Publications
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DRAGON: A Dynamic Risk-Aware Graph Optimization Network for Adaptive Building Evacuation Using Graph Convolutional Network and Q-Learning
Efficient evacuation route planning in dynamic environments remains a major challenge, particularly when environmental risks and accessibility conditions change rapidly during emergencies. Traditional shortest-path algorithms, while effective in static graphs, often fail to adapt to evolving conditions, leading to suboptimal or unsafe evacuation guidance. This study aims to develop an adaptive and intelligent evacuation routing […]
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Coupling Pricing Decisions with Aircraft and Gate Scheduling in Airport Operations
Airlines increasingly emphasize operational flexibility to adapt schedules to fluctuating de mand. Pricing decisions directly influence expected passenger numbers, and when demand is insufficient for a large aircraft, companies may reassign a smaller one, provided capacity, aircraft–gate compatibility, and crew constraints are satisfied. Such changes require gate reassignment, making pricing, aircraft assignment, gate allocation, and […]
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A NSGA-II Approach for Energy-Efficient Flexible Flow Shop Scheduling with Renewable Energy and Storage Integration
Manufacturing industries face increasing pressure to reduce energy costs while maintai ning production efficiency. Time-of-use electricity pricing structures offer opportunities for cost reduction through strategic production scheduling. Integrating renewable energy sources such as wind and Photovoltaic, along with Energy Storage System further enhances sustainability and economic benefits. This work presents a multi-objective optimization approach using […]
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ADAP-GNN: Adaptive property-aware graph neural network for intrusion detection in IoT networks
New and sophisticated attacks are threatening Internet of Things (IoT) networks, compromising the security and trustworthiness of devices. Consequently, Network Intrusion Detection Systems (NIDS) have become critical for protecting these networks, and AI-based NIDS have emerged as a promising solution. A relatively new subfield of deep learning, Graph Neural Networks (GNNs), has further advanced this […]
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Durability and microstructural evolution of metakaolin-based geopolymers under various coupled temperature and relative humidity conditions
This study investigates the long-term durability of a metakaolin-based geopolymer composite formulated with potassium silicate, fine sand, limestone filler, and short carbon fibers, after 9 months of exposure to three hygrothermal conditions: 20 ◦C – 100 % RH, 90 ◦C – 100 % RH, and 90 ◦C – 0 % RH. A comprehensive evaluation was […]
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A Literature Review of Public Transport OD Matrix Estimation
Origin–Destination matrices (ODms) are a fundamental input for public transport planning and optimization, as they characterize travel demand across a network. Traditionally estimated from user surveys, ODms are now increasingly inferred from large-scale automatically collected data, such as Automated Fare Collection (AFC), Automated Passenger Counting (APC), and Automated Vehicle Location data (AVL). This review focuses […]
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An innovative power converter based technique for on-site photovoltaic I-V characterization under natural irradiance
This paper presents a standalone PV curve tracer designed to extract current-voltage (I-V) and power-voltage (P-V) characteristics, as well as the five parameters of a Multiple-Diode Model (MDM) with identical diodes, effectively reducing it to a single-diode model for parameter extraction, under real sunlight conditions. The system consists of a custom-built synchronous boost converter operating […]
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Hybrid Modeling of a Lithium-Ion Battery Using an Extended Shepherd Model Enhanced with an MLP Neural Network Model
Exact modeling of lithium‐ion batteries is essential for the optimal design and functioning of contemporary energy storage systems. This research introduces a hybrid modeling approach that integrates an extended Shepherd equivalent circuit model (ECM) with a multilayer perceptron (MLP) neural network to improve voltage prediction precision. The ECM parameters are determined utilizing the Red‐Tailed Hawk […]
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A comparative environmental assessment of an automotive component processed by laser powder bed fusion (LPBF) versus CNC machining, with steel powder reuse impact analysis
The study reported in this paper presents a comparative life cycle assessment (LCA) of a maraging steel automotive hub carrier manufactured through using Laser Powder Bed Fusion (LPBF) metal additive manufacturing (metal AM) versus conventional Computer Numerical Controlled (CNC) machining. The manufacturing processes are modelled with primary data collected from dedicated metal AM unit and […]
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Machine learning-driven solutions for sustainable and dynamic flexible job shop scheduling under worker absences and renewable energy variability
This paper addresses the Dynamic Sustainable Flexible Job Shop Scheduling Problem (DSFJSSP) by going beyond the traditionally emphasized economic dimension — such as makespan, flow time, or resource utilization — to include human and environmental factors, along with their related disruptions. Specifically, it considers human-related constraints such as workers’ skills and ergonomic risks, as well […]
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Influence of organizational culture on sustainable mobility behaviours in higher education
Purpose -In the face of climate change, higher education institutions can play a role in fostering a sustainable future. This study aims to examine how their green organizational culture, including values, social norms and practices, affects students’ mobility choices. Specifically, the authors examine the direct impact of green organizational culture on polluting commuting behaviours, the […]
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Robust Pitch-Angle Control of Floating Offshore Wind Turbines Using Optimized Active Disturbance Rejection Control
This paper presents a robust pitch-angle control strategy for a floating offshore wind turbine (FOWT) based on an optimized active disturbance rejection controller (OADRC). The suggested controller uses the Red-Tailed Hawk (RTH) optimization algorithm to automatically adjust the ADRC parameters. This makes the system better at rejecting disturbances and more robust overall. The optimization process […]