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Researchers are employing advanced mathematical techniques to tackle complex problems in diverse fields. A recent study highlights the application of the hinge function in fluvial geomorphology, providing a solution for predicting bedload sediment transport in rivers. Additionally, mathematicians have used mathematical modeling to unravel the mystery behind the striped patterns of "broken" tulips, a phenomenon that has puzzled scientists for centuries. These examples demonstrate the power of mathematical methods in understanding and predicting phenomena across various scientific disciplines.
A team at Washington State University has developed a new forecasting model that helps businesses predict customer demand more accurately, even when key data is missing. This model, published in Production and Operations Management, uses a mathematical modeling method to estimate customer interest beyond just completed transactions and traditional forecasting techniques. By analyzing real-world sales data, the model provides a clearer view of how many customers considered a purchase but ultimately did not buy due to factors like pricing or timing. The researchers utilized a computational technique called the sequential minorization-maximization algorithm to improve forecasting accuracy. Furthermore, researchers at the University of Alberta have solved a centuries-old floral mystery by using a mathematical model to explain how striped tulips get their distinctive pattern. The study, published in Nature Communications Biology, reveals that the tulip-breaking virus inhibits the production of anthocyanins, the pigments that give tulips their vibrant colors. The mathematical model incorporates two key mechanisms—the substrate-activator mechanism and Wolpert's positional information mechanism—to simulate the interaction between the virus, pigment production, and cellular resources within the plant, ultimately creating the striped pattern.
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Recent research has spotlighted the diverse applications of mathematical and computational methods across multiple critical fields. One notable study, published in ACM Transactions on the Web, details the use of advanced mathematical techniques and software to investigate the collapse of the TerraUSD stablecoin and its associated currency, LUNA. Led by Dr. Richard Clegg at Queen Mary University of London, the research team employed temporal multilayer graph analysis to uncover suspicious trading patterns indicative of a coordinated attack, which led to the loss of $3.5 billion. The study highlights the power of mathematical tools in unraveling complex financial events.
Scientists have also made significant strides in fluid dynamics through the development of AI-powered simulation models. Researchers at Osaka Metropolitan University have created a machine learning model that dramatically reduces computation time for fluid simulations while maintaining accuracy. This innovation, which utilizes graph neural networks, has potential applications in offshore power generation, ship design, and real-time ocean monitoring, offering a scalable solution that balances accuracy with efficiency. The new model cuts simulation time from 45 minutes to just three minutes. The 23rd International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2025) also focuses on the integration of mathematical and computational methods across science and engineering. A session within the conference aims to unite researchers and practitioners in discussing novel ideas, methodologies, and applications that bridge the gap between mathematics and its practical implementations. The session welcomes contributions focusing on analytical and numerical techniques, algorithm development, and computational modeling, particularly those providing new insights into solving complex systems.
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The International Conference of Numerical Analysis and Applied Mathematics (ICNAAM) 2025 will feature a symposium on Statistical Modeling and Data Analysis. The event, organized by Luis M. Grilo from the University of Évora and the Research Centre for Mathematics and Applications in Portugal, aims to gather researchers from various fields with expertise in statistical models and data analysis. Academics, professionals, and students interested in these areas are encouraged to submit original, unpublished results for peer review.
Applications with real-world data are particularly welcome, spanning disciplines such as Health Sciences, Natural and Life Sciences, Social and Human Sciences, Economics, Engineering, Education, Sports, and Tourism. The conference aims to foster collaboration and knowledge sharing within the international Numerical and Applied Mathematics community. It is organized with the cooperation of the European Society of Computational Methods in Sciences and Engineering (ESCMCE).
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