Tunable operation of a gain-switched diode laser by nonresonant self-injection seeding

In this letter, we report tunable operation of a gain-switched diode laser by nonresonant self-injection seeding from an uncoated glass slide used as an external cavity reflector. A spectral linewidth reduction from 11 to 0.05 nm has been achieved for picosecond pulses with little effect on other laser characteristics. Good agreement with numerical simulations based on a compound-cavity laser model is also reported

Nonresonant self-injection seeding of a gain-switched diode laser

We demonstrate step-tunable single-mode operation of a gain-switched diode laser by nonresonant self-injection seeding from an uncoated glass slide used as an external cavity reflector. A spectral bandwidth reduction from 11 mn to 0.05 nm and wavelength tunability has been achieved for picosecond (near-transform-limited) pulses with little effect on other laser characteristics. Good agreement with numerical simulations based on a compound-cavity laser model is also reported

The elements of a computational infrastructure for social simulation

Applications of simulation modelling in social science domains are varied and increasingly widespread. The effective deployment of simulation models depends on access to diverse datasets, the use of analysis capabilities, the ability to visualize model outcomes and to capture, share and re-use simulations as evidence in research and policy-making. We describe three applications of e-social science that promote social simulation modelling, data management and visualization. An example is outlined in which the three components are brought together in a transport planning context. We discuss opportunities and benefits for the combination of these and other components into an e-infrastructure for social simulation and review recent progress towards the establishment of such an infrastructure

Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach

Small area estimation and in particular the estimation of small area income deprivation has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold based as opposed to benefit count based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small area measures. This paper briefly reviews a number of approaches to small area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small area HBAI rates at the small area level in Wales in 2003-5. The paper discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results

UKRI open access review : consultation analysis

In August 2021, UKRI announced a new open access policy for publications that acknowledge funding from UKRI or any of its councils. UKRI held a public consultation on a draft open access policy in 2020. This report is the analysis of the responses, carried out by CFE Research

The prospects for environmental accounting and accountability in China

Foucault’s ideas on episteme change are used to help understand change taking place in China from the “industrial civilization” to an “ecological civilization”. If episteme change is taking place this could be reflected in the philosophies and attitudes of Chinese accountants and their environmental accounting work will be developing. The conclusions are that: China is slowly moving towards an ecological civilisation; based around the thinking of Chinese accountants an epistemic change is in evidence in tandem with an emerging interest in ancient Chinese philosophy; Chinese accountants’ engagement with environmental accounting and accountability is evidence of reduced specialisation

Developing an Individual-level Geodemographic Classification

Geodemographics is a spatially explicit classification of socio-economic data, which can be used to describe and analyse individuals by where they live. Geodemographic information is used by the public sector for planning and resource allocation but it also has considerable use within commercial sector applications. Early geodemographic systems, such as the UK’s ACORN (A Classification of Residential Neighbourhoods), used only area-based census data, but more recent systems have added supplementary layers of information, e.g. credit details and survey data, to provide better discrimination between classes. Although much more data has now become available, geodemographic systems are still fundamentally built from area-based census information. This is partly because privacy laws require release of census data at an aggregate level but mostly because much of the research remains proprietary. Household level classifications do exist but they are often based on regressions between area and household data sets. This paper presents a different approach for creating a geodemographic classification at the individual level using only census data. A generic framework is presented, which classifies data from the UK Census Small Area Microdata and then allocates the resulting clusters to a synthetic population created via microsimulation. The framework is then applied to the creation of an individual-based system for the city of Leeds, demonstrated using data from the 2001 census, and is further validated using individual and household survey data from the British Household Panel Survey

Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours

Evolving consumer behaviours with regards to store and channel choice, shopping frequency, shopping mission and spending heighten the need for robust spatial modelling tools for use within retail analytics. In this paper, we report on collaboration with a major UK grocery retailer to assess the feasibility of modelling consumer store choice behaviours at the level of the individual consumer. We benefit from very rare access to our collaborating retailers’ customer data which we use to develop a proof-of-concept agent-based model (ABM). Utilising our collaborating retailers’ loyalty card database, we extract key consumer behaviours in relation to shopping frequency, mission, store choice and spending. We build these observed behaviours into our ABM, based on a simplified urban environment, calibrated and validated against observed consumer data. Our ABM is able to capture key spatiotemporal drivers of consumer store choice behaviour at the individual level. Our findings could afford new opportunities for spatial modelling within the retail sector, enabling the complexity of consumer behaviours to be captured and simulated within a novel modelling framework. We reflect on further model development required for use in a commercial context for location-based decision-making