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This paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.

This study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.

The results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.

Body of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.

With the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.

The results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.

The study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.

The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.

The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.

The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.

To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

The purpose of this paper is to propose an attention alignment method for opinion mining of massive open online course (MOOC) comments. Opinion mining is essential for MOOC applications. In this study, the authors analyze some of bidirectional encoder representations from transformers (BERT’s) attention heads and explore how to use these attention heads to extract opinions from MOOC comments.

The approach proposed is based on an attention alignment mechanism with the following three stages: first, extracting original opinions from MOOC comments with dependency parsing. Second, constructing frequent sets and using the frequent sets to prune the opinions. Third, pruning the opinions and discovering new opinions with the attention alignment mechanism.

The experiments on the MOOC comments data sets suggest that the opinion mining approach based on an attention alignment mechanism can obtain a better F1 score. Moreover, the attention alignment mechanism can discover some of the opinions filtered incorrectly by the frequent sets, which means the attention alignment mechanism can overcome the shortcomings of dependency analysis and frequent sets.

To take full advantage of pretrained language models, the authors propose an attention alignment method for opinion mining and combine this method with dependency analysis and frequent sets to improve the effectiveness. Furthermore, the authors conduct extensive experiments on different combinations of methods. The results show that the attention alignment method can effectively overcome the shortcomings of dependency analysis and frequent sets.

The primary objective of this study was to investigate the viability of Lacticaseibacillus rhamnosus GR-1 (LGR-1) when combined with four prebiotic-rich fruit powders – apple, papaya, mango, and red beetroot – in probiotic yogurt. Additionally, the study aims to assess customer acceptability of the yogurt fortified with these fruit powders through a sensory evaluation using a nine-point hedonic scale.

The yogurt samples, inoculated with the LGR-1 probiotic strain, underwent fermentation at 38 °C for 0, 2, 4 and 6 h. Following fermentation, the samples were stored in a refrigerator at 4 °C for 1, 15 and 30 days. Throughout the study, microbial counts and pH level measurements were performed to assess the viability of LGR-1. A sensory evaluation consisted of 89 participants. A nine-point hedonic scale, ranging from 1 (dislike extremely) to 9 (like extremely), along with a questionnaire were used to assess criteria such as appearance, flavor, texture and overall acceptability of the samples.

All treatments at all time points maintained a minimum viable microbial count of 10⁷ CFU/mL (colony-forming units per mL), which indicated that the addition of fruit powders supported the growth and survival of LGR-1 in yogurt. Treatment 5, fortified with papaya powder, was the only group that exhibited a significant change of microbial count after 30 days of fermentation (p = 0.018). Although there were no statistically significant differences in pH values at the 0- and 2-h time points within each treatment, the pH remained relatively stable after day 15, with an average mean pH of 4.29. Treatment 2 fortified with mango powder obtained the highest overall acceptability score because of its smooth and firm texture as well as mild mango-sweet flavor.

This study explored the viability of probiotics and the sensory properties of yogurt fortified with various fruit powders, while also examining the potential prebiotic effects of fruit powders in enhancing overall sensory appeal. The findings suggested that papain may play a role in increasing probiotic viability in yogurt. Given the inconvenience and inaccessibility of fresh fruits and the generally inadequate prebiotic intake, this research addressed the gap in prebiotic consumption by offering novel ideas for health-enhancing dairy products.

Adopting large eddy simulation (LES) to simulate the complex flow in turbomachinery is appropriate to overcome the limitation of current Reynolds-Averaged Navier–Stokes modelling and it provides a deeper understanding of the complicated transitional and turbulent flow mechanism; however, the large computational cost limits its application in high Reynolds number flow. This study aims to develop a three-dimensional GPU-enabled parallel-unstructured solver to speed up the high-fidelity LES simulation.

Compared to the central processing units (CPUs), graphics processing units (GPUs) can provide higher computational speed. This work aims to develop a three-dimensional GPU-enabled parallel-unstructured solver to speed up the high-fidelity LES simulation. A set of low-dissipation schemes designed for unstructured mesh is implemented with compute unified device architecture programming model. Several key parameters affecting the performance of the GPU code are discussed and further speed-up can be obtained by analysing the underlying finite volume-based numerical scheme.

