7th International Academy of Engineering and Technology Forum Future Manufacturing Technologies and Systems
20 - 22 August 2026
University of Strathclyde, Glasgow, UK
(AET Forum and Technical Tour)
* For AET Forum registration, please visit the AET Forum registration
* For AET Tour registration, please visit the AET Tour registration
Committee
Forum Chair
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Prof. Lihui Wang, KTH Royal Institute of Technology, Sweden
Scientific Committee
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Prof. József Váncza, Institute for Computer Science and Control, Hungary
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Prof. Kornel Ehmann, Northwestern University, USA
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Prof. Fengzhou Fang, Tianjin University, China & University College Dublin, Ireland
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Prof. Nabil Anwer, Paris-Saclay University, France
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Dr. Christian Wenzel, Innolite GmbH, Germany
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Prof. Kazuya Yamamura, Osaka University, Japan
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Prof. Xichun Luo, University of Strathclyde, UK
Keynote Talks
Topic: Manufacturing Paradigm III
Prof. Komel Ehmann
Northwestern University, USA
AI in Manufacturing Research – A Swedish Perspective
Prof. Lihui Wang
KTH Royal Institute of Technology, Stockholm, 10044, Sweden
Abstract
AI technology enables smart manufacturing that depends on the timely acquisition, distribution, and utilisation of information from machines and processes from shop floors and across product lifecycles. Effective AI-based information processing can improve production quality, reliability, resource efficiency and the recyclability of end-of-life products. Smart manufacturing built on digitalisation and AI also aims for better sustainability. As emerging technologies, cyber-physical systems, digital twins and human-centric collaborative assembly provide new opportunities to achieve the goals of smart manufacturing. Within the context, this presentation will focus on AI in Manufacturing (AIM) and cover the following topics:
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A brief intro to Swedish research environment
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Latest advancement and future opportunities in smart manufacturing
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Showcases and future research directions
While advanced AI methods show great promise in smart manufacturing, challenges and future trends remain to be identified and will be highlighted in the presentation.
Lihui Wang is the current President of AET, a Chair Professor of Sustainable Manufacturing and the Director of Centre of Excellence in Production Research (XPRES) at KTH Royal Institute of Technology, Sweden. His research interests are primarily focused on human-robot collaboration, brain robotics, real-time monitoring and control, cyber-physical systems, and adaptive manufacturing systems. Professor Wang is actively engaged in various professional activities. He is the Editor-in-Chief of Robotics and Computer-Integrated Manufacturing, International Journal of Manufacturing Research, and Journal of Manufacturing Systems. He has published 12 books and authored in excess of 800 scientific publications. Professor Wang is a Fellow of Canadian Academy of Engineering, AET, CIRP, SME and ASME; the President (2020-2021) of North American Manufacturing Research Institution, and the Chairman (2018-2020) of Swedish Production Academy. He was elected one of the 20 Most Influential Professors in Smart Manufacturing in 2020, and a Gold Medal recipient from Society of Manufacturing Engineers in 2024.
On Atomic-scale Manufacturing
Prof. Fengzhou Fang
Centre of Micro/Nano Manufacturing Technology (MNMT-Dublin), University College Dublin
Dublin 4, Ireland
Abstract
The evolution of manufacturing technologies and systems can be fundamentally classified into three paradigms, distinguished by the scale of manufacturing precision, functional features, modes of material manipulation (removal, migration, and addition), and their underlying theoretical foundations. This inherent law of manufacturing development indicates that the emergence of Atomic and Close to atomic Scale Manufacturing (ACSM) is an inevitable outcome shaped by these three paradigms of manufacturing advancement.
This keynote presents a research framework for ACSM, outlining its key scientific issues and research themes. The framework aims to achieve cost effective, deterministic, and scalable manufacturing of next generation products with atomic level precision by addressing quantum uncertainty in atomic scale material manipulation. ACSM is regarded as the foundational technology enabling a new manufacturing paradigm, namely Manufacturing III. The keynote will also introduce the fundamental scientific knowledge, theoretical principles, and potentially viable industrial processing technologies required to realize ACSM, followed by a discussion of scientific and technological challenges and future research perspectives.
