Articles tagged with Computer Modeling
Researchers use machine learning to combine mismatched datasets and reduce variation by over 95%, retaining meaningful differences. The approach has potential to provide deeper understanding of normal metabolism and identify biomarkers for disease.
Researchers at UBC Okanagan have developed a new modeling framework called TOSCA, which helps improve wind energy forecasts and productivity. The framework can capture the interaction between large wind farms and the oncoming wind, leading to more accurate estimates of power output.
Researchers at George Mason University are exploring security concerns around prompts for large language models (LLMs), focusing on attack and defense strategies with explainable AI and human involvement.
Researchers use advanced electron microscopy and computational modeling to understand tantalum oxide formation, which can impede qubit performance. The study reveals a 'suboxide' layer at the interface between tantalum and oxide, with ordered crystalline lattice features.
Researchers created an AI-enabled model to help mitigate global ammonia emissions from agriculture. By optimizing fertilizer management, the model can effectively reduce emissions by up to 38%, with Asia having the highest potential for reduction.
Researchers at University of Virginia Health System developed a new approach to machine learning that identifies drugs minimizing harmful scarring after heart attacks. The tool predicts and explains drug effects for other diseases as well.
A multidisciplinary team created a new algorithm to provide optimal personalized treatment options for individuals with esophageal cancer. The framework uses various heterogeneous factors that facilitate the progression of the disease and demonstrated high accuracy in controlling esophageal cancer.
Researchers developed a novel AI technique to detect Medicare fraud in big data, using Random Undersampling and supervised feature selection. The method improved classification performance, especially when reducing features, and outperformed models with all available features.
Physicists at Leipzig University have developed a neural network that uses active colloidal particles for artificial intelligence. The system reduces noise and increases efficiency in calculations by utilizing past states of the reservoir.
A new study by Northwestern University found that lab-trained AI models are easily misled by tissue contaminants, resulting in errors in diagnoses and vessel damage detection. The researchers suggest improving the problem of quantifying and addressing biological impurities in AI models to enhance accuracy.
Researchers developed an AI-driven algorithm that accurately predicts death and complications after percutaneous coronary intervention (PCI). The tool uses patient feedback to provide a patient-centered, individualized risk prediction score.
Researchers found that more extreme and frequent droughts could lead to a nearly five-fold increase in total area burned, from 231 sq mi in 2016 to over 17,000 sq mi by 2100. Fires would also become more frequent, returning to certain forest points every five years.
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.
Researchers at ISTA investigated the crucial set of synapses between neurons within the cerebellum, uncovering details of their function and development. The study used advanced techniques to look at the inhibitory synapses in great detail, revealing how they delicately influence the cell's signal output.
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.
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.
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.
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 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.
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.
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 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.
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.
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.
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.
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 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 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 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.
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 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.
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 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 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 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.
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.
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.
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 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 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.
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.
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.
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.