Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Integrated Impact of Circular Economy, Industry 4.0, and Lean Manufacturing on Sustainability Performance of Manufacturing Firms.

In today's business environment, contributions made by the manufacturing sector to the economy and social development is evident. With a focus on long-term development, the manufacturing sector has adopted advanced operating strategies, such as lean manufacturing, industry 4.0, and green practices in an integrated manner. The integrated impact of circular economy, industry 4.0, and lean manufacturing on sustainability performance has not been adequately addressed and investigated. Therefore, the aim of this study is to investigate the integrated impact of circular economy, industry 4.0, and lean manufacturing on the sustainability performance of organizations in Saudi Arabia. Data were collected through a questionnaire-based survey as a primary data instrument. A total of 486 organizations have responded to the survey within the timeframe. Moreover, the structural equation modeling method is utilized for data analysis through SmartPLS tool for the developed hypotheses of the research. The findings highlight the positive impact of circular economy on the sustainability of the organizations. Furthermore, the results indicate that industry 4.0 and lean manufacturing have positive mediating impacts as enablers for the successful implementation of circular economy toward the sustainable performance of organizations in Saudi Arabia. The study finding confirms that lean manufacturing is a substantial mediating variable that is essential for the successful implementation of industry 4.0 technologies. Moreover, the study indicates the recognition and acknowledgment of companies on circular economy principles, industry 4.0 technologies, and lean manufacturing tools to achieve the desired sustainability.

A neural network-based model for estimating the delivery time of oxygen gas cylinders during COVID-19 pandemic.

Since COVID-19 was declared as a pandemic by World Health Organization in March 2020, 169,682,828 cases have been reported worldwide, with 151,416,570 recovered, and 3,526,647 deaths by May 28, 2021. Oxygen gas cylinders demand is booming globally due to its need for COVID-19's for intensive care. Thus, it is critical for hospitals to know exactly the time of receiving oxygen gas cylinders since this will help in minimizing the fatality rate. In this regards, this paper proposes a Multilayer Perceptron Neural Network-based model to predict the delivery time of oxygen gas cylinders for a real-life logistics data from a company that delivers oxygen gas cylinders to all cities around Saudi Arabia. Besides, Multilayer Perceptron Neural Network is benchmarked to supported vector machine and multiple linear regression. Although all the considered models have the ability to provide accurate prediction results, the findings indicate that the proposed supported vector machine and Multilayer Perceptron Neural Network model provide better prediction results. The analysis was achieved through a methodology to identify factors with the highest impact and build a neural network model. The model was further optimized to identify the best order and select the best subset of input variables. The analysis showed that the neural network model can be used effectively to estimate the delivery time of oxygen gas cylinders. The model illustrated high accuracy of prediction by comparing the predicted values to the actual values.