Dr. Fengzhou Fang has been engaged in the fields of freeform optics design and manufacturing, ultra precision machining and metrology, and atomic and close to atomic scale manufacturing since he became a university faculty member in 1982. He developed the theoretical framework of the Three Paradigms of Manufacturing Advancement (TPMA), which reveals the inherent law governing the evolution of manufacturing and demonstrates the inevitability of the era of Atomic and Close to atomic Scale Manufacturing (ACSM). In recognition of his contributions, he has been elected Fellow of the International Academy of Engineering and Technology (AET), the International Society for Nanomanufacturing (ISNM), the International Academy for Production Engineering (CIRP), and the Society of Manufacturing Engineers (SME).
Professor Fang is a Member of the Royal Irish Academy (RIA) and the Academia Europaea (AE). He is the Founding President of AET, a former President of CIRP, and the Editor in Chief of Nanomanufacturing and Metrology.
Physics-Informed and AI-Enhanced Manufacturing: From Process Control to Quality Assurance
Prof. Robert X. Gao
Case Western Reserve University, Cleveland, OH, 44106, USA
Abstract
Continued advances in AI and machine learning are creating new opportunities to enhance process control and quality assurance for smart manufacturing. This keynote highlights the benefits of integrating model-based and data-driven methods, illustrated by combining mathematical models such as the ridgelet transformation with image processing techniques based on convolutional neural network (CNN) for non-contact evaluation of surface roughness in machined parts, showcasing AI-enhanced surface metrology. Next, AI-enhanced Model Predictive Control (MPC) for the incremental forming of skeletal fixation plates is highlighted for point-of-care manufacturing in reconstructive surgery to restore both the function and appearance of patients’ facial structure. The keynote concludes with an overview of the Engineering Research Center on Hybrid and Autonomous Manufacturing: Moving from Evolution to Revolution (ERC-HAMMER), a multi-university consortium supported by the US National Science Foundation.
Robert Gao is the Cady Staley Professor and Department Chair of Mechanical and Aerospace Engineering at Case Western Reserve University. Since receiving his Ph.D. from the Technical University of Berlin, Germany in 1991, he has been working on physics-based sensing, multiresolution signal processing, stochastic modelling, and monitoring and control of manufacturing systems and processes. Prof. Gao is internationally recognized as a leader in application of AI and machine learning for manufacturing. He has published over 450 technical papers, three books, and holds 13 patents, served as Chairman of the CIRP Collaborative Working Group on AI in Manufacturing (2021-2024), and Scientific Committee Chair of the SME North Americal Manufacturing Research Institute (2022-2024). He is a Fellow of CIRP, IEEE, SME, and ASME, has received the SME Gold Medal (2024), ASME Milton C. Shaw Manufacturing Research Medal (2023), IEEE Instrumentation and Measurement Society Technical Award (2013), and was named one of SME’s 20 Most Influential Professors in Smart Manufacturing in 2020.
Topic: Quantum Technology
Prof. Michael Strain
University of Strathclyde, UK
Invited Talks
Atomic layer processing: advancing 3D electronic architectures and new applications in nanofiltration membranes
F. Roozeboom
Faculty of Science & Technology,
University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands
Abstract
The author starts with a short review on key thin-film and 3D integration & packaging technologies developed by his team at Philips. In ~2003-2009 they were frontrunners in heterogeneously integrated RF-device packages containing passive Si-chips with 3D MIM high-density trench capacitors, which were filled with ~10 nm Al2O3 dielectric and TiN conductor layer stacks grown with Atomic Layer Deposition (ALD), and reached world-record capacitance of 0.4 µF/mm2 and 6 MV/cm breakdown voltage [1-2], and predicted values >1 µF/mm2 when using high-k dielectrics like HfO2 [3]. Today, the semiconductor community is widely revisiting heterogeneous IC integration [4-5].
The second part discusses Molecular Layer Deposition (MLD) in nanofiltration membranes. Here, inorganic precursors and organic co-reactants are sequentially fed to form atomic-scale conformal metalcone films in a ceramic support with initial average pore size of 20 nm. Once the pores are fully filled, post-deposition calcination causes the MLD-coating to decompose, creating pores with sizes <1 nm and enabling tunable hydrophobicity for optimum waste water filtration [6].
References
1) F. Roozeboom et al., ECS Symp. Proc. 2005, 8, pp. 16-31 ; ibid, ALD 2008 Conference, June 29-July 2, Bruges, Belgium.
2) J.H. Klootwijk, …. , and F. Roozeboom, IEEE Electron Device Letters 2008, 29, 740-742.
3) F. Roozeboom et al., First Int. Workshop on Power Supply on Chip (PowerSoC08), Sept. 22-24, 2008, Cork, Ireland.
