Delayer Pays Principle: Examining Congestion Pricing with Compensation

Despite its virtues, congestion pricing has yet to be widely adopted. This paper explores the issues of equity and use of toll revenue and several possible alternatives. The equity and efficiency problems of conventional (uncompensated) congestion pricing are outlined. Then, several alternatives are discussed and developed. A new compensation mechanism is developed, called the delayer pays principle. This principle ensures that those who arecause delay to others pay a toll to compensate those who are delayed. We evaluate the effectiveness of this idea by simulating alternative tolling approaches and evaluating the results across several measures, including delay, social cost, consumer surplus, and equity. Different tolling approaches can satisfy widely varying policy objectives, thus this principle is applicable in diverse situations. Such a system is viable and can eliminate some common hurdles of congestion pricing while remaining revenue neutral.

Power to the Workers? A qualitative study of workers' experiences of a 4-day working week

The pandemic has resulted in seismic shifts to all aspects of our lives, including views concerning the organisation of work. One impact is the acceleration of workers questioning traditional life stages, of work then retirement, and what they want out of life (Cable & Gratton, 2022) As quality of life is acknowledged as a driving force for many employees leaving their current jobs (Fuller & Kerr, 2022), the implications of a four-day working week are currently being investigated (Miller, 2022). Drawing on a small-scale study at an automotive supplier, based in the North-East of England, this case study will present findings from qualitative interviews conducted with employees who are experiencing a newly established 4-day working week. From the findings presented, discussions will highlight implications of this shift in the organisation of the working week for employees across the organisation. It is intended that the findings and discussions will raise relevant, contemporary questions for the business community more generally

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

Introduction Undiagnosed diabetes is a global health issue. Previous studies have estimated that about 24.1%–75.1% of all diabetes cases are undiagnosed, leading to more diabetic complications and inducing huge healthcare costs. Many current methods for diabetes diagnosis rely on metabolic indices and are subject to considerable variability. In contrast, a digital approach based on retinal image represents a stable marker of overall glycemic status.Research design and methods Our study involves 2221 subjects for developing a classification model, with 945 subjects with diabetes and 1276 controls. The training data included 70% and the testing data 30% of the subjects. All subjects had their retinal images taken using a non-mydriatic fundus camera. Two separate data sets were used for external validation. The Hong Kong testing data contain 734 controls without diabetes and 660 subjects with diabetes, and the UK testing data have 1682 subjects with diabetes.Results The 10-fold cross-validation using the support vector machine approach has a sensitivity of 92% and a specificity of 96.2%. The separate testing data from Hong Kong provided a sensitivity of 99.5% and a specificity of 91.1%. For the UK testing data, the sensitivity is 98.0%. The accuracy of the Caucasian retinal images is comparable with that of the Asian data. It implies that the digital method can be applied globally. Those with diabetes complications in both Hong Kong and UK data have a higher probability of risk of diabetes compared with diabetes subjects without complications.Conclusions A digital machine learning-based method to estimate the risk of diabetes based on retinal images has been developed and validated using both Asian and Caucasian data. Retinal image analysis is a fast, convenient, and non-invasive technique for community health applications. In addition, it is an ideal solution for undiagnosed diabetes prescreening