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The business of belonging: Homocapitalism, homonormativity, and cu/queer economic geographies in São Paulo, Brazil
Get PDFThis paper examines the rise of corporate LGBTQ+ activism and homocapitalism in Brazil, highlighting the productive incorporation of queers into capitalism. Mobilizing transnational queer materialist critiques in tandem with critical perspectives from teoria do cu, the paper sheds light on how homonormativity operates not simply as a set of cultural norms or representational tropes, but as a structural and ideological formation that integrates queer inclusion into global circuits of capitalist accumulation while obscuring the material and historical violences sustaining those circuits. The paper draws from ethnographic fieldwork conducted at the Out & Equal Forum in São Paulo, exploring how corporate investments in LGBTQ+ diversity operate within wider economic geographies that differentially fold queers into global capitalism. In so doing the paper contributes to extant debates in queer economic geography about the relationship between queerness and (homo)capitalism, underlining the importance of moving beyond hegemonic homonormativity critiques to grasp the complex, uneven ways in which queers are incorporated into (homo)capitalism
Divergence in DNACPR and resuscitation policies: institutional survey in England
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Objectives
Our objective was to analyse the policies of hospitals and care homes in England as regards the use of do not attempt cardiopulmonary resuscitation (DNACPR) recommendations. We sought to identify (i) variations among policies at different institutions, and (ii) divergence of local policies from national guidance, particularly with reference to decisions either (a) to initiate cardiopulmonary resuscitation (CPR) despite the presence of a DNACPR recommendation, or (b) not to initiate CPR in the absence of a DNACPR recommendation.
Methods
We conducted a survey of 14 DNACPR and/or resuscitation policies, drawn from care homes, NHS trusts and hospices.
Results
Many of the policies we surveyed diverge significantly from national guidance. Some require that CPR be administered in all cases where no DNACPR recommendation has been made. Others fail to specify that CPR may be appropriate even in the presence of a DNACPR recommendation.
Conclusions
Local DNACPR policies currently place both patients and healthcare professionals at significant risk.
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Leveraging Digital Transformation Strategy and Data-Driven Decision Making to Improve Organisational Performance in a Hostile Environment
Get PDFPurpose: This study examines whether and how a digital transformation strategy (DTS) can improve organisational performance (OP) when confronted with environmental hostility (EH) disruptions. Based on the Resource Base View, Dynamic Capability theories, and literature straddling digital transformation and organisational development, this study developed and tested a conceptual model consisting key elements and relationships between digital transformation strategy (DTS), business analytic capabilities (BAC), organisational agility (OA), data-driven decision making (DDM), organisational performance (OP) and environmental hostility (EH).
Design/methodology: The study is based on a quantitative cross-sectional design, using a survey sample of 309 respondents, working in mainly IT and different industry sectors in North America. Structural equation modelling was used and five hypotheses have been tested.
Findings: The results suggest that environment hostility has a negative effect on organisational performance, while DTS via business analytics capability and organisational agility enable Data Driven Decision (DDM) to enhance the organisational performance. The specific paths are uncovered - both digital capability and organisational agility drive dynamic capability development. DDM, in the form of decision automation by non-human entities and collective sense-making, leads to superior performance and reduces the impact of environmental hostility.
Originality/Value: The study contributes to the theoretical development of the dynamic capability framework from digital capability and organisational agility perspectives. The practical implications will support IT managers in designing IT strategies and agile practices that embed business analytic capabilities for data-driven decision making that increases organisational performance
‘My child is a blessing’: Exploring the role of religion for Muslim parents of children with special educational needs and disabilities (SEND)
Get PDFThis study explores the role of religion in the experiences of Muslim parents raising children with special educational needs and disabilities (SEND) in the United Kingdom. While parental experiences in SEND research have received increasing attention, there continues to be limited focus on the role of religion, both within educational psychology and broader psychological research. A qualitative approach was adopted, using reflexive thematic analysis to analyse semi-structured interviews with eight Muslim parents, including six mothers and two fathers. The study was grounded in an Islamic ontological and epistemological framework, aligning with a growing tradition of decolonising research by centring non-western worldviews and reclaiming religious ways of knowing. The findings highlight the central role of a religious lens in shaping how parents perceive their child's SEND, make meaning of their experiences, adopt coping strategies, and engage with community and professional support systems. Drawing on these findings, the study developed a conceptual framework illustrating the link between religious beliefs, practices, and social contexts in shaping parental resilience and religious identity. This framework demonstrates that religion is a primary lens through which lived experience is interpreted. The research highlights the need for educational and psychological practice to engage more meaningfully with religious worldviews when supporting Muslim families, challenging secular assumptions and contributing to culturally and religiously responsive models of SEND support
Intensive case supervision during child and adolescent psychoanalytic psychotherapy training: An IPA study of supervisors’ and supervisees’ accounts of their experience