4) International Roadmap for Devices and Systems (IRDS™) 2024 Edition - IEEE IRDSTM, and references therein.
5) C.J. Lockhart de la Rosa and G.S. Kar, Semiconductor Digest, Nov./Dec. 2024, pp. 16-21.
6) H. Sondhi, …, F. Roozeboom, et al., Appl. Surf. Sc. Adv. 2026, 31, 100922 ; ibid, Appl. Surf. Sc. 2025, 683, 161790 ; ibid, Membranes 2025, 15(3) 86.
Fred Roozeboom holds a doctorate in technical sciences from University of Twente (Netherlands) with specialization in inorganic chemistry and catalysis. After three years in catalysis at ExxonMobil R&D Labs in Baton Rouge (USA), he joined Philips Research (from 2007: NXP) in Eindhoven, Netherlands to work most of his life on thin-film technology and plasma processing (1983-2009). From 1997-2009 he led a team that focused here on applications in 3D passive and heterogeneous integration for System-in-Package devices for wireless communication and power management. In 2007 he became Research Fellow and also full professor at TU Eindhoven (2007-2021), working on atomic layer deposition and etching. In 2009 he left NXP to join TNO Holst Centre to work on spatial atomic layer process and reactor design.
In 2021 he left TU Eindhoven and TNO to join University of Twente as guest (emeritus) professor, where his research focuses on inorganic membranes for nanofiltration applications. Since 2021 he is or was also consultant for high-tech industry in applications of thin-film processing in EUV optics lifetime, 3D Li-ion batteries, etching and greenhouse gas emission reduction.
Fred holds over 50 US patents, granted or pending, and published 200+ papers in journals (h-index 44 Scopus). He is ECS Fellow and AVS Fellow, and the winner of the ECS 2023 Gordon E, Moore Medal Award and ECS 2026 Electronics and Photonics Division Award. He chaired the 11th International Atomic Layer Etching Workshop (ALE2024) as part of ALD2024 and is an editorial board member of the International Journal of High-End Manufacturing.
Spatiotemporal Learning for Uncertainty-Aware Contour Error Prediction in Blade Milling
Prof. Xu Han
School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China
Abstract
Contour-error prediction in thin-walled blade milling is challenged by limited data and non-uniform cutting engagement along the blade span. A toolpath-length-aware spatiotemporal alignment method is proposed to map multi-channel CNC servo signals to span-wise blade positions, improving feature–error consistency under varying cutting conditions. A lightweight CNN–BiLSTM–Attention model is then trained to predict span-wise contour error profiles, with quantile regression incorporated to characterize predictive uncertainty. The predicted quantiles are converted into a continuous probability density via kernel density estimation, enabling tolerance-exceedance probability computation and cost-sensitive alarm formulation for quality screening. Experiments are conducted on thin-walled aluminum alloy blades machined under varying cutting parameters on a five-axis CNC center, with full-field errors measured by structured-light 3D scanning. Results show that the proposed alignment markedly improves feature–error correlation, the sequential model achieves accurate span-wise predictions, and the probabilistic outputs provide well-calibrated uncertainty estimates for risk-aware in-process quality evaluation.
Xu Han is a full professor of mechanical engineering at Hebei University of Technology. He received his bachelor’s and master’s degrees from Harbin Institute of Technology and his Ph.D. from the National University of Singapore. He has contributed greatly to high performance engineering optimization design theory and method system, and developed the non-probabilistic reliability modeling and design of the theoretical framework. He is the author of 4 monographs and has published more than 300 peer-reviewed papers with over 20,000 citations, and has been named as a Highly Cited Chinese Researcher of Mechanical Engineering in the years of 2014 to 2025.
Topic: Ultra-precision Polishing Large Optics
Prof. Thomas Arnold
Leibniz Institute of Surface Engineering (IOM), Germany
Neural Operators on Riemannian Manifolds
Prof. Yingguang Li
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, China
Abstract
In the decision-making process for mechanical design and manufacturing process planning, we typically need to integrate predictions from physical fields—such as deformation, stress, temperature, and pressure—that reflect performance or manufacturing status, in order to iteratively optimize design or process variables. Since actual components often have complex geometries, the mathematical essence of these physical field prediction problems lies in operator mappings defined over complex geometries. Numerical simulation models suffer from low prediction efficiency making them incapable of supporting adequate iteration of design or process variables. Conventional neural networks can approximate functions with arbitrary precision but struggle to learn operators at a theoretical level. Neural operators, capable of approximating operators with arbitrary precision, have been the latest advancements in artificial intelligence, offering a new feasible approach for physical fields prediction. However, the original neural operators are defined on Euclidean spaces, and are thus inherently limited to regular geometries. This research introduces Neural Operators on Riemannian Manifolds (NORM) applicable to arbitrary complex geometries, proves the universal approximation theorem for NORM, and thus extends neural operator modelling methods from Euclidean spaces to non-Euclidean spaces. Furthermore, multiple case studies of NORM-driven design optimization and manufacturing process optimization will be presented.
