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Este estudo foi realizado no distrito de Alagoinha, em Araripe (CE), e tem como objetivo avaliar a viabilidade técnica da integração do reúso de águas cinzas, originárias das residências, e de água de chuva, por meio de barragens subterrâneas, para fertirrigação de cultura do milho. A proposta metodológica utilizada foi do tipo descritiva, quantitativa e técnica, com uma análise por meio de abordagem teórica, dados e estimativas. O sistema se mostrou favorável com indicação da capacidade de armazenamento de 49.659,89 m³, sendo 10.344,43 m³ de águas de chuva e 39.315,46 m³ de águas cinzas, o que atende a demanda necessária na irrigação da cultura do milho em uma área de 10,68 ha. Portanto, com a implantação desse sistema se pôde concluir que, considerando uma variação dos valores do milho de R$ 85,00 a R$ 100,00, haveria um faturamento anual de R$ 27.179,55, garantindo benefícios ambientais e financeiros à comunidade.

Deep learning networks, more specifically convolutional neural networks, have shown a notable distinction when it comes to computer vision problems. Their versatility spans various domains, where they are applied for tasks such as classification and regression, contingent primarily on the availability of a representative dataset. This work explores the feasibility of employing this approach in the domain of agriculture, particularly within the context of olive growing. The objective is to enhance and facilitate cultivar identification techniques by using images of olive tree leaves. To achieve this, a comparative analysis involving ten distinct convolutional networks (VGG16, VGG19, ResNet50, ResNet152-V2, Inception V3, Inception ResNetV2, XCeption, MobileNet, MobileNetV2, EfficientNetB7) was conducted, all initiated with transfer learning as a common starting point. Also, the impact of adjusting network hyperparameters and structural elements was explored. For the training and evaluation of the networks, a dedicated dataset was created and made available, consisting of approximately 4200 images from the four most representative categories of the region. The findings of this study provide compelling evidence that the majority of the examined methods offer a robust foundation for cultivar identification, ensuring a high level of accuracy. Notably, the first nine methods consistently attain accuracy rates surpassing 95%, with the top three methods achieving an impressive 98% accuracy (ResNet50, EfficientNetB7). In practical terms, out of approximately 2016 images, 1976 were accurately classified. These results signify a substantial advancement in olive cultivar identification through computer vision techniques.

A digestão anaeróbia é um processo que gera o metano (0,132 Nm³CH4 m-3AR), que pode ser utilizado para geração de energia elétrica (0,562 KWh m-3AR). Todavia, deve-se saber se existe viabilidade nesses sistemas. Dessa forma, faz-se necessário definir a facticidade de investir em plantas de biogás em ETEs (Estações de Tratamento de Esgoto) projetadas para atender a 10.000, 50.000 e 100.000 habitantes. Assumindo isso, levantaram-se todos os custos de investimento, operação e manutenção, assim como as receitas, e por meio disso identificou-se que as ETEs nas condições estabelecidas, atendendo uma população de 10.000 habitantes, não apresenta viabilidade, nem rentabilidade, mostrando payback elevado, evidenciando um alto risco de investimento. Associado a isso, demostrou-se valor de VPL (Valor Presente Líquido) negativo e TIR (Taxa Interna de Retorno) inferior ao mínimo. Os casos de médio e grande porte foram os que apresentaram melhor rentabilidade e estabilidade economia, com valores de TIR superior à TMA (Taxa Mínima de Atratividade) e tempo de pagamento do investimento adequado, mesmo com tal valor elevado. Existem outras receitas que podem ser exploradas com plantas de biogás, como a venda de créditos de carbono.

