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Linkages between research, scholarship and teaching are a topic of contemporary interest in UK universities, driven by pressures such as traditional views of the nature and purpose of universities, reputation, student expectations of their teachers, educational enhancement through up‐to‐date research and scholarly input, and personal ambitions and satisfaction. The paper describes a study of these linkages at the Beijing Institute of Technology (BIT) during 2006 within the Sino‐UK Higher Education Leadership Development Programme, which allows for senior academics from China and the UK to study a particular management issue to identify good practice which they can apply in their institution. The activities included a preliminary workshop in the UK, a two‐week visit to BIT in and a workshop in Beijing. My study was conducted through a semi‐structured interview programme with a wide range of academics and administrators. It was enlightening to find that a leading Chinese university, which operates within quite different systems and cultures from the UK, nevertheless has similar issues, imperatives and problems. My overall conclusion is that there is international agreement that research and scholarly performance underpins the credibility of academic staff to teach at a university, which in turn attracts good students and research staff.

The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme.

This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score.

The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model.

The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

Given the popularity of online health communities (OHCs) and medical question-and-answer (Q&A) services, it is increasingly important to understand what constitutes useful answers and user-adopted standards in healthcare domain. However, few studies provide insights into how health information characteristics, provider characteristics and recipient characteristics jointly influence user information adoption decisions. To fill this research gap, this study examines the combined effects of physicians' certainty tone as information characteristics, seniority as provider characteristics and disease severity as recipient characteristics on patients' health information adoption.

Drawing on dual-process theory and information adoption model, an extended information adoption model is established in this study to examine the effect of attitude certainty on patients' health information adoption, and the moderating effects of online seniority and offline seniority, as well as patient motivation level—disease severity. Utilizing logit regression models, the authors empirically tested the hypotheses based on 4,224 Q&A records from a popular Chinese OHC.

The results show that (1) attitude certainty has a significant positive impact on patients' health information adoption, (2) the relationship between attitude certainty and information adoption is negatively moderated by physicians' online seniority, but is positively moderated by offline seniority; (3) there is a negative three-way interaction effect of attitude certainty, online seniority and disease severity on patients' health information adoption.

This study extends the information adoption model to examine the two-way interaction between argument quality and source reliability, as well as the three-way interaction with user motivation level, especially for health information adoption in the healthcare field. These findings also provide direct practical applications for knowledge contributors and OHCs.

Although the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. Many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM).

The approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems.

The use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM; hence, the decision-maker should consider using more quality measures if the objective is to preserve the original PCM characteristics.

For the first time, a many-objective approach reduces the CR to consistent levels with the ability to consider one or more quality measures and allows the decision-maker to adjust each pairwise comparison individually.

This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.

Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.

The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.

A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.

The highlights of the paper are as follows:

1. A new grey prediction model with a unified nonlinear structure is proposed.

2. The new model can be fully compatible with multiple traditional grey models.

3. The new model solves the defect of poor adaptability of the traditional grey models.

4. The parameters of the new model are optimized by PSO algorithm.

5. Cases verify that the new model outperforms other models significantly.

A new grey prediction model with a unified nonlinear structure is proposed.

The new model can be fully compatible with multiple traditional grey models.

The new model solves the defect of poor adaptability of the traditional grey models.

The parameters of the new model are optimized by PSO algorithm.

Cases verify that the new model outperforms other models significantly.

Given the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is to propose a new dynamic seasonal grey model based on PSO-SVR to forecast the production and consumption of electric energy.

In the model design, firstly, the parameters of the SVR are initially optimized by the PSO algorithm for the estimation of the dynamic seasonal operator. Then, the seasonal fluctuations in the electricity demand data are eliminated using the dynamic seasonal operator. After that, the time series after eliminating of the seasonal fluctuations are used as the training set of the DSGM(1, 1) model, and the corresponding fitted, and predicted values are calculated. Finally, the seasonal reduction is performed to obtain the final prediction results.

This study found that the electricity supply and demand data have obvious seasonal and nonlinear characteristics. The dynamic seasonal grey model based on PSO-SVR performs significantly better than the comparative model for hourly and monthly data as well as for different time durations, indicating that the model is more accurate and robust in seasonal electricity forecasting.

