The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent
nature of the solar resource highlights the importance of power forecasting for the grid …

Technologies have driven big data collection across many fields, such as genomics and
business intelligence. This results in a significant increase in variables and data points …

Characterizing the potential health effects of exposure to risk factors such as red meat
consumption is essential to inform health policy and practice. Previous meta-analyses …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

As a leading behavioral risk factor for numerous health outcomes, smoking is a major
ongoing public health challenge. Although evidence on the health effects of smoking has …

Purpose It is analyzed whether working from home improves or impairs the job satisfaction
and the work–life balance and under which conditions. Design/methodology/approach …

Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

While the field of electricity price forecasting has benefited from plenty of contributions in the
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …