Nature has adapted different methods for surviving dry, arid, xeric conditions. It is the focus of this comparative review to pull together the relevant information gleaned from the literature that could be utilized to design moisture harvesting devices informed by biomimetics. Most water harvesting devices in current use are not informed by nature and those that do are usually based on a biomimetic principle that has been based on one species only. This review draws on the published literature to establish a list of species (animals (vertebrates/invertebrates) and plants) whose habitat is in mainly dry or arid regions and that are known to harvest airborne moisture. Key findings have been outlined and review comments and discussion set out. Following this, surface feature convergences have been identified, namely hexagonal microstructures, groove-like and cone-like geometries. This has been coupled with direction of water flow that is driven by surface energy. As far as the authors are aware, this convergent evolution has not been brought together in this manner before. In the future this information could be translated into an engineered device for collecting water from airborne sources.

The proliferation of generative artificial intelligence (GenAI) has disrupted academic institutions across the world, presenting transformative challenges for decision makers, and leading to questions around existing methods and practices within higher education (HE). The widespread adoption of GenAI tools and processes highlights an ongoing change to existing perceptions of the role of humans and machines. Academics have expressed concerns relating to: academic integrity, undermining critical thinking, lowering of academic standards and the threat to existing academic models. This study presents a mixed methods approach to developing valuable insight to the key underlying challenges impacting GenAI adoption within HE. The results highlight many of the key challenges impacting decision makers in the formation of policy and strategic direction. The findings identify significant interdependencies between the key underlying challenges associated with GenAI adoption in HE. We further discuss the implications in the findings of the high levels of driving power of the factors: (i) perceived risks from Large Language Model training and learning; (ii) the reliability of GenAI outputs in the context of impact on creativity and decision making; (iii) the impact from poor levels of GenAI platform regulation. We posit this research as offering new insight and perspective on the changing landscape of HE through the widespread adoption of GenAI.

Four species of cacti were chosen for this study: Copiapoa cinerea var. haseltoniana, Ferocactus wislizenii, Mammillaria columbiana subsp. yucatanensis and Parodia mammulosa. It has been reported that dew condenses on the spines of C. cinerea and that it does not on the spines of F. wislizenii, and our preliminary observations of M. columbiana and P. mammulosa revealed a potential for collecting dew water. This study found all four cacti to harvest dew on their stems and spines (albeit rarely on the spines of F. wislizenii). Dew harvesting experiments were carried out in the UK, recording an increase in cacti mass on dewy nights. By applying a ranking relative to a polymethyl methacrylate (Plexiglas) reference plate located nearby, it was found that C. cinerea collected the most airborne moisture followed by M. columbiana, P. mammulosa and F. wislizenii respectively, with mean efficiency ratio with respect to the Plexiglas reference of 3.48 ± 0.5, 2.44 ± 0.06, 1.81 ± 0.14 and 1.27 ± 0.49 on observed dewy nights. A maximum yield of normalized performance of 0.72 ± 0.006 l/m(-2) on one dewy night was recorded for C. cinerea. Removing the spines from M. columbiana was found to significantly decrease its dew harvesting efficiency. The spines of three of the species were found to be hydrophilic in nature, while F. wislizenii was hydrophobic; the stems of all four species were hydrophilic. The results of this study could be translated into designing a biomimetic water collecting device that utilizes cactus spines and their microstructures.