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Shaping Socially Engaged Art Practice And Critical Pedagogy In The Uk Arts And Cultural Sector: An Auto/Biographical Framing Of Class, Gender And Political Activism, Chrissie Tiller

Doctoral

Situated at the intersection of critical pedagogy, radical theatre and political activism, this PhD thesis reflects critically on a series of publications and an interdisciplinary counterhegemonic socially engaged art practice between 2010-20. Mobilising the auto/biographical as a conceptual framework for critical analysis, the thesis addresses questions of systemic inequalities, cultural democracy and social transformation in the UK arts and cultural sector. By examining my lived experience through the analytical lenses of intersectionality and feminist standpoint theory, the thesis foregrounds how interconnecting oppressions of gender and class have contributed to the formation of my creative practice and its pedagogical interventions, constituting …

Active Learning To Enhance The Architecture, Engineering, And Construction Sector, Shannon M. Chance

Research Outputs: 2025-Present

Active learning methods are integrated across the honors-level Bachelor of Science in Building Information Modelling (BIM/Digital Construction) at Technological University Dublin. Now in its fifth year of operation, the course provides mature students working (or aiming to work) in the Architecture, Engineering, and Construction (AEC) Sector across Ireland with a wide range of well-integrated and intellectually engaging hands-on learning opportunities. The Irish government financially supports this course because the Irish AEC sector needs state-of-the-art knowledge and skills to help modernize construction methods for this small island nation. The Irish government's support underscores the significance of the course in addressing the …

A Novel Measure Of Habits And Goal-Directed Control, Katie Wood

School of Psychology Theses

Repetitive habitual behaviour is thought to occur even when this directly conflicts with goals. This is termed an action slip. The current research aimed to present a simple procedure that captures action slips and is easy to implement and interpret. Chapter 1 reviews the current literature. Chapters 2-4 then report nine experiments that aimed to test our novel procedure. A congruency effect, where participants’ performance was good on congruent trials but comparatively poor on incongruent trials, was consistently found in our experiments (with one exception), providing evidence of action slips. Chapter 2 details three experiments that used an Outcome (O)-Stimulus …

Exploring The Behavioural And Neural Bases Of Impulsivity In A Transdiagnostic Approach Relevant For Addiction., Kenza Kadri

School of Psychology Theses

This thesis aims to investigate the behavioural and neural basis of impulsivity, which is known to be a risk factor in the development of psychiatric disorders such as addiction. To tackle this issue, the thesis uses neuroimaging, behavioural, and machine-learning techniques to identify transdiagnostic markers of impulsivity and their relationship with striatal connectivity and decision-making variability. This thesis was divided into two studies. The first one aims to associate a set of transdiagnostic markers relevant to addiction with the connectivity profiles of the striatum, which is known to be implicated in processes of reward, motivation, and decision-making. In particular, this …

The Role Of Technology In Online Health Communities: A Study Of Information-Seeking Behavior, Leann Boyce, Ahasan Harun, Gayle Prybutok, Victor R. Prybutok

Information Systems Faculty Publications

This study significantly contributes to both theory and practice by providing valuable insights into the role and value of healthcare in the context of online health communities. This study highlights the increasing dependence of patients and their families on online sources for health information and the potential of technology to support individuals with health information needs. This study develops a theoretical framework by analyzing data from a cross-sectional survey using partial least squares structural equation modeling and multi-group and importance-performance map analysis. The findings of this study identify the most beneficial technology-related issues, like ease of site navigation and interaction …

Double Testing With Lateral Flow Antigen Test Devices For Covid-19: Does A Second Test In Quick Succession Add Value?, Matthias E Futschik, Sarah A Tunkel, Elena Turek, David Chapman, Zareen Thorlu-Bangura, Raghavendran Kulasegaran-Shylini, Edward Blandford, Andrew Dodgson, Paul E Klapper, Malur Sudhanva, Derrick Crook, John Bell, Susan Hopkins, Tim Peto, Tom Fowler

School of Biomedical Sciences

BACKGROUND/OBJECTIVES: We investigated if performing two lateral flow device (LFD) tests, LFD2 immediately after LFD1, could improve diagnostic sensitivity or specificity for detecting severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) antigen.

