Abstract
This study presents a simulation-based procedure to analyse a production line of a metalworking company. We use a simulation tool, ProModel ® software, to reproduce the existing production line layout of the company best-selling product which represents about 70% of the total sales. Our purpose is to get information about the existing system behaviour, and to find strategies to increase actual production level to meet the market’s demand. Based on an initial simulation model, different production scenarios were proposed and results have shown that it is possible to increase the production level allowing to meet the increasing demand for the product. The following changes in the production system were considered: (i) the use of intermediate stock of work-in-process items, (ii) the introduction of new equipment, and (iii) a mixed strategy where the introduction of new equipment is combined with the use of intermediate stock of work-in-process items. In summary, this research exhibits the flexibility of the simulation technique to address manufacturing problems throughout the creation of different scenarios providing some of the behaviour of the systems allowing the anticipation of final outputs. © 2021, Springer Nature Switzerland AG.

Abstract
We present K-band interferometric observations of the PDS 70 protoplanets along with their host star using VLTI/GRAVITY. We obtained K-band spectra and 100 mu as precision astrometry of both PDS 70 b and c in two epochs, as well as spatially resolving the hot inner disk around the star. Rejecting unstable orbits, we found a nonzero eccentricity for PDS 70 b of 0.17 0.06, a near-circular orbit for PDS 70 c, and an orbital configuration that is consistent with the planets migrating into a 2:1 mean motion resonance. Enforcing dynamical stability, we obtained a 95% upper limit on the mass of PDS 70 b of 10 M-Jup, while the mass of PDS 70 c was unconstrained. The GRAVITY K-band spectra rules out pure blackbody models for the photospheres of both planets. Instead, the models with the most support from the data are planetary atmospheres that are dusty, but the nature of the dust is unclear. Any circumplanetary dust around these planets is not well constrained by the planets' 1-5 mu m spectral energy distributions (SEDs) and requires longer wavelength data to probe with SED analysis. However with VLTI/GRAVITY, we made the first observations of a circumplanetary environment with sub-astronomical-unit spatial resolution, placing an upper limit of 0.3 au on the size of a bright disk around PDS 70 b.

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Abstract
We present a giant sensitivity displacement sensor combining the push-pull method and enhanced Vernier effect. The displacement sensor consists in two interferometers that are composed by two cleaved standard optical fibers coupled by a 3 dB coupler and combined with a double-sided mirror. The push pull-method is applied to the mirror creating a symmetrical change to the length of each interferometer. Furthermore, we demonstrate that the Vernier effect has a maximum sensitivity of two-fold that obtained with a single interferometer. The combination of the push-pull method and the Vernier effect in the displacement sensors allows a sensitivity of 60 +/- 1 nm/mu m when compared with a single interferometer working in the same free spectral range. In addition, exploring the maximum performance of the displacement sensors, a sensitivity of 254 +/- 6 nm/mu m is achieved, presenting a M-factor of 1071 and M-Vernier of 1.9 corresponding to a resolution of 79 pm. This new solution allows the implementation of giant-sensitive displacement measurement for a wide range of applications.

Abstract
The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms require careful hyperparameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, which frequently require the reconfiguration of hyperparameters. In this article, we present SSPT, an extension of the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically-sized samples, converging to optimal settings in a single pass over data while using a relatively small number of hyperparameter configurations. In addition, our proposal automatically readjusts hyperparameters when concept drift occurs. To assess the effectiveness of SSPT, the algorithm is evaluated with three different machine learning problems: recommendation, regression, and classification. Experiments with well-known data sets show that the proposed algorithm can outperform previous hyperparameter tuning efforts by human experts. Results also show that SSPT converges significantly faster and presents at least similar accuracy when compared with the previous double-pass version of the SPT algorithm.