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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 study the isothermal and nonisothermal crystallisation kinetics of pure polypropylene (PP), 1 kGy pre‐irradiated PP and 1 kGy pre‐irradiated PP/syndiotactic 1,2‐polybutadiene (s‐1,2 PB) (90/10) blends by differential scanning calorimetry.

The Avrami equation, modified Avrami equation, Ozawa equation and the treatment by combining the Avrami and Ozawa equation were used to analyse the isothermal and nonisothermal crystallisation of various samples.

The s‐1,2 PB acted as a heterogeneous nucleation agent during the crystallisation of the PP/s‐1,2 PB blends and accelerated the crystallisation rate. The Avrami exponent n of the blends implied that the isothermal crystallisation kinetics of the blends followed a three‐dimensional growth via heterogeneous nucleation. The modified Avrami equation was limited to describe the nonisothermal crystallisation process of pure PP and 1 kGy pre‐irradiated PP, but it was successful for the blends. The treatment by combining the Avrami and Ozawa equation described appropriately the nonisothermal crystallisation process and obtained the kinetic parameter F(T) with specific physical meaning. The crystallisation activation energy for isothermal crystallisation and nonisothermal crystallisation of the blends was reduced due to the s‐1,2 PB acting as a heterogeneous nucleating agent during the crystallisation of the blends and accelerating the crystallisation rate.

The Avrami equation, modified Avrami equation, Ozawa equation and the treatment by combining the Avrami and Ozawa equation were compared for analysis of the isothermal and nonisothermal crystallisation of samples. The crystallisation activation energy for isothermal crystallisation and nonisothermal crystallisation was also calculated according to the Arrhenius and the Kissinger method.

The fundamental research on the crystallisation properties of PP/s‐1,2‐PB blends is essential to understand the mutual effects of two components on their crystallisation mechanisms, facilitating to improve the mechanical properties of the final materials.

The isothermal and nonisothermal crystallisation behaviours of PP/s‐1,2 PB blends, especially pre‐irradiated PP/s‐1,2 PB blends, have not been studied systematically yet, though PP/s‐1,2 PB blends were promising materials in terms of both PP toughening and the application of s‐1,2 PB thermal plastic elastomer.

This study aims to reveal the positive side of poor voice quality. Grounding on sociometer theory, this study proposes a model to explore how poor voice quality affects employee's motivation to make high-quality voice via managerial non-endorsement and employee's self-perception of poor voice quality.

The sample consisted of 247 employees and immediate supervisors of employees in China. To minimize potential common method biases and reduce participants' fatigue, a three-wave method for the data collection with each wave separated by one month was executed. Path analysis and bootstrapping approach were adopted to verify the hypotheses.

The results illustrated that employee's poor voice quality was able to promote employee's motivation to make high-quality voice via managerial non-endorsement and employee's self-perception of poor voice quality.

First, this study extends our knowledge of the consequences of employee voice. Second, this study further contributes to the literature on voice quality by emphasizing the positive effects of poor voice quality. Third, this study enriches the sociometer theory by the explication of chain mediation as a key mechanism through which poor voice quality affects employee's motivation to make high-quality voice.

This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.

The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.

This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).

This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.

This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

This study identifies key challenges to adopting smart real estate (SRE) technologies and offers insights and recommendations to enhance decision-making for stakeholders, including buyers and property investors.

To achieve the aim of the study, a rigorous research approach was employed, conducting an in-depth analysis of 41 academic papers utilising PRISMA guidelines and checklists. The chosen methodology also applies a PEST (Political, Economic, Social and Technological) framework to identify factors influencing technology adoption in the real estate sector.

The study uncovers critical challenges to adopting smart real estate technologies, such as regulatory ambiguity, high implementation costs, and societal resistance. PEST analysis reveals that unclear standards and guidelines, coupled with the high financial burden of technology implementation, are significant obstacles. Socially, resistance to change and difficulties in integrating new technologies are prevalent. The study also underscores the potential of artificial intelligence (AI) for predictive analytics and blockchain for secure transactions and records, though their adoption is currently hindered by inadequate infrastructure and regulatory challenges. These findings underscore the need for strategic interventions to address these challenges and facilitate the effective integration of advanced technologies in the real estate sector, thereby enhancing industry innovation and competitiveness.

The study offers insights for real estate stakeholders to embrace technology effectively, with a conceptual framework contributing to industry advancements.

The study’s key contribution is offering real estate stakeholders execution tactics and recommendations to navigate challenges and utilise technology, thereby driving industry innovation and enhancing competitiveness.