Tchawou Tchuisseu Consulting

  Visualizing Customer Distribution with a Geospatial Heatmap In today's data-driven world, understanding your customers' geographical distribution is essential for making informed business decisions. Visualizing this information can provide invaluable clarity, whether you're optimizing your delivery routes, targeting specific markets for promotions, or just gaining insights into your customer base. In this post, I'll walk you through how I used geospatial heatmaps to visualize customer locations based on data from the Fecom Inc. E-commerce Marketplace Orders Data CRM dataset available on Kaggle. What is a Heatmap? A heatmap is a data visualization technique that uses color gradients to represent values in a two-dimensional space. In this case, we're using a heatmap to show the concentration of customer locations worldwide. Areas with higher concentrations of customers will be highlighted in warmer colors, making it easy to spot where most of your custome...

This article deals with data analysis and provides a basic example of creating a dashboard using Looker or Data Studio. We start by defining data, which is simply a collection of facts. For instance, the annual sales of a shopping company can be considered data. Facts can also be regarded as measurements, and they can be qualitative or quantitative. Data analysis involves the collection, transformation, and organization of data to draw conclusions, make predictions, and drive decision-making. Therefore, a data analyst is someone who collects, transforms, and organizes data to facilitate informed decision-making. For those unfamiliar with the term, decision-making refers to using facts to guide business strategy. The data analysis process comprises six phases: Ask: The analyst must ask the right and effective questions, define the problem, use structured thinking, and communicate with others. Prepare: In this phase, the analyst understands how the data is generated and collected. ...

For many years, creators or designers of patterns used, for the reproduction of drawings observed in nature, the usual drawing tools such as pencil, eraser, ruler, colors... In order to mimic the patterns or drawings observed in nature, the Turing model used, in particular, the simple BVAM model which is defined as a system of two coupled partial differential equations named reaction-diffusion equations. After determining the mathematical model, a study of the dynamic behavior of this system led to the determination of the condition to obtain Turing patterns. The numerical simulations of this system with Fortran 90, gave the results which, plotted on Matlab, permitted to reproduce of many complex Turing patterns for instance patterns drawn with lines, which describe the skin of zebra; and those with points which describe a skin of a leopard. The method used to obtain these patterns constitutes the first automaton called numerical-mathematical automaton. With the aim of controllin...

 

Abstract Blade Tip Timing (BTT) technology is concerned with the estimation of turbomachinery blade stresses. The stresses are determined from BTT data by relating the measured tip deflection to the stresses via Finite Element (FE) models. The correlation of BTT measurements with FE predictions involves a number of uncertainties. This paper presents the process for validating the FE stress and deflection predictions of aero-engine compressor blades under non-rotation conditions as a critical preliminary step toward the complete understanding of their dynamic behavior under rotating conditions when using BTT measurements. The process steps are described in detail, including the FE modeling and analysis of the blades and the blade-disk assembly, and the measurements of the blade tip deflection and blade stress. Furthermore, the uncertainties associated with the FE modeling and the measurement processes are quantified. The results show that the FE model is valid considering the control of...