Articles tagged with Mathematical Modeling
The structure of families is projected to change significantly, with grandparents and great-grandparents increasing in age and number, while cousins, nieces, nephews, and grandchildren declining. This shift could limit their ability to help raise grandchildren.
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.
Suzanne Lenhart, Chancellor's Professor in UT's Department of Mathematics, will deliver the American Mathematical Society's Gibbs Lecture at JMM2024, recognizing outstanding achievement in applied mathematics. Her lecture explores how mathematical modeling can represent natural system dynamics and guide ecological management strategies.
Korea Maritime & Ocean University researchers have developed a new method for assessing the path-following performance of autonomous ships in adverse weather conditions. The computational fluid dynamics model can provide more accurate predictions of path-following performance and enhance safety in autonomous marine navigation.
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.
Researchers developed a mathematical model that accurately predicts gastrulation flows in chick embryos, which are similar to human embryos. The model then predicted cellular flows observed in frog and fish embryos, suggesting common physical principles behind multicellular self-organization across vertebrate species.
A study by Kyoto University researchers uses hierarchical mathematical modeling to analyze the shapes of skin eruptions and link them to the in vivo pathological dynamics of CSU. The Criteria for Classification of Eruption Geometry (EGe criteria) was developed, demonstrating high reliability among dermatologists.
Researchers used AI-selected natural images and synthetic images to probe visual processing areas of the brain, finding that predicted maximal activator images significantly activated targeted areas. The study suggests individualized models for each subject can improve understanding of visual system organization across populations.
Researchers develop a mathematical model that analyzes the future survival of plants in a changing climate by studying how far wind can carry seeds. The model provides fast and reliable predictions of seed movement, considering factors like seed type, plant height, and wind speed.
A research team at City University of Hong Kong has developed a novel performance evaluation method called Information Exchange Surrogate Approximation (IESA) to calculate blocking probabilities in queueing systems with overflow. IESA provides ways to allocate limited resources better, enhancing resource allocation and capacity plannin...
New research from University of Illinois Chicago identifies a potential 'stop growing' switch triggered by steroid hormone ecdysone in fruit flies, which may relate to human puberty. The study's findings have implications for understanding the growth-stopping process in humans, particularly given recent changes in puberty onset.
Researchers have successfully observed the operating principle of promoters in a catalytic reaction in real-time. Using high-tech microscopy methods, they visualized individual La atoms' role in hydrogen oxidation. The study revealed that two surface areas of the catalyst act as pacemakers, controlled by promoter lanthanum.
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 new machine learning model, combining two techniques, has been found to be 30% more effective in predicting who will be cured of disease. This allows for targeted treatment strategies to minimize unnecessary interventions and optimize patient care.
Researchers developed a deep learning model that can identify previously unknown quasicrystalline phases in multiphase crystalline samples. The model achieved a prediction accuracy of over 92% and successfully detected an unknown phase in Al-Si-Ru alloys.
Researchers at Purdue University developed a new tool to visualize neural network decisions, making it easier to identify errors in image recognition. The tool uses graph-topological data analysis to provide a bird's-eye view of all images in a database, revealing areas where the network struggles to distinguish between classifications.
Researchers found that language coexistence is influenced by interaction between communities with opposing language preferences. Individual preferences play a pivotal role in language dynamics, sometimes overcoming social prestige.
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.
A new mathematical model suggests that mammalian sperm cells exhibit two distinct swimming modes, which may be linked to fertilization. The model captures the interactions between motor kinetics and flagella deformations, revealing a second mode with stronger wave-shaped beating.
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.
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 describe traveling waves of acetylcholine in the striatum, revealing a new kind of neurochemical wave. The study proposes a mathematical mechanism by which simultaneous waves of acetylcholine and dopamine arise, maintaining balance in the brain's striatum.
Researchers from Austria and France join forces to unravel the secrets of gene regulation during mammalian development using stem cell-derived 3D culture models. The project aims to understand how key molecular events influence gene transcription and regulation over hours and days.
Researchers found that apoptotic cells induce apoptosis in neighboring hair follicle cells during the regression cycle. The study proposes a mathematical model of the hair follicle regression cycle, which suggests that the dermal papilla plays an essential role in initiating apoptosis.
A new study by Klick Labs reveals that AI technology can screen for Type 2 diabetes using six to 10 seconds of a person's voice, with high accuracy rates. The research used acoustic features to analyze recordings from over 18,000 participants and identified significant vocal variations between individuals with and without the condition.
Researchers created a 3D printed tumor model using bioprinting and synthetic chips to better understand complex cancers. The model simulates the surrounding environment, addressing limitations of traditional 2D models.
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.
Researchers studied how insects evolved two distinct flight strategies using robots and biophysicists. They found that asynchronous flight evolved together in a common ancestor, but some groups reverted back to synchronous flight.
Researchers found that antigen testing significantly reduces the probability of cluster occurrence by identifying and isolating infected persons. However, it may not be effective against highly infectious mutant strains like Omicron, highlighting the need for booster vaccination campaigns and other infection control measures.
A mathematical model suggests females infer attractiveness by watching peers' choices, learning to prefer rare traits that distinguish successful males. This learning process helps maintain variation in male traits and female preferences over time.
New plant cell walls exhibit significantly different mechanical properties compared to surrounding parental walls, enabling cells to alter their local shape and influence the growth of plant organs. Researchers have discovered that new cell walls in some plants are 1.5 times stiffer than the parental cell walls.
Researchers say current models fail to project oxygen dynamics in coastal ecosystems with high photosynthetic activity, such as seagrass meadows and coral reefs. Fluctuations in oxygen levels have been observed in systems like the Venice Lagoon and Red Sea coral reefs, where marine species adapt to changing conditions.
A new analysis of COVID-19 cases in Africa found that the rate of infection was likely much higher than reported in the initial stages. The researchers estimated that approximately 66% of all infections were asymptomatic, while about 5% were severe and 27% were mild.
Researchers at the University of Bristol studied the damping and natural frequency characteristics of a 150m tall building in London. The findings suggest that amplitude and time are the dominant factors affecting damping ratios, which can improve occupant comfort and reduce failure risk.
Researchers from University of Cambridge and Cornell University have developed a method to build machine learning models that can understand complex equations using far less training data. This breakthrough enables the construction of more time- and cost-efficient models for physics, engineering, and climate modeling applications.
Researchers from the University of Oldenburg developed a new stochastic method to mitigate sudden swings in wind turbine power output. The study found that control systems are mainly responsible for short-term fluctuations and can be optimized to ensure more consistent energy output.
The iHEART Simulator combines electromechanics, haemodynamics, and cardiac perfusion into a single platform for unprecedented biophysical accuracy. Researchers can study coronary artery disease, cardiac arrhythmias, and guide cardiac surgeons in delicate procedures.
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.
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.
Researchers at the University of Melbourne have developed a new simulation model that can predict flooding during an ongoing disaster more quickly and accurately. The Low-Fidelity, Spatial Analysis and Gaussian Process Learning (LSG) model can produce predictions as accurate as advanced models but at speeds 1000 times faster.
Researchers at the University of Sydney and Max Planck Institute have developed new methods to describe synchronicity in complex systems. Their findings suggest that convergent walks on a network structure can lead to poor quality synchronisation.
Researchers have designed a hand-held model to demonstrate the concept of entropy for students, allowing them to confront the topic with new intuition. The model uses everyday materials to effectively demonstrate the quantitative nature of entropy, enabling students to reason correctly about its definition.
A new study suggests that synchronizing internal clocks can help alleviate jet lag and age-related problems. Researchers found that eating a larger meal in the morning can help overcome jet lag, while constant nighttime eating can lead to misalignment between internal clocks.
Tubificine worms can form entangled blobs that behave as a single organism to adapt to extreme environments and migrate more efficiently. Researchers successfully simulated collective movements of worm blobs in confined terrain, facilitating design of future swarm robotic systems.
A team of researchers at the University of Waterloo and Dalhousie University have developed a method for forecasting short-term disease progression using limited data. The Sparsity and Delay Embedding-based Forecasting model, or SPADE4, uses machine learning to predict epidemic progressions with high accuracy.
Augusta University researcher Arni S.R. Srinivasa Rao calls for updating the UN's traditional approach to measuring population replacement levels. He proposes a new formula that allows for more timely and accurate measurement, which is essential for understanding emerging global demographics.
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.
Researchers found a lognormal distribution of neuron densities in mammalian brains, influencing network connectivity and potentially promoting efficient information transmission. The discovery is relevant for modeling the brain accurately and designing brain-inspired technology.
Researchers develop a new model predicting up to twice the original amount of subglacial water may be draining into the ocean, accelerating glacial melt and sea level rise. The theory uses satellite measurements and is a simple equation that can predict exfiltration in a fraction of a second on a laptop.
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.
Researchers developed a new mathematical model to study circadian rhythm resilience and develop ways to improve it in individuals with weak internal clocks. Sustained disruptions can lead to disorders like diabetes and memory loss.
Researchers propose a hypothesis that astrocytes, non-neuronal cells in the brain, can perform core computation as transformers, providing insights into human brain function and machine learning success. This discovery could spark future neuroscience research and help explain transformer performance across complex tasks.
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 used a mathematical theory called the free energy principle to predict how real neural networks learn and organize themselves. The study successfully mimicked this process in rat embryo neurons grown in a culture dish, demonstrating the principle's guiding force behind biological neural network learning.
Researchers used Fibonacci's method to calculate the Moon's shape, finding that its poles are half a kilometre closer to its centre of mass than its equator. This information is crucial for applying GPS technology to the Moon.
GOBI overcomes model-free and model-based inference method limitations by introducing an easily testable condition for a general monotonic ODE model to reproduce time-series data. It successfully infers positive and negative regulations in various networks, distinguishing between direct and indirect causation.
A University of Ottawa study reveals that a diverse brain's ecosystem is key to maintaining normal function while responding to changes. This concept is inspired by Charles Darwin's idea that biodiversity is crucial for survival, suggesting cell-to-cell diversity helps prevent failures in brain circuits.
A mathematical model of UK cat populations suggests that neutering of owned cats affects not only their own population but also those of feral, stray, and shelter subpopulations. The model shows that lower rates of neutering of female owned cats lead to population booms within the other subpopulations, especially for stray cats.
Researchers developed a new method using Copula models to restore the true statistical model from data processed by Differential Privacy technology, even with many missing values. This enables highly accurate data analysis for pandemic mitigation and various societal applications.