The most remarkable difference between electric vehicles (EVs) and conventional ones is the fuel burning dependency of the internal combustion engine, while the emerging EVs operate on electric motors. These alternations create staggering shifts in both lubricants' market demand and performance specifications. Lubricants for electrical powertrain constitutes greases, transmission oils, and lubricants for auxiliary systems and do not rely on engine oils as internal combustion vehicles. The new standards will be more focused on lubricants' electrical properties such as breakage voltage and conductivity, coupled with tribological performance under high rpm, corrosion resistance and thermal management benchmarks. This paper thematically reviews the different studies performed with nanolubricants, and how they match EVs' operational requirements. Conclusions from this study can be considered as guidelines for the potential application of nanolubricants in EVs and possible future research that can be accomplished on the topic.

In the last few decades, in the lubricant industry, the request for new performing additives has been becoming imperative. In this scenario, control at the nanoscale can be the key factor for the improvement of more efficient nanolubricants. Herein, after a discussion about the nanoparticles’ four main lubrication mechanisms, considerable attention is devoted to the usage of reduced graphene oxide/graphene oxide (rGO/GO) nanosheets in tribology. Moreover, graphene surface functionalization is reviewed, also including unexplored results in the field of lubrication. As far as the literature is concerned, it can be postulated that rGO/GO nanosheets can reduce wear and friction. Wear reduction is obtained by deposition and film formation, while friction reduction is related more to the shear and lamination of the sheets on the contacting surfaces. Nevertheless, the two phenomena are interrelated and work in sync. In this context, it is of high importance to form a homogenous suspension for a continuous nanosheet supply after deposition and shearing. The focus of this review was placed on the main issues still to be overcome, e.g., the literature results in rationalization; dispersion stability enhancement; and finding the optimum concentration in the delicate balance of different components. Possible solutions for their efficient overcoming are eventually reported.

In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains where SOPPs naturally occur include natural language processing, speech recognition, and computer vision. Typical SOPPs have an extremely large label set, which grows exponentially as a function of the size of the output. Existing generalization analysis implies generalization bounds with at least a square-root dependency on the cardinality $d$ of the label set, which can be vacuous in practice. In this paper, we significantly improve the state of the art by developing novel high-probability bounds with a logarithmic dependency on $d$. Moreover, we leverage the lens of algorithmic stability to develop generalization bounds in expectation without any dependency on $d$. Our results therefore build a solid theoretical foundation for learning in large-scale SOPPs. Furthermore, we extend our results to learning with weakly dependent data.