The spinning industry makes a major contribution to environmental pollution due to the excessive use of natural assets and the generation of remarkable amounts of waste during manufacturing processes. Now, the spinning industries are concentrating on sustainable activities due to environmental issues. While textile recycling efforts have been widely explored, the utilization of soft waste (process waste) in yarn production remains underexplored. This study addresses this gap by investigating a sustainable approach incorporating soft waste into producing sustainable yarn using the ring-spinning technique. The research explores the properties of yarns manufactured from a blend of virgin cotton and soft waste, and 100% virgin cotton yarn is produced for comparison. The results indicate that incorporating soft waste leads to an increase in CVm% (13 vs. 11), hairiness (6.9 vs. 5.1), and IPI (165 vs. 125) compared to virgin cotton yarn. However, the elongation percentage (7.1% vs. 8%) and tensile strength (12.6 cN/tex vs. 16.2 cN/tex) showed a reduction, highlighting potential trade-offs in mechanical properties. The statistical analysis applies one-way ANOVA to evaluate the significance of variations in yarn characteristics made from the mixture of soft waste + virgin cotton and only virgin cotton. The manufactured yarns were examined in a modern weaving machine as weft yarn for fabric (denim) manufacturing and found to be perfect for normal operation. The article focuses on reducing negative impacts on the fabric (denim) manufacturing environment by incorporating soft waste to produce sustainable yarn. This research provides important insights into the production of sustainable yarns, focusing on environmental concerns.

This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in travel time due to load-dependent factors. Significant delays are transferred to subsequent groups to simulate delay propagation. The primary objective is to minimize the network clearance time—the total time required for the last group of evacuees to reach safety from the start of the evacuation. Given the model’s computational intensity, a simplified version is introduced for comparison. Both the original and simplified models are tested on small networks and benchmarked against the Cell Transmission Model, a well-regarded method in dynamic traffic assignment literature. Additional objectives, including average travel time and average evacuation time, are explored. A sensitivity analysis is conducted to assess how varying the number of evacuee groups impacts model outcomes.

The integration of hybrid renewable energy systems (HRES) has introduced both opportunities and challenges in managing multisource power systems such as wind and solar. Accurate forecasting of HRES performance is critical to efficient planning and grid stability. This paper proposes a data efficient hybrid framework that combines Grey Wolf Optimization (GWO) for feature selection with Deep Belief Networks (DBN) for predictive modeling. GWO effectively selects relevant features from high dimensional environmental and system parameters, reducing computational burden and enhancing learning performance. The DBN is then trained on the optimized input set to forecast system performance. Two public datasets capturing wind and solar power production across distinct geographic conditions were used for validation. The proposed model significantly outperforms conventional methods, achieving a mean square error of 0.0207, RMSE of 0.144, and an energy efficiency of 98.32%. These results demonstrate the framework’s potential for deployment in smart grid forecasting environments.