Articles tagged with Mathematical Modeling
Researchers from Washington University in St. Louis have discovered that the pore size of a battery separator plays a crucial role in determining the stability and safety of a battery. The study reveals that smaller pores can lead to localized metal ion penetration and increased risk of short circuits.
GIST researchers propose a new strategy for crime prevention using artificial intelligence, trained on a large-scale dataset of deviant incident reports and corresponding images. The model, called DevianceNet, can accurately classify and detect deviant places, making it a useful tool in urban safety development.
A new UNSW-led study reveals that global warming has amplified the water cycle, with an estimated 46,000-77,000 cubic kilometers of freshwater transported from the equator to the poles since 1970. This finding suggests broader changes to the global water cycle and highlights the need for improved climate models.
Researchers from NIST have developed a mathematical model that predicts the strength and timing of changes in velocity required for crane operators to apply when transporting heavy loads. This equation can be applied to various situations, including moving a load with initial rest and large distances.
Researchers found that the master clock and slave clock operate via distinct molecular mechanisms, allowing for robustness and flexibility in regulating bodily rhythms. The master clock's ability to adapt to environmental changes enables quick adjustment to new time zones after international flights.
A Harvard T.H. Chan School of Public Health study suggests that US elementary schools should only remove masks when local COVID-19 case rates are below a certain threshold to avoid increased transmission. The study provides a framework for school decision-makers to estimate the level of mitigation needed to meet their goals.
Researchers developed patient surrogates to evaluate potential prostate cancer treatments, identifying genes that could be targeted with drugs. The models predicted synergistic effects of treatments and identified promising drug combinations.
Researchers at RIT have created a biophysical model that can predict changes in cartilage mechanics and function during disease pathways. The model, informed by experimental data, enables noninvasive predictions using MRI scans, potentially reducing the need for invasive procedures.
A study published in AGU Advances found that water depletion from rivers and lakes accelerates near urban centers due to factors like increased evaporation and groundwater exploitation. Researchers analyzed satellite imagery of the US from 1984 to 2018 and developed a model predicting surface water loss proximity to cities.
In a recent study, Dalla Torre and his team ran a collaborative mathematical game on different technologies to evaluate the systems' ability to demonstrate quantum mechanical properties. The Quantinuum System Model H1-1 outperformed classical results by returning correct answers 97% of the time.
Mathematicians at the University of Leicester have developed a new mathematical model that could greatly increase the efficiency of pest control. The model uses field data and computer simulations to predict the distribution of slugs in arable fields, accounting for individual creature movements.
Researchers at Tel Aviv University found that a specific type of bacteria, Weissella, becomes dominant in the gut microbiomes of locusts when they form swarms. This change may provide the bacteria with an evolutionary advantage, allowing them to spread and infect more locusts.
Researchers at the University of Pennsylvania found that changing social strategies between settings can lead to more cooperative behavior in a society. In a model with spillover between domains, cooperation is favored when actors can observe and imitate each other's behaviors in different contexts.
A University of Nebraska-Lincoln researcher has developed a virtual immune system model that can simulate the complex interactions between different components of the human immune system. The goal is to identify more effective drug targets and accelerate the development of new treatments for immune-related diseases.
A mathematical modeling study predicts that routine immunization with TCV could avert 46-74% of all typhoid fever cases in 73 countries. Vaccination is also expected to reduce the relative prevalence of antimicrobial-resistant typhoid by 16%, preventing millions of cases and deaths.
Researchers at UC3M have developed a mathematical model that analyzes the appearance of oscillations in flow networks, which may help explain how blood circulates in the brain. The model takes into account the size of the network and predicts the frequency of pressure oscillations.
A team of researchers from Ritsumeikan University developed an unprecedented stream cipher using chaos theory to create highly secure cryptographic systems. The new system is resistant to statistical attacks and eavesdropping, even against quantum computers, making it a promising solution for post-quantum era cryptosystems.
Researchers at Pusan National University have developed a new model using 'double Mellin transforms' and the 'method of dimension reduction' to accurately price vulnerable exchange options. The model provides an effective and accurate mathematical solution, enabling quick deployment without long calculations.
Researchers at Boston University developed a real-time method for projecting COVID-19 quarantine needs in congregate housing settings ten days in advance. The team's findings were published in the American Journal of Public Health and can be used as a predictive tool to allocate resources.
