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The Afnan Equilibrium: a coupled welfare model for balancing technology, resilience, and externalities
Get PDFRapid technological change is reshaping economies, raising productivity while intensifying systemic risks and environmental pressures. This paper introduces the Afnan Equilibrium, a coupled welfare framework that evaluates trade-offs among technology adoption T, productivity P, resilience R, and externalities E. The model defines a balanced rest point
(T*, R*), shows that technology and resilience are strategic complements and demonstrates that the planner's allocation can be implemented with simple instruments such as Pigouvian pricing and resilience support. Computational experiments—including phase diagrams, welfare surfaces, and policy wedges—illustrate how resilience broadens the safe range for technology and how incentives align decentralized choices with the social optimum. A two-region extension captures coordination gaps when only part of the damages is internalized. An illustrative cross-country comparison (Bangladesh, India, Nigeria, the Netherlands, and the United States) demonstrates empirical mapping and structural trade-offs. At the same time, sectoral sketches (agriculture, manufacturing, governance) highlight policy applications under uncertainty. By embedding resilience and externalities directly into welfare analysis, the framework moves beyond growth-centric metrics. It offers a structured way to frame trade-offs for governments, firms, and international institutions. It provides a quantitative lens for balancing innovation, resilience, and ecological integrity. It may inform climate adaptation planning, sustainable development pathways, and resilience governance under global environmental change, provided it is appropriately calibrated and supported by institutional detail. The framework is intentionally stylized and diagnostic at this stage; although not yet calibrated for operational policy use, its tractable structure allows future development as data and institutional specificity are incorporated
Punishments enhance reward learning by modulating striatal prediction errors
Get PDFPeople often make decisions in contexts where rewards and punishments co-occur, yet most human research still examines reward and punishment learning as independent processes. Here, across three studies, we address this gap by demonstrating that punishments amplify reward learning and its neural correlates in healthy human participants. In Study 1 (N = 102, 69 females and 33 males), participants performed a probabilistic learning task involving monetary rewards and punishments presented in either intermixed or separated contexts. In intermixed contexts, punishments enhanced reward learning accompanied by changes in computational parameters, including higher learning rates from reward prediction errors. In Study 2 (N = 26, 18 females and 8 males), fMRI revealed that punishments amplified reward prediction errors signals in the caudate. Study 3, an fMRI metaanalysis, confirmed that striatal reward responses are consistently stronger when punishments are present. Across studies, we found no reciprocal enhancement of punishment learning by rewards. Together, these findings demonstrate that punishments sharpen reward learning through striatal modulation and underscore the extent to which reward learning is influenced by its broader outcome context
Global energy corporations and climate change: the role of formal and informal institutions in shaping climate change risk disclosure
Get PDFThis study examines climate change risk disclosure in the global energy sector, where firms face intense stakeholder scrutiny and legitimacy pressures. We develop a novel domain-specific textual analysis measure to capture climate change risk disclosures, improving on prior approaches based on generic environmental terminology. Drawing on neo-institutional theory, we analyze how national cultural development and governance quality shape firms' strategic responses to climate-related risk and stakeholder expectations. Using a global panel of listed energy firms from 2016 to 2021, we find that stronger cultural development and higher governance quality are associated with significantly greater disclosure of climate change risk. These findings suggest that climate risk disclosure functions as a strategic mechanism of legitimacy through which firms manage stakeholder pressure and signal their responsiveness to institutional demands. The study contributes to the literature by linking macro-institutional contexts to firm-level risk management and strategic transparency in environmentally sensitive industries
Optimised murine HFpEF models for translational pre-clinical studies
No full textBackground and aims: The most clinically representative murine models of HFpEF include a "2-hit" model combining nitrosative stress with metabolic perturbation and a "3-hit" model that also includes ageing. Both models have important limitations with regard to sub-strain and sex. Methods: The 2-hit model protocol was modified to reproduce HFpEF in both C57BL/6N and 6J mice by increasing L-NAME doses (0.5 g/L to 1.75 g/L) and protocol lengths (7 weeks to 13 weeks). For the 3-hit model, in addition to deoxycorticosterone pivalate [DOCP], we added 1% NaCl drinking water to enhance and prolong the effect of DOCP ("4-hit"). To maintain the phenotype, a second bolus of DOCP was administered after 8 weeks. Results: HFpEF was successfully induced in C57BL/6J mice when exposed to a 13week 2-hit L-NAME protocol with gradually increasing dosage from 1.0 g/L to 1.75 g/L. For the 4-hit mice, a clear HFpEF phenotype was observed in C57BL/6N and 6J mice in both male and females, and maintained for up to 12 weeks. Conclusions: These modifications ensure the 2-hit model is induced in J substrain of C57BL/6 mice. The 4-hit model prevents aldosterone escape and enhances reproducibility across sexes and substrain
Tenascin-c functionalised self-assembling peptide hydrogels for critical-sized bone defect reconstruction
