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Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals
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Data will be made available on request.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0277953624001618?via%3Dihub#appsec1 .The advent of AI has ushered in a new era of patient care, but with it emerges a contentious debate surrounding accountability for algorithmic medical decisions. Within this discourse, a spectrum of views prevails, ranging from placing accountability on AI solution providers to laying it squarely on the shoulders of healthcare professionals. In response to this debate, this study, grounded in the mutualistic partner choice (MPC) model of the evolution of morality, seeks to establish a configurational framework for cultivating felt accountability towards AI among healthcare professionals. This framework underscores two pivotal conditions: AI ethics enactment and trusting belief in AI and considers the influence of organizational complexity in the implementation of this framework. Drawing on Fuzzy-set Qualitative Comparative Analysis (fsQCA) of a sample of 401 healthcare professionals, this study reveals that a) focusing justice and autonomy in AI ethics enactment along with building trusting belief in AI reliability and functionality reinforces healthcare professionals’ sense of felt accountability towards AI, b) fostering felt accountability towards AI necessitates ensuring the establishment of trust in its functionality for high complexity hospitals, and c) prioritizing justice in AI ethics enactment and trust in AI reliability is essential for low complexity hospitals.National Natural Science Foundation of China (Grants number 72372111)
Impact of population aging on future temperature-related mortality at different global warming levels
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All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data were collected within the MCC Collaborative Research Network under a data sharing agreement and cannot be made publicly available.Code availability:
A sample of the analysis code is available from https://github.com/CHENlab-Yale/MCC_ProjAging_Temp .Supplementary information is available online at: https://link-springer-com.ezproxytest.brunel.ac.uk/article/10.1038/s41467-024-45901-z#Sec15 .Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. K.C. was supported by the Yale Planetary Solutions Project seed grant. A.G., A.S., and S.R. were supported by the European Union’s Horizon 2020 Project Exhaustion grant (820655). A.G. was also supported by the Medical Research Council UK grant (MR/V034162/1). J.M. received funding from the Fundação para a Ciência e a Tecnlogia Grant (SFRH/BPD/115112/2016). A.T. was supported by the MCIN/AEI/10.13039/501100011033 grant (CEX2018-000794-S). A.U. and J.K. were supported by the Czech Science Foundation (22-24920S). F.S. was supported by the Italian Ministry of University and Research (MUR), Department of Excellence project 2023-2027 ReDS ‘Rethinking Data Science’ - Department of Statistics, Computer Science and Applications - University of Florence. MNM. was supported by the European Commission (H2020-MSCA-IF-2020) under REA grant agreement no. 101022870. A.V.C. acknowledges the support of the Swiss National Foundation (TMSGI3_211626). V.H. received funding from the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie Grant Agreement No.: 101032087)
The Ukraine War and the Shift in Russian Intelligence Priorities
Get PDFThe war in Ukraine has transformed Russian intelligence activities. It has drawn the bulk of Russian intelligence collection resources, both inside Ukraine and further afield, to focus on war-related, often low-level operational/tactical targets. Even strategic collection is related to the war, especially directed toward bolstering Russia’s global reputation. However, the war has also led to the dismantling of a large portion of Russia’s intelligence apparatus, including both human and signals intelligence, especially in Europe, just when it is needed the most. It has prompted greater scrutiny and international counterintelligence cooperation against Russian intelligence activities than has been seen since the 1980s. Russia’s own actions have drawn those reactions. Nevertheless, Russian intelligence services are resilient and persistent. They learn from mistakes and adapt to changing circumstances
A Crime by Any Other Name: Gender Differences in Moral Reasoning When Judging the Tax Evasion of Cryptocurrency Traders
Get PDFData Availability Statement: The data presented in this study are available on request from the corresponding author, Jori Grym.Tax evasion is a major issue for authorities worldwide. Understanding the factors that influence individuals’ intrinsic motivation to pay taxes, known as their tax morale, is important for improving tax compliance. This study investigated gender differences in judging tax evasion in the context of cryptocurrency trading. Specifically, a survey study explored whether different moral foundations, financial literacies, and political orientations among females vs. males might explain potential gender differences in judging tax evasion. In an online survey, 243 U.S. adults read a vignette about a friend evading taxes in a cryptocurrency trading context. In a correlational analysis, we