Improving occupational safety in office spaces in the post-pandemic era

The rise of COVID-19 and its consequent socio-economic losses raised concerns regarding the resilience of workplaces against widespread infectious diseases. During the COVID-19 pandemic, several outbreaks occurred in workplaces. As a result, local authorities implemented restrictive interventions (e.g., lockdown and social distancing) to control the spread of this disease in different contexts. Despite the short-term positive impacts of these interventions, they are not sustainable in the long run due to their associated economic costs to industries. Hence, in the post-pandemic era, novel and non-restrictive interventions are needed to limit the spread of similar diseases inside workplaces during epidemics. Herein, several non-restrictive interventions have been introduced to limit the spread of COVID-19 in office spaces. The effectiveness of these interventions is tested in generic office space by a disease spread simulator (CoDiSS), which is based on stochastic agent-based modeling. As a result, this research identifies the most impactful interventions based on the simulation outcomes and offers practical strategies to improve occupational safety within office environments. Our findings help enhance safety in the ever-transforming occupational environment by limiting the spread of infectious diseases in workplaces using non-restrictive interventions

The Relationship between Sleep Quality and Happiness in Men with Coronary Artery Disease

Background and Aim: Good sleep quality has beneficial effects on happiness and being unhappy is significantly associated with negative cardiac outcomes. The present research aimed at studying the relationships between sleep quality and happiness in male coronary patients. Methods: One hundred male coronary patients that having been referred to Madani Heart Hospital, Tabriz, Iran, completed the Pittsburgh Sleep Quality Index (PSQI) and Oxford Happiness Questionnaire (OHQ). All participants were selected by purposive sampling (aged 37 to 67 years). Data were analyzed by multiple regression (simultaneous method) through the SPSS 18 software.Results: There was a significant negative difference between happiness with sleep disturbances and use of sleeping medication in coronary patients. Conclusion: This study showed that poor sleep quality in coronary patients has negative effects on their happiness. Therefore, the quality of sleep in these patients should be given more consideration by community health care providers.Keywords: Sleep Quality, Happiness, Coronary Artery Disease

An Artificial Intelligence-Based Framework for Automated Information Inquiry from Building Information Models Using Natural Language Processing and Ontology

Building information modeling (BIM), a novel technology in the architectural engineering and construction (AEC) industry, contains various data and information, which is so practical and can be required by many stakeholders during the project\u27s life cycle. For non-technical users with limited or no skill in dealing with BIM software, access to this data can be time-consuming, and tedious. Automating the information extraction from BIM models can efficiently address this need. In this regard, this research proposes an artificial intelligence (AI)-based framework to facilitate information extraction from BIM models. Therefore, the user can ask questions and receive answers from the framework. Utilizing natural language processing (NLP), an ontology database (IfcOWL) and an NLP method [latent semantic analysis (LSA)], the purpose of the user is understood by the framework through syntactic analysis and semantic understanding of the question and answer to the user, based on functions. The results show that the speed of answering the questions in this framework is up to five times faster than the manual while maintaining high accuracy

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation

LEVERAGING NATURAL LANGUAGE PROCESSING FOR AUTOMATED INFORMATION INQUIRY FROM BUILDING INFORMATION MODELS

Building Information Modeling (BIM) is a trending technology in the building industry that can increase efficiency throughout construction. Various practical information can be obtained from BIM models during the project life cycle. However, accessing this information could be tedious and time-consuming for nontechnical users, who might have limited or no knowledge of working with BIM software. Automating the information inquiry process can potentially address this need. This research proposes an Artificial Intelligence-based framework to facilitate accessing information in BIM models. First, the framework uses a support vector machine (SVM) algorithm to determine the user\u27s question type. Simultaneously, it employs natural language processing (NLP) for syntactic analysis to find the main keywords of the user\u27s question. Then it utilizes an ontology database such as IfcOWL and an NLP method (latent semantic analysis (LSA)) for a semantic understanding of the question. The keywords are expanded through the semantic relationship in the ontologies, and eventually, a final query is formed based on keywords and their expanded concepts. A Navisworks API is developed that employs the identified question type and its parameters to extract the results from BIM and display them to the users. The proposed platform also includes a speech recognition module for a more user-friendly interface. The results show that the speed of answering the questions on the platform is up to 5 times faster than the manual use by experts while maintaining high accuracy