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Towards an ontological approach to browser fingerprinting detection and privacy risk assessment.
No full textBrowser fingerprinting poses a significant privacy and security threat by covertly collecting unique user attributes such as device information, screen resolution, and installed fonts, enabling persistent tracking without relying on cookies. This paper introduces a novel hybrid detection method that combines JavaScript function interception and network request monitoring to collect and analyse fingerprinting scripts across the top 1,000 TRANCO websites, categorising them into widespread baseline, niche and highly aggressive scripts based on their attribute collection behaviour. Furthermore, we present our work towards the development of an ontology for browser fingerprinting detection, enabling structured reasoning about tracking techniques, relationships between websites and attributes, and classification of fingerprinting severity. The proposed ontology can be extended and integrated with future knowledge graphs to facilitate automated detection and reasoning about invasive tracking practices
Four futures one solution for a sustainable reduction in construction delay and disruption disputes by 2030.
Get PDFScenario planning is a research method that enables leaders to prepare for the future by revealing the impending opportunities and threats to businesses and markets. Although scenario planning has been used to forecast forces that will impact the construction sector, none of the recent studies investigates the potential of dispute reduction solutions to mitigate the negative impact of such future developments. Thus, the objectives of this study are to (i) offer a conceptual dispute reduction solution; (ii) establish the characteristics of four hypothetical scenarios based on the main forces that will impact the construction sector in 2030 and (iii) seek verification of the offered solution, including its ability to mitigate the impact of these forces. Therefore, the research methods are literature analysis, synthesis and evaluation, to fulfil objective one, and scenario planning, to realise objectives two and three. The research findings reveal that source materials, contract provisions and technology are the three tenets of a conceptual solution that can reduce delay and disruption disputes over matters of fact. Climate change and profit margins are the main forces that will impact the construction sector in 2030. As the consequences of climate change are likely to increase, contract terms that allocate risks associated with it are likely to be modified and insurance companies are liable to increase indemnification premiums, or become unable to cover such risks. This paper offers an innovative conceptual solution that increases contractual certainty through a risk allocation mechanism that mitigates the impact of those forces
Status and implications of the knowledge, attitudes and practices towards AWaRe antibiotic use, resistance and stewardship among low- and middle-income countries.
Get PDFThere are concerns globally with rising rates of antimicrobial resistance (AMR), particularly in low- and middle-income countries (LMICs). AMR is driven by high rates of inappropriate prescribing and dispensing of antibiotics, particularly Watch antibiotics. To develop future interventions, it is important to document current knowledge, attitudes and practices (KAP) among key stakeholder groups in LMICs. We undertook a narrative review of published papers among four WHO Regions including African and Asian countries. Relevant papers were sourced from 2018 to 2024 and synthesized by key stakeholder group, country, WHO Region, income level and year. The findings were summarized to identify pertinent future activities for all key stakeholder groups. We sourced 459 papers, with a large number coming from Africa (42.7%). An appreciable number dealt with patients' KAP (33.1%), reflecting their influence on the prescribing and dispensing of antibiotics. There was marked consistency of findings among key stakeholder groups across the four WHO Regions, all showing concerns with high rates of prescribing of antibiotics for viral infections despite professed knowledge of antibiotics and AMR. There were similar issues among dispensers. Patients; beliefs regarding the effectiveness of antibiotics for self-limiting infectious diseases were a major challenge, although educational programmes did improve knowledge. The development of the AWaRe (Access, Watch and Reserve) system, including practical prescribing guidance, provides a future opportunity for the standardization of educational inputs. Similar KAP regarding the prescribing and dispensing of antibiotics across LMICs and stakeholder groups presents clear opportunities for standardization of educational input and practical training programmes based on the AWaRe system
A drilling forensic framework and algorithm for the analysis and diagnoses of drilling dysfunction.
