Articles tagged with Statistical Clustering
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Researchers used machine learning to classify older adults in Japan by physical and cognitive functions. Five functional subtypes were identified, including severe multicomponent and moderate physical types, which showed higher risks of death and hospitalization respectively.
A cluster randomized trial involving 31 Canadian high schools and 3,800 students found that delivering personality-targeted brief cognitive behavioral interventions to students in the 7th grade reduced risk for substance use disorders by the end of high school. The intervention was associated with a 23%-80% reduced odds of SUD compared...
A new study found that routine laboratory tests are not reliable for diagnosing Long COVID, with no biomarker among 25 clinical laboratory values providing a clinically useful diagnosis. The researchers suggest treating symptoms rather than relying on lab results to diagnose the condition.
Researchers identified six clinical subtypes in older adults starting long-term care in Japan, including cardiac disease, respiratory disease/cancer, and insulin-dependent diabetes, which incur higher mortality risks and worsen care needs. These findings can inform optimal interventions for each subtype and influence healthcare policy.
Researchers at the University of Utah and Max Planck Institute have discovered an intermediate-mass black hole in the Omega Centauri cluster, providing crucial evidence for a long-theorized class of black holes. The discovery offers insights into galaxy evolution and the formation history of globular clusters.
A new study by David Atance and colleagues found that global mortality patterns are converging, with increased life expectancies and narrowing gender disparities. The researchers analyzed data from 194 countries and predicted continued convergence towards a unique pattern of mortality and longevity by 2030.
A new method can help estimate the prevalence of disease in free-ranging wildlife by accounting for animal clustering. This approach may reduce the number of samples needed to detect a disease. The researchers focused on Chronic Wasting Disease (CWD) in deer, which tends to cluster in family groups, making this method particularly useful.
Physicists investigate systems of self-propelled particles whose speed depends on orientation, discovering a series of new effects, including spontaneous cluster formation with permanent flow and programmable shapes. The findings have practical importance for technical applications, such as realising programmable matter.
Researchers at National Korea Maritime and Ocean University developed a close contact identification algorithm that outperforms conventional clustering algorithms. The algorithm enables accurate tracking and physical isolation of individuals in ship environments, contributing to the health and safety of passengers.
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.
Stepped wedge cluster randomized trials offer statistical power, incentivized recruitment, and staggered resource allocation. However, challenges include time-sensitive recruitment, retention issues, and the Hawthorne effect.
Researchers at Penn State found that Beowulf clusters, composed of low-cost personal computers linked together, can offer supercomputer-like capabilities. The clusters can run multiple processors in parallel, providing a cost-effective solution for scientists who need to run complex codes.
A new data classification scheme based on human perception is being incorporated into a hand-held chemical sensor system. This method, called VERI, can group real-world objects seen near each other by superimposing an invisible dumbbell shape, allowing for quick and accurate identifications.
A new method for clustering data on computers has been developed by Prof. Eytan Domany, enabling the analysis of vast amounts of information without prior knowledge of its structure or categories. The algorithm mimics human intuition and can automatically identify clusters in various types of data.
The discovery of EUV emissions in the Coma cluster suggests a large cloud of cooler matter, totaling up to 10 trillion of our Suns, which could help clear up a major problem with these galactic groupings. The findings also hint at the presence of normal baryonic matter instead of dark matter or exotic particles.
Scientists successfully modeled the evolution of a massive X-ray galaxy cluster using CTC's SP, achieving unprecedented complexity and resolution. The simulation explores the collapse of high-density regions in a cube of 256 million light years on a side, incorporating gravity and hydrodynamics.