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Should I Stay or Should I Go? The Effect of Allied Brands Negative Publicity on Brand Managers' Decision-Making
No full text© 2023, Elsevier. The attached document (embargoed until 13/07/2026) is an author produced version of a paper published in Industrial Marketing Management uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it
A Theology of Becoming:Body, Blood, Birth, and Sacrament
No full textModern theological approaches to birth have been filtered through an androcentric lens, focusing more on ethical questions of contraception and abortion than on the significance of birth for what it means to be human. In the Catholic tradition, this has been influenced by doctrines and traditions surrounding Mary's virginal conception of Christ and painless birth. This Element considers the challenges posed by maternal life to ideas and theories about pregnancy, childbirth, and the relationship between a woman and her newborn child. Reflecting on her maternal experiences through the lenses of feminist theory and Marian theology, the author sketches the contours of an incarnational theology that endows the birthing body with sacramental significance. She concludes by asking what it would mean for theological anthropology to adopt this as the normative model of the person reborn through baptism into the body of the maternal Church
Differential Associations of Pitch Discrimination and Rapid Auditory Processing With Emotional Prosody Recognition in Autistic and Non-autistic Children
No full textLiberator:Visual Haskell
No full textLiberator is a visual programming environment for Haskell, designed to support the teaching of functional programming at A-Level Computer Science. It grew out of a conviction that students find functional thinking easier to grasp when they can see the data flow directly — when a function is a node, an argument is a wire, and evaluation is just following the graph to its leaves
Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction
No full textPredicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignment between image and transcriptomic features. To address these limitations, we propose a unified multimodal learning framework that integrates histological imaging and spatial transcriptomics through a shared latent representation space. Specifically, histological H&E images are encoded by a ResNet50-based convolutional stem and a MobileViT Transformer backbone to extract hierarchical visual representations. Both modalities are projected into a shared latent space via linear–GELU–dropout transformation blocks, enabling cross-modal alignment through a contrastive learning objective that maximizes agreement between the corresponding image and the spot embeddings. Experimental results on the 10x Genomics Visium dataset of human liver tissue demonstrate that MViTGene achieves significantly higher prediction accuracy than existing methods across multiple gene subsets, with improvements of 20%, 33%, and 12% in predicting marker genes, highly expressed genes, and highly variable genes, respectively. The significant improvement in relevance indicates that the model can more accurately capture the true correspondence between tissue morphology and gene expression, therefore enabling more reliable biological interpretation. It provides a computational tool for high-throughput spatial gene expression prediction that balances performance and interpretability
How Students in Developing Economies Navigate Economic Hardship Through Conditional Informal-Necessity Entrepreneurial Bricolage
Full text linkThis paper examines how and why student entrepreneurs in developing economies engage in informal, necessity-based entrepreneurship while pursuing their education. Developing economies face unique challenges, including high rates of youth unemployment, poverty, informality, and limited resources. This paper investigates entrepreneurial bricolage and frugal business models by which students engage in entrepreneurship to navigate economic hardship. An exploratory qualitative approach based on interviews with 18 student entrepreneurs in Nigeria was the preferred research method. The qualitative research is grounded in the principles of Interpretative Phenomenological Analysis and Gioia’s thematic methodology, which enable qualitative rigor and an “inductive approach. By defining and framing the first-order concepts and second-order themes from the qualitative data, the thematic findings revealed the nature of self-employment, side hustles, frugality, and bricolage innovations associated with student entrepreneurship. The findings enable us to conceptually frame conditional entrepreneurial bricolage (CEB), which describes the frugal, innovative process by which students in developing economies establish informal necessity enterprises as a primary source of income, a side income, or an alternative to employment. Student entrepreneurs rely on informal learning, low-cost resources, cheap products, improvisation, and social networks to sustain their ventures. This paper contributes to an understanding of student entrepreneurship, conditional entrepreneurship, and frugal innovation in the context of developing economies. This study identifies essential elements of informal necessity entrepreneurship and proposes future avenues for advancing knowledge on student entrepreneurship, bricolage, and frugal business model innovations. © 2026, Emerald Insight. This is an author produced version of a paper published in International Journal of Entrepreneurial Behaviour & Research uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it
Dynamic Subsidy Design for Sustainable Fresh Agricultural Supply Chains: A Differential Game Approach
Full text linkFresh agricultural products are highly perishable, and inadequate preservation leads to food loss and supply chain inefficiency, undermining sustainability. This study develops a continuous-time differential game model to analyze dynamic pricing and cold-chain investment decisions in a two-echelon fresh agricultural produce supply chain under government intervention. Two subsidy regimes are examined: one targeting suppliers’ cold-chain investments and another supporting the retailer based on sales volume. By explicitly modeling the dynamic evolution of product freshness, we analyze how subsidy intensity and allocation influence firms’ strategies, market outcomes, and social welfare over time. The results show that when initial freshness is low, firms consistently adopt a penetration pricing strategy and increase cold-chain investment irrespective of subsidy intensity. In contrast, when initial freshness is high, a critical subsidy threshold emerges: Below this threshold, firms employ skimming pricing and reduce investment, whereas above it, they switch to penetration pricing and raise investment. Under equal government expenditure, supplier subsidies achieve higher product freshness but raise retail prices, while retailer subsidies lower prices and stimulate demand, albeit with more modest freshness improvements. Welfare effects are non-linear: supplier subsidies are more effective at low intensities, whereas retailer subsidies become superior beyond a specific threshold. These findings provide actionable insights for designing sustainable, targeted subsidy policies in fresh agricultural supply chains
