Abstract
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.

Abstract
The need for faster and energy-efficient computing technologies has recently pushed for major developments on alternative computing paradigms to the common von Neumann architecture. Amongst those, reservoir computing framework is an emerging concept that leverages a simple training process and eases transference to hardware implementations, allowing any given nonlinear physical system to act as a computing platform. In this work, we explore how we can make use of a discrete chain of solitons to create an effective reservoir computing framework, investigating not only the ability to learn data but also to predict models depending on the strength of the nonlinear interaction of the media. Probing the role of the nonlinear separation for tasks involving nonlinear separable data, these results open new possibilities for a multitude of physical implementations in the context of optical sciences, from optical fibers to nonlinear crystals.

Abstract
The classification of the type of trapped particles is a crucial task for an efficient integration of optical-tweezers in intelligent microfluidic devices. In the recent years, the use of the temporal scattering signal of the trapped particle paved for the use of versatile optical fiber solutions for performing such tasks, a feature previously unavailable as most methods required conventional optical tweezer setups. This work presents a comprehensive comparison of performances achieved with two distinct implementations – i)optical fiber and ii)conventional optical tweezers – for the classification of the material of particles through the analysis of the scattering signal with machine learning algorithms. The results suggest that while micron-sized particles can be accurately classified using the forward scattering information in conventional optical tweezers, when equipped with a quadrant photodetector, the optical fiber tweezers solutions can easily surpass its performance using the back-scattered information if the laser is modulated. Together with the advantages of being simpler, less expensive and more versatile, the results presented suggest that optical fiber solutions can become a valuable tool for miniaturization and integration of intelligent microfluidic devices working towards nanoscopic scales. © 2021 The Authors

Abstract
Light propagating in nonlinear optical materials opens the possibility to emulate quantum fluids of light with accessible tabletop experiments by taking advantage of the hydrodynamical interpretation. In this context, various optical materials have been studied in recent years, with nematic liquid crystals appearing as one of the most promising ones due to their controllable properties. Indeed, the application of an external electric field can tune their nonlocal response, and this mechanism may be useful for producing fluids of light and developing optical analogues. In this work, we extend the applicability of nematic liquid crystal to support optical analogues and study the possibility of emulating turbulent phenomena by using two fluids of light. These fluids interact with each other through the nonlinearity of the medium and generate instabilities that will lead to turbulent regimes. We also explore the possibility of exciting turbulent regimes through the decay of dark soliton stripes. The preliminary results are presented. © 2022, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Laser-induced breakdown spectroscopy (LIBS) is a technique that leverages atomic emission towards element identification and quantification. While the potential of the technology is vast, it still struggles with obstacles such as the variability of the technique. In recent years, several methods have exploited modifications to the standard implementation to work around this problem, mostly focused on the laser side to increase the signal-to-noise ratio of the emission. In this paper, we explore the effect of pulse duration on the detected signal intensity using a tunable LIBS system that consists of a versatile fiber laser, capable of emitting square-shaped pulses with a duration ranging from 10 to 100 ns. Our results show that, by tuning the duration of the pulse, it is possible to increase the signal-to-noise ratio of relevant elemental emission lines, an effect that we relate with the computed plasma temperature and associated density for the ion species. Despite the limitations of the work due to the low-resolution and small range of the spectrometer used, the preliminary results pave an interesting path towards the design of controllable LIBS systems that can be tailored to increase the signal-to-noise ratio and thus be useful for the deployment of more sensitive instruments both for qualitative and quantitative purposes. © 2022, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
One of the caveats of laser-induced breakdown spectroscopy technique is the performance for quantification purposes, in particular when the matrix of the sample is complex or the problem spans over a wide range of concentrations. These two questions are key issues for geology applications including ore grading in mining operations and typically lead to sub-optimal results. In this work, we present the implementation of a class of clustered regression calibration algorithms, that previously search the sample space looking for similar samples before employing a linear calibration model that is trained for that cluster. For a case study involving lithium quantification in three distinct exploration drills, the obtained results demonstrate that building local models can improve the performance of standard linear models in particular in the lower concentration region. Furthermore, we show that the models generalize well for unseen data of exploration drills on distinct rock veins, which can motivate not only further research on this class of methods but also technological applications for similar mining environments.