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Objective and subjective emotional face classification in non-clinical depression
Get PDFBackground: Many previous studies highlighting a relationship between depression and emotional face recognition have relied on measures of classification accuracy to determine recognition deficits. However, the perception of emotions is also related arousal levels and valence, and more research is needed to determine how depression impacts these dimensions. Aims: To compare performance on both an objective forced choice emotional recognition task and subjective emotional face valence rating task in participants with self-reported high depression. Methods: Based on screening using the depression sub-scale of the DASS-42, 46 participants (23 males, 23 female) were in the high depression group (mean DASS-42 34±5) and 50 participants in the control groups (25 males, 25 females) with DASS-42 scores of either 0 or 1. All participants completed both a performance-based task (objective) as well as a rating task (subjective) of emotional facial expressions. Results: The data indicate that difference in performance exist in classification accuracy between the groups, with depressed participants demonstrating reduced accuracy for anger, sadness and neutral facial expressions. Additionally differences in subjective ratings exist in the depressed group, but with the important caveat that these only relate to faces display positive emotional expressions. Discussion: The limitations of relying solely on objective tasks where recognition accuracy is the main outcome measure are discussed as well as the data quantitatively demonstrating a reduced response in the depression group to positive stimuli. This study justifies the need for future studies using both objective and subjective measures to assess emotion classification deficits in depression.This work was internally funded by Brunel University London via funds awarded by the Centre for Cognitive and Clinical Neuroscience
The <i>Psy</i>chosis <scp>MRI</scp><i>Share</i>d <i>D</i>ata Resource (Psy‐<scp>ShareD</scp>)
No full textData Availability Statement:
The data that support the findings of this study are openly available in Psy_ShareD at https://psyshared.com/Home.html. We will link our data catalogues to DATAMIND (https://datamind.org.uk) and we are exploring the possibility of also linking to HDRUK (https://www.hdruk.ac.uk).Neuroimaging research in the field of schizophrenia and other psychotic disorders has sought to investigate neuroanatomical markers, relative to healthy control groups. In recent decades, a large number of structural magnetic resonance imaging (MRI) studies have been funded and undertaken, but their small sample sizes and heterogeneous methods have led to inconsistencies across findings. To tackle this, efforts have been made to combine datasets across studies and sites. While notable recent multicentre initiatives and the resulting meta- and mega-analytical outputs have progressed the field, efforts have generally been restricted to MRI scans in one or two illness stages, often overlook patient heterogeneity, and study populations have rarely been globally representative of the diversity of patients who experience psychosis. Furthermore, access to these datasets is often restricted to consortia members who can contribute data, likely from research institutions located in high-income countries. The Psychosis MRI Shared Data Resource (Psy-ShareD) is a new open access structural MRI data sharing partnership that will host pre-existing structural T1-weighted MRI data collected across multiple sites worldwide, including the Global South. MRI T1 data included in Psy-ShareD will be available in image and feature-level formats, having been harmonised using state-of-the-art approaches. All T1 data will be linked to demographic and illness-related (diagnosis, symptoms, medication status) measures, and in a number of datasets, IQ and cognitive data, and medication history will also be available, allowing subgroup and dimensional analyses. Psy-ShareD will be free-to-access for all researchers. Importantly, comprehensive data catalogues, scientific support and training resources will be available to facilitate use by early career researchers and build capacity in the field. We are actively seeking new collaborators to contribute further T1 data. Collaborators will benefit in terms of authorships, as all publications arising from Psy-ShareD will include data contributors as authors.United Kingdom Medical Research Council. Grant Number: MR/X010651/1.This work was supported by the United Kingdom Medical Research Council grant number MR/X010651/1. A list of funders and acknowledgements for Psy-ShareD datasets can be found at https://psyshared.com/Team.html
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
Get PDFTo alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62132004);
Jiangsu Major Project on Basic Researches (Grant Number: BK20243059)
The effect of a community-based health insurance on the out-of-pocket payments for utilizing medically trained providers in Bangladesh
Get PDFSupplementary data are available online at: https://academic.oup.com/inthealth/article/12/4/287/5627757#supplementary-data .Background: We aimed to estimate the effect of the community-based health insurance (CBHI) scheme on the magnitude of out-of-pocket (OOP) payments for the healthcare of the informal workers and their dependents. The CBHI scheme was piloted through a cooperative of informal workers, which covered seven unions in Chandpur Sadar Upazila, Bangladesh. Methods: A quasi-experimental study was conducted using a case-comparison design. In total 1292 (646 insured and 646 uninsured) households were surveyed. Propensity score matching was done to minimize the observed baseline differences in the characteristics between the insured and uninsured groups. A two-part regression model was applied using both the probability of OOP spending and magnitude of such spending for healthcare in assessing the association with enrolment status in the CBHI scheme while controlling for other covariates. Results: The OOP payment was 6.4% (p < 0.001) lower for medically trained provider (MTP) utilization among the insured compared with the uninsured. However, no significant difference was found in the OOP payments for healthcare utilization from all kind of providers, including the non-trained ones. Conclusions: The CBHI scheme could reduce OOP payments while providing better quality healthcare through the increased use of MTPs, which consequently could push the country towards universal health coverage.This work was supported by Grand Challenge Canada (GR# 01009, 2012)
Pain in adults with cerebral palsy: A systematic review
Get PDFWhat this paper adds:
• The prevalence of chronic pain, defined as pain for longer than 3 months, was 75% to 78%.
