Articles tagged with Computer Modeling
A new study proposes a framework for connecting AI risk prediction systems with scheduling platforms to detect emerging risks and adjust project plans before delays occur. This approach aims to close the gap between early warning and actionable response, enabling more resilient project delivery.
Researchers found that agriculture intensity is the main driver of accelerated bird decline in three regions. The study analyzed data from the North American Breeding Bird Survey and estimated a 15% total loss of bird abundance across North America over 35 years.
Researchers used machine learning techniques to compress a large model of the visual cortex, creating smaller versions that predict neural responses with high accuracy. The compact models revealed specific computational patterns in how neurons detect important features, offering insights into how visual information is processed.
A study reveals that identical photons in optical circuits exhibit Hopfield Network behavior, enabling associative memory mechanisms similar to the human brain. The research finds a fundamental limit to memory capacity, with quantum coherence allowing correct retrieval but transitioning to disorder as data volume increases.
Epic Games has acquired Meshcapade, a Max Planck startup that develops solutions for creating and animating digital humans. The technology, based on the SMPL body model, enables realistic human movement and expression in 3D.
Researchers at the University of East London identified a critical weakness in Industrial Internet of Things networks that can destabilize factories and safety-critical infrastructure. Attackers can exploit subtle 'delay attacks' to quietly disrupt coordination, making timing just as important as information in highly automated systems.
Researchers found that personalization features in LLMs can increase the likelihood of sycophancy, a phenomenon where models mirror users' viewpoints. The study suggests that context and user profiles play a significant role in shaping this behavior.
Researchers developed a free-to-use software tool, PSBench, to verify the accuracy of artificial intelligence-based protein structure predictions. The database includes 1.4 million annotated protein models, verified by experts, and provides reliable information for building more accurate AI systems.
Researchers developed an AI-based system to generate high-resolution soybean yield maps across Brazil, leveraging knowledge from U.S.-based models through transfer learning. The approach achieved strong predictive performance without using municipal-level yield data, improving estimates for this key agricultural region.
Researchers will gather to discuss how to engineer AI systems that work in the real world, focusing on composition, optimization, verification, and evaluation. The conference aims to establish shared foundations for a new class of software, including methods for evaluating models and ensuring durability, efficiency, and dependability.
Researchers discovered functional gradients in elephant and cat whiskers, allowing for precise touch sensing. The stiff-to-soft transition enables elephants to navigate their environment with ease, including picking up delicate objects.
Researchers at the University of Arkansas developed an AI diagnostic tool to detect masked hypertension, a condition where people have high blood pressure but normal readings during exams. The tool uses machine learning and health indicators to predict masked hypertension with accuracy, potentially saving lives and improving patient care.
A new study assesses indoor heat vulnerability for each single-family home in Austin, finding that 85% of homes would pose significant risk to an elderly person during a power outage. The city can now take a methodical approach to mitigating risk through cooling centers and home weatherization programs.
A recent NSF grant will support the development of new diagnostics and predictive models for understanding self-competition and weak asymmetry in turbulent flows. The project aims to uncover hidden patterns that current models miss, leading to improved simulations in weather forecasting, climate modeling, and engineering design.
A new AI tool uses discovery learning to predict battery cycle life with just a few days' data, saving months to years of testing and substantial energy. The tool leverages physics-based features to establish parallels between historical battery designs, allowing for accurate prediction performance.
Researchers have developed a new AI-powered correction that addresses instability in hybrid climate models, allowing for reliable simulation of months or years-long processes. The CondensNet architecture learns from reference simulations to correct condensation errors, ensuring physically consistent results.
Researchers at OIST found that combining inner speech with working memory enhances AI learning, enabling easier adaptation to new situations. This discovery holds promise for developing more human-like AI systems capable of handling complex, dynamic environments.
The Association for Computing Machinery has named 72 new Fellows for their technical innovations and service to the field. The honorees represent the top 1% of professionals worldwide, with contributions in AI for healthcare, computer graphics, data management, and more.
Researchers propose a new design principle for QM/MM simulations, enabling the objective and automatic determination of the quantum-mechanical region based on electronic-state changes. This approach addresses long-standing challenges in multiscale molecular simulations, demonstrating consistent applicability across different systems.
SPH simulations provide physically consistent insights into flow dynamics, impact forces, and affected areas in catastrophic hazard scenarios. High-quality visualization enables intuitive representations that support emergency planning and decision-making.
