Electrically Active States of Charge Capture and Transfer Causing Slow Recombination in Thallium-Bromide Crystals at Low Temperatures

TlBr single crystals grown by the Bridgman–Stockbarger method are studied. It is established that frozen-conductivity effects manifest themselves under interband excitation by light at temperatures below 200 K. Herewith, clearly pronounced superlinear dependences of the induced photoconductivity on the strength of the applied electric field manifest themselves. The results of studying thermally stimulated conductivity evidence that these phenomena can be associated with the filling of trap states with thermal activation energies of 0.08–0.12 eV. This state can be removed due to thermal quenching at temperatures of ?180 K because of the emptying of energy states with an activation energy of 0.63–0.65 eV filled after optical generation.     

New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods

Neural Computing and Applications - Successfully organizing the transport of hazardous materials and handling them correctly is a very important logistical task that affects both the overall flow...     

Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks

The presented work deals with the application of artificial neural networks in the modelling of the thermal decomposition process of friction composite systems based on polymer matrices reinforced by yarns. The thermal decomposition of the automotive clutch friction composite system consisting of a polymer blend reinforced by yarns from organic, inorganic and metallic fibres impregnated with resin, as well as its individual components, was monitored by a method of non‐isothermal thermogravimetry over a wide temperature range. A supervised feed‐forward back‐propagation multi‐layer artificial neural network model, with temperature as the only input parameter, has been developed to predict the thermogravimetric curves of weight loss and time derivative of weight loss of studied friction composite system and its individual components acquired at a fixed constant heating rate under a pure dry nitrogen atmosphere at a constant flow rate. It has been proven that an optimized model with a 1‐25‐6 architecture of an artificial neural network trained by a Levenberg‐Marquardt algorithm is able to predict simultaneously all the analyzed experimental thermogravimetric curves with a high level of reliability and that it thus represents the highly effective artificial intelligence tool for the modelling of thermal stability also of relatively complicated friction composite systems.