Get PDFThis study explores the experiences both of supervisors and supervisees in the intensive psychoanalytic supervision of their psychotherapeutic work with children and young people, and how both groups of participants understand its role and function as part of the child and adolescent psychoanalytic psychotherapy training. Using the qualitative research method of Interpretative Phenomenological Analysis (IPA), the study explores through the data gathered from four participants’ communications during semi-structured interviews, in the context of the existing literature, how intensive case supervision supports and develops clinical confidence and a sense of growing clinical capacity in trainees during the child and adolescent psychoanalytic psychotherapy training. Findings are concerned with six experiential themes: ‘Understanding an unconscious emotional experience’; ‘Using another person for help and support’; ‘Working with negative feelings’; ‘Growing up and finding one’s own way’; ‘The learning experience as an attitude towards difference’; and, ‘Feelings about the centrality and legacy of the experience’. Implications and recommendations for the provision and enhancement of intensive case supervision during child and adolescent psychotherapy training that facilitates authentic growth and development in a trainee, in parallel with the patients they work with, are considered. Areas of further research, including the need to understand better the experiences of trainees and supervisors from minority backgrounds and of different genders as well as how issues such as negative feelings towards trainees are managed in supervision, are identified
MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems
Get PDFContinuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today’s Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively
The factors influencing the effectiveness of policy implementation: Insights from social media analysis during the COVID-19 crisis in the UK
Get PDFIn the era of social media, the effective implementation of governmental policies has become increasingly crucial for achieving desired outcomes and addressing societal issues. Public discourse surrounding these policies often generate negative and misleading influences that can hinder their successful execution. A lack of understanding of the factors that drive policy success undermines policymakers' ability to design effective interventions for managing public events. Therefore, this thesis aims to investigate the dynamics influencing the implementation performance of policies, contributing not only to the academic literature but also offering practical implications for enhancing public welfare and the efficacy of governmental policies. The central research question focuses on exploring the factors that influence the effectiveness of policy implementation. Utilizing data mining techniques, the thesis extracts 144K Twitter posts from the United Kingdom and employs research methods, including regression analysis, machine learning, and text data analysis, to comprehensively examine the underlying dynamics. Consequently, this thesis has discovered that minimizing certain public emotions can enhance policy effectiveness, thereby facilitating improved implementation outcomes. Besides, the thesis advises policymakers that increasing the volume of detailed descriptions of protective behaviours and implementing strategies aimed at cultivating public trust can reduce misinformation about government policies. Furthermore, the thesis reveals the diverse impacts of risk perceptions on policy implementation performance, suggesting that risks perceived at individual, group, and societal levels should be addressed differently to achieve optimal policy implementation outcomes. Overall, this thesis makes a significant theoretical contribution through a novel investigation of the impact of public emotions and varying risk perceptions on policy performance, which enriches the existing literature in information systems, public management, and social media data analysis. Moreover, this thesis advocates for implementing more targeted measures for managing misinformation, providing practical implications that aid policymakers in navigating challenges and enhancing management effectiveness during similar crisis scenarios in the future
AdaptEEG: A Deep Subdomain Adaptation Network with Class Confusion Loss for Cross-Subject Mental Workload Classification
Get PDFEEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation techniques have shown effectiveness in enhancing the accuracy of cross-subject classification. However, current state-of-the-art methods for cross-subject mental workload classification primarily focus on global domain adaptation, which may lack fine-grained information and result in ambiguous classification boundaries. We proposed a novel approach called deep subdomain adaptation network with class confusion loss (DSAN-CCL) to enhance the performance of cross-subject mental workload classification. DSAN-CCL utilizes the local maximum mean discrepancy to align the feature distributions between the source domain and the target domain for each mental workload category. Moreover, the class confusion matrix was constructed by the product of the weighted class probabilities (class probabilities predicted by the label classifier) and the transpose of the class probabilities. The loss for maximizing diagonal elements and minimizing non-diagonal elements of the class confusion matrix was added to increase the credibility of pseudo-labels, thus improving the transfer performance. The proposed DSAN-CCL method was validated on two datasets, and the results indicate a significant improvement of 3∼10 percentage points compared to state-of-the-art domain adaptation methods. In addition, our proposed method is not dependent on a specific feature extractor. It can be replaced by any other feature extractor to fit new applications. This makes our approach universal to cross-domain classification problems
Essays on conditional cooperation
