The PLATO mission

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2R ) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases

Semantic model for artificial intelligence based on molecular computing

In this work, a new DNA-based semantic model is proposed and described theoretically. This model, referred to as semantic model based on molecular computing (SMC) has the structure of a graph formed by the set of all attribute-value pairs contained in the set of represented objects, plus a tag node for each object. Attribute layers composed of attribute values then line up. Each path in the network, from an initial object-representing tag node to a terminal node represents the object named on the tag. Application of the model to a reasoning system was proposed, via virtual DNA operation. On input, object-representing dsDNAs will be formed via parallel self-assembly, from encoded ssDNAs representing (value, attribute)-pairs (nodes), as directed by ssDNA splinting strands representing relations (edges) in the network. The computational complexity of the implementation is estimated via simple simulation, which indicates the advantage of the approach over a simple sequential model

A study of particle motion in rotary dryer

The purpose of this work was to study the performance of a rotary dryer in relation to number of flights. In this work an equationing was proposed to calculate the area used by the solids in two-segment flights of with any angle between the segments. From this area, the flight holdup and the length of fall of the particles were calculated for different angle positions and the results obtained were compared to experimental values. The results show an increase in dryer efficiency with the increase in number of flights up to a limit value, for ideal operational conditions. The experimental data on average residence time were compared to results obtained by calculations using equations proposed in the literature. The equation proposed for predicting flight holdup and length of fall of particles generated very accurate estimations