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- Decoding Musical Valence and Arousal: Exploring the Neural Correlates of Music-Evoked Emotions and the Role of Expressivity FeaturesPublication . Sayal, Alexandre; Guedes, Ana Gabriela; Almeida, Inês A. T.; Jardim Pereira, Daniela; Lima, César F.; Panda, Renato; Paiva, Rui Pedro; Sousa, Teresa; Castelo-Branco, Miguel; Bernardino, Inês; Direito, BrunoMusic conveys both basic emotions, like joy and sadness, and complex ones, such as tenderness and nostalgia. Its effects on emotion regulation and reward have attracted much research attention, as the neural correlates of music-evoked emotions may inform neurorehabilitation interventions. Here, we used fMRI to decode and examine the neural correlates of perceived valence and arousal in music excerpts. Twenty participants were scanned while listening to 96 music excerpts, classified beforehand into four categories varying in valence and arousal. Music modulated activity in cortical regions, most noticeably in music-specific subregions of the auditory cortex, thalamus, and regions of the reward network such as the amygdala. Using multivoxel pattern analysis, we created a computational model to decode the perceived valence and arousal of the music excerpts with above-chance accuracy. We further explored associations between musical features and brain activity in valence-, arousal-, reward-, and auditory-related networks. The results emphasize the involvement of distinct musical features, notably expressive features such as vibrato and tonal and spectral dissonance in valence, arousal, and reward brain networks. Using ecologically valid music stimuli, we contribute to delineating the neural correlates of music-evoked emotions with potential implications in the development of novel music-based neurorehabilitation strategies.
- Remote Monitoring of Energy-autonomous Constructed WetlandsPublication . Lopes, Simão; Barros, F.M.; Ferreira, Carlos; Mateus, D. M. R.; Matos, Pedro; Neves, Pedro; Pinho, Henrique J. O.Constructed Wetlands systems (CW) are nature-based and sustainable technology for treating wastewater, contributing to the management and protection of freshwater resources. Moreover, CW can contribute to valorizing waste materials, producing reclaimed water for diverse applications, and producing plant biomass that can be material and energetically valorized. Because CW efficiency depends on several mechanisms such as physical, chemical, and biological, its real-time monitoring is essential to provide a better use of this technology. This work describes a smart framework for monitoring CW based on IoT devices and sensors, and data science tools providing real-time processing of gathered water quality parameters and environmental variables. Furthermore, the framework manages renewable energy sources to provide the required energy for CW operation and monitoring. Data collected from the sensor network show significant daily variations in water quality parameters. The future processing of these data can provide the development of models to improve the efficiency of the CW.
- A Comparison Study of Deep Learning Methodologies for Music Emotion RecognitionPublication . Louro, Pedro; Redinho, Hugo; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoClassical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such necessity. Nonetheless, there are limitations, such as the need for large amounts of quality labeled data, a common problem in MER research. To understand the effectiveness of these techniques, a comparison study using various classical machine learning and deep learning methods was conducted. The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1 score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, combining handcrafted features with feature learning.
- A Usability Study on Widget Design for Selecting Boolean OperationsPublication . Chambel Lopes, Diogo; Mendes, Helena; Portal, Ricardo; Klerk, Rui de; Nogueira, Isabel; Lopes, Daniel SimõesApplying the correct set of Boolean operations is a fundamental task in constructive solid geometry (CSG), which is a staple in automated manufacturing systems. Although textual buttons and icons are the most common interfaces to apply such operations, these require an unnecessary cognitive load that hampers the solid modeling process. This study presents VennPad, a novel CSG widget that gathers all Boolean operations under the same user interface control element and is represented as a two-set Venn diagram. Contrary to conventional CSG widgets, VennPad supports a graphical interface that gives simultaneous access to several types of Boolean operations (intersection, union, difference, symmetric difference and split). A usability study was conducted to ascertain whether VennPad is a more natural interface compared to textual buttons and icon-based widgets for different solid modeling tasks. VennPad proved to be an effective interface to perform Boolean operations. Qualitative feedback places VennPad as the preferred interface, but efficiency results are operation dependent, thus, opening the way to new design iterations.
- Envisaging a global infrastructure to exploit the potential of digitised collectionsPublication . Groom, Quentin; Dillen, Mathias; Addink, Wouter; Ariño, Arturo H.; Bölling, Christian; Bonnet, Pierre; Cecchi, Lorenzo; Ellwood, Elizabeth R.; Figueira, Rui; Gagnier, Pierre-Yves; Grace, Olwen; Güntsch, Anton; Hardy, Helen; Huybrechts, Pieter; Hyam, Roger; Joly, Alexis; Kommineni, Vamsi Krishna; Larridon, Isabel; Livermore, Laurence; Lopes, Ricardo Jorge; Meeus, Sofie; Miller, Jeremy; Milleville, Kenzo; Panda, Renato; Pignal, Marc; Poelen, Jorrit; Ristevski, Blagoj; Robertson, Tim; Rufino, Ana C.; Santos, Joaquim; Schermer, Maarten; Scott, Ben; Seltmann, Katja; Teixeira, Heliana; Trekels, Maarten; Gaikwad, JitendraTens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.
