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This paper investigates the vibration compaction mechanism and evaluates the impact of vibration frequencies on the stability of coarse-grained soil, aiming to optimize the subgrade filling process.

This study examines the vibratory compaction behavior of coarse-grained soils through indoor vibration tests and discrete element simulations. Focusing on angular gravel (breccias) of varying sizes, the simulations were calibrated using parameters such as Young’s modulus, restitution and friction coefficients. The analysis highlights how particle shape influences compaction, revealing mesoscopic mechanisms that drive macroscopic compaction outcomes.

This study investigates the influence of vibration frequency on the compaction behavior of coarse-grained soils using discrete element simulation. By analyzing particle contact and motion, the mesoscopic mechanisms driving compaction are explored. The study establishes a positive linear correlation between contact force anisotropy (Cv) and deformation, demonstrating that higher anisotropy leads to greater structural disruption. Additionally, the increase in sliding contact percentage (SCP) at higher frequencies indicates instability in the skeletal structure, driven by uneven contact force distribution. These findings reveal how frequency-induced stress concentration affects the stability and deformation of the soil skeleton.

This research explores the effect of various vibration frequencies on the compaction behavior of coarse-grained soils, examining microscopic interactions to reveal their impact on soil stability and deformation.

This study aims to explain the market-oriented transformation dilemma of Chinese urban investment and development companies (UIDCs; also known as local government investment and financing companies) and objectively evaluate their transformation efficiency from both static and dynamic perspectives. The results of the research provide methodological bases for improving the transformation efficiency of UIDCs, thus pointing out the direction for the rational planning of their transformation path.

This study takes Chinese UIDCs in market transformation during 2015–2019 as the research object and uses principal component analysis to screen the index system for measuring the efficiency of market transformation. It then uses a three-stage data envelopment analysis model and the Malmquist productivity index to evaluate the market transformation efficiency of these companies during 2015–2019 and comprehensively analyzes the influence of external environmental factors on the market transformation of Chinese UIDCs.

Research results show that the transformation efficiency of Chinese UIDCs is low and slow overall and that large spatial and temporal differences exist. The transformation efficiency of UIDCs located in eastern China is higher than that of UIDCs in central and western China. The higher the external environmental factors of regional GDP, local debt service pressure and credit rating, the more likely they are to cause input redundancy in the transformation process of Chinese UIDCs, which is not conducive to their market-oriented transformation. In addition, the higher the urbanization rate, the more effective it is to improve the efficiency of market-oriented transformation of UIDCs. If the influence of environmental factors is stripped away, both the overall efficiency value and pure technical efficiency value of market-oriented transformation of Chinese UIDCs will increase while the scale efficiency value becomes smaller.

This research measures the transformation efficiency of Chinese UIDCs and comprehensively analyzes the influence of external environmental factors on their market-oriented transformation. The goal is to enrich the study of the market-oriented transformation efficiency evaluation index system of Chinese UIDCs at the theoretical level and provide important reference values for improving the efficiency of market-oriented transformation of Chinese UIDCs at the practical level.

The precise and sensitive methods for authentication and differentiation of natural and synthetic indigo dyes are required for assurance of textile safety and public health. This study aims to develop a fast and simple method to distinguish natural indigo from synthetic one.

A static headspace gas chromatography-mass spectrometry (GC-MS) method was developed for identification of natural and synthetic indigo samples. Natural indigo samples prepared from three different plants and synthetic indigo samples from three famous manufacturers in China, were involved in this study, along with some nonindigo blue samples (such as direct blue, active blue and neutral blue). The yarns and fabrics dyed with natural and synthetic indigo were also analyzed by the GC-MS method.

High levels of aniline (21.87%–71.59%) or N-methylaniline (25.26%–38.73%) were detected only in synthetic indigo samples (1 g) using the static headspace GC-MS method. The yarns and fabrics dyed with the synthetic indigo were also detected with residual aniline (0.47%–14.86%) or N-methylaniline (6.59%–40.93%).

The results clearly demonstrated that aniline or N-methylaniline can be used a diagnostic marker for distinguishing natural indigo from synthetic indigo. The proposed static headspace GC-MS method is a rapid, simple and convenient approach for differentiation of natural and synthetic indigo, as well as for the yarns and fabrics dyed with synthetic indigo.

This study explores the potential impact of enterprise social media (ESM) communication visibility on knowledge sabotage to reduce knowledge sabotage within organizations.

