Articles tagged with Cluster Analysis
A University of Utah-led study found a surge in anti-Asian hate language on Twitter between January and March 2020, with clusters of hateful tweets spreading across the US. The researchers identified 15 geographic regions where anti-Asian hate was statistically higher than expected.
A new study reveals that urban residents have a simplistic understanding of coastal ecosystems and are less likely to take pro-environmental actions. The research highlights an issue known as urbanized knowledge syndrome, which can harm natural ecosystems and undermine community resilience.
A new approach for measuring physician adherence to best practices has been developed, taking into consideration patient characteristics and physician training background. The study found that pulmonary and critical care medicine training was associated with higher recognition of ARDS, a form of lung failure.
Researchers developed an algorithm to classify 16 distinct adult sleep patterns based on arm acceleration data from 100,000 UK Biobank participants. These patterns can be used to diagnose insomnia and develop personalized treatment methods, offering new insights into human sleep behavior.
A new 'image analysis pipeline' called TDAExplore gives scientists rapid insight into how cells are changed by disease, using a combination of microscopy, topology, and artificial intelligence. This approach can provide objective information on cell changes, such as the movement of proteins like actin, even with limited training data.
Researchers identified clusters of asphyxia-associated neonatal mortality in cities with low per capita GDP, highlighting the need for targeted healthcare interventions. The study's methodology can help policymakers plan strategies to reduce avoidable newborn deaths.
Researchers developed an integrated framework combining single-cell and metagenomics to characterize microbes. The approach showed higher accuracy and precise binning, revealing more bacterial genera and intra-species diversity.
Researchers analyzed 25,766 film tropes and identified 42 cluster groups, with some associated with specific genres and high ratings. The study provides insights into the film industry's narrative patterns, potentially informing new film development and story generation.
Astronomers used Hubble and Gaia data to analyze the positions and velocities of stars in NGC 6397, finding strong evidence for invisible mass in the dense central regions. The bulk of this unseen mass is likely made up of stellar-mass black holes, rather than white dwarfs or neutron stars.
Researchers found that 73% of announced recalls occurred in clusters, with a 16-day gap between cluster formations. Leading recalls are associated with higher stock market penalties than following recalls, growing as time since the last cluster increases.
Researchers have identified a common approach to clustering, a phenomenon that leads to nonequilibrium multiscale assembling in biological systems. The proposed method could simplify studying cluster systems in both natural and artificial objects.
A recent study revealed neural clustering in V4 that preserves visual acuity, enabling the separation of local and global features. This discovery challenges existing models and offers new insights into how the brain processes visual information.
Observations of a nearby star-forming region have revealed that large stars are more prevalent than predicted by models. The study found that there were many more stars at high masses, with a top-heavy initial mass function expected to generate exotic objects like black holes and neutron stars.
Researchers found statistically elevated rates of pediatric cancers in urban areas within the Miami metro region and an area just west of the Everglades. The clusters showed differences in cancer types, racial composition, and timing, but not a clear link to environmental exposures.
A computer program has identified various subtypes of asthma, including one associated with poor quality of life and depression in obese women. The analysis used machine learning algorithms to track 112 variables for nearly 400 people, leading to potential personalized treatments.
Researchers at the University of York have developed a new technology allowing them to observe and analyze single atoms in dynamic experiments under realistic conditions. This breakthrough has significant implications for understanding reactions in physical sciences, medicine, and energy sources.
A new statistical technique has been developed to analyze all possible causes of severity in a traffic accident. The researchers used data-mining techniques, including Latent Class Clustering and Bayesian Networks, to identify the variables that affect accident severity.
Researchers create detection tool that spots flight glitches without prior knowledge of parameters to check. The technique uses cluster analysis to identify abnormal flights, allowing analysts to further inspect reports and prevent potential accidents.
A study analyzing over 61,000 Italian professors found significant nepotism in academia, with certain disciplines showing a high likelihood of hiring family members. The research suggests that nepotism is a pervasive problem, contributing to Italy's brain drain and poor university rankings.
University of Washington geophysicist George Parks analyzed Louis Frank's ultraviolet camera images and concluded that the white snow in space is likely instrument noise. A closer examination of the data revealed statistical evidence supporting this finding, suggesting that the 'snowballs' are not actual cosmic phenomena.
Starlight, a 3D visualization technology, was developed to help decision-makers navigate complex data and identify critical details. It enables users to analyze massive volumes of information across various media types, including structured and unstructured text, maps, digital data, video, and satellite imagery.