Abstract
The last years saw the emergence of nonlinear optical materials, with local and nonlocal nonlinearities, as experimentally accessible systems to implement optical analogues of quantum fluids. In these systems, a light beam propagating in the nonlinear medium can be interpreted as a fluid, where the diffraction in the transverse plane to the propagation gives the effective mass of the fluid and the medium nonlinearity mediates the required interactions between the photons. This fluid interpretation and its application have been extensively studied, from the creation of superfluid-like flows and the study of phenomena associated with this effect to the implementation of gravity analogues. Furthermore, many optical materials have been considered, with a special interest in the ones that offer tunable mechanisms that allow to easily control the system properties to better explore and emulate the different phenomena. Recently, nematic liquid crystals have been proposed as an interesting tunable material capable of supporting superfluids of light. These systems have a nonlocal character and offer external mechanisms that can be used to tailor the nonlinearity to better emulate the desired analogue system. Indeed, through the application of an external electric field perpendicular to the direction of propagation, is it possible to control the nonlocal length of the nonlinearity. This mechanism offers interesting opportunities in the present context. In this work, through numerical methods based on GPGPU supercomputing, we explore the possibility of observing superfluid effects in defocusing nematic liquid crystals. In particular, we explore the possibility of observing the drag force cancellation and the emission of quantized vortices, which are two manifestations of a superfluid flow. Furthermore, we also discuss the possibility of using these systems for creating an analogue of quantum turbulence with these materials. These studies constitute a stepping-stone towards the implementation of gravity analogues with nematic liquid crystals.

Abstract
Adaptive Deep Brain Stimulation has recently emerged to tackle conventional DBS limitations by measuring disease fluctuations and to adapt stimulation parameter accordingly. In early 2020, Medtronic launched in the European Union the first certified DBS neurostimulator capable of simultaneously stimulate and read signals from the deep brain structures, the PerceptTMPC. In epilepsy, the most common target brain structure is the Anterior Nucleus of Thalamus and the Local Field Potentials analysis requires prior synchronization of data recorded from the Percept PC with video-Electroencephalography (vEEG) equipment. Fine-grained synchronization (sub-second resolution) is mandatory for multimodal neurodata analysis and may be achieved by aligning artefacts perceived in both systems. In this work we study two methods aiming for neurodata streams clock synchronization: one based on DBS stimulation artefacts and another on tapping maneuver artefacts. For this purpose, we studied the data collected from the first epileptic patient that underwent 1-week vEEG-PerceptTMPC monitoring at a Hospital monitoring unit. We found that tapping maneuver-based methodology allowed a more accurate synchronization in relation to the stimulation artefact-based method (0.56s vs. 2.07s absolute average uncertainty). This method was also more complete one since tapping timestamps can be determined by video timeframes and do not require a prior identification of artefacts in EEG data by clinicians.

Abstract
Modern communications have moved away from point-to-point models to increasingly heterogeneous network models. In this article, we propose a novel controller-based architecture to deploy adaptive causal network coding in heterogeneous and highly meshed communication networks. Specifically, we consider using the software-defined network as the main controller. We first present an architecture for highly meshed heterogeneous multi-source multi-destination networks that represent the practical communication networks encountered in the fifth generation of wireless networks and beyond. Next, we present a promising solution to deploy network coding over the new architecture. We also present a new controller-based setting with which network coding modules communicate to attain the required information. Finally, we briefly discuss how the proposed architecture and network coding solution provide a good opportunity for future technologies.

Abstract
Learning computer programming is a complex activity and requires a lot of practice. The viral pandemic that we are facing has intensified these difficulties. In this context, programming learning platforms play a crucial role. Most of them are characterized by providing a wide range of exercises with progressive complexity, multi-language support, sophisticated interfaces and automatic evaluation and gamification services. Nevertheless, despite the various features provided, others features, which influence user experience, are not emphasized, such as performance and usability. This article presents an user experience evaluation of the LearnJS playground, a JavaScript learning platform which aims to foster the practice of coding. The evaluation highlights two facets of the code playground: performance and a usability. In the former, lab and field data were collected based on Google Lighthouse and PageSpeed Insights reports. In the later, an inquiry was distributed among students from a Web Technologies course with a set of questions based on flexibility, usability and consistency heuristics. Both evaluation studies have a twofold goal: to improve the learning environment in order to be officially used in the next school year and to foster the awareness of user experience in all phases of the software development life-cycle as a key facet in Web applications engagement and loyalty. © Ricardo Queirós, Mário Pinto, and Teresa Terroso; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).

Abstract
To remain competitive, companies must continuously improve the processes at hand, be they administrative, production, or logistics. The objective of the study described in this paper was to develop a decision-making tool based on a simulation model to support the production of knits and damask fabrics. The tool was used to test different control strategies for material flow, from the raw material warehouse to the finished product warehouse, and thus can also be used to evaluate the impacts of these strategies on the productivity. The data upon which the decision support tool was built were collected from five sectors of the plant: the raw material warehouse, knit production, damask production, finishing work, and the finished product warehouse. The decision support tool met the objectives of the project, with all five strategies developed showing positive results. Knit and damask production rates increased by up to 8% and 44%, respectively, and a reduction of 75% was observed in the waiting time on the point of entry to the finishing work area, compared to the company's existing system.

Abstract
Association football has been the subject of many research studies. In this work we present a study on player similarity using passing sequences extracted from games from the top-5 European football leagues during the 2017/2018 season. We present two different approaches: first, we only count the motifs a player is involved in; then we also take into consideration the specific position a player occupies in each motif. We also present a new way to objectively judge the quality of the generated models in football analytics. Our results show that the study of passing sequences can be used to study player similarity with relative success. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.