found that females judged tax evasion harsher, as being more morally wrong than males. Of the psychographic factors, only individualizing moral foundation values (i.e., fairness and harm avoidance) explained the harsher moral judgment by females. That is, individualizing moral foundation values were at a higher level among females, which further predicted females’ harsher judgment of tax evasion. While females also had, on average, lower financial literacy and knowledge of cryptocurrencies than males, these did not predict their harsher judgment of tax evasion. The findings contribute to research on gender differences in moral judgments and highlight that a given transgression, or a specific crime, may violate different moral values in men and women. The results demonstrate to policy makers that it is important to take into account gender differences, in campaigns promoting tax morale and compliance.This research received no external funding
A novel optimization method of carbon reduction strategies implementation for industrial parks
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.The effects of various energy conservation and carbon reduction (ECCR) strategies can differ significantly despite equal investment. Given limited amount of capital expenditure, managers and planners of industrial parks must carefully select from different ECCR strategies and implementation technologies to maximize investment returns. This study establishes mathematical models for four ECCR strategies: forestry carbon sequestration (FCS), carbon capture and utilization (CCU), waste heat recovery (WHR), and photovoltaic (PV). A universal ECCR planning optimization model is constructed to maximize annual economic benefits or carbon emission reduction. Using an industrial park in southern China as a case study, genetic algorithms are utilized to solve the model and validate its feasibility. The study analyzes three key parameters: capital expenditure caps, carbon trading price in the Emission Trading Scheme, and transportation distance of captured CO2 products for sensitivity. The results demonstrate considerable economic benefits of the CCU strategy when demand matches appropriately. However, in cases with limited capital expenditure, implementing small-scale FCS strategies in industrial parks is not advisable from both an economic and environmental perspective.Science and technology project of State Grid Zhejiang Electric Power Co., Ltd. entitled “Research on Energy Efficiency Enhancement Technologies in End-Use Energy” (5211LS220004)
Renewable energy sources integration via machine learning modelling: A systematic literature review
Get PDFThe use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided
A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data
No full text© Elsevier Ltd. All rights reserved. This manuscript version is under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | https://www.elsevier.com/about/policies/sharingThrough the application of deep learning and multi-sensor data, fault features can be automatically extracted and valuable information can be integrated to tackle intricate challenges in motor bearing fault diagnosis. Most existing fusion models focus primarily on the original time series signal with information extraction largely restricted to the time domain (without extensions into multiple transformation domains). Also, in most fusion models, the sensor fusion level is kept relatively simple which could lead to the oversight of correlations and complementarities among the information. To enhance the recognition capability of diagnostic network features, in this paper, we propose a novel framework for motor bearing fault diagnosis from the perspectives of multi-transformation domain and multi-source data fusion. Within this framework, feature extraction and fusion from various source data are achieved in the time domain, frequency domain, and time–frequency domain. Distinct independent networks are set up within these domains: one network is designated for overseeing feature fusion, while the others are dedicated to extracting features from individual sensors. To support the extraction of pivotal features across multiple fusion layers in various transformation domains, several fusion nodes are inserted between the layers of the multiple feature extraction networks and the feature summarization network. Furthermore, a channel attention mechanism is introduced as a fusion strategy that serves to pinpoint the significance of different features, thus enhancing the efficiency of feature extraction. Experimental evaluation reveals the efficacy of the proposed model and highlights its noteworthy performance attributes such as scalability and universality.In part by the National Natural Science Foundation of China, under Grants 61973209 and 61933007, the Capacity Building Project of Shanghai Local Colleges and Universities of China under Grant 22010501100, the Royal Society of the UK, the BRIEF Award of Brunel University London, and the Alexander von Humboldt Foundation of German
Mapping Situated Practice from an Insider's Perspective
No full textThe images are available online. Image Credits:
Image 1: The Researcher reporting at Harpenden Xmas Lights in November 2017. (Author’s personal collection.)
Image 2: The Researcher’s Observational Forms for PhD research. (Author’s personal collection.)
