Carrier localization mechanisms in InGaN/GaN quantum wells

Localization lengths of the electrons and holes in InGaN/GaN quantum wells have been calculated using numerical solutions of the effective mass Schr\"odinger equation. We have treated the distribution of indium atoms as random and found that the resultant fluctuations in alloy concentration can localize the carriers. By using a locally varying indium concentration function we have calculated the contribution to the potential energy of the carriers from band gap fluctuations, the deformation potential and the spontaneous and piezoelectric fields. We have considered the effect of well width fluctuations and found that these contribute to electron localization, but not to hole localization. We also simulate low temperature photoluminescence spectra and find good agreement with experiment.Comment: 7 pages, 7 figure

Intelligent Resource Use to Deliver Waste Valorisation and Process Resilience in Manufacturing Environments

© 2020 Johnson Matthey Circular economy (CE) thinking has emerged as a route to sustainable manufacture, with related cradle-to-cradle implications requiring implementation from the design stage. The challenge lies in moving manufacturing environments away from the traditional linear economy paradigm, where materials, energy and water have often been designed to move out of the system and into receivership of waste management bodies after use. Recent applications of industrial digital technologies (IDTs: for example internet of things, data-driven modelling, cyber-physical systems, cloud manufacturing, cognitive computing) to manufacturing may be instrumental in transforming manufacturing from linear to circular. However, although IDTs and CE have been the focus of intensive research, there is currently limited research exploring the relationship between IDTs and the CE and how the former may drive the implementation of CE. This article aims to close the knowledge gap by exploring how an IDT (data-driven modelling) may facilitate and advance CE principles within process manufacturing systems, specifically waste valorisation and process resilience. These applications are then demonstrated through two real-world manufacturing case studies: (a) minimising resource consumption of industrial cleaning processes and (b) transforming wastewater treatment plants (WWTPs) into manufacturing centres

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

This paper develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data which comes principally from Risk metrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. They also allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal-posterior cingulate cortex-precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity

Legume based plant mixtures for delivery of multiple ecosystem services: An overview of benefits

As costs for mineral fertilizers rise, legume-based leys are recognised as a potential alternative nitrogen source for crops. Here we demonstrate that including species-rich legume-based leys in the rotation helps to maximize synergies between agricultural productivity and other ecosystem services. By using functionally diverse plant species mixtures these services can be optimised and fine-tuned to regional and farm-specific needs. Field experiments run over three years at multiple locations showed that the stability of ley performance was greater in multi-species mixtures than in legume monocultures. In addition, mixing different legume species in the ley helps to suppress both early and late weeds. Further, combining complementary phenologies of different legume species extended forage availability for key pollinator species. Finally, widening the range of legume species increases opportunities to build short term leys into rotations on conventional farms via cover cropping or undersowing

From ‘other’ to involved: User involvement in research: An emerging paradigm

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 The Author(s). This is an Open Access article. Non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly attributed, cited, and is not altered, transformed, or built upon in any way, is permitted. The moral rights of the named author(s) have been asserted.This article explores the issue of ‘othering’ service users and the role that involving them, particularly in social policy and social work research may play in reducing this. It takes, as its starting point, the concept of ‘social exclusion’, which has developed in Europe and the marginal role that those who have been included in this construct have played in its development and the damaging effects this may have. The article explores service user involvement in research and is itself written from a service user perspective. It pays particular attention to the ideological, practical, theoretical, ethical and methodological issues that such user involvement may raise for research. It examines problems that both research and user involvement may give rise to and also considers developments internationally to involve service users/subjects of research, highlighting some of the possible implications and gains of engaging service user knowledge in research and the need for this to be evaluated

Country differences in transmissibility, age distribution and case-fatality of SARS-CoV-2: a global ecological analysis.

Objectives The first COVID-19 pandemic waves in many low-income countries appeared milder than initially forecasted. We conducted a country-level ecological study to describe patterns in key SARS-CoV-2 outcomes by country and region and explore associations with potential explanatory factors, including population age structure and prior exposure to endemic parasitic infections. Methods We collected publicly available data and compared them using standardisation techniques. We then explored the association between exposures and outcomes using random forest and linear regression. We adjusted for potential confounders and plausible effect modifications. Results While mean time-varying reproduction number was highest in the European and Americas regions, median age of death was lower in the Africa region, with a broadly similar case-fatality ratio. Population age was strongly associated with mean (β=0.01, 95% CI, 0.005, 0.011) and median age of cases (β=-0.40, 95% CI, -0.53, -0.26) and deaths (β= 0.40, 95% CI, 0.17, 0.62). Conclusions Population age seems an important country-level factor explaining both transmissibility and age distribution of observed cases and deaths. Endemic infections seem unlikely, from this analysis, to be key drivers of the variation in observed epidemic trends. Our study was limited by the availability of outcome data and its causally uncertain ecological design

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuitsj els_1260 482..510

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases