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This study aims to examine the effects of the characteristics of apparel professionals on their attitude toward sustainability-related technology in the context of a developing country, Bangladesh.

A quantitative approach was used to investigate the apparel professionals’ perception of sustainability-related technology. A survey was conducted, and 204 valid responses were used in data analysis. The structural equation modeling technique was used to analyze the data.

The findings demonstrate that apparel professionals’ personal innovativeness positively impacts their knowledge of apparel technology. Knowledge of apparel technology and environmental issues in apparel manufacturing both significantly and positively impact their level of awareness of sustainability-related technology in apparel manufacturing. The findings also suggest that managers’ level of awareness of sustainability-related technology has a significant positive impact on their attitude toward sustainability-related technology.

Fishbein’s attitude theory was applied to examine how the various characteristics of apparel professionals (i.e. personal innovativeness in technology, knowledge of apparel technology, knowledge of environmental issues of apparel manufacturing) affect their awareness of and attitude toward sustainability-related technology. This study expands our understanding of the causal flow among cognitive variables of apparel professionals, including their innovativeness, knowledge, awareness and attitudes. The findings of the study can be helpful to the apparel industry to improve apparel professionals’ adoption of sustainable technology.

This study aims to analyze online responses to gain insights into public attitudes and concerns regarding traditional Chinese medicine (TCM) among Hong Kong residents. By addressing gaps in understanding public sentiment, this study contributes to the development of effective health-care policies.

Responses were collected from Baby-Kingdom.com using Python, gathering 17,568 TCM-related comments from 2016 to 2023. Analysis involved an eight-theme codebook and sentiment and semantic network analyses with DiVoMiner.

Most responses expressed positive sentiments and attitudes toward TCM. The analysis revealed recurring topics related to conditioning and specific diseases, including gynecological problems. Clinic service quality, fair pricing and convenient locations were also highlighted.

This study examines the networked public sphere and the Theory of Planned Behavior regarding TCM, emphasizing online forums’ impact on attitudes and highlighting gaps in service access, using big data and an interdisciplinary approach.

The findings of this study from Baby-Kingdom.com emphasize the need to improve the accessibility of TCM-related discussions. An official platform for professionals is proposed, with government support for reliable information and partnerships with local universities to expand services.

This study provides valuable insights into the popularity of TCM in Hong Kong, which may encourage uptake and use of TCM services in the health-care sector in not only Hong Kong but also the Greater Bay Area, China and potentially other countries in the future.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

The purpose of this paper is to investigate how educators perceive and adapt their roles in the face of changes in technology-driven learning environments. The Gioia methodology explores how educators enable adaptive learning, broaden their pedagogical practice and promote cultural inclusivity to educate diverse students.

This paper involves a qualitative interpretive phenomenological research design using the Gioia methodology. Data was collected through semi-structured interviews with 14 educators across various disciplines. Gioia methodology is a structured exploration of first- and second-order themes and aggregate dimensions that capture the nuanced ways in which educators navigate adaptive learning contexts.

This study highlights how digital tools help enable personalized self-directed learning, how adaptive learning adapts educators to become more than just traditional teachers and how the culturally responsive teaching that is required in a globalized learning environment promotes inclusivity and resilience in a diverse group of learners.

This study contributes to the growing body of literature on adaptive learning and identifies educators’ critical, expanded roles in a technology-centred world. This research provides a structured, in-depth analysis of educator perspectives on adaptive learning using the Gioia methodology, offering unique insights into the policy and practice implications.

In the context of macroeconomic fluctuations and uncertainty in policy changes, it is essential to understand how companies adapt their environmental strategies and marketing tactics to ensure survival and growth. This study, therefore, examines the impact of perceived economic policy uncertainty on corporate greenwashing.

Based on panel data from listed companies on the Chinese A-share market between 2013 and 2022, this paper employs a high-dimensional fixed effects model to explore the impact of perceived economic policy uncertainty (PEPU) on corporate greenwashing behavior.

