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Information and communication technologies enable migrants to maintain bonds with multiple communities. Little is known about the association between migrants’ connections to their country of origin and different integration practices in online and offline communities in the receiving society. We draw on a survey conducted amongst migrants in Iceland (N = 2,139) and conduct three regression analyses to identify determinants of migrants’ use of media and social media from their country of origin. Contrary to other studies, we do not find evidence of reactive transnationalism (i.e., migrants seeking out connections to their places of origin due to dissatisfaction with life in the receiving society) as a response to negative attitudes towards the receiving society. We identify distinct patterns of online and offline integration: Migrants with frequent contact with their countries of origin are less integrated locally in terms of offline activities. However, they are more integrated in digital communities of the receiving society, and use receiving-country media more frequently, thus following a strategy of digital biculturalism.

The tilted-wave interferometer is an interferometrical measurement system for the accurate optical form measurement of optical aspheres and freeform surfaces. Its evaluation procedure comprises a high-dimensional inverse problem to reconstruct the form of the surface under test from measured data. Recent work has used a deep learning hybrid approach to solve the inverse problem successfully in a simulation environment. A quantification of the model uncertainty was incorporated using ensemble techniques. In this paper, we expand the application of the deep learning approach from simulations to measured data and show that it produces results similar to those of a state-of-the-art method in a real-world environment.

This paper proposes three different black-box models for a 10-kVA 7.967 kV–120/240 V distribution transformer that is suitable to narrowband power line communication studies. One is valid in the 3–500 kHz range, while the remaining two are simpler models valid for the CENELEC band (3–148.5 kHz). The models are represented as equivalent circuits derived from the rational approximation of the transformer admittance matrix using either phase- or modal-domain theory. All parameters needed for their implementation in electrical circuit solvers are given. Validation is performed not only in frequency domain, but also in time domain, with the application of voltage signals that excite the 3–500 kHz and 3–148.5 kHz frequency bands uniformly. The models are shown to accurately predict the attenuation and distortion of signals transferred through the transformer for a wide range of load conditions for both downstream and upstream signal transmission. Signal attenuation is shown to be load dependent, frequency dependent, and sensitive to the considered transmission path (downstream or upstream).

A review of the book: Nafisa Yeasmin, Waliul Hasanat, Jan Brzozowski, and Stefan Kirchner, eds. Immigration in the Circumpolar North: Integration and Resilience (London: Routledge, 2021)

Deep neural networks have been successfully applied in many different fields like computational imaging, medical healthcare, signal processing, or autonomous driving. In a proof-of-principle study, we demonstrate that computational optical form measurement can also benefit from deep learning. A data-driven machine learning approach is explored to solve an inverse problem in the accurate measurement of optical surfaces. The approach is developed and tested using virtual measurements with known ground truth., Comment: 11 pages, 8 figures

Uncertainty quantification by ensemble learning is explored in terms of an application from computational optical form measurements. The application requires to solve a large-scale, nonlinear inverse problem. Ensemble learning is used to extend a recently developed deep learning approach for this application in order to provide an uncertainty quantification of its predicted solution to the inverse problem. By systematically inserting out-of-distribution errors as well as noisy data the reliability of the developed uncertainty quantification is explored. Results are encouraging and the proposed application exemplifies the ability of ensemble methods to make trustworthy predictions on high dimensional data in a real-world application., 23 pages, 20 figures

O presente trabalho discute algumas técnicas para análise de dados de medidas repetidas, utilizando modelos univariado, multivariado e misto. Discutem-se também algumas questões sobre o problema, bem como alguns procedimentos para seleção do melhor modelo, quando a abordagem de estimação dos parâmetros do modelo misto é via máxima verossimilhança e máxima verossimilhança restrita. Quanto ao modelo univariado de medidas repetidas, foram realizadas simulações para verificação da acurácia dos testes quando as suposições exigidas são válidas, ou não. Este trabalho também discute e compara os procedimentos GLM e MIXED do SAS®, quando utilizados para análise de dados de medidas repetidas. Some technical aspects of repeated measures analysis, using univariate, multivariate and mixed models are discussed in this dissertation. Some problems and procedures to select the adequate model, when the parameters estimation are effectuated through maximum likelihood and restricted maximum likelihood estimation. As for repeated measures, univariate models simulations were done to verify the accuracy of the tests, both when the assumptions are valid or not. This dissertation also discusses and compares the SAS procedures GLM and MIXED when utilized for repeated measures analysis.

After publishing the auction conditions for the transmission facilities, concessionaires must assess whether the proposed condition is advantageous for them, taking into account various cost factors, directly financial or not. Given the short time for making this decision, it is important that there is a tool to assist them in this process. In this work, we present an optimization methodology for obtaining the optimal routing of transmission lines between two points, taking into account several financial, social and environmental factors. This problem has been discussed in many previous works, but the most significant difference of the proposed methodology is that it is an adaptation of a preeminent technique for the motion planning of robots: (Optimal) Rapid Random Trees, since there is a strong analogy between the problem of optimal routing of transmission lines and that of optimal motion planning of robots. The algorithm is detailed and results for three scenarios with moderate complexity are presented. These results are compatible with the experience of professionals from the field who supervised this research.

The Brazilian power system is mainly composed of hydroelectric generation. Due to the stochasticity of inflows each hydroelectric plant receives an assured energy certificate, also known as physical guarantees. In general, the assured energy certificates correspond to the energy that each plant can produce at a 95% confidence level., They also represent the maximum energy each hydro unit can contract. So, the hydro generators are constrained by the assured energy and they determine their portfolio of energy contracts with free consumers, other generators, distribution companies and trade companies accordingly. The portfolio is composed of bilateral contracts that are, mainly composed of long term contracts negotiated significantly before the power delivery. Even considering that the assured energy is constant, each generator can seasonalize it on a monthly base, but keeping to the total annual assured energy. This paper aims to use stochastic dual dynamic programming to optimize the seasonalization of this assured energy considering the bilateral contracts previously assigned and the projection of the spot price. The results obtained show a considerable increase in the revenues of a generator utility from the Brazilian system.

In this study, we use a stochastic representation of wind for medium/long-term planning problems that are associated with the operation of hydro-thermal systems. The stochastic dual dynamic programming (SDDP) technique is used in this study. Synthetic wind and hydrological scenarios are generated using a periodic autoregressive model (PAR (p)). This algorithm has wide applicability in countries with a predominantly hydroelectric energy matrix that is associated with high penetration of thermal and wind generation, as in the Brazilian power system. The nonlinearities of the hydraulic production function also was been taking into account. The developed technique can be applied due to the global expansion of power generation over the last two decades with the increasing integration of alternative sources.