The Institute of Science and Technology Austria (ISTA) has received a significant donation to advance trustworthy AI technology. The €5 million gift from Garrett Camp will support fundamental research in artificial intelligence, focusing on interdisciplinary collaboration and long-term impact.
Researchers have developed a GNSS-only method that consistently outperforms existing methods in dense urban areas, enabling safer and more reliable autonomous navigation. The approach uses a probabilistic framework to estimate position without relying on carrier-phase integer ambiguity resolution.
Scientists develop ultra-selective crystalline membranes to recycle polluted textile wastewater and improve pharmaceutical medicine purity. The technology could significantly reduce energy consumption, enabling large-scale water reuse in industries.
Researchers developed ZENN, an AI framework that recognizes and adapts to hidden differences in data quality. By using thermodynamic principles, the model distinguishes meaningful signals from uncertainty and noise.
A machine-learning-based approach has been developed to map real-time tumor metabolism in brain cancer patients, helping doctors discover effective treatment strategies. The digital twin uses patient data to calculate metabolic flux, predicting which treatments are likely to work for individual cases of glioma.
Researchers at Cornell University have developed a new technique to create digital images of cloth that more accurately captures the texture of textiles. The method models how light interacts with yarns, both as it passes through and reflects off the fabric, enabling more realistic renderings for the gaming and animation industry.
Researchers at Duke University have created a new method to use analog radio waves to boost energy-efficient edge AI, enabling devices to run powerful AI models without heavy chips or distant servers. The approach, called Wireless Smart Edge networks (WISE), achieves nearly 96% image classification accuracy while consuming significantl...
A reconfigurable optical computing platform based on a double-layer liquid crystal structure has been developed to enable multifunctional all-optical image processing. The platform integrates eight types of image processing functions in one go, including bright-field imaging, vortex filtering and edge enhancement, promising substantial...
Researchers developed a portfolio optimization framework to maximize offshore energy production by identifying optimal locations for wind turbines and marine hydrokinetic technologies. The study found that combining these technologies in suitable locations can significantly reduce costs and increase energy stability.
Researchers create powerful computational tools, sCCIgen and QuadST, to map how cells communicate in diseases like Alzheimer's and cancer. These tools provide a controlled environment to test methods for detecting cell communication, enabling the discovery of new mechanisms and therapies.
A team of researchers used innovative approaches, including public data and machine learning models, to create more accurate predictions of coastal erosion and infrastructure damage. The project aims to improve storm preparedness and timeliness.
The UAE has launched an AI ecosystem to support climate-resilient farming in vulnerable communities worldwide. The CGIAR AI Hub and four other initiatives aim to drive digital transformation and innovation in agriculture, providing practical solutions for farmers, governments, and development actors.
New research by MIT researchers found that even with the same CO2 reduction, air travel causes about three times more damage to air quality than comparable electricity purchases. The study highlights the importance of considering co-pollutants and regional factors when evaluating climate actions.
A breakthrough AI system called OmniPredict can predict human pedestrian behaviors with unprecedented accuracy, revolutionizing self-driving cars and urban mobility. The model combines visual cues with contextual information to anticipate pedestrians' next moves, reducing the risk of accidents and improving traffic safety.
The new Spectra supercomputer at Sandia National Laboratories features a unique chip architecture that prioritizes tasks in real time, promising increased performance and reduced power consumption. Researchers will push the limits of this technology to simulate complex national security tasks.
A new study from Michigan Medicine found that its data-driven command center increased hospital bed use efficiency by 63%, reducing emergency department boarding time by 33% and patient transfers by 13%. The initiative also streamlined discharge processes, resulting in a 12% drop in waiting times for patients ready to leave the hospital.
Researchers have developed a novel computational form-finding method that allows for the creation of complex, lattice-structured gridshells. This breakthrough method reduces computation cost by 98% and provides a standardized approach to designing attractive and robust gridshell structures.
The EBRAINS Summit 2025 will bring together experts to assess how neuroscience can drive medical progress, digital innovation, and responsible data use. Preliminary results from the EPINOV clinical trial, integrating virtual brain technology for epilepsy surgery planning, will be presented.
University of Missouri researchers are combining in-home sensor technology with artificial intelligence to monitor daily changes in ALS patients' health. The system uses machine learning to estimate a patient's score on the ALS Functional Rating Scale Revised, predicting potential problems before they occur.
A new computer modeling approach has simulated retinal development and identified key stages of cell diversification. The research paves the way for understanding retinal diseases and regenerative research using stem cells.
