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The aim of this paper is presenting a new application of material obtained from the acrylonitrile butadiene styrene (ABS) recycling process from electronic equipment housings. Elements of computer monitors were used to prepare re-granulate, which in turn was used to manufacture a filament for fused filament fabrication (FFF) additive manufacturing technology.

The geometry of test samples (i.e. dumbbell and bar) was obtained in accordance with the PN-EN standards. Samples made with the FFF technology were used to determine selected mechanical properties and to compare the results obtained with the properties of ABS re-granulate mould pieces made with the injection moulding technology. The GATE device manufactured by 3Novatica was used to make the prototypes with the FFF technology. Processing parameters were tested with the use of an Aflow extrusion plastometer manufactured by Zwick/Roell and other original testing facilities. Tests of mechanical properties were performed with a Z030 universal testing machine, a HIT 50P pendulum impact tester and a Z3106 hardness tester manufactured by Zwick/Roell.

The paper presents results of tests performed on a filament obtained from the ABS re-granulate and indicates characteristic processing properties of that material. The properties of the new secondary material were compared with the available original ABS materials that are commonly used in the additive technology of manufacturing geometrical objects. The study also presents selected results of tests of functional properties of ABS products made in the FFF technology.

The test results allowed authors to assess the possibility of a secondary application of used elements of electronic equipment housings in the FFF technology and to compare the strength properties of products obtained with similar products made with the standard injection moulding technology.

How does the human brain absorb information and turn it into skills of its own in psychotherapy? In an attempt to answer this question, the authors will review the intricacies of processing channels in psychotherapy and propose the term transprocessing (as in transduction and processing combined) for the underlying mechanisms. Through transprocessing the brain processes multimodal memories and creates reparative solutions in the course of psychotherapy. Transprocessing is proposed as a stage-sequenced mechanism of deconstruction of engrained patterns of response. Through psychotherapy, emotional-cognitive reintegration and its consolidation is accomplished. This process is mediated by cellular and neural plasticity changes.

This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.