Articles tagged with Experimental Data
Researchers have made the first-ever observations of how lambda particles, a form of strange matter, are produced by a specific process called semi-inclusive deep inelastic scattering (SIDIS). The study reveals that diquarks, pairs of quarks and gluons, can march through atomic nuclei, contributing to the formation of lambdas.
Researchers analyzed 2,571 patients treated with monoclonal antibodies and found a 39% reduced risk of hospitalization or death compared to non-treated peers. The study also showed greater benefit in patients with immunocompromising conditions and those treated earlier during the pandemic.
The UTSA ScooterLab will collect data on riders' mobility, context and environment to improve sustainable transportation solutions. The project aims to transform the way we think about micro-mobility.
The ExPaNDS project is holding four topic-based webinars showcasing how efficient management of data can increase its value through sharing and reuse. The webinars cover Life Sciences, Cultural Heritage Science, Tomography/Imaging, and Industry topics.
Astronomers have cataloged over 51,863 Lyman-alpha-emitting galaxies, 123,891 star-forming galaxies, and 4,976 active galactic nuclei using HETDEX's spectroscopic data. The survey is a non-targeted, moon-sized survey that collects spectra from 35,000 fiber optic cables, providing a unique dataset for future galaxy mapping.
A team of researchers developed a model-free approach using deep reinforcement learning to optimize estimation of multiple parameters in quantum sensors. The protocol achieved significantly better estimations compared to nonadaptive strategies, demonstrating enhanced performance in resource-limited regimes.
New research computes first step toward predicting lifespan of electric space propulsion systems by developing a model that bridges scales between molecular dynamics simulations and experiments. The model resolves limitations and uncertainties in experimental data, gaining insight into critical phenomenon and surface morphology over time.
A recent study discovered a link between soluble urokinase plasminogen activator receptor (suPAR) and the development of atherosclerosis, a hardening of arteries that affects over a billion people worldwide. SuPAR was found to cause inflammation in blood vessels, leading to cardiovascular events and increasing risk of heart disease.
A new study explores the value of 'trash data' from cancer genome sequencing, identifying new strategies to uncover previously unexplored information. The researchers found that genomic and transcriptomic data contain relevant information that can help elucidate carcinogenesis and discover putative biomarkers with clinical applications.
The proposal aims to provide a central repository for battery test information, enabling researchers to use advanced data science methods to accelerate battery technology development. The availability of open-source information on batteries is limited, but the Battery Data Genome could help address this challenge.
Researchers have validated a new theory for molecular diffusion in polymer matrices, explaining how molecules move through complex media. The study found that temperature and molecule size significantly impact transport rates, enabling the design of more selective polymer membranes.
A comprehensive study compared the accuracy of human speaker identification with that of an automatic-speaker-recognition system. The forensic-voice-comparison system performed better than all tested listeners, including judges and jurors. This finding has significant implications for courtroom proceedings.
Recent CERN experiments provide evidence for the existence of new particles called pentaquarks, which consist of four quarks and one antiquark. The discovery raises the possibility that a whole new class of matter is at the cusp of being discovered.
Researchers discovered that humans automatically integrate extralinguistic information into grammatical processing, improving communication. The study used EEG data from 37 native Russian speakers, finding both linguistic and extralinguistic features processed simultaneously.
Researchers developed an electronic laboratory notebook that uses knowledge graphs to describe material properties and experimental processes. The platform enables automated analysis, lossless sharing, and discovery of new materials with potential applications in energy-related devices.
Research institutes from Finland, Germany, Norway, Spain, and Sweden have launched the Federated European Genome-phenome Archive (Federated EGA) to improve data sharing across national borders. The new platform enables secure access to sensitive human data for research while respecting national data protection regulations.
A high-precision experiment reveals that protons and neutrons in small nuclei prefer to pair up with others of the same kind more often than expected. The study provides new details about short-distance interactions between particles and may impact results from experiments seeking to tease out further nuclear structure details.
Researchers at IISc used widely-used computational techniques to predict and verify migration barrier values in lithium-ion batteries. They propose robust guidelines to choose accurate frameworks for testing materials. The study found SCAN functional had better accuracy overall, while GGA was faster but less accurate.
A Tel Aviv University study reveals that corals' fluorescence serves as a lure for plankton, which are then consumed by predators like corals. The researchers found that green-fluorescent corals were 25% more preyed upon than yellow-fluorescent ones.
