Journal of Transactions in Systems Engineering

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

Albana Leti, Ermira Shehi, Silva Sula Spahija — Thu, 11 Jun 2026 00:00:00 +0200

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

Tue, 14 Jul 2026 00:00:00 +0200

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.

Integrating MPLS and SDN for Improved Routing in VANETs: A Comparative Study of AODV and OLSR Protocols

Ronild Hako, Evjola Spaho — Mon, 03 Aug 2026 00:00:00 +0200

Vehicular Ad Hoc Networks (VANETs) enable wireless communication among vehicles and roadside infrastructure, supporting safety, traffic optimization and infotainment in Intelligent Transportation Systems (ITS). However, the high mobility of vehicles causes frequent topology changes and link breaks, degrading routing performance. This paper investigates the integration of Multi-Protocol Label Switching (MPLS) and Software-Defined Networking (SDN) with two widely used VANET routing protocols, Ad hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR). We evaluate four progressive configurations for each protocol: the default protocol, the protocol with MPLS-enhanced forwarding, the protocol with SDN-based centralized route optimization, and the combined SDN-MPLS integration. The simulation environment uses a realistic urban topology with 50 vehicles and 5 Road-Side Units (RSUs). Results are analysed while using several traffic patterns and measuring Key Performance Indicators, such as: Packet Delivery Ratio (PDR), average end-to-end delay, jitter, throughput, and routing overhead. The simulations show that each integration layer contributes measurable improvements to the protocol performance, with the SDN-MPLS combination achieving the best overall results.

Vulnerability Assessment of Operational Technology Systems in Industry 4.0 Manufacturing Environments

John Hughes, Carlene Campbell, Simon Thomas — Mon, 03 Aug 2026 00:00:00 +0200

The increasing convergence of Information Technology (IT) and Operational Technology (OT) under the Industry 4.0 paradigm has significantly expanded the cyberattack surface of industrial environments, making the manufacturing sector one of the most frequently targeted industries. This study investigates cybersecurity vulnerabilities in a brownfield OT environment that integrates legacy industrial assets with modern connected technologies. A vulnerability assessment was conducted using a grey-box testing methodology, which provides partial knowledge of the target infrastructure while accurately reflecting the security perspective of an internal assessment. Automated reconnaissance and vulnerability scanning were performed using tools available in the Kali Linux distribution, including Nmap, Nikto, and Wifite, to identify security weaknesses and potential attack paths across the OT network. The assessment revealed multiple vulnerabilities that could be exploited to compromise the confidentiality, integrity, and availability of industrial systems, highlighting the need for systematic security evaluation and risk-based remediation. Although individual vulnerability assessment tools have been widely studied, limited research has examined their integrated application in contemporary brownfield OT environments that combine legacy operational infrastructure with Industry 4.0 technologies. The principal contribution of this work is the adaptation and validation of an established vulnerability assessment methodology in a real-world industrial production environment. Additionally, the study demonstrates a transparent approach for quantifying cyber risk and prioritizing remediation actions, providing practical guidance for strengthening the cybersecurity posture of manufacturing organizations undergoing digital transformation.