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A Theory Repair Based Traffic Regulations Generalisation for Autonomous Vehicles
No full textThe development of AI products poses unique challenges to law reform, especially specific normative reform. In particular, some AI technologies like autonomous driving technologies blur the boundaries of these fundamental concepts of law, such as human beings, objects and behaviours. This gives rise to inconsistency and inapplicability of existing human-specific laws when turned to AI, such as irrational division of responsibility, conceptual ambiguities, conflicting guidance on behaviour, and conflicts with legislative intent. Considering the complexity and scale of the legal structure and how AI affects this structure, especially in a dynamic context, pure manual legal adjustments will naturally face difficulties in terms of accuracy and efficiency. This study therefore extended an automated theory repair system to design an intelligent legal aid system for the revision of driving rules in the context of automated driving and provided the code necessary to implement these functions
New tools in hierarchical hyperbolicity: A survey
No full textThe aim of this short survey is to advertise various tools that have been developed to study hierarchically hyperbolic spaces (HHSs) in recent years, with particular emphasis on those that require little to no knowledge of the HHS machinery to be used
Examining the Nonlinear and Spatial Heterogeneity of Housing Prices in Urban Beijing: An Application of GeoShapley
No full textHousing is essential for human well-being and economic stability. Major metropolitan areas, particularly in developing countries, face severe housing price challenges. Traditional Hedonic Pricing Models (HPM) have extensively examined the determinants of housing prices, often assuming linear relationships and overlooking submarket segmentation. While approaches such as Geographically Weighted Regression (GWR) address spatial heterogeneity, they may still struggle with capturing complex nonlinear interactions between housing attributes, neighborhood factors, and spatial dependencies. To overcome these limitations, this study combines Extreme Gradient Boosting (XGBoost) with the GeoShapley to better model nonlinear and spatially varying effects on housing prices. The GeoShapley summary plot reveals that spatial location (GEO) is the most influential feature, followed by distance to the CBD, housing age, and housing size, along with their interactions with GEO. Further analysis uncovers that larger suburban homes show weaker market performance compared to smaller units in central districts, revealing distinct submarket dynamics. Properties near the CBD, particularly in school districts and green landscapes, maintain higher value due to the spillover effects of educational and environmental amenities. Conversely, the negative correlation between proximity to Xizhimen Metro Station and housing prices highlights the complexity of metro accessibility, where factors such as station design might diminish the expected premium. These insights inform real estate policy and sustainable urban planning by spotlighting the importance of spatial heterogeneity and threshold effects, thus extending classical theories of urban housing markets to account for submarket-specific price formation processes
Evaluating Megacity Resilience to Pandemics: The Case of China
No full textMegacities’ inherent complexity and dense populations heighten vulnerability to health crises, necessitating pandemic resilience research. This study pioneers a tailored resilience assessment framework for pandemic-facing megacities, building upon a refined Tyler and Moench urban resilience model. Applying grey correlation-technique for order preference by similarity to ideal solution (TOPSIS) methodology and barrier diagnosis modeling, we evaluated eight Chinese megacities. Key findings reveal: First, pandemic resilience scores exhibited fluctuating growth across all cities from 2014 to 2021. Shanghai demonstrated the most rapid improvement (23.13% increase), contrasting with Shenzhen's marginal gain (1.82%). Second, Shanghai achieved optimal coordination across systems, agents, and institutional dimensions in 2017, 2020, and 2021, whereas Shenzhen displayed the least dimension integration during 2016–2021. This dimensional equilibrium critically determines overall urban resilience. Finally, barrier analysis identified population scale, urban size, resource allocation efficiency, and mobility patterns as dominant resilience constraints. The findings and policy recommendations of this study can inform megacity development and pandemic response planning.</p
Evaluating Megacity Resilience to Pandemics: The Case of China
No full textMegacities’ inherent complexity and dense populations heighten vulnerability to health crises, necessitating pandemic resilience research. This study pioneers a tailored resilience assessment framework for pandemic-facing megacities, building upon a refined Tyler and Moench urban resilience model. Applying grey correlation-technique for order preference by similarity to ideal solution (TOPSIS) methodology and barrier diagnosis modeling, we evaluated eight Chinese megacities. Key findings reveal: First, pandemic resilience scores exhibited fluctuating growth across all cities from 2014 to 2021. Shanghai demonstrated the most rapid improvement (23.13% increase), contrasting with Shenzhen's marginal gain (1.82%). Second, Shanghai achieved optimal coordination across systems, agents, and institutional dimensions in 2017, 2020, and 2021, whereas Shenzhen displayed the least dimension integration during 2016–2021. This dimensional equilibrium critically determines overall urban resilience. Finally, barrier analysis identified population scale, urban size, resource allocation efficiency, and mobility patterns as dominant resilience constraints. The findings and policy recommendations of this study can inform megacity development and pandemic response planning.</p
Cohesion at 40: A Commentary on (Re)conceptualizing Cohesion Through Identity, Interdependence, and Teamwork in Sport and Exercise