Antioxidant properties of phenolic compounds are presumed responsible for several health benefits due to their capacity to scavenge free radicals and chelate metals prevenient oxidative stress. Since these compounds are ubiquitous in plants, including cereals, the available antioxidant potential profile of wheat genotypes could allow the development of new cultivars with an increased nutritional value that may result in antioxidant-rich foods. In this study, total phenolics content (TPC) was quantified in the whole grain flour of 92 wheat (46 genotypes each from bread and durum) genotypes, which were evaluated under field conditions during 2 different crop seasons (2004-05 and 2009-10). Of the ninety-two, fourteen (six and eight from bread and durum wheat, respectively) genotypes were comprehensively evaluated for antioxidant activity [2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) and reducing power] and ferulic acid contents. The results revealed that substantial genotypic variability exists for TPC in wheat genotypes with a mean value of 991 and 787 µg ferulic acid equivalents (FAE)/g DW in bread and durum wheat, respectively. Moreover, bound phenolics as a source of ferulic acid, including both cis- and trans-isomers, were observed as a major contributor to the total phenolics, which must be considered in assessing the antioxidant activity in wheat grain. In conclusion, these findings may also help improve wheat grains as natural sources of antioxidants utilizing selected genotypes in breeding programs.

The cultivation of walnuts (Juglans regia L.) has become increasingly popular worldwide due to the nutritional value of the nuts. Plant biostimulants (PBs) and nutrient-rich products have been increasingly used in agriculture to improve yield, quality, and abiotic stress tolerance. However, farmers need fast laboratory studies to determine the most suitable treatment per crop or ecosystem to take full advantage of these products. Evaluating nucleolar activity and protein content can provide clues about the most appropriate treatment. This study aimed to determine how five commercial products, four PBs based on seaweed extract and/or free amino acids and one boron-enriched fertiliser used as foliar sprays, affect walnut cv’s nucleolar activity and protein content. “Franquette” from an orchard located in NE Portugal was compared to untreated (control) plants. All treatments brought a low leaf mitotic index. The control showed the smallest nucleolar area, highest protein content, and highest frequency of nucleolar irregularities. Fitoalgas Green®, Sprint Plus®, and Tradebor® showed the highest nucleolar area and lowest frequencies of nucleolar irregularities. The recruitment of proteins/enzymes for response against abiotic stresses may explain the high protein content in the control. Hence, the enhanced abiotic stress tolerance of the treated trees explains their lower protein content and frequency of nucleolar anomalies. Globally, the Fitoalgas Green®, Sprint Plus®, and Tradebor® seem better suited for “Franquette” walnut trees under the edaphoclimatic conditions where trials were conducted.

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

Wildfires act as a selection force threatening the sustainability and diversity of forest genetic resources. Few studies have investigated the genetic effects of forest wildfires. Species with perennial canopy seed banks in serotinous cones and soil or with long-distance seed and pollen dispersion can preserve genetic diversity and population differentiation under normal fire regimes. To test this hypothesis, we characterised molecularly Pinus pinaster Aiton (maritime pine) seedlings produced from seeds sampled in post-fire, naturally regenerated populations that had been subject to different fire regimes in the North of Portugal using inter-simple sequence repeats (ISSRs). The sampled populations burned once (A), twice (B), or three (D) times or had no prior fire history (C, control). Given the globally low seed germination ability, only 104 plantlets regenerated and were described. These plantlets were grouped according to their origin population. Intra-group ISSR polymorphism ranged from 72.73% (B) to 89.41% (D), revealing genetic differentiation among groups originating from populations that had experienced different fire recurrence. Overall, the unaffected genetic diversity of the regenerated plantlets allowed us to accept the hypothesis. Our findings enhance our understanding of the species ability to withstand fire-induced challenges and their responses to wildfires, guiding conservation endeavours and forest management strategies to bolster ecosystem resilience.

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer's disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Despite numerous published literature on the impacts of agriculture on water quality, knowledge gaps persist regarding which farming systems are of most concern for their impact on water quality, which would allow water resource planners to better target water management efforts. Seeking to understand how these relationships vary across different farming systems, we used data on water quality status in watersheds of an agricultural region in southern Portugal and overlaid it with a map of farming systems for the same region provided by a previous study. By intersecting both data layers, we characterized the areal shares of the farming systems in each watershed and inspected how these shares relate to water quality status using logistic regression. The results show that the impact of agriculture on water quality is primarily related to specific farming systems. We believe this type of information can be of significant interest to agricultural planners and policymakers interested in meeting water quality standards, and we conclude this study by suggesting innovative policy options based on payments to farmers operating selected farming systems as a cost-effective way to reconcile agricultural and environmental policy objectives.

