Canonical horizontal visibility graphs are uniquely determined by their degree sequence

Horizontal visibility graphs (HVGs) are graphs constructed in correspondence with number sequences that have been introduced and explored recently in the context of graph-theoretical time series analysis. In most of the cases simple measures based on the degree sequence (or functionals of these such as entropies over degree and joint degree distributions) appear to be highly informative features for automatic classification and provide nontrivial information on the associated dynam- ical process, working even better than more sophisticated topological metrics. It is thus an open question why these seemingly simple measures capture so much information. Here we prove that, under suitable conditions, there exist a bijection between the adjacency matrix of an HVG and its degree sequence, and we give an explicit construction of such bijection. As a consequence, under these conditions HVGs are unigraphs and the degree sequence fully encapsulates all the information of these graphs, thereby giving a plausible reason for its apparently unreasonable effectiveness

Cucurbita plants: From farm to industry

The Cucurbita genus, a member of Cucurbitaceae family, also known as cucurbits, is native to the Americas. Genus members, like Cucurbita pepo and Cucurbita maxima, have been used for centuries in folk medicine for treating gastrointestinal diseases and intestinal parasites. These pharmacological effects are mainly attributed to their phytochemical composition. Indeed, Cucurbita species are a natural source of carotenoids, tocopherols, phenols, terpenoids, saponins, sterols, fatty acids, functional carbohydrates, and polysaccharides, that beyond exerting remarkable biological effects, have also been increasingly exploited for biotechnological applications. In this article, we specifically cover the habitat, cultivation, phytochemical composition, and food preservative abilities of Cucurbita plants.This work was supported by CONICYT PIA/APOYO CCTE AFB170007. N. Martins would like to thank the Portuguese Foundation for Science and Technology (FCT-Portugal) for the Strategic project ref. UID/BIM/04293/2013 and “NORTE2020-Northern Regional Operational Program” (NORTE-01-0145-FEDER-000012)

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics

LL’s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1

Does AI-technology-based indoor environmental quality impact occupants’ psychological, physiological health, and productivity?

Over the past century, because of increased global travel, there high growth in the travel and tourism sector. But the outbreak of an ongoing pandemic has changed this scenario, which has put tremendous focus on the Indoor Environmental Quality (IEQ) embedded with the application of technologies, especially artificial intelligence (AI). This study aims to investigate the effect of AI-technology-based IEQ in the hospitality industry on occupants’ productivity through their psychological and physiological health. Drawing from Job demand - resource theory and Nudging philosophy, we formulated the hypoth-esis and conceptual model, which was empirically tested by structural equation model-ing (SEM). The results show that AI-technology-based IEQ is statistically significant in people’s behavioral change, which reflects on occupants’ health and productivity. Notably, AI-technology-based IEQ of the hospitality industry had a greater influence on occupants’ productivity, followed by their psychological and physiological health

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