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The objective of this study is to use non-destructive testing of corrosion on coated aluminium alloys using differential eddy current detection (DECD), with the aim of elucidating the relationship between the characteristics of corrosion defects and the detection signal.

Pitting corrosion defects of varying geometrical dimensions were fabricated on the surface of aluminium alloy plates, and their impedance signals were detected using DECD to investigate the influence of defect diameter, depth, corrosion products and coating thickness on the detection signals. Furthermore, finite element analysis was used to ascertain the eddy current distributions and detection signals under different parameters.

The size of the defect is positively correlated with the strength of the detection signal, with the defect affecting the latter by modifying the distribution and magnitude of the eddy current. An increase in the diameter and depth of corrosion defects will enhance the eddy current detection (ECD) signal. The presence of corrosion products in the corrosion defects has no significant effect on the eddy current signal. The presence of a coating results in a decrease in the ECD signal, with the magnitude of this decrease increasing with the thickness of the coating.

The objective is to provide experimental and theoretical references for the design of eddy current non-destructive testing equipment and eddy current testing applications.

Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

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.

This study aims to propose and examine a conceptual model that shows how green performance measurement systems (GPMS) mediate the relationship between green intellectual capital components and environmental performance.

The research surveyed 407 Vietnamese publicly listed companies to gather empirical data. Then, the exploratory factor analysis (EFA) and structural equation modeling (SEM) are used to examine the degree of emphasis firms place on using GPMS to transform green intellectual capital into firm value.

The results indicate that both green human capital and green organizational capital have a direct positive impact on environmental performance. On the contrary, the influence of green social capital on environmental performance was found to be indirect through the mediation of GPMS.

GPMS can be considered a tool that helps managers renew, develop and synchronize their systems to take advantage of green resources in environmental performance improvement.

The effective assimilation of GPMS within industrial entities holds the potential to mitigate air pollution and hazardous waste, thereby ameliorating social conditions for both employees and the neighboring community. Besides that, proficient implementation of GPMS enhances collaborative efforts within the industrial sphere, yielding collective societal benefits.

This study emphasizes the importance of aligning green intellectual capital with appropriate control mechanisms, such as performance measurement systems, to maximize the benefits derived from these capital resources. The findings provide insights for organizations seeking to enhance their environmental performance and sustainability practices by effectively using their intellectual and social capital while implementing robust measurement systems.

Blockchain technology is one of the major contributors to supply chain sustainability because of its inherent features. However, its adoption rate is relatively low due to reasons such as the diverse barriers impeding blockchain adoption. The purpose of this study is to identify blockchain adoption barriers in sustainable supply chain and uncovers their interrelationships.

A three-phase framework that combines machine learning (ML) classifiers, BORUTA feature selection algorithm, and Grey-DEMATEL method. From the literature review, 26 potential barriers were identified and evaluated through the performance of ML models with accuracy and f-score.

The findings reveal that feature selection algorithm detected 15 prominent barriers, and random forest (RF) classifier performed with the highest accuracy and f-score. Moreover, the performance of the RF increased by 2.38% accuracy and 2.19% f-score after removing irrelevant barriers, confirming the validity of feature selection algorithm. An RF classifier ranked the prominent barriers and according to ranking, financial constraints, immaturity, security, knowledge and expertise, and cultural differences resided at the top of the list. Furthermore, a Grey-DEMATEL method is employed to expose interrelationships between prominent barriers and to provide an overview of the cause-and-effect group.

The outcome of this study can help industry practitioners develop new strategies and plans for blockchain adoption in sustainable supply chains.

The research on the adoption of blockchain technology in sustainable supply chains is still evolving. This study contributes to the ongoing debate by exploring how practitioners and decision-makers adopt blockchain technology, developing strategies and plans in the process.

The Architecture, Engineering and Construction (AEC) industry has been transformed by the increasing adoption of existing and emerging technologies of Industry 4.0, leading to the concept of Construction 4.0. However, the AEC industry is falling behind in comparison to other industries. The purpose of this chapter is to provide a comprehensive overview of digital transformation within the Construction 4.0 framework and explore the uses and challenges associated with adopting the most prominent digital technologies. This study is a literature-based exploration of published works to evaluate knowledge gaps and comprehend the Construction 4.0 concept in the context of the AEC industry. The results present a set of possible uses of 10 promising technologies in Construction 4.0 along with challenges that hinder its adoption. These are categorised as technical, legal, financial, organisational, industry and data security barriers. This research is limited to several promising technologies. Future studies should focus on rapidly developing technologies and finding better solutions for implementation. Besides increasing the awareness of practitioners, policymakers and clients towards Construction 4.0 technologies, it might assist them in making decisions on selecting and implementing key technologies. This chapter contributes to the literature by providing an updated and inclusive review that focuses on the uses and challenges of 10 trending technologies in the specific context of Construction 4.0. Therefore, the findings of this study provide a basis for different actors in the AEC industry to accelerate digital transformation and pave the way for future research.

This study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.

The research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.

Cognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.

This research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

This study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models.

Repair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors.

The LSTM model’s learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models’ learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects.

Gathering a real-world high-quality data set of apartment’s repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

The study investigates the influence of managerial discretion over accruals on banks' financial reporting quality. Furthermore, it examines the role of ownership in shaping managerial incentives to manipulate banks’ reporting quality in a developing economy.

The sample includes 37 Indian public- and private-sector banks from the fiscal year 2001–2022. The discretionary LLP (DLLP) is used to examine various managerial incentives and accounting quality. The models are estimated using panel fixed-effect regression and the system generalized method of moments. The results survive several sensitivity checks.

The results exhibit a low quality of financial reporting in public-sector banks, which is evident through the higher use of DLLP for income smoothing and signaling. In contrast, the low-capitalized private-sector banks employ DLLP to manage capital.

The study’s sample size is relatively small and focuses on a single country. Future researchers can investigate other emerging economies to better generalize the findings of this study.

The study highlights the influential role of ownership in shaping managerial incentives in the banking industry. Moreover, the study is of utmost importance for governments, regulators and policymakers in devising policies that reduce agency conflicts and improve financial stability in emerging economies.

The study subscribes to the growing literature on the role of ownership in influencing the banks’ financial reporting quality. To the best of the author’s knowledge, this is one of the limited studies in the context of government-owned vs private-owned banks in an emerging economy.