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Solute micro-segregation profile and associated precipitation in cast Al-Mg-Si alloy
Get PDFThe micro-segregation in the as-cast AA6082 aluminium alloy were investigated across a range of length scales using a combination of analytical electron microscopes. It is found that the micro-segregation bands form an inter-connected network following grain boundaries and inter-dendritic channels. The micro-segregation can be divided into major micro-segregation and minor micro-segregation; the former is mainly on the grain boundaries consisting of iron-bearing intermetallic; the latter occurs both, along the grain boundaries and inter-dendritic channels, consisting mainly of Mg and Si alloying elements. The atomic-scale imaging reveals that in the minor-segregation bands, the supersaturated solute concertation has formed precipitates that had either nucleated heterogeneously on the dislocation network or homogeneously inside the aluminium matrix. The heterogeneously nucleated precipitates in the dislocation lines are composed of a mixture of phases down the precipitation sequence meanwhile, the homogenously grown ones are discrete phases that appear at the early stages of the precipitation sequence.UK Engineering and Physical Sciences Research Council LiME [EP/N007638/1]
Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks
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The original data presented in the study are openly available in [25] Fuglsang, S.A.; Märcher-Rørsted, J.; Dau, T.; Hjortkjær, J. Effects of sensorineural hearing loss on cortical synchronization to competing speech during selective attention. J. Neurosci. 2020, 40, 2562–2572..Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain–computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.This research received no external funding
Experimental Implementation of an Economic Model Predictive Control for Froth Flotation
Get PDFWe present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The EMPC's optimal control problem was solved using full discretization with orthogonal collocation over finite elements, employing automatic differentiation via CasADi. This approach was applied in a 30-litre laboratory-scale flotation cell, significantly improving mineral recovery from 9% to 29% under feed flowrate disturbances while maintaining a minimum concentrate grade of 20%
Does a novel digital physical activity intervention delivered via the Kidney BEAM platform improve health-related quality of life in people living with polycystic kidney disease? A mixed methods randomised controlled trial
No full textSupplementary data are available online at: https://academic.oup.com/ckj/advance-article/doi/10.1093/ckj/sfaf041/8010850#supplementary-data .Background:
In people living with polycystic kidney disease (PKD), physical inactivity may contribute to poor health-related quality of life (HRQoL). To date, no research has elucidated the impact of a PKD-specific physical activity programme on HRQoL and physical health. This sub-study of the Kidney BEAM Trial evaluated the impact of a PKD-specific 12-week educational and physical activity digital health intervention for people living with PKD.
Methods:
This study was a mixed-methods, single-blind, randomised waitlist-controlled trial. Sixty adults with a diagnosis of PKD, were randomised 1:1 to the intervention or a wait-list control group. Primary outcome was difference in the Kidney Disease QoL Short Form 1.3 Mental Component Summary (KDQoL MCS) between baseline and 12 weeks. Six participants completed individualised semi-structured interviews.
Results:
All 60 individuals (mean 53 years, 37% male) were included in the intention-to-treat analysis. At 12 weeks, there was a significant difference in mean adjusted change in KDQoL MCS score between the intervention group and waitlist control (4.2 [95% confidence interval, CI: 1.0–7.4] arbitrary units [AU], p = 0.012). Significant between-group differences in KDQoL sub-scales; burden of kidney disease (p = 0.034), emotional wellbeing (p = 0.001), and energy/fatigue (p = 0.001) were also achieved. There was no significant between-group difference in KDQoL PCS scores (p = 0.505). Per protocol analyses revealed significant between group differences in the PAM-13 patient activation score (p = 0.010) and body mass (p = 0.027). Mixed-methods analyses revealed key influences of the programme, including opportunities for peer support and to build on new skills and knowledge, as well as the empowerment and self-management.