Advancing Atomic and Close-to-Atomic Scale Manufacturing: From Structural Modulation to Functional Device Fabrication
Prof. Chengyong Wang
State Key Laboratory of High Performance Tools, Guangdong University of Technology, Guangzhou, 510006, China
Abstract
Atomic and close-to-atomic scale manufacturing (ACSM) shifts the engineering focus from macroscopic geometric shaping to the deterministic manipulation of atoms, offering a transformative approach to materials science and engineering.
This presentation reviews our group's efforts in ACSM. By deploying cross-scale energy fields such as electron beams, focused ion beams, and ultrafast lasers, we modulate energy-matter interactions to precisely control atomic migration, localized relaxation, and nucleation pathways. On this basis, we have established a cross-scale "shape-property" modulation method for amorphous alloys, effectively linking cluster structures, material performance, and nanoscale geometries. Additionally, we fabricated Hf-B series coatings featuring theoretically optimized, HCP-arranged covalent interfaces, which more than doubled their service life. Furthermore, we achieved sub-0.5 nm manufacturing precision in fabricating 2.3 nm Si3N4 nanopores, successfully validating their performance in single-molecule sensing tests.
These efforts bridge theoretical materials science with extreme manufacturing capabilities, providing valuable references for atomic-scale material tailoring engineering and next-generation manufacturing technologies.
Prof. Chengyong Wang is the Director of the National Key Laboratory of High-Performance Tools. His primary research focuses on the mechanics, processes, and equipment for machining difficult-to-cut materials, as well as the development of advanced medical devices. This interdisciplinary work spans multiple frontier domains, including laser manufacturing, direct acoustic manufacturing, additive manufacturing, space manufacturing, bio-manufacturing, as well as atomic-scale manufacturing and intelligent tooling. He has published over 300 papers in top-tier journals, such as IJMTM and JMPT, and holds more than 100 authorized invention patents. His work has successfully translated fundamental manufacturing theories into practical industrial applications.
Bio-inspired Functional Interfaces
Prof. Jining Sun
Dalain University of Technology, China
Abstract
Re-entrant microstructures are essential for robust liquid repellency, yet fabricating them on durable metallic substrates while balancing scalability, geometry, and speed remains challenging. Here, we introduce confined shear-induced structure discretization (CSSD)—a one-step mechanical cutting strategy generating high-density re-entrant architectures directly on planar and curved metals. Using a custom cave-trapezoid tool during ultra-precision diamond turning, we effectively convert continuous ridges into discrete re-entrant units. The resulting surfaces sustain a highly repellent Cassie–Baxter state across various liquids, achieving a water contact angle of 161.1 ± 1.0°. Importantly, CSSD enables rapid, large-area production with a material removal rate of 1.068 mm³ s⁻¹ over areas up to 1.5 × 10⁴ mm². Furthermore, these textures exhibit exceptional durability, remaining intact after 1,000 wear cycles. Ultimately, this geometry-driven approach provides a predictive, scalable blueprint for discrete microstructure manufacturing adaptable to other machining modalities.
Prof. Sun received his B.S. and M.S. in Physics from Peking University and his Ph.D. from Heriot-Watt University in the UK. Currently, he is a Professor at the School of Mechanical Engineering, Dalian University of Technology. He holds a National-level fellowship in Science and Technology in China and the distinction of being a Fellow of the Higher Education Academy (FHEA) in the UK. Throughout his career, he has provided technical consultancy for world-renowned companies such as Renishaw, STMicroelectronics, Contour Fine Tooling, ltd., and Wontai Power etc. His research has made fundamental contributions to the design and fabrication of functional surfaces and interfaces, micro/nano-manufacturing technologies and atomic and close-to-atomic scale manufacturing (ACSM). To date, he has authored over 80 papers in high-impact SCI journals and served as editorial board members of multiple international journals. Recently, he is focusing on advancing precision manufacturing processes and nanomaterials for functional surfaces..
