Development of the Universal Model of Mechatronic System with a Hydraulic Drive

The growing demands to performance of mechatronic systems with a hydraulic drive of movable operating elements of self-propelled machines require application of new approaches to the process of their development and design. Functional parameters of the mechatronic systems depend on a rational choice of operating modes of the hydraulic system and the design implementation of the mechatronic modules of these systems. Quality of the mechanically driven mechatronic system is largely determined by its dynamic characteristics. In order to improve dynamic characteristics, a universal model describing dynamic and static processes occurring in the elements of the mechatronic system was proposed. The pump, the hydraulic motor, the safety valve and the working fluid are considered interrelated as a single whole. The universal model takes into account peculiarities of functioning and mutual influence of all elements of the mechatronic system as well as the features of the working fluid and can be used with any hydraulic machines of a volumetric action. The study of dynamics of the changes in functional parameters of the mechanically driven mechatronic system was carried out for four stages of its operation: acceleration of the hydraulic drive (triggering of the safety valve); valve closure; completion of acceleration and steady-state operation. The conducted studies have established that when activating the hydraulic drive of the mechatronic system from the moment of the safety valve activation and to its closure, operating conditions do not affect changes in the functional parameters. In the steady-state operation, there are fluctuations caused by unevenness of the pump feed and load fluctuations. It should also be noted that the mechatronic system with a hydraulic motor having larger working volume has better dynamic characteristics than that with smaller working volume

‘Superior to Disney’: colour animation at Lenfilm, 1936-41

This article examines the phenomenon of colour-film animation at Lenfilm during the period 1936–1941. It discusses the development of colour technologies at the studio during the 1930s and the ways in which its artists responded to the aesthetic challenge of colour. Three of the seven short films produced during this period have been selected as case studies; they are examined here in the context of filmed animations in the Soviet Union during the 1930s, in particular the debates prompted by the screening of three Disney animations in Technicolor at the Moscow International Film Festival in 1935. The formal analysis of the case studies is based on the digital restorations in recent years at the Russian State Film Archive (Gosfilmfond), but also the inspection of one nitrate-positive of Mstislav Pashchenko’s Dzhiabzha (1939), which has survived intact at the archive. The technical difficulties posed by the hydrotype process developed at Lenfilm, as well as the challenge of producing sufficient prints for mass distribution, also form part of the discussion

A fine-grained data set and analysis of tangling in bug fixing commits

Abstract Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objectives: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusions: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise