Abstract
Anaphora resolution is one of the problems in natural language processing. It is the process of disambiguating the antecedent of a referring expression from the set of entities in a discourse. The correct interpretation of pronouns plays an important role in the construction of meaning Thus, the resolution of pronominal anaphors remains a very important task for many natural language processing applications. Additionally, it plays an increasingly significant role in computational linguistics. However, a significant amount of work on anaphora resolution is focused on English; anaphora resolution for other languages, including Arabic, is still limited. In this paper, we present a new set of computational and linguistic features to resolve Arabic anaphors using a machine learning approach. In this paper, an in-depth study was conducted on a set of computational and linguistic features to exploit their effectiveness and investigate their effect on anaphora resolution. The aim was to efficiently integrate different feature sets and classification algorithms to synthesize a more accurate classification procedure. Four well-known machine learning algorithms k-nearest neighbor, maximum entropy, decision tree and meta-classifier, were employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on Quran datasets was conducted, the discussion presented, and conclusions were drawn. The experimental results show that our approach gives satisfactory results. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario. © 2021 IEEE.

Abstract
Deriving from the scholarly debate surrounding design policies in the past 25 years, this paper proposes a novel triangulation of fundamental issues which remain unsolved, by highlighting them and analyzing how they relate to each other. The issues under analysis are innovation policies, the role of design in research, development and innovation and the multiplicity of design definitions. These are intrinsically linked with Design policies, but their interconnections are not well established or visible, leading to conflicting perspectives and tensions. Ultimately, this approach provides new insights on the rationale and construction of Design policies, towards prospective interpretations for their role and aspirations.

Abstract
Sensors based on polarization are suitable for application in power grids due to their excellent characteristics, such as high electrical insulation, non-magnetic saturation, oil-free, no risk of explosive failures, and high bandwidth. Utility companies are incorporating new technologies that are driving the evolution of electrical systems. Thus, it is interesting to evaluate the possibility of using polarization sensors in a network configuration. In this work, we present an experimental study of a current and voltage polarization sensor network applied to a medium voltage distribution grid. The current sensor is based on the Faraday effect, and the voltage sensor uses the Pockels effect. Both sensors use a 90° polarization degree between the two output ports to compensate for the various impairments on the measurements by applying the difference-over-sum. The network uses a DWDM topology centered at the 1550 nm range, and both current and voltage sensors in this work used this spectral band. We evaluated the sensor node in terms of accuracy according to IEC standard 61869-10 and IEC standard 61869-11. Considering that an important application of this sensor network is in the aerial cable of medium voltage networks, sensor node accuracy was also estimated in the presence of cable vibration. The calculated power budget of the proposed network indicates that reaching ten nodes of current and voltage sensors in a 10 km optical link is possible, which is enough for a medium urban voltage distribution network.

Abstract
In this work we use the concept of paraxial fluids of light to explore quantum turbulence, probing a turbulent regime induced on an optical beam propagating inside a defocusing nonlinear media. For that purpose, we establish a physical analogue of a two-component quantum fluid by making use of orthogonal polarizations and incoherent beam interaction, obtaining a system for which the perturbative excitations follow a modified Bogoliubov-like dispersion relation. This dispersion relation features regions of instability that define an effective range of energy injection and that are easily tuned by manipulating the relative angle of incidence between the two components. Our numerical results support the predictions and show evidence of direct and inverse turbulent cascades expected from weak wave turbulence theories, which may inspire new ways to explore to quantum turbulence with optical analogues.

Abstract
The materials used in the transport industry have been changing in the last decades. The traditional and heavy steel have been switching by the light alloys like aluminum alloys. However, despite their advantages as low density and high corrosion resistance, the manufacturing process, especially fusion welding, is very demanding and challenging. In the transport industry, most of the hyperstatic components made in aluminum alloys are welded manually with the associate financial costs as well as the lack of quality and repeatability. For these reasons, it is urgent to develop new methodologies to automate this process. The present work intends to show a scientific method to automate the welding process of hyperstatic frames, very common in bicycles, made in aluminum alloy. This methodology involves two steps, the first one in which is performed numerical simulations to determine the optimal welding parameters to minimize the distortion and residual stresses. The second step is experimental one, and it is created an automated welding cell with a robot to weld the frames. It has been proved that it is possible to obtain welding aluminum frames with acceptable quality in agreement with the ASME IX standard. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.