Articles tagged with Computer Modeling
A team of MIT engineers developed an algorithm to identify causal links in complex systems, taking data from various sources and analyzing interactions between variables. The method generates a causality map linking variables with likely cause-and-effect relationships, including synergistic and redundant links.
Researchers developed a new index combining human comfort and social vulnerability with heat island mitigation strategies. Trees were found to provide the best relief from heat in vulnerable areas, while cool roofs and green roofs were preferred in less vulnerable neighborhoods.
A new study reveals that ocean eddies off the leeward side of Hawaiian Islands supply nutrients to both sides of the island chain, stimulating blooms of phytoplankton and boosting biological productivity. This mechanism may also impact fisheries near Hawaii and other nutrient-poor regions.
A computer model uses camera pose estimation and phototourism data to reconstruct the 3D scene of Antarctic penguin colonies, allowing for their identification and tracking. This innovative approach enables researchers to study and monitor penguin populations more effectively.
Researchers developed a method to assess which patients with metastatic triple-negative breast cancer can benefit from immunotherapy. The tool uses predictive biomarkers and machine learning-based approaches to identify patients who are likely to respond to treatment.
A new study used the 2023 Texas-Louisiana heat wave as a test case to establish processes for evaluating extreme weather events. The researchers found that the heat wave was directly related to climate change and will see even hotter heat waves in the future.
MIT researchers developed a versatile technique that combines diverse data from various sources into a shared language for generative AI models. This approach outperformed traditional techniques by 20% in simulation and real-world experiments.
Researchers at Wayne State University are developing new AI-powered methods to design and develop new drugs, including carbohydrate-based treatments for cancer. The study aims to improve the accuracy of simulations and machine learning models to predict the behavior of complex biological molecules.
A team of University of Houston engineers developed an AI tool to predict and control pandemic spread by analyzing international air travel. The analysis found that reducing flights in Western Europe can lead to fewer global COVID-19 cases, making it a key strategy for controlling the pandemic.
Researchers discovered that Titan's icy surface is warmed by an insulating layer of methane clathrate ice, which relaxes the impact craters' shape. This finding helps explain Titan's unique hydrological cycle and climate.
Researchers found that restoring coastal marshes can significantly help protect coastlines at a reasonable cost. A study by MIT graduate student Ernie I. H. Lee and professor Heidi Nepf shows that enhancing salt marshes in front of protective seawalls can reduce construction costs while still providing adequate protection from storms.
A team of researchers developed a wearable camera system that uses artificial intelligence to detect potential medication delivery errors. The AI achieved high sensitivity and specificity in identifying vial-swap errors, making it a critical safeguard in operating rooms, intensive-care units, and emergency-medicine settings.
Researchers found that verified users with entrenched opinions can trigger echo chambers, while centrist ideologues may actually facilitate consensus. The study used a computational model to simulate how people post and receive messages on social media platforms.
A new tool, SymGen, enables users to verify AI model responses more quickly and easily by displaying data citations. This speeds up the manual validation process by 20 percent, making it easier for users to spot errors in LLMs deployed in various real-world situations.
A team of scientists and experts led by PNNL has developed a cloud computing approach to democratize access to emerging resources. They demonstrated that cloud computing can provide an agile complement to high-performance computing facilities, enabling complex chemistry workflows to be completed in days instead of months. The initiativ...
A new study suggests that Betelgeuse's pulsing is due to an orbiting companion star known as the 'Betelbuddy'. The star acts like a snowplow, pushing light-blocking dust out of the way and making Betelgeuse appear brighter. Researchers used computer simulations to confirm this hypothesis, ruling out other possible causes.
A UVA professor has developed a new computational algorithm to find tightly connected clusters, or triangle-dense subgraphs, within large networks. This breakthrough can help uncover suspicious activity in fraud detection and identify community dynamics on social media with greater precision.
Researchers at Chung-Ang University developed a novel GAN model, PMF-GAN, to address stability and efficiency issues. The model utilizes kernel functions and histogram transformations to improve the generator's ability to produce diverse outputs, reducing mode collapse and gradient vanishing.
A new project, 'Crowd-Assisted Human-AI Teaming with Explanations,' aims to develop an interactive AI system that leverages the collective strengths of human crowd workers and machine learning models. The researchers will use crowdsourcing platforms to recruit experts and non-experts to perform tasks, making the system more robust and ...
Researchers at UVA have developed an AI-driven system that optimizes manufacturing processes, improving speed and quality while reducing waste. The system uses Multi-Agent Reinforcement Learning to coordinate tasks in real-time, leading to faster production and reduced downtime across various industries.
