A Tale of Two Entrepreneurs: Understanding Differences in the Types of Entrepreneurship in the Economy

Policymakers and pundits who use entrepreneurship as a "catch-all" phase to capture a single economic activity make an important mistake. There are two distinct types of entrepreneurship with different economic roles, requiring individually tailored policies to support each. This report examines the difference between IDE Entrepreneurship (innovation-driven enterprises) and SME Entrepreneurship (small and medium enterprises) and the type of policies required to support each

Defining social exclusion in Western Sydney: exploring the role of housing tenure

Over the past decade social exclusion has increasingly been positioned at the forefront of political, academic and lay discourse as the cause of disadvantage. While the definition, measurement and solutions to social exclusion remain open to debate, housing has progressively been positioned as a central variable creating neighbourhoods of exclusion. Much of this debate has positioned areas of public housing as the most disadvantaged and socially excluded neighbourhoods. However, the multiplicity of social exclusion questions the simple identification of areas of public housing as the most excluded. By exploring six dimensions of exclusion (neighbourhood, social and civic engagement, access, crime and security, community identify and economic disadvantage) we argue that there is relatively little difference between areas dominated by public housing and those characterised by private rental for each of these individual dimensions of exclusion (with a number of exceptions). Rather, it is the experience of multiple dimensions of exclusion which marks areas of public housing as unique

A Mathematical Model of Human Narrative

Understanding the stability and evolution of personality disorders, particularly narcissism, remains a critical challenge in psychology [1]. Traditional longitudinal approaches often focus on linear changes and rank-order stability, potentially overlooking nuanced patterns and oscillatory behavior [1, 2]. In this study, we adopt a dynamical systems approach to model the temporal evolution of narcissistic traits and other personality dimensions, capturing both linear and cyclic dynamics that static models might miss. Using eigenvalue analysis and numerical integration techniques [3, 5], we examine how traits such as superiority and exploitative tendencies exhibit growth trends, while others, like exhibitionism, demonstrate oscillatory or decreasing behavior. Our results contrast with previous meta-analytic findings [1], which reported that most personality disorder criteria decrease over time, with some stability in antisocial and obsessive-compulsive traits. Additionally, Grijalva et al. [2] found a consistent decline in narcissistic traits across the lifespan, with agentic, antagonistic, and neurotic narcissism all demonstrating negative growth trajectories. Unlike these prior works, our analysis highlights the coexistence of increasing, de- creasing, and oscillatory dynamics within individual traits, reflecting a more complex temporal landscape. This novel perspective not only questions the assumption of linear decline [1, 2], but also provides a framework to understand how personality dynamics may adapt or fluctuate in response to social and cultural influences. Our findings suggest that integrating dynamical systems theory into per- sonality research could lead to more nuanced therapeutic strategies [6], where clinicians capitalize on oscillatory periods of trait relaxation to foster breakthroughs in therapeutic settings. Future studies with larger datasets and extended observation periods are essential to generalize these find- ings and further explore the interplay between trait evolution and sociocultural dynamics

System framework for autonomous data processing onboard next generation of nanosatellite

Progress within nanosatellite systems development makes niche commercial Earth observing missions feasible; however, despite advances in demonstrated data rates, these systems will remain downlink limited able to capture more data than can be returned to the ground cost-effectively in traditional raw or near-raw forms. The embedding of existing ground-based image processing algorithms into onboard systems is non-trivial especially in limited resource nanosatellites, necessitating new approaches. In addition, mission opportunities for systems beyond Earth orbit present additional challenges around relay availability and bandwidth, and delay-tolerance, leading to more autonomous approaches. This paper describes a framework for implementing autonomous data processing onboard resource-constrained nanosatellites, covering data selection, reduction, prioritization and distribution. The framework is based on high level requirements and aligned to existing off-the-shelf software and international standards. It is intended to target low-resource algorithms developed in other sectors including autonomous vehicles and commercial machine learning. Techniques such as deep learning and heuristic code optimization have been identified as both value-adding to the use cases studied and technically feasible. With the framework in place, work is now progressing within the consortium under UKSA Centre for Earth Observation and Instrument funding to deliver an initial prototype data chain implemented within a representative FPGA-based flight computer system