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The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

The efficient application of building information modeling (BIM) methodology in the sustainable building design process, known as green BIM, provides ideal leverage to significantly enhance multidiscipline team collaboration. However, the practical execution of green BIM is characterized by issues such as duplication of work, information silos and poor cross-party coordination. Besides, there are limited studies on the specific components that are critical to driving green BIM collaborative design. This study aims to establish the critical components of green BIM collaborative design to enable the multidiscipline team to effectively use diverse software to collaboratively exchange accurate information, thus ensuring informed decision-making in the sustainable building design process.

Data were obtained by using a questionnaire to survey 360 respondents comprising mainly architects and engineers (civil, mechanical and electrical) in Malaysia. Subsequently, data were analyzed via confirmatory factor analysis. Afterward, a measurement model was established and used to test the 11 hypotheses of this study.

A covariance-based structural equation model of the critical components for successful BIM-based sustainable building design collaboration was established.

The research findings will guide the multidisciplinary team to collaboratively exchange accurate information in green BIM practices.

To the best of the authors’ knowledge, this research is the first attempt in the literature to provide a pragmatic approach for practitioners to combine the established critical components of green BIM to collaboratively exchange heterogeneous sustainability criteria and efficiently design buildings with high sustainability performance, particularly in emerging countries like Malaysia.

The purpose of this article is to provide a conceptual framework for measuring the performance of multidiscipline teams in knowledge‐creating organizations.

The author conducts a meta‐analysis of case studies on individual and team performance and technical communication in the context of creating organizational knowledge. This analysis provides a basis for a new conceptual framework for team functions and performance.

This framework updates the definition of team functions to include knowledge creation and communication. The framework describes several factors that contribute to successful performance and provides a scale for measuring successful performance based on the additional team functions.

An organization's ability to create and manage knowledge may be their only lasting competitive advantage. Managers, whose responsibilities include fostering purposeful knowledge creation and transfer, need a framework for understanding how these goals can be factored into team performance and what factors contribute to the successful achievement of these goals. This paper provides such a framework.

This is the first conceptual framework for team performance that includes knowledge creation and stakeholder communication as principle team functions.

Organizational process improvement plays an important role for sustaining business in a competitive environment. Therefore, enterprise leaders are increasingly prone to adopt business process improvement (BPI) practices. However, organizations are unable to implement formal and reusable solutions, representing a gap between academic research and practical use. In addition, companies adopt discipline-dependent solutions, lacking BPI representations of best practices applicable to all organizational divisions. This paper aims to propose some constructs on top of the Quintessence kernel for representing the practice systematic development of the BPI in the BPI lifecycle and we conduct two case studies in a multinational company.

A quantitative research design was adopted for recognizing gaps in previous approaches and identifying best BPI practices. Subsequently, characterization of practices and activities are represented based on a unified definition model and the Quintessence kernel. Finally, two case studies are developed for applying the solution.

The formal representation is applicable to multiple disciplines in organizational environments. Besides, the sub-alphas (abstract level progress health attribute) states and the work products resulting from each activity completion criteria evidence the health and progress accomplished during the practice execution.

The practice representation serves as a formal, graphical, reusable and multidiscipline guide compiling activities and tasks for systematically developing BPI during the radical/incremental improvement lifecycle.

The purpose of this study is to analyze and compare the impact of project-factors and organization-factors on five indicators of project performance for small and medium enterprise (SME) and large construction contracting firms that are fully responsible for the successful completion of the projects. The five performance indicators are time, cost, safety, quality and financial.

A questionnaire survey was conducted to solicit responses from project managers/directors from 342 construction firms in Malaysia. The construction firms included in this study came from various sectors: civil, building and infrastructure; oil and gas; marine and multidiscipline. Hierarchical multiple-regression was used to analyze the data.

The salient findings are as follows: (1) impacts of project-factors and organization-factors on performance indicators are different for SMEs and large construction firms and (2) relative impact of organization-factors on performance is much higher than the project-factors.

Analyzing the relative impact of project- and organization-factors on the performance of SMEs and large construction firms can significantly enhance the body of knowledge about performance levels and boost best practices in this respect related to construction industry.

