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In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

The purpose of this paper is to predict and control the composition during laser additive manufacturing, since composition control is important for parts manufactured by laser additive manufacturing. Aluminum and steel functionally graded material (FGM) were manufactured by laser metal deposition, and the composition was analyzed by means of spectral analysis simultaneously.

The laser metal deposition process was carried out on a 5 mm thick 316L plate. Spectral line intensity ratio and plasma temperature were chosen as two main spectroscopic diagnosis parameters to predict the compositional variation. Single-trace single-layer experiments and single-trace multi-layer experiments were done, respectively, to test the feasibility of the spectral diagnosis method.

Experiment results showed that with the composition of metal powder changing from steel to aluminum, the spectral intensity ratio of the characteristic spectral line is proportional to the elemental content in the plasma. When the composition of deposition layers changed, the characteristic spectrum line intensity ratio changed obviously. And the linear chemical composition analysis results confirmed the gradient composition variation of the additive manufacturing parts. The results verified the feasibility of composition analysis based on spectral information in the laser additive manufacturing process.

The composition content of aluminum and steel FGM was diagnosed by spectral information during laser metal deposition, and the relationship between spectral intensity and composition was established.

POWER spectral methods for computing loads on aircraft due to atmospheric turbulence have been developed over the past dozen years or so. Such calculations are carried out on all new aircraft, and to date the results have been used to amplify fatigue and environmental data. The United States Federal Aviation Agency (FAA) is proposing the introduction of power spectral gust design procedures for and computing design limit loads on commercial aircraft. A parallel programme for the development of taxi load criteria is also being considered. The FAA have sponsored/research on the development of such power spectral gust methods and currently two approaches are being examined by industry. These are a design envelope approach and a mission analysis approach. A similar British investigation is now under way to establish the validity of the American proposals.

The dynamics of coupling between spectrum and resolvent under ε‐perturbations of operator and matrix spectra are studied both theoretically and numerically. The phenomenon of non‐trivial pseudospectra encountered in these dynamics is treated by relating information in the complex plane to the behaviour of operators and matrices. On a number of numerical results we show how an intrinsic blend of theory with symbolic and numerical computations can be used effectively for the analysis of spectral problems arising from engineering applications.

Understanding cyclical activity is an important component of efficient portfolio management. Property appraisal models that do not explicitly take into account cyclical fluctuations may produce unrealistic valuation estimates resulting in property assets being incorrectly added to or removed from the general investment portfolio. In this paper we use conventional spectral analysis techniques to examine property and financial assets for evidence of cycles and co‐cycles. One finding is that the very pronounced cyclical patterns that appear in direct real estate markets and the economy as a whole are very much less obvious once they have filtered through to securitised property markets and financial assets markets.

The transient simulation of integrated circuit has become very expensive in terms of computer time due to increase in the number of transistors in typical simulation. Spectral technique and Chebyshev polynomials offers an efficient alternative algorithm for simulation of integrated circuits. In this paper an automatic formulation of circuit elements and transistor models, built in MOS technology, for analysis using spectral technique is presented. The algorithm is implemented and the simulation is proven to require less computer time than in the case of SPICE or ASTAP

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R² = 0.924, and the average relative error is 6.608%.

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

The purpose of this paper is to investigate the cross-spectra of stock, real estate and bond of ten selected Asian economies in the pre- and post-global financial crisis periods to detect whether there is greater cyclical co-movement post-financial crisis, and whether any observed increased co-movement measures the outcomes of contagion or integration.

Co-spectral approach is the proper econometric tool to deliver economic insight for this research.

Results indicate that Asian stock markets, and to a lesser degree, bond and real estate markets are more correlated post-financial crisis. Similarly, Asian financial markets have experienced increased co-movements with the US financial markets post-financial crisis. Moreover, these observed increased co-movements measure the outcomes of contagion in some cases of within-asset and cross-asset classes, as well as for some cross-US-Asian asset factor relationships along the high-frequency components of between two and four weeks. The stock markets are the most contagious, followed by the real estate markets and bond markets.

The results provide short-term investors with additional co-movement information at higher frequencies in order to identify short-term fluctuations of different asset classes. The empirical study also underscores the role of Asian real estate in investment portfolios in a mixed real estate, stock and bond context from a frequency domain perspective.

The practical implication of this research is that benefits to investors from international diversification may not be as great during the present time compared to previous periods because financial/asset market movements have become more correlated. However, it does not imply the complete absence of diversification benefits. This is because although cyclical correlations increase in the short run, many of the values are still between low and moderate range, indicating that some diversification benefits may still be realized.

In advancing the body of knowledge in international financial markets, this research is probably the first study to consider a multi-asset class portfolio context that includes stock, real estate and bond across the ten Asian economies and the USA in a single study. The frequency domain analysis conducted in this paper adds to the understanding of real estate, stock and bond market co-movement, integration and contagion dynamics, as well as the Asian cross-asset factor and US-Asian asset factor relationships in global mixed-investing environment.

The purpose of this paper is to examine whether the banking crisis in the USA and Western Europe that began in August 2007 precipitated a change in the relationship between the currencies of the Baltic States and the Euro, such that it could be described as shift contagion. The paper also considers whether the “hardness” of the currency peg affects the market reaction to that crisis.

Shift contagion is said to be revealed if there a change in the co‐movements of exchange rates after August 2007 compared with before. Change is revealed by coherence and phase shifts. Both are drawn from cross‐spectral analysis.

Rather than weaken, the bonds between the currency board‐managed Kroon and the Litas, in a similar way to the Lat, exhibited greater bonding after the banking crisis began compared with before. The phase angles suggest some shift in money flows between the Baltic currencies and the Euro. With the Lat, the delays appear to be the same but at longer periodicies. The other two appear be subject to a reversal of money flows at various periodicies.

Spectral analysis reveals that bonding between currencies of ERMII countries and the Euro increased, but the structure of money flows changed as a result of the Western banking crisis in related geographical and financial markets, before a local crisis became evident. To what extent this is an improvement over correlation methods could be the basis of further research. The phase switch is a structural change that other techniques could not have revealed.

The paper shows that spectral analysis could be more widely used in financial economics to reveal the impact of events on term structures.