A Life Cycle Assessment of Resource Use and Environmental Impacts in the Albanian Denim Garment Industry

2026-06-10T23:39:30+02:00

The textile and clothing industry is among the most resource-intensive sectors, consuming significant amounts of water, energy, and chemical products, particularly in denim production. This study analyses resource use and explores environmentally sustainable alternatives through a case study of a denim manufacturing company in Albania. The company operates under the Cut-Make-Trim (CMT) production model. A Life Cycle Assessment (LCA), in accordance with ISO 14040 and ISO 14044 standards, was conducted using a denim sample as the functional unit. Data were collected across key production stages, including cutting, assembly, industrial washing, dyeing, bleaching, ironing, packaging, and waste management. The results indicate that the washing process is the most resource-intensive stage, with a single pair of jeans requiring approximately 70 litres of water, contributing significantly to water pollution due to chemical discharge. Given a daily production of 2,500 pairs, monthly water consumption ranges between 2.75 and 3.8 million litres, depending on the product model. To reduce environmental impact, sustainable alternatives such as ozone washing, wastewater treatment and recycling systems, and the use of energy-efficient servomotors are recommended.

Digital Twin-Enabled Personalized E-Learning Using Deep Learning and Real-Time Learning Analytics

2026-07-01T10:20:35+02:00

The rise of online learning platforms has led to the need for smart and personalized educational solutions. Existing e-learning systems offer standardized learning solutions that do not consider individual differences in learning preferences, styles, engagement levels, and other factors. This study proposes the Digital Twin-Enabled Personalized E-Learning Framework that combines deep learning and real-time learning analytics. This framework develops dynamic learner digital twins based on data provided by learning management systems, assessments, discussion forums, learning videos, and clickstream activities. The hybrid LSTM-DNN algorithm is used to predict learner performance, engagement levels, chances of completing a particular course, and even risk of dropping out of courses. Real-time learning analytics provides continuous updating of learners' profiles and personalized recommendations and adaptation. The proposed framework was tested using learners' interaction data in online learning environments. As a result of experimentation, the hybrid LSTM-DNN model was able to achieve a prediction accuracy of 95.2%, which is better than the prediction accuracy of conventional deep learning algorithms. The application of personalized learning paths led to a significant increase in learners' engagement levels, academic performance, resource usage, and course completion rates. Besides, the recommendation engine provided 93.8% recommendation accuracy. The results show that a combination of digital twin technology, deep learning, and real-time learning analytics can significantly improve personalization, retention rates, academic performance, and educational effectiveness of e-learning environments.

Journal of Transactions in Systems Engineering

A Life Cycle Assessment of Resource Use and Environmental Impacts in the Albanian Denim Garment Industry

Digital Twin-Enabled Personalized E-Learning Using Deep Learning and Real-Time Learning Analytics