STUDY DESIGN: Individuals aged ≥16 years attending UK community testing sites (February-May 2021) performed two successive LFD tests and provided a nose-and-throat sample for a polymerase chain reaction (PCR) test. Using the PCR result as the reference diagnosis, we assessed whether improvements could be achieved in sensitivity (by counting a positive result in either LFD as a positive overall test result) or specificity (by using LFD2 as confirmatory test). …

Rapid Antigen Testing For Sars-Cov-2 By Lateral Flow Assay: A Field Evaluation Of Self- And Professional Testing At Uk Community Testing Sites, Matthias E Futschik, Samuel Johnson, Elena Turek, David Chapman, Simon Carr, Zareen Thorlu-Bangura, Paul E Klapper, Malur Sudhanva, Andrew Dodgson, Joanna R Cole-Hamilton, Nick Germanacos, Raghavendran Kulasegaran-Shylini, Edward Blandford, Sarah Tunkel, Timothy Peto, Susan Hopkins, Tom Fowler

School of Biomedical Sciences

BACKGROUND: The advent of lateral flow devices (LFDs) for SARS-CoV-2 detection enabled widespread use of rapid self-tests during the pandemic. While self-testing using LFDs is now common, whether self-testing provides comparable performance to professional testing was a key question that remained important for pandemic planning.

METHODS: Three prospective multi-centre studies were conducted to compare the performance of self- and professional testing using LFDs. Participants tested themselves or were tested by trained (professional) testers at community testing sites in the UK. Corresponding qRT-PCR test results served as reference standard. The performance of Innova, Orient Gene and SureScreen LFDs by users (self) …

Robust Triso-Fueled Pebble Identification By Digit Recognition, Roshan Kenia, Jihane Mendil, Ahmed Jasim, Muthanna Al-Dahhan, Zhaozheng Yin

Chemical and Biochemical Engineering Faculty Research & Creative Works

Nuclear Power Plays a Vital Role in Providing Reliable and Clean Energy to Fulfill Increasing Demands in Electricity Worldwide. It Continues to Be an Essential Source of National Power Supply as Growing Concerns About Fossil Fuel Depletion, Global Warming, and Emissions Require Utilizing Sustainable Energy Sources. One Area Contributing to the Growth of Nuclear Power is the Development of Reactors that Have Enhanced Protection and Security, Thermal Efficiency, and Design. Reactor Efficiency Can Be Studied by the Burnup that Occurs When a TRISO-Fueled Pebble is Inserted into the Nuclear Core and Subsequently Removed. the Levels of Burnup Are Measured based …

Improving Insar Accuracy For Slow Deformation And Change Detection With Lidar And Gps, Yusupujiang Aimaiti, Vasit Sagan, Cagri Gul, Jeremy Maurer, Jackson D. Cothren, Carla Klehm, Elizabeth A. Koenig, Nathan Scott

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Interferometric Synthetic Aperture Radar (InSAR) uses two or more SAR images over the same region for mapping ground surface displacements through time. InSAR displacements are relative to a reference point due to the 2π phase ambiguity that from unwrapping, and as a result ground control points (GCPs) are often used to calibrate the displacement model and obtain absolute measurements. Errors in InSAR time-series are typically measured with respect to the reference point, but absolute errors are not currently well constrained. To develop absolute error models for InSAR, we generate Sentinel-1 InSAR time-series for Belleville, Illinois and compare time-series with Global …

Towards A Concurrency Platform For Scalable Multi-Axial Real-Time Hybrid Simulation, Marion Sudvarg, Oren Bell, Tyler Martin, Benjamin Standaert, Tao Zhang, Sun Beom Kwon, Chris Gill, Arun Prakash

Computer Science Faculty Research & Creative Works

Multi-axial real-time hybrid simulation (maRTHS) uses multiple hydraulic actuators to apply loads and deform experimental substructures, enacting both translational and rotational motion. This allows for an increased level of realism in seismic testing. However, this also demands the implementation of multiple-input, multiple-output control strategies with complex nonlinear behaviors. To realize true real-time hybrid simulation at the necessary sub-millisecond timescales, computational platforms will need to support these complexities at scale, while still providing deadline assurance. This paper presents initial work towards supporting (and is influenced by the need for) envisioned larger-scale future experiments based on the current maRTHS benchmark: it discusses …

Novel Re-Crosslinkable Preformed Particle Gels (Rppg) For Parent- And Infill-Well-Fracture Interactions Mitigation, Xiaojing Ge, Adel Alotibi, Ahmed Al-Hlaichi, Yanbo Liu, Tao Song, Junchen Liu, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Hydraulic fracturing treatments in unconventional infill (or "child") wells can be significantly affected by depletion from existing parent-well, resulting in asymmetrical fracture growth. These issues may lead to excessive load-water production, proppant deposition, casing deformation in the parent well, and unbalanced stimulation of infill wells. To mitigate these effects, various strategies have been proposed, including the use of far-field diverters in child wells and repressurization of parent wells. Additionally, an increasingly popular strategy involves injecting near-wellbore diverters to temporarily plug entry points into the parent wellbores during frac operations on infill wells. To achieve better application, a novel low-cost, self-degradable, …