No full textIn the oil and gas industry, drilling wells can be time-consuming, largely influenced by the rate of penetration of the drill bit. Drilling dysfunctions result in wasted energy, reduced penetration rates, and potential damage to the bottom hole assembly or drill bit which can significantly impact overall drilling performance, costs, and emissions. These dysfunctions often stem from bit-rock interactions or drill string vibrations. The recent availability of digital data and real-time information has enabled instantaneous measurement of drilling parameters and loads, enhancing forensic assessments of drilling dysfunctions. However, consistent approaches to drilling dysfunction identification at the rig site are still lacking. Over the past decade, machine learning algorithms have been developed to enhance drilling performance, yet there are limited applications of artificial intelligence for real-time drilling dysfunction identification. This thesis aims to improve drilling performance by evaluating drilling data and developing an advanced forensic workflow to diagnose drilling dysfunctions. In this study, a workflow for drilling forensics was created, identifying the necessary information for effective assessments. Data from five wellbores were used in a case study to test this methodology, applied actively during the drilling of these wells. The raw data files were reprocessed using physics-based drilling mechanics equations, and a comparative analysis was conducted to correlate variables linked to drilling dysfunctions. A novel algorithm was developed for post-run analysis, detecting and categorizing drilling dysfunctions, and verified through manual post-run analysis. The impacts of drilling dysfunctions on machine learning capabilities were then analysed. The rate of penetration was predicted using the full data set and compared to predictions from a data set with dysfunctions removed, highlighting the effects of removing dysfunctions prior to machine learning training. The novelty of this study lies in the proposed drilling dysfunction forensics framework and the unique algorithm that combines physics-based and machine learning models to create an automated driller's roadmap. This includes the innovative use of resistivity to detect interfacial severity. Additionally, the study provides new insights into the impacts of drilling dysfunctions on machine learning predictions. Following the execution of the five 17.5-inch hole sections analysed in this study, implementing practices to tackle drilling dysfunctions resulted in the well campaign being delivered within 309 days, 79 days ahead of plan, with 40 of these days attributable to the performance drilling aspects described in this thesis. The approach for generating the algorithm demonstrated that utilizing this novel process can create an automated, integrated driller's roadmap in a fraction of the time required for manual methods. The machine learning models created showed a coefficient of determination ranging from 0.94 to 0.98 on training data and 0.60 to 0.86 on test data, indicating a favourable overall model fit. The forensics framework introduced in this study has formed the basis of a supporting industry guideline as part of an ongoing global effort by the International Association of Drilling Contractors (IADC) to upgrade the current drill bit and bottom hole assembly dull code manual. This study also aligns with 10 out of the 17 United Nations Sustainable Development Goals, promoting a sustainable future
Preparing students for the global virtual workplace: examining student, faculty, and employer perceptions of collaborative online international learning (COIL).
No full textThis exegesis offers a critical overview of the portfolio based on research completed between 2018 and 2024, incorporating 40 publicly available outputs. Eight of these have been submitted for closer consideration by the PhD assessors. The primary topic underpinning this work is Collaborative Online International Learning (COIL) and how it can help prepare students for the global, virtual workplace. Within this primary topic, the work focuses on four distinct but interrelated sub-topics incorporating intercultural competence, transferable skills and employability; COIL practice in Higher Education; and inclusive approaches to COIL design. Across all four topics, the contribution to the advancement of knowledge and practice within the field of COIL is demonstrated. The portfolio is contextualised at the beginning with an overview of theory and concepts associated with the characteristics and requirements of the contemporary global virtual workplace. This is followed by an analysis of core pedagogical arguments relating to transferable skill development, experiential learning, and employability and how they relate to the global, virtual workplace. Recent literature concerning internationalising and democratising the curriculum, and its significance for inclusive approaches to educational practice and the future workplace, is examined and placed within the context of the emerging field of COIL. The research topics and overarching research paradigm are presented and justified concerning the context and methodological theory, and the output portfolio is linked directly to the research topics through an interactive conceptual model and supporting narrative. The discussion reflects critically on the quality of output, its potential impact on COIL theory and practice, and the implications for students entering the global, virtual workplace. The researcher argues that the portfolio's main theoretical contribution is to understand further how COIL develops intercultural sensitivity and competency; how the DYNAMITE model can support transferable skill development; and how inclusive approaches to COIL design may strengthen the employability of students entering the global, virtual workplace. The narrative explains how the methodological approach, which involved examining a range of stakeholder perceptions, supported novel developments in COIL practice, including the creation of the COIL@RGU and COIL@UArctic digital resources. The limitations and potential wider impact of the portfolio concerning student employability, global sustainability, and inclusivity in Higher Education are considered, together with recommendations and opportunities for future research. The exegesis builds on existing pedagogical theory by suggesting that a flexitive learning approach to COIL creates an opportunity to bridge the gap between Higher Education and the global, virtual workplace
Predictive simulation of caprock failure mechanisms in depleted hydrocarbon reservoirs.
