Articles tagged with Mathematical Modeling
Rice University engineers have demonstrated a way to control the optical properties of T centers, paving the way toward leveraging these point defects for building quantum nodes. By embedding a T center in a photonic integrated circuit, they increased the collection efficiency for single photon emission by two orders of magnitude.
A new study on comets suggests that Kuiper Belt Objects like Arrokoth may contain ancient ices from billions of years ago. Researchers found a domino effect where the object's interior gets colder, preserving volatile gases for billions of years.
New mathematical modelling reveals that the Atlantic meridional overturning circulation has a high degree of complexity, with switches between states potentially leading to major climate changes. The study warns against relying on early warning indicators, as minor transitions might escalate into more significant tipping points.
Researchers at Klick Labs developed an algorithm to detect deepfakes with 80% accuracy by analyzing speech pause patterns, offering a solution to the growing problem of AI-generated content. The study's findings suggest that vocal biomarkers can distinguish between real and fake voices, providing a novel approach to flagging deepfakes.
Researchers at Lehigh University found that low- to mid-level flooding events are the most vulnerable to cyber attacks, which can overwhelm the system and cause a flood downstream. The study developed a mathematical framework to identify vulnerabilities in stormwater systems, allowing municipalities to assess their risks.
Scientists from UC3M and Johns Hopkins University have developed a computational model that simulates the invasion process of cancer cells based on the characteristics of surrounding tissue and cell junctions. The model allows for predicting tumor evolution in patients by analyzing mechanical properties of the microenvironment.
A study discovers that traditional Chinese ice-ray lattice designs can provide unique stiffness and strength under asymmetric loads, offering an alternative to conventional gridshells. The research also explores the potential of integrating complex geometry into facade design and micro-scale material design.
A KAIST-Seoul National University Hospital research team has developed a computational workflow that predicts metabolites and metabolic pathways associated with somatic mutations in cancers. The workflow uses genome-scale metabolic models and mutation data to identify altered metabolic pathways that contribute to cancer progression.
Researchers found large individual differences in roundworm neural activity despite conserved neural circuits. Computer simulations including noise accurately model whole-brain activity, shedding light on neuronal connectivity and essential role of noise in brain function.
A team of researchers developed a time-optimal control method for autonomous ships, optimizing maneuvering performance in dynamic sea conditions. The new control system accounts for various forces affecting ship movement and can improve efficiency and safety, reducing shipping costs and carbon emissions.
A new study led by Worcester Polytechnic Institute aims to determine whether AI can help doctors predict which patients will benefit from mindfulness-based stress reduction in managing chronic lower back pain. The research uses machine learning and physiological data from fitness sensors to detect patterns that may not be apparent to d...
A Kyoto University research group developed RENGE, a computational model to estimate gene regulatory networks in multicellular organisms. The method measures time-series gene expression and uses the proprietary model to infer regulatory dynamics.
Researchers develop framework to assess relative value of rules and data in AI models, improving efficiency and accuracy in scientific problems. The framework optimizes model training by tweaking the influence of different rules, filtering out redundant ones, and identifying synergistic relationships between rules.
Researchers aim to address LVAD shortcomings, reducing blood damage and clotting risk through innovative designs and coatings. A novel flexible stented blood inlet and slippery hydrophilic coatings will be used to prevent flow stasis and clot formation.
Researchers found that stereotypes can significantly reduce cooperation by making individuals less likely to engage in positive behaviors if they believe their actions won't be recognized. This can lead to a decrease in overall cooperation. However, the use of individual-based reputations could potentially improve cooperative outcomes.
Researchers developed a mathematical model to optimize World-class 400m and 1500m running performances by considering variability of motor control, oxygen consumption, and energy expenditure. The study found that improving aerobic metabolism and maintaining optimal velocity are crucial for achieving top performance.
The new AI model uses a visual map to explain each diagnosis, helping doctors follow its line of reasoning and check for accuracy. The tool aims to catch diseases in their earliest stages, making it easier on doctors and patients alike.
A new study using a high-resolution global climate model found that cloud clustering causes more extreme rain in the tropics, leading to increased severity of precipitation events. The researchers also discovered that more extreme rain occurs at the cost of expansion of dry areas, further shifting towards extreme weather patterns.
