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This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Although many scholars have investigated different aspects of the notion of innovation ambidexterity, the conceptualization of examining this concept in a technological setting remained unclear, as no serious attempts have been made to figure out the core concept of innovation ambidexterity in a technological context, which is a critical concept for high-tech firms.

With the development of a new generation of digital technologies, digital transformation (DT) has become an inevitable trend for enterprises to achieve development in various industries. Nevertheless, the contradiction between the “fragmented” use of digital technologies and the “systematic” transformation of the industry leads to the underperformance of DT in the construction industry. Whilst previous studies have examined why DT is needed and how separate digital technologies can be used in construction projects, they failed to specify effective tools that can help enterprises identify key resources that facilitate DT from the organisational perspective.

This study established an objective assessment framework for evaluating the digital transformation capability (DTC) of construction enterprises in identifying limitations in their transformation efforts. This study also established a management entropy quantitative model and a comprehensive capability evaluation model of DT to analyse the DT performance of construction enterprises from the internal and external perspectives. Data were collected from 95 listed enterprises in China’s construction industry in 2020 as a case study.

This study concluded that enterprise profitability provides a strong endogenous driving force for DT. Research and development capabilities and DT proficiency of enterprises are the most critical factors in facilitating DT. In addition, China’s construction enterprises' DT was characterised by uneven development and low orderliness. The lack of a unified digital integration platform is key to cracking the dilemma.

This paper systematically identified key DTC in construction enterprises and proposed an objective framework for measuring DTC to enhance the DT performance of these enterprises.

This study aims to explore whether and how the two potential antecedents (i.e. relationship quality at the dyadic level and network density at the network level) affect firms’ internet-interactive capability (FIIC), referring to the capability of a specific firm to communicate and interact with the relevant partner firms on the basis of internet-interactive technologies in the internet environment and, at the same time, the following influence of FIIC on collaborative activities (i.e. joint planning and joint problem-solving).

This study designed a questionnaire and collected data on-site from 400 manufacturers. SmartPLS is used to validate the research model.

The results suggest that the dyadic relationship quality and network density of the partner group are both positively connected with a firm’s FIIC. Besides, FIIC is positively related to collaborative activities with its partners.

Given the nature of our data (i.e. cross-sectional), the authors can collect longitudinal or experimental data to retest the hypotheses.

This study gives certain guidance for firms to be aware of the factors that motivate FIIC and use their FIIC to influence their employees’ collaborative activities in their relationships with partners, thereby promoting cooperation performance.

This study attempts to extend the resource-based theory based on the logic of motivation-capability by exploring the potential antecedents of FIIC and makes contributions to the current studies on the antecedents of FIIC, which provides actionable insights for firms to play the role of FIIC in interfirm interactions.

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.

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 investigates the effectiveness of problem-based learning (PBL) in information literacy curricula using library e-resources to enhance information literacy self-efficacy (ILSE) of students in two universities in Indonesia, in Java Island (University A) and Sumatra Island (University B).

A comparison of the effectiveness of the approach in the two universities forms the focus of the study, which has adopted a single group quasi-experimental design which was conducted in one online teaching-learning session. The authors compared the pre-test and post-test scores of 65 library and information science (LIS) students from both universities.

The results show that the treatment enhanced ILSE effectively. After the treatment, University A students performed better than those at University B.

The factors which affected the success of PBL using library e-resources have not been identified in this study, a task for a future qualitative research study.

This study will both inspire the use of library e-resources in learning activities and promote ILSE.

Integrating PBL with library e-resources provides opportunity to identify the advantages of library e-resources in supporting student ILSE, resulting a better learning achievement.

Malaysian rice wines (tapai) manufactured in small-scale industries are usually formulated based on personal experiences under minimally controlled conditions for generations, especially in Sabah, one of the East Malaysian states. However, the quality and safety of rice wines were receiving increased attention to ensure better quality control, particularly those produced on an industrial scale. Therefore, this research aims to determine the fermentation dynamics and consumers’ acceptance of Malaysian rice wines produced using different sasad (a local term for starter culture from Sabah).

The physicochemical [total soluble solids (TSS), alcohol content, total titratable acidity (TTA) and pH] and microbiological [total yeast and mould count (TYMC) and total lactic acid bacteria (LAB) count] changes in Malaysian rice wines were determined to better understand the fermentation process for future process optimisation. Additionally, sensory evaluations were conducted to determine the consumers’ preferences for the rice wines.

The overall fermentation dynamics of rice wines exhibited similar trends with slight variations between the samples, demonstrating the effect of microbial compositions of sasad on the quality of final rice wines. Additionally, consumer acceptance tests showed that rice wines with darker shades of yellow and a stronger alcoholic aroma were preferable.

This is the first research that provides important insights into both the fermentation dynamics and consumers’ acceptance of Malaysian traditional rice wines, enriching the rice wine literature from the academic perspective and contributing to the production of safe and high-quality rice wines.

Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.