Researchers at UVA's School of Engineering and Applied Science have developed an AI-driven intelligent video analyzer capable of detecting human actions with unprecedented precision and intelligence. The system, called SMAST, promises to transform industries such as surveillance, healthcare, and autonomous driving.
Researchers designed custom-fit PAP masks using computational modelling to reduce air leakage and increase comfort. The masks were tested against commercially available ones, showing promising results.
The two-year study aims to explore biases in AI systems and develop a 'human-in-the-loop' framework for quality data discovery. It will investigate how humans can be involved as labelers, prompters, and validators to improve data sets and user interfaces.
Researchers at Lehigh University are developing predictive models for gene editing with CRISPR to improve outcomes and expand medical applications. The team is using AI and advanced computer models to simulate the effects of altering a single gene on the entire genome, enabling them to predict and avoid unintended consequences.
Researchers developed a new AI model that predicts optical properties across a wide range of light frequencies using only a material's crystal structure as input. This enables highly precise predictions, making it suitable for screening materials for high-performance solar cells and detecting quantum materials.
Researchers at MIT developed a security protocol that leverages quantum mechanics to guarantee secure data transmission during deep-learning computations. The protocol encodes data into laser light, making it impossible for attackers to copy or intercept information without detection.
Researchers developed a model to project Italy's energy storage needs for a renewable energy system, accounting for daily and seasonal fluctuations. The model suggests that increasing short-term energy storage capacity is critical for decarbonizing the power sector.
Researchers trained an automatic speech recognizer on recordings from people with dysarthria related to Parkinson's disease, achieving a 30% accuracy improvement. The study, led by Mark Hasegawa-Johnson, provides valuable data for improving voice recognition devices.
Researchers at UT Austin developed AI model EvoRank to design protein-based therapies and vaccines by leveraging nature's evolutionary processes. The model identifies useful mutations in proteins, offering a new approach to biomedical research and biotechnology.
A Kennesaw State University researcher is developing secure AI models that not only protect personal information but also reduce energy use. Her goal is to create a federated learning system that overcomes specific vulnerabilities of current systems, improving data transmission security and confining AI training to individual devices.
A study reveals that the gender gap in physics has remained stable for over a century due to established scientists' adoption patterns and network inclusion. The gap can be closed by adjusting parameters, but interventions such as funding and promotion opportunities are more challenging.
A new study warns that future climate change will intensify wildfires in the Arctic region, leading to an abrupt switch from no fires to very intense ones within just a few years. The accelerated permafrost thawing is expected to increase soil water and vegetation biomass, exacerbating fire fuel.
Researchers from Kyushu University found that reducing plastic waste entering oceans by 32% by 2035 would result in more than 50% less plastic in the oceans by 2050. The study aims to provide a clear numerical target for global efforts to tackle marine plastic pollution.
Researchers at Mizzou have developed Cryo2Struct, a computer program that uses AI to build the three-dimensional atomic structure of large protein complexes from cryo-electron microscopy images. This breakthrough enables scientists to better understand protein interactions, critical for developing effective treatments for diseases like...
Researchers found that large language models used in home surveillance can make inconsistent decisions about calling the police, even when videos show no crime. Models often disagreed with each other and exhibited inherent biases influenced by neighborhood demographics.
Researchers apply computational technique to understand the 'pseudogap', a long-standing puzzle in quantum physics with ties to superconductivity. The discovery helps scientists in their quest for room-temperature superconductivity, enabling lossless power transmission and faster MRI machines.
A Concordia-led team developed a framework that enables crowdsourced deep reinforcement learning as a service, using blockchain technology. This allows smaller organizations to access complex AI tasks previously out of reach, reducing costs and risk.
A team of researchers from Shibaura Institute of Technology developed a moving particle simulation-aided soil plasticity analysis for earth pressure balance shield tunnelling. The study found that earth pressure is a reliable indicator for analyzing soil plasticity and proposed a computer-aided analysis system that precisely reflects e...
A team of researchers at Pusan National University developed a pricing formula for perpetual American strangle options (PASOs) using a stochastic volatility model. The formula is accurate and provides a better understanding of the risks and returns associated with PASOs, especially in low-volatility environments.
Researchers analyzed COVID-19 cases in US states, finding oscillating waves every six months that start near the southern border in July-August. The data suggests a larger pattern sweeping up and down North America, but further research is needed to understand the mechanisms behind these seasonal oscillations.
