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Live Demonstration: Real-Time High-Amplitude Signal Acquisition with 2-Channel Modulo ADC
Get PDFModulo analog-to-digital converters (ADCs) offer a potential solution to the clipping challenges in conventional ADCs by folding signals that exceed the threshold. This makes them suitable for high-amplitude signal acquisition in wide dynamic range applications. In this demonstration, we present a 2-channel modulo ADC system implemented on a field-programmable gate array (FPGA) with an integrated real-time recovery algorithm. By managing both signal folding and recovery entirely in hard-ware, the system ensures low-latency processing. The FPGA efficiently handles high-bandwidth signals, making it a promising option for applications that require robust performance in high-amplitude signal environments
Digital twin with automatic disturbance detection for an expert-controlled SAG mill
Get PDFThe article version is a preprint. It has not been certified by peer review. It was published as a technical note in Minerals Engineering, Volume 220, January 2025, 109076. DOI URL: https://doi.org/10.1016/j.mineng.2024.109076.This study presents the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin integrates three key components of the closed-loop operation: (1) fuzzy logic for expert control, (2) a state-space model for regulatory control, and (3) a recurrent neural network to simulate the SAG mill process. The digital twin is combined with a statistical framework for automatically detecting process disturbances (or critical operations), which triggers model retraining only when deviations from expected behaviour are identified, ensuring continuous updates with new data to enhance the SAG supervision. The model was trained with 68 hours of operational industrial data and validated with an additional 8 hours, allowing it to predict mill behaviour within a 2.5-minute horizon at 30-second intervals with errors smaller than 5%
Mining a Decade of Event Impacts on Contributor Dynamics in Ethereum: A Longitudinal Study
Get PDFWe analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes
Gene therapy for children with X-linked myotubular myopathy: a plain language summary of publication for the ASPIRO study
No full textAvailability of data and materials: Details for how researchers may request access to anonymized participant level data, trial-level data, and protocols from Astellas sponsored clinical trials can be found at https://www.clinicaltrials.astellas.com/transparency/.What is this summary about?
This summary describes the results of a research study (clinical trial) called ASPIRO that was published in the Lancet Neurology in 2023. This study looked at an investigational gene therapy called resamirigene bilparvovec (also known as AT132) as a possible treatment for children with a disease called X-linked myotubular myopathy (abbreviated as XLMTM).The ASPIRO trial was sponsored by Astellas Gene Therapies. The authors acknowledge Lucy Smithers, PhD, and Sinead Stewart of Alpha (a division of Prime, Knutsford, UK), for medical writing and editorial support. This was funded by Astellas Gene Therapies in accordance with Good Publication Practice guidelines
The role of Earth Observation in supporting holistic coastal lagoon management
Get PDFWhite paper. This report has been prepared by the Lagoons for Life network following a peer review process and represents the views of the authors. The report does not necessarily reflect the opinions or policies of affiliated organisations.Lagoons for Life is an initiative of the Future Earth Coasts programme. See: https://www.futureearthcoasts.org/lagoons-for-life/welcome/ .Coastal lagoons, representing 13% of the world’s coastline, are dynamic and globally significant ecosystems that serve as vital habitats for diverse species, provide crucial ecosystem services, and support the livelihoods of millions of people. These unique systems are characterised by their position at the interface of land and sea, acting as complex socio-ecological systems where environmental and human dynamics are deeply interconnected. Despite their importance, coastal lagoons are understudied and often overlooked in national and international policies due to their transitional nature, neither fully marine nor fully terrestrial. This White Paper emphasises the urgent need to address the data, knowledge, and management gaps surrounding coastal lagoons. It highlights the applicability of EO as a transformative tool for advancing holistic and sustainable lagoon management. EO can provide critical insights into the physical, ecological, and social dimensions of coastal lagoons, addressing limitations in current approaches and complementing other data