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Integrated Hydrate Phase Behavior Modeling: Key to Hydrate Mitigation and Removing Pipeline Blockages
No full textGas hydrates, which are ice-like crystalline structures formed under low-temperature and high-pressure conditions, present significant flow assurance challenges in hydrocarbon transportation systems by causing pipeline blockages. This study underscores the critical importance of advanced thermodynamic modeling in predicting hydrate phase behavior and developing effective mitigation strategies, particularly in cold climates. By implementing sophisticated equations of state, particularly the Cubic-Plus-Association (CPA) model alongside the comprehensive van der Waals-Platteeuw (vdWP) model for predicting chemical potential and fluid-hydrate equilibrium, coupled with experimental data and field insights, the research offers a comprehensive framework for understanding hydrate formation, dissociation, and blockage mechanisms. The study highlights the necessity of accurately modeling hydrate phase boundaries to differentiate hydrate blockages from other solid deposits, such as ice or salt, and to optimize inhibitor dosing strategies for efficient flow assurance. A detailed case study of hydrate blockage in a buried gas pipeline illustrates the practical application of these modeling techniques. The analysis reveals that while depressurization can initially dissociate hydrates, it can lead to ice formation at low pressure/temperature conditions, exacerbating blockage issues (as ice does not respond to pressure). The study evaluates the effectiveness of various inhibitors, including methanol and monoethylene glycol (MEG), and proposes an innovative approach combining nitrogen (N2) injection with atomized methanol to address existing or future hydrate, ice, and salt blockages. This method successfully resolved the pipeline blockage, demonstrating the value of integrating advanced modeling, experimental validation, and field data for mitigating hydrate blockage. The findings emphasize the indispensable role of robust thermodynamic modeling in predicting fluid/solid behavior under operational conditions, optimizing inhibitor use, and minimizing economic and environmental impacts
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
No full textPrevious research on behaviour-based attack detection for networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited and often not demonstrated. This paper presents IoTGeM, an approach for modelling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance. We first introduce an improved rolling window approach for feature extraction. To reduce overfitting, we then apply a multi-step feature selection process where a Genetic Algorithm (GA) is uniquely guided by exogenous feedback from a separate, independent dataset. To prevent common data leaks that have limited previous models, we build and test our models using strictly isolated train and test datasets. The resulting models are rigorously evaluated using a diverse portfolio of machine learning algorithms and datasets. Our window-based models demonstrate superior generalization compared to traditional flow-based models, particularly when tested on unseen datasets. On these stringent, cross-dataset tests, IoTGeM achieves F1 scores of 99% for ACK, HTTP, SYN, MHD, and PS attacks, as well as a 94% F1 score for UDP attacks. Finally, we build confidence in the models by using the SHAP (SHapley Additive exPlanations) explainable AI technique, allowing us to identify the specific features that underlie the accurate detection of attacks.</p
Investigation of reaction parameters for esterification of acidic crude palm oil using bubble column reactor
No full textAcidic crude palm oil (ACPO) offers a sustainable option as a non-edible feedstock for biodiesel production. This study investigated a lab-scale bubble column reactor (BCR) for free fatty acid (FFA) esterification of APCO as a pretreatment step for biodiesel production. Air bubbles were sparged through the BCR column to facilitate homogeneous mixing of reactants, and the FFA esterification reaction was catalysed using sulphuric acid (H2SO4) and p-toluenesulfonic acid monohydrate (PTSA). Under optimised conditions, FFA esterification catalysed using H2SO4 required reaction conditions of 3 wt% catalyst dosage, 20:1 methanol-to-oil molar ratio, 30 min reaction time, 30 °C reaction temperature and 0.5 L/min air flow rate, achieving high FFA to FAME conversion of 84.06 %. PTSA-catalysed esterification reaction required similar reaction conditions as H2SO4, albeit at 5 wt% catalyst dosage and 15:1 methanol-to-oil molar ratio, achieving 79.51 % FFA conversion. Changes in the aspect ratio did not significantly affect the FFA conversion. The FFA esterification reaction trends were determined to fit the pseudo second-order reaction rate with activation energies of 28.59 and 22.23 kJ/mol for H2SO4 and PTSA, respectively. This study demonstrates the promising use of BCR for FFA esterification with lower reaction conditions and improved mixing
Artificial intelligence and machine learning for the diagnosis of Huntington disease: a narrative review