Conclusion:
A PKD-specific digital health educational and physical activity intervention is acceptable and has the potential to improve HRQoL. Further research is needed to better understand how specific education and lifestyle management may help to support self-management behaviour.This study was funded by The PKD Charity and Kidney Research UK. J.B. is supported by a Kidney Research UK Allied Health Professional Fellowship (Clinical), reference number: AHPF_001_20230628. H.M.L.Y. is funded by the National Institute for Health and Care Research (NIHR) [NIHR302926]. J.O.B. is funded (Senior Investigator Award) by the NIHR. The views expressed are those of the authors and not necessarily those of NIHR or the Department of Health and Social Care
Enhancing empirical SRP model for BDS-3 MEO satellites via semi-analytical accelerations analysis
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The observations are available from: ftp://igs.gnsswhu.cn/pub/gps/(accessed on Feb. 26, 2024). The satellite information of BDS is available from: http://en.beidou.gov.cn/SYSTEMS/Officialdocument (accessed on Feb. 26, 2024).Currently, the significant challenge in achieving Precise Orbit Determination (POD) for the BeiDou Global Satellite Navigation System (BDS-3) lies in the Solar Radiation Pressure (SRP) model. The empirical CODE orbit model (ECOM) and its extended version, ECOM2, initially developed for the Global Positioning System (GPS) and the Russian Global Navigation Satellite System (GLONASS), are now widely employed not only for GPS and GLONASS but also for BDS and Galileo. However, applying ECOM and ECOM2 to BDS-3 reveals discrepancies, especially during eclipse seasons. To overcome this challenge, in this study, we proposed a refined ECOM with consideration of characteristics of SRP-induced accelerations acting on BDS-3 Medium Earth Orbit (MEO) satellites. First, we generate SRP-induced accelerations using the Adjustable Box-wing (ABW) model. Subsequently, we employ Fast Fourier Transform (FFT) to analyze the spectrum characteristics of these accelerations. The results indicate that the most prominent periodic terms are 1pr and 2pr terms in the D-direction and 1pr term in the B-direction. Parameter correlation analysis reveals that Dc,1 has a correlation coefficient nearly 1.0 with the parameter B0. Consequently, we eliminate Dc,1 from the model. In eclipse seasons, the proposed model achieves an orbit Day Boundary Discontinuity (DBD) accuracy of about 30 mm. Compared to other ECOMs, it demonstrates an improvement ranging from 8 % to 44 %. Additionally, the orbit prediction precision reaches approximately 60 mm, surpassing other ECOMs by 9 %–43 %. The STD of SLR residuals for CAST satellites during eclipse season also shows improvements of 8 %–25 % compared to other ECOMs. These results affirm the proposed model is well-suited for BDS-3 MEO satellites POD, especially during eclipse seasons.This work was supported by the Programs of the National Natural Science Foundation of China (42374025), and the program of China Scholarship Council (CSC) (Grant No. 202306560087)
Low Concentrating Photovoltaic Geometry for Retrofitting Onto European Building Stock
Get PDFThe most appropriate low concentrating photovoltaic (LCPV) technology suitable for European buildings located in mid-high latitudes under both maritime and continental climatic conditions has been identified as the asymmetric compound parabolic concentrator (ACPC). To date, there is no published experimental data at different latitudes on the long-term performance of these systems at these latitudes nor how location would modify the optical characteristics of deployed systems. Previous theoretical research by the authors has demonstrated the superiority of the ACPC with this additional work experimentally confirming the robustness of the design. To investigate how seasonal and locational variations affect their measured technical performance two identical ACPC-LCPVs were installed, instrumented, and monitored at two different climatic locations (Uxbridge, UK, and Vevey, Switzerland) from May 2020 to September 2020. A valid comparative performance investigation characterizing two geometrically equivalent ACPC-based LCPV systems using real-life experimental data collected is presented in this paper. Locations at higher latitudes experience greater transverse angles more frequently compared to locations nearer the equator making ACPC geometries more appropriate than symmetrical concentrator configurations for building retrofit. This is shown in this paper over a latitudinal expanse of 31.35 deg for four separate locations; Tessalit (20.19 deg N, 1.00 deg E; Mali), Timimoun (28.03 deg N, 1.65 deg E; Algeria), Uxbridge (51.54 deg N, 0.48 deg E, UK), and Vevey (46.6 deg N, 6.84 deg E, Switzerland).This project has received funding from the European Union’s H2020 research and innovation programme under grant agreement no 768576 (ReCO2ST project). David Redpath acknowledges the funding received from the Bryden Centre project (91) which was supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB)
Context-Aware Frequency-Embedding Networks for Spatio-Temporal Portfolio Selection
Get PDFRecent developments in the applications of deep reinforcement learning methods to portfolio selection have achieved superior performance to conventional methods. However, two major challenges remain unaddressed in these models and inevitably lead to the deterioration of model
performance. First, asset characteristics often suffer from low and unstable signal-to-noise ratios, leading to poor learning robustness of the predictive feature representations. Second, existing literature fails to consider the complexity and diversity in long-term and short-term spatio-temporal
