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In order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.

Firstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.

The results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.

The estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

The purpose of the paper is to analyse the performances of closures and compressibility corrections classically used in turbulence models when applied to highly-compressible turbulent boundary layers (TBLs) over flat plates.

A direct numerical simulation (DNS) database of TBLs, covering a wide range of thermodynamic conditions, is presented and exploited to perform a priori analyses of classical and recent closures for turbulent models. The results are systematically compared to the “exact” terms computed from DNS.

The few compressibility corrections available in the literature are not found to capture DNS data much better than the uncorrected original models, especially at the highest Mach numbers. Turbulent mass and heat fluxes are shown not to follow the classical gradient diffusion model, which was shown instead to provide acceptable results for modelling the vibrational turbulent heat flux.

The main originality of the present paper resides in the DNS database on which the a priori tests are conducted. The database contains some high-enthalpy simulations at large Mach numbers, allowing to test the performances of the turbulence models in the presence of both chemical dissociation and vibrational relaxation processes.

This study investigates firms' innovation behaviour under environmental change. Therefore, it examines the effect of trade disputes on corporate technological innovation and how product market competition moderates this relationship.

This research tests the hypotheses using the fixed effects model based on panel data of publicly listed enterprises in China from 2007–2020.

The empirical results validate the positive association between trade disputes and corporate research and development (R&D) intensity as well as the U-shaped relationship between trade disputes and radical innovation. Additionally, the moderating effect of product market competition is verified: a concentrated market with less competition flattens the U-shaped curve of radical innovation induced by trade disputes; as the market becomes more concentrated and less competitive, the U-shaped relationship eventually turns into an inverted U.

First, this study contributes to the corporate innovation and trade dispute literature by expanding the environmental antecedents of technological innovation and the firm-level consequences of trade disputes. Second, this study enriches the theoretical framework of the environment–innovation link through an integrated perspective of contingency theory and dynamic capabilities view. Third, instead of the traditional linear mindset which had led to contradictory results, this study explores a curvilinear effect in the environment–innovation relationship.

This article aims to investigate how institutional characteristics affect the level of financial development of economies collectively and compare between developed and undeveloped economies.

A dynamic panel with 131 countries, including developed and developing ones, was utilized; the estimators of the generalized method of moments system (GMM system) model were selected because they have econometric characteristics more suitable for analysis, providing superior statistical precision compared to traditional linear estimation methods.

The results from the full panel suggest that concrete and well-defined institutions are important for financial development, confirming previous research, with a more limited scope than the present work.

Limitations of this research include the availability of data for all countries worldwide, which would make the research broader and more complete.

A panel of countries was used, divided into developed and developing countries, to analyze the impact of institutional variables on the financial development of these countries, which is one of the differentiators of this work. Another differentiator of this research is the presentation of estimates in six different configurations, with emphasis on the GMM system model in one and two steps, allowing for comparison between results.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

This study seeks to explore the intricate relationship among supply chain transparency, digitalization and idiosyncratic risk, with a specific focus on newly public firms. The objective is to determine whether supply chain transparency effectively mitigates idiosyncratic risk within this context and to understand the potential impact of digitalization on this dynamic interplay.

The study utilizes data from Initial Public Offerings (IPOs) on China’s Growth Enterprise Board (ChiNext) over the last five years, sourced from the CSMAR database and firms’ annual reports. The research covers the period from 2009 to 2021, observing each firm for five years post-IPO. The final sample comprises 2,645 observations from 529 firms. The analysis employs the Hausman test, considering the panel-data structure of the sample and favoring fixed effects over random effects. Additionally, it applies the high-dimensional fixed effects (HDFE) estimator to address unobserved heterogeneity.

The analysis initially uncovered an inverted U-shaped relationship between supply chain transparency and idiosyncratic risk, indicating a delicate equilibrium where detrimental effects diminish and beneficial effects accelerate with increased transparency. Moreover, this inverted U-shaped relationship was notably more pronounced in newly public firms with a heightened level of firm digitalization. This observation implies that firm digitalization amplifies the impact of transparency on a firm’s idiosyncratic risk.

This study distinguishes itself by providing distinctive insights into supply chain transparency and idiosyncratic risk. Initially, we introduce and substantiate an inverted U-shaped correlation between supply chain transparency and idiosyncratic risk, challenging the conventional linear perspective. Secondly, we pioneer the connection between supply chain transparency and idiosyncratic risk, especially for newly public firms, thereby enhancing comprehension of financial implications. Lastly, we pinpoint crucial digital conditions that influence the relationship between supply chain transparency and idiosyncratic risk management, offering a nuanced perspective on the role of technology in risk management.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

The positive illiquidity–return relationship (so-called liquidity premium) is a well-established pattern in international developed stock markets. The magnitude of liquidity premium should increase with market illiquidity. Existing studies, however, do not confirm this conjecture with regard to frontier markets. This may result from applying different approaches to the investors' holding period. The paper aims to identify the role of the holding period in shaping the illiquidity–return relationship in emerging and frontier stock markets, which are arguably considered illiquid.

The authors utilise the data on stocks listed on fourteen exchanges in Central and Eastern Europe. The authors regress stock returns on liquidity measures variously transformed to reflect the clientele effect in a liquidity–return relationship.

The authors show that the investors' holding period moderates the illiquidity–return relationship in CEE markets and also show that the liquidity premium in these markets is statistically and economically relevant.

The findings may be of great interest to investors, companies and regulators. Investors and companies should take liquidity into account when making decisions; regulators should employ liquidity-enhancing actions to decrease companies' cost of capital and expand firms' investment opportunities, which will improve growth perspectives for the entire economy.

These findings enrich the understanding of the role that the investors' holding period plays in the illiquidity–return relationship in CEE markets. To the best knowledge, this is the first study which investigates the effect of holding period on liquidity premium in emerging and frontier markets.

Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices.

The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result.

The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Building Index certification. The empirical evidence from this study suggests that green certification significantly affects the sales price of residential properties in Malaysia. The findings of this research will help investors identify measurable factors that affect the transaction prices of green-certified residential real estate. These identifications will facilitate the development of strategic plans aimed at achieving sustainable rates of return in the sustainable residential real estate market.

Specifically, this research will contribute to achieving area 4 of the 11th Malaysia Plan, which pertains to pursuing green growth for sustainability and resilience. This will be achieved by enhancing awareness among investors and homebuyers regarding the importance of green residential buildings in contributing to the environment, the economy and society.

The regression model for housing attributes and green certification on house price developed in this study could offer valuable benefits to support and advance Malaysia in realising its medium and long-term goals for green technology.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.