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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 use the local correlation technique to measure flight to quality, which is defined as a pronounced and generally rapid increase in risk aversion. Flight to quality between American, British, German, Japanese, and Hong Kong spot equity indices and index futures is examined.

The technique of non‐linear local correlation is employed to detect flight to quality in both spot and futures markets. The use of this methodology allows us to properly process both normally or non‐normally distributed time series. In addition, the estimation of local correlation minimizes the theoretical restrictions resulting from the selection of conditional events and the use of linear regression.

As market risk grows, an increase in flight to quality is documented. For example, a crash in the US stock market results in the flight of capital to the Treasury bond market. Evidence of flight to quality from domestic and foreign spot equity markets to US Treasury bonds is provided. Furthermore, flights to quality from domestic and foreign index futures to US bond futures are revealed. The strength of the reaction from one market to the other is measured and reported. Surprisingly, the authors observe that when market risk becomes extremely high, flight to quality diminishes.

To the best of the authors' knowledge, this is the first study that examines flight to quality in the futures markets by applying local correlation analysis. This study broadens the application of local polynomial regression and local correlation analysis.

This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy.

Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA.

Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally.

To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

The purpose of this paper is to assess price linkages and patterns of transmission among producer and consumer markets for apple in Slovenia.

Non-linear error correction models are applied. Non-linearities are allowed by means of threshold and multivariate local linear regression estimation techniques. Monthly prices over the period 2000-2011 are used in the empirical application.

Both techniques provide evidence of non-linearities in price adjustments. Findings suggest that producer and consumer prices tend to increase rather than decrease. Results also indicate that parametric threshold approaches may have difficulties in adequately representing price behavior dynamics.

The main contribution of this work to the literature relies on the fact that this is the first attempt to assess vertical price transmission in the apple sector in Central and Eastern European Country markets. Further, it is the first attempt to use multivariate local linear regression techniques in this context.

The purpose of this paper is to propose and analyze the use of local surrogate models to improve differential evolution’s (DE) overall performance in computationally expensive problems.

DE is a popular metaheuristic to solve optimization problems with several variants available in the literature. Here, the offspring are generated by means of different variants, and only the best one, according to the surrogate model, is evaluated by the simulator. The problem of weight minimization of truss structures is used to assess DE’s performance when different metamodels are used. The surrogate-assisted DE techniques proposed here are also compared to common DE variants. Six different structural optimization problems are studied involving continuous as well as discrete sizing design variables.

The use of a local, similarity-based, surrogate model improves the relative performance of DE for most test-problems, specially when using r-nearest neighbors with r = 0.001 and a DE parameter F = 0.7.

The proposed methods have no limitations and can be applied to solve constrained optimization problems in general, and structural ones in particular.

The proposed techniques can be used to solve real-world problems in engineering. Also, the performance of the proposals is examined using structural engineering problems.

The main contributions of this work are to introduce and to evaluate additional local surrogate models; to evaluate the effect of the value of DE’s parameter F (which scales the differences between components of candidate solutions) upon each surrogate model; and to perform a more complete set of experiments covering continuous as well as discrete design variables.

The purpose of this paper is to investigate for symmetries – in sign and size – between spot and futures prices in the markets of energy commodities.

The aforementioned objective is pursued using daily observations of spot and futures prices for the commodities of crude oil, Brent, heating oil, gasoline and natural gas, along with local nonlinear regression.

Symmetry in sign and size cannot be rejected. This means that, shocks of the same absolute magnitude, but of different sign, are transmitted from futures prices to spot prices with the same intensity. In addition, larger absolute value price shocks in the futures are transmitted to the spot markets with the same intensity compared with smaller ones. The findings of symmetry in the comovements among prices reveal a lack of those commodities on diversifying the investors’ investment risk.

To the best of the authors’ knowledge, this is the first study to use local nonlinear regression to test for sign and size symmetry between futures and spot prices in the energy commodities markets.

The purpose of this study is to determine the main total quality management (TQM) factors adopted and the respective results achieved by local authorities – municipalities. Determining the degree to which TQM influences the respective results is also an aim of the present study.

A research study was designed to be carried out in Greek local authorities – municipalities, 125 of which were approached through interviews with chief executive officers (CEOs) based on a structured questionnaire. The TQM practices and results identified in the literature were used as measured variables of the questionnaire. Descriptive statistics and linear regression analyses were applied for the purpose of the study.

The TQM philosophy is not highly adopted by the Greek local authorities participating in the present study. Moreover, the Greek local authorities do not seem to derive significant TQM benefits. Finally, adopting TQM strongly and positively influences, first, operational and quality performance of the local authorities; second, citizen satisfaction and society results; and third, employee satisfaction.

Subjective data were collected from the CEOs of a small sample of local authorities operating in a European Union country, under circumstances of financial crisis. Based on these limitations, future research studies are recommended.

Areas for improvement are identified for a local authority to develop a robust TQM model, approach business excellence and derive significant benefits.

This is the first research study in the field of TQM to be conducted at city-level administration in Greece.

The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA.

Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing.

The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works.

The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

The purpose of this paper is to examine the effects of school attendance on learning and child labour in Ghana.

The paper uses a nationally representative sample of household and individual data in 2005/06 and 2011/12 for the analysis. Regression discontinuity, the capitation grant in 2005 as exogenous, is used to estimate the impact of school attendance on child labour and learning outcomes.

The study found that children who were exposed to the capitation grant spent more hours in school and were more likely to enrol in primary school. School attendance was found to increase the likelihood to read and write a standardised test in English. Also, the improvement in children’s school attendance was found to enhance the likelihood of performing a written calculation. The authors could not find any evidence that school attendance affected child labour.

This research is the first causality analysis in sub-Saharan Africa that uses a nationally representative dataset to study the impact of school attendance on child labour and learning outcomes using a regression discontinuity estimator to deal with endogeneity issues.

The purpose of this paper is to present an exploration of multiple slips and temperature dependent thermal conductivity effects on the flow of nano Williamson fluid over a slendering stretching plate in the presence of Joule and viscous heating aspects. The effectiveness of nanoparticles is deliberated by considering Brownian moment and thermophoresis slip mechanisms. The effects of magnetism and radiative heat are also deployed.

The governing partial differential equations are non-dimensionalized and reduced to multi-degree ordinary differential equations via suitable similarity variables. The subsequent non-linear problem treated for numerical results. To measure the amount of increase/decrease in skin friction coefficient, Nusselt number and Sherwood number, the slope of linear regression line through the data points are calculated. Statistical approach is implemented to analyze the heat transfer rate.

The results show that temperature distribution across the flow decreases with thermal conductivity parameter. The maximum friction factor is ascertained at stronger magnetic field.

In the current paper, the magneto-nano Williamson fluid flow inspired by a stretching sheet of variable thickness is examined numerically. The rationale of the present study is to generalize the studies of Mebarek-Oudina and Makinde (2018) and Williamson (1929).