Abstract
We experience the world around us using all our senses, however, multimedia content still relies majorly on audiovisual stimuli. With technology advancements, multisensory stimuli started to be introduced in multimedia experiences. Still, very few contemplate a wide range of different modalities simultaneously, approaching the stimulation one would receive in reality. This paper explores the effects of trimodal multisensory stimuli on the sense of Presence, Perceptual Realism, and Quality of Experience (QoE) during video visualisation. Namely, we study the impact of heat, wind, and smell during video visualization to investigate how each stimulus contributes to the QoE. A correlational analysis was also performed to understand better how the different variables interact. The results indicate that multisensory stimulation improved significantly the sense of presence satisfaction and perceptual realism. Furthermore, smell contributed the most to the QoE, followed by heat and wind. We highlight the use of multisensory stimulation on video visualization over audiovisual only, as it benefits substantially the user experience.

Abstract

Abstract
Nutrient quantification in hydroponic systems is essential. Reagent-less spectral quantification of nitrogen, phosphate and potassium faces challenges in accessing information-rich spectral signals and unscrambling interference from each constituent. Herein, we introduce information equivalence between spectra and sample composition, enabling extraction of consistent covariance to isolate nutrient-specific spectral information (N, P or K) in Hoagland nutrient solutions using orthogonal covariance modes. Chemometrics methods quantify nitrogen and potassium, but not phosphate. Orthogonal covariance modes, however, enable quantification of all three nutrients: nitrogen (N) with R = 0.9926 and standard error of 17.22 ppm, phosphate (P) with R = 0.9196 and standard error of 63.62 ppm, and potassium (K) with R = 0.9975 and standard error of 9.51 ppm. Including pH information significantly improves phosphate quantification (R = 0.9638, standard error: 43.16 ppm). Results demonstrate a direct relationship between spectra and Hoagland nutrient solution information, preserving NPK orthogonality and supporting orthogonal covariance modes. These modes enhance detection sensitivity by maximizing information of the constituent being quantified, while minimizing interferences from others. Orthogonal covariance modes predicted nitrogen (R = 0.9474, standard error: 29.95 ppm) accurately. Phosphate and potassium showed strong interference from contaminants, but most extrapolation samples were correctly diagnosed above the reference interval (83.26%). Despite potassium features outside the knowledge base, a significant correlation was obtained (R = 0.6751). Orthogonal covariance modes use unique N, P or K information for quantification, not spurious correlations due to fertilizer composition. This approach minimizes interferences during extrapolation to complex samples, a crucial step towards resilient nutrient management in hydroponics using spectroscopy.

Abstract
Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and acceptability of these diagnostic methods. Attempts to provide concept-based explanations are based on post-hoc approaches, which depend on an additional model to derive interpretations. In this paper, we propose an inherently interpretable framework to improve the interpretability of concept-based models by incorporating a hard attention mechanism and a coherence loss term to assure the visual coherence of concept activations by the concept encoder, without requiring the supervision of additional annotations. The proposed framework explains its decision in terms of human-interpretable concepts and their respective contribution to the final prediction, as well as a visual interpretation of the locations where the concept is present in the image. Experiments on skin image datasets demonstrate that our method outperforms existing black-box and concept-based models for skin lesion classification.

Abstract
Medical rescuers are the frontline for COVID-19 and their psychological experience and health are major concerns to our society and healthcare system. This study aims to understand how medical rescuers psychologically experienced this pandemic and explore the contributing variables to COVID-19 anxiety. Portuguese medical rescuers (n = 203) answered questions about their COVID-19 experience, the COVID-19 Anxiety Scale, Patient-Health Questionnaire, Perceived Stress Scale, Obsessive-Compulsive Inventory, and Well-Being Questionnaire. Rescuers presented low COVID-19 anxiety and low-moderate levels of fear. Most already faced or were facing changes in their job-related tasks, did not change household and did not feel stigma/discrimination. COVID-19 workplace security measures were considered moderately adequate and low anxiety, depression and obsessive-compulsive symptoms, low to moderate stress and moderate well-being were found. Only COVID-19 fear and security measures, anxiety, depression and obsessive-compulsive symptoms explained COVID-19 anxiety. Overall, findings showed these rescuers were psychologically well adjusted during the pandemic's initial stages. © 2023 Associacao Portuguesa de Psicologia. All rights reserved.

Abstract
The spectral response of a SPR (surface plasmon resonance) sensor depends on the engineering of the conducting layer. In this paper, we analyze theoretically the spectra of a D-shaped SPR PCF (photonic crystal fiber) refractive index sensor considering four different plasmonic materials: Ag, Au, Ga-doped zinc oxide (GZO) and an Ag-nanowire metamaterial. The sensing properties provided by each material and how they form the bases to design multiplasmonic resonance sensors are the focus of our discussion.