Attentional bias toward health-threat in health anxiety: a systematic review and three-level meta-analysis

Attentional bias toward health-threat may theoretically contribute to the development and maintenance of health anxiety, but the empirical findings have been controversial. This study aimed to synthesize and explore the heterogeneity in a health-threat related attentional bias of health-anxious individuals, and to determine the theoretical model that better represents the pattern of attentional bias in health anxiety. Four databases (Web of Science, PubMed, PsycINFO, and Scopus) were searched for relevant studies, with 17 articles (N = 1546) included for a qualitative review and 16 articles (18 studies) for a three-level meta-analysis (N = 1490). The meta-analytic results indicated that the health anxiety group, compared to the control group, showed significantly greater attentional bias toward health-threat (g = 0.256). Further analyses revealed that attentional bias type, paradigm, and stimuli type were significant moderators. Additionally, compared to the controls, health-anxious individuals displayed significantly greater attention maintenance (g = 0.327) but nonsignificant attention vigilance to health-threat (g = −0.116). Our results provide evidence for the attention maintenance model in health-anxious individuals. The implications for further research and treatment of elevated health anxiety in the context of coronavirus disease-2019 (COVID-19) were also discussed.

Validation of copula-based weather generator for maintenance model of offshore wind farm

This article discusses the aspect of modeling weather conditions in marine environment for implementation in the offshore wind farm domain. It is clear that harsh sea weather conditions influence many characteristics of any offshore installation. The accessibility to the infrastructure, maintenance procedures, failure ratios of components, energy provision levels, or utilization of vessels—are the examples of weather-related issues connected to the offshore wind industry. Regarding the growing popularity of digital twin methodology, authors present a novel view to generate weather data with copula-based method. The results obtained are compared to selected historical data and implemented into the maintenance model. The selected indicators of maintenance service are used for usability assessment of proposed copula-based method.

Research on Intelligent Operation and Maintenance Model of Large Database for Electric Power Industry

Existing research on automated database operation and maintenance for the electrical industry mainly focuses on distributed and cloud platforms, and there is a lack of traditional large-scale database intelligent operation and maintenance research. This paper designs an overall operation and maintenance model framework of “intelligent perception-intelligent decision-intelligent execution”, and proposes feasible implementation plans, including: (1) Use the prophet time series forecasting model to perceive and predict important database performance indicators, and dynamically adjust the threshold of each performance indicator according to the predicted value; (2) Perform correlation analysis on abnormal indicators through the association rule model to construct “Indicators”->Operation” optimized combination operation strategy library for intelligent decision making; (3) According to the intelligent decision library, automatically restrict the associated operations under abnormal conditions to ensure the normal operation of the service and realize intelligent execution.

An integrated space-based building maintenance management model using multi-objective optimization

PurposeThe purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.Design/methodology/approachThis research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.FindingsFollowing 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.Originality/valueThe model is flexible and can be modified by facility managers to align with the required codes or standards.

BIG DATA ANALYTICS FOR PREVENTIVE MAINTENANCE MANAGEMENT

Maintenance data for government buildings in Putrajaya, Malaysia, consists of a vast volume of data that is divided into different classes based on the functions of the maintenance tasks. As a result, multiple interactions from stakeholders and customers are required. This necessitates the collection of data that is specific to the stakeholders and customers. Big data can also forecast for predictive maintenance purposes in maintenance management. The current data practise relies solely on well-structured statistical data, resulting in static analysis and findings. Predictive maintenance under the Big Data idea will also use non-visible data such as social media and web search queries, which is a novel way to use Big Data analytics. The metamodel technique will be used in this study to evaluate the predictive maintenance model and faulty events in order to verify that the asset, facilities, and buildings are in excellent working order utilising systematic maintenance analytics. The metamodel method proposed a predictive maintenance procedure in Putrajaya by utilising the big data idea for maintenance management data.