Researchers developed a school simulation model that shows how the virus spreads in different settings and calculates the effectiveness of measures against its spread. The model reveals that elementary and lower secondary schools can keep reproduction rates below 1 with classroom ventilation, wearing masks, and class size reduction.
A multidisciplinary team at UNIGE has developed a simple mathematical equation to explain the complex distribution of scales in ocellated lizards. This discovery contributes to a better understanding of skin color pattern evolution and provides an optimal pattern for animal survival.
Researchers developed MonoCon, a new AI technique that enables accurate identification of 3D objects in 2D images. By incorporating auxiliary context, the method improves object detection and estimation accuracy, paving the way for safer and more robust autonomous vehicles.
A mathematical model developed by researchers at Massachusetts General Hospital has identified biological markers that can predict treatment responses in COVID-19 patients. The model reveals differences in patient characteristics and response to therapy, enabling clinicians to provide optimal care for diverse patients.
Mathematical modeling can assess toxicants' impact on river populations without endangering the environment. A new model developed by Peng Zhou and Qihua Huang describes the interactions between a population and a toxicant in an advective environment.
Researchers develop a symmetrized N-center representation that provides a natural, fully equivariant framework for learning properties associated with multiple atoms. The approach gives excellent accuracy for predicting atomic properties despite using only linear or kernel regression.
A recent study found that conventional metrics for detecting audio adversarial examples are unreliable and fail to accurately represent human perception. Researchers proposed a more robust evaluation method, but acknowledge the complexity of modeling auditory perception with mathematical metrics.
New research suggests that ultra-low velocity zones in the deep mantle may be regions made of different rocks than the rest of the mantle, with compositions potentially linked to the early Earth. The study's findings imply the presence of layered structures within these zones, shedding light on their origin and evolution.
Researchers developed a fair-diverse allocation optimization framework to optimize the distribution of COVID-19 vaccines, treatments, and testing supplies. The model aims to get limited resources to the most vulnerable subgroups while ensuring fairness and diversity, independent of demographic background.
A new study published in PLOS Computational Biology found that the order of COVID-19 symptoms varies depending on the SARS-CoV-2 variant. In the USA, cough was most likely to be the first symptom, while fever and diarrhea were more common third symptoms.
The study predicts that the Balearic Islands will lose between 56 and 65% of their beach area permanently due to climate change. This loss is mainly attributed to sea level rise, which will cause devastating inroads on the coastline.
Researchers at Linköping University developed a mathematical model to describe global climate target achievement and predict unanimity in climate talks. The model shows how countries influence each other over time, highlighting the importance of inclusive plenary sessions and informed parties.
A simplified mathematical model found a 'lag time' between human intervention and an actual decrease in CO2 levels, which could still lead to climate tipping points. The researchers used a system of stochastic delay differential equations to analyze various emission reduction and carbon capture strategies.
Researchers at Chalmers University of Technology have developed an algorithm that learns optimal energy usage for electric delivery-vehicles. By focusing on overall energy usage instead of just distance travelled, the vehicles can reduce their energy consumption by up to 20% and minimize battery usage.
Researchers developed AI that extracts hidden equations of motion from observational data, creating models faithful to the laws of physics. This enables physics-based investigations and simulations for ecosystem sustainability, among other applications.
Researchers developed a mathematical model to predict mosquito breeding ground temperatures, estimating populations and tracking vector-borne diseases. The model revealed that even in winter, certain breeding grounds can support mosquito growth.
Researchers found that major COVID-19 models were wrong and not very useful in predicting the pandemic course. They used physics-informed neural networks (PINNs) to capture changing parameters and make predictions with improved accuracy.
A team of researchers at UC Davis Health has developed a novel PET imaging-based tool to detect liver inflammation in non-alcoholic fatty liver disease. The tool uses dynamic PET imaging and mathematical modeling to measure glucose transport from blood to liver tissue, allowing clinicians to quantify liver inflammation.
New Cornell University research suggests that social connections can distort voting behavior, favoring minority candidates. The study found that complacency and dejectedness – conditions caused by social connections – can skew election outcomes.
Mathematicians analysed global COVID-19 data to identify two constants that drastically change a country's rate of infection. Social stress is key to understanding the multi-wave dynamics of COVID-19 outbreaks.
Researchers have successfully created an experimental model of a skyrmion particle in a beam of light, providing a real system to demonstrate the behavior of this elusive type of fundamental particle. The study reveals the intricate structure and topological properties of skyrmions, which can be distorted but not broken.
