Articles tagged with Computer Modeling
Researchers analyze tidal disruption events (TDEs) to estimate the properties of supermassive black holes and stars. The CN22 model, proposed by Syracuse University researchers, provides a new way forward for understanding TDEs and their implications for galaxy evolution.
A new European research project, SMARTS, aims to improve air travel efficiency by redesigning flexible airspace sectors using artificial intelligence. The €2million project will create accurate predictive models and develop innovative sector configuration plans to reduce passenger delays, increase productivity, and lower emissions.
The METEOR Expedition M197 is a research project studying the Eastern Mediterranean Sea's future changes in response to climate change and human activities. The project investigates nutrient supply, marine ecosystems, and carbon export from surface to deep waters.
Researchers developed mathematical models based on real events data from the Swedish Trauma Registry, showing that AI models outperformed clinical outcomes. The study highlights the potential of AI-powered decision support to improve ambulance staff's ability to assess injury severity and potentially save more lives.
A new study uses machine learning and satellite imagery to create the first global map of large vessel traffic and offshore infrastructure, finding a remarkable amount of activity previously unknown. The analysis reveals that industrial fishing and transport activities are concentrated around Africa and south Asia.
New ASU research models the benefits of combining heat adaptation strategies with mitigation efforts to lessen extreme heat exposure in major US cities. The study finds that Northeast and Midwest regions can expect greater benefits, while Sun Belt cities face limited reductions.
A recent study using AI to analyze registry data on people's residence, education, income, health, and working conditions can predict life events such as personality and time of death. The model outperforms other advanced neural networks and provides precise answers despite ethical concerns about sensitive data and bias.
Klick Applied Sciences unveils LOVENet, an AI framework that rapidly identifies new therapeutic indications for existing drugs. The algorithm integrates large language model and structured knowledge graph technology to offer a fresh perspective on new potential applications.
Researchers used a fiber optic cable to study the Arctic seafloor's seismic structure and temperature. They identified areas with large amounts of ice and detected changes in temperature over seasons, which will help understand global climate change.
A NSF-funded project, MABLE, is developing a digital app using crowdsensing, AI, and robotics to empower individuals with responsive maps and turn-by-turn instructions. The app aims to improve accessibility and navigation for persons with visual or mobility impairments, such as those with low vision and wheelchair users.
The US Department of Transportation has awarded the University of Virginia's Center for Applied Biomechanics eight competitive research contracts totaling $4.1 million to further automotive safety research. The center will study demographic variations in injury risk, vehicle impact, and pedestrian fatalities.
A new study from MIT shows that computational models trained on auditory tasks display an internal organization similar to the human auditory cortex. Models trained on diverse tasks and background noise more closely mimic brain activation patterns.
Researchers found that space weather events can trigger 'wrong side' failures in rail signalling systems, which are more hazardous than 'right side' failures. This study highlights the need for the industry to consider the risks of space weather and explore mitigation strategies.
Researchers found that aging can accelerate evolution, favoring faster adaptation to changing worlds. This means senescence becomes an advantageous characteristic under natural selection.
Researchers from the University of Córdoba used machine learning models to predict reference evapotranspiration in Southern Spain until 2100. The projections indicate a significant increase in water needs, with air temperature being the key factor in calculating this parameter.
The University of Texas at Arlington is awarded a $1.1 million grant to train and mentor researchers in mathematical techniques for addressing cancer biology, neurology, and vector-borne diseases. The program aims to increase diversity in science research by recruiting underrepresented scholars.
A new AI-powered satellite analysis technique reveals the economic conditions of regions with limited data, such as North Korea. The approach combines human input with machine learning to provide detailed economic maps and monitor progress towards Sustainable Development Goals.
Researchers use AI to develop dynamic modeling of brain graphs, capturing dynamics in continuous time for more accurate predictions and personalized treatment of brain diseases. The project aims to track disease development in individual patients and identify biomarkers associated with brain disorders.
Researchers at West Virginia University are using artificial intelligence to analyze habanero peppers and develop new methods for predicting genetic traits. The goal is to improve crop yields and prevent genetic diseases, with potential applications in human health.
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.
A study led by Keck School of Medicine of USC used AI detection technology to analyze influencer content on TikTok between 2019 and 2022, finding an increase in posts that promote e-cigarettes. The prevalence of pod devices, e-juice flavor names, and nicotine warning labels increased significantly over time.
Dr. Ning Zhang's AntiFake tool uses adversarial techniques to make it difficult for AI tools to read voice recordings, preventing synthesis of deceptive speech. The tool has achieved over 95% protection rate against state-of-the-art speech synthesizers and is accessible to diverse populations.
A new framework demonstrates that proportionately more multi-homing consumers lead to significant efficiency gains when integrating two business platforms. However, this trend also creates higher barriers to entry for new platform firms and may require policy guidance to mitigate potential harms of platform mergers.
Researchers from IOCB Prague and MED-EL have created a complete computer model of the ear, allowing for detailed simulation of sound conversion and hearing processes. This model may help improve cochlear implants and better compensate hearing impairments.
A simulation study suggests that a soft drink tax in Germany would reduce sugar consumption and illness rates, including type 2 diabetes, resulting in significant financial savings. The estimated economic benefits of introducing such a tax range from 9.5 billion to 16 billion euros over the next two decades.
A computer simulation by Nagoya University researchers found that human behavior, such as lockdowns and isolation measures, influenced the evolution of new COVID-19 strains. The study discovered that SARS-CoV-2 variants with higher peak viral loads were more successful at spreading, but also had shorter infection durations.
The new AI system can reliably recognize symbols on cuneiform tablets, allowing for the search and comparison of multiple tablets. This breakthrough enables new research questions and access to ancient texts.
A paper by Anthony Chemero explains how AI thinking differs from human thinking, highlighting the limitations of large language models trained on biased data. Despite generating impressive text, these models can make up facts and produce biased outputs due to their lack of embodiment and understanding of context.
Scientists at the University of Copenhagen and University of Victoria have developed an AI formula to predict rogue waves, which can split apart ships and damage oil rigs. The new knowledge can make shipping safer by identifying the likelihood of being struck by a monster wave at sea.
A new tool called Facemap uses deep neural networks to relate mouse facial movements to neural activity in the brain. This allows researchers to track and quantify movements and correlate them with brain activity, bringing them one step closer to understanding how the brain uses persistent, widespread signals.
A new economic-pandemic model predicts that lockdowns and spontaneous risk reduction lead to similar trade-offs between health and the economy. The model, developed by an international team of researchers, accurately predicted death rates and economic impact in New York City during the first wave of the pandemic.
A study published in Advances in Atmospheric Sciences found that record-setting Canadian wildfires had a significant impact on air quality across the Northern Hemisphere. The research used numerical air quality models to simulate the dispersal of pollutants from the fires, revealing widespread effects beyond Canada and the US.
A novel robotic system developed by USC researchers can help clinicians accurately assess a patient's rehabilitation progress. The method generates an 'arm nonuse' metric using machine learning and a socially assistive robot to track how much a patient is using their weaker arm spontaneously.
The UTSA MATRIX AI Consortium has received a $2 million grant to create new AI models that rapidly learn, adapt, and operate in uncertain conditions. The team aims to bridge the gap between human brain processing efficiency and current AI limitations, enabling more efficient and adaptive AI systems.
Yu Yang's NSF-funded research aims to reduce vehicle emissions and promote the use of electric bikes and scooters by developing socially informed traffic signal control systems. The project involves a three-pronged method that uses low-cost mobile air-quality sensing, spatial-temporal graph diffusion learning, and reinforcement learnin...
Researchers at Oak Ridge National Laboratory used quantum biology and artificial intelligence to sharpen the CRISPR Cas9 genome editing tool, improving its efficiency on microbes. The new model revealed key features about nucleotides that enable better guide RNA selection.
Researchers at UC Berkeley introduce prediction-powered inference (PPI), a method to correct machine learning model output and provide valid confidence intervals. PPI allows scientists to incorporate AI predictions into their work without making assumptions about the model's limitations or data biases.
Researchers used computer modeling to analyze cancer incidence and mortality rates among diverse populations, confirming the validity of the approach. The studies aimed to address critical public health issues by identifying leverage points to increase equity in cancer burden among Black populations.
Researchers redesigned IVF needles to reduce fluid flow damage to eggs, improving oocyte collection and IVF success rates. The study uses computer models and mathematical simulations to optimize the design, which has been successfully tested in cattle, with plans for human trials.
Researchers developed a deep learning system to detect and predict joint space narrowing and erosions in hand radiographs of RA patients. The AI model achieved over 90% accuracy in detecting joints, but its performance may be lower than human radiologists for wrist joint analysis.
A UNIGE team has developed a super-model to simulate the spread of three green technologies in Swiss municipalities by 2050. The results show that Switzerland is unlikely to achieve zero net carbon emissions by 2050 without significant policy changes, highlighting the need for increased efforts and updated policies.
