Abstract: Benzene and/or monochlorobenzene were chlorinated with molecular chlorine to obtain paradichlorobenzene with high selectivity. A batch reactor was used for this purpose, with a highly selective catalytic system consisting of SbCl3 and a phenothiazine derivative. The entire process was improved with the introduction of a new catalytic system recovery method, which was based on returning the mother liquid containing the catalytic system to the process after prior separation from the fresh post-reaction mixture by distillation of unreacted raw materials under reduced pressure and recycling them, as well as crystallization of paradichlorobenzene from the depleted liquid after vacuum distillation.

Abstract: The disclosure relates to methods, apparatuses and systems for improving a neural architecture search (NAS). For example, A computer-implemented method using a searching algorithm to design a neural network architecture is provided, the method including: obtaining a plurality of neural network models; selecting a first subset of the plurality of neural network models; applying the searching algorithm to the selected subset of models; and identifying an optimal neural network architecture by repeating the selecting and applying for a fixed number of iterations; wherein at least one score indicative of validation loss for each model is used in or alongside at least one of the selecting and applying.

Abstract: A method of predicting performance of a hardware arrangement or a neural network model includes: obtaining one or more of a first hardware arrangement or a first neural network model, obtaining a first graphical model comprising a first plurality of nodes corresponding to the obtained first hardware arrangement or the obtained first neural network model, wherein each node of the first plurality of nodes corresponds to a respective component or device of the first plurality of interconnected components or devices or a respective operation of the first plurality of operations; extracting, based on the first graphical model, a first graphical representation of the obtained first hardware arrangement or the obtained first neural network model; predicting, based on the first graphical representation, performance of the obtained first hardware arrangement or the obtained first neural network model; and outputting the predicted performance.

Abstract: Disclosed are an electronic device and a method for controlling thereof.