It is crucial to evaluate indoor personal thermal comfort for a comfortable and green thermal environment. At present, the research on individual thermal comfort does not consider its implementation mode. Moreover, the improvement of energy saving efficiency under the premise of increasing human comfort is an urgent problem that needs to be solved. In this paper, we proposed a Building Information Model (BIM) and Artificial Neural Network (ANN) based system to solve this problem. The system consists of two parts including an ANN predictive model considering the Predicted Mean Vote (PMV) index, the persons’ position, and an innovative plugin of BIM to realize dynamic evaluation and energy efficient design. The ANN model has three layers, considering three environment parameters (air temperature, air humidity, and wind speed around the person), three human state parameters (human metabolism rate, clothing thermal resistance, and the body position) and four body parameters (gender, age, height, and weight) as inputs. The plugin provides two functions. One is to provide corresponding personal thermal comfort evaluation results with dynamic changes of parameters returned by Wireless Sensor Networks (WSN). The other one is to provide energy saving optimization suggestions for interior space design by simulating the energy consumption index of different design schemes. In the data test, the Mean Squared Error (MSE) of the established ANN model was about 0.39, while the MSE of traditional PMV model was about 2.1. The system realized the integration of thermal information and a building model, thereby providing guidance for the creation of a comfortable and green indoor environment.

The construction of smart cities is a theme of urban development, and building fires greatly threaten public safety and urban environmental governance, in which fire emergency management is one of the key factors. However, most studies on the evaluation of emergency response capacity ignore the process of improvement, as well as the intelligence and practicality of the results. The evaluation system of building fire emergency response capability maturity (FE-CMM) was innovatively proposed based on the capability maturity model (CMM), including the evaluation index, evaluation grade, evaluation method, and evaluation process. At the same time, a plug-in for evaluating fire emergency response capability was developed based on the building information modeling (BIM) platform. Finally, an empirical study was carried out in combination with the case of a district fire center. The research demonstrates that the evaluation system can effectively judge the maturity of fire emergency response capability, and the established plug-in can preliminarily realize the intelligent evaluation of building fire emergency response capability, which improves the practice and intelligence of the fire emergency response capability evaluation system when fully considering the process of improvement. It has guiding significance for ex ante control and refined management of building fires, thus providing support for urban public safety and environmental governance.

Online learning has captured much attention, while given for high dropout rate, continuance of MOOCs is now a most concerned critical topic in both research and practical field. From teaching-based quality and platform-based quality perspectives, this study aims to investigate the impact of quality elements on continuance intention based on Expectation Confirmation Model, Task Technology Fit, flow theory and trust. We conducted our research through online questionnaire from July to September in 2020 and collected 555 valid responses which were mainly from university students who had already participated in MOOCs. A Partial Least Square Structural Equation Model approach is employed to test the research model. The results show that teaching-based quality will increase both students’ task technology fit and confirmation, and platform-based quality can improve the confirmation and perceived value about learning in MOOCs. Task technology fit, confirmation and perceived value will further facilitate the using experience and enhance trust and satisfaction. This research comprehensively illustrates the importance of quality relevant to teaching and platforms on continuance intention of MOOCs.

PurposeThe reviews submitted by users are the foundation of user-generated content (UGC) platforms. However, the rapid growth of users brings the problems of information overload and spotty content, which makes it necessary for UGC platforms to screen out reviews that are really helpful to users. The authors put forward in this paper the factors influencing review helpfulness voting from the perspective of review characteristics and reviewer characteristics.Design/methodology/approachThis study uses 8,953 reviews from 20 movies listed on Douban.com with variables focusing on review characteristics and reviewer characteristics that affect review helpfulness. To verify the six hypotheses proposed in the study, Stata 14 was used to perform tobit regression.FindingsFindings show that review helpfulness is significantly influenced by the length, valence, timeliness and deviation rating of the reviews. The results also underlie that a review submitted by a reviewer who has more followers and experience is more affected by review characteristics.Originality/valuePrevious literature has discussed the factors that affect the helpfulness of reviews; however, the authors have established a new model that explores more comprehensive review characteristics and the moderating effect reviewer characteristics have on helpfulness. In this empirical research, the authors selected a UGC community in China as the research object. The UGC community may encourage users to write more helpful reviews by highlighting the characteristics of users. Users in return can use this to establish his/her image in the community. Future research can explore more variables related to users.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0186.

In view of the problems such as the basic properties, usage, and location of prefabricated concrete building components, which are easy to be omitted, missed, and difficult to query in the field management, this study introduces building information modeling (BIM) and radio frequency identification (RFID) technologies to visualize the state information of prefabricated concrete components, such as component type, manufacturer, location, and temperature. In the design stage, a new RFID family can be built in the actual model in order to solve the lack of definition of RFID family through the Industry Foundation Class (IFC) standard, and the databases of BIM and RFID can be connected with C# language, realizing the effective integration of the two engineering technologies. In the application stage, through the secondary development of Revit, the information connection between PC terminal and RFID equipment is completed, and the component data collected by RFID tags are transmitted to the BIM model to realize the integration and visualization of prefabricated component state information. In this study, the traceability of prefabricated components of prefabricated concrete buildings can be improved, providing a basis for quality responsibility tracking in the later period, reducing unnecessary waste of human and material resources and helping to maximize economic benefits.