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8th International Conference on Cloud and Big Data computing, ICCBDC 2024

作者： Almardoud, Layla Dawood Tawfik, Hissam Majzoub, Sohaib Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom

ISBN: (纸本)9798400717253

Dementia is a disease that imposes medical, social, and economic challenges on medical professionals, caregivers, and the patients themselves. Dementia monitoring and prognosis are critical factors besides dementia diagnosis. However, recent studies on dementia prognosis involve people with diagnosed dementia and not non-demented with a high risk of being demented, like people with cognitive difficulties. The aim of this paper is to use Machine Learning (ML) algorithms to predict patients with the risk of deterioration from medical histories containing clinical, cognitive, and profile data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The best model was the Random Forest model with a sensitivity of 0.79, accuracy of 0.77, specificity of 0.76, F1-score of 0.78, and an AUROC of 0.83. Moreover, the model was interpreted through permutation importance. Using the permutation importance tool, the study highlighted the strong effect of diagnosis information and specific symptoms like muscle pain for dementia deterioration prediction. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Information Flow and Laplacian Dynamics on Local Optima Networks 13

Information Flow and Laplacian Dynamics on Local Optima Netw...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Richter, Hendrik Thomson, Sarah L. Htwk Leipzig University of Applied Sciences Faculty of Engineering Leipzig Germany School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350308365

We propose a new way of looking at local optima networks (LONs). LONs represent fitness landscapes;the nodes are local optima, and the edges are search transitions between them. Many metrics computed on LONs have been proposed and shown to be linked to metaheuristic search difficulty. These have typically considered LONs as describing static structures. In contrast to this, Laplacian dynamics (LD) is an approach to consider the information flow across a network as a dynamical process. We adapt and apply LD to the context of LONs. As a testbed, we consider instances from the quadratic assignment problem (QAP) library. Metrics related to LD are proposed and these are compared with existing LON metrics. The results show that certain LD metrics are strong predictors of metaheuristic performance for iterated local search and tabu search. © 2024 IEEE.

关键词： Tabu search

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Computational Investigation of Laminar Premixed Hydrogen/Ammonia flames at Sub-Atmospheric Conditions

Computational Investigation of Laminar Premixed Hydrogen/Amm...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Tule, Davud Tingas, Efstathios-Al School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom Furtwangen University Germany

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The increasing urgency to address global warming has highlighted the need to explore alternative fuels for the transport sector, a significant contributor to carbonemissions. Hydrogen and ammonia have emerged as promising candidates, each with distinct advantages and challenges. Hydrogen boasts high reactivity and beneficial cooling properties but suffers from low volumetric density and storage difficulties. Conversely, ammonia offers better infrastructure compatibility and higher volumetric density but has low flammability. This study investigates computationally hydrogen/ammonia flames under sub-atmospheric conditions pertinent to high altitude aviation, focusing on laminar flame dynamics. One-dimensional simulations of freely propagating laminar flames were conducted using the PREMIX software and analyzed with computational singular perturbation (CSP) tools to elucidate the flame structure and dynamics. Results reveal that hydrogen/ammonia blends improve flame speed and stability compared to pure ammonia, especially under lean conditions. CSP analysis identified key reactions driving flame behavior, highlighting differences in chemical dynamics between hydrogen and ammonia. The findings underscore the potential of hydrogen/ammonia blends in aviation applications, particularly for high-altitude relight scenarios, and provide insights into optimizing fuel mixtures for enhanced performance. Further research is needed to validate these findings under varied conditions and explore practical implementation strategies. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Premixed flames

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UNPACKING THE PERCEPTIONS OF HIGH-DENSITY LIVING AND ZONING LAWS: A CASE OF PHILADELPHIA AREA, USA 40

UNPACKING THE PERCEPTIONS OF HIGH-DENSITY LIVING AND ZONING ...

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40th Annual Conference on Association of Researchers in Construction Management, ARCOM 2024

作者： Lawani, Kenneth Thomson, Craig Hare, Billy Duplicki, Alexander School of Computing Engineering and Built Environment Glasgow Caledonian University Cowcaddens Road GlasgowG4 0BA United Kingdom