A mathematical model suggests that SARS-CoV-2 Delta-like variants, which combine enhanced transmissibility and partial immune escape, will cause more severe pandemics than variants with either trait alone. Vaccination is predicted to be highly beneficial in these cases, preventing more infections and breakthroughs.
Iowa State University researchers validated the CDC's COVID-19 vaccine rollout strategy, which prioritized frontline essential workers, older age groups, and individuals with underlying health factors. The model showed that this approach was highly effective in curbing mortality while keeping case numbers at bay.
Researchers developed a predictive model that maps electric activity of mouse, rabbit, and human cardiac cells, allowing translation of findings across species. The model is expected to accelerate drug development and improve understanding of disease mechanisms.
Research published in JNeurosci found that children with dyslexia take longer to gather visual evidence, indicating altered motion processing and decision-making. This slowdown corresponds to differences in brain activity, particularly in centro-parietal regions involved in decision making.
A new Illinois study compares performance of static and dynamic nitrogen recommendation tools, finding that complex dynamic tools do not consistently increase profits but offer a 15% reduction in nitrogen leaching. The researchers highlight the need to refine these tools and prioritize environmental outcomes over economic gains.
Researchers developed a mathematical model that can predict the effectiveness of immunotherapy treatment for individual patients based on standard clinical measures. The model showed high sensitivity in predicting treatment response within two months, with minimal false-negative results.
Researchers from academia and industry will converge at Lehigh University to discuss innovative solutions for optimizing efficiency and resiliency in the global supply chain. The workshop aims to leverage machine learning for prescriptive analytics, enabling proactive optimization of supply chain operations.
A team of researchers from Flinders University has developed a single mathematical equation that can accurately model and predict the behavior of ventricular fibrillation, a life-threatening heart rhythm. The equation uses principles from renewal theory to predict the population dynamics of 'rotors' found in the heart during VF.
Researchers develop a more accurate mathematical model to predict solar cell output power, considering degradation and external factors. The new model will aid policymakers in making informed decisions on solar power installations.
A team of researchers has developed an ensemble-based machine learning model that can predict how cancer patients will respond to certain drugs with high accuracy. The model was trained on data from 499 independent cell lines and validated against a clinical dataset containing seven chemotherapeutic drugs.
Researchers applied a physics method to Balinese rice irrigation practices, finding that a balance between water stress and pest stress is crucial for equilibrium. The traditional method, which involves synchronized flooding, can help maintain social harmony and prevent chaos in water schedules.
A new analysis spanning 10,000 years of history and ten major world regions found that world population size, major technological advances, and geographical connectivity are key drivers of military technology evolution. The study's methodology employed a resource called Seshat: Global History Databank to empirically test prior hypotheses.
A team of researchers has developed a novel machine learning model that can identify medication orders requiring pharmacy intervention using provider behavior and contextual features. This approach reduces the risk of exposing sensitive patient data, while alleviating the workload of pharmacists and increasing patient safety.
A new mathematical model suggests that patients with anemia can be effectively treated with transfusions of blood substitutes, which are more readily available than real blood. This approach could reduce the negative effects of blood transfusion and lower costs.
A study of 5,000 simulated undergraduates found that weekly surveillance testing was associated with a substantial reduction in infections. Even at low vaccine effectiveness rates, quarantine protocols were effective in reducing infections by up to 93.6%.
Assistant Professor Kang Hao Cheong and his team discovered that chaotic switching for quantum coin Parrondo's games has similar underlying ideas to encryption. They found that using pre-generated chaotic sequences enhances the work, making it easier to invert the encrypted message to obtain the original state.
A study found that snakes rapidly diversified their diets following the K-Pg mass extinction event, incorporating birds, fish, and small mammals into their diet. This diversification was triggered by ecological opportunities presented by the extinction, with some lineages experiencing further bursts of adaptive evolution.
Researchers find that most countries are not growing wind and solar power fast enough to meet global warming targets of 1.5°C or 2°C. The study analyzes data from 60 countries and finds that only a few countries, like Portugal and Ireland, have seen rapid growth in onshore wind and solar power.
Liquid marbles' unique hydrophobic outer layer allows for faster evaporation than bare water droplets due to particle-particle and liquid-particle interactions. The team's mathematical model accurately predicts evaporation behavior, providing insights into these tiny biological structures.
A team of scientists at NAIST successfully used automatic differentiation to accelerate calculations of model parameter extraction, reducing computation time by 3.5 times compared to conventional methods. This breakthrough enables the design of more efficient power converters with increased performance and reduced energy consumption.