Image 3: Participant observation at The Eye in 2018. (Author’s personal collection.)Flow is a critical forum on media and culture published by the Department of Radio-Television-Film (https://rtf.utexas.edu/) at the University of Texas at Austin (https://www.utexas.edu/). Flow’s mission is to provide a space where scholars and the public can discuss media histories, media studies, and the changing landscape of contemporary media.When I began my doctoral research exploring how community radio practitioners make programmes by sourcing, shaping and sharing content on-air and online, I already had decades of experience as a radio producer/presenter and volunteer. It made sense to research a field that I was passionate about. I decided to apply Theodore Schatzki’s framework conceptualising practitioner knowledge, know-how, and arrays of tasks as ‘social sites’ of practice. For Schatzki, a practice is situated in time and space, influenced by a web of understandings and conventions, relationships, emotions and expectations interlinked with access to the appropriate tools and equipment (Schatzki, 2002, 2017). In my data gathering, then, I needed to get as close as possible to my research participants
Protocol for the development of a tool (INSPECT-SR) to identify problematic randomised controlled trials in systematic reviews of health interventions
Get PDFIntroduction: Randomised controlled trials (RCTs) inform healthcare decisions. It is now apparent that some published RCTs contain false data and some appear to have been entirely fabricated. Systematic reviews are performed to identify and synthesise all RCTs that have been conducted on a given topic. While it is usual to assess methodological features of the RCTs in the process of undertaking a systematic review, it is not usual to consider whether the RCTs contain false data. Studies containing false data therefore go unnoticed and contribute to systematic review conclusions. The INveStigating ProblEmatic Clinical Trials in Systematic Reviews (INSPECT-SR) project will develop a tool to assess the trustworthiness of RCTs in systematic reviews of healthcare-related interventions.
Methods and analysis: The INSPECT-SR tool will be developed using expert consensus in combination with empirical evidence, over five stages: (1) a survey of experts to assemble a comprehensive list of checks for detecting problematic RCTs, (2) an evaluation of the feasibility and impact of applying the checks to systematic reviews, (3) a Delphi survey to determine which of the checks are supported by expert consensus, culminating in, (4) a consensus meeting to select checks to be included in a draft tool and to determine its format and (5) prospective testing of the draft tool in the production of new health systematic reviews, to allow refinement based on user feedback. We anticipate that the INSPECT-SR tool will help researchers to identify problematic studies and will help patients by protecting them from the influence of false data on their healthcare.
Ethics and dissemination: The University of Manchester ethics decision tool was used, and this returned the result that ethical approval was not required for this project (30 September 2022), which incorporates secondary research and surveys of professionals about subjects relating to their expertise. Informed consent will be obtained from all survey participants. All results will be published as open-access articles. The final tool will be made freely available.National Institute for Health and Care Research (NIHR) Research for Patient Benefit programme (NIHR203568)
Both identity and non-identity face perception tasks predict developmental prosopagnosia and face recognition ability
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The data supporting this manuscript is available in OSF: https://osf.io/va4jh/ .Supplementary Information is available online at: https://www.nature.com/articles/s41598-024-57176-x#Sec27 .Developmental prosopagnosia (DP) is characterised by deficits in face identification. However, there is debate about whether these deficits are primarily perceptual, and whether they extend to other face processing tasks (e.g., identifying emotion, age, and gender; detecting faces in scenes). In this study, 30 participants with DP and 75 controls completed a battery of eight tasks assessing four domains of face perception (identity; emotion; age and gender; face detection). The DP group performed worse than the control group on both identity perception tasks, and one task from each other domain. Both identity perception tests uniquely predicted DP/control group membership, and performance on two measures of face memory. These findings suggest that deficits in DP may arise from issues with face perception. Some non-identity tasks also predicted DP/control group membership and face memory, even when face identity perception was accounted for. Gender perception and speed of face detection consistently predicted unique variance in group membership and face memory; several other tasks were only associated with some measures of face recognition ability. These findings indicate that face perception deficits in DP may extend beyond identity perception. However, the associations between tasks may also reflect subtle aspects of task demands or stimuli
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