Students learn about various crystals, such as kidney stones, within the human body. They also learn about how crystals grow and ways to inhibit their growth. They also learn how researchers such as chemical engineers design drugs with the intent to inhibit crystal growth for medical treatment purposes and the factors they face when attempting to implement their designs. A day before presenting this lesson to students, conduct the associated activity, Rock Candy Your Body.
This covers the implementation of database clustering through Open Source technologies. It is designed to teach the students on how to get, install and configure the required software and eventually set-up the cluster. It also provides an example on how a web application connects randomly to any database server in the cluster and still gets the same data. Through this example, high data availability solution is clearly demonstrated in the sense that if and when one database server in the cluster is down, the other database server can continue providing the needed data.
This project presents the method for the segmentation and detection of tumor of Magnetic Resonances brain images using intuitionistic fuzzy representation and intuitionistic fuzzy divergence method. In this proposed method, skull stripping is carried out for the removal of unwanted portion from the brain image using morphology. A Restricted equivalence function from automorphisms is used for intuitionistic fuzzy representation of image. Sugeno type intuitionistic fuzzy generator is used to calculate non-membership and hesitation degree. A new distance measure, Intuitionistic Fuzzy Divergence is used to find the optimum threshold to detect the brain tumour from MR images. The results showed a much better performance on poor illuminated brain MR images, where the brain tumor is detected properly.
El OA presenta el método de agrupamiento K-medias mostrando sus características, estrategia y procedimiento. El estudiante debe poseer conceptos básicos sobre métodos de agrupamiento aglomerativos y partitivos.
This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:
"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."
The rest of the transcript, along with a link to the research itself, is available on the resource itself.