Browsing by Issue Date, starting with "2024-09-16"
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- “Once upon a time… a beach sand grain”: a bed-time story and scientific outreach activity for young children to increase sediment literacyPublication . Lira, Cristina Ponte; Valverde, Fátima; Matias, AnaPurposeLearning science in early years can cultivate children's curiosity and enjoyment in exploring the world around them, laying the foundation for the progression of science learning and ultimately increasing science literacy. Here, we present an example of a tailored preschool scientific activity designed to enhance literacy about sediments and illustrate their importance to both humans and nature.MethodsThe activity centres around a captivating story detailing the journey of a sand grain from the mountains to the sea. This storytelling experience is enriched with hands-on observation of various sand grains, informative cards on key topics, and culminates in a creative colouring activity.ResultsTo date, the activity has been repeated five times, engaging 110 children (from 2 to 10 years). It has yielded positive outcomes with both preschool and primary school students, as they were actively engaged in the story and delighted in handling and observing the magnified sand grains.ConclusionsThe activity was successfully implemented for preschool and primary school students, fostering engagement with the story and the sand samples. However, while the immediate engagement was evident, the impact on sediment literacy remains to be measured. Future structured evaluations are needed to assess the long-term effectiveness of such initiatives in enhancing sediment literacy among young learners.
- Socially acceptable feed formulations may impact the voluntary feed intake and growth, but not robustness of Nile Tilapia (Oreochromis niloticus)Publication . Mendes, Rodrigo; Rena, Paulo; Dias, Jorge; Fachadas Gato Coelho Gonçalves, Ana Teresa; Teodósio, Rita; Engrola, Sofia; Sánchez-Vázquez, Francisco J.; Conceição, Luís E. C.Society is becoming more demanding with aquaculture’s environmental footprint and animal wellbeing. In order to potentially mitigate these concerns, feed formulations could be based on eco-efficient (circular economy-driven) or organic ingredients. This study aimed to investigate the growth performance, feed utilization, and health status of juvenile Nile tilapia (Oreochromis niloticus) when fed with such feeds. The growth trial lasted for 8 weeks, and fish had an initial weight of 31.0 ± 0.5 g (mean ± SD). Fish were fed until visual satiation, in quadruplicate, with one of three isonitrogenous and isoenergetic experimental feeds: a commercial-like feed without fishmeal (PD), a diet based on ingredients compatible with organic certification (ORG), or a feed formulated using circular economy-driven subproducts and emergent ingredients (ECO). Fish fed ECO showed a tendency for decreased feed intake, while ORG fish significantly reduced their intake compared to those fed PD. Consequently, fish fed ECO (62.7 ± 5.4 g) exhibited almost half the growth than those fed PD (107.8 ± 6.1 g), while ORG fish almost did not increase their weight (32.7 ± 1.3 g). ECO and ORG diets had a lower digestibility for protein, lipid, and energy when compared to PD. Feed utilization of fish fed ECO or ORG was also lower than those fed PD. From the health-related genes analyzed, only glutathione reductase (gsr) showed statistically significant differences, being more expressed in fish-fed ECO than those fed PD. Thus, even when such novel formulations induced extreme effects on voluntary feed intake, their impact was noted only in fish growth, but not in robustness.
- Automated detection of hillforts in remote sensing imagery with deep multimodal segmentationPublication . Canedo, Daniel; Fonte, João; Dias, Rita; Pereiro, Tiago do; Gonçalves‐Seco, Luís; Vázquez, Marta; Georgieva, Petia; Neves, António J. R.Recent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR-derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post-processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F-1 score of 66%. In England experiments, the system achieved a 59% F1 score when fine-tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.