The textile and apparel industry faces significant sustainability challenges due to the high volume of waste it generates and the limitations of current recycling systems. Automation in textile waste management has emerged as a promising solution to enhance material recovery through accurate and efficient sorting. This systematic literature review, conducted using the PRISMA-guided PSALSAR methodology, examines recent advancements in computer-based sorting technologies applied in textile recycling. This study identifies and evaluates major technological methods often integrated with machine learning, deep learning, or computer vision models. The strengths and limitations of these approaches are discussed, highlighting their impact on classification accuracy, reliability, and scalability. This review emphasizes the need for further research on blended fiber detection, data availability, and hybrid models to advance automated textile waste management and support a sustainable circular economy.

With the rapid advancement of quantum computing technology, there is a growing need for new debugging tools for quantum programs. Recent research has highlighted the potential of assertions for debugging quantum programs. In this paper, we investigate assertions in quantum ternary systems, which are more challenging than those in quantum binary systems due to the complexity of ternary logic. We propose quantum ternary circuit designs to assert classical, entanglement, and superposition states, specifically geared toward debugging quantum ternary programs.