How Have Employment Transitions for Older Workers in Germany and the UK Changed?

Extending working life is an objective for many nations. However, the UK government has recently reported only modest improvement "compared to many nations". A comparison of European, Labour Force Surveys show that Germany has reversed early retirement much faster than the UK since 2003. This was not forecast by previous researchers. In particular, Ebbinghaus' influential cross-national analysis of early retirement, published in 2006, had predicted that liberal welfare states regimes like the UK would react faster than conservative ones like Germany. A review of changes to pensions and employment policies suggests the UK puts more emphasis on recruitment of older workers, flexible working and gradual retirement while Germany puts more emphasis on retention of older workers through age-management and employment protection. The paper compares the employment transitions of older workers using data covering 1993 to 2013 from the longitudinal surveys British Household Panel Survey, Understanding Society and the German Socio-Economic Panel. It finds little evidence for the recruitment of older workers or gradual retirement in either the UK or Germany and concludes it was the greater employment protection for older workers in Germany that enabled the employment rate for older workers to increase even during the recent recession

Deep learning in the construction industry: A review of present status and future innovations

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed