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...

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,...

This paper is intended to provide an assessment of the impact of the silent revolution of the last three decades on moving governments closer to people to establish fair, accountable, incorruptible and responsive governance. To accomplish this, a unique data set is constructed for 182 countries...

This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model...

The primary objective in the one-dimensional cutting stock problem is to minimize material cost. In real applications it is often necessary to consider auxiliary objectives, one of which is to reduce the number of different cutting patterns (setups). This paper first presents an integer linear...

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...