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Epistemology of religion and phenomenology of revelation in post-revolutionary Iran: The case of Abdolkarim Soroush
Get PDFLessons from Ancient Indian Scriptures for Business and Society
No full textThe current worldwide Covid-19 health crisis has brought upon us, like few before, eye-opening response choices. It demanded a sharp focus on the immediate human condition and, indirectly, revived cli- mate concerns. However, the return to the malaise of non-inclusive growth and reducing overall wellbeing looms large. Discussing the relationship of growth with happiness and wellbeing in the modern world, this chapter presents institutional lack of trust and inclusion as the cause for reducing wellbeing. It proposes lessons from ancient Indian scriptures for institutional transformation towards inclusivity and happiness
Spatiotemporal gender differences in urban vibrancy
No full textUrban vibrancy is the dynamic activity of humans in urban locations. It can vary with urban features and the opportunities for human interactions, but it might also differ according to the underlying social conditions of city inhabitants across and within social surroundings. Such heterogeneity in how different demographic groups may experience cities has the potential to cause gender segregation because of differences in the preferences of inhabitants, their accessibility and opportunities, and large-scale mobility behaviours. However, traditional studies have failed to capture fully a high-frequency understanding of how urban vibrancy is linked to urban features, how this might differ for different genders, and how this might affect segregation in cities. Our results show that (1) there are differences between males and females in terms of urban vibrancy, (2) the differences relate to `Points of Interest` as well as transportation networks, and (3) that there are both positive and negative `spatial spillovers` existing across each city. To do this, we use a quantitative approach using Call Detail Record data--taking advantage of the near-ubiquitous use of mobile phones--to gain high-frequency observations of spatial behaviours across the seven most prominent cities of Italy. We use a spatial model comparison approach of the direct and `spillover` effects from urban features on male-female differences. Our results increase our understanding of inequality in cities and how we can make future cities fairer
Human Machine Interaction Using Zero Force Sensing Switches Incorporating Self-adaptation
Get PDFA novel human machine interface is presented that ‘self-adapts’ to accommodate for changes in position between an operator and a non-contact sensor. Zero force sensing has been especially suitable for people with small amounts of movement force, making switch operation difficult or impossible. A common issue with existing switches concerned maintaining a workable operating position for a user. Testing of new “auto adapting” sensors demonstrated the viability of the approach and optical sensors provided a workable solution, but problems were encountered in strong light. Further work addressed this problem
Towards inferring network properties from epidemic data
Get PDFEpidemic propagation on networks represents an important departure from traditional mass-
action models. However, the high-dimensionality of the exact models poses a challenge to
both mathematical analysis and parameter inference. By using mean-field models, such as the
pairwise model (PWM), the high-dimensionality becomes tractable. While such models have
been used extensively for model analysis, there is limited work in the context of statistical
inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-
recovered (SIR) epidemic can be used to infer disease- and network-related parameters. Data
from an epidemics can be loosely categorised as being population level, e.g., daily new cases, or
individual level, e.g., recovery times. To understand if and how network inference is influenced
by the type of data, we employed the widely-used MLE approach for population-level data and
dynamical survival analysis (DSA) for individual-level data. For scenarios in which there is no
model mismatch, such as when data are generated via simulations, both methods perform well
despite strong dependence between parameters. In contrast, for real-world data, such as foot-
and-mouth, H1N1 and COVID19, whereas the DSA method appears fairly robust to potential
model mismatch and produces parameter estimates that are epidemiologically plausible, our
results with the MLE method revealed several issues pertaining to parameter unidentifiability
and a lack of robustness to exact knowledge about key quantities such as population size and/or
proportion of under reporting. Taken together, however, our findings suggest that network-
based mean-field models can be used to formulate approximate likelihoods which, coupled with
an efficient inference scheme, make it possible to not only learn about the parameters of the
disease dynamics but also that of the underlying network