Call for Participation
Advanced manufacturing is the backbone of our economy and a driving force to fulfill a vision of a sustainable, prosperous and competitive future. Currently, advanced manufacturing is undergoing a significant transformation as the sector looks to decarbonise and digitise. Meanwhile, technological advances in artificial intelligence, quantum technology, and atomic and close-to-atomic scale manufacturing are profoundly reshaping future manufacturing technologies and systems, with the potential for enormous advances in our productivity and way of life. Strong collaboration between academia, industry, and research institution is essential to this vision of prosperity, and levelling-up the global economy.
It is our great pleasure to invite you to the 7th AET Forum on Future Manufacturing Technologies and Systems.
AIMS: The aim of the event is to provide an intimate and high-level forum for in-depth discussions on future manufacturing technologies and systems and emerging challenges and opportunities at the interface of academia and industry.
Submission
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If you are interested in presenting a technical talk, please submit your abstract (1-page) to info@aet-ac.org before 10 May 2026. (Please find the abstract template at the following link)
Programme at a Glance
* Registration and Welcome Reception (20 Aug 2026)
University of Strathclyde (JW7.06b, James Weir Building, Glasgow G1 1XJ, UK)
* AET Forum (21 Aug 2026)
Ross Priory (Ross Loan, Alexandria G83 8NL)
(Coach transportation will be arranged to and return from University of Strathclyde.)
* Optional technical Tour (22 Aug 2026)
(Coach transportation will be arranged to and return from University of Strathclyde.)
Travel to Glasgow
* By Air
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Glasgow International Airport (11 miles)
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Edinburgh Airport (38 miles)
* Travel from Airpor
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Glasgow Airport: Glasgow Airport Express Bus 500 (every 10 mins, traveling time 20 mins), Stance 1, Drop off @ Glasgow Geore Square/Glasgow Buchann Bus Station/ £12; or Taxi (£35)
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Edinburgh Airport, Citylink Air (every 20 mins, traveling time: 62 mins), Stance C, Drop off @ Glasgow Buchann Bus Station, or Taxi (£90-£100)
* By Train
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Glasgow Centre Station (0.7 miles, 16 mins by walk) for train from London
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Glasgow Queen Street Station (0.3 miles, 7 mins by walk) for trains from Edinburg
* By Car
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Off M8 Joint 15, parking at NCP Glasgow George Street Car Park @ 68 Montrose St, Glasgow G1 1R
Hotels
Many hotels (3-star to 5-stat are in Glasgow City Centre, here are the closed ones to Registration and Welcome Reception Venue: JW7.06b, James Weir Building, University of Strathclyde, 75 Montrose Street, Glasgow, G1 1XJ, UK
* 4-star Hotels
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Millennium Hotel Glasgow (4-star hotel @ George Square, Glasgow G2 1DS, (2 mins by walk) | Visit
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AC hotel, new 4-star hotel @ 260 George St, Glasgow G1 1QX (2 mins by walk) | Visit
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Maldron Hotel, 4-star hotel @ 50 Renfrew St, Glasgow G2 3BW (0.5 miles, 12 mins by walk) | Visit
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citizenM Glasgow, 4-star hotel @ 60 Renfrew St, Glasgow G2 3BW (0.6 miles, 13 mins by walk) | Visit
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DoubleTree by Hilton Glasgow Central, 4-start hotel @ 36 Cambridge Street, Glasgow G2 3HN (0.7 miles, 16 mins by walk) | Visit
* 3-star Hotels
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Premier Inn Glasgow (George Square) 3-star hotel at 187 George St, Glasgow G1 1YU (1 min by walk) | Visit
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Moxy Glasgow Merchant City, 3-star hotel @ 210 High St, Glasgow G4 0QW (0.4 miles, 8 mins by walk) | Visit
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Holiday Inn (City Centre, Theatreland), 3-star at 165 West Nile Street, Glasgow G1 2RL (0.5 miles, 11 mins by walk) | Visit
Registration
* Registration Fees for Early-bird (before 10 May 2026)
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General delegate £350
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AET members/students £260
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Optional technical tour £150
* Regular Registration Fees (after 10 May 2026)
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General delegate £400
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AET members/students £310
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Optional technical tour £200
* For AET Forum registration, please visit the AET Forum registration
* For AET Tour registration, please visit the AET Tour registration
Organisers
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International Academy of Engineering and Technology (AET)
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University of Strathclyde