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Cultural Criminology, Counterextremism and the Contemporary Far Right
Get PDFThis article draws together existing criminological work as well as developments from sociology, political science and media studies to argue that cultural criminology can offer a useful corrective to current ‘counterextremist’ thinking about the contemporary far right. The first part of the article introduces the contemporary far right, describes how it differs from previous instances, and explains that this resurgent far-right movement has to date primarily been analysed through the lens of ‘counterextremism’. The second part of the article problematises the concepts of ‘extremism’, ‘radicalisation’ and ‘terrorism’. The article argues that these concepts are ambiguous, imprecise and normative, and that they are freighted with ideological baggage and unsupported by empirical evidence. The third part of the article argues that cultural criminology can better inform our understanding of the contemporary far right owing to its focus on subculture and style, its attendance to networked digital media and its foregrounding of emotion and affect. The article concludes by outlining a tentative programme for cultural criminological research into the contemporary far right
Timing Legitimacy: Identifying the Optimal Moment to Launch Technology in the Market
Get PDFHow do managers time the launch of new technologies? Without actionable frameworks to ensure consumers and other stakeholders are ready, innovation releases remain a risky endeavor. Previous work on legitimacy has focused on stages following a product launch. However, launch timing concerns shared expectations of when actions should occur prior to launch. This conceptual article evaluates the alignment between firm and stakeholder expectations regarding launch timing. It proposes that the market timing of new technology launches is structured by two dimensions: firm-led coordination and stakeholders’ willingness to change. Combining these dimensions, the authors map four types of market timing situations managers can encounter: antagonistic, synergistic, flexible, and inflexible timing. Temporal legitimacy is achieved when a firm and its key stakeholders share timing norms about the ideal moments when activities should occur in a market process. The authors conceptualize proto-markets as prefacing the well-known market legitimacy stages. This article concludes with a detailed managerial decision tree on how to create the optimal technology product launch moment and avenues of future research on market timing beyond technology launches
What defines the practice of existential coaching? A qualitative study of the perspectives of existential coaches
Get PDFThe coaching literature offers some definitions of existential coaching yet there is limited empirical research that describes the process of this approach. The aim of this study is to contribute to the field by exploring existential coaches’ perspectives on the process and value of existential coaching. Qualitative data were gathered through semi-structured in-depth interviews with nine existential coaches and analysed thematically. We highlight three key themes. First, the coaches reported that this approach helps their clients deal with their existential questions by fostering skills such as self-reflection, and by transforming their mindset regarding freedom of choice, the human condition and finitude. Second, their main tool is their inner philosophy that is implicit in their work. Finally, the boundaries between existential coaching and existential therapy are significantly blurred. These findings serve as an initial ground for future guidelines and regulations to be established by the individual coaching practices and professional bodies
An iterative least-squares Monte Carlo approach for the simulation of cohort based biometric indices
Get PDFThis paper tackles the problem of approximating the distribution of future biometric indices under a cohort-based perspective. Unlike period-based evaluations, cohort-based schemes require the computation of conditional expectations for which explicit solutions often do not exist. To overcome this issue, we suggest the application of a well-established methodology, i.e., the Least-Squares Monte Carlo approach. The idea is to approximate conditional expectations by combining simulations and regression techniques, thus avoiding a straightforward but computationally demanding nested simulations method. To show the extreme flexibility and generality of the proposal, we provide extensive numerical results concerning two main longevity indices, life expectancy and lifespan disparity, obtained by adopting both single- and multi-population mortality models. Comparisons between period- and cohort-based results are made as well. Finally, the paper shows that the proposed methodology can be used to approximate other biometric indices at future dates for which cohort-based estimations are often replaced by period ones for computational simplicity
On indication, strict monotonicity, and efficiency of projections in a general class of path-based data envelopment models
No full textData envelopment analysis (DEA) theory formulates a number of desirable properties that DEA models should satisfy. Among these, indication, strict monotonicity, and strong efficiency of projections tend to be grouped together in the sense that, in individual models, typically, either all three are satisfied or all three fail at the same time. Specifically, in slacks-based graph models, the three properties are always met; in path-based models, such as radial models, directional distance function models, and the hyperbolic function model, the three properties, with some minor exceptions, typically all fail. Motivated by this observation, the article examines relationships among indication, strict monotonicity, and strong efficiency of projections in the class of path-based models over variable returns-to-scale technology sets. Under mild assumptions, it is shown that the property of strict monotonicity and strong efficiency of projections are equivalent, and that both properties imply indication. This paper also characterises a narrow class of technology sets and path directions for which the three properties hold in path-based models
Lessons on AI implementation from senior clinical practitioners: An exploratory qualitative study in medical imaging and radiotherapy in the UK
Get PDFIntroduction
Artificial Intelligence (AI) has the potential to transform medical imaging and radiotherapy; both fields where radiographers' use of AI tools is increasing. This study aimed to explore the views of those professionals who are now using AI tools.
Methods
A small-scale exploratory research process was employed, where qualitative data was obtained from five UK-based participants; all professionals working in medical imaging and radiotherapy who use AI in clinical practice. Five semi-structured interviews were conducted online. Verbatim transcription was performed using an open-source automatic speech recognition model. Conceptual content analysis was performed to analyze the data and identify common themes.