Researchers have developed a method to decode mouse neural activity, enabling accurate determination of location and direction within an open environment. This breakthrough could inform the design of intelligent machines that navigate autonomously without GPS or satellite guidance.
Researchers at MSU and Carnegie Science have developed a model connecting microscopic plankton relationships to predictably affect food webs on an ecosystem level. This new model could deepen understanding of nature's laws and create opportunities in ecosystem management.
A recent study from Argonne National Laboratory has uncovered new insights into the relationship between genetic ancestry and prostate cancer risk. The research, which included large increases in representation among men of African, Hispanic, and Asian ancestries, found 187 new genetic markers that provide more accuracy in calculating ...
A team of Earth scientists and statisticians has developed a method that can identify underground nuclear explosions with 99% accuracy. This breakthrough, published in Geophysical Journal International, uses data from known US nuclear tests and seismic patterns to distinguish between nuclear and natural earthquakes.
Researchers found that larger, more diverse, and less segregated cities experience decreased implicit racial biases due to increased social interactions. This is attributed to a lack of cosmopolitan public spaces where people can interact with others from different groups, creating barriers to equity.
Researchers created mathematical models to examine aspects of human behavior affecting pandemic trends. They found that risk perception and adherence to preventive measures are crucial in understanding why similar policies resulted in different outcomes.
Researchers have developed a novel approach to determine the age of mosquitoes, which could help improve pesticide strategies and reduce the spread of diseases like malaria. The method uses surface-enhanced Raman spectroscopy (SERS) to analyze biomolecules in mosquito water extract.
Researchers introduce a mathematical model that connects innovation and obsolescence to unify insights across economics, biology, and science. The model reveals three possible scenarios: an ever-expanding scenario, Schumpeterian dystopia, and creation and destruction.
Researchers developed a mathematical model showing how cicadas come to a consensus about local average temperature, leading to large-scale swarms. The model is related to those describing 'avalanches' in decision-making among stock market traders, highlighting the importance of communication in collective behavior.
A multidisciplinary team created a new algorithm to provide optimal personalized treatment options for individuals with esophageal cancer. The framework uses various heterogeneous factors that facilitate the progression of the disease and demonstrated high accuracy in controlling esophageal cancer.
A new tool, developed with a machine learning-based approach, can predict diuretic response in patients with ADHF. The BAN-ADHF score may lead to personalized strategies for effectively managing congestion in hospitalized patients.
A team of mathematicians solved Feynman's Sprinkler Problem by combining precision lab experiments with mathematical modeling. They found that the reverse sprinkler spins in the opposite direction when taking in water, and the cause is subtle and surprising.
Researchers have developed a model-free approach to identify topological systems, enabling the discovery of new materials. The method uses experimental approaches to measure topologically protected soft or fragile spots in unknown mechanical metamaterials.
A new model developed by MIT engineers predicts how certain shoe properties will affect a runner's performance, incorporating factors like stiffness and springiness. The model aims to help designers create high-performing shoes with novel properties.
Researchers at Iowa State University have identified common design principles in Boolean network models used to simulate complex systems and find solutions. The study highlights the importance of 'canalization' and other evolutionary mechanisms shaping gene regulatory networks.
Researchers analyzed large datasets of neural wiring in fruit flies, mice, and worms to compare networks across species. They created a mathematical model based on Hebbian plasticity, which shows how strong connections form and leads to clustering, suggesting a shared principle of self-organization governs brain network formation.
The review discusses the optical aspects of QPAT, including mathematical models for light propagation and interaction with biological tissues. The authors outline two approaches to estimating chromophore concentrations from absorbed optical energy density data, highlighting the challenges associated with practical implementation, such ...
A new study from the University of Birmingham suggests that epidemic modeling can significantly overestimate infection numbers due to ignoring network structures. The researchers found that heterogeneous networks can lead to smaller epidemic waves and fewer infections, contradicting standard 'random mixing' models.
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 a new mathematical framework to analyze genetic interactions and identified key regulators in entire biological networks. The approach, which combines geometry and statistics, reveals how individual genes and species influence network dynamics.
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.
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.
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.
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.
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...
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.
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.