Sensor indutivo para guiamento de veículos

Este artigo apresenta um sensor indutivo vocacionado para o guiamento de veículos. Trata-se de um trabalho desenvolvido no âmbito da cadeira de Projecto de Electrónica do Curso de Engenharia de Sistemas das Telecomunicações e Electrónica. Esta comunicação descreve o enquadramento, projecto e implementação do referido sensor.info:eu-repo/semantics/publishedVersio

First report of Sydowia polyspora causing disease on Pinus pinea shoots

Short CommunicationThe fungus Sydowia polyspora is frequently isolated from conifers worldwide and is considered a pathogen on several hosts. Stone pine (Pinus pinea) is one of the most important forestry species throughout the Mediterranean basin due to the value of the edible pine nut. Stone pines showing tip dieback, needles with tan- to yellowcoloured lesions and shoot death, observed in stands in Portugal, were sampled for analysis. Fungal colonies covered with cream-coloured spore masses, were consistently obtained. Morphological and phylogenetic analyses of the ITS rDNA region enabled identification of these isolates as S. polyspora. Inoculation tests showed that the fungus caused lesions on excised P. pinea shoots. The symptoms observed might have a negative effect on pine nut production, and thus, evaluation of the impact of this disease is of relevance to future research. This paper is the first to report S. polyspora causing disease on P. pineainfo:eu-repo/semantics/publishedVersio

Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms

Sheet metal forming tools, like stamping presses, play an ubiquitous role in the manufacture of several products. With increasing requirements of quality and efficiency, ensuring maximum uptime of these tools is fundamental to marketplace competitiveness. Using anomaly detection and predictive maintenance techniques, it is possible to develop lower risk and more intelligent approaches to maintenance scheduling, however, industrial implementations of these methods remain scarce due to the difficulties of obtaining acceptable results in real-world scenarios, making applications of such techniques in stamping processes seldom found. In this work, we propose a combination of two distinct approaches: (a) time segmentation together with feature dimension reduction and anomaly detection; and (b) machine learning classification algorithms, for effective downtime prediction. The approach (a)+(b) allows for an improvement rate up to 22.971% of the macro F1-score, when compared to sole approach (b). A ROC AUC index of 96% is attained by using Randomized Decision Trees, being the best classifier of twelve tested. An use case with a decentralized predictive maintenance architecture for the downtime forecasting of a stamping press, which is a critical machine in the manufacturing facilities of Bosch Thermo Technology, is discussed.publishe

Pestalotiopsis pini sp. nov., an emerging pathogen on stone pine (Pinis pinea L.)

Research Highlights: Pestalotiopsis pini sp. nov. is an emerging pathogen on stone pine, Pinus pinea L., in Portugal. Background and Objectives: Stone pine is one of the most important forest tree species in Portugal and in the whole Mediterranean basin. Pestalotiopsis species are common endophytes, saprobes or pathogens in a variety of hosts and environments. The objective of the present study was to identify the Pestalotiopsis species associated with the symptomatic stone pine trees. Materials and Methods: Samples of stone pine trees showing shoot blight and stem necrosis were obtained from stone pine orchards and urban areas in Portugal, and the isolated Pestalotiopsis species were identified based on morphology and combined ITS, TEF and TUB DNA sequence data. Artificial inoculations on one-year-old stone pine seedlings were performed with the two species most frequently found in association with shoot blight disease. Results: Five Pestalotiopsis spp. were isolated. A taxonomic novelty, Pestalotiopsis pini is described, representing a new pathogen for stone pine. Conclusions: Pestalotiopsis species may represent a threat to the health of pine forests in the Mediterranean basin. Future research should be done in order to increase our knowledge about the potential impact of pestalotioid species in stone pine, in order to develop management strategies against these pathogensinfo:eu-repo/semantics/publishedVersio

Potential application of propolis extracts to control the growth of stemphylium vesicarium in 'Rocha'pear

PA54101 PA54102 PA54103 UIDB/04077/2020Stemphylium vesicarium (Wallr.) E. G. Simmons is the pathogen responsible of brown spot disease in pear and has become one of the main concerns for European pear producers. In Portugal, S. vesicarium is responsible for significant yield reduction and economic losses in "Rocha" pear (Pyrus communis L. cv Rocha) production. Considering the antimicrobial potential of propolis, the high incidence of brown spot in pears and the emergence of fungicides resistance in S. vesicarium, this study aimed to evaluate the potential of Portuguese propolis as an alternative strategy to control brown spot disease in "Rocha" pear. In vitro assays showed that propolis extracts were able to inhibit up to 90% the S. vesicarium mycelial growth. In vivo assays in artificially wounded and inoculated "Rocha" pears showed that, compared to the control, the disease incidence decreased up to 25% and the lesions diameter up to 57%, in fruits treated with propolis. Moreover, propolis seems to be more efficient in reducing the disease incidence when applied after pathogen inoculation (curative assay) than when applied before pathogen inoculation (prophylactic assay). Thus, the results suggest that propolis extracts have potential to be applied as part of an integrated approach for the control of brown spot of pear.publishersversionpublishe