ISBN: (纸本)9780995546387

America's residential housing model of suburban sprawl has its own environmental impact compared to high-density residential models that are believed to operate more efficiently in other places. There is hesitation in America regarding enacting changes towards a more sustainable model. An understanding of the barriers inhibiting the implementation of high-density residential model is fundamental to changes in the current system. This study evaluated American perceptions of current living models;high-density models and understanding of zoning laws. A mixed-method approach was adopted involving 207 questionnaires with 14 follow-up interviews from participants in the Philadelphia area. Findings suggest young adults' preference for high-density living with concerns for housing affordability and inequality crisis in America. The sampled population lacked awareness of residential high-density models and what it physically embodies including its impact on the surrounding population. Findings suggest lack of understanding of the zoning laws that have perpetuated America's sprawling living model. These results are significant for planners;developers;and proponents of high-density models regarding planning housing models that sets up America into a more sustainable future. © Association of Researchers in Construction Management

关键词： Zoning

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Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS's Digital Twin 5

Accurate AI-Driven Emergency Vehicle Location Tracking in He...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Al-Shareeda, Sarah Celik, Yasar Bilgili, Bilge Al-Dubai, Ahmed Canberk, Berk Istanbul Technical University Department of AI and Data Engineering Turkey BTS Group Turkey United States Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798331519957

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time, and tracking their real-Time location is crucial to the medical authorities. Despite the claim of real-Time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance's location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle's next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN's key role in significantly reducing the witnessed gap within the HITS's DT. This transformative approach enhances real-Time synchronization in emergency HITS by approximately 88% to 93%. © 2025 IEEE.

关键词： Deep neural networks

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An assessment for the viability of recovering heat from a smoke extract system

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Energy and built environment 2023年第4期4卷 458-466页

作者： Liam Hancox Siliang Yang Paul Hallam Michael White Saim Memon School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeeds LS28AGUnited Kingdom Department of Engineering and Technology University of HuddersfieldHuddersfield HD13DHUnited Kingdom

Over the course of industrial manufacturing,additional heat within the extract systems is usually released into the atmosphere and its intrinsic energy is *** paper investigated a cold abatement smoke extract system for a fire testing wall furnace to determine the viability in recovering heat from the hot *** scenarios were investigated:1)the extract system was closed and only 300°C smoke was present;2)the system took in ambient air around the furnace and heat recovery occurred at 80°C in smoky air;3)the smoke had been removed from the air and the temperature was 60°*** was found that there was a significant build-up of soot on Scenarios 1&2 with a build-up rate of 0.25𝜇m/s which totalled 2.7 mm of soot after a three-hour *** soot had a low heat transfer rate and therefore acted as an insulator on the heat exchanger which reduced the efficiency significantly of it over *** to this loss in efficiency,it was more viable to recover heat in Scenario 3 at 60°C in clean air than it was to recover heat at 300°C or 80°C in smoky *** results show that having clean air was more important than a higher temperature when it came from recovering heat from a cold abatement system for a fire testing *** paper contributes to reveal the possibilities of harnessing the“waste heat”for use in other applications in the vicinity of the manufacturing processes,such as heating water within a central heating plant,domestic hot water or electricity generation,or re-cycled within the industrial plant itself.

关键词： Smoke heat recovery Smoke extract process Heat transfer Soot thermal efficiency

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Lightweight Blockchain Prototype for Food Supply Chain Management 17

Lightweight Blockchain Prototype for Food Supply Chain Manag...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Rusakov, Alexey Moradpoor, Naghmeh Akbarzadeh, Aida School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Gjovik Norway

ISBN: (纸本)9798331509736

The modern food supply chain often involves multiple layers of participants spread across different countries and continents. This complex system offers significant benefits to businesses worldwide;however, it also presents several challenges. One major problem is the inability to trace the product flow back to its origin, a critical issue in many industries. Another issue is the lack of trust among supply chain participants. Blockchain technology can help address these and other challenges faced by the supply chain industry. However, it is surprising that, globally, there are still not many examples of the technology's adoption, with most projects remaining in the pilot stage. This paper explores the field of custom blockchain design tailored to specific applications, with a focus on supply chain operations in the food industry. It includes the development of a lightweight yet fully featured Python prototype for a decentralized blockchain system. In addition to common features like block validation and state updates, the prototype includes a newly designed type of transaction tailored specifically for supply chain operations. These transactions eliminate the need for smart contracts, making the system more lightweight compared to general-purpose blockchain platforms such as Ethereum and less prone to security vulnerabilities. The prototype is designed as a public blockchain network, with Proof of Work selected as the consensus algorithm. The novelty of this research work lies in advancing the concept of a custom blockchain solution for the food industry. The key elements of the prototype have been unit tested. The overall evaluation was completed using a Python script that simulates product flow through an example supply chain, allowing product provenance to be determined by tracing the product flow back to its origin. © 2024 IEEE.