The results show that higher PEPU increases greenwashing, with agency costs and investor sentiment mediating the relationship. Corporate credit availability and managerial short-sightedness positively moderate this effect. Heterogeneity analysis reveals that non-state-owned enterprises in central and western regions, particularly those with weak environmental regulation and high pollution, are most impacted by PEPU.

This paper provides practical guidance for how to avoid the phenomenon of green reshuffle in economic and environmental policies and encourages enterprises to take more real and effective environmental protection measures.

These findings highlight the importance of considering corporate responses to policy uncertainty when formulating economic and environmental policies. They provide valuable insights for emerging economies in fostering genuine corporate environmental behavior and promoting sustainable development.

Heterogeneous risk disclosure has been proven to improve the efficiency of new stock issuance, but excessive risk disclosure during the IPO may lead to irrational underestimation of the company, which is different from the original intention of management's detailed disclosure. Therefore, this study aims to examine the impact of IPO heterogeneous risk disclosure on earnings management motivations from the information transfer perspective of earnings management.

The sample includes 2,000 listed companies listed firms on Shanghai and Shenzhen Stock Exchanges from 2007 to 2022. This study uses the pretrained ERNIE model to measure text similarity in the prospectus to measure the heterogeneity of IPO risk disclosure.

This study empirically finds that heterogeneous IPO risk disclosure suppresses the opportunistic motivation of earnings management because managers tend to use earnings management to leverage information transmission functions. Such an effect is more pronounced in firms with higher analyst attention, lower marketization levels and non-state-owned. And heterogeneous risk disclosure may inhibit management’s over-investment behavior, thereby reducing the possibility of management engaging in opportunistic earnings management. Besides, price discounts are used to distinguish opportunistic and non-opportunistic earnings management and carry out a quasi-natural experimental design to demonstrate that marketization can enhance the relationship between heterogeneous risk disclosure and earnings management.

This study contributes evidence regarding the economic consequences of managerial earnings management behavior related to heterogeneous IPO risk disclosure. It supports highlighted firms in the IPO risk information disclosure to mitigate potential adverse outcomes through earnings management. This contributes to the literature and enhances information transparency in the capital market, fostering the healthy development of China’s capital market.

Metadiscourse is an important dialogue technique used in productive knowledge building to help a group evaluate and advance their knowledge progress. Previous studies have identified and defined various types of metadiscourse. However, there is scant knowledge about how different metadiscourse types emerge among different groups or what implicit correlations lie between progressive discourse and metadiscourse. Moreover, research on how different types of metadiscourse influence groups' knowledge advancement and artifacts is still inadequate. Therefore, this study aims to further examine the roles that different types of metadiscourse play in the collaborative knowledge building community on both a fine-grained (i.e. progressive discourse) and coarse-grained (i.e. group knowledge advancement and group artifacts) level.

Data for this study are drawn from the behaviour of undergraduate students participating in a 12-week course at a key university in China. On the fine-grained level, epistemic network analysis (ENA) is applied to illustrate how metadiscourse promotes the development of progressive discourse. On the coarse-grained level, two different chi-square tests are conducted to examine the roles of different types of metadiscourse in groups' knowledge advancement and artifacts.

The analysis allowed several conclusions to be drawn. First, the types of metadiscourse that students most often adopted were reflecting on ideas development (RD) and commenting on ideas (CI); they less frequently adopted setting group goals (SG) and making group plans (MP). Second, most types of metadiscourse correlated with developments in progressive discourse, particularly RD and CI. Third, the metadiscourse types RD, CI and coordinating group efforts (CE) played essential roles in knowledge advancement. Fourth, higher-quality artifacts could be created by using the metadiscourse type reviewing the state of knowledge building progress (RP).

A more profound comprehension of the role that metadiscourse plays in the collaborative knowledge building community not only contributes to the literature in the knowledge building field but also carries a significant meaning in regulating community, promoting learner agency and sustained knowledge, and consequently improving collaborative learning performance.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.