• There was low certainty in the evidence that pain is more prevalent in adults with cerebral palsy.
• There was moderate certainty that pain is more prevalent in adults with better communication.
• There was low certainty that the prevalence of pain does not differ across Gross Motor Function Classification System levels.
• There was very low to low certainty for the effectiveness of pharmacological and non-pharmacological interventions to reduce pain.Data Availability Statement:
No data available.Supporting Information is available below and online at: https://onlinelibrary.wiley.com/doi/10.1111/dmcn.16254#support-information-section .Aim:
To describe the prevalence and incidence of pain, identify prognostic factors for pain, determine psychometric properties of tools to assess pain, and evaluate effectiveness of interventions for reducing pain among adults with cerebral palsy (CP).
Method:
Six databases were searched to identify studies published since 1990 in any language that met eligibility criteria defined for each objective. Titles, abstracts, and full texts were screened by two independent reviewers.
Results:
Sixty-three studies were identified; 47 reporting prevalence, 28 reporting prognostic factors, four reporting psychometric properties, five evaluating intervention effectiveness. Pain prevalence ranged from 24% to 89%. Prevalence was higher among adults with CP than in adults without it. Communication function, sex, and age were prognostic factors for pain prevalence. Numerical, verbal, and pictorial rating scales were valid for assessing pain intensity in adults with CP. Pharmacological and surgical interventions had no effect on pain. An active lifestyle and sports intervention reduced pain in adults with CP compared with usual care.
Interpretation:
Many adults with CP experience pain, although prevalence estimates vary considerably. The quality of evidence for prognostic factors and interventions is very low to low. There is a lack of evidence about effective pain management among adults with CP.This project was supported by a grant from the Cerebral Palsy Foundation
Book review of Stephen Quick, 'The Dhofar War'
No full textThis is a holistic politico-military history of British operations in Oman, built on an impressive range of archival sources. Stephen Quick’s book examines the communist-led Dhofar region insurgency in western Oman. Foundational themes tying together this book echo other British counterinsurgencies: build-up of (mostly local) forces; hearts and minds civic-action programmes; overall British political direction; SAS-run psychological-operations to split Marxist insurgents from Islamic Dhofaris; the use of turned surrendered insurgents as loyalist firqa forces. Britain’s removal of Oman’s feudal Sultan Said in a managed coup in 1970 in favour of his more forward-thinking son, Qaboos, was key, highlighting the politically driven, Clausewitzian quality to overall strategy. Qaboos facilitated the ‘carrot’ part of operations to win the population. His antediluvian father hated Dhofaris and demanded the ‘stick’ of collective punishment after every action by the People’s Front for the Liberation of the Occupied Arab Gulf (PFLOAG). As with so many counterinsurgencies, without source materials, we see the insurgents through a glass, darkly
Machine learning-based failure prediction in United States of America lobbying firms: The role of director networks
No full textData availability:
Data will be made available on request.The role of directors and their associated social networks is critical in determining corporate failure, particularly within lobbying firms in the United States. However, predicting corporate failure in increasingly complex organizational structures remains an underexplored challenge. Using firm-level director data from the United States between 2005 and 2018, we apply both traditional machine learning models – Logistic Regression, Random Forest, and Support Vector Machines (SVM) – and more recent tabular deep learning approaches, including TabTransformer and Feature Tokenizer Transformer, to predict and uncover complex non-linear relationships between director network attributes and firm failure. Our results demonstrate that corporate failure can be accurately predicted based on director network characteristics, achieving a mean performance of 99.98% Area Under the Receiver Operating Characteristic Curve (AUC-ROC), 85.10% Precision, and 80.23% Recall using a stratified 5-fold cross-validation procedure with a weighted averaging strategy. Furthermore, network-derived attributes such as centrality, betweenness, and remuneration significantly influence failure risk in lobbying firms. These findings contribute to the management literature by highlighting the predictive value of director networks in corporate failure and offer practical insights for directors, engineering practitioners, and policymakers seeking to mitigate organizational risk
