Zahra Saki

Graphs and charts have been used for hundreds of years for data visualization, perhaps thousands of years if we count pictographs and symbols in early written languages. Creating these graphs and charts was very time‐consuming when done by hand, and journals and book publishers were reluctant to include them because of the cost. Text and tables were far easier to print. This mindset began to change about 50 years ago with the advent of computer software that made creating graphs and charts easier, as well as a series of books by Edward Tufte, John Tukey, and others that explained how the exploration of large amounts of data benefited from visualizations. Over the past decade, the development of new software and applications has enabled dynamic, interactive data visualizations, making sophisticated presentations accessible to modern researchers across various fields. No longer are these command‐and‐control centers with dynamic interactive displays limited to well‐funded organizations such as nuclear power plants, chemical company dashboards, and NASA; they are now becoming more commonplace on websites, smartphone displays, automobile dashboards, and many other business and industrial applications. In this paper, we provide numerous examples, guidelines for creating dynamic and interactive data visualizations, and offer some cautions regarding data quality.

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.

Purpose: The purpose of this study is to identify US textile and apparel (TAP) products and categories that demonstrate export comparative advantage and their respective country destinations.Design/methodology/approach: Revealed comparative advantage (RCA) and normalized revealed comparative advantage (NRCA) in the long term (1996-2016) and short term (2010-2016) using two-digit Harmonized Commodity Description and Coding System codes (HS codes) provide an insight into export advantage of TAP products. Non-parametric rank correlation (i.e. Spearman rank correlation) tests consistency between RCA and NRCA. Subsequently, NRCA using four-digit HS codes identify textile sub-categories with export comparative advantage. Bilateral trade data identify export destinations for sub-categories that indicate US export comparative advantage in the short term.Findings: Aggregated product-level comparison of RCA and NRCA ranking indicates a consistent positive correlation between the two indices. The disaggregated findings reveal cotton fiber HS5201, artificial filament tow HS5502, non-wovens HS5603, cotton yarn HS5205, carpet and other floor coverings HS5703 and worn clothing HS6309 as sources of US TAP export advantage.Originality/value: This research provides a comprehensive, current analysis of the US competitive position within the TAP global environment. Incorporation of NRCA into the study’s design demonstrates the flexibility of this measure in a new industry context.

United Nations COMTRADE data are used to examine worn clothing trade flows over the most recent five years (2013-2017). The focal metric for analysis is export values expressed in U.S. dollars. Export data were queried using various time scenarios including: 2013-2017, 1996-2008 and pre and post Multi-Fibre Arrangement (MFA) (1996-2005, 2006-2017, respectively). The five year period was selected based on the goal to identify current impacts and policy implications. To identify complex patterns of trade flows between exporting and destination countries, a visual tool is adopted. An interactive Sankey diagram presented in static form illustrates destination countries for the top seven global worn clothing exporters.