predictive relations between the feature sequences and portfolio objectives. To tackle these problems, we propose a novel Context-Aware Frequency-Embedding Graph Convolution Network (Cafe-GCN) for spatio-temporal
portfolio selection. It contains three important modules: (1) frequency-embedding block that explicitly captures the short-term and long-term predictive information embedded in asset characteristics meanwhile filtering out noise; (2) context-aware block that learns multiscale temporal dependencies in the feature space; and (3) multi-relation graph
convolutional block that exploits both static and dynamic spatial relations among assets. Extensive experiments on two real-world datasets demonstrate that Cafe-GCN consistently outperforms proposed techniques in the literature.This work was supported by the National Natural Science Foundation of China (NSFC) 62272172, Guangdong
Basic and Applied Basic Research Foundation 2023A1515012920, and Zhuhai Science and Technology Plan Project (2320004002758)
Effects of antidepressant drug pollution on molluscs
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThere are growing concerns molluscs may be more vulnerable to the effects of antidepressant drug pollution than any other animal phylum as a multitude of key physiological processes are regulated by monoamines, the target of antidepressants, in molluscs. However, target-mediated effects of different classes of antidepressants at environmental levels in molluscs remain to be understood as currently existing studies have largely focused on effects of concentrations that are not environmentally relevant. Moreover, the effects of antidepressants on overarching physiological processes such as feeding and reproductive behaviours (shell mounting and intromission) have not been reported in any molluscan species in addition to the absence of report on the effects of two major classes of antidepressant drugs on fecundity in molluscs. Furthermore, according to the read-across hypothesis, pharmacological responses (target-mediated effects) of a pharmaceutical would occur in an organism in which the drug targets are conserved if the plasma concentrations of the pharmaceutical approaches its human therapeutic plasma concentration. Despite this, hemolymph levels of antidepressants have not also been reported in any molluscan species. To address this critical knowledge gap, a systematic review of the literature was conducted; and the effects and hemolymph levels of environmentally relevant concentrations of widely prescribed antidepressant drugs (3 classes) including amitriptyline (10, 100, 500 and 1000 ng/L), fluoxetine (10, 100 and 500 ng/L) and venlafaxine (0.5, 1, 2, 4 and 55 μg/L) in the freshwater mollusc, Biomphalaria glabrata, were investigated over a period of 28 days. The endpoints included feeding, reproduction (fecundity), growth, substrate attachment, spatial distribution, shell mounting and intromission. Feeding and reproduction were disrupted at particularly low hemolymph levels of antidepressants (with venlafaxine being the least potent), while the systematic review reveals that immunosuppression is a major target-mediated effect of antidepressants in molluscs. The study provides novel critical data relevant to regulatory decision-making and future research direction.TETfund, Nigeri
Searches for Higgs boson production through decays of heavy resonances
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Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use and open access policy (https://cms-docdb.cern.ch/cgi-bin/PublicDocDB/RetrieveFile?docid=6032%26filename=CMSDataPolicyV1.2.pdf%26version=2).A preprint version, arXiv:2403.16926 [hep-ex], of this article is available on arXiv at https://arxiv.org/abs/2403.16926v1 . It has not been certified by peer review.Supplementary data are available at: https://www.sciencedirect.com/science/article/pii/S0370157324003223?via%3Dihub#appSC .The discovery of the Higgs boson has led to new possible signatures for heavy resonance searches at the LHC. Since then, search channels including at least one Higgs boson plus another particle have formed an important part of the program of new physics searches. In this report, the status of these searches by the CMS Collaboration is reviewed. Searches are discussed for resonances decaying to two Higgs bosons, a Higgs and a vector boson, or a Higgs boson and another new resonance, with proton-proton collision data collected at √s = 13 TeV in the years 2016-2018. A combination of the results of these searches is presented together with constraints on different beyond-the-standard model scenarios, including scenarios with extended Higgs sectors, heavy vector bosons and extra dimensions. Studies are shown for the first time by CMS on the validity of the narrow-width approximation in searches for the resonant production of a pair of Higgs bosons. The potential for a discovery at the High Luminosity LHC is also discussed.the Armenian Science Committee, project no. 22rl-037; the Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the Bulgarian Ministry of Education and Science, and the Bulgarian National Science Fund; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, the National Natural Science Foundation of China, and Fundamental Research Funds for the Central Universities; the Ministerio de Ciencia Tecnología e Innovación (MINCIENCIAS), Colombia; the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research and Innovation Foundation, Cyprus; the Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; the Estonian Research Council via PRG780, PRG803, RVTT3 and the Ministry of Education and Research TK202; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucléaire et de Physique des Particules/CNRS, and Commissariat à l’Énergie Atomique et aux Énergies Alternatives/CEA, France; the Shota Rustaveli National