Results
Participants spoke about the possibility of AI deskilling staff and changing their roles, they discussed issues around data protection and data sharing strategies, the important role of effective leadership of AI teams, and the seamless integration into workflows. Participants thought that the benefits of adopting AI were smoother clinical workflows, support for the workforce in decision-making, and enhanced patient safety/care. They also highlighted the need for tailored AI education/training, multidisciplinary teamwork and support.
Conclusion
Participants who are now using AI tools felt that clinical staff should be empowered to support AI implementation by adopting new and clearly defined roles and responsibilities. They suggest that attention to patient care and safety is a key to successful AI adoption. Despite the increasing adoption of AI, participants in the UK described a gap in knowledge with professionals still needing clear guidance, education and training regarding AI in preparation for more widespread adoption
Mutual fund performance: The model for selecting persistent winners
Get PDFStandard Fama-French-Carhart (FFC) models are widely used by academics to assess risk-adjusted fund performance versus market, size, style and momentum factors. However, they fail to reflect the industry standard, following which the performance of money managers is commonly evaluated relative to a corresponding benchmark and the peer group. In this paper, we introduce a new approach that augments the Carhart model and enables investors to identify the funds that outbid both the benchmark and the peer group. In addition, it allows discovering more certain winners by eliminating the under/outperformance of funds driven by the bias in the FFC factor construction. The application of our model is illustrated on Large Cap Value and Large Cap Growth US active equity mutual funds using contingency tables. The performance and persistence in performance are assessed by comparing our novel and the standard Carhart models. Our model identifies more winners than the Carhart; those winners earn higher returns net of benchmark and peer- group than the Carhart’s winners, and show persistence in performance 36 months ahead. The results are robust to different specifications of contingency tables, holding periods or style categories of funds
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous
Get PDFResearch on developing deep learning techniques for autonomous spacecraft relative navigation challenges is continuously growing in recent years. Adopting those techniques offers enhanced performance. However, such approaches also introduce heightened apprehensions regarding the trustability and security of such deep learning methods through their susceptibility to adversarial attacks. In this work, we propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes based on the explainability concept. We develop for an orbital rendezvous scenario an innovative relative pose estimation technique adopting our proposed Convolutional Neural Network (CNN), which takes an image from the chaser’s onboard camera and outputs accurately the target’s relative position and rotation. We perturb seamlessly the input images using adversarial attacks that are generated by the Fast Gradient Sign Method (FGSM). The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting. Simulation results show that the proposed adversarial attack detector achieves a detection accuracy of 99.21%. Both the deep relative pose estimator and adversarial attack detector are then tested on real data captured from our laboratory-designed setup. The experimental results from our laboratory-designed setup demonstrate that the proposed adversarial attack detector achieves an average detection accuracy of 96.29%
ltl Synthesis Under Environment Specifications for Reachability and Safety Properties
Get PDFIn this paper, we study ltlf synthesis under environment specifications for arbitrary reachability and safety properties. We consider both kinds of properties for both agent tasks and environment specifications, providing a complete landscape of synthesis algorithms. For each case, we devise a specific algorithm (optimal wrt complexity of the problem) and prove its correctness. The algorithms combine common building blocks in different ways. While some cases are already studied in literature others are studied here for the first time
Factors influencing the uptake of culturally tailored diabetes self-management education and support programmes among ethnic minority patients with type 2 diabetes: A systematic review
Get PDFPurpose
This systematic review aimed to evaluate the factors influencing the uptake of culturally-tailored Diabetes Self-Management Education and Support (DSMES) programmes among ethnic minority patients diagnosed with type 2 diabetes mellitus (T2DM).
Methods
A systematic review, following PRISMA guidelines, was conducted, including quantitative research studies published in peer-reviewed journals from January 2013 to January 2023. Studies were extracted via the following databases, AMED, MEDLINE, CINAHL, EMBASE, EMCARE, PSYCHINFO, Ovid Nursing, and grey literature. Studies were selected based on eligibility criteria including the evaluation of DSMES programmes tailored for ethnic minorities and involving adult participants with T2DM. The factors affecting the uptake of these programs were mapped against the three categories of the Andersen's Behavioural Model of Health Services Use: predisposing, enabling, and need factors. The quality of the included studies was assessed using the Critical Appraisal Skills Program (CASP) checklist, and a narrative synthesis was conducted to analyse the findings.
Results
Nine studies met the inclusion criteria, demonstrating that culturally-tailored DSMES programmes significantly improve uptake among ethnic minorities. Key factors influencing participation included demographic characteristics, diabetes knowledge, emotional support, and cultural beliefs. Barriers such as language proficiency, cost, and diabetes fatalism were identified, while enablers included the use of local champions and culturally specific strategies.
Conclusions
This systematic review highlights the effectiveness of culturally-tailored DSMES programmes in improving health outcomes among ethnic minority groups. It suggests that more research is needed to explore these barriers and develop strategies to enhance the uptake of DSMES programmes among underserved populations
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