Deep Reinforcement Learning for Sustainable Urban Mobility: A Bibliometric and Empirical Review
Full text linkThis paper provides an empirical basis for a Computational Integration Framework (CIF), a systematic and scientifically supported implementation of artificial intelligence (AI) in smart city applications. This study is a methodological framework-with-validation study, where large-scale bibliometric analysis is used as a justification for design in the identification of strategically relevant urban areas rather than a single research study. This evidence determines urban mobility as the most mature and computationally optimal domain for empirical verification. The exploitation of CIF is realized using a DRL-driven traffic signal control system to show that bibliometrically informed domain selection can be put into application by way of an algorithm. The empirical results show that the most traditional control strategies accomplish significant performance gains, such as about 48% reduction in average wait time, over 30% increase in traffic efficiency, and considerable reductions in fuel consumption and CO2 emissions. A federated DRL solution maintains around 96% of central performance while still maintaining data privacy, which suggests that deployment in real-world situations is feasible. The contribution of this study is threefold: evidence-based domain selection through bibliometric analyses; introduction of CIF as an AI decision support bridge between AI techniques and urban application domains; and computational verification of the feasibility of DRL for sustainable urban mobility. These findings reveal policy information relevant to goals governing global sustainability, including the European Green Deal (EGD) and the United Nations Sustainable Development Goals (SDGs), and thus, the paper is a methodological framework paper based on literature and validated through computational experimentation
Community health intervention through musical engagement (CHIME) in South Africa: A formative exploration of the feasibility and development of a music-based intervention to support perinatal mental health
No full textIn South Africa, perinatal depression, stress or anxiety affect an estimated 16% to 50% of women posing serious concerns for both mothers and infants. The vast majority of women receive no perinatal mental healthcare through the public health system, partly due to high levels of stigma and a lack of culturally sensitive mental health care. South African musical traditions such as group singing are culturally significant for supporting social connection and coping with challenges experienced in everyday life. However, there is little research on how group music making could be used to support perinatal mental health in South Africa. This study aimed to explore the potential for developing a culturally embedded, music-based intervention to support women in the perinatal period. Using Community-Based Participatory Research, we held five focus group discussions with: 1) community health workers, 2) music experts, 3) traditional healers, 4) professional healthcare workers, and 5) the management team of a rural health NGO. Through thematic analysis, four themes were identified. Theme 1 encompasses the various challenges that contribute to perinatal mental distress, including social determinants of mental health, unhelpful coping strategies, stigma, and isolation. Theme 2 reflects existing community music practices: the way music is embedded in culture, processes of cultural change, and musical practices associated with perinatal health. Theme 3 encompasses the perceived benefits of music making in supporting social connections and effecting transformation in relation to individual mood and spiritual experiences. Theme 4 includes consideration of factors that are important for the development of a music-making intervention to support perinatal mental health. The findings suggest strong potential for implementing music-based mental health interventions in South Africa, adaptable to various facilitators and community contexts. [Abstract copyright: Copyright: © 2026 Sigwebela et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Evaluating the Requirements Engineering Process in Model Transformation Development: A State of Practice Analysis
Full text linkModel Transformations (MT) are a central element of Model-Driven Engineering (MDE) methods. As MT adoption increases in both industry and academia, there is a growing need for systematic software engineering practices, particularly in Requirements Engineering (RE) for MT development.This paper investigates the state of RE in MT through two complementary empirical studies: semi-structured interviews with industry practitioners and a systematic literature review (SLR) analyzing published transformation cases. Both studies address the same research questions but differ in the populations they cover. The interviews focus on industrial settings, while the SLR reviews published work, the majority of which comes from academic sources. Our findings reveal that the RE processes used in MT development tend to be largely informal and lack structured methodologies. While some RE techniques such as prototyping and scenario-based generalization are used, they are typically applied in an ad-hoc manner based on personal experience rather than through a well-defined RE framework.Our studies highlight challenges in stakeholder engagement in MT RE, particularly limited access to stakeholders, which restricts the effective application of RE techniques. Furthermore, our analysis identifies a predominant focus on MT implementation, with limited MT specification and systematic RE activities, which often leads to requirements being implicitly defined rather than explicitly documented. Despite these shared findings, the interview study and SLR differ in their perspectives: the interview study reflects real-world industrial constraints on requirements engineering, while the SLR reflects more research-driven RE practices.These findings underscore the gap between research and practice in model transforma- tions, and highlight the need for lightweight, structured RE frameworks tailored to MT development. Future work should focus on bridging this gap by integrating agile RE techniques with structured methodologies to support flexibility, traceability and stakeholder collaboration in MT projects