No full textCritical-sized bone defects are unable to heal spontaneously and receive poor clinical prognosis due to limitations in modern treatment strategies. Next-generation therapies are applying biomaterials incorporating BMP-2 to effectively promote and support bone regeneration, but adverse effects are linked to uncontrolled BMP-2 egress from the biomaterial. Implementing extracellular matrix proteins to biomaterials is a favourable approach to alleviate these drawbacks, and self-assembling peptide hydrogels are rapidly emerging as modulable and versatile biomaterials. Here, we describe the creation of a tenascin-c-functionalised peptide hydrogel designed to regenerate critical-sized bone defects. A recombinant fragment of tenascin-c spanning from the 3rd to 5th fibronectin-like domains is integrated into the fibre network. We demonstrate that this nascent construct effectively retains BMP-2 to differentiate mesenchymal stem cells into mature osteoblasts and achieves complete unionisation of murine critical-sized bone defects under low BMP-2 dose. All in all, we demonstrate tenascin-c as a suitable candidate to functionalise biomaterials intended for bone engineering applications and the promising potential of self-assembling peptide hydrogels in treating critical-sized bone defects
Temporal reachability dominating sets: contagion in temporal graphs
Get PDFGiven a population with dynamic pairwise connections, we ask if the entire population could be (indirectly) infected by a small group of k initially infected individuals. We formalise this problem as the Temporal Reachability Dominating Set (TaRDiS) problem on temporal graphs. We provide positive and negative parameterized complexity results in four different parameters: the number k of initially infected, the lifetime τ of the graph, the number of locally earliest edges in the graph, and the treewidth of the footprint graph G↓. We additionally introduce and study the MaxMinTaRDiS problem, where the aim is to schedule connections between individuals so that at least k individuals must be infected for the entire population to become fully infected. We classify three variants of the problem: Strict, Nonstrict, and Happy. We show these to be coNP-complete, NP-hard, and ΣP2-complete, respectively. Interestingly, we obtain hardness of the Nonstrict variant by showing that a natural restriction is exactly the well-studied Distance-3 Independent Set problem on static graphs
Artificial intelligence transformations in geotechnics: progress, challenges and future enablers
Get PDFOur reliance on the underground space to deliver critical civil engineering infrastructure is growing: to accommodate utility and transport infrastructure in urban environments, to provide innovative housing and commercial solutions, and to support proliferating renewable energy infrastructure, particularly offshore. Artificial intelligence (AI) is arguably the most promising enabler to transform geotechnical engineering by extracting knowledge from data to achieve step-change increases in efficiency, sustainability, reliability and safety. This paper seeks to develop a shared understanding of the state of the art of AI in geotechnics and to explore future developments. By way of example, specific popular use cases in geotechnics are considered to highlight current progress in AI applications including intelligent site investigation, predictive modelling for soil behaviour, and optimisation of design and construction processes. The paper then addresses key research challenges, such as data scarcity and interpretability, and discusses the opportunities that lie ahead in the integration of AI with geotechnical engineering. Finally, priority technological enablers are identified for future transformations
Tailored Modelling Approaches to Assess the Feasibility of Closed-loop Systems
No full textThe use of the phrase ‘Geothermal Everywhere’ has grown as deep closed-loop systems have emerged. These include Advanced Geothermal Systems, defined by IRENA as “deep, large, artificial closed-loop circuits in which a working fluid is circulated and heated by sub-surface rocks through conductive heat transfer”, and Deep Borehole Heat Exchangers, which employ coaxial pipe configurations typically. Closed-loop systems are not only for heat extraction applications, but can provide effective heat storage. Their use in connection with geological facilities for nuclear waste disposal has also been explored. Through a range of recent case studies, this paper demonstrates the key role of tailored modelling frameworks and approaches in assessing the pre-feasibility of closed-loop projects by identifying key impacts on overall system performance and estimating its associated uncertainty. Depending on the geological settings, system engineering design, and intended use of the energy, the paper offers examples of how analytical and numerical models can assist with estimating - for example - the radius of the thermal plume around closed-loop sections, the decay heat released from nuclear waste canisters and interceptable by closed-loop systems, the effect of ground water flow, how much and how quickly thermal energy can be discharged from/charged to the system, and the long-term sustainability of the energy extraction or storage. The paper also offers example of when potentially complex and time-consuming numerical simulation can be replaced by simpler and more transparent analytical models and nomograms to support project developers and investors with quick look analyses
R&D intensity, development costs’ capitalization intensity and stock returns: a variance decomposition analysis
Get PDFThis study examines the value relevance of R&D intensity and development costs’ capitalization intensity under IFRS, by gauging their contribution to the informational components of the volatility of unexpected returns. We use a multivariate time-series approach that can be reconciled to a log-linear valuation model by also accommodating time-varying discount rates. Our results show that R&D intensity has a significant positive impact upon the variance contribution of cash flow and accrual news to the variance of unexpected returns. Thus, R&D intensity indeed conveys value relevant information about shocks to both future operating cash flows and accruals. We also show that this association is stronger as the capitalization intensity of development costs increases, but only for accrual news. Overall, the return decomposition that we employ shows the channel through which the return variation related to R&D and development costs’ capitalization intensity occurs. Additional analysis shows that our results are not driven by country level growth option risk but are weaker in countries with higher uncertainty avoidance. This study contributes to the R&D and return variance decomposition strands of the literature and raises policy implications by providing evidence in favor of development costs’ capitalization
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