Get PDFThis thesis consists of three chapters, studying the role of reciprocity and the nature of conditional cooperation. In Chapter 1, we theoretically investigate how reciprocity can be modelled so that it remains compatible with a wide range of experimental findings. We introduce a new definition of kindness with two components in our model: intentional kindness and consequential kindness. We also propose a new definition of efficient strategy that resolves paradoxes found in earlier behavioural models. Finally, we show that our framework reflects the results of a host of laboratory games, including the ultimatum game and the sequential prisoner’s dilemma, which neither standard theory nor existing reciprocity models can fully explain. In Chapter 2, we experimentally study conditional cooperation, an instance of reciprocity that is particularly applicable to social dilemmas. Reciprocity can be broadly defined as taking a more altruistic action in response to a more generous action. This chapter aims to better understand the nature of conditional cooperation and in turn the nature of reciprocity, given that existing models of reciprocity fail to explain some of the empirical regularities. We use sequential prisoner's dilemma games to conduct a thorough study on payoffs that can potentially affect conditional cooperation. We experimentally investigate conditional cooperation by considering generosity separately in terms of first-mover payoffs and second-mover payoffs, which has not been done previously. We find that both aspects of generosity are present and affect the choices of the second-mover. The findings suggest the need for richer frameworks of reciprocity than those are currently used. In Chapter 3, we further analyze the nature of conditional cooperation in sequential prisoner's dilemma games using revealed preference methods. We disentangle context-free quasi-monotone preferences—where individuals prefer choices that improve their own payoff at least as much as they do for others—from conditional cooperation. By definition, conditional cooperation is context-dependent and closely tied to reciprocity. To capture this, we propose the concept of reciprocal preferences, which reflects how varying contexts affect conditional cooperation. Our model offers a method for identifying conditional cooperation in experimental settings
Explainable Artificial Intelligence for the identification of novel mammalian enhancers and their epigenetic code
No full textEnhancers are non-coding regions of the genome responsible for controlling the activity of genes. Approximately 80-90% of mutations causing human diseases (including cancers) are located in the non-coding part of the human genome, often in enhancers. To provide a better understanding of how these mutations lead to disease states, both experimental and machine learning approaches have been developed to annotate enhancers. However, these techniques fail to provide genomics and clinical researchers with an accurate explanation of how these models predict specific regions as enhancers over other regions. Hence, there is a need for eXplainable Artificial Intelligence (XAI) IF/THEN rules systems that can be easily understood, analysed, and augmented by domain experts. In this thesis, we developed several Artificial Intelligence (AI) models (Convolutional Neural Networks (CNNs), XGBoost, Logistic Regression and a Type-2 Fuzzy logic rule-based eXplainable Artificial Intelligence (XAI)) for enhancer prediction in different human and mouse cell lines. While all models display high accuracy, only our XAI models (AUC 0.79) are explainable and provide a set of IF/THEN rules that decipher the underlying combinatorial epigenetic code of enhancers. Furthermore, our results confirmed that only XAI and partially CNN perform consistently well in the other two human cell lines, i.e. K562 and IMR90, with an AUC (XAI: 0.73, CNN: 0.7 & 0.54). The other opaque models did not generalise well (i.e. logistic regression (AUC: 0.67) and XGBoost (AUC: 0.55)), further supporting the generalisation abilities of the XAI model. Our AI models identified many novel enhancers (i.e. H3K18ac and H3K14ac), which display the same epigenetic signatures as experimentally identified ones. Interestingly, seven Features i.e. (epigenetic marks) in human (XAI AUC: 0.79) and five features in mouse (XAI AUC: 0.8) are sufficient to annotate enhancers without losing accuracy. Furthermore, the 7 epigenetic mark minimal human model was applied to annotate enhancers in 10 brain tumour (glioblastoma) patient-derived lines. Most importantly, the XAI provides insights on the specific combinations of epigenetic modifications that classify enhancers instead of only providing the importance of one or several features. In particular, we present an interpretable IF/THEN rule architecture that helps us model how features interact in high-dimensional biological data, handling some major drawbacks of post-hoc explainability methods like SHAP and LIME. Unlike ML methods that just hand out static importance scores or look at features individually, our rule base lays out combinatorial logic in a clear way. It shows how epigenetic markers work together to activate enhancers (e.g. “IF H3K27ac is high enriched and H3K4me1 is high enriched in a genomic region, THEN the region is defined as an enhancer region”). The approach clears up the confusion around feature importance, while univariate analysis might wrongly classify epigenetic markers because of inconsistent individual correlations, our rules pinpoint their predictive strength only when they show up alongside other markers. This highlights how linear or additive models can miss out on crucial conditional dependencies. This framework has significant implications for personalised medicines, by mapping patient-specific epigenetic profiles to rule-based logic clinician could identify individualised enhancers activation patterns linked to disease. For example, a tumour might show enhancers that are classified by the following rule: “IF H3K18ac and H3K14ac is high enriched in the genome”, instead of the usual individual pairing of the H3K27ac and H3K4me1. This points to the possibility of personalised therapies, as by enabling clinicians to develop drugs that specifically control the enrichment level of these enhancer markers. For example, A personalised drug can be designed to modulate epigenetic modifications, specifically by reducing H3K18ac enrichment from high to low and adjusting H3K14ac enrichment from high to medium. This targeted regulation ultimately disrupts key oncogenic pathways, thereby inhibiting tumour growth. This level of explainability is unattainable with methods like SHAP or LIME, as they don't have the framework needed to suggest actionable biomarkers that depend on specific condition