This demonstration of study focalizes the melting transport and inclined magnetizing effect of cross fluid with infinite shear rate viscosity along the Skan-Falkner wedge. Transport of energy analysis is brought through the melting process and velocity distribution is numerically achieved under the influence of the inclined magnetic dipole effect. Moreover, this study brings out the numerical effect of the process of thermophoresis diffusion and Brownian motion. The infinite shear rate of viscosity model of cross fluid reveals the set of partial differential equations (PDEs). Similarity transformation of variables converts the PDEs system into nonlinear ordinary differential equations (ODEs). Furthermore, a numerical bvp4c process is imposed on these resultant ODEs for the pursuit of a numerical solution. From the debate, it is concluded that melting process cases boost the velocity of fluid and velocity ratio parameter. The augmentation of the minimum value of energy needed to activate or energize the molecules or atoms to activate the chemical reaction boosts the concentricity.
The main problem when querying a database is the response time. The research was of an applied type, using two databases: control and experimental. Three computers have been used to execute ten queries to the two databases, running 4 consecutive times and obtaining an average. The results found were that, of the ten consultations carried out, seven consultations obtained better results in the experimental DB and three in the control DB. It is concluded that the best practices to optimize a database are: to create clustered indexes on columns frequently used in searches or to perform sorts, create non-clustered indexes on primary or foreign keys that do not have clustered indexes, use calculated columns, operators, and listing of proper columns in queries, however, the use of indexes should be restricted because they affect insert, update, and delete operations.
Keywords: relational database, database optimization, SQL, indexing.
The objective was to implement an artificial neural network to improve the recognition of geometric figures that later allow making estimates about shapes in a real context to create virtual planes. A Matlab program was developed to create the neural network and two knowledge bases containing fifteen different geometric figures that were developed for the training stage and for the recognition stage. The results of the training phase were carried out in three processes, obtaining a percentage of 11.35%, 3.55%, and 2% error margin respectively, later came the recognition stage with three processes, obtaining 40%, 100%, and 100% figures recognized respectively. It is concluded that the implemented neural network performed the recognition of fifteen geometric figures correctly (100%), requiring three training processes and three recognition processes to verify their learning.
Keywords: Image recognition, Machine learning, artificial neural networks, artificial intelligence.
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