Fiber-based coalescers are widely used to accumulate droplets from aerosols and emulsions, where the accumulated droplets are typically removed by gravity or shear. This Letter reports self-propelled removal of drops from a hydrophobic fiber, where the surface energy released upon drop coalescence overcomes the drop-fiber adhesion, producing spontaneous departure that would not occur on a flat substrate of the same contact angle. The self-removal takes place above a threshold drop-to-fiber radius ratio, and the departure speed is close to the capillary-inertial velocity at large radius ratios.

Hotspot cooling is critical to the performance and reliability of electronic devices, but existing techniques are not very effective in managing mobile hotspots. We report a hotspot cooling technique based on a jumping-drop vapor chamber consisting of parallel plates of a superhydrophilic evaporator and a superhydrophobic condenser, where the working fluid is returned via the spontaneous out-of-plane jumping of condensate drops. While retaining the passive nature of traditional vapor-chamber heat spreaders (flat-plate heat pipes), the jumping-drop technique offers a mechanism to address mobile hotspots with a pathway toward effective thermal transport in the out-of-plane direction.

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Supervised learning models are one of the most fundamental classes of models. Viewing supervised learning from a probabilistic perspective, the set of training data to which the model is fitted is usually assumed to follow a stationary distribution. However, this stationarity assumption is often violated in a phenomenon called concept drift, which refers to changes over time in the predictive relationship between covariates X and a response variable Y and can render trained models suboptimal or obsolete. We develop a comprehensive and computationally efficient framework for detecting, monitoring, and diagnosing concept drift. Specifically, we monitor the Fisher score vector, defined as the gradient of the log-likelihood for the fitted model, using a form of multivariate exponentially weighted moving average, which monitors for general changes in the mean of a random vector. In spite of the substantial performance advantages that we demonstrate over popular errorbased methods, a score-based approach has not been previously considered for concept drift monitoring. Advantages of the proposed score-based framework include applicability to any parametric model, more powerful detection of changes as shown in theory and experiments, and inherent diagnostic capabilities for helping to identify the nature of the changes.

No abstract