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Previous studies have shown that sea-ice drift effectively promotes the onset of a globally ice-covered snowball climate for paleo Earth and for tidally locked planets around low-mass stars. Here, we investigate whether sea-ice drift can influence the stellar flux threshold for a snowball climate onset on rapidly rotating aqua-planets around a Sun-like star. Using a fully coupled atmosphere–land–ocean–sea-ice model and turning sea-ice drift on or off, circular orbits with no eccentricity (e = 0) and an eccentric orbit (e = 0.2) are examined. When sea-ice drift is turned off, the stellar flux threshold for the snowball onset is 1250–1275 and 1173–1199 W m−2 for e = 0 and 0.2, respectively. The difference is mainly due to the poleward retreat of sea ice and snow edges when the planet is close to the perihelion in the eccentric orbit. When sea-ice drift is turned on, the respective stellar flux threshold is 1335–1350 and 1250–1276 W m−2. This means that sea-ice drift increases the snowball onset threshold by ≈80 W m−2 for both e = 0 and 0.2, promoting the formation of a snowball climate state. We further show that oceanic dynamics have a small effect, ≤26 W m−2, on the snowball onset threshold. This is because oceanic heat transport becomes weaker and weaker as the sea-ice edge is approaching the equator. These results imply that sea-ice dynamics are important for the climate of planets close to the outer edge of the habitable zone, but oceanic heat transport is less important.

Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.