Get PDFCaprock integrity is critical for the long-term containment of injected CO2 in geological sequestration projects. Typically composed of low-permeability shale or claystone, caprock acts as a natural seal, preventing the upward migration of CO2 into overlying formations or the atmosphere. However, maintaining its integrity is challenging due to complex geomechanical and operational factors. Failure of caprock can create leakage pathways, undermining sequestration efficiency and posing environmental and regulatory risks. This research investigates the geomechanical behaviour of caprock in depleted hydrocarbon reservoirs considered for CO2 storage. Using the static structural module of ANSYS software, geomechanical changes leading to potential leakage are simulated under varying stress, pressure, and temperature conditions. A caprock core sample is modelled to reflect real-field stress scenarios, evaluating the sealing efficiency under CO2 injection operations. The study aims to establish a conservative baseline for assessing caprock stability in deep formations and to understand the mechanisms - such as induced stress, fracture propagation, and fault slip-that could compromise containment. Despite rigorous site assessments, caprock leakages have been reported, highlighting knowledge gaps in predicting failure under dynamic conditions. This work addresses these gaps by providing insights into the stress conditions that trigger leakage, supporting safer and more reliable CO2 sequestration strategies
AI-enhanced imaging and multimodal detection of rare-earth fluorescence-based security features for document authentication and border control.
No full textAdvanced document forgeries using color-matched inks and counterfeit ultraviolet pigments pose significant challenges for border security, evading conventional optical scanners. This study introduces an AI-enhanced imaging framework for authenticating documents via rare-earth-based fluorescence security features, such as invisible waveguides, which are robust against replication. Our approach leverages deep learning to eliminate the need for specialized optics, enabling reliable detection with standard cameras. We developed a dataset of co-registered white-light and fluorescence image pairs, trained a Conditional Generative Adversarial Network (CGAN) to generate synthetic fluorescence from white-light inputs, and implemented a YOLO-v8-based detector for real-time identification of embedded security features. This pipeline achieves a signal-to-noise ratio gain of +8.5 dB and 97% detection accuracy, offering a scalable, cost-effective solution for border checkpoints. By integrating the physical resilience of rare-earth luminescence with AI adaptability, our framework enhances document authentication, strengthens anti-counterfeiting measures, and facilitates efficient passenger processing
Tailoring education with AI: balancing personalized learning and critical thinking.
No full textArtificial Intelligence (AI) is increasingly reshaping higher education by offering new ways to support learning and teaching. In particular, AI has been celebrated for its capacity to personalise education, allowing students to engage with content in formats that align with their preferred learning styles. Yet, alongside these opportunities, concerns persist that over-reliance on AI may undermine the cultivation of critical thinking, a cornerstone of higher education. This paper draws on survey data from students across disciplines and academic levels to explore how AI is perceived as a tool for learning. The findings highlight both the potential and the limitations of AI in tailoring education while raising important considerations about maintaining intellectual autonomy and reflective practice
WIP: COMPASS: A gamified visual framework for competency tracking in computing education.
