The purpose of this study is to compare experimentally three electronic components : A Flat Plate, A Pinned Surface – similar to a tubular heat exchanger, A Finned Surface – similar to the fins on air-cooled engines or electrical heat sinks, in both heat transfer modes (free and forced) from the point of view Nusselt Number for forced and free convection by using TecQuipment's free and forced convection apparatus, by imposing a constant thermal flux of different values while calculating the cooling efficiency indicators as: the convective heat exchange coefficient (h), the number of Nusselt (Nu), and the average temperature Tm. This experimental study showed that the forced convection beam plate, is the best point of view cool it as is illustrated in the chapter of calculation.

Given the importance of DC machine in the industrial field and with the technological development, researchers are interested in developing the design of DC machine of all kinds. In this work, an advanced design of different type for DC machines using finite element method simulator. Design series DC machine, shunt DC machine and compound DC machine were implemented using Ansys RMXprt and study electromagnetic phenomena in Maxwell software, we will study flux lines and magnetic flux density and identify areas of temperature distribution. The speed and torque rates for each design are evaluated with consideration of time. Then, a 3D design of a compound DC machine and series DC machine was created to evaluate the difference between the DC machine types.

For more than a decade, PMT and SMT models have dominated the field of machine translation, and neural machine translation has emerged as a new paradigm for machine translation. The latest neural machine translation not only performs better than systems that consider the structure of ordinary words and sentences, but is also able to find complex relationships between source and target words. Neural machine translation provides a simpler modeling mechanism that makes it easier to use in practice and science. Neural machine translation no longer requires concepts such as word rank, which is a key component of a system that takes into account word and sentence structure. While this simplicity can be seen as an advantage, on the other hand, the lack of careful wording is a loss of control over translation. Systems that take into account word and sentence structure generate translations that consist of word sequences in the training data. On the other hand, neural machine translation is more flexible for translation that does not exactly match the training data. This provides more opportunities for such models, but frees the translation from predefined constraints. Lacking a specific word connection can make it difficult to link the target words you create to the source word. The widespread use of neural machine translation in translation systems has the advantage of allowing users to translate certain terms and translate untrained data to a certain extent, but in some cases often results in distorted sentence structure. This paper aims to address issues such as neural machine translation control, more accurate translation of unrecognized data, correct sentence structure and grammar boundaries, and the creation of independent machine translation system.