Articles tagged with Mathematical Modeling
A new machine learning model developed by NYU researchers can predict food crises up to 12 months in advance by analyzing news articles and their frequency. The model shows a high correlation between news coverage and on-the-ground occurrences of risk factors, indicating its potential as an early-warning system.
Tumour cells exhibit an innate randomness in their ability to respond to chemotherapy, which can lead to resistance. Researchers identified a marker for resistance and propose combining chemotherapy with drugs targeting this 'noise' to improve treatment outcomes.
Researchers at the University of Bath developed a new theory that unifies conflicting viewpoints on lane formation, predicting curved and straight lanes in crowded spaces. The study reveals a new class of structures in daily life that may go unnoticed.
Researchers at Cornell University developed a new model called swarmalators, which can simulate swarming behaviors and synchronized timing in microrobots. The model mimics diverse emergent phenomena, such as aggregation, dispersion, and vortices, and can be used for precision medicine and drone applications.
A new mathematical model has been developed to optimize treatment for prostate cancer, showing promising results in eliminating cancer cells. The model explores different therapeutic strategies, including single drugs and drug combinations, and suggests that alternating therapies could significantly improve cancer treatment.
A new study by Dartmouth College researchers finds that sanctions on wealth managers would be more effective than asset seizure in crippling Russia's oligarchs. The research uses network science to map the connections between wealthy individuals and their financial experts, revealing a vulnerable network that can be disrupted with targ...
A team of researchers from Rensselaer Polytechnic Institute has developed a system to optimize TV ad scheduling, resulting in a 3-5% revenue increase for networks. The model combines mathematical programming and machine learning to assign ads to specific breaks and positions.
A University of Queensland-led research team is using an unusual caesium atom to search for dark matter particles. The team's work may also improve atomic theory calculations and technology, such as navigation systems.
Researchers have developed a new model of black hole collisions that reveals nonlinear effects in gravitational waves, allowing for more accurate modeling of the behavior. This breakthrough has significant implications for understanding black hole collisions observed by LIGO and testing Einstein's general theory of relativity.
The Heidelberg Laureate Forum Foundation offers journalist travel grants to cover the 10th HLF from September 24-29, 2023. Recipients of prestigious awards in mathematics and computer science will gather with young researchers for a week of interdisciplinary dialogue.
A new mathematical model uses wastewater samples to predict COVID-19 cases five days in advance, detecting SARS-CoV-2 RNA with high sensitivity. The approach could help authorities tailor infection control policies when clinical surveillance is lacking.
Researchers investigated the relationship between slow slip events and tectonic strain in Japan's Bungo Channel, Tokai, and Boso-Oki regions. They found that not all accumulated strain is released during SSEs, but rather builds up in shallower areas before a megathrust earthquake can occur.
A recent study by Crystal Lim found that obesity prevention programs combining nutrition and exercise components over an extended period had the most success in changing students' daily behaviors. Successful programs also included evidence-based strategies like goal setting, problem solving, and self-regulation techniques.
Researchers tested three common techniques to make algorithms fairer and found that one approach didn't reduce social norm bias at all. They proposed a new technique: a formula to directly measure social norm bias in an algorithm so it can be corrected. This bias can persist even after overt discrimination is removed.
Scientists at Stanford University and SLAC National Accelerator Laboratory have made progress toward building a novel quantum simulator. The device can simulate interactions between two quantum objects, paving the way to study complex systems and answer fundamental questions in physics.
A new mathematical model simulates how the body regulates potassium levels, shedding light on the relationship between kidneys and muscles. The study suggests that muscle-kidney cross-talk signal hypothesis is essential in maintaining healthy potassium homeostasis.
A UAB researcher discovered a new definition of Petri nets that reconciles two longstanding mathematical approaches, allowing for the reconciliation of geometric and algebraic semantics. This breakthrough has been used in epidemiological modeling, including COVID-19 research.
A team of researchers from FSU and Cleveland State University have developed a mathematical model that explains how bacteria communicate within larger ecosystems. The model can predict environmental responses from bacterial communities and is flexible for different applications.
Researchers at Aston University have created the world's first computer reconstruction of a virus, including its complete native genome. This breakthrough could lead to the development of targeted treatments to kill bacteria that are dangerous to humans, reducing the threat of antibiotic resistance.
