Search results

The purpose of this study to investigate the high temperature tribological performances of CrN and CrAlN coatings on AISI H13 steel, which was beneficial to improve the wear resistance of hot work molds.

Arc ion plating was used to deposit the CrN and CrAlN coatings on AISI H13 steel, and the tribological performances of CrN and CrAlN coatings were evaluated using a ball-on-plate wear tester.

The average coefficients of friction and wear rate of CrAlN coating in the normal wear period are 0.33 and 5.34 × 10^–9 mm³·N^–1·mm^–1, respectively, which are lower than those of CrN coating, exhibiting that the outstanding friction reduction. The formations of Cr and Al oxides during the wear process are the main factor in enhancing the tribological performance of CrAlN coating.

CrN and CrAlN coatings were applied for hot work molds, and their tribological performances were comparatively investigated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0359/

Although various stakeholder groups frequently advocate and call for greater heterogeneity among directors and managers, it remains unknown whether team heterogeneity can be beneficial for audit committee to exercise the auditor selection functions. This study aims to address this question.

Drawing on a sample of domestically listed nonfinancial A-share firms in China from 2008 to 2022, the authors empirically examine whether and how firm’s audit committee heterogeneity associates with the selection of auditors.

Firms with higher levels of audit committee heterogeneity are more likely to be associated with lower-quality auditors. Further examination reveals the mediating role of risk-taking: higher levels of heterogeneity are associated with higher levels of risk-taking, influencing firms to employ lower-quality auditors. Moreover, the authors document that increased audit committee heterogeneity is associated with more audit committee meetings and lower audit efficiency, and that hiring lower-quality auditors can influence the market value of firms with high audit committee heterogeneity.

To the best of the authors’ knowledge, this study is the first to examine whether and how audit committee erogeneity associates with the selection of auditors. Moreover, because China is a high-power distance, collectivism-oriented, more relations-based (i.e. guanxi-based) than rules-based society, it is critical to examine the influence of team heterogeneity based on the unique cultural context and transitional nature of China’s business environment.

The current study aims to ascertain how green entrepreneurial orientation (GEO) affects green innovation performance (GIP) through the mediating mechanism of the knowledge creation process (KCP) and whether or not these associations can be strengthened or hampered by the moderating impacts of resources orchestration capabilities (ROC).

The research used data from managers at various levels in 154 manufacturing enterprises in Pakistan to evaluate the relationships among the constructs using hierarchical regression analysis and moderated mediation approach.

The study indicates that GEO substantially impacts firms' GIP. GEO and GIP's relationship is partially mediated by two KCP dimensions: knowledge integration (KI) and knowledge exchange (KE). Furthermore, ROC amplifies not only the effects of GEO on KE but also the effects of KE on GIP. The moderated mediation results demonstrate that KE has a greater mediating influence on GEO and GIP when ROC is higher.

To better understand GEO's advantages and significance, future studies should look into the possible moderating mechanisms of environmental, organizational culture/green capability in the association between GEO, KCP and GIP.

The research helps expand the field of green entrepreneurship and GIP literature by providing a deeper knowledge of GEO and offering insight into how to boost GI in manufacturing firms.

This research helps fill in knowledge gaps in the field by delving further into the mechanisms by which GEO promotes GIP, both directly and indirectly, via the mediating role of KCP and the moderating impacts of ROC.

Promoting technological innovation is important to address the complexity of major engineering challenges. Technological innovations include short-term innovations at the project level and long-term innovations that can enhance competitive advantages. The purpose of this study is to develop an incentive mechanism for the public sector that considers short-term and long-term efforts from the private sector, aiming to promote technological innovation in major engineering projects.

This study constructs an incentive model considering the differences in short-term and long-term innovation efforts from the private sector. This model emphasizes the spillover effect of long-term efforts on current projects and the cost synergy effect between short-term and long-term efforts. It also explores the factors influencing the optimal incentive strategies for the public sector and innovation strategies for the private sector.

The results indicate that increasing the output coefficient of short-term and long-term efforts and reducing the cost coefficient not only enhance the innovation efforts of the private sector but also prompt the public sector to increase the incentive coefficient. The spillover effect of long-term innovation efforts and the synergy effect of the two efforts are positively related to the incentive coefficient for the public sector.

This research addresses the existing gap in understanding how the public sector should devise incentive mechanisms for technological innovation when contractors acting as the private sector are responsible for construction within a public-private partnership (PPP) model. In constructing the incentive mechanism model, this study incorporates the private sector's short-term efforts at the project level and their long-term efforts for sustained corporate development, thus adding considerable practical significance.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.

This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.

The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.

The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The aim was to investigate the effect of normal load on the tribological performance of laser cladded FeCoCrMoSi amorphous coating, which might choose the appropriate normal load for the friction reduction and wear resistance.

A FeCoCrMoSi amorphous coating was prepared on 45 steel using laser cladding, and the tribological performance of obtained coating under the different normal loads was investigated using a ball-on-disk tribometer.

The FeCoCrMoSi amorphous coating is composed of M₂₃C₆, Co₆Mo₆C₂ and amorphous phases, where the M₂₃C₆ hard phase enhances the coating hardness to increase the wear resistance and the Co₆Mo₆C₂ with the vein shape forms the strong mechanical interlock to play the role of friction reduction. The average coefficients of friction of containing amorphous FeCoCrMoSi coating under the normal loads of 3, 4 and 5 N are 0.68, 0.65 and 0.53, respectively, and the corresponding wear rates are 17.7, 23.9 and 21.9 µm3•N⁻¹•mm⁻¹, respectively, showing that the appropriate normal load is beneficial for improving its friction reduction and wear resistance. The wear mechanism is composed of adhesive wear, abrasive wear and oxidative wear, which is attributed to the high hardness of amorphous coating by the amorphous phase.

The FeCoCrMoSi amorphous coating was first applied for the improvement of 45 steel, and the effect of normal load on its tribological performance was investigated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0304/

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and text-based corporate innovation based on a sample of India’s ESG-disclosed companies from financial year 2011–2012 to 2021–2022. Further, it endeavors to investigate the moderating role of heightened climate policy uncertainty (CPU) in this relationship.

To verify these hypotheses, the authors first construct a corporate innovation index for India using a sophisticated natural language processing model on each firm-year’s management discussion and analysis reports. Next, the authors use a panel fixed effects model to examine how ESG performance impacts corporate innovation and its moderating and mediating components.

Empirical evidence suggests higher ESG performance bolsters text-based corporate innovation. After addressing endogeneity issues with the system GMM estimator and two-stage least square IV, incorporating additional control variables and using alternative innovation measurement, the baseline results remain unchanged. Next, the authors find this link is mediated by reducing information asymmetry, financial constraints and managerial myopia. The authors also observe that increased CPU favorably moderates the ESG-innovation nexus. Additionally, the heterogeneity research shows that ESG only positively impacts innovation in specific industries and firms in their growth and mature life cycle phases.

The results demonstrate that sustainable and ethical business practices can foster corporate innovation. Thus, this study may provide valuable insight for investors, managers and policymakers.

To the best of the authors’ knowledge, this is the first study to examine the relationship between ESG performance and text-based corporate innovation using a machine learning model.