- Isolation, Identification, and Characterization of Phosphate-Solubilizing Bacteria from Tunisian SoilsPublication . Amri, Marwa; Rjeibi, Mohamed Ridha; Gatrouni, Marwa; Mateus, D. M. R.; Asses, Nedra; Pinho, Henrique J. O.; Abbes, ChaabaneSoil microorganisms play an important role in maintaining natural ecological balance through active participation in carbon, nitrogen, sulfur, and phosphorous cycles. Phosphate-solubilizing bacteria (PSB) are of high importance in the rhizosphere, enhancing the solubilization of inorganic phosphorus complexes into soluble forms available for plant nutrition. The investigation of this species of bacteria is of major interest in agriculture, as they can be used as biofertilizers for crops. In the present study, 28 isolates of PSB were obtained after the phosphate enrichment of soil samples from five Tunisian regions. Five PSB species were identified by 16S rRNA gene sequencing including Pseudomonas fluorescens, P. putida, and P. taiwanensis, Stenotrophomonas maltophilia, and Pantoea agglomerans. Solid and liquid Pikovskaya’s (PVK) and National Botanical Research Institute’s (NBRIP) media containing insoluble tricalcium phosphate were used for the evaluation of the phosphate solubilization ability of the bacterial isolates by two methods: visual evaluation of the solubilization zone around colonies (halo) and determination of solubilized phosphates in liquid medium by the colorimetric method of the vanado-molybdate yellow. Based on the results of the halo method, the isolate of each species that showed the higher phosphate solubilization index was selected for evaluation of phosphate solubilization by the colorimetric method. In the liquid media, the bacterial isolates showed phosphate solubilization ranging from 535.70 to 618.57 µg mL−1 in the NBRIP medium, and 374.20 to 544.28 µg mL−1 in the PVK medium, with the highest values produced by P. fluorescens. The best phosphate solubilization ability and higher reduction in broth pH, which indicates higher organic acid production, were achieved in NBRIP broth for most of the PSB. Strong correlations were observed between the average capability of PSB to solubilize phosphates and both the pH and total phosphorous content in the soil. The production of the hormone indole acetic acid (IAA), which can promote plant growth, was observed for all five PSB species. Among them, P. fluorescens obtained from the forest soil of northern Tunisia showed the highest production of IAA (50.4 ± 0.9 µg mL−1).
- Physics of Sound to Raise Awareness for Sustainable Development Goals in the Context of STEM Hands-On ActivitiesPublication . Costa, Maria Cristina; Ferreira, Carlos; Pinho, Henrique J. O.This paper aims to present an interdisciplinary approach intended to raise awareness for Sustainable Development Goals in the context of STEM (Science, Technology, Engineering, and Mathematics) hands-on activities targeted to elementary and secondary school. In particular, contents related to the physics of sound are used to warn about the dangers of noise pollution and its consequences for health, well-being, and productivity. Therefore, it is crucial to inform and raise community awareness on this issue, as well as on the measures needed to prevent its consequences. This research is inserted in a broader pedagogical project that includes primary school and secondary school teachers’ professional development and visits to schools to perform several hands-on activities in class aiming to provide students with 21st-century skills related to STEM education. Based on the literature, questionnaires, and participant observation, an empirical study was conducted with teachers who participated in a professional development programme. It is concluded that teachers and students understood the dangers of noise pollution and the measures to be taken to prevent them. Therefore, higher education institutions have a crucial role in the community, namely, through partnerships with schools and teachers’ training centres to raise awareness and disseminate and increase Sustainable Development practices in the community.
- Bioenergy routes for valorizing constructed wetland vegetation: An overviewPublication . Pinho, Henrique J. O.; Mateus, D. M. R.Valorizing constructed wetlands vegetation into biofuels can be a way to contribute to mitigating the increasing energy demand, avoiding the use of arable land, freshwater, and fertilizers consumption, while simultaneously treating wastewater with eco-friendly technology. This work shortly overviews the main genera of wetland plants and the main routes of vegetal biomass conversion into biofuels including biochemical and thermochemical processes, and through a cross-search, in the Scopus database, the research intensity in bioenergy application for each genus was assessed. A total of 283 genera of wetland plants were identified and classified into five groups, from very common to very rare genera. The very common group includes 10 genera and contributes to 62% of the literature hits, while the 147 genera classified as very rare contribute to only 3% of the hits. Concerning the bioenergy applications, four genera stand out from the remaining. The plants of the genus Sorghum are the most referred to in bioenergy applications, followed by the genera Brassica, Miscanthus, and Saccharum. Miscanthus is a less common wetland plant, while the other genera are rarely applied in constructed wetlands. The relevance of bioenergy routes depends on the plants' group. For common wetland plants, the most relevant applications are biogas production, followed by bio-ethanol production, and pyrolysis processing. As a recommendation for future research works the genera with high energy potential should be evaluated as wetland vegetation, and it is recommended that the goal to recover wetland vegetation for bioenergy applications be viewed as an integral step of the design and implementation of constructed wetlands facilities.
- Wastewater Electrolysis for Hydrogen ProductionPublication . Cartaxo, Marco; Fernandes, José; Gomes, Mário; Pinho, Henrique J. O.; Nunes, Valentim; Coelho, PauloDue to its highest gravimetric energy density, H2 has been regarded as the preferred clean-energy carrier, with potentially environmentally-friendly production through the solar-assisted WS. Since human activities generate enormous amounts of WW, H2 production from this new resource has gained attention as an emergent technology. This paper addresses the most relevant and current aspects of H2 production from WWEL, and electricity generation from RES. In this sense, the state of art of H2 production, especially from WS, is presented here, as well as the main approaches to electricity generation from RES, with the greatest potential for viability. A new approach on this matter, which is part of the work that is being developed by the authors of this study, was also herein presented.
- Audio Features for Music Emotion Recognition: a SurveyPublication . Panda, Renato; Malheiro, Ricardo; Paiva, Rui PedroThe design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Finally, although the focus of this article is on classical feature engineering methodologies (based on handcrafted features), perspectives on deep learning-based approaches are discussed.