Science Foundation, Georgia; the Bundesministerium für Bildung und Forschung, the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 - GRK2497, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Innovation and the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288, Greece; the National Research, Development and Innovation Office (NKFIH), Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and National Research Foundation (NRF), Republic of Korea; the Ministry of Education and Science of the Republic of Latvia; the Research Council of Lithuania, agreement No. VS-19 (LMTLT); the Ministry of Education, and University of Malaya (Malaysia); the Ministry of Science of Montenegro; the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Education and Science and the National Science Centre, Poland; the Fundação para a Ciência e a Tecnologia, grants CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019, Portugal; the Ministry of Education, Science and Technological Development of Serbia; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765, projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00, and Plan de Ciencia, Tecnología e Innovación de Asturias, Spain; the Ministry of Science, Technology and Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Ministry of Higher Education, Science, Research and Innovation, and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Energy, Nuclear and Mineral Research Agency; the National Academy of Sciences of Ukraine; the Science and Technology Facilities Council, UK; the US Department of Energy, and the US National Science Foundation.
Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union) the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010 and USTC Research Funds of the Double First-Class Initiative No. YD2030002017 (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Hungarian Academy of Sciences, the New National Excellence Program - ÚNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Scientific and Industrial Research, India; ICSC – National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration program, Italy; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia, grant FCT CEECIND/01334/2018; the National Priorities Research Program by Qatar National Research Fund; the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00, and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation ; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA)
An investigation into the generalisability of fake news detection models
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonFake news has emerged as a significant societal challenge, influencing public
discourse, spreading disinformation, and eroding trust in democratic institutions.
While supervised machine learning has become the predominant approach to addressing
this issue, existing methods often struggle with generalisability. These
limitations stem from an overreliance on coarsely labelled datasets, which fail to
capture nuanced distinctions between fake and real news, and the widespread use of
token-based features, such as Bag-of-Words, TF-IDF, Word2Vec, and BERT. These
features, while effective within specific datasets, are highly sensitive to dataset biases
and source-specific patterns. Traditional evaluation techniques, such as holdout
testing and K-fold cross-validation, exacerbate this issue by by assuming the
data is representative, an assumption often invalid when models are tested against
real-world data.
This thesis addresses these limitations by exploring strategies to enhance the generalisability
of fake news detection models. It proposes the use of stylistic features,
which focus on linguistic characteristics such as sentence structure, punctuation,
readability, and persuasive language. These features are less reliant on specific word
patterns and more robust to source biases. Additionally, the thesis introduces a
novel set of ‘social-monetisation’ features to capture the economic motivations behind
fake news. These include the presence of advertisements, social media share
buttons and affiliate links. Together, these features offer a new perspective on detecting
disinformation by focusing on the financial incentives driving its production.
To assess generalisability, the research combines K-fold cross-validation with external
validation. In this approach, models are tested internally within each fold
and externally on a manually labelled dataset after every fold. This dual framework
ensures performance is rigorously evaluated under both experimental conditions and
real-world scenarios. By combining these strategies, the research addresses the shortcomings
of traditional methods, providing a robust understanding of generalisability.
Results demonstrate that token-based models, while effective within specific
datasets, perform poorly in cross-dataset scenarios. In contrast, stylistic and socialmonetisation
features show greater resilience to dataset-specific biases and provide
a more nuanced understanding of fake news characteristics. External validation
further highlights the importance of evaluating models on diverse data to assess
real-world performance.
This research advances fake news detection by identifying the limitations of current
approaches, proposing robust feature sets, and advocating for rigorous evaluation
methods. Specifically, it has made four key contributions: demonstrating the advantages of stylistic features in improving fake news detection, introducing a
novel category of features focused on social dissemination behaviors and economic
incentives, developing a reduced and simplified feature set to enhance generalisability
and efficiency, and establishing a novel evaluation framework for assessing model
performance in this domain
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