ERAS em Obstetrícia Da evidência à vivência da Maternidade

Introdução : The mission of ERAS Society is to develop perioperative care and to improve recovery through research, education, audit, and implementation of evidence-based practice.info:eu-repo/semantics/draf

merlin v4: an updated platform for reconstructing genome-scale metabolic models

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) software is an open source user-friendly Java application developed for Windows and Unix, aimed towards the reconstruction of genome-scale metabolic models. The development of merlin follows a design philosophy of automating time-consuming steps in the reconstruction of genome-scale metabolic models, while allowing users to control the parameters of operations and manually curate the results. All major steps involved in the reconstruction of a metabolic model are implemented in merlin, including genome retrieval and its functional annotation, construction of the reactions set and associated entities, model compartmentalization and conversion to standard SBML formats. The fourth iteration of merlin includes a major overhaul of the user interface, implementation of new features, improvements to existing features, and most notably, the implementation of the object-relational mapping framework Hibernate. The graphical layout has been significantly streamlined, while supporting the latest version of AiBench, providing users with an intuitive and responsive interface. Development was also focused at new quality of life improvements, aimed mainly towards importing, exporting and duplicating merlin user projects. The development of the latest version of merlin followed a modular approach, culminating in the implementation of a plugin manager which simplifies and hastens the process of updating and debugging the various features of merlin. In addition, TranSyT, a state-of-the-art genome-wide transmembrane transport system annotation tool has been implemented to overcome the limitations of the previously available TRIAGE module. Finally, it is noteworthy to mention the implementation of BioISO, a tool aimed at evaluating a genome-scale metabolic network or biomass formulation, based on the previously available COBRA and FBA frameworks.info:eu-repo/semantics/publishedVersio

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.Centre of Biological Engineering (CEB, UMinho); Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit; this work is a result of the project 22231/01/SAICT/2016: Biodata.pt Infraestrutura Portuguesa de Dados Biologicos, supported by the ´ PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF); FCT for providing PhD scholarships [DFA/BD/08789/2021 J.C, DFA/BD/8076/2020 E.C., SFRH/BD/139198/2018 to F.C., SFRH/BD/131916/2017 R. Rodrigues]; FCT for the Assistant Research contract of Oscar Dias obtained under CEEC Individual 2018.info:eu-repo/semantics/publishedVersio

Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal

Pathogen genome sequencing during epidemics enhances our ability to identify and understand suspected clusters and investigate their relationships. Here, we combine genomic and epidemiological data of the 2022 mpox outbreak to better understand early viral spread, diversification and transmission dynamics. By sequencing 52% of the confirmed cases in Portugal, we identified the mpox virus sublineages with the highest impact on case numbers and fitted them into a global context, finding evidence that several international sublineages probably emerged or spread early in Portugal. We estimated a 62% infection reporting rate and that 1.3% of the population of men who have sex with men in Portugal were infected. We infer the critical role played by sexual networks and superspreader gatherings, such as sauna attendance, in the dissemination of mpox virus. Overall, our findings highlight genomic epidemiology as a tool for the real-time monitoring and control of mpox epidemics, and can guide future vaccine policy in a highly susceptible population.info:eu-repo/semantics/publishedVersio

Variables influence analysis of gas leak testing using belief propagation over factor graphs

Leak testing provides nondestructive quality measurements and is a vital stage in the manufacturing of components that require leak-tight properties. Yet, leak tests are notorious for their sensitivity towards external and environmental factors, hampering accuracy and reliability of test results, leading to a more costly and less efficient production cycle. The classical approach to this issue is through the use of mathematical or physical models of leakage behaviour, which are of difficult creation and low generalisation. Yet, resulting calibrations made to compensate for small deviations in testing conditions seldom consider each test-jig's unique set-up, and may still lead to unsatisfactory results. Alternatively, data-driven methods, such as the novel approach presented in this work, allow for failure analysis based solely on historical data. To achieve that, we employ a set of Variable Influence Analysis (VIA) models based on the application of the Belief Propagation algorithm to factor graphs where different events are related through correlation relationships. What differentiates our approach from other data-driven methods, such as the ones based on machine learning or deep learning methods, is the interpretability of the results and more efficient and swift implementation. We then apply VIA models to a real-world use case at Bosch Thermotechnology, centred around a differential pressure leak tester where testing frequently resulted in false rejections. Our approach is able to formally determine in a data-driven manner that, unlike initial suspicion, environmental factors show negligible impact on false rejections, and issues likely stem from equipment fault.publishe