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 examines the effect of internal dynamic capabilities i.e., digital leadership, environmental awareness, and organizational learning, on sustainable supply chain performance as studied in the logistics sector. It builds on the Dynamic Capabilities Theory by combining notions of green innovation and sustainability and fills the growing gap in the existing literature. Despite the fact that these domains have been extensively studied independently, there has been limited research examining how internal capabilities contribute to green supply chain innovation (GSCI) that in turn results in sustainability outcomes, especially in the case of emerging markets. Seven hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of data collected from 312 logistics and supply chain professionals in Jordan. This study shows that each of the three capabilities has a major effect on GSCI and therefore sustainable performance. Linking the most influential predictor of sustainability outcome to sustainable supply chain performance, as indicated by the strongest effect (β = 0.825, p < 0.001) between GSCI and sustainable supply chain performance, and followed by significant coefficients between the sustainable information processing (β = 0.261, p < 0.01), and information capabilities (β = 0.297, p < 0.001), indicates that the theory is more suited to GSCI. In particular, digital leadership had the largest impact on the green innovation (β = 0.481, p < 0.001), indicating that the role of digitally driven leadership is to facilitate eco-innovation. In addition, this intermediate factor, GSCI, serves as a variable that mediates relationships between the capabilities and the sustainability outcomes. As the results here suggest, leveraging internal capabilities is a very tangible channel for green innovation that has important ramifications for practitioners and policymakers facing resource constraints.

The aim of this study is to obtain the non-similar solution of power law conducting fluid flow over a nonlinear moving permeable wedge utilizing the Levenberg–Marquardt scheme-artificial neural network. In this paper, a novel theoretical analysis that consists of the influence of a variable magnetic field on power law fluid flow past a stretchable porous wedge embedded in a Darcy-Forchheimer porous medium is performed. The second objective is to perform the local sensitivity analysis for fluid friction and heat transfer rate over the porous permeable wedge. The governing nonlinear partial differential equations are transformed into a system of nonlinear ordinary differential equations via the local non-similarity method. The system of ordinary differential equations is numerically solved using a finite difference scheme implemented in MATLAB. To evaluate parameter sensitivity, response surface methodology based on central composite design, along with analysis of variance, is employed using MINITAB 21. The Levenberg–Marquardt scheme shows excellent agreement with the numerical results, with absolute errors consistently in the range of 10−4–10−5. The model achieved optimal validation performance with mean squared errors of 9.1185 × 10−10 for skin friction and 1.0251 × 10−11 for the heat transfer rate. Results indicate that the presence of a Darcy–Forchheimer porous medium leads to increased velocity and enhanced heat transfer, evidenced by reduced temperature profiles and elevated skin friction coefficients and the Nusselt numbers. Furthermore, the Forchheimer parameter is the most sensitive parameter for skin friction and Nusselt number as compared to other parameters.

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.