Abstract: A magnetoelectric nanocomposite (MEN) is described. The MEN are used as a colorectal cancer treatment. The MEN includes a shell having at least one ferroelectric compound and a rare earth (R) metal doped spinel ferrite nanoparticle (SFNP) core, of a formula of CoxMn1-xR2-yFeyOz wherein x= 0.1-0.9, y = 1.90-1.99, and z = 3-5; and R is at least one rare earth metal selected from the group consisting of cerium (Ce), europium (Eu), gadolinium (Gd), terbium (Tb) and thulium (Tm). A method of making MENs is also provided.

Abstract: Systems and methods for determining an amount of correlation between non-orthogonal vectors characterizing quantified employee request submissions include generating a prioritization matrix for a request submission including entries associated with characteristics of the request submission considered important by one or more approvers; generating a request attribute vector characterizing attributes of a request extracted from the request submission; calculating a product for each entry of the prioritization matrix by multiplying corresponding entries of the prioritization matrix and the request attribute vector; applying a weighting factor to the product for each entry of the prioritization matrix resulting in weighted products for each entry of the prioritization matrix; accumulating the weighted products into a request quality score indicating an amount of correlation between the prioritization matrix and the request attribute vector; and transmitting an enhanced data packet to another computing device incl

Abstract: Systems and methods for determining an amount of correlation between non-orthogonal vectors characterizing quantified curricula participation include generating a prioritization matrix for the scholarship including entries associated with ideal characteristics of scholarship applicants; generating an applicant attribute vector characterizing attributes of an applicant extracted from the application; calculating a product for each entry of the prioritization matrix by multiplying corresponding entries of the prioritization matrix and the applicant attribute vector; applying a weighting factor to the product for each entry of the prioritization matrix resulting in weighted products for each entry of the prioritization matrix; accumulating the weighted products into an applicant quality score indicating an amount of correlation between the prioritization matrix and the applicant attribute vector; transmitting an enhanced data packet to another computing device including the applicant attribute vector, the priori

Abstract: Systems and methods for determining an amount of correlation between non-orthogonal vectors characterizing quantified employee request submissions include generating a prioritization matrix for a request submission including entries associated with characteristics of the request submission considered important by one or more approvers; generating a request attribute vector characterizing attributes of a request extracted from the request submission; calculating a product for each entry of the prioritization matrix by multiplying corresponding entries of the prioritization matrix and the request attribute vector; applying a weighting factor to the product for each entry of the prioritization matrix resulting in weighted products for each entry of the prioritization matrix; accumulating the weighted products into a request quality score indicating an amount of correlation between the prioritization matrix and the request attribute vector and transmitting an enhanced data packet to another computing device inclu

Abstract: Systems and methods for determining an amount of correlation between non-orthogonal vectors characterizing quantified curricula participation include generating a prioritization matrix for the scholarship including entries associated with ideal characteristics of scholarship applicants; generating an applicant attribute vector characterizing attributes of an applicant extracted from the application; calculating a product for each entry of the prioritization matrix by multiplying corresponding entries of the prioritization matrix and the applicant attribute vector; applying a weighting factor to the product for each entry of the prioritization matrix resulting in weighted products for each entry of the prioritization matrix; accumulating the weighted products into an applicant quality score indicating an amount of correlation between the prioritization matrix and the applicant attribute vector; transmitting an enhanced data packet to another computing device including the applicant attribute vector, the priori