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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.

In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Presentation of the different industrial carbon linkages of India. The purpose of this paper is to understand the direct and indirect impact of these industrial linkages.

This study uses a hypothetical extraction method with its various extensions. Under this method, different carbon linkages of a block are removed from the economy, and the effects of carbon linkages are determined by the difference between the original and the post-removal values. Energy and non-energy carbon linkages are also estimated.

“Electricity, gas and water supply (EGW)” at 655.61 Mt and 648.74 Mt had the highest total and forward linkages. “manufacturing and recycling” at 231.48 Mt had the highest backward linkage. High carbon-intensive blocks of “EGW” plus “mining and quarrying” were net emitters, while others were net absorbers. “Fuel and chemicals” at 0.08 Mt had almost neutral status. Hard coal was the main source of direct and indirect emissions.

Net emitting and key net forward blocks should reduce direct emission intensities. India should use its huge geographical potential for industrial accessibility to cheaper alternative energy. This alongside with technology/process improvements catalyzed by policy tools can help in mitigation efforts. Next, key net-backward blocks such as construction through intermediate purchases significantly stimulate emissions from other blocks. Tailored mitigation policies are needed in this regard.

By developing an understanding of India’s industrial carbon links, this study can guide policymakers. In addition, the paper lays out the framework for estimating energy and non-energy-based industrial carbon links.

This study aims to explore how transformational leadership influences employees' creativity by considering the role of the environment and psychology. The study aims to provide insights into the impact of transformational leadership on team innovation climate, team reflexivity, psychological capital and employee creativity while also examining the moderating effect of environmental dynamism on these relationships.

This study employed a multi-source, multi-wave approach, utilizing data from 618 participants in 118 teams. It constructed a multilevel structural equation model and estimated the confidence intervals of the mediated and moderated effects using the Markov chain Monte Carlo method.

The results of the multilevel analyses indicated that transformational leadership positively influenced the team innovation climate, team reflexivity, psychological capital and employee creativity. Moreover, the study found that environmental dynamism positively moderates the relationships among transformational leadership, team reflexivity, psychological capital and employee creativity.

Drawing on social cognitive theory and the motivated information processing in groups model, this study offers new insights into the interplay between transformational leadership and creativity. It examines the moderating role of cross-level process linkages and environmental dynamism, thereby validating and extending relevant theories.

The purpose of this paper is to investigate the effects of Sn-Ag-x leveling layers on the mechanical properties of SnBi solder joints. Four Sn-Ag-x (Sn-3.0Ag-0.5Cu, Sn-0.3Ag-0.7Cu, Sn-0.3Ag-0.7Cu-0.5 Bi-0.05Ni and Sn-3.0Ag-3.0 Bi-3.0In) leveling layers were coated on Cu pads to prepare SnBi/Sn-Ag-x/Cu solder joints. The microstructure, hardness, shear strength and fracture morphology of solder joints before and after aging were studied.

The interfacial brittleness of the SnBi low-temperature solder joint is a key problem affecting its reliability. The purpose of this study is to improve the mechanical properties of the SnBi solder joint.

Owing to the addition of the leveling layers, the grain size of the ß-Sn phase in the SnBi/Sn-Ag-x/Cu solder joint is significantly larger than that in the SnBi/Cu eutectic solder joint. Meanwhile, the hardness of the solder bulk in the SnBi/Cu solder joint shows a decrease trend because of the addition of the leveling layers. The SnBi/Cu solder joint shows obvious strength drop and interfacial brittle fracture after aging. Through the addition of the Sn-Ag-x layers, the brittle failure caused by aging is effectively suppressed. In addition, the Sn-Ag-x leveling layers improve the shear strength of the SnBi/Cu solder joint after aging. Among them, the SnBi/SACBN/Cu solder joint shows the highest shear strength.

This work suppresses the interfacial brittleness of the SnBi/Cu solder joint after isothermal aging by adding Sn-Ag-x leveling layers on the Cu pads. It provides a way to improve the mechanical performances of the SnBi solder joint.

This paper aims to present the result of the pilot run of a research project which aims at evaluating the applicability of project-based action learning (PAL) to shop floor organizational learning (OL) component in a manufacturing company in Dongguan, China. How the PAL framework was introduced and implemented is described. The factors that influence PAL implementation are examined in this pilot implementation. The implications and effectiveness are discussed. The findings shed light on the modifications needed in the PAL framework for shop floor implementation.

