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This research considers the question of whether unemployment insurance benefit and labour-market activation measures induce the likelihood of re-employment and whether this effect differs for natives and immigrants.

Statistical processing was carried out on the European Union Statistics on Income and Living Conditions cross-sectional data for Finland for the period 2004 to 2016. Propensity score matching analysis was undertaken to investigate whether a treatment effect (unemployment insurance benefit) was a predictor of success in increasing re-employment rates, when controlling for participation in labour-market policy measures, subsidized employment and personal background characteristics.

We find that the probability of re-employment for recipients of unemployment benefits is half that of non-recipients of benefits. Due to the influence of subsidized employment, subsequent employment income decreases for recipients of unemployment benefits and especially for immigrants. Finally, we find that due to the influence of subsidized employment, time spent as a full-time employee decreases for recipients of unemployment benefits and especially for immigrants.

Although our results indicate that benefit determination has a marked impact on re-employment probabilities, unobserved variables turn to play a significant role in selection of labour-market activation measures. In this respect, we find the treatment assignment to activation policy measures depends on influence of unobserved variables and this effect is more important for the re-employment rates of natives than it is for immigrants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2019-0668.

Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application.

This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain.

A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework.

This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application.

The purpose of this paper is to evaluate the effect of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃) nanoparticles-reinforced pure aluminium (Al) on the mechanical properties of hybrid aluminium matrix nanocomposites (HAMNCs).

The HAMNCs were fabricated via a vacuum die-assisted stir casting route by a two-step feeding method. The varying weight percentages of TiO₂ and Y₂O₃ nanoparticles were added as 2.5, 5, 7.5 and 10 Wt.%.

Scanning electron microscope images showed the homogenous dispersion of nanoparticles in Al matrix. The tensile strength by 28.97%, yield strength by 50.60%, compression strength by 104.6% and micro-hardness by 50.90% were improved in HAMNC1 when compared to the base matrix. The highest values impact strength of 36.3 J was observed for HAMNC1. The elongation % was decreased by increasing the weight percentage of the nanoparticles. HAMNC1 improved the wear resistance by 23.68%, while increasing the coefficient of friction by 14.18%. Field emission scanning electron microscope analysis of the fractured surfaces of tensile samples revealed microcracks and the debonding of nanoparticles.

The combined effect of TiO₂ and Y₂O₃ nanoparticles with pure Al on mechanical properties has been studied. The composites were fabricated with two-step feeding vacuum-assisted stir casting.