AI sensation and engagement: Unpacking the sensory experience in human-AI interaction
Get PDFGiven the limited studies on AI sensation and its impact on consumer emotional response and engagement, we investigate its impact to drive engagement. Employing a mixed-methods approach, we began with a qualitative phase consisting of 68 interviews (18 healthcare employees, 37 users of Wearable Health Devices, 7 AI developers, and 6 academics). Grounded in the theories of constructed emotion and the uncanny valley, as well as insights from the qualitative phase, we developed a robust model investigating the role of AI sensation on costumer emotional responses and engagement. This was followed by a survey of 557 healthcare employees. Data analysis was conducted using SPSS for descriptive statistics and reliability assessments, and AMOS for confirmatory factor analysis to validate the robustness of our measurement models. our findings show that AI sensation can drive customer subjective feeling state and AI affects. We also found empirical evidence that both can mediate the relationship between AI sensation, customer subjective feeling state, AI affects and activation engagement. Our findings can offer valuable understanding for managers and AI developers, underscoring the important role of AI sensation for driving engagement.The authors would like to note their appreciation for the grant received from the "Marketing Trust" in support of their research (Sensory brand experience: Conceptualisation, measurement and impact on performance outcomes)
The Refugee Integration Industry: Stakeholder Power, Market Logic, and the (De)Humanisation of Refugee Labour
Get PDFData Availability Statement:
The authors have nothing to report.This paper theorises the refugee integration industry by examining how institutional configurations and stakeholder arrangements shape labour market integration outcomes for refugees in Germany and Turkey. Drawing on Spender's theory of industrial recipes, we conceptualise the integration industry as a network of public, private and third-sector actors governed by competing logics of humanitarianism and market efficiency. Through a comparative case study approach based on more than 200 policy, institutional, and civil society sources, we demonstrate how power asymmetries and economic imperatives systematically marginalise refugees' human agency, producing both humanising and dehumanising effects. We introduce a fourfold typology of (de)humanitarianism, indifference, assimilation, integration and multiculturalism models that reveals how different national and organisational contexts mediate the moral, economic, and political tensions at the heart of refugee labour market integration. Despite stark contrasts in governance models and economic capacity, both countries institutionalise forms of exclusion that limit meaningful participation and recognition. Our analysis advances the theoretical understanding of the refugee integration industry as a contested and relational space where policy, discourse and institutional practice interact to shape refugee subjectivities and futures. In doing so, we call for more reflexive, inclusiv, and agency-centred approaches to integration that foreground social justice and co-determination.The authors received no specific funding for this work
Editorial: Voices from the academy: a response to President Donald Trump’s anti-DEI policies
Get PDFAuthor's extract from the editorial published by Emerald Publishing Ltd on 10 April 2025: Aguinis, H., Ashcraft, K., Benschop, Y., Blancero, D.M., Cheng, C., Cornelius, N., Davidson, M., Ford, D., Hebl, M., King, E., Stone, D., Hennekam, S., Holmes IV, O., Konrad, A.M., Kossek, E.E., Ozbilgin, M., Powell, G.N., Pullen, A., Morgan Roberts, L.M., Roberson, Q., Stone, D., Syed, J., Williams, J., Williamson, I.O. and Zanoni, P. (2025), "Editorial: Voices from the academy: a response to President Donald Trump’s anti-DEI policies", Equality, Diversity and Inclusion, Vol. 44 No. 2, pp. 151-157. https://doi.org/10.1108/EDI-03-2025-550.Trump’s anti-DEI executive orders exemplify the broader responsibilization of inequality – shifting systemic failures onto individuals while dismantling institutional mechanisms of justice. My research on EDI leadership, alt-right movements and gendered organizations demonstrates that such attacks embolden exclusionary practices and legitimize strategic ignorance as an unearned privilege – allowing dominant groups to deny discrimination while deepening systemic inequities. These orders erode workplace protections, delegitimize inclusion efforts and silence critical scholarship. As DEI scholars and practitioners, we must counter this erosion through empirical evidence, institutional advocacy and transnational solidarity. DEI is not an ideology – it is a foundation for equitable economies and sustainable democracies
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