Articles tagged with Computer Modeling
Post-Acute Sequelae of COVID-19 research aims to track long-term health symptoms in survivors. A $3.7 million grant will support the development of self-supervised deep learning technologies to recognize post-COVID lung progression phenotypes.
Researchers developed a novel approach to predict therapeutic targets for aging and age-related diseases. They trained a domain-specific BioGPT model on biomedical literature, which improved its performance in identifying prospective targets.
Researchers from Imperial College London and the University of Nottingham used machine learning to identify 'atomic shapes' that form basic pieces of geometry in higher dimensions. The findings reveal unexpected patterns in these shapes and demonstrate the potential for machine learning to accelerate mathematical discoveries.
A machine learning model developed by researchers can predict lung cancer risk with an 83.9% sensitivity, identifying those who need screening for the disease. The model uses just three predictors: age, smoking duration, and pack-years of smoking, making it a simplified approach to determine high-risk populations.
A new AI model integrates imaging and non-imaging patient data for improved diagnostic performance on chest X-rays. The multimodal model outperformed other models for diagnosing up to 25 conditions, showing potential as an aid to clinicians in high-pressure diagnoses.
A new AI method leverages causal relationships in genome regulation to efficiently identify optimal genetic perturbations for cellular reprogramming. The technique reduces experimental costs by prioritizing the most informative interventions, leading to faster convergence and more effective results.
A University of Virginia team has developed a new analytical tool using hydrogels to cultivate vascular sprouting from mouse lung tissue, providing new insight into idiopathic pulmonary fibrosis. The research aims to understand the biomechanical and biochemical cues to blood vessels in the lungs during disease progression.
A new modeling method powered by interconnected processors removed human bias from the debate over dinosaurs' demise. The study suggests that the outpouring of climate-altering gases from the Deccan Traps alone could have been sufficient to trigger global extinction, consistent with volcanic eruptions contributing to the mass extinction.
A new clinical and research partnership has created an AI model that can predict whether cancerous tissue has been fully removed from the body during breast cancer surgery. The model performed as well as humans in identifying positive margins, especially in patients with higher breast density.
Scientists developed computational eye models to help patients and surgeons select ideal intraocular lenses and predict visual outcomes. The technology uses anatomical information of the patient's eye to provide guidance on expected optical quality post-operatively.
A recent study found that dopamine release makes decisions faster but tends to be less accurate. Researchers analyzed response times to understand the role of dopamine in decision-making processes, revealing a speed-accuracy trade-off.
The project aims to assess the operational resilience of microgrids on DoD installations and ships, using new operational resilience indexes developed by Lehigh University researcher Javad Khazaei. The team will develop a dashboard to monitor resilience indexes in real-time, providing recommendations for improving the systems.
Researchers have successfully solved a problem in graph theory that has attracted attention from within the field. The team's research involves packing coloring, which deals with labelling parts of a graph to comply with certain rules and avoid specific conflicts.
Researchers developed an AI foundation model for eye care that can identify sight-threatening eye diseases and predict general health. The model, RETFound, was trained on millions of eye scans from the NHS and outperforms existing state-of-the-art AI systems across complex clinical tasks.
A digital twin of the bladder has been developed to simulate normal and bladder outlet obstruction (BOO)-affected function. The model will help researchers better understand the connection between changes in BOO bladder wall structure and functionality, enabling them to develop new treatments and predict treatment success rates.
A University of Houston study found that most of Houston's ozone exceedance is due to transported pollutants from the central and northern US, while local photochemistry contributes to elevated ozone production. The research highlights the importance of reducing emissions at the Houston Ship Channel to mitigate future ozone pollution.
A novel AI system developed by City University of Hong Kong improves predictive accuracy in dense traffic, reducing latency and increasing efficiency. QCNet achieves speed and accuracy in predicting road users' movements, even with long-term predictions, making autonomous driving safer and more human-like.
A team of scientists from Ames National Laboratory developed a new machine learning model that predicts Curie temperatures of new material combinations. This breakthrough discovery is crucial for designing high-performance magnets with reduced critical materials.
A study published in Radiology found that combining short- and long-term breast cancer risk models using artificial intelligence can improve cancer risk assessment. The combined model showed an overall improved risk assessment for both interval and long-term cancer detection, identifying women at high risk for breast cancer.
A hybrid system of electronic encoding and diffractive optical decoding transmits optical information with high fidelity through random, unknown diffusers. The system outperforms traditional approaches that only utilize a diffractive optical network or an electronic neural network for optical information transfer.