Laboratory experiments show rapid antibiotic switches can prevent resistance, with collateral-sensitive drug pairs suitable for therapy. However, environmental conditions in patients differ from labs, and natural degradation affects treatment.
A study published in Journal of Managed Care & Specialty Pharmacy found that an individual's prior adherence behavior is a key predictor of future adherence. The research suggests interventions can move patients to adherence at any time, leading to better adherence in the future.
A new digital twin of laser-directed energy deposition repair technology has been developed to improve industrial sustainability. The system automatically determines optimum forming conditions, reducing metal powder waste and increasing the effectiveness of the repair process.
The São Paulo School of Advanced Science on Epidemic Preparedness will bring together researchers from various fields to discuss core tracks such as mathematical modeling, genomics, epidemiological surveillance, and public health policies. Participants will engage in simulated situation rooms and hands-on experience with leading faculty.
Researchers at Rensselaer Polytechnic Institute developed a model to predict cryptocurrency scams using Benford's Law and found that scam addresses deviated from the law. They also advocated for robust blockchain interoperability to provide stability in decentralized systems.
A mathematical model predicts the long-term effectiveness of COVID-19 vaccine booster doses in various patient populations, including those with healthy immune systems and cancer patients. The model suggests that robust antibody-based responses can be achieved through booster doses, but may wane quickly in immunosuppressed individuals.
A team of researchers from Xi'an Jiaotong-Liverpool University and other institutions has identified a flexible and user-friendly model for predicting flood frequency in a changing environment. The fractional polynomial-based regression method is more effective than existing models, which often fail to account for factors like climate ...
The CNIC study reveals two distinct mechanisms by which cells detect and respond to forces of varying strength, one mediated by caveolae and the other by newly discovered dolines. This finding has significant implications for understanding pathological processes such as atherosclerosis and neurodegenerative diseases.
A new mathematical model has found a clear trend towards mutualism in ecological interactions, with most relationships eventually leading to a mutually beneficial partnership. The researchers used adaptive dynamics and classical models of population dynamics to study the transitions between different ecological relationships.
Researchers from Brown and MIT developed a new framework that uses machine learning and sequential sampling to predict rare disasters like earthquakes and pandemics with less data. The framework, called DeepOnet, has been shown to outperform traditional modeling efforts in predicting scenarios, probabilities and timelines of rare events.
A University of Houston researcher has developed a method to describe complex systems using the least number of variables possible, reducing complexity from millions to just one. This advancement speeds up science with efficiency and ability to understand and predict natural system behavior.
Researchers at the University of Texas at Dallas have developed a computer-based platform for drug discovery using topological data analysis. The approach allows for virtual screening of thousands of compound candidates, narrowing them down to the most promising ones for laboratory and clinical testing.
UVA researchers developed a new tool to analyze genetic data, reducing noise and bias in cancer diagnosis. The tool uses mathematical modeling to identify patterns in chromatin, helping scientists detect tiny numbers of disease cells.
Researchers at Singapore University of Technology and Design (SUTD) have developed a novel phase-change key for new hardware security. The device, known as the physical unclonable function (PUF), is scalable, energy-efficient, and secure against AI attacks compared to traditional silicon PUFs.
A new study published in Collective Intelligence journal suggests that evolutionary forces may be fueling collective tendencies to discriminate. The research recommends fostering environments where desired behavior emerges naturally through evolutionary dynamics rather than regulating against undesired outcomes.
Researchers at KAUST develop a novel multivariate skew-elliptical link model to address the challenges of highly imbalanced health data. The new model provides a better fit to COVID-19 datasets and offers flexibility over existing models.
Assistant Professor Nourridine Siewe developed a new mathematical model to assess different approaches for treating metastatic cancer. The model evaluates the interactions among immune cells and cancer, helping clinicians decide on the best treatment strategies.
Researchers found that the Eya protein regulates cell contact behavior, allowing for self-organization of epithelial cells with nurse cells. This mechanism enables complex development processes, including egg chamber formation and sperm cell development, in Drosophila.
Researchers have developed a new model that combines nuclear physics and string theory to describe the transition to dense and hot quark matter in neutron star collisions. The model allows for the calculation of gravitational-wave signals, showing that both hot and cold quark matter can be produced.
Physicists at Ural Federal University have developed a theory regulating the solidification of iron-nickel alloys to control characteristics and improve uniformity. This technology will affect high-precision instruments like clocks, seismic sensors, and engines.
