We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
Online ordering will be unavailable from 17:00 GMT on Friday, April 25 until 17:00 GMT on Sunday, April 27 due to maintenance. We apologise for the inconvenience.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure [email protected]
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens. Still, there are limited numbers of studies using clustering techniques to analyse the recent Global Dietary Database. This study aims to extend an existing multivariate time-series clustering algorithm to allow for greater customisability and to provide the first cluster analysis of the Global Dietary Database to explore temporal trends in country-level nutrition profiles (1990-2018).
Design:
Trends in sugar-sweetened beverage intake and nutritional deficiency were explored using the newly developed program ‘MTSclust’. Time-series clustering algorithms are different from simple clustering approaches in their ability to appreciate temporal elements.
Setting:
Nutritional and demographical data from 176 countries were analysed from the Global Dietary Database.
Participants:
Population representative samples of the 176 in the Global Dietary Database.
Results:
In a 3-class test specific to the domain, the MTSclust program achieved a mean accuracy of 71.5% (Adjusted Rand Index [ARI]=0.381) while the mean accuracy of a popular algorithm, DTWclust, was 58% (ARI=0.224). The clustering of nutritional deficiency and sugar-sweetened beverage intake identified several common trends among countries and found that these did not change by demographics. Multivariate time-series clustering demonstrated a global convergence towards a Western diet.
Conclusion:
While global nutrition trends are associated with geography, demographic variables such as sex and age, are less influential to the trends of certain nutrition intake. The literature could be further supplemented by applying outcome-guided methods to explore how these trends link to disease burdens.
Thermohaline staircases are a widespread stratification feature that impacts the vertical transport of heat and nutrients and are consistently observed throughout the Canada Basin of the Arctic Ocean. Observations of staircases from the same time period and geographic region form clusters in temperature-salinity (T–S) space. Here, for the first time, we use an automated clustering algorithm called the hierarchical density-based spatial clustering of applications with noise to detect and connect individual well-mixed staircase layers across profiles from ice-tethered profilers. Our application only requires an estimate of the typical layer thickness and expected salinity range of staircases. We compare this method to two previous studies that used different approaches to detect layers and reproduce several results, including the mean lateral density ratio $ {R}_L $ and that the difference in salinity between neighboring layers is a magnitude larger than the salinity variance within a layer. We find that we can accurately and automatically track individual layers in coherent staircases across time and space between different profiles. In evaluating the algorithm’s performance, we find evidence of different physical features, namely splitting or merging layers and remnant intrusions. Further, we find a dependence of $ {R}_L $ on pressure, whereas previous studies have reported constant $ {R}_L $. Our results demonstrate that clustering algorithms are an effective and parsimonious method of identifying staircases in ocean profile data.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.