The WVU team evaluated Code Interpreter's features, finding it accessible to students but limited for scientists working with biological data. The plugin breaks down barriers for coding, but lacks internet access and parallel processing capabilities.
A new climate modeling method called ensemble boosting can simulate a large set of extreme but plausible heat waves, providing a worst-case scenario for planning and preparation. This method helps prepare for the potential loss of tens of thousands of lives in extreme heat waves.
MIT researchers developed a new algorithm that can execute complex maneuvers like loops and rolls for tailsitter aircraft, enabling agile trajectories with fast-changing accelerations. The algorithm uses differential flatness to ensure feasibility and planning in real-time.
The University of Texas at El Paso has been awarded a $5.3M grant from the Air Force Research Laboratory to enhance digital engineering training programs. The project will provide pre-professional experiences to 200 students and offer courses in digital engineering.
A study by Cornell University researchers found that current methods for measuring malaria parasite multiplication rates vastly overestimate the actual rates, which has significant implications for vaccine efficacy and understanding of drug resistance.
Aerodynamic researchers at University of Illinois create wind tunnel experiment to study internal boundary layers and their impact on flow behavior. They identify a new internal boundary layer that changes the flow's behavior, providing insights into aerodynamics physics and improving turbulence models for complex designs.
A team led by Dr. Zixiang Xiong at Texas A&M University aims to understand the fundamental limits of learned source coding, a machine learning-based data compression method. They hope to develop more powerful compression methods for efficient use of wireless communication and less energy consumption by mobile devices.
A new study by Paula Mayer proposes a practical tool for identifying areas of high human-bear conflict in the Abruzzo region. The model considers factors such as habitat suitability, migration corridors, and human-made food resources to inform local measures promoting coexistence.
A new study found that a massive North Atlantic cooling event led to the disruption of early human occupation in Europe, with climate stress changing the course of early human history. The study used observational and modeling evidence to document the unprecedented climate anomaly.
Researchers used AI to accurately classify four subtypes of Parkinson's disease from patient-derived stem cells, with one subtype reaching an accuracy of 95%. The study suggests that personalized medicine and targeted drug discovery could be possible using this approach.
Researchers aim to create machine learning tools that can analyze and quantify shape information from images, enabling more accurate diagnoses and improving patient care. A new family of deep neural networks, called DSNNs, will be developed to tackle AI's blind spot in image analysis.
Researchers have developed a new explainable AI model to reduce bias and enhance trust in machine learning-generated decisions. The Pattern Discovery and Disentanglement (PDD) model can predict medical results with rigorous statistics and explainable patterns, leading to more reliable diagnoses and better treatment recommendations.
Researchers developed an AI model called OncoNPC that can analyze genetic data to predict cancer type and origin. The model accurately classified at least 40% of tumors with unknown origin, leading to a 2.2-fold increase in eligible patients for targeted treatments.
Researchers find that Acinetobacter baumannii can achieve significant functional modifications in protein complexes over short evolutionary time spans, particularly in hair-like cell appendages. This diversity may affect the pathogen's interaction with its environment and inform personalized therapies.
Researchers developed a generative AI tool, AniFaceDrawing, to assist users in creating high-quality anime portraits. The tool uses a sketch-to-image framework and employs stroke-level disentanglement to match raw sketches with latent vectors of the generative model.
Researchers developed a computational technique to quickly design and evaluate cellular metamaterial structures with unique properties. The new interface enables users to explore the entire space of potential shapes, allowing for faster development of complex materials.
A new open-source Python toolbox called simpleNomo has been made available, enabling the creation of nomograms directly from logistic regression coefficients. This facilitates the translation of research findings into practical use, particularly in resource-poor settings or areas without internet access.
A recent study revealed that attention plays a crucial role in shaping our conscious experience of the world. Researchers found that manipulating attention can modify an individual's ability to consciously perceive visual stimuli, highlighting the intricate relationship between attention and conscious perception. By using advanced neur...
Researchers at UCLA found that GPT-3 performs well on analogy problems, similar to human undergraduates, but struggles with understanding physical space. The AI model's ability to mimic human reasoning is unclear, raising questions about its underlying cognitive processes.
Researchers at the University of Pittsburgh have developed a system that uses fluid mechanics and chemo-mechanical processes to autonomously assemble hierarchical 3D structures. The system utilizes sticky bonds to drive self-organization, allowing for the construction of complex devices with minimal external intervention.