Researchers observe a significant increase in electrical conductivity when mica is thinned down to few molecular layers, exhibiting semiconductor-like behavior. The findings suggest that thin mica flakes have the potential to be used in two-dimensional electronic devices with exceptional stability and durability.
Researchers reveal the likely composition of Charon's dynamic methane atmosphere and propose a possible source for its red polar zone. The team's novel experiments and atmospheric modeling suggest that ultraviolet light breaking down methane molecules is key to understanding the moon's unique albedo.
A recent study published in Angewandte Chemie found that AI models struggle to predict reaction yields due to biased data, mainly caused by a lack of reported failed experiments. The researchers attribute this failure to three possible causes: experimental error, personal bias, and underreporting of negative results.
Alexander Austregesilo, a staff scientist at Jefferson Lab, has been awarded a $2.5 million grant to study novel forms of nuclear matter within the spectrum of hadrons. He aims to develop new tools and resources to analyze large datasets generated by the GlueX experiment in search of exotic particles or hints of their existence.
The study uses a global climate system model to simulate large ensemble experiments, which capture internal variabilities' impact on surface air temperature and land precipitation. The research helps improve understanding of forced climate changes and provides guidance for policymakers.
A study conducted by Kaunas University of Technology researchers analyzed how people with Alzheimer's disease process visual information from faces. The study found that brain signals in patients are significantly noisier, making it harder for them to focus and be attentive.
Scientists have developed a machine learning algorithm that can accurately predict the lifetimes of different battery chemistries using as little as a single cycle of experimental data. The technique could reduce costs and accelerate the development of new battery materials, enabling researchers to quickly evaluate and test multiple ma...
A new study from MIT suggests that genome loops, which were believed to play a crucial role in controlling gene expression, are actually short-lived and fleeting. The researchers found that these loops only exist for about 3-6% of the time and last for only 10-30 minutes.
Researchers propose an open platform to integrate data collection, processing, and publication, ensuring FAIR (findable, accessible, interoperable, reusable) data and enabling machine-learning algorithms to learn from failed experiments. The platform aims to bridge the gap between data and machine learning in chemistry.
A team of researchers used the National Ignition Facility (NIF) to create a laboratory replica of galaxy-cluster plasmas, discovering strong suppression of heat conduction in these turbulent environments. The experiments provide insight into complex physics processes and raise additional questions that may be answered in future studies.
A study published in PNAS found that risk-tolerant and impatient individuals are more likely to commit crimes. The research used data from economic experiments involving over 7,000 young Danish men, who were shown to be more prone to theft and other property offenses.
Researchers from Argonne National Laboratory have created a set of new practices to guide the curation of high energy physics datasets, making them more FAIR and reusable. The goal is to automate the finding and use of data for humans and streamline the development of AI tools for scientific discovery.
Researchers developed a methodology to compress extensive genetic data libraries, reducing their size while preserving essential information.
Researchers at UTHealth Houston found online focus groups reduced barriers to participation, while maintaining high-quality data collection. The study also revealed a cost-effective alternative to in-person focus groups, with reduced resources needed.
A new study reveals that the Omicron variant is sensitive to inhibition by the interferon response, an unspecific immune reaction present in all body cells. This provides the first explanation for why COVID-19 patients infected with Omicron are less likely to experience severe disease.
A study published in Developmental Psychology found that short Taekwondo courses improved primary school children's self-regulation and reduced symptoms of conduct disorders. The classes were well-received by pupils and led to higher levels of value placed on self-control, according to the researchers.
A researcher developed a computer simulation to predict asteroid impacts and study crater formation. The simulation, built using NASA-designed methods, can generalize results to all metal asteroid impacts.
A Los Alamos National Laboratory team uses transfer learning to predict fault slip in laboratory earthquakes from limited field observations. The approach shows promise for predicting fault-slip behavior and possibly earthquakes in the field.
Scientists developed an all-season smart-roof coating that automatically switches between cooling and heating, outperforming commercial cool-roof systems in energy savings. The technology uses vanadium dioxide to regulate its rate of radiative cooling, overcoming the problem of overcooling in winter.
International researchers used machine learning to forecast marsh establishment under various environmental conditions, revealing that controllable local factors are more important than global climate change. The study suggests smart management of tidal flats can counteract threats and strengthen wetlands.
An international research team has measured neutron form factors with previously unattained precision, filling a blank space on the map. The new data provides a more comprehensive picture of the neutron's size and lifetime, and reveals oscillating patterns in its form factor.
