In reducing waste and protecting natural resources benefits in a circular economy platform, performing remanufacturing tasks are complex, as it may be associated with costs such as investment, setup and disposal cost. Thus, many studies those aims to find the optimal number of remanufacturing has been investigated whether it is an infinite or a constant number of remanufacturing via trial-and-error method. During the investigation, the disposal rate is assumed as a fixed value for each unique case, which needs further focus. The current study aims to propose a novel decision model to figure out an optimal number of remanufacturing regarding to the various ratio of used units returned for recovery. The proposed model was extended in context of remanufacturing opportunities of PVC products. The obtained findings are useful for companies in managing remanufacturing processes by knowing optimal remanufacturing times, and results in enhanced economic–ecological–social gains in the circular economy.

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature.

In today’s world, energy efficiency is becoming increasingly crucial, due to its impact on sustainability in production. Designing systems that consume less energy and manage resources efficiently is essential. Variations in operating speed can affect processing time, energy consumption, idle times of subsequent machines, work delays, and missed deadlines. While most studies focus on prediction parameters like cut depth and cut area to estimate the energy consumption or processing time, our approach emphasizes variations in G-code motion parameters. To enhance both precision and the adaptability of the model to all Computer Numerical Control (CNC) machines in no-load condition, we propose an intelligent hybrid model that combines physical and data-driven approaches. The first approach employs curve fitting based on the physical model, while the second utilizes deep neural network (DNN) models optimized through hyperparameter tuning. The DNN topologies we evaluated exhibit high performance, with prediction errors of less than 1% for all models. Our approach provides a more precise and comprehensive understanding of energy consumption patterns in manufacturing processes, enabling manufacturers to make informed decisions about energy utilization, cost reduction, and sustainability. Our study aims to advance the field of energy efficiency in manufacturing and contribute to a more sustainable future.

Digital transformation has provided the decision-making infrastructure needed to produce custom solutions for each person. Traditional decision support systems have begun to evolve into intelligent systems and mass customization has begun to be applied in many areas of life, such as education, transportation, health, and production. In order to achieve digital transformation in teaching, it is necessary not only to focus on the teaching process but also to focus on environmental factors. The provision of digital content and the performance of the student are carried out in the digital environment with education technologies. It is aimed to maintain sustainability of the environmental quality by following the digital environment by means of digital transformation and to improve the teaching processes with intelligent systems that will interfere immediately in emergency situations.