No full textBackground and Objective: Huntington's disease (HD) is a neurodegenerative disorder currently diagnosed by genetic tests and motor symptoms observation. However, these methods are either invasive or lack precision in diagnosing different stages, including presymptomatic states. These limitations have driven interest in the application of machine learning (ML) techniques to analyze patient data, identify HD patients, and uncover valuable biomarkers for diagnosis. Despite the growing body of research, a review of ML applications for HD diagnostics has been lacking. The review aims to provide a summary of ML methods used to diagnose HD and key diagnostics biomarkers that distinguish it from other neurodegenerative diseased (NDDs). Methods: A narrative review of English, peer-reviewed articles and conference papers that conducted experimental designs and employed ML or artificial intelligence (AI) algorithms for diagnostics. This includes those studies published from 2010 until 2023 on PubMed, IEEE and Heriot-Watt Discovery digital libraries. Amongst them, a total of 54 papers were found relevant and included in this review.Key Content and Findings: The review revealed the power of ML models for diagnosing HD from healthy controls, commonly by using physiological signals. Besides, decision tree-based models were the most used ML approach, offering a favourable balance between diagnostics performance and interpretability. Furthermore, despite that HD clinical scores emerged as crucual diagnostic features for identifying HD and discriminating them from control and other NDD conditions, more impactful features, such as brain structures, like caudate volume were found to improve the diagnosis. Conclusions: This review offers valuable insight for researchers and healthcare professionals, highlighting common ML applications for diagnosing HD, including data sources, modalities, preprocessing methods, and key biomarkers. Future research can refine diagnostic techniques by advancing from classical ML models to advanced approaches, leveraging state-of-the-art techniques, such as transformers to enhance performance, utilizing them for clinical decision-making, tailoring therapy development
Integrating External Tools with Large Language Models (LLMs) to Improve Accuracy
No full textThis paper deals with improving querying large language models (LLMs). It is well-known that without relevant contextual information, LLMs can provide poor-quality responses or tend to hallucinate. Several initiatives have proposed integrating LLMs with external tools to provide them with up-to-date data to improve accuracy. In this paper, we propose a framework to integrate external tools to enhance the capabilities of LLMs in answering queries in educational settings. Precisely, we develop a framework that allows accessing external APIs to request additional relevant information. Integrated tools can also provide computational capabilities such as calculators or calendars. The proposed framework has been evaluated using datasets from the Multi-Modal Language Understanding (MMLU) collection. The data consists of questions on mathematical and scientific reasoning. Results compared to basic OpenAI model show that the proposed approach significantly improves performance. On mathematical questions, our framework scores 83% where basic OpenAI scores 36%. In scientific reasoning, the difference is even more significant with 88% for the proposed method as compared to 56% for the basic OpenAI model. These promising results open the way to creating complex computing ecosystems around LLMs to make their use more natural to support various tasks and activities.</p
Advanced synthesis methods for graphene
No full textGraphene-based nanomaterials have lately gained considerable attention owing to their outstanding physicochemical characteristics and their capacity to enhance various applications. This chapter delves into advanced synthesis methods and the characteristics of graphene. Graphene synthesis techniques are typically categorized into two primary types: top-down and bottom-up processes. Among these processes, liquid-phase exfoliation, oxidative exfoliation of graphite, and chemical-vapor deposition hold promise for large-scale graphene fabrication due to their ease of manufacturing, high product quality, and scalability. Nonetheless, the current trend in graphene synthesis emphasizes sustainability and ultraprecision manufacturing. This chapter explores advanced synthesis methods such as molecular beam epitaxy (MBE), laser-induced processing, microfluidization, electrochemical exfoliation, biomass-derived graphene, flash Joule heating (FJH), and supercritical fluid (SCF) exfoliation, along with the characteristics of the synthesized graphene. These advanced methods offer advantages in meeting specific application needs due to their superior control over graphene size, quality, and structural properties. MBE can produce high-quality epitaxial layers with atomic precision, while laser-induced processing enables precise and rapid graphene synthesis without significant thermal damage. Microfluidization offers the benefit of producing graphene with fewer defects due to its mild exfoliation conditions, and electrochemical exfoliation is recognized as a facile and environmentally friendly synthesis method. This chapter also discusses green precursors, such as agricultural waste and other carbon-rich biomass, for synthesizing graphene. Biomass-derived graphene is sustainable, cost-effective, and versatile. FJH provides a rapid, economical, and sustainable approach, converting diverse carbon-rich waste into high-quality graphene without external gases or solvents. SCF exfoliation enables scalable graphene production with minimum chemical waste, preserving graphene’s intrinsic properties while offering a green and efficient alternative to traditional exfoliation methods. Finally, the prospects and challenges of advanced graphene synthesis methods are discussed. Maintaining product purity and quality, developing application-specific functionalization methods, and establishing standardized protocols and characterization methods are major requirements for the large-scale adoption of advanced synthesis methods
BREA-Depth: Bronchoscopy Realistic Airway-Geometric Depth Estimation
No full textMonocular depth estimation in bronchoscopy can significantly improve real-time navigation accuracy and enhance the safety of interventions in complex, branching airways. Recent advances in depth foundation models have shown promise for endoscopic scenarios, yet these models often lack anatomical awareness in bronchoscopy, overfitting to local textures rather than capturing the global airway structure–particularly under ambiguous depth cues and poor lighting. To address this, we propose Brea-Depth, a novel framework that integrates airway-specific geometric priors into foundation model adaptation for bronchoscopic depth estimation. Our method introduces a depth-aware CycleGAN, refining the translation between real bronchoscopic images and airway geometries from anatomical data, effectively bridging the domain gap. In addition, we introduce an airway structure awareness loss to enforce depth consistency within the airway lumen while preserving smooth transitions and structural integrity. By incorporating anatomical priors, Brea-Depth enhances model generalization and yields more robust, accurate 3D airway reconstructions. To assess anatomical realism, we introduce Airway Depth Structure Evaluation, a new metric for structural consistency. We validate BREA-Depth on a collected ex-vivo human lung dataset and an open bronchoscopic dataset, where it outperforms existing methods in anatomical depth preservation
Changing EAP assessment practices in the age of generative artificial intelligence: The case of Scottish higher education institutions
No full textThe impact of generative artificial intelligence (GenAI) on higher education has been widely discussed since the public release of ChatGPT-3.5 in late 2022. However, there has been little empirical research on changes in English-for-Academic-Purposes (EAP) assessment practices in response to GenAI. This qualitative case study intends to fill this gap by examining how Scottish universities changed EAP assessments in response to GenAI, how effective those changes were perceived by EAP academics, and what recommendations EAP academics offered for future assessment practices. Data were collected from six semi-structured interviews conducted with EAP academics at five Scottish universities in mid-2024 and thematically analysed. The findings reveal that while substantial changes in assessment task design were limited, modifications to task requirements (e.g., GenAI declarations, context-specific prompts) and grading practices were more common. Moreover, our participants expressed scepticism about the effectiveness of some changes (e.g., AI use declarations) but positively perceived others (e.g., the use of context-specific questions, spontaneous speaking tasks, and named marking). As for their recommendations, the participating EAP academics generally advocated authentic and innovative tasks, such as portfolio-based assessment, reflections, multimodal projects, and GenAI output evaluation over reverting to traditional exams while simultaneously highlighting issues with workload and learning outcomes. The study implies a need for clearer institutional guidance, ongoing professional dialogue, and support for experimentation with GenAI-integrated assessment design in EAP contexts
Changing EAP assessment practices in the age of generative artificial intelligence: The case of Scottish higher education institutions
No full textThe impact of generative artificial intelligence (GenAI) on higher education has been widely discussed since the public release of ChatGPT-3.5 in late 2022. However, there has been little empirical research on changes in English-for-Academic-Purposes (EAP) assessment practices in response to GenAI. This qualitative case study intends to fill this gap by examining how Scottish universities changed EAP assessments in response to GenAI, how effective those changes were perceived by EAP academics, and what recommendations EAP academics offered for future assessment practices. Data were collected from six semi-structured interviews conducted with EAP academics at five Scottish universities in mid-2024 and thematically analysed. The findings reveal that while substantial changes in assessment task design were limited, modifications to task requirements (e.g., GenAI declarations, context-specific prompts) and grading practices were more common. Moreover, our participants expressed scepticism about the effectiveness of some changes (e.g., AI use declarations) but positively perceived others (e.g., the use of context-specific questions, spontaneous speaking tasks, and named marking). As for their recommendations, the participating EAP academics generally advocated authentic and innovative tasks, such as portfolio-based assessment, reflections, multimodal projects, and GenAI output evaluation over reverting to traditional exams while simultaneously highlighting issues with workload and learning outcomes. The study implies a need for clearer institutional guidance, ongoing professional dialogue, and support for experimentation with GenAI-integrated assessment design in EAP contexts
Origins and fate of polycyclic aromatic hydrocarbons (PAHs) in sustainable drainage systems (SuDS) in a Scottish urban area: Implications for groundwater systems
No full textIncreasing urbanisation and the effects of climate change have resulted in a decrease in water quality and availability worldwide. At the same time, flooding in urban areas has become one of the most prevalent natural disasters worldwide. Sustainable drainage systems (SuDS) are resilient stormwater management solutions that can help reduce flooding by mimicking natural drainage processes and promoting infiltration. Numerous studies have focused on the benefits SuDS provide. But, to date, studies fail to investigate the risks that detention basins pose to groundwater quality, particularly the potential for infiltration of stormwater pollutants such as polycyclic aromatic hydrocarbons (PAHs). To address this important knowledge gap, this study combines gas chromatography–mass spectrometry (GC–MS) techniques for PAHs characterisation with numerical modelling tools to investigate organic pollutant infiltration patterns. We find that high levels of PAHs originating from urban areas are temporarily stored in SuDS basins and are likely to reach shallow water tables within one year. Multiple factors such as vegetation, precipitation, drainage area size, total organic carbon content in the soil and soil saturation influence the PAHs infiltration rates within the basins. More broadly, this study highlights the need for more research regarding SuDS dynamics to prevent both flooding and groundwater deterioration.<br/
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