Would Self-Supported Fracture Contribute To The Hydrocarbon Production In Shale Reservoir Besides Proppant-Supported Fracture, Y. Sun, G. Li, S. Zeng, J. Wu, J. Liu, M. Xu, C. Dai, Baojun Bai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

During hydraulic fracturing in deep shale gas reservoirs, it is difficult to pump proppants long distances, and self-supported fractures are formed at the far and upper ends of the fractures. The self-supported fracture could hold the fracture space by its surface structure again fracture closure. However, it faces various aspects of impairment during shale gas reservoir development, which affect its conductivity. Among the impairment, long-term production results in a decline of bottom hole pressure, which will compact the fracture space, and self-supported fractures without proppant are more sensitive than those with proppant. In this paper, we conducted a series of …

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. the optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. the primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

On The K-Weak Coverage Of Random Mobile Sensors, Sajal K. Das, Rafal Kapelko

Computer Science Faculty Research & Creative Works

This paper studies the fundamental problem of energy consumption in the movement of mobile random sensors ensuring k-weak coverage on the domain. In particular, we analyze two notions of k-weak coverage on the unit square, namely (1) (k, x)-weak coverage in which every straight-line path across the width of the unit square passes through the sensing range of at least k sensors; and (2) (k, x, y)-weak coverage in which every straight-line path across the width and the length of the unit square passes through the sensing range of at least k sensors. The number of reliable and p-reliable sensors …

Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …

Trusted Digital Twin Network For Intelligent Vehicles, Asad Malik, Ayan Roy, Sanjay Madria

Computer Science Faculty Research & Creative Works

Vehicle-to-vehicle (V2V) infrastructure facilitates wireless communication among vehicles within close proximity. This allows sharing of contextual information such as speed, location, direction, traffic, route closures, human behavior mental conditions to improve traffic flow, reduce collisions, and enhance safety on the road. However, the assumption of honest peers along with the over-reliability on the information shared in the network can pose a serious threat to human safety. A digital twin is a concept that enables a system to develop a virtual environment that mimics the real-life scenario for any situation. The availability of powerful computing equipment inside vehicles can be leveraged …

Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …

Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das

Computer Science Faculty Research & Creative Works

Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this paper, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables …

Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra

Computer Science Faculty Research & Creative Works

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals' GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) …

Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li

Computer Science Faculty Research & Creative Works

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties: …

Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio

Computer Science Faculty Research & Creative Works

Domain Name System (DNS) tunneling is a well-known cyber-attack that allows data exfiltration - the attackers exploit this tunnel to extract sensitive information from the system. Advanced Persistent Threat (APT) attackers encapsulate malicious traffic in a DNS connection to elude security mechanisms such as Intrusion Detection System (IDS). Although different techniques have been implemented to detect these targeted attacks, their rise induces a threat to Cyber-Physical Systems (CPS). The DNS over HTTPS (DoH) tunnel detection is a challenge because the encrypted data prevents an analysis of DNS traffic content. In this paper, we present a novel detection system that identifies …

Quantifying The Impact Of A Constructed Wetland On Downstream Nitrate Concentrations And Loads In The U.S. Midwest, Elliot Anderson, Keith E. Schilling, Craig L. Just, Bong Chul Seo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Constructed wetlands are standard conservation practices used to reduce nitrate loads in agricultural watersheds. Many studies have examined the efficiency of denitrification in wetlands under various scenarios, but quantifying the watershed-scale impact of wetlands on downstream nitrate levels is rarely done using field observations. In this study, we estimated nitrate removal in a constructed wetland in the headwaters of Mud Creek, a HUC12 watershed in eastern Iowa, from May–September 2022 and May–September 2023 (a ten-month period). We also measured nitrate loads at four successive downstream sites, three along Mud Creek and one below its confluence with the larger Cedar River. …

A Fuzzy Model And Decision-Support Tool For Assessing And Predicting The Probability Of Bankruptcy Of Construction Companies, Rayan H. Assaad, Ghiwa Assaf, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction firms face considerable risks that might lead to business bankruptcy. Failed construction companies leave behind unfinished projects, which leads to huge losses to project owners. While previous studies were conducted to understand the factors that contribute to the bankruptcy of construction organizations, little to no research was performed to quantitatively assess the risk of construction business bankruptcy. Hence, this paper addresses this knowledge gap by developing a fuzzy model for predicting the probability of business bankruptcy of construction companies. First, the following six failure warning signs were considered: financial management system, borrowed credit, estimating and job-cost reporting, project management, …