Researchers at Pusan National University have developed a new framework for predicting vessel turnaround time by leveraging queuing-based operation indicators. This dynamic approach captures time-varying fluctuations in port operations, offering a more accurate and actionable forecast.
A team of researchers developed a groundbreaking Full Earth System Simulation at 1 km resolution, capturing energy, water, and carbon flow through the atmosphere, ocean, and land. This innovation has enormous potential to provide detailed global information on local scales about future warming implications.
The ACM Gordon Bell Prize-winning team developed a full-physics Bayesian inversion framework called 'digital twin' to predict tsunami early warning. This approach enabled real-time, data-driven forecasting with dynamic adaptivity, achieving a ten-billion-fold speedup over existing state-of-the-art methods.
The TUM researchers created a benchmark that standardizes lightweight design methods by comparing six reference strategies, including classical topology optimization and lattice-based layouts. This allows users to evaluate the mechanical and geometric characteristics of components with different resolutions and material use.
A new study found the Rio Grande basin is severely depleted, with only half of used water being replenished by natural processes. The primary cause of overconsumption is irrigated agriculture, particularly cattle-feed crops like alfalfa and grass hay.
A new analytical tool can improve a hospital's ability to limit the spread of antibiotic-resistant infections by inferring asymptomatic carriers. The method, developed by Columbia University researchers, combines multiple data sources to predict the spread of an infection in the hospital setting.
The University of Liverpool has announced an ambitious plan to establish the UK's flagship AI-driven materials discovery centre, AIM-HI. The centre will focus on areas such as catalysis, materials for net-zero, soft matter, and product formulation, driving innovation in sectors crucial to the UK economy.
A UVA-led team is part of a national project funded by ARPA-H to create an intelligent indoor air system that detects pathogens and responds with interventions. The BRAVE system aims to revolutionize public health by anticipating and mitigating outbreaks before they spread.
Researchers at RIKEN successfully simulated the Milky Way Galaxy with over 100 billion individual stars, far surpassing previous state-of-the-art models. This achievement demonstrates the power of AI-accelerated simulations in tackling complex multi-scale problems in astrophysics and beyond.
The new statistical method adapts to data structure, resisting outliers and providing greater stability on non-Euclidean spaces. This improves the reliability of analysis in areas like medical imaging, computer vision, and machine learning.
Researchers from Chung-Ang University have developed a novel AI-based approach for producing high-fidelity and defect-aware ultrasonic images, outperforming traditional techniques. This technology has the potential to revolutionize non-destructive testing in industries such as semiconductors, energy, and automotive.
Stefano Baroni, a renowned physicist, has been awarded the 2026 Aneesur Rahman Prize for his pioneering work in first-principles methods and open-source software development. His research has significantly advanced our understanding of material properties and paved the way for cutting-edge technologies.
A new physics-based approach predicts how lightning strikes aircraft, identifying vulnerable regions for protection. The tool can be applied to any shape of aircraft, including unconventional designs.
A new study from Arizona State University and the University of Michigan explores the strategic trade-offs individuals make when facing punishment for dissent. The research reveals that self-censorship is a rational response shaped by the interplay of boldness, surveillance, and punishment severity.
Researchers at the University of Freiburg are developing boronic acid-catalysed amidation reactions that use sustainable solvents, consume little energy, and produce only water as a by-product. The AI-powered approach aims to improve resource efficiency in the chemical industry and achieve United Nations' global sustainability goals.
The Association for Computing Machinery's global Technology Policy Council recommends six key pillars for successful government digital transformation, emphasizing reliability, security, and accessibility. The report aims to inform policymakers on how to modernize responsibly and sustainably.
Researchers develop AI-powered methods for modeling the Gulf of Mexico's dynamics, achieving higher accuracy for short-term predictions and emulating 10-year dynamics without hallucinations. This breakthrough drives forward critical management of natural resources in the U.S. and Mexico, advancing AI technology in earth sciences.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
Researchers found that strong supernatural belief systems can effectively reduce environmental harm by encouraging restraint and promoting sustainable relationships between humans and nature. The study's model showed that supernatural punishments are effective when the fear of punishment outweighs short-term gains and is not too extreme.
Researchers at Washington State University found that early planting to avoid heat damage may actually hinder spring wheat productivity due to other growth issues. The study used computer modeling to show that moving crop plantings earlier in the season can expose crops to elevated heat or cold stress in later growth stages.