Greenbury Report (UK)

The Greenbury Report on Directors Remuneration (1995) (hereafter called the Greenbury Report) was one of the first comprehensive governance codes directly addressing executive and director remuneration. The Greenbury Report was commissioned by the Confederation of British Industry in response to public concerns over recently privatized public utilities and the salaries and bonuses earned by executives, while they implemented job cuts, and service price increases. The Greenbury Report recommended an independent remuneration committee, linking executive pay to corporate financial and operational performance measures, and increased the requirements for disclosure and transparency on directors’ remuneration. However, the credibility of the Greenbury Report was challenged due to the composition of the group; it was not deemed to be independent of the sector it was to investigate, and it was argued that its recommendations did not go far enough. The financial crisis of 2008 highlighted the failure of the Greenbury Report’s recommendations for limiting excessive executive pay. In particular, the Walker Review of the Banking Sector found that performance-based bonus schemes in banking corporations that are supposed to align executive objectives with shareholder objectives increased corporate risk in the period leading up to the financial crisis. In addition, during the crisis, executive pay in large publicly listed corporations (PLCs) continued to increase, while workers’ wages stagnated. Therefore, despite Greenbury’s recommendations, executive pay continued, and still continues, to be a concern for the public and policymakers alike. Nonetheless, improved transparency on remuneration and a greater linking of pay to performance followed from the Greenbury Report and most corporations now include operational measures linked to performance and sustainability

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.</p