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Fault interruption scheme for HVDC systems using GaN-HEMT and VCB
No full textPower electronics switching devices played an important role in high-voltage DC circuit breaker development. Timely isolation of faulty portions of an HVDC transmission line from a healthy system is a basic requirement for a fault interruption. In this scenario, the integration of hybrid DC circuit breakers (HDCCBs) with wideband-gap semiconductor devices enables the effective management of high power, currents, and voltages. The SiC-MESFET and the GaN-HEMT are commonly used wideband-gap-based semiconductor devices. This paper introduces a fault interruption scheme for HVDC power systems, featuring the advancement of a hybrid DC circuit breaker. The proposed HDCCB design consists of two parts, one part is based on a VCB as a mechanical circuit breaker, and the second part involves electronic switches for fault interruption. The electronic switches are designed through the combination of GaN and HEMT to achieve fast switching to achieve rapid interruption of fault current. The system model is implemented through a Simulink model to perform a comparative analysis between the presented and existing protection topologies. Current commutation is achieved through the attainment of artificial zero current crossing to interrupt the DC fault. GaN-HEMT emerges as a more reliable and fast switching element compared to other electronic switches like Sic-MESFET as validated by the presented simulative results. The presented model shows better fault-clearing times of 2.2 ms and 2 ms for experimental parameters of (500 kV and 9kA) and (100 kV and 10kA), respectively. This fault-clearing time shows an improvement of 52.38% and 50% compared to the SiC-MESFET-based electronic switches used by the existing mechanisms. The outcomes of the proposed design are evaluated in terms of fault current, commutated current, and voltage across the commutated capacitor.</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
Modelling Regional Geochemistry and As-Bi-Co-Cu-Fe-Ni Mineralisation Using G-BASE in the Lake District, UK
No full textGeochemical data for the UK Lake District including both G-BASE stream sediment data and newly collected samples are shown here as a tool for modelling whole rock geochemistry at a regional scale and as a case study for identifying potential As-Bi-Co-Cu-Fe-Ni mineralisation. Regional whole rock concentrations for the Skiddaw Group and Borrowdale Volcanic Group (BVG) were modelled using G-BASE stream sediment data and found to align closely with newly collected in-situ XRF measurements of host rock samples. Average concentrations of elements such as Ag, Al, As, Fe, Ni, and Ti differed by only 1–2 wt.% or ∼20 ppm between the two datasets. Six areas identified by G-BASE as potential As-Co-Cu-Ni targets were visited. Of these, Keld and Devoke Water showed evidence for sulphide dissemination within the host rock rather than visible veins, while Black Combe, Seathwaite, Coniston, and Tilberthwaite were confirmed to host vein-type, quartz-sulphide mineralisation geochemically similar to known deposits at Dale Head North, Scar Crag, and Ulpha. This study highlights the successful application of G-BASE data for regional geochemical modelling and exploration targeting. The workflow could be adapted for other areas covered by preexisting stream sediment geochemical data or integrated into exploration strategies for new regions
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
Modelling Regional Geochemistry and As-Bi-Co-Cu-Fe-Ni Mineralisation Using G-BASE in the Lake District, UK
No full textGeochemical data for the UK Lake District including both G-BASE stream sediment data and newly collected samples are shown here as a tool for modelling whole rock geochemistry at a regional scale and as a case study for identifying potential As-Bi-Co-Cu-Fe-Ni mineralisation. Regional whole rock concentrations for the Skiddaw Group and Borrowdale Volcanic Group (BVG) were modelled using G-BASE stream sediment data and found to align closely with newly collected in-situ XRF measurements of host rock samples. Average concentrations of elements such as Ag, Al, As, Fe, Ni, and Ti differed by only 1–2 wt.% or ∼20 ppm between the two datasets. Six areas identified by G-BASE as potential As-Co-Cu-Ni targets were visited. Of these, Keld and Devoke Water showed evidence for sulphide dissemination within the host rock rather than visible veins, while Black Combe, Seathwaite, Coniston, and Tilberthwaite were confirmed to host vein-type, quartz-sulphide mineralisation geochemically similar to known deposits at Dale Head North, Scar Crag, and Ulpha. This study highlights the successful application of G-BASE data for regional geochemical modelling and exploration targeting. The workflow could be adapted for other areas covered by preexisting stream sediment geochemical data or integrated into exploration strategies for new regions
3D Printing in the Construction Sector: Identification of Key Topics, Technologies, Applications and Relevant Factors Discussed in the Literature
No full text3D printing is transforming the construction industry by reducing material waste, enhancing design flexibility, and shortening construction timelines. As a rapidly advancing technology, it offers innovative solutions for efficient, sustainable, and advanced building practices. This study aims to give an overview of the contents treated in the literature on 3D printing of concrete, with a focus on key topics, technologies, applications, and parameters influencing printability. Following a systematic review process, 1079 studies were analyzed in terms of objectives, structural applications, and printing technologies. The findings reveal a strong emphasis on parameters such as strength, interlayer bonding, and rheological properties, while durability-related aspects like freeze-thaw resistance and water absorption are explored more seldom. The study underscores the need for material optimization to balance fresh-state and hardened-state properties, ensure long-term structural performance, and incorporate sustainable materials. By addressing these gaps, this research identifies critical pathways for advancing 3D printing in construction and provides recommendations for achieving durable, efficient, and environmentally sustainable solutions
Real-time data-driven multi-objective optimization in B2B marketing using digital twins
No full textIn the competitive B2B marketing environment, optimal decision-making requires intelligent and data-driven models that can respond to real-time market changes. This research presents an innovative framework for multi-objective optimization in B2B marketing that uses digital twins and real-time data to maximize profits, reduce marketing costs, and increase customer engagement. To achieve these goals, two algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Whale Optimization Algorithm (WOA), provide a combination of high convergence speed and accuracy in finding optimal solutions. The results show that NSGA-II performs better when quick decision-making is required, while WOA provides more optimal solutions in some cases. Also, examining the role of digital twins showed that the proposed model can reduce additional costs and improve decision accuracy by continuously adjusting marketing strategies. Sensitivity analysis also confirmed that increasing marketing budgets and improving customer engagement rates directly impact growing profitability. The results of this research show that the proposed optimization framework, by integrating digital twins and multi-objective meta-heuristic algorithms, has the ability to improve resource allocation and performance indicators in dynamic and uncertain environments. This approach can be used in areas such as B2B digital marketing, supply chain management, and resource allocation in online service systems.</p
Parametric estimation of conditional Archimedean copula generators for censored data
No full textA novel framework is introduced for estimating Archimedean copula generators in a conditional setting by embedding endogenous variables directly within the generator function. Unlike standard copula constructions that rely on a fixed dependence structure across all covariate levels, the proposed methodology allows both the strength and the shape of dependence to evolve with the covariates. To identify the values of a continuous risk factor at which the dependence pattern undergoes substantive changes, an iterative splitting algorithm is developed to determine optimal partitioning points within the covariate range. The approach is evaluated through applications to a diabetic retinopathy study and a claims reserving analysis, illustrating that explicitly modelling covariate effects yields a more accurate representation of dependence and enhances the practical relevance of copula models in medical and actuarial settings
A fractional partition of unity finite element method for transient anomalous diffusion problems
No full textA fractional partition of unity finite element method is proposed for the solution of the transient anomalous diffusion equation. The Caputo integro-differential operator is employed to represent the fractional time-derivative in these problems. To approximate the Caputo fractional derivative, we propose a new numerical differentiation formula using quadratic splines. For the spatial discretization, we implement an enriched finite element method on unstructured meshes. In the present study, a category of exponential functions incorporating fractional orders is introduced as enrichment functions to refine the finite element approximation. These functions are designed to capture the fractional characteristics of the solution more effectively. By integrating these enrichment functions through the partition of unity framework, the method utilizes prior knowledge of the fractional problem, leading to a substantial enhancement in approximation accuracy while preserving the fundamental advantages of the traditional finite element method. Consequently, the proposed approach delivers precise numerical solutions even with coarse meshes and requires significantly fewer degrees of freedom compared to conventional finite element techniques. Moreover, the mesh resolution remains unaffected by variations in the fractional order, allowing for a consistent mesh structure regardless of changes in fractional parameters. Through extensive numerical simulations, we consistently verify the effectiveness of the proposed technique in achieving high levels of accuracy. This approach not only ensures reliable and precise results but also broadens the applicability of the finite element method, making it more capable of handling time-fractional transient diffusion problems that have traditionally been challenging for standard methods
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