关键词： Food industry

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How do Employees Engage Within an Online Wellbeing Platform? Insights FromAssociation Rule Mining Analysis 32

How do Employees Engage Within an Online Wellbeing Platform?...

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32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Cameron, Gillian Mulvenna, Maurice Bond, Raymond Ennis, Edel O’Neill, Siobhan Cameron, David Bunting, Alex Inspire Belfast United Kingdom School of Computing Faculty of Computing Engineering & the Built Environment Ulster University Belfast United Kingdom School of Psychology Faculty of Life and Health Sciences Ulster University Coleraine United Kingdom

The increasing availability of mental health data presents both opportunities and challenges, particularly due to the unstructured and noisy nature of such data. Data mining—an analytical approach for extracting knowledge from large datasets—is becoming increasingly prevalent in the fields of medicine and mental health. By employing data mining techniques, the insights can help inform the development of enhanced digital tools for mental health and fostering a more personalised user experience. One notable method within this domain is association rule mining, which identifies frequent relationships between items in a dataset. This study aims to apply association rule mining to a dataset generated by users of a digital employee wellbeing platform, focusing on the relationships between various tools and resources utilised on the platform. The Inspire Support Hub is a digital employee wellbeing platform featuring tools such as a mood tracker, a chatbot for self-assessments, and psychoeducational resources. User interactions with the platform are logged as anonymous events, including clicks, mood entries, and self-assessment results, each associated with a unique user ID and timestamp. Upon registration, users enter a company pin and their sector is recorded. From February 2019 to April 2023, 11,583 users engaged with the platform over 16,657 sessions. The analysis was conducted using R Studio, employing the dplyr and tidyverse packages for data cleaning and wrangling, along with ggplot2 for visualisation. The event logs were transformed into transaction data for association rule mining using the arules package. The Apriori algorithm was applied with a minimum support threshold of 0.05 and a confidence level of 0.8, ensuring that only rules with at least 80% accuracy were included. Applying association rule mining on the employee wellbeing platform dataset revealed distinct sets of co-associations, with significant emphasis on the chatbot and mood tracker. This is predic

关键词： Economic and social effects

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Hydrogen Production Rate of Temperature Controlled Proton Exchange Membrane Electrolyzer 12

Hydrogen Production Rate of Temperature Controlled Proton Ex...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Hussnain, Usama Bani, Nurul Aini Muhtazaruddin, Mohd Nabil Muhammad-Sukki, Firdaus Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur Malaysia Edinburgh Napier University School of Computing Engineering and Built Environment Edinburgh United Kingdom

ISBN: (纸本)9798350340204

This study investigates the hydrogen production through the Proton exchange membrane electrolyzer. It focuses on the hydrogen production rates, power, voltage and stack temperature to elaborate the operating dynamic behavior and efficiency of the system. The simulation reveals the characteristically behavior of these parameters including the activation phases, peak performances and stabilization trends. This study provides a framework for designing and optimization the system which contribute in the advancement of renewable hydrogen production technologies. © 2024 IEEE.

关键词： Electrolysis

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Dynamic Event-Triggered Resilient Heading Control for Unmanned Surface Vehicle With Encrypted Data

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-10页

作者： Zhao, Huarong Xu, Dezhi Zhou, Zhigang Yu, Hongnian Engineering Research Center of Internet of Things Applications Ministry of Education Jiangnan University Wuxi Jiangsu China School of Electrical Engineering Southeast University Nanjing China China Ship Scientific Reserch Center Wuxi Jiangsu China School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh U.K

This paper studies data-driven dynamic event-triggered heading control issues for an unmanned surface vehicle (USV) with enciphered data and aperiodic denial of service (DoS) attacks. First, we establish a compact form dynamic linearization model for the USV by employing the control input and a formulated virtual output. Next, we design a quantization-based encoding-decoding scheme, in which the data is quantized and encoded before transmission and decoded upon reception by the controller. This approach not only compresses data bits but also prevents information leakage. Meanwhile, we design a dynamic variate and develop a dynamic event-triggered mechanism to reduce the communication burden. Moreover, we employ a radial basis function neural network (RBFNN) to create an RBFNN-based compensation method to offset the effects of aperiodic DoS attacks. As a result, we propose a data-driven dynamic event-triggered resilient heading control (DET-RHC) approach, and we rigorously prove its convergence. Additionally, we verify the effectiveness of the proposed DET-RHC method through several simulation studies. IEEE

关键词： Cryptography

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