As academic social Q&A networking websites become more popular, scholars are increasingly using them to meet their information needs by asking academic questions. However, compared with other types of social media, scholars are less active on these sites, resulting in a lower response quantity for some questions. This paper explores the factors that help explain how to ask questions that generate more responses and examines the impact of different disciplines on response quantity.

The study examines 1,968 questions in five disciplines on the academic social Q&A platform ResearchGate Q&A and explores how the linguistic characteristics of these questions affect the number of responses. It uses a range of methods to statistically analyze the relationship between these linguistic characteristics and the number of responses, and conducts comparisons between disciplines.

The findings indicate that some linguistic characteristics, such as sadness, positive emotion and second-person pronouns, have a positive effect on response quantity; conversely, a high level of function words and first-person pronouns has a negative effect. However, the impacts of these linguistic characteristics vary across disciplines.

This study provides support for academic social Q&A platforms to assist scholars in asking richer questions that are likely to generate more answers across disciplines, thereby promoting improved academic communication among scholars.

A simple interaction‐potential model has been established to calculate the higher order elastic constants of intermetallic YbAl2 in the temperature range from 10‐300K. Temperature dependent second and third order elastic constants are used for the determination of the ultrasonic attenuation, velocity, Grüneisen numbers, Acoustic‐coupling constants, and thermal relaxation time at the different temperatures. Temperature dependency of the ultrasonic properties of YbAl2 is similar at low temperatures to that of pure metals and the low carrier heavy fermion systems ‐ LaSb, YbAs and YbP having simple NaCl‐type structures. Thermal energy density makes significant contribution to the total attenuation in the compound at the higher temperatures from 100‐300K. Effect of the magnetic field on the ultrasonic attenuation is also evaluated using the magneto resistance data. At 100K, the effect of the magnetic field becomes insignificant. The attenuation decreases with the field at 3K to 50K.

The Internet of Things-based digital twin (IoT-DT) technologies offer a transformative approach to building retrofitting for reducing operational carbon (ROC) emissions. However, a notable gap exists between the potential and adoption of the two emerging technologies, further exacerbated by the nascent state of research in this domain. This research aims to establish the best practices that innovatively strengthen the identified enablers to decisively tackle challenges, ensuring the efficient implementation of IoT-DT for ROC emissions in buildings.

This study adopted a mixed-method approach. Questionnaire data from 220 multidiscipline professionals were analysed via structural equation modelling analysis, while interview data obtained from 18 stakeholders were analysed using thematic content analysis. The findings were triangulated for cohesive interpretation.

After the analysis of questionnaire data, a structural model was established, depicting the critical challenges (inadequate data security, limited technical expertise and scalability issues) and key enablers (robust data security measures, skill development and government incentives) of implementing IoT-DT for ROC. Sequentially, analysis of in-depth interview data revealed the IoT-based DT best practices (safeguarding data, upskilling and incentivization). Upon triangulating the questionnaire and interview findings, this study explicitly highlights the potential of the established best practices to strategically strengthen enablers, thereby mitigating challenges and ensuring the successful implementation of IoT-based DT for ROC emissions in buildings.

This study provides practical guidance for stakeholders to effectively implement IoT-DT in ROC in buildings and contributes significantly to climate change mitigation.

Robotic ironing needs multidiscipline and requires a quantitative analysis of garment unfolding and ironing motion. This paper investigates the trajectories and orientation of the ironing process where particular geometry is presented in an analytical way. The trajectories produced from this process are analysed and presented with mathematical models to be possibly implemented in robotic automation. This paper further investigates the orientation of iron during the ironing process. It is revealed that the orientation is dependent on the regions of garment and on the closeness to an operator. The orientation is then integrated into the trajectory and presented in a 3D form in which the vertical axis represent the orientation and horizontal axis represent the position. This type of orientation analysis is then used to find similarity in motions to determine the most effective and efficient way of ironing a garment.

THE INTRODUCTION of Tribology, the multidiscipline consequence of the incidence of interacting surfaces in relative motion, gave rise to the recognition of the arising complications not so discernable under the expression Lubrication. The complexity of the subject matter enveloped by Tribology has to be appreciated to be believed and it may be the readiness to appreciate only a fraction of the whole that causes the acceptance of the nondirectional route of the activities of those engaged in the field of Tribology.