No full textThis innovative practice work-in-progress proposes a gamified tracking system that visually represents skill progression through academic journeys. The Competency-Oriented Mapping and Progress Assessment System (COMPASS) employs a "class card" model aggregating points across high-level skill categories. Educators assign predetermined points to competencies reflecting each class's focus, creating visual representations of contributions to overall competency development. This facilitates targeted instruction, early identification of skill gaps, and alignment with industry expectations. COMPASS encourages active participation and self-monitoring, transforming traditional assessments into an engaging visual narrative of learning progress. It builds upon frameworks from Computing Curricula 2020 and the Skills Framework for the Information Age by mapping competency models into a gamified, visual structure. Currently in pilot phase, the system is being implemented in select curricula with planned analyses of skill point distributions and stakeholder feedback on clarity, engagement and usability
Performance of septic tanks for pharmaceutical removal and their impact to river water quality.
No full textPharmaceuticals enter surface water after incomplete removal in wastewater treatment works (WWTWs) and are known for their potential ecotoxicological hazard to the environment. Septic tanks (STs) treat wastewater of individual houses and small communities in rural and semi-urban areas and are an understudied pathway of surface water contamination. Therefore, the aim of this research was to understand the performance of STs for pharmaceutical removal and their impact to river water quality. A methodology for the analysis of 68 pharmaceuticals, including prescription and over-the-counter drugs, related metabolites, hormones, and other human wastewater marker (emerging contaminants; ECs), by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) in ST influent and effluent, total suspended solids (TSS), sludge and river water was developed. Five community STs and the receiving rivers were studied over 12 months. Concentrations were similar in influent and effluent, exceeded those previously found in centralised WWTWs and surpassed freshwater predicted no-effect concentrations (PNECs) for some pharmaceuticals and samples. Hence, dilution of the ST discharges is required to mitigate environmental hazard. Influenced by the small contributing population, monthly variability was high, especially for pharmaceuticals with acute use, e.g., antifungals. Generally, the contribution of TSS to the total pharmaceutical concentration was small, but high contributions of TSS were for example observed for fluoroquinolone antibiotics and antidepressants. Approximately half of pharmaceuticals are chiral, existing as two or more enantiomers with differences in their environmental occurrence, fate, and toxicity. For 26 chiral pharmaceuticals, the enantiomer composition of ST was determined. Most pharmaceuticals were found racemic in wastewater, containing the same amount of both enantiomers. However, a strong enantioselectivity in influent and effluent was for example found for fluoxetine, naproxen, citalopram, omeprazole, and cotinine. The similar enantiomeric composition of ST influent and effluent suggests that unlike in aerobic WWTWs, no enantioselective degradation occurs in STs. Potentially, the unchanged enantiomeric composition in ST wastewater, can be used to distinguish between pharmaceutical discharges from STs and other WWTWs in the environment, e.g., naproxen. A wide range of ECs were also detected in rivers upstream and downstream of the ST discharge points, and concentrations increased by up to 95% downstream. In general, hazards in the rivers were low. However, PNECs were exceeded for ibuprofen, diclofenac and ciprofloxacin in few samples. Overall, the similarities in number of compounds detected, concentrations, and enantiomeric composition, indicate that STs are less effective in removing pharmaceuticals than centralised WWTWs. Alternative wastewater treatment might be needed at locations with low dilutions. Finally, pharmaceuticals were analysed in influent and effluent of twelve pilot-scale STs (control, insulated, 20°C and 30°C) to assess the temperature effect on removal, as biodegradation is influenced by microbial activities and can increase with increasing temperature. For most pharmaceuticals, no temperature effect was found. However, lower concentrations of metformin and sanitary determinands at 30°C, highlight the potential for a seasonal effect for some pharmaceuticals. Higher temperatures above 30°C may further enhance the removal. In summary, STs contribute to pharmaceutical concentrations in rivers, but due to the mostly high dilutions, hazards are low
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