NishMath - #modeling

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.


References :

• www.sciencedaily.com: Modeling method provides smarter way to predict customer demand

Classification:

• HashTags: #Mathematics #Applications #Modeling
• Target: Solutions
• Product: Applications
• Feature: Applications
• Type: Research
• Severity: Medium