Browsing by Author "Silva, L."
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- Asdas responses in patients with axial spondyloarthritis starting bdmards: results from a multicentre prospective cohortPublication . Santos, M. E.; Ramiro, S.; Van der Heijde, D.; Landewé, R.; Santos, F. Pimentel; Machado, A. R. Cruz; Ferreira, C.; Gomes, C.; Soares, C. Dantas; Miguel, C.; Albuquerque, F.; Martins, F.; Silva, L.; Santos, H.; Almeida, I.; Bernardes, M.; Khmelinskii, N.; Valente, P.; Teixeira, P. M.; Matias, S. Emídio; Fraga, V.; Branco, J. C.; Sepriano, A.ASAS and EULAR recommend the use of an improvement ≥1.1 in ASDAS at 12 weeks to determine the continuation of a bDMARD. However, it is debated whether improvements can occur and whether patients’ characteristics influence (time to) response.
- Balanced Scorecard as a management model in the waste sectorPublication . Mendes, Paula; Almeida, S.; Santos, Ana Carina; Ribau Teixeira, Margarida; Murta, E.; Silva, L.All Organizations recognize that internal methodologies and frameworks are very important to have a thorough knowledge of their potential, simultaneous increase competitiveness and, correspondingly, enhance and optimize the performance of their service. Waste management is a challenge of modern society, and there is awareness that responsibility of waste management should be shared by all community, to cooperate and ensure a sustainable development with the best principles and best management practices. It is a citizenship issue, where citizens contribute adopting preventative behaviours in the production of waste, as well as practices that facilitate waste recycling, reuse and recovery, which contribute to reduce the waste life cycle. This concern and the service improvement involve the minimization of the environmental impacts, the conservation of the natural resources, the reduction of pollutant emissions, as well as the design of the solutions for the collection, transfer and transport, treatment and waste final deposition, and the allocation of human and financial resources. The need for a sustainable management of resources has led to the design and development of management models in waste systems to assess in what extent the various tasks or activities are (or are not) carried out in accordance with the objectives established in advance and the efforts, decisions and operational actions developed by organisations to improve the quality of its work. Thus, given the problems and requirements of the waste sector, it is necessary to outline and plan sustainable strategies for the management system. Therefore, the objective of the present work is the application and study of key concepts related to design and implementation of a management model, the Balanced Scorecard (BSC). This is a management method based on critical success factors, which propose is to translate through a systemic approach, the mission and strategy of Organizations (private or public / nonprofit) in operational objectives, arranged into perspectives interconnected in a cause-effect relation. It connects the strategic objectives to measurable measures (performance indicators) that indicate the success or failure of the adopted strategy, contributing to a review. The BSC is a very useful and simple management tool, which perfectly suits the needs of the waste sector. It works as a measurement and management system, and a basis for the strategy communication to all elements of the system, and through a joint analysis, demonstrates of the importance of all stakeholders to the overall management, encouraging their involvement and motivation.
- Ontogeny of swimming behaviour in sardine Sardina pilchardus larvae and effect of larval nutritional condition on critical speedPublication . Silva, L.; Faria, Ana Margarida da Silva; Chicharo, Maria Alexandra Teodosio; Garrido, SusanaThe ontogeny of swimming behaviour in sardine Sardina pilchardus larvae was studied, from hatching to 75 days post-hatch (dph), by measuring the critical swimming speed (Ucrit) and observing locomotory behaviour. In addition, the effect of larval nutritional condition on Ucrit at the onset of their swimming abilities (20 to 25 dph) was evaluated by rearing larvae under 4 different feeding treatments. Diets consisted of different concentrations of dinoflagellates, rotifers and the copepod Acartia grani, and a wild plankton assemblage. Recently hatched larvae were mostly inactive, but from 2 dph onwards larvae started to swim freely in the rearing tank, and time spent swimming increased throughout ontogeny. Larvae younger than 20 dph (i.e. <7.90 mm TL) could not swim for the entire adjustment period at the minimum current speed, but thereafter Ucrit increased significantly with larval age and length, reaching a maximum of 9.47 cm s-1 at 19.10 mm TL and 55 dph. Growth, survival and the nutritional condition of sardine larvae, assessed by the RNA residual index, were significantly higher for larvae reared with the high-concentration diet, contrary to the other derived nucleic acids indices (RNA/DNA and DNA/DW), which showed no differences between diets. Despite differences in the survival and growth rates of sardine larvae, Ucrit at the onset of swimming did not differ significantly among diets, but was significantly related to larval nutritional condition as assessed by the RNA residual index. Overall, our results show that early larval stages of sardines have poor swimming ability and probably rely on food patches in the wild to survive; however, close to metamorphosis (especially from 45 dph onwards), larvae spend most of the time swimming and are capable of resisting the mean current speeds of their natural environment, which may strongly enhance chances for survival.
- SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopyPublication . A. Martins, J.; Guerra, Rui Manuel Farinha das Neves; Pires, R.; Antunes, M.D.; Panagopoulos, T.; Brázio, A.; Afonso, A.M.; Silva, L.; Lucas, M.R.; Cavaco, A.M.This work presents a new deep learning architecture, SpectraNet-53, for quantitative analysis of fruit spectra, optimized for predicting Soluble Solids Content (SSC, in Brix). The novelty of this approach resides in being an architecture trainable on a very small dataset, while keeping a performance level on-par or above Partial Least Squares (PLS), a time-proven machine learning method in the field of spectroscopy. SpectraNet-53 performance is assessed by determining the SSC of 616 Citrus sinensi L. Osbeck 'Newhall' oranges, from two Algarve (Portugal) orchards, spanning two consecutive years, and under different edaphoclimatic conditions. This dataset consists of short-wave near-infrared spectroscopic (SW-NIRS) data, and was acquired with a portable spectrometer, in the visible to near infrared region, on-tree and without temperature equalization. SpectraNet-53 results are compared to a similar state-of-the-art architecture, DeepSpectra, as well as PLS, and thoroughly assessed on 15 internal validation sets (where the training and test data were sampled from the same orchard or year) and on 28 external validation sets (training/test data sampled from different orchards/years). SpectraNet-53 was able to achieve better performance than DeepSpectra and PLS in several metrics, and is especially robust to training overfit. For external validation results, on average, SpectraNet-53 was 3.1% better than PLS on RMSEP (1.16 vs. 1.20 Brix), 11.6% better in SDR (1.22 vs. 1.10), and 28.0% better in R2 (0.40 vs. 0.31).