Abstract
Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evolve to become more capable and computational power keeps increasing, the datasets available are being retracted and removed from public access. Privacy and ethical concerns are relevant topics within these domains. Through generative artificial intelligence, researchers have put efforts into the development of completely synthetic datasets that can be used to train face recognition systems. Nonetheless, the recent advances have not been sufficient to achieve performance comparable to the state-of-the-art models trained on real data. To study the drift between the performance of models trained on real and synthetic datasets, we leverage a massive attribute classifier (MAC) to create annotations for four datasets: two real and two synthetic. From these annotations, we conduct studies on the distribution of each attribute within all four datasets. Additionally, we further inspect the differences between real and synthetic datasets on the attribute set. When comparing through the Kullback-Leibler divergence we have found differences between real and synthetic samples. Interestingly enough, we have verified that while real samples suffice to explain the synthetic distribution, the opposite could not be further from being true.

Abstract
We present a pilot study where spectral unmixing is applied to hyperspectral images captured in a controlled environment with a threefold purpose in mind: validation of our experimental setup, of the data processing pipeline, and of the usage of spectral unmixing algorithms for the aforementioned research avenue. Results from this study show that classical techniques such as VCA and FCLS can be used to distinguish between plastic and nonplastic materials, but struggle significantly to distinguish between spectrally similar plastics, even in the presence of multiple pure pixels.

Abstract
This study reports the fabrication of three-dimensional microfluidic channels in fused silica, using femtosecond laser micromachining, to achieve two-dimensional hydrodynamic flow focusing in either the horizontal or the vertical directions. Spatial focusing of 3 µm polystyrene particles was successfully demonstrated, showing the ability of the fabricated devices to confine microparticles within a 6 µm layer over a channel width of 420 µm and within a 5 µm layer over a channel height of 260 µm. Integration of laser-direct written optical waveguides inside a microfluidic chip and orthogonal to the channel also enabled the implementation of a dual-beam optical trap, with trapping of polystyrene microparticles using a 1550 nm beam being demonstrated. © The Authors.

Abstract
Over recent decades, precision agriculture has revolutionized farming by optimizing crop yields and reducing resource use through targeted applications. Existing portable spray quality assessors lack precision, especially in detecting overlapping droplets on water-sensitive paper. This proposal aims to develop a smartphone application that uses the integrated camera to assess spray quality. Two approaches were implemented for segmentation and evaluation of both the water-sensitive paper and the individual droplets: classical computer vision techniques and a pre-trained YOLOv8 deep learning model. Due to the labor-intensive nature of annotating real datasets, a synthetic dataset was created for model training through sim-to-real transfer. Results show YOLOv8 achieves commendable metrics and efficient processing times but struggles with low image resolution and small droplet sizes, scoring an average Intersection over Union of 97.76% for water-sensitive spray segmentation and 60.77% for droplet segmentation. Classical computer vision techniques demonstrate high precision but lower recall with a precision of 36.64% for water-sensitive paper and 90.85% for droplets. This study highlights the potential of advanced computer vision and deep learning in enhancing spray quality assessors, emphasizing the need for ongoing refinement to improve precision agriculture tools. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

Abstract
Traditionally, proportional-integral (PI) control has ensured the successful application of automatic generation control (AGC). Two design features of AGC-PI are the following: (1) it is merely a reactive system which does not take full advantage of existing knowledge about the system and (2) the control signal sent to all units is divided proportionally to their participation in the AGC. These two features ensure simplicity and, thus, reliability for the regular functioning of the power system. However, when the power system is recovering from a loss of generation, such features can become shortcomings. This paper proposes a model predictive control (MPC) to improve performance of AGC in such a scenario. The contrast with the traditional approach is as follows: instead of using merely two system measures which are also the control objectives (frequency and interconnection flow), the proposed controller relies on an internal model that takes advantage of further known variables of the power system, especifically the ramping capabilities of participating units. While still respecting the participation factors, it is shown that the proposed model allows to exhaust earlier the availability of faster units, such as some demand response, as the one to be provided by hydrogen electrolysers, and thus reestablishes the frequency and interconnection flows in a faster way than typical AGC-PI.

Abstract
Resilience is an important theoretical construct that helps to conceptualise the ways individuals and organisations attempt to countervail the effects of poverty and austerity. As a response to prolonged crises, such as the global economic crisis and the COVID-19 pandemic, this chapter focuses on tracing the psychological, behavioural, sociological and spatial perspectives of resilience, advancing our current understanding of resilience theory within the marketing and consumption context of crises and austerity. The chapter reviews recent research exploring the importance of resilience and, more specifically, the notion of persistent resilience in response to long-term stressors, such as unemployment, triggered by the austerity measures imposed by European governments following the global economic crisis as well as the COVID-19 pandemic. In advancing previous research in this area, we offer a broader perspective by underlining the impetus for businesses and communities to employ a range of resilience strategies while also highlighting the importance for individuals to develop a sustainable set of resilience capacities to help creatively navigate the market and flexibly adapt to the long-term effects of intense and long-standing crises © 2024 selection and editorial matter, Caroline Moraes, Morven G. McEachern and Deirdre O’Loughlin; individual chapters, the contributors. All rights reserved.