Research from Harvard John A. Paulson School of Engineering and Applied Sciences estimates that humans have increased atmospheric mercury levels sevenfold, with a pre-anthropogenic baseline of around 580 megagrams. Human emissions from coal-fired power plants and waste-incineration are responsible for the majority of this increase.
Researchers are combining biology, physics, computer science, and engineering to design electric circuits that mimic the brain's adaptive behavior. The goal is to create a more efficient AI application that can learn from history and adapt without significant energy consumption.
A team of scientists discovered two types of neurons in fruit flies and mice that enable them to identify distinct smells. With experience, these animals can learn to differentiate between very similar odors, a process that could improve machine-learning models and AI systems.
Researchers found self-supervised models generate activity patterns similar to mammalian brains, suggesting an organizing principle. The models learn representations of the physical world to make accurate predictions, potentially unlocking human-labeled data limitations.
Researchers analyzed whiteschist from the Dora Maira Massif to study rapid upward movements, revealing a sharp decrease in pressure or decompression. This suggests that UHP rocks may not have reached a depth of 120 kilometers before returning to the surface.
A new method called TWC-Swin effectively restores holographic images even under low spatial coherence and arbitrary turbulence, surpassing traditional convolutional network-based methods. The study demonstrates strong generalization capabilities, extending its application to unseen scenes.
A new study suggests that Venus once had plate tectonics similar to those on early Earth, which could have supported microbial life. The researchers used atmospheric data and computer modeling to show that the planet's current atmosphere and surface pressure would only be possible with an early form of plate tectonics.
A University of Córdoba team developed an algorithm that predicts student performance in online education, dividing students into four categories and providing personalized assistance. The algorithm uses ordinal classification and fuzzy logic to make more accurate predictions than previous models.
Researchers at MIT found that similarity-focused generative AI models falter when tasked with designing new products, highlighting the need to prioritize innovation in engineering tasks. By adjusting training objectives and metrics, AI can be an effective 'co-pilot' for engineers, enabling faster creation of innovative products.
Researchers developed a novel physical theory that can accurately predict protein folding, surpassing existing models like AlphaFold 2. The new model, WSME-L, can elucidate folding processes without limitations, enabling a comprehensive understanding of protein structures and behaviors.
A recent study discovered two subgroups of people with idiopathic generalized epilepsy, one experiencing highest incidence during sleep and the other during daytime. The researchers found that either dynamics of cortisol or sleep stage transition explained most of the observed distributions of epileptiform discharges.
A new computer model reconstructs the evolution of Alpine ice cover with unprecedented precision, allowing scientists to understand past climate interaction with glaciers. The simulation provides a direct visualization of phenomena, making them accessible to a wide audience.
Research investigates impact of storm fronts, tropical storms, and cyclones on ocean circulation, finding changes in atmospheric synoptic variability (ASV) slow down ocean circulation and decrease primary productivity. ASV variations also affect mixing of ocean's layers and strength of oceanic circulation systems.
A study reveals that variable C:N:P ratios of phytoplankton are essential for regulating dissolved oceanic nutrient ratios, while also influencing atmospheric CO2 levels on geological time scales. The findings challenge the commonly hypothesized strong link between phytoplankton and seawater nutrient ratios.
A team has developed a comprehensive platform called HydroBIM for digital design, intelligent construction, and smart operation of hydropower engineering projects. The platform improves efficiency by 1.5 to 2 times compared to conventional methods, enhancing cooperation, work efficiency, and information integration.
A new model describes microswimmer self-propulsion energy requirements, enabling optimized shape designs and applications in microfluidics, biophysics, and material science. The study reveals surprising similarities between artificial and natural shapes.
Researchers at the University of Virginia Health System are developing computer models to better understand the cellular processes and gene activity of multi-drug resistant bacteria Staphylococcus aureus and Pseudomonas aeruginosa. The goal is to identify vulnerabilities in these bacteria and advance the development of new treatments.
Researchers tested the feasibility of using locally run LLMs like Vicuna-13B to label key findings in chest X-ray reports while preserving patient privacy. The results showed moderate to substantial agreement with non-LLM computer programs, suggesting that these models can be a viable option for AI research.
A new generative model named scPoli enables multi-scale representations of cells and samples, facilitating the integration of high-quality large-scale datasets for novel biological insights and disease understanding. This model accelerates atlas building and usage, ultimately accelerating disease understanding and therapy development.