The results show that an acceleration ratio of approximately 84 (on a single GPU) for double precision algorithm can be achieved with this unstructured GPU code. The transitional flow inside a compressor is simulated and the computational efficiency has been improved greatly. The transition process is discussed and the role of K-H instability playing in the transition mechanism is verified.

The speed-up gained from GPU-enabled solver reaches 84 compared to original code running on CPU and the vast speed-up enables the fast-turnaround high-fidelity LES simulation.

The GPU-enabled flow solver is implemented and optimized according to the feature of finite volume scheme. The solving time is reduced remarkably and the detail structures including vortices are captured.

This study aims to examine the impact of social advertising (informative, entertainment, credibility, ease of use, privacy and contents) on the buying behavior of Muslim consumers toward the fashion clothing brands during the Holy Month of Ramadan along with the moderating role of brand image. Precisely, it focuses on the marketing techniques and strategies in social advertising to enhance buying behavior.

Using the convenience sampling technique, data was collected from 304 Muslim consumers during the Holy Month of Ramadan. Questionnaires were self-administered, and data was analyzed via Smart partial least square structural equation modeling.

Social advertising (informative, entertainment, credibility, privacy, ease of use, contents) and brand image have a positive relationship with the buying behavior of Muslim consumers toward the fashion clothing brands during the Holy Month of Ramadan, while the brand image has nonmoderating effects. Furthermore, social advertising has a positive and significant relationship with the brand image.

This study is only limited to fashion clothing brands in the Malaysian Muslim community and is based only on the few dimensions of the theory of reasoned action and technology acceptance model (TAM).

Results clarified the impact of social advertising and brand image on the buying behavior of Muslim consumers toward the fashion clothing brands during the Holy Month of Ramadan and the moderating role of brand image in achieving the business objectives.

This study has evaluated the effects of social advertising and brand image in enhancing the buying behavior of Muslim consumers during the Holy Month of Ramadan toward the fashion clothing brands along with the moderating role of brand image based on the theory of reasoned action and TAM model. Precisely, this study examined the unique characteristics of social advertising and the relative importance of informative, entertainment, credibility, ease of use, privacy and content in enhancing the buying behavior of Muslim consumers during the Holy Month of Ramadan, where consumers are emotionally involved in buying fashion clothing brands due to Eid al Fitr celebration.

At the end of the 1970s, the Chinese government enacted the one-child policy; now the one-child successively enters into the labor market and reaches the age for marriage and childbirth. The floating population group of China’s interior regions also experiences the heterogeneity changes. The purpose of this paper is to analyses the reasons for the difference of family migration between one-child and non-only child using the binary logit regression model – from the three aspects of individual characteristics, family endowment and institutional factors were investigated.

Family migration or individual migration of the floating population is the dichotomous dependent variable and therefore the binomial logistic regression analysis model is selected.

It is found that the tendency of one-child family migration is significantly higher than that of non-only child. The main reason is that the one-child has obvious advantages in terms of individual characteristics, family endowment and institutional factors.

The previous researches on family migration: first, the previous researches mainly analyzed the impact of the human capital and family income on the family migration from the perspective of economics and neglected the discussion on the family structure, life cycle, family level factors and Hukou’s limitation; second, most researches considered the migration as a whole. In fact, the migration population is no longer a highly homogeneous group and gradually become diversified.

T Education is a leading educational science and technology enterprise in China with technology-driven, talent intimacy and quality leadership as the core development objectives. Since its inception, it has been committed to creating better learning experience for children. As the predecessor of T-education, X-education was founded in Beijing in 2003. At first, it mainly provided after-school math counseling for school-age children. Over the past 10 years, its business has been expanding, covering almost every aspect of school-age education. This case studies accounting issues and business ethics challenges that firms may face when they transform from a single (traditional education) line of business to a multiple channel business.