An international team of scientists, led by Professor Owen Long, explored supersymmetry as an extension of the Standard Model. They conducted experiments at the Large Hadron Collider and found no signs of supersymmetric particles, but their null result is still a significant scientific progress.
A novel machine learning approach has been developed to understand symmetry and trends in materials, enabling researchers to group similar classes of material together. The technique uses a large, unstructured dataset gleaned from 25,000 images to identify structural similarities and trends.
Researchers from the University of Innsbruck found that Covid-19 pandemic did not completely displace other social and political concerns. Participants in a donation experiment donated more to causes when Covid-19 incidence was high, indicating a correlation between pandemic presence and willingness to donate.
The 2021 Fall Meeting of the APS Division of Nuclear Physics presents cutting-edge research on nuclear astrophysics, quantum technology, and rare isotopes. Researchers will discuss breakthroughs such as the most precise measurement of neutron lifetime and novel experiments measuring neutron skin in calcium.
Researchers used a supercomputer to emulate Google's quantum processor and discovered a reachability deficit, a performance limitation induced by a problem's constraint-to-variable ratio. The study showed that future experiments will require significantly more quantum resources to overcome this limit.
Researchers have measured the energy dependence of e+e- -> B-anti-B, B-anti-B* and B*-anti-B* reactions in a new energy range, providing insight into exotic Upsilon mesons. The data was collected by the Belle experiment and has the potential to shed light on the structure of highly excited states.
Researchers developed an algorithm to rank apps based on their privacy scores, allowing users to easily find and install non-intrusive apps. The system considers two scores: permission and listener access, providing a ranking of apps from least intrusive to most private.
A new study by Newcastle University researchers has developed dynamic DNA data structures that can store and retrieve information in an ordered way. The study presents an in vitro implementation of a stack data structure using DNA polymers, which stores and retrieves information in a last-in-first-out order.
Earthquakes generated by controlled fluid injection at the Rangely oil field were caused by destabilizing fault pore pressure changes, according to a new mechanistic model. The study revisits data from the decades-old project in light of increased seismicity due to fluid injection.
A Duke University study found that combining risk data with an imagination exercise helps participants make more realistic decisions about their own risky behaviors. The intervention, now integrated into a publicly accessible data dashboard, aims to improve public health decisions during the pandemic.
Researchers developed a novel evidence-based material recommender system that predicts high entropy alloy formation without data descriptors, overcomes data bias and poor availability. The method recommends an FeMnCoNi alloy as the most probable HEA and successfully synthesizes it, confirming its validity.
Researchers are exploring autonomous discovery techniques to reduce data required for scientific discovery, enabling faster and more efficient exploration of multi-dimensional parameter spaces. The field is gaining traction across various disciplines, including physics, chemistry, biology, materials science, and more.
A new theoretical model can reliably predict the properties of compounds at low temperatures using only two adjustable parameters. The model reproduces experimental data for thirty different systems, including simple glasses and complex organic compounds.
Researchers have developed a tool to predict the biological activity of any molecule using deep machine learning methods. The system integrates experimental data with AI models to complete incomplete information about bioactivity profiles, enabling the selection of suitable candidates for drug discovery.
Researchers developed P-Flash, an AI-powered tool predicting flashover in burning buildings. It uses temperature data from heat detectors and shows promise in anticipating simulated flashovers, identifying unmodeled physical phenomena that can improve forecasting in real fires.
A research team has successfully measured tritium production rates in a water-cooled ceramic breeder blanket mock-up, validating its design and function under D-T neutron environment. The experimental results are in good agreement with Monte Carlo simulations, meeting the requirements for tritium self-sustaining in future fusion reactors.
Researchers at the Chinese Academy of Sciences extracted the proton mass radius from experimental data, finding it to be 0.67 ± 0.03 femtometers, which is smaller than the charge radius.
Researchers use aerodynamic levitation and laser heating to suspend small samples of refractory oxides in mid-air, allowing for precise data collection. Machine learning algorithms are then used to predict structural changes and interactions between atoms at high temperatures.
The study determines that the ALT rotational transformation accurately describes the relativistic effects in rotating frames, revealing a fundamental framework for spacetime. The results confirm length contraction and time dilation within an absolute simultaneity framework.
Scientists from several Chinese universities investigated the physics of stone skipping and its relation to space vehicle reentry. They found that vertical acceleration and spinning direction affect bouncing and surfing motions, providing new insights for aerospace and marine engineering.