knowledge, this is the first study to report the shrinkage behaviour of laser-sintered PA 3200GF parts fabricated under different sintering conditions.