Considering the seasonal and nonlinear fluctuation characteristics of electricity data. In this paper, a dynamic seasonal grey model based on PSO-SVR is established to predict the consumption and production of electric energy.

The purpose of this paper is to evaluate the impact of the International Financial Reporting Standards (IFRS) and eXtensible Business Reporting Language (XBRL) on audit fees based on evidence from listed companies operating in an emerging economy. Whilst IFRS constitute high-quality accounting standards, XBRL represents a technology standard that can enhance the usability of IFRS and overall financial reporting transparency.

Multivariate analyses are used on a sample of 1,798 firm-year observations between 2000 and 2011 from companies listed in the Shanghai Stock Exchange that were subject to XBRL and IFRS adoption mandates.

The main results suggest that XBRL has a main negative effect on audit fees which is weaker for larger firms. Additionally, the authors find that IFRS increases audit fees for all companies. Whilst this effect is positive for firms of different sizes, it is weaker for larger firms.

Whilst the findings are applicable to the selected sample and may or may not be generaliseable to other economies, they can provide important implications for both regulators and companies that are undertaking IFRS convergence and XBRL implementation projects in developing economies around the world.

This study offers a timely assessment of the economic consequences of IFRS and XBRL on listed companies operating in an emerging economy, in addition to providing an important basis upon which further research can be designed in order to extend the analysis.

The purpose of this paper is to explore the joint effects of different types of IT resources and top management support. Especially, the authors attempt to mainly examine a negative synergy or substitution relationship between IT infrastructure resources and CEO support, and a positive synergy or complementary relationship between IT human resources and CEO support among the large-sized enterprises.

A research model that integrates IT infrastructure resources, IT human resources, CEO support and the degree of usage of IT for business objectives (i.e. IT business spanning capability) is developed. Based on a sample of 112 large-sized enterprises, partial least squares is used to analyze the research model.

Whereas the positive moderating role of CEO support in the effectiveness of IT human resources is insignificant, CEO support and IT infrastructure resources have a substitution relationship in predicting IT business spanning capability. Furthermore, the results can explain under which conditions IT infrastructure resources insignificantly or significantly affect IT business spanning capability in large-sized enterprises. Specially, IT infrastructure resources significantly affect IT business spanning capability only when CEO support is low. Thus, in the presence of high CEO support, IT executives in large-sized enterprises should prioritize developing highly effective IT resources, such as IT human resources.

This paper highlights the joint effects of two critical IT resource types (i.e. IT infrastructure and IT human resources) and CEO support in the IT assimilation process among the large-sized enterprises, ultimately contributing to information systems theories and practices.

The aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user.

This work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions.

The three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression.

As the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO₂ trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator “used to measure manufacturing efficiency” which is the overall equipment effectiveness used in the authors’ case to measure the improvement of the production system.

This study aims to investigate technology-based health and safety (H&S) management to control the spread of disease on construction sites using a partial least squares structural equation modelling (PLS-SEM) approach.

An extensive literature review is conducted to develop a conceptual framework. The variables identified from the literature review are included in a cross-sectional survey which gathered a total of 203 valid feedback. The variables for challenges are grouped under their relevant construct using exploratory factor analysis. Then, a hypothesized model is developed for PLS-SEM analysis using Smart PLS software. Later, the outcome of the model is further validated by nine construction experts using a semi-structured questionnaire survey.

The results rationalized the relationships between the COVID-19 H&S measures, challenges in implementing COVID-19 H&S measures on construction sites and the innovative technologies in transforming construction H&S management during the COVID-19 pandemic. The possible challenges that obstruct the implementation of H&S measures are highlighted. The potential technologies which can significantly transform H&S management by reducing the impact of challenges are presented.

The findings benefited the industry practitioners who are suffering disruption in construction operations due to the pneumonic plague.

By developing a conceptual model, this study reveals the contribution of technology-based H&S management for construction projects during the COVID-19 pandemic, which remains under-studied, especially in the context of the developing world.