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Categories: Geoscience: Oceanography, Mathematics: Statistics

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New global modelling predicts the devastating socioeconomic impacts of future extreme coastal flooding for developing nations caused by climate change, with Asia, West Africa and Egypt facing severe costs in the coming decades.

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In the course of five years, citizens who went on sailing cruises to the Arctic surveyed and collected plastic debris that had washed up on the shores of Svalbard. This has now been analyzed. According to the findings, one third of the plastic debris which still bore imprints or labels allowing an analysis of their origin came from Europe, and much of that number from Germany.

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Researchers have pinpointed two intervals when ice and ocean conditions would have been favorable to support early human migration from Asia to North America late in the last ice age, a new paper shows.

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The Arctic is rapidly losing sea ice, even during winter months when temperatures are below freezing and ice should be recovering from the summer melt. A new study found powerful storms called atmospheric rivers are increasingly reaching the Arctic in winter, slowing sea ice recovery and accounting for a third of all winter sea ice decline, according to a team led by Penn State scientists.

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Researchers tested a large sample of the major AI technologies available today and found that not only did they reproduce human biases in facial age recognition, but they exaggerated those biases.

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Synthetic populations are computer-generated models that mimic real-world populations in terms of characteristics such as age, gender, and occupation; they are useful when conducting social simulations. In a recent study, researchers developed a new approach to assign workplaces to individuals in a synthetic Japanese population with household information, based on ODI (Origin-Destination-Industry) data. Their efforts will enable more accurate, realistic simulations of the day-time distribution of workers in Japan, helping to improve decision-making and planning.

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A new paper presents a mathematical model for the motion of bacteria that includes cell division and death, the basic ingredients of the cell cycle.

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A new study shows machine-learning models trained using simple symptoms, demographic features are effective in predicting COVID-19 infections.

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A new statistical method provides a more efficient way to uncover biologically meaningful changes in genomic data that span multiple conditions -- such as cell types or tissues.

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America was unprepared for the magnitude of the pandemic, which overwhelmed many counties and filled some hospitals to capacity. A new study suggests there may have been a mathematical method, of sorts, to the madness of those early COVID days.

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People often wonder why one nutritional study tells them that eating too many eggs, for instance, will lead to heart disease and another tells them the opposite. The answer to this and other conflicting food studies may lie in the use of statistics, according to a new report.

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Researchers followed the birth and death of tens of thousands of nematode worms using the 'Lifespan Machine', which collects lifespan data at unprecedented statistical resolution. They found that worms have at least two distinct 'biological ages', and that these have consistent correlations between each other, suggesting the existence of an invisible hierarchical structure that regulates the ageing process. The findings challenge the idea of living organisms having a single, universal biological age. It also means mean that biomarkers used to assess biological age can be changed by interventions such as diet, exercise, or drug treatments without actually turning a 'fast ager' into a 'slow ager'. The study calls into question the use of ageing biomarkers -- what exactly are they measuring?

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The contact.engineering platform enables users to create a digital twin of a surface and thus to help predict, for example, how quickly it wears out, how well it conducts heat, or how well it adheres to other materials.

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A commentary advocates the proper application of artificial intelligence in healthcare and warns of the dangers when machine learning algorithms are misused.

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Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to improvements in computer performance and its potential applications in artificial intelligence. However, this statistical approach requires complex calculations of integrals, which are often considered approximations only. In their new study, the research team successfully derived new exact integral formulas. Their findings contribute to improving the accuracy of methods to identify data symmetries, possibly extending their applications to wider areas of interest, such as genetic analysis.

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A groundbreaking Bayesian method and new statistical analyses of genomic data from geckos in the Philippines shows that during the ice ages, the timing of gecko diversification gives strong statistical support for the first time to the Pleistocene aggregate island complex (PAIC) model of diversification, or 'species pump.'

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Fine-tuning the thermal conductivity of amorphous silicon used in technologies such as solar cells and image sensors should become much easier thanks to the computational topology and machine-learning-assisted discovery of the relationship between nano-scale structures and physical properties.

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A quantum system with only 51 charged atoms can take on more than two quadrillion different states. Calculating the system's behavior is child's play for a quantum simulator. But verifying the result is almost impossible, even with today's supercomputers. A research team has now shown how these systems can be verified using equations formulated in the 18th century.

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The famous double slit experiment shows that particles can travel on two paths at the same time -- but only by looking at a lot of particles and analysing the results statistically. Now a two-path-interference experiment has been designed that only has to measure one specific particle to prove that it travelled on two paths.

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Mathematicians have developed statistical methods that lay the framework for the crucial first step of determining whether there are any linkages between exposures and health outcomes from the 2010 Deepwater Horizon oil spill, which is considered the largest marine oil spill in the history of the U.S.