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The purpose of this paper is to improve active sonar detection performance in shallow water. A stochastic‐like model multivariate elliptically contoured (MEC) distributions is defined to model reverberation, which helps to reveal structure information of target signatures.

Active sonar systems have been developed with wider transmission bandwidths and larger aperture receiving array, which improve the signal‐to‐noise ratio and reverberation power ratio after matched filtering and beamforming. But, it has changed the statistical distribution of the reverberation‐induced envelope from the traditionally assumed Rayleigh distribution. The MEC is a kind of generalized non‐Gaussian distribution model. The authors theoretically derive the compound Gaussian, Rayleigh‐mixture, Weibull, K distributions are all special cases of MEC. It is known that Weibull and K distributions have obvious heavy‐tail than Rayleigh distribution. MEC is a suitable model to characterize non‐Rayleigh heavy‐tailed distribution of reverberation.

The analysis of test data shows reverberation envelopes obviously deviate Rayleigh distribution. In a broad non‐Gaussian framework, reverberation is modelled as MEC distribution, which is suitable to characterize non‐Rayleigh reverberation. The received data in trials validate the effectiveness of MEC model. The real data envelops follows K distribution, which is a special case of MEC. So, the MEC can be applied to develop novel signal‐processing algorithms that mitigate or account for the effects of the heavy‐tailed reverberation distributions on the target detection.

The limited sea test data are the main limitation to prove model validation in further.

A very useful model for representing reverberation in shallow‐water.

The MECs in fact represent an attractive set data model for adaptive array, and it provides a theoretic framework to design an optimal or sub‐optimal detector.

This study aims to provide a new method of multiscale directional Lyapunov exponents (MSDLE) calculated based on the state space reconstruction for the nonstationary time series, which can be applied to detect the small target covered by sea clutter.

Reconstructed state space is divided into non-overlapping submatrices whose columns are equal to a predetermined scale. The authors compute eigenvalues and eigenvectors of the covariance matrix of each submatrix and extract the principal components σ_ip and their corresponding eigenvectors. Then, the angles ψ_ip of eigenvectors between two successive submatrices were calculated. The curves of (σ_ip, ψ_ip) reflect the nonlinear dynamics both in kinetic and directional and form a spectrum with multiscale. The fluctuations of (σ_ip, ψ_ip), which are sensitive to the differences of backscatter between sea wave and target, are taken out as the features for the target detection.

The proposed method can reflect the local dynamics of sea clutter and the small target within sea clutter is easily detected. The test on the ice multiparameter imaging X-ban radar data and the comparison to K distribution based method illustrate the effectiveness of the proposed method.

The detection of a small target in sea clutter is a compelling issue, as the conventional statistical models cannot well describe the sea clutter on a larger timescale, and the methods based on statistics usually require the stationary sea clutter. It has been proven that sea clutter is nonlinear, nonstationary or cyclostationary and chaotic. The new method of MSDLE proposed in the paper can effectively and efficiently detect the small target covered by sea clutter, which can be also introduced and applied to military, aerospace and maritime fields.

H₂O, CO₂ and CO are three main species in combustion systems which have high volume fractions. In addition, soot has strong absorption in the infrared band. Thus, H₂O, CO₂, CO and soot may take important roles in radiative heat transfer. To provide calculations with high accuracy, all of the participating media should be considered non-gray media. Thus, the purpose of this paper is to study the effect of non-gray participating gases and soot on radiative heat transfer in an inhomogeneous and non-isothermal system.

To solve the radiative heat transfer, the fluid flow as well as the pressure, temperature and species distributions were first computed by FLUENT. The radiative properties of the participating media are calculated by the Statistical Narrow Band correlated K-distribution (SNBCK), which is based on the database of EM2C. The calculation of soot properties is based on the Mie scattering theory and Rayleigh theory. The radiative heat transfer is calculated by the discrete ordinate method (DOM).

Using SNBCK to calculate the radiative properties and DOM to calculate the radiative heat transfer, the influence of H₂O, CO₂, CO and soot on radiation heat flux to the wall in combustion system was studied. The results show that the global contribution of CO to the radiation heat flux on the wall in the kerosene furnace was about 2 per cent, but that it can reach up to 15 per cent in a solid fuel gasifier. The global contribution of soot to the radiation heat flux on the wall was 32 per cent. However, the scattering of soot has a tiny influence on radiation heat flux to the wall.

This is the first time H₂O, CO₂, CO and the scattering of soot were all considered simultaneously to study the radiation heat flux in combustion systems.

This paper seeks to review the literature on methods for solving the radiative transfer equation (RTE) and integrating the radiant energy quantities over the spectrum required to predict the flow, the flame and the thermal structures in chemically reacting and radiating combustion systems.

The focus is on methods that are fast and compatible with the numerical algorithms for solving the transport equations using the computational fluid dynamics techniques. In the methods discussed, the interaction of turbulence and radiation is ignored.