No full textWe provide a brief commentary to accompany Eys and Beauchamp’s (2025) work, (Re)conceptualizing cohesion: A theoretical realignment and roadmap for future research. We emphasize and extend the conceptual advances offered in the paper around four focal areas: the revised definition of cohesion (Propositions 1–2), the repositioning of individual attractions within a social identity framework (Proposition 3), the roles of task and outcome interdependence (Propositions 4, 5, 7) and teamwork processes (Proposition 6). Building on these propositions, we contribute to their conversation on theoretical refinement and methodological rigor. Specifically, we highlight cohesion theory can be further strengthened with dedicated research on social cohesion and more precisely placing (group) goals within cohesion theory. As mentioned by the authors, there is a need to conduct targeted studies in individual-sport contexts and utilize multilevel modeling to capture nested team dynamics in any group setting. We further consider that framing interdependence as a subjective, dynamic state offers novel insights into its relationship with cohesion. To enhance knowledge about cohesion and teamwork, we suggest measuring observable teamwork behaviors in combination with survey-based methods. We offer this commentary to enrich the paper’s conceptual foundation and support the evolution of cohesion research in the coming 40 years
The synergistic effects of demand-pull and supply-push policies on firm’s green innovation: Evidence from China
No full textAddressing escalating environmental challenges requires effective government intervention to guide firms toward green innovation. This study examines how such policies influence firms’ green innovation, focusing on the dual roles of supply-side (environmental subsidies) and demand-side (green public procurement) interventions. Using a dataset of Chinese A-share listed firms from 2015 to 2022, we find that the joint implementation of these policies generates a significant positive synergy, surpassing the additive effects of isolated interventions. The synergy effects are more pronounced in non-heavy-pollution industries, state-owned firms, and large-scale firms. Mechanism analysis reveals that this synergy operates by enhancing environmental strategic awareness, relaxing financing constraints, and mitigating the dual uncertainty of R&D and market returns. Additionally, green public procurement generates spillover effects at both industry and city levels, whereas environmental subsidies generate spillovers only within industries. Our study deepens the understanding of supply-side and demand-side green transformation support policies and provides important insights for policymakers aiming to promote sustainable development
The limits to state capital: on the hubris of state intervention in the age of Trump
No full textState capitalism is no longer the exception – it’s the rule. Across the political spectrum, from left to right, in democracies and autocracies alike, the state has reclaimed a dominant role in shaping economies. Industrial policies and national development plans are now openly embraced, with governments actively steering markets through state-owned enterprises, sovereign wealth funds and state-backed financial institutions. Where direct state involvement is absent, protectionist trade policies and strategic interventions safeguard national industries. This resurgence of state power has fractured the global economy, deepening divides between nations and heightening fears that escalating economic rivalries could spill over into geopolitical conflict. In this commentary, based on a FinGeo-RSA keynote presented at the Regional Studies Association annual conference in May 2025, I examine ideological justifications that legitimize this expanded state intervention, from mission-oriented governance to abundance agendas to conservative industrial policy. Drawing on insights from the liberal tradition – particularly Adam Smith and Friedrich Hayek – I argue that these contemporary frameworks systematically underestimate the knowledge problems inherent in centralised economic planning. I conclude by arguing for renewed engagement with liberal political economy within financial geographical research as a source of analytical scepticism for understanding the limits and possibilities of state intervention
Artificial Immune System Approaches to Classify Ambiguous Data on Device Quality
No full textSemiconductor devices must be characterised and measured to ensure that they perform in accordance with predefined specifications. The manual process of data-driven segregation of devices to detect anomalies is laborious and time-consuming. Therefore, there is an unmet need to automate data classification tasks in order to reduce the extensive manual review process. In order to address the issue of classifying MOSFET device characteristics, this paper explores the real-valued negative selection algorithm (RNSA) and the conservative self-pattern recognition algorithm (CSPRA), and proposes a CSPRA-SHAP classifier based on Shapley Addition interpretation (SHAP The results demonstrate that the CSPRA-SHAP classifier achieves significantly higher recall rates and accuracy in detecting and classifying anomalies than the traditional model
A Theory Repair Based Traffic Regulations Generalisation for Autonomous Vehicles
No full textThe development of AI products poses unique challenges to law reform, especially specific normative reform. In particular, some AI technologies like autonomous driving technologies blur the boundaries of these fundamental concepts of law, such as human beings, objects and behaviours. This gives rise to inconsistency and inapplicability of existing human-specific laws when turned to AI, such as irrational division of responsibility, conceptual ambiguities, conflicting guidance on behaviour, and conflicts with legislative intent. Considering the complexity and scale of the legal structure and how AI affects this structure, especially in a dynamic context, pure manual legal adjustments will naturally face difficulties in terms of accuracy and efficiency. This study therefore extended an automated theory repair system to design an intelligent legal aid system for the revision of driving rules in the context of automated driving and provided the code necessary to implement these functions
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