This paper demonstrates the existence of adverse selection in the group insurance market with no individual choice. We provide evidence against the “conventional wisdom” that group insurance mitigates adverse selection because of the mixture of high risks and low risks. We show, however,...

With the development of electrochemical energy storage systems with intrinsic safety, zinc–nickel batteries (ZNBs) have attracted widespread attention. However, the poor cycling life and shelf life restrict their further application. Herein, a porous-structured “Ryomen Sukuna” (RS)...

There is considerable debate about the sustainability of the hydraulic fracturing (HF) water cycle in North America. Recently, this debate has expanded to China, where HF activities continue to grow. Here, we provide a critical review of the HF water cycle in China, including water withdrawal...

Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis...

Even though blockchain as a research topic incites broad academic interest, there is arguably a balkanization that occurred along the natural delineation between public-permissionless blockchains and private-permissioned blockchains. IS researchers, for instance, have been more inclined to...

Zn–Ni battery is prospective green energy storage device. However, Zn(OH)42–, the dissolved product of ZnO in the electrolyte, redistributes during the cycle, leading to the shape changing of the anode. This issue severely limits the lifespan of zinc anodes. Despite the success in improving...

We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function. This function integrates high-frequency,...

Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis...