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Synthesis of Multimodal Cardiological Signals using a Conditional Wasserstein Generative Adversarial Network
Get PDFCardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Recent advancements in machine learning have significantly enhanced early detection and treatment strategies for CVDs. While electrocardiogram (ECG) signals are commonly used for detection, additional signals like arterial blood pressure (ABP) and central venous pressure (CVP) provide a comprehensive view of the cardiovascular system. However, acquiring such extensive datasets is challenging due to resource constraints, privacy issues, and ethical considerations. This paper introduces a novel Multichannel Conditional Wasserstein Generative Adversarial Network (MC-WGAN) capable of simultaneously generating synthetic ECG, ABP, and CVP signals. The MC-WGAN model addresses the data scarcity issue by providing high-fidelity synthetic data that mirrors real physiological signals, facilitating better simulation, diagnosis, and treatment planning. Evaluation against the MIT-BIH Arrhythmia Database demonstrated the model’s strong performance, with competitive metrics such as RMSE, PRD, and FD, particularly excelling in the generation of ECG and ABP signals. MC-WGAN surpasses other generative models by simultaneously replicating multiple physiological signals, offering a comprehensive view of cardiovascular health. This advancement enhances diagnostic accuracy and risk stratification, setting a new standard in synthetic biomedical signal generation, and paving the way for more personalized and effective clinical interventions.10.13039/501100000274-British Heart Foundation (Grant Number: FS/19/73/34690)
Addressing Antimicrobial Resistance: The Potential Role of Parental Health Literacy and Intensive Parenting Attitudes in Antibiotic Use
Get PDFEditorial
Unlocking the Dynamics of Online Team Based Learning: A Comparative Analysis of Student Satisfaction and Engagement Across Psychology Modules
Get PDFTeam based learning (TBL) is a co-operative learning method, increasingly used online within our digitalised society. The aim of this study is to better understand the factors influencing student satisfaction and engagement with online TBL. The study measures student satisfaction, accountability and preference for online TBL across three compulsory psychology undergraduate modules; PY1604 (Clinical Psychology), PY1608 (Employability in Psychology) and PY1702 (Academic Skills in Psychology).. Seventy-two psychology students enrolled at a UK university completed the TBL-SAI online survey, where they answered 33 statements relating to online TBL on a five-point Likert-type scale. It was found students were significantly more accountable in the TBL method for PY1608 and PY1604 and significantly less accountable for PY1702. It was also found that there was significantly higher satisfaction for PY1604 compared to both PY1702 and PY1608, as well as students attending significantly more PY1604 sessions than PY1702 and PY1608. Overall, there was no significant difference in preference for online TBL over online lectures when comparing the three modules. The findings support our hypothesis, that there would be differences across the three modules in terms of satisfaction, accountability and preference for online TBL
Chatting with the Future: A Comprehensive Exploration of Parents’ Perspectives on Conversational AI Implementation in Children’s Education
Get PDFRevolutionary technological advancements have introduced the integration of Conversational AI into a multitude of different settings. This widespread implementation has raised questions about the impact of AI on learning, its benefits are and its potential costs. This study aims to explore the perspectives of parents, investigating their confidence in allowing conversational AI tools to become part of children’s learning. The study analyses survey responses of 101 parents to investigate how they feel about this emerging tool. Our primary hypothesis was that parents would be overall positive toward implementing conversational AI into education. Secondly, it was hypothesised that alongside the positivity, parents would also show moderate apprehension to their children using conversational AI. Results showed most participants would allow their children to use these tools in confidence, and believed they will learn quicker with authentic information provided by AI and when educational apps have AI integrated in their systems. In conclusion, participants generally had a positive outlook on the potential impact of conversational AI on the future of their children’s education, in duality the study also finds a coinciding level of uncertainty and apprehension toward conversational AI is and its uses
Corruption and insider trading
Get PDFData availability: Data will be made available on request.JEL classification: C23; G14; D73; M14.We investigate firm corruption in China by extracting a measure of corruption from published financial statements and use this to demonstrate that corruption impacts the trading decisions of insiders. Specifically, we show that insiders in firms that are more corrupt trade more aggressively, and they are more willing to trade on their private information as evidenced by the increased informativeness of their trades, in respect of both purchases and sales. This link between firm corruption and trade informativeness is robust to the inclusion of a number of factors that are known to influence the informativeness of such trades, including trade characteristics, insider characteristics and the firm's information environment. We also consider the effect of the appointment of a new CEO or Chair. Overall, corruption related trade informativeness holds consistently for both purchases and sales. Finally, we show that this measure of corruption is robust to the inclusion of several alternative indicators of corporate misconduct
Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting
Get PDFData Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.This research received no external funding
A Comprehensive Experimental Investigation of NOx Emission Characteristics in Hydrogen Engine Using an Ultra-Fast Crank Domain Measurement
Get PDFData Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and IP protection.Adopting zero-carbon fuels, like hydrogen, can significantly reduce environmental harm and pave the way for a decarbonised trajectory with zero carbon emissions. The hydrogen internal combustion engine (ICE) technology has demonstrated its reliability and capacity to seamlessly integrate into the current ICE platform, originally designed for diesel and gasoline operation. The direct utilisation of pure hydrogen eradicates steady-state carbon dioxide and hydrocarbon emissions. It is important to highlight that efforts to comprehend and comprehensively tackle NOx emissions are underway. A comprehensive study was carried out to assess the NOx emissions for a hydrogen ICE with different injection modes from gasoline. The study involved varying the relative air-to-fuel ratio (AFR) from stoichiometric to the lean-burn limit in a boosted spark ignition (SI) engine fuelled with gasoline or hydrogen. A fast NOx emissions analyser was employed to measure the instantaneous NO and NO2 emissions in the engine exhaust. The study provides a detailed analysis of NOx emissions, including steady-state averaged emissions, average crank angle domain NOx distribution and emissions, in-cylinder pressure analysis, as well as time and cycle analyses of NOx emissions’ temporal and cyclic variations. The primary discovery was that NOx emissions are almost zero between lambda 2.75 and 3.7, and hydrogen produces 13.8% less NOx emissions than gasoline at stoichiometric operation. Finally, the full NOx time analysis revealed that the consistency of NOx emissions is higher with hydrogen than with gasoline by using a novel approach by identifying the coefficient of variation of the NOx emission of each cycle.This research was funded by [UKRI] grant number [10014250]
Gendered transitions to self-employment and business ownership: a linked-lives perspective
Get PDFWe apply the sociological lens of linked lives to show how household contexts channel transitions to self-employment in ways strongly differentiated by gender. We investigate the impact of demographic transitions to marriage, cohabitation and having children on the transition to self-employment using fixed-effects models on 10 waves of the UK’s nationally representative survey, Understanding Society. Men’s transitions to self-employment and separately to business ownership are remarkably impervious to the arrival of a new child in the household. In contrast, second births raise the odds of self-employment for women and have a strong and statistically significant association with business ownership, highlighting the role of birth parity as a household influence. Within the subset of opposite-sex couples, lives are indeed linked: a partner’s long hours precipitate the other partner’s transition into self-employment for men and women. However, the effect is asymmetric to the extent that women are much more likely to have a partner working long hours. Marriage is associated with a much higher likelihood of transitioning to business ownership for both men and women, which does not hold for self-employment overall
The Corporate Purpose in search of a new meaning: the journey continues A View from the UK and elsewhere
No full textThe neuroanatomy of visual extinction following right hemisphere brain damage: Insights from multivariate and Bayesian lesion analyses in acute stroke
Get PDFData availability statement: Online materials are publicly available at OSF under a CC BY license: https://doi.org/10.17605/OSF.IO/NVP54. These include descriptive and statistical topographies and extended demographic data. The clinical datasets analysed in the current study are not publicly available due to the data protection agreement approved by the local ethics committee.A PsyArXiv preprint is available online at: https://doi.org/10.31234/osf.io/7kzs2 . It has not been certified by peer review.Multi-target attention, that is, the ability to attend and respond to multiple visual targets presented simultaneously on the horizontal meridian across both visual fields, is essential for everyday real-world behaviour. Given the close link between the neuropsychological deficit of extinction and attentional limits in healthy subjects, investigating the anatomy that underlies extinction is uniquely capable of providing important insights concerning the anatomy critical for normal multi-target attention. Previous studies into the brain areas critical for multi-target attention and its failure in extinction patients have, however, produced heterogeneous results. In the current study, we used multivariate and Bayesian lesion analysis approaches to investigate the anatomical substrate of visual extinction in a large sample of 108 acute right hemisphere stroke patients. The use of acute stroke patient data and multivariate/Bayesian lesion analysis approaches allowed us to address limitations associated with previous studies and so obtain a more complete picture of the functional network associated with visual extinction. Our results demonstrate that the right temporo-parietal junction (TPJ) is critically associated with visual extinction. The Bayesian lesion analysis additionally implicated the right intraparietal sulcus (IPS), in line with the results of studies in neurologically healthy participants that highlighted the IPS as the area critical for multi-target attention. Our findings resolve the seemingly conflicting previous findings, and emphasise the urgent need for further research to clarify the precise cognitive role of the right TPJ in multi-target attention and its failure in extinction patients.Deutsche Forschungsgemeinschaft. Grant Numbers: HA5839/4-1, KA 1258/23-1;
Fonds National de la Recherche Luxembourg. Grant Number: FNR/1160116
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