This is a longitudinal case research which is conducted in a case company (APT) located in Dongguan, China. PAL framework was introduced and implemented in APT. A wave-like approach was used to repeat launching PAL projects in cycles. Data of learning and work performances were collected for review during each phase and each cycle of the project. The result presented here is from the early round of the pilot PAL implementation.

Result indicated that the PAL framework is applicable in the shop floor level OL. Learning and performance have been found to have improved in all the PAL groups participating in the pilot run. Result also shows that the PAL implementation framework needs some fine tuning to meet the needs of the unique shop floor environment particularly with regard to performance measure and evaluation.

No such case study has been conducted in shop floor level before. The research study will be valuable experience and example for other organizations to implement OL in shop floor level.

The purpose of this study is to examine the impact of chief executive officer (CEO) hometown identity on company audit fees in the Chinese setting.

This study uses data from Chinese public companies in the Shanghai Stock Exchange and the Shenzhen Stock Exchange for the period 2008–2019. This study investigates the impact path of CEO hometown identity on company audit fees and further examines the moderating role of internal and external governance level.

This study finds that CEO hometown identity is significantly and negatively related to company audit fees. In addition, CEO hometown identity can reduce audit fees by alleviating agency risk and litigation risk. Moreover, the negative effect of CEO hometown identity on audit fees is more pronounced in companies with a higher percentage of institutional investors shareholding and more analysts tracking quantity.

This study may provide new references for executives’ selection, auditors’ optimization decisions and regulators’ information disclosure system.

This study contributes to the literature by exploring the effect of CEO hometown identity on audit fees in the context of China.

This study aims to investigate the impact of supply chain concentration (SCC) on corporate research and development (R&D) investment and determine the moderating roles of industry concentration and financing constraints on the relationship between SCC and R&D investment.

The study collected data from Chinese listed companies, used the fixed effects model to test the research hypotheses and further used the two-stage Heckman test and propensity score matching (PSM) to address potential endogeneity issues.

The result reveals a negative impact of SCC on corporate R&D investment. In addition, industry concentration mitigates the negative impact of SCC on corporate R&D investment, but financing constraints strengthen the negative impact.

This study introduces the concept of SCC and empirically tests its effect on R&D investment, further explaining the lack of corporate innovation. This study inspires companies to strengthen SC management and weigh the level of SCC with environmental factors.

The purpose of this article is to understand how academics in management deal with the concept of generation in the workplace. We begin by conducting an interdisciplinary literature analysis, thereby elaborating a conceptual framework concerning generational diversity. This framework consists of four levels of analysis (society, career, organisation and occupation) and three dimensions (age, cohort and event/period). We then conduct a meta-analysis using this conceptual framework to analyse papers from the management field. The results from this analysis reveal the existence of a diversity of generational approaches, which focus on the dimensions of age and cohort on a societal level. Four factors seem to explain these results: the recent de-synchronisation of generational dimensions and levels, the novelty of theoretical models, the amplification of stereotypes by mass media and the methodologies employed by researchers. In sum, this article contributes to a more realistic view of generational diversity in the workplace for both academics and practitioners.

Macro synthetic steel fibers were incorporated into the concrete material as a toughening agent to improve the corrosion and cracking resistances of concrete in a sulfate-containing service environment.

To study the basic mechanical properties of this system, an accelerated concrete degradation test was designed to evaluate the influence of the sulfate ions on the concrete. A three-point bending test was carried out in the laboratory to evaluate the fracture toughness. The thickness of the damaged concrete layer and changes of microstructure of the degraded concrete were monitored by using ultrasound, scanning electron microscopy and X-ray diffraction detection methods.

The results showed that compared to the performance of ordinary concrete, in an exposure environment containing sulfate ions, the structure compactness of macro synthetic steel fiber concrete was improved, degradation resistance to the sulfate solution was enhanced and the fracture resistance performance was improved significantly.

The thickness of the degradation layer on the macro synthetic steel fiber concrete was less than a half of that of ordinary concrete in the sulfate environment, and was generally unchanged with increase in the sulfate concentration. Through micro-structural analysis, it was confirmed that macro synthetic steel fiber improved the compactness of the concrete structure, inhibiting access of sulfate ions to the interior of the concrete and thereby reducing the degree of sulfate degradation to the concrete.