The overview is limited to four methods (differential approximation, discrete ordinates, discrete transfer, and finite volume) for predicting radiative transfer in multidimensional geometries that meet the desired requirements. Greater detail in the radiative transfer model is required to predict the local flame structure and transport quantities than the global (total) radiation heat transfer rate at the walls of the combustion chamber.

The RTE solution methods and integration of radiant energy quantities over the spectrum are assessed for combustion systems containing only the infra‐red radiating gases and gas particle mixtures. For strongly radiating (i.e. highly sooting) and turbulent flows the neglect of turbulence/radiation interaction may not be justified.

Methods of choice for solving the RTE and obtaining total radiant energy quantities for practical combustion devices are discussed.

The paper has identified relevant references that describe methods capable of accounting for radiative transfer to simulate processes arising in combustion systems.

The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S² control chart designed to monitor the sample variance of samples from a normally distributed population.

The proposed chart monitors a statistic computed as a logarithmic transformation of the sample variance; the introduction of the sample variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.

A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S² chart has been assessed through a comparison with an EWMA‐S² control chart proposed in the literature to monitor the process dispersion.

The paper only deals with uncorrelated normally distributed data.

The obtained results show how the CUSUM‐S² chart is particularly suitable when reduction in the process dispersion should be detected by means of subgroups having limited sample sizes.

The paper shows the new CUSUM‐S² control chart allows a decreasing of the variability to be detected faster than the corresponding EWMA‐S² control chart proposed earlier in the literature.

Because of new requirements related to the employment of lead‐free manufacturing and the diversity of components and metal finishes on high density printed circuit boards, better understanding and control of the reflow process is required in order to achieve acceptable yields and reliability of SMT assemblies. Accurate control of the temperature distributions within components and boards during the reflow process is one of the major requirements, especially in lead‐free assembly. This paper outlines a scheme for reflow modelling and presents an oven‐level model of the steady state flow‐field inside a reflow oven, which will be needed in subsequent transient analysis and small‐scale modelling. The model is constructed by utilising the advanced computational fluid dynamics (CFD) technology using commercial software. The computational results are discussed and compared with measured data.

The purpose of this study is twofold: first is to examine the nonlinear relationship between tourism development and housing prices, and secondly, the role of economic growth in facilitating the materialization of tourism development and housing prices nexus in G7 countries.

The authors used the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data from 1995 to 2018.

Results confirmed that the nexus between the tourism development and housing prices is nonlinear and regime dependent. Moreover, the results showed that the threshold level of economic growth above which tourism development increases the housing prices is 2.63%. The relationship above the threshold value is positive and growth enhancing, while below the threshold, tourism development has a negative effect on housing prices. The economic growth and housing prices also showed the U-shape relationship implying that at a certain level increase in economic growth decreases the housing prices but after a certain level increase in economic growth increases the housing prices.

This paper makes a unique contribution to the literature with reference to developed economies, being a pioneering attempt to investigate the nonlinear relationship between tourism development and housing prices and applying more rigorous and advanced econometric techniques like PSTR.

This paper studies which aggregate socio‐economic factors determine the long run entry and exit process of the retail sector at the provincial level by using a data set of 30 provinces over eight years (1985–1992). The following results are obtained: (1) All predictor variables, especially population growth rate, average wage rate and employees per store, have statistically significant effects on the entry and exit process. Per capita national income, store per capita, average wage, mobility are significant in accounting for the variations in sales per store. (2) The 1988 austerity programme only affected the short run variable, sales per store but not the long run variable, store per capita. This suggests that the short run recession induced by the austerity programme could not affect the long term trend of the retail sector.

Academicians and practitioners alike recognise that logistics services and operating systems are an integral part of the overall marketing strategy of firms. Consequently, there is a need for an ongoing, interactive review of actual logistics system performance, so that firms can achieve operating systems improvements as well as select and implement the most profitable corporate strategies.

The authors investigate the growth–inequality relationship, using panel data from 13 Southern African Development Community (SADC) countries over the period 1990–2015, to test the validity of the Kuznets and Tribble theories. Furthermore, the authors seek to determine the threshold level at which excessive growth hampers inequality.

The panel smooth transition regression (PSTR) model has several stages. The authors applied the Lagrange multiplier (LM) test to find the appropriate transition variable amongst all candidate variables, to assess the linearity between economic growth and income inequality and to find the sequence for selecting the order m of the transition function. The authors then estimated the PSTR model, but before facilitating the results, the authors first used the wild cluster bootstrap (WCB)–LM-type test to assess the appropriateness of the selected transition.

The authors found that at lower growth, income inequality tends to be lower, while if growth increases above US$8,969, inequality tends to increase in the SADC region. The findings combine into a U-shaped relationship, contradicting the Kuznets and Tribble theories.

The contribution of this paper is that it becomes the first to provide the threshold level at which excessive growth increases inequality in the selected countries. This study proposes that policymakers should focus on activities aimed at stimulating growth, in other words, activities such as spending more on infrastructure, drawing up a suitable investment portfolio and spending on technological investment for countries that are below US$8,969. An improvement in these activities will create job opportunities, which in turn will add to economic growth and thus lead to lower income inequality and better social cohesion.