The paper describes the possibilities of implementing cobots for the execution of manual tasks in human-cobot collaborative teams to reduce waste within manufacturing systems from the perspective of Industry 5.0. Particular attention is paid to those manufacturing systems where, due to the high...

In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

Remanufacturing processes are one of the key pillars of the so-called closed-cycle economy. Unfortunately, the design and organization of such processes pose a significant problem, due to, among other things, the high variability of the process as a consequence of both the degree of wear and tear and the quality of the remanufactured components. This article presents a method for optimizing the flow of the brake caliper remanufacturing process in the MTO (make-to-order) model. For the optimization process, the system dynamics (SD) method, which belongs to a group of simulation methods, was used. The developed model allows, in particular, the prediction of the number of components at the input to the regeneration process and the method of defining the size of the transport batch and evaluating its impact on the length of the production cycle of the batch of regenerated products. The issues of staffing management at individual positions and the organization of transport operations with variable production batch sizes were also discussed. The provided experiments have shown that reducing the size of the transport batch contributes to shortening the production order execution time, both with standard and increased staffing.

Sediment accumulation in dam reservoirs significantly impacts hydropower efficiency and infrastructure sustainability. Bottom sediments often contain heavy metals such as Cr, Ni, Cu, Zn, Cd, and Pb, which can pose ecological risks and affect water quality. Moreover, excessive sedimentation reduces reservoir capacity, increases turbine wear, and raises operational costs, ultimately hindering energy production. This study examined the ecological risk of heavy metals in bottom sediments and explored predictive approaches to support sediment management. Using 27 sediment samples from Zemborzyce Lake, the concentrations of selected heavy metals were measured at two depths (5 cm and 30 cm). Ecological risk index (ERI) values for the deep layer were predicted based on surface data using artificial neural networks (ANNs) and multiple linear regression (MLR). Both models showed a high predictive accuracy, demonstrating the potential of data-driven methods in sediment quality assessment. The early identification of high-risk areas allows for targeted dredging and optimized maintenance planning, minimizing disruption to dam operations. Integrating predictive analytics into hydropower management enhances system resilience, environmental protection, and long-term energy efficiency.

This study explores the use of machine learning models to predict the percentage of the population unable to keep their houses adequately warm in European countries. The research focuses on applying three machine learning models—ElasticNet, decision trees, and neural networks—using macro-energy indicator data from Eurostat for 27 European countries. Neural networks with Bayesian regularization (BR) achieved the best performance in terms of prediction accuracy, with a regression value of 0.98179, and the lowest root mean squared error (RMSE) of 1.8981. The results demonstrate the superior ability of the BR algorithm to generalize data, outperforming other models like ElasticNet and decision trees, which also provided valuable insights but with lower precision. The findings highlight the potential of machine learning to predict the percentage of the population unable to keep their houses adequately warm, enabling policymakers to allocate resources more efficiently and target vulnerable populations. This research is the result of the application of machine learning models to solve the problem of energy poverty.

The purpose of this article is to propose a fuzzy logic system as a tool for automated risk identification of potential technical challenges and social barriers during the implementation of artificial intelligence-based co-bots on workstations in manufacturing enterprises. On the basis of an...