A new study in PNAS suggests that the spread of COVID-19 in US counties followed a hybrid lognormal-Pareto distribution, with 99% of counts conforming to Taylor's Law and the top 1% matching the Pareto distribution with infinite variance.
Researchers from University of Colorado Boulder develop equation learning technique, WSINDy, to grasp how body rebuilds skin and accelerate wound healing. The method can apply to various phenomena in the natural world, including flocking behavior in birds.
A new study found that increasingly complex mathematical models can produce more uncertain estimates, limiting their usefulness for informing real-world policy decisions. Researchers recommend reassessing the drive to create detailed models and suggest calculating the model's effective dimensions before adding complexity.
A mathematical model suggests that social interactions between sedentary and moderately active people can stimulate higher levels of overall physical activity in a population. The model found that interacting with moderately active individuals helped sedentary populations become more physically active over time.
Researchers at the University of Technology Sydney have extended the theory of acoustic levitation to account for asymmetrical particles, which is more applicable to real-world experience. This new understanding enables precise control and sorting of tiny objects using ultrasonic waves.
A recent review article summarizes the latest developments in finite-control-set model predictive control (FCS-MPC) strategies for PMSMs. FCS-MPC is a promising approach to optimize drive systems, but challenges remain, including computational complexity and parameter uncertainty.
Researchers developed computational models to identify massage businesses at risk of violating laws related to sex and labor trafficking. The models provide probability scores on the likelihood that a business is engaged in illegal activity, allowing law enforcement and organizations to prioritize investigations.
A breakthrough computer model from Chalmers University of Technology reveals the properties of an atomic nucleus, providing insights into the strong force that governs neutron star behavior. The model predicts a surprisingly thin neutron skin, which could lead to increased understanding of heavy element creation in neutron stars.
Mathematicians at Aston University are developing computational modelling techniques to examine biological drag reduction methods, such as fish slime secreted by predator-avoiding fish. This project aims to reduce skin-friction drag and increase the performance range of electric vehicles, contributing to a reduction in CO2 emissions.
Aneurysms occur when blood vessel thins and expands, leading to increased stress on walls and rising rupture probability. The new model uses computed tomography scans to reconstruct geometry and blood flow patterns.
A team of scientists from the University of Exeter has made a key breakthrough in predicting fluctuations in the rotation of the Earth and the length of the day. They used mathematical modeling to show that changes in the atmosphere can be predicted more than a year in advance, linking geodesy with climate prediction.
Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.
A team of scientists from Ural Federal University has developed a complex mathematical model to understand the dynamics of nanoparticles and viruses in cells. The model reveals how viruses cluster inside endosomes and interact with cellular proteins, shedding light on their behavior and replication mechanisms. This breakthrough can hel...
The new equation developed by University of Bristol scientists can be used to model accurately particle motion through porous materials like biological tissues, polymers, rocks, and sponges. This could lead to exciting advances in medical procedures, natural gas extraction, and plastic packaging production.
Xiu Yang, a 2022 NSF CAREER award recipient, is working on an algorithmic approach to model and overcome hardware errors in quantum computing. He aims to enable the technology to achieve its promise of unparalleled speed in solving complex problems.
Researchers developed a mathematical model of cilia beating due to mechanical instability caused by the cilium motor protein dynein. This knowledge will aid in understanding and treating cilia-related diseases.
Physicists at Ural Federal University developed a mathematical model to predict El Niño's behavior, accounting for wind, humidity, temperature, and ocean currents. They found that stronger winds can cause unpredictable weather phenomena.
Undergraduate students at Ohio University applied kinetic techniques learned in physical chemistry to analyze the COVID-19 spread data from the CDC. They developed a model that considers the effect of vaccination and made predictions for the following months, correctly predicting the surge in cases in Ohio.
Researchers at Cedars-Sinai created complex computer models of individual brain cells using artificial intelligence. These models capture the electrical signals that neurons fire to communicate with each other, allowing researchers to replicate brain activity at the single-cell level.
A new mathematical framework predicts the most effective combinations of drugs to treat postmenopausal bone loss in patients. The model identifies optimal orderings that boost bone density and reduce long-term bone loss, improving treatment outcomes.
Researchers at Princeton University used artificial intelligence to simulate ice formation by individual atoms and molecules with quantum accuracy. This breakthrough enables tracking of hundreds of thousands of atoms over longer timespans than previous simulations.