Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis

In this paper, an optimized multivariate singular spectrum analysis (MSSA) approach is proposed to find leading indicators of cross‐industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French, and UK economies. Both recurrent and vector forecasting algorithms of horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate singular spectrum analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data are rigorously tested for normality, seasonal unit root hypothesis, and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that, for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r, which minimizes a loss function.     

Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models. We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. Copyright © 2012 John Wiley & Sons, Ltd.     

An Organizational Cybernetics Framework for Designing a Viable Higher Education System

Systemic Practice and Action Research - The Viable System Model (VSM) is an Organizational Cybernetics framework mainly used to handle systems complexity. The fundamental role of the VSM is to...     

Polyimides Based on Flexible Diamines:Synthesis, Characterization and Thermal Properties

1 Results and DiscussionTwo diamines 1,4-phenylene di(oxy-4,4′-aniline) and 4-aminophenyloxy-N-4-[(4-amiophenyloxy)benzylidene]aniline were prepared via the nucleopilic substitution reaction and were polymerized with 3,3′,4,4′-benzophenonetetracarboxylic acid dianhydride (BP), 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (HF) , 3,4,9,10-perylenetetracarboxylic acid dianhydride (PD) and pyromellitic dianhydride (PMDA) either by one step solution polymerization reaction or two step procedure. The l...     

Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

Improving the prediction accuracy of agricultural product futures prices is important for investors, agricultural producers, and policymakers. This is to evade risks and enable government departments to formulate appropriate agricultural regulations and policies. This study employs the ensemble empirical mode decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently, three models—support vector machine (SVM), neural network (NN), and autoregressive integrated moving average (ARIMA)—are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model is then compared with the benchmark individual models: SVM, NN, and ARIMA. Our main interest in this study is on short-term forecasting, and thus we only consider 1-day and 3-day forecast horizons. The results indicate that the prediction performance of the EEMD combined model is better than that of individual models, especially for the 3-day forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods in forecasting high-frequency volatile components. However, there is no obvious difference between individual models in predicting low-frequency components.     

Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

An artificial neural network and regression procedures were used to predict the recovery and collision probability of quartz flotation concentrate in different operational conditions. Flotation parameters, such as dimensionless numbers (Froude, Reynolds, and Weber), particle size, air flow rate, bubble diameter, and bubble rise velocity, were used as inputs to both methods. The linear regression method shows that the relationships between flotation parameters and the recovery and collision probability of flotation can achieve correlation coefficients (R2) of 0.54 and 0.87, respectively. A feed-forward artificial neural network with 3-3-3-2 arrangement is able to simultaneously estimate the recovery and collision probability as the outputs. In testing stages, the quite satisfactory correlation coefficient of 0.98 was achieved for both outputs. It shows that the proposed neural network models can be used to determine the most advantageous operational conditions for the expected recovery and collision probability in the froth flotation process.     

Investigation on the homogenization treatment and element segregation on the microstructure of a γ/γ′-cobalt-based superalloy

The aim of the present study was to investigate the effect of element segregation on the microstructure and γ′ phase in a γ/γ′ cobalt-based superalloy. Several samples were prepared from a cast alloy and homogenized at 1300°C for different times, with a maximum of 24 h. A microstructural study of the cast alloy using wavelength-dispersive spectroscopic analysis revealed that elements such as Al, Ti, and Ni segregated mostly within interdendritic regions, whereas W atoms were segregated within dendrite cores. With an increase in homogenization time, segregation decreased and the initial dendritic structure was eliminated. Field-emission scanning electron microscopy micrographs showed that the γ′ phases in the cores and interdendritic regions of the as-cast alloy were 392 and 124 nm, respectively. The size difference of γ′ was found to be due to the different segregation behaviors of constituent elements during solidification. After homogenization, particularly after 16 h, segregation decreased; thus, the size, chemical composition, and hardness of the precipitated γ′ phase was mostly uniform throughout the samples.     

Kinetic modeling of copper bioleaching from low-grade ore from the Shahrbabak Copper Complex

The copper recovery from low-grade copper sulfide ore was investigated using microbial leaching. Several parameters substantially affect the bioleaching of copper; among them, pulp density and nutrient media were selected for investigation. The optimum conditions for copper recovery were a pulp density of 5 g/mL, a mixed-mineral salt medium of Acidithiobacillus thiooxidans (70vol%) and Acidithiobacillus ferrooxidans (30vol%), and 10vol% of inoculum. Under these conditions, the maximum bioleaching capacity of the medium for copper recovery was determined to be approximately 99%. The effect of pulp density on the kinetics of the bioleaching process was surveyed using both da Silva's method and constrained multilinear regression analysis. The kinetics of copper dissolution followed the shrinking core model, and the process was diffusion controlled at a pulp density of 5 g/mL. Nevertheless, at higher pulp densities, the process was controlled by chemical reaction.     

采用超声组装与滚压工艺制备铝–碳纳米管复合材料

This study introduced a novel fabrication of aluminum–carbon nanotube (CNT) composites by employing bulk acoustic waves and accumulative roll bonding (ARB). In this method, CNT particles were aligned using ultrasonic standing wave in an aqueous media, and the arrayed particles were precipitated on the aluminum plate substrate. Then, the plates rolled on each other through the ARB process with four passes. Optical and scanning electron micrographs demonstrated the effective aligning of CNTs on the aluminum substrate with a negligible deviation of arrayed CNTs through the ARB process. The X-ray diffraction pattern of the developed composites showed no peaks for carbon and aluminum carbide. In addition, tensile tests showed that the longitudinal strength of the specimens processed with aligned CNTs was significantly greater than that of the specimens with common randomly dispersed particles. The proposed technique is beneficial for the fabrication of Al–CNT composites with directional mechanical strength.