Articles tagged with Computer Modeling
Physicists have created a new code, QUADCOIL, to design stellarators, which could lead to simpler and more affordable fusion facilities. The code helps balance physics and engineering by quickly ruling out unstable plasma shapes and predicting magnet complexities.
A Virginia Tech study reveals that machine learning models are inadequate in detecting critical patient conditions, missing 66% of injuries. The research emphasizes the need for more responsive models incorporating medical knowledge to improve healthcare outcomes.
A team of computer engineers developed PhyloFrame, a machine-learning tool that accounts for ancestral diversity in genetic data. This improves precision medicine outcomes by predicting differences between subtypes of diseases and suggesting personalized treatments regardless of patient ancestry.
Researchers have developed a novel technique to overcome the spurious correlations problem in AI by eliminating a small portion of the training data that contains hard-to-understand features. This approach improves performance even when conventional techniques are ineffective.
Scientists from the University of California San Diego have discovered that liquid water separates into two distinct phases under certain conditions, one high-density and one low-density. This finding reveals a unique property of water and has potential applications in fields such as water desalination and pollutant capture.
Anna Balazs, a world-renowned pioneer in materials theory and soft matter science, has been awarded the 2025 Gutenberg Research Award. Her recent research on 'Confine: Sculpting Confined Fluids for Transport using Self-Organization and Information Transfer' has garnered significant attention.
Researchers have employed artificial intelligence and data analysis to improve forest protection, enabling real-time monitoring and predictive analysis. The innovative forest regeneration model forecasts how forests will grow and change over time, identifying best-suited tree species for different environments.
Researchers at UC San Francisco have enabled a paralyzed man to control a robotic arm through a device that relays signals from his brain to a computer. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted.
The new AI model leverages hypergraphs to quickly and accurately identify therapeutic gene targets for diseases. HIT outperformed existing models in all tested metrics, demonstrating its accuracy in classifying therapeutic gene targets with great precision.
The Morpho software can model a range of soft materials, including biological tissues and shape-shifting fluids, with high accuracy. Researchers and engineers can solve complex optimization problems using the open-source program.
A new machine learning algorithm can fully characterize systems of merging neutron stars in under a second, compared to traditional methods which take around an hour. This allows for rapid localization of the source and pointing of telescopes towards the merging neutron stars.
Researchers from Cornell and Tel Aviv University developed ProtoSnap to copy cuneiform characters from photos of tablets. The approach allows for accurate reproduction of characters and whole tablets, making it easier for experts to analyze and compare ancient texts.
TaxaBind, a new multimodal AI tool, addresses ecological problems by combining multiple models for species classification, distribution mapping, and other tasks. The tool outperformed state-of-the-art methods in zero-shot classification and cross-modal retrieval tasks.
Dr. Sarah Du, an associate professor at Florida Atlantic University's College of Engineering and Computer Science, has been selected as a Senior Member of the National Academy of Inventors for her significant contributions to advancing medical technology. Her research focuses on developing point-of-care diagnostic tools and monitoring ...
A recent study by Washington University in St. Louis explores the potential of large language models to predict postoperative complications from preoperative assessments and clinical notes. The work shows that specialized LLMs can significantly outperform traditional machine learning methods in forecasting postoperative risks.
Researchers have developed a new AI tool that uses sensors and real-time data to predict water quality across the US. This tool can be applied nationwide, benefiting communities by providing water quality forecasts, streamlining operations, and informing strategies for managing turbidity in basins worldwide.
Researchers from Carnegie Mellon University and UC-Riverside developed a system to monitor and analyze beehive health using sensors and forecasting models. The Electronic Bee-Veterinarian (EBV) uses real-time temperature data to calculate the hive health factor, helping beekeepers take corrective actions before colony collapse.
A study developed by the US Department of Energy's Thomas Jefferson National Accelerator Facility aims to lower data center costs using machine learning. The Digital Data Center Twin (DIDACT) system detects anomalies and diagnoses their source using AI continual learning, reducing downtime for scientists processing data from experiments.
A new collaboration between ASU, MSU, and LLNL aims to answer the question of how planets form by analyzing the atmospheres of seven young exoplanets. The team will use the James Webb Space Telescope and powerful computers to create atmospheric models that can reveal insights into planetary formation and evolution.
A new study developed a computer model to predict household displacement duration in US communities after disasters, accounting for socioeconomic factors. The model combines physical damage estimates with socioeconomic characteristics to help inform risk mitigation strategies.
Researchers at Mizzou have developed a proactive approach using artificial intelligence to address evolving threats against smart grids. The CIBR-Fort system can predict cyberattacks with 91.88% accuracy and defend against them in real-time, enabling scalable security for power grids of the future.
Researchers developed a new method to search through billions of molecules to identify potential anti-inflammatory drug candidates. The method uses computer algorithms to explore vast chemical space and has the potential to speed up the costly drug development process.
An international research team developed a user-friendly software method called Segment Anything for Microscopy, which can precisely segment images of tissues, cells, and similar structures. The new model improved performance for cell segmentation, enabling researchers to automate tasks that previously took weeks of manual effort.
A Cornell University study used a computer model to predict the transmission of bird flu to backyard poultry when migratory mallards stop to rest. The researchers found that mallard ducks are natural carriers of avian influenza and can spread the disease to backyard poultry if they stop to rest in an area with high risk for introduction.
Researchers developed innovative techniques and optical solutions to overcome long-standing challenges in facial projection mapping. They achieved unprecedented speed in dynamic facial projection mapping while maintaining high accuracy, and proposed methods to simulate video annotations and minimize alignment artifacts.
Researchers found that large language models use a semantic hub mechanism to process diverse data, integrating information from various modalities. They demonstrated that models can change their outputs by intervening with text in the dominant language, even when processing other languages.
Researchers developed a synergetic strategy combining millimeter-wave-terahertz-infrared photo-monitoring and computer-vision three-dimensional modeling for ubiquitous non-destructive inspections. The approach allows for material composition identifications and structural reconstructions of composite multi-layered objects.
The qHPC-GREEN project aims to model quantum mechanical systems relevant to environmental and energy challenges using a hybrid approach combining classical and quantum computing hardware. This will help understand biocatalysts, enabling the development of new industrial catalysts for more sustainable production processes.
A new method developed by Osaka Metropolitan University accurately predicts housing prices in Osaka City, with neighborhood perception being a key factor. The approach achieves nearly 75% accuracy by combining existing property data with machine-learning-processed street view images.
Hannah Dailey, an associate professor of mechanical engineering at Lehigh University, has been recognized with the Presidential Early Career Award (PECASE) for her innovative research on fracture healing. Her virtual mechanical test can identify nonunions early in the healing process, allowing for earlier surgical intervention.
A team of MIT engineers has developed a training method for multiagent systems that can guarantee their safe operation in crowded environments. The method enables agents to continually map their safety margins, allowing them to scale up to any number of agents while maintaining system safety.
Researchers created an automated system that enables developers to build algorithms taking advantage of two types of data redundancy, boosting computation speed and reducing energy consumption. The system can optimize machine-learning algorithms for a wide range of applications, including scientific computing.
Researchers developed a computer model called MANAscore to identify tumor-fighting immune cells in lung cancer patients treated with immune checkpoint inhibitors. The three-gene model helped find differences associated with patient response to immunotherapy, including the presence of stem-like memory T cells.
Researchers at King's College London have developed a complex model of molecular 'wear-and-tear' that sheds light on how proteins age. The study found that chromatin, the DNA-protein mix, is more resilient to aging than previously thought, suggesting new avenues for anti-aging treatments.
The project's recordings help improve voice recognition tools by providing diverse speech patterns to train artificial intelligence models. Microsoft has seen significant improvements in recognizing non-standard English speech, with accuracy gains ranging from 18% to 60%, depending on the speaker's disability.
Researchers at the University of Surrey propose a new approach to treating inflammatory diseases using personalized probiotic therapies. By analyzing large-scale computer models and computational methodologies, they can identify potential targets and design tailored treatments.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
Vision-Language Models (VLMs) inherit biases from uncurated datasets, leading to poor group robustness and biased predictions. Researchers are exploring strategies to mitigate these biases in discriminative models, but generative tasks like image captioning and image generation require attention.
Neuromorphic computing is poised to emerge into full-scale commercial use, driven by the need for energy-efficient solutions. The review article proposes strategies for building large-scale neuromorphic systems that can tackle complex real-world challenges.
A new hybrid machine learning model predicts ultimate axial strength of CFRP-strengthened CFST columns with high accuracy, enabling safer and more efficient designs. The model can be used to optimize construction processes and enhance the safety of structures at a lower cost.
Researchers created digital images of iridescent bird feathers that change colors at different angles, incorporating tiny variations for a glittery appearance. The new method uses specimens from the Cornell Lab of Ornithology as a reference and has potential applications in video games and animation.
A groundbreaking AI model developed by researchers at Emory University accurately predicts the likelihood of blood transfusion in non-traumatic ICU patients, addressing longstanding challenges in predicting transfusion needs. The model achieved exceptional performance metrics, including an AUROC of 0.97 and an accuracy rate of 0.93.
Scientists at the University of Lausanne used AI to simulate the last Alpine glaciation, finding ice covers 35-50% thinner than previous models. The new approach enables unprecedented accuracy and resolution, making it possible to describe complex topography numerically.
The Aging Research and Drug Discovery Meeting (ARDD2025) will feature Nobel laureates Morten Meldal and Michael Levitt. Their groundbreaking discoveries have had a lasting impact on drug development and molecular design, highlighting the conference's pivotal role in advancing healthspan and longevity science.
Researchers developed ProteinReDiff, an AI-powered method to redesign proteins for improved ligand binding. The approach uses initial protein sequences and ligand SMILES strings, reducing reliance on detailed structural data.
A new study reveals that a colossal 'megaflood' refilled the Mediterranean Sea around 5.97-5.33 million years ago, marking the end of the Messinian Salinity Crisis. The Zanclean Megaflood is believed to have had discharge rates and flow velocities dwarfing any other known floods in Earth's history.
A new study assesses the historical knowledge of AI chatbots like ChatGPT-4 and finds they struggle with nuanced, PhD-level inquiry. The models performed best on legal systems and social complexity but struggled with topics such as discrimination and social mobility.
Anil Jain and Michael I. Jordan received the BBVA Foundation Award in Information and Communication Technologies for their pioneering work on machine learning, enabling transformative technologies like biometrics and artificial intelligence. Their research has unlocked applications of far-reaching impact on society.
Neuroscientists at WashU develop a novel method to create personalized brain models, shedding light on individual variations in brain dynamics and their impact on cognition and behavior. The study reveals new insights into the mechanisms underlying person-specific differences in brain electrophysiology.
Researchers at UVA have developed computer models to target specific bacteria in specific parts of the body, reducing the chance of antibiotic resistance. This approach could lead to more effective treatments and reduce the need for broad-spectrum antibiotics.
A novel method called RESQUE predicts computational and energy costs for updating deep learning/AI models, allowing users to make informed decisions about when to update models to improve AI sustainability. The researchers conducted extensive experiments to validate the performance of RESQUE.
The University of Texas at Austin appoints Charles 'Charley' Taylor to lead a new Center for Computational Medicine, developing advanced medical applications and modeling. This move strengthens collaborations between Dell Medical School and the Oden Institute, driving innovation in health and technology.
Researchers developed a computer model to predict which medication works best for each individual patient with CML. The approach has the potential to improve treatment outcomes for patients who previously relied on stem cell transplantation.
Researchers at Columbia University Irving Medical Center have developed an AI method that can accurately predict the activity of genes within any human cell, revealing the cell's inner mechanisms. The system can also uncover hidden biology of diseased cells and explore the role of genome's 'dark matter' in cancer and other diseases.
A new AI model developed by researchers at Penn State College of Medicine can predict the progression of autoimmune disease among those with preclinical symptoms up to 1,000% more accurately. The GPS model integrates data from large genetic studies and electronic health records to identify individuals at high risk of disease progression.
Computer models predict that climate change will lead to an increase in spongy moth outbreaks, causing widespread damage to forests. The fungus that normally curbs the moths' spread is expected to decline, allowing the moths to destroy more trees as temperatures rise.
A new study published in Nature Medicine shows that AI-based models can accurately identify ovarian cancer in ultrasound images, achieving an accuracy rate of 86.3%. The models also reduce the need for expert referrals and misdiagnosis rates by 63% and 18%, respectively.
Researchers at Seoul National University propose PV2DOC, a software that converts presentation videos into searchable, well-organized PDFs with text summaries and relevant images. The tool enhances video accessibility and reduces storage space, making it easier for users to find specific information.
The US Department of Energy is investing $179 million in three Microelectronics Science Research Centers to develop next-generation microelectronics designed for extreme environments. PNNL will lead projects on neuromorphic computing, EUV lithography, and heterogeneous computing.
Researchers developed Concurrent Dynamic Quantum Logic (CDQL) to verify quantum protocols with concurrent actions, enhancing expressiveness and speeding up verification. CDQL provides a rigorous framework for verifying both sequential and concurrent models of quantum protocols.