We collected data from 389 Chinese employees across three stages and used hierarchical regression analysis and the bootstrap method to test our hypotheses.

Communication visibility negatively affects knowledge sabotage, and the loss of knowledge power mediates the relationship between communication visibility and knowledge sabotage. Digital work connectivity strengthens the negative relationship between message transparency and loss of knowledge power but weakens the negative relationship between network translucence and loss of knowledge power. Therefore, digital work connectivity plays a dual role.

Managers can encourage employees to share their knowledge advantages through ESM and seek cross-disciplinary knowledge cooperation, which helps restrain knowledge sabotage from the source. At the same time, maintaining appropriate digital work connectivity enables employees to leverage their knowledge interaction advantages of ESM, thereby fostering their knowledge competitiveness.

This study is the first to reveal the internal mechanism (loss of knowledge power) through which ESM communication visibility affects knowledge sabotage and explores the boundary condition (digital work connectivity) impacting the effectiveness of communication visibility. It contributes to a deeper understanding of the inherent nature of knowledge sabotage from an information technology perspective and offers novel technical insights into its management.

Based on the cognitive appraisal theory of emotion and social comparison theory, this study explores the mediating mechanism and boundary conditions of supervisor bottom-line mentality on employee presenteeism.

Using hierarchical regression and bootstrapping, we test the hypothesized relationships with three-stage data from 265 full-time employees in China.

Supervisor bottom-line mentality has a significant positive influence on employee presenteeism. Workplace fear of missing out plays a mediating role between supervisor bottom-line mentality and employee presenteeism. Employees’ status-striving motivation positively moderates the influence of supervisor bottom-line mentality on employees’ workplace fear of missing out and enhances the mediating effect of workplace fear of missing out.

Presenteeism can be detrimental to employees’ health, and ultimately leads to a decrease in organizational productivity. Research conclusions warn companies to be vigilant about supervisors’ bottom-line mentalities and to strengthen employee health management.

This study explains when and how supervisor bottom-line mentality affects employee health, contributing to the literature on the antecedents of presenteeism and enriching the research on supervisor bottom-line mentalities and employee and organizational outcomes. This study clarifies the emotional mechanisms and boundary conditions of supervisor bottom-line mentality affecting presenteeism.

This study aims to investigate the negative interpersonal antecedents, emotional mediators and boundary conditions of knowledge sabotage behavior.

The authors collected data from 275 Chinese employees using convenience sampling and snowball sampling across three stages. Subsequently, the authors used both hierarchical regression and bootstrap methods to test the proposed hypotheses.

The results confirmed that workplace ostracism has positive effects on employee knowledge sabotage behavior both directly and via employee anger. In addition, the authors found that employee bottom-line mentality (BLM) moderates not only the direct effect of workplace ostracism on employee anger but also the indirect effect of employee anger in this context. Employee conscientiousness moderates only the direct effect of workplace ostracism on employee anger and does not moderate the indirect effect.

To the best of the authors’ knowledge, this study not only explores the influence of workplace ostracism on employee knowledge sabotage behavior for the first time but also elucidates the underlying emotional mechanisms (anger) and boundary conditions (employee BLM and conscientiousness) by which workplace ostracism influences employee knowledge sabotage behavior, thus deepening the understanding of how knowledge sabotage emerges in organizations.

Based on social cognitive theory, this study aims to explore the influence of supervisor bottom-line mentality (SBLM) on employee knowledge behavior (knowledge territorial behavior and knowledge sabotage behavior). The study first investigates the role of an ethical decision-making mechanism (moral disengagement) in mediating this relationship. In addition, it considers the possible boundary conditions to supplement research on the influence of SBLM in the knowledge management field.

The authors collected 256 data points from employees across three stages using convenience sampling. The authors then tested the proposed hypothesis using hierarchical regression and bootstrap methods.

The results demonstrated that SBLM promotes employees’ moral disengagement, leading to more knowledge territorial behavior and knowledge sabotage behavior. Furthermore, high power distance orientation among employees exacerbates the ill effects of SBLM according to the first stage of a moderated mediation model. Employees with such an orientation are more likely to respond to a SBLM by exhibiting a higher level of moral disengagement, thus increasing their knowledge territorial behavior and knowledge sabotage behavior.

Research on the influence of SBLM in the knowledge management field is limited. This study not only clarifies the relationships between SBLM and two types of knowledge behavior (knowledge territorial behavior and knowledge sabotage behavior) but also enriches the research on the antecedents of these two types of knowledge behavior.

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.