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This paper aims to model the aerodynamic flow characteristics of NACA0010 for various angle of attacks including stall for incompressible flows using panel methods. This paper also aims to quantify the surface pressure distribution on streamlined bodies and validate the results with analytical Jukouwski method and inverse panel methods that can predict the aerodynamic flow behaviour using the geometric iteration approach.

The 2 D panel method was implemented in Qblade software v.06 which uses the fundamental panel method which rely on source strengths and influence coefficients to determine the velocity and pressure fields on the surface. The software implements the boundary layer or viscous effects to determine the influence on aerodynamic performance at various angles of attack. Jukouwski method is also evaluated for predicting aerodynamic characteristics and is based on the geometric iteration approach. Then complex aerodynamic flow potentials are determined based on the source strengths which are used to predict the pressure and velocity fields.

At low to moderate angles of attack, panel and Jukouwski methods predict similar results for surface pressure coefficients comparable to Hess and Smith inverse method. In comparison to panel method, results from the Jukouwski mapping method predicted the pressure coefficient conservatively for the same free stream conditions. With increase in Reynolds number, lift coefficient and aerodynamic performance improved significantly for un-tripped aerofoil when stall angle is approached when compared to tripped aerofoil.

This study demonstrated that panel methods have higher efficacy in terms of computational time or resources and thus can provide benefits to many real-world aircraft or aerospace design applications.

Even though panel and Jukouwski methods have been studied extensively in the past, this paper demonstrates the efficacy of both methods for modelling aerodynamic flows that range between moderate to high Reynolds number which are critical for many aircraft applications. Both methods have been validated with analytical and inverse design methods which are able to predict aerodynamic flow characteristics for simple bluff bodies, streamlined aerofoils as well as bio-inspired corrugated aerofoils.

The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.

Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.

The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.

The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

This paper aims to fast predict vibration responses of specific locations in the satellite subject to acoustic environment. It proposes a set of vibro-acoustic simulation methods of satellite components to represent their conditions in the whole satellite during the ground tests or launching. This study aims to use vibro-acoustic models of satellite components to replace that of hard modeling and time-consuming whole satellite when only local responses are concerned.

This paper adopted experimental and numerical studies, with the latter based on the finite element (FE), statistical energy analysis (SEA) and FE-SEA hybrid theories. The vibro-acoustic model of the whole satellite was built and verified by experimental data. Based on the whole satellite model and experimental results, the fast vibro-acoustic simulation methods of all kinds of typical satellite components were discussed.

This paper shows that the models about satellite components not only show high consistency but also reduce 61.6% to 99.8% times compared with the whole satellite model. The recommended fast simulation methods for all kinds of typical satellite components were given in comprehensive consideration of the model accuracy, time required and response accessibility.

This paper fulfils an identified need to perform fast vibro-acoustic prediction of the local positions in satellites.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.

This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.

This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.

The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.

The development of a 3D printing method for the textile hybrid structure that can both be a solution to the conventional drawbacks of 3D printing method and a step forward to a garment making industry.

A novel 3D printing method using the textile hybrid structure was developed to generate 3D object without support structures.

3D printing of curved panels without support structure was possible by using fabric tension and residual stress.

Garment panels can be 3D printed without support structures by utilizing the idea of textile hybrid structure. Garment panels are expected to be modelled and printed easily using the Garment Panel Printer (GPP) software developed in this study.

3D printing method developed in the study is expected to reduce the time and material previously needed for support structures.

Comprehensive preparatory experiments were made to determine the design parameters. Various experiments were designed to test the feasibility and validity of proposed method.

The purpose of this paper is to review and analyse Vision 2023: the Turkish National Technology Foresight project. The paper aims to review the process of conducting the project, how it was implemented afterwards and how it compares to other national technology foresight projects

Through a literature search, a process framework was conducted. The analysis was then conducted in four phases. First a process review, second a comparative review, third content review and finally a post project review. Expert interviews and site visits to Turkish State Planning Organization and TUBITAK (Scientific and Technical Research Council of Turkey) helped the authors to collect the data on Vision 2023 including how it was established, which areas were involved and what the recommendations were. Finally an expert panel was organized as part of a recent Portland International Conference on Management of Engineering and Technology. This included experts involved in the project as well as leading researchers who have been analyzing this project. This panel helped to validate the results.

Processes used in the Turkish project were similar to the other national projects, however lack of political ownership and change in leadership had been blocking the recommendations coming out of this project from being implemented. A second effort is required to modify the results of the first one and to establish political ownership and leadership. Several other national projects had multiple rounds before solid actions were taken. Industry needs to be a part of the effort as the panelists indicated that several key corporations were missing in the first project.

The project provides comparative details on running national technology foresight projects. This should be useful for those responsible for planning similar projects.

The paper reviews the project implementation process and what happened after the implementation providing feedback on what should have been done or should be done in similar foresight projects.