sources. The paper also identifies the challenges associated with EO technologies, including the need for algorithm validation, data standardisation, and enabling data accessibility to under-resourced regions, and provides a set of bullet points with further recommendations. Overall, the White Paper argues that coastal lagoons, with their distinctive characteristics and critical role in ecosystems and communities, demand global attention.Gema Casal’s contributions were supported by funding from Ramón y Cajal 2023 grant RYC2023-044898-I funded by the Spanish State Plan for Scientific and Technical Research and Innovation 2021–2023. Sónia Cristina’s contributions were supported under the programme contract CEECINSTLA/00018/2022 funded by the Portuguese Foundation for Science and Technology (FCT) for the performance of research activities at CIMA (https://doi.org/10.54499/UIDB/00350/2020) under the scope of the Associated Laboratory ARNET (LA/P/ 0069/2020) (https://doi.org/10.54499/LA/P/0069/2020)
US Futures Markets Interconnectedness During Crisis Periods
No full textThis is a working paper. It is not certified by peer review.This study contributes new empirical evidence on the interconnectedness of crude oil, precious metals, and financial futures during crisis/non-crisis periods. Dynamic spillovers from US to Europe and Asian futures markets are also examined. Empirical results show a significant and changing relationship among oil, gold, and financial futures, especially during the global financial crisis. Dynamic analysis (rolling window and subsamples) demonstrates that crude oil markets become notably more inuential during the crisis, while gold turns from a net giver, in the pre- and post-crisis periods, to a net receiver during turbulent times. West Texas Intermediate and Brent provide valuable information about return dynamics, while S&P500 and FTSE100 play a key role in volatility spillovers. Asian futures markets are strongly influenced by changes in the US and UK oil and stock futures markets. Finally, using different permutations of Cholesky orderings (Klobner and Wagner, 2013), provides additional support that the spillover index for both return, and volatility is overestimated when the generalized forecast error decompositions are employe
The Legacy of Wilson's Sociobiology for the Human Evolutionary Behavioral Sciences, Fifty Years On
Get PDFWhen Wilson’s 1975 book Sociobiology was published it ignited a media firestorm. His bombastic style and speculations on the role of biological explanations for human behavior attracted considerable attention—and criticism
Machine learning methods for AI-enhanced DCE-MRI breast cancer diagnosis
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBreast cancer remains one of the most prevalent and challenging diseases affecting women globally. Early and accurate diagnosis plays a pivotal role in improving patient outcomes and reducing mortality rates. This project aims to improve the current state of the art by employing several deep learning methodologies to be used as diagnostic support in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI).
The work has been carried out as part of an InnovateUK project named “Intelliscan” (project reference: 104192), funded by UK Research and Innovation. UK Research and Innovation did not have any involvement in the study design, or the collection, analysis, and interpretation of data. The data collection was carried out in collaboration with consultant radiologist Dr Naveed Altaf (North Tees and Hartlepool NHS Foundation Trust) and Dr Susann Wolfram (Teesside University).
The research begins by providing an overview of neural networks, including structure, activation, regularization, training, architectures, loss functions, and performance metrics for model evaluation.
The first contribution to knowledge is provided as the diagnostic process is then presented from a clinician’s point of view, along with empirical evidence of the subjectivity of the process prevents the application of ground truth data for the development of algorithms in this area.
The core contributions of this thesis lie in the development of several deep learning methodologies that are aimed at reducing human errors and increasing the speed of the diagnosis in clinical settings. These methodologies include: the first lesion detection algorithm based on unsupervised deep learning, which matches the performance of the current state of the art, while not relying on manually annotated data, significantly lowering the cost of research in the field; the development of a state of the art deep learning methodology to segment the organs within the chest wall from the breast; a novel application of deep learning in lesion morphology characterisation.
Overall, this thesis contributes to advancing the state-of-the-art in breast DCE-MRI diagnosis by introducing innovative deep learning methodologies that offer enhanced accuracy, efficiency, and interpretability
Resilience of the acute sector in recovery from COVID-19 pressures
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The authors do not have permission to share data.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0277953625003922?via%3Dihub#appsec1 .Ethics approval statement:
This project was approved by the Yorkshire and Humber Research Ethics Committee (REC Reference: 20/YH/0287 and IRAS ID: 288138). Hospital Episode Statistics are Copyright 2018–2021, re-used with the permission of NHS Digital (DARS-NIC-378657-B8F3K-v0.13). All rights reserved.The COVID-19 pandemic had a profound impact on the management and delivery of acute healthcare. To tackle the pandemic, hospitals redesigned their organisational models to provide a rapid increase in acute care assessment and treatment capacity for patients with COVID-19 whilst also trying to maintain delivery of care for patients with non-COVID-19 healthcare needs. This capacity to adjust and recover after COVID-19 might be shaped by both measures taken by acute hospitals and wider hospital pre-pandemic characteristics. The aim of this study is to examine how hospital characteristics in acute care are associated with recovery of elective activity after the height of the COVID-19 pandemic compared to pre-pandemic levels. Using patient-level data from Hospital Episode Statistics aggregated at monthly-trust level for all English National Health Service (NHS) acute hospital trusts in 2019 and 2021, we estimate the associations between hospital recovery rate and hospital pre-pandemic characteristics by employing linear regressions of the proportional change over time in elective activity against a set of explanatory variables related to supply factors (e.g., hospital size, workforce, type of hospital, regional location), demand factors (e.g., population need, patient case-mix) and time factors. On average, English NHS acute hospital trusts did not fully recover from the COVID-19 pandemic in 2021. The results show that the explanatory variables are not systematically associated with hospital recovery rate, excepting regional differences. Hospital trusts not located in London, especially in the North of England, are associated with a lower recovery (less resilience) of total elective activity and orthopaedic and vascular surgical elective activity. The implication for policy development is that the evolution of hospital recovery rates in elective activity varied across English regions, especially for high-volume and high-risk elective specialties, with better recovery in London than elsewhere.This project is funded by the National Institute for Health and Care Research (NIHR) [NIHR Policy Research Programme (Award ID: NIHR200718)]. This research is supported by the NIHR Applied Research Collaboration (ARC) West Midlands, the NIHR Community Healthcare MedTech and IVD Cooperative (MIC) and the NIHR Oxford Biomedical Research Centre (BRC). Laia Bosque-Mercader is funded by the Fundación Ramón Areces Postdoctoral Fellowship. Russell Mannion is in part funded by the NIHR West Midlands Patient Safety Research Collaborative
The role of foreign direct investment and environmental taxation in promoting renewable energy sustainability
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Data will be made available on request.This study examines the relationship between Foreign Direct Investment (FDI) and the achievement of Sustainable Development Goal 7 (SDG 7), which aims to ensure affordable, reliable, sustainable, and modern energy for all. Using a panel dataset of 891 country-year observations, the study analyzes how FDI influences SDG 7, while controlling for variables such as GDP, inflation, population growth, patents, and research and development expenditures. The research specifically investigates the moderating role of environmental taxation in this relationship. The findings show a statistically significant negative correlation between FDI and SDG 7, suggesting that foreign investment may hinder the achievement of sustainable energy objectives in some contexts. Specifically, countries with lax environmental regulations tend to attract FDI that undermines sustainable energy efforts, supporting the Pollution Haven Hypothesis. In contrast, higher environmental taxes are shown to mitigate the negative impact of FDI on SDG 7, indicating that stronger regulatory frameworks can help align foreign investments with sustainable energy goals. Further, the study reveals that the impact of FDI on SDG 7 varies by income levels: in high-income countries, FDI has a more detrimental effect on sustainable energy development, whereas in low-income countries, FDI appears to stimulate technological transfer and innovation in clean energy solutions. This research contributes to the literature by providing a nuanced understanding of how environmental taxation can moderate the negative effects of FDI on SDG 7. The findings underscore the importance of policy design in directing FDI flows toward sustainable energy outcomes. Policymakers are encouraged to implement stricter environmental tax policies, particularly in high-income countries, to ensure that FDI supports sustainable energy practices and contributes to achieving SDG 7.The authors received no financial support for the research, authorship, and/or publication of this article
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