The new Corporate Lab aims to advance collaboration between industry and public sector researchers to address real-world challenges in sustainable living and digital technologies. It will develop and trial novel digital technologies prioritizing sustainable ecosystems and lifestyles, including energy-efficient AI algorithms, green clou...
Researchers have found a new mechanism for the formation of impermeable horizontal ice layers below the surface of ice sheets, which could improve estimates of sea level rise. This process is critical for determining the contribution of ice sheet meltwater to sea level rise.
Researchers found that coastal dams can either increase or decrease flood risks, depending on the duration of a surge event and friction from water flow. The study, published in the Journal of Geophysical Research: Oceans, analyzed data from Charleston Harbor and other estuaries to make this surprising discovery.
A Dartmouth-led study projects that Antarctica's glaciers will rapidly retreat and potentially collapse by 2200, increasing global sea levels by up to 5.5 feet by 2300. The researchers used 16 ice-sheet models to refine the projection of ice loss over the next 300 years.
A novel network computer model, DiWANN, allows for efficient searches of cancer genetic data, identifying co-occurring mutations and similarities among DNA sequence elements across several types of cancer. The model provides a scalable solution to prioritize possible treatment targets.
A research team at the University of Würzburg has achieved electrically controlled modulation of light antennas, paving the way for ultra-fast active plasmonics. This breakthrough could lead to significantly faster computer chips and new insights into energy conversion and storage technologies.
A new AI model generates detailed images of cancer tissue that imitate what its staining would look like, reducing the need for resource-intensive lab analyses. The VirtualMultiplexer uses contrastive unpaired translation to create accurate virtual pictures of diagnostic tissue colorations.
Researchers found that sustained neck exertions alter the cervical spine's curvature, leading to muscle fatigue and increased risk of neck pain. The study provides new insights into the mechanical changes that occur in the spine and muscles when performing repetitive tasks.
Researchers found that control of most genes doesn't deteriorate with age, but coordination between cellular processes becomes less effective. The study suggests a more complex approach to understanding aging is needed, analyzing all genes simultaneously and their protein interactions.
Researchers at Duke University have developed a computer model that simulates nerve responses to electrical stimulation, enabling the efficient design of more effective and targeted neuromodulation therapies. The new tool, called S-MF, runs thousands of times faster than current industry standards without sacrificing accuracy or detail.
A new editorial in Oncoscience explores the application of functional information to adapt radiation therapy to individual patients. This approach aims to reduce uncertainty and improve outcomes in cancer treatment by analyzing signal data uncertainty.
Researchers at the University of South Australia have developed an AI sensor that can accurately measure the orientation of the Milky Way in low light, using a technique inspired by the dung beetle. This system could improve navigation for drones and satellites in difficult lighting conditions.
Researchers at the University of Vienna developed a software tool called MolCompass to identify areas of 'chemical space' where machine learning models are weak. This helps toxicologists and regulatory authorities increase confidence in these models, reducing the need for animal testing.
Researchers developed a framework called SigLLM that uses large language models to detect anomalies in time-series data. The approach converts time-series data into text-based inputs and can be deployed right out of the box, offering an efficient anomaly detection solution for complex systems.
The UFZ forest condition monitor tracks dynamic changes in Germany's forests due to climate change extreme events. The index displays seasonal and annual maps of forest condition, highlighting areas with severe damage, such as the Harz region.
Researchers at University of Bath and Technical University of Darmstadt found that large language models like ChatGPT cannot learn independently or acquire new skills, making them controllable and predictable. The study concluded that LLMs remain inherently safe, but misuse is still possible.
Researchers at Carnegie Mellon University propose guidelines for using interpretable machine learning methods in computational biology to tackle complex problems. The guidelines address pitfalls such as relying on a single method and cherry-picking results, emphasizing the need for multiple approaches and human-centric considerations.
A new interpretable deep learning modeling architecture, LACG, is proposed to handle complex variable interactions in chemical processes. It achieves high-accuracy modeling results via the coupling of three sub-modules based on underlying logics. The model outperforms widely used neural networks in terms of interpretability.
Researchers found that powerful waves triggered deep within the Earth can cause continental surfaces to rise by over a kilometre. The study explains why parts of continents experience substantial uplift and erosion, forming sweeping elevated regions known as plateaus.
Researchers developed a novel clustering technique that considers both basic characteristics and target material properties, enabling the categorization of over 1,000 oxides into material groups. This approach uses machine learning to predict target properties and incorporates basic feature information into the analysis.