Researchers developed a divide & conquer approach to leads-to model checking, mitigating the state explosion problem and improving performance. The technique, DCA2L2MC, divides the reachable state space into smaller sub-state spaces, making it feasible for large-scale systems.
A study by University of Toronto researchers found that child language development and language evolution share a common cognitive foundation, based on a core knowledge base. The team built a computational model that predicts word meaning extension patterns across languages and time scales, highlighting the role of visual, associative,...
A new open-source software, NMSM Pipeline, enables clinicians and engineers to create personalized computer models of patient movement to optimize treatment designs. The software uses physics-based models to predict and optimize functional outcomes for patients with various mobility impairments.
A University of Central Florida engineer is leading a $3.3 million ARPA-E funded project to develop simulation software for floating offshore wind turbines. The goal is to improve turbine design and increase their use as a renewable energy source. The software will be licensed or commercialized and can be hosted on a university web page.
A team of Lithuanian researchers has created an AI-based system to facilitate the rehabilitation process for stroke patients. The system, which uses wearable equipment and electromyography (EMG) technology, enables patients to track their progress and receive feedback on their exercises.
A new game theory model developed at ISTA finds that both information and ignorance can lead to cooperative outcomes in changing environments. The model, based on stochastic games and algebra, quantifies the benefits of ignorance in around 80-90% cases, highlighting counter-intuitive instances where it is optimal for cooperation.
A new editorial explores the potential of machine learning to enhance early cancer detection in primary care, leveraging extensive patient data and improving risk stratification accuracy. The authors emphasize the need for responsible implementation, collaboration, and validation across diverse populations.
Researchers developed an AI algorithm to predict the properties of new 2D materials with point defects, achieving 3.7 times greater accuracy than other machine learning algorithms. The model operates 1000 times faster than quantum mechanical computations and can handle multiple defects simultaneously.
Researchers create a new privacy metric, Probably Approximately Correct (PAC) Privacy, that enables the addition of minimal noise to protect sensitive data. The algorithm automatically determines the optimal amount of noise needed without requiring knowledge of the inner workings of the model or training process.
A Lehigh University professor has received $4 million in NIH grants to develop an AI-driven approach for precision mental health diagnosis and care. The project aims to identify biomarkers in the brain that can predict treatment response and personalize interventions for patients with depression and other mental disorders.
A new machine learning-based simulation method called Materials Learning Algorithms (MALA) has been developed, enabling accurate electronic structure calculations at large scales. MALA achieves this by utilizing a hybrid approach that combines physics-based approaches with machine learning to predict the electronic structure of materials.
The University of Houston's CYBER-CARE research center aims to prevent cyberattacks that could compromise the safe movement of people and goods in the US. The center will focus on four goals, including exploring advanced theories to mitigate the impact of large-scale cyberattacks.
The study combines real and robotic insects to understand how they sense forces in their limbs while walking. Campaniform sensilla (CS) are force receptors found in insect limbs that respond to stress and strain, providing critical information for controlling locomotion.
Researchers at EPFL have found a way to teach quantum computers to learn and process information using principles inspired by quantum mechanics. By training quantum neural networks (QNNs) on a few simple examples called 'product states', the computer can effectively grasp complex dynamics of entangled quantum systems.
Researchers at Nagoya University developed an AI-based technique to predict crystal orientation in polycrystalline materials, revolutionizing the industry. The method uses optical photographs and reduces measurement time from 14 hours to 1.5 hours, enabling large-area materials analysis.
A new computer model forecasts yield for four key crops in the southeastern US, drawing on climate, groundwater, and agricultural data. The tool helps farmers and water resource managers identify ways to maximize crop yields while efficiently utilizing water and energy.
A team of researchers at UC Irvine has developed a freely available computer model to estimate the economic costs of hurricanes and typhoons. The model combines data from climate change science and household vulnerability information, providing return periods of asset losses and helping countries better prepare for these disasters.
A new study models the spread of African swine fever in US swine farms, finding that control actions can reduce secondary infections by up to 79% but an outbreak could still be persistent and costly. The model suggests that between-farm movements are the primary route of transmission.
Research highlights how generative AI models encode biases and negative stereotypes in their users, particularly marginalised groups. This can lead to the mass generation and spread of nonsensical information, influencing human beliefs and perpetuating existing inequalities.
Researchers at the University of Washington created an app called FeverPhone that uses existing phone sensors and screens to estimate whether people have fevers. The app was tested on 37 patients in an emergency department and showed accuracy comparable to consumer thermometers, with potential for early intervention in viral outbreaks.