Predicting High-Impact Research In The Construction Engineering And Management Domain Using Computational Machine Learning, Islam H. El-Adaway, Gasser G. Ali, Muaz O. Ahmed, Radwa Eissa, Mohamad Abdul Nabi, Tamima Elbashbishy, Ramy Khalef

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction engineering and management (CEM) domain covers multiple evolving sub-disciplines and directions. To understand those directions, numerous research efforts have conducted scientometric analysis of publications to explore their significance and impact. Despite their valuable insights, such research endeavors have been limited to exploratory analyses, whereas no previous attempt has been conducted to predict the future impact of research in CEM. This study fills this knowledge gap by performing a predictive analysis of citation metrics of publications in CEM using machine learning (ML). The developed model was trained on a dataset of 93,868 publications using an XGBoost algorithm. Testing showed that …

Predicting Construction Costs Under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based On Deepar, Ghiwa Assaf, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Projects Often Experience Cost Overruns Due To Market Uncertainty And Price Escalations. Traditional Cost Estimation Methods That Rely On Point Estimation Are Incapable Of Providing Prediction Intervals As Well As Probabilistic Assessment. Thus, There Is Need For An Innovative Approach To Predict The Changes And Uncertainties In Construction Material Costs. This Paper Proposes A Novel Stochastic Model To Estimate Construction Material Costs By Applying Probabilistic Forecasting Using Autoregressive Recurrent Networks. First, Price Data Was Collected For Four Different Construction Materials. Second, Data Was Divided Into A Training Set (Pre-COVID-19) And A Testing Test (Post-COVID-19). Third, The State-Of-The-Art DeepAR Algorithm Was …

An Accurate-Pricing Estimate Game-Theoretic Model For Determining Price Escalations In Construction Projects During Economic Uncertainties, Yasser Jezzini, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Economic Market Uncertainties, Such As Those Experienced During The COVID-19 Pandemic, Can Make Determining Accurate Prices Estimate For Construction Materials A Challenging Task. While Previous Research Focused On The Contractual Aspect Of This Issue By Studying Price Escalation Clauses, There Is Still A Gap In The Literature When It Comes To Proposing An Accurate Pricing Model. Thus, This Study Develops An Accurate Pricing-Estimate Game-Theoretical Model That Can Efficiently And Competitively Account For Escalations In Construction Materials Prices During Uncertain Market Conditions. First, Data On Past Producer Price Indexes (PPIs) Of Different Construction Materials Were Collected. Second, The Percentage Changes In …

Circular Economy Strategies For Decarbonization Of The Built Environment Value Chain Scope 3 Emissions: A Network Analysis, Radwa Eissa, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

With The Anticipated Growth In Construction Activities, Minimizing Value Chain Emissions, Including Those Attributed To Activities Beyond An Organization's Direct Control - Aka "Scope 3" Emissions, And Adopting Circular Economy (CE) Practices Have Become Critical To Attaining The 2050 Climate Goals. Extant Literature Lacks A Value-Chain-Comprehensive Set Of CE Strategies On One Hand, And An Analysis Of Their Actual Project Applications On The Other. This Research Aims To Build A Holistic Portfolio Of CE Actionable Strategies That Span Across The Entire Built Environment Value Chain, Analyzing Their Extent Of Implementation And Interlinkages Within The Literature Versus Actual Projects. To This …

Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose …

Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns. It is important to note that 3HGs are identified in individual spaces, lacking natural cross-subject correspondences. To address this issue, we have developed a learning-based method to encode anatomical features of 3HGs into a set of embedding vectors that can …

Prediction Of Surfactant Performance From Surfactant Huff-Puff In Carbonate Reservoirs Using A Data-Driven Approach And Desktop Application Development, Y. Yao, Mingzhen Wei, Y. Cui, M. Ali, J. Leng, Y. Qiu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Surfactants have been widely used to alter the wettability of carbonate rocks from oil-wetness to water-wetness and enhanced oil recovery. One of primary methods implemented in filed applications for enhanced oil recovery is surfactant huff-puff. Currently, a large number of surfactant treatments are conducted in laboratories prior to field applications to optimize the design of surfactant huff-puff, test the performance of surfactants, and minimize failure risk. This process is time-consuming since a treatment could last from several days to more than 300 days. Moreover, a fraction of treatments could not improve oil recovery as reported in the literature. In this …