O lodo gerado nas Estações de Tratamento de Esgoto é considerado um resíduo sólido e pode ter diferentes destinações: disposição em aterro sanitário e oceânica, uso agrícola, etc. A Política Nacional de Resíduos Sólidos afirma que este pode ser aproveitado para usos diversos, uma vez garantida a seguridade. A pirólise vem sendo recomendada como tratamento para esse resíduo por possibilitar a transformação do lodo em biocarvão, agregando valor ambiental e econômico ao processo. Com isso, essa pesquisa de revisão bibliográfica buscou diagnosticar a aplicabilidade da conversão pirolítica do lodo de esgoto em biocarvão e mostrar as possíveis aplicações desse produto. Entre os estudos apresentados, ficam evidentes as vantagens do biocarvão produzido a partir do lodo de esgoto, como o aumento da produtividade de culturas agrícolas e florestais, remediação de solos, adsorvente de poluentes, etc. Contudo, percebe-se uma lacuna sobre os reais efeitos deste biocarvão no meio ambiente.

A espiritualidade é o cuidado com a vida em sua integralidade. Como define Faustino Teixeira no artigo “Malhas da hospitalidade”, ela é a capacidade de celebrar a vida em profundidade, ou seja, a capacidade humana de reconhecer que a vida tem uma dimensão profunda – da qual irradiam amor desinteressado, gratuidade, atenção, cortesia e hospitalidade – e de se alegrar com isso. Diferentemente do que geralmente se pensa, a espiritualidade não se restringe aos templos e espaços religiosos, antes, diz respeito a pessoas e não a almas etéreas. Portanto, perpassa toda a vida cotidiana. Na atualidade, diante dos desafios dos tempos sombrios em que vivemos, tanto na dimensão política como na de saúde pública, a casa se torna um lugar privilegiado para o cultivo da espiritualidade. Neste artigo, buscamos explicitar a dimensão espiritual da comensalidade de acordo com uma análise interpretativa do filme “A Festa de Babette” (1987), dirigido por Gabriel Axel, baseado na obra homônima da escritora dinamarquesa Karen Blixen. Em nossa análise, buscamos ressaltar a compreensão ecumênica do mistério eucarístico que o filme inspira.

The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an ϵ-greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.

Climate change is expected to affect the agricultural sector in ways that are often unclear to predict. If in the short- and medium-terms farmers may adapt to climate change by adjusting their agricultural practices, in the long-term, these adjustments may become insufficient, forcing farmers to change their farming systems. The extent and direction in which these farming system transitions will occur is still a subject that is underexplored in the literature. We propose a new framework to explore the effect of climate change on the choice of farming system while controlling the effect of other drivers that are also known to influence the farming system choice. Using a spatially explicit farming system choice model developed by a previous study in an extensive agricultural region of southern Portugal, we applied a space-for-time approach to simulate the effect of climate change on the future dynamics of the farming systems in the study area. The results suggest that climate change will force many farmers to change the farming system in a foreseeable future. The extent of the projected changes in farming systems is likely to trigger significant social, economic, and environmental impacts, which should require early attention from policy makers.

Fire is the origin of serious environmental and social impacts in Mediterranean-like landscapes, such as those in California, Australia, and southern Europe. Portugal is one of the southern European countries most affected by fire, which has increased in intensity and extent in the recent decades in response to variations in climate, but mostly due to changes in land systems (LSs), characterized by land use and land cover and also by factors such as management intensity, livestock composition, land ownership structure, and demography. Agricultural activities, which contributed to the management of fuel in the overall landscape, were allocated to the most productive areas, while the steepest areas were occupied by extensive areas of shrubland and monospecific forests, creating landscapes of high fire-proneness. These challenging circumstances call for landscape transformation actions focusing on reducing the burned area, but the spatial distribution of LS is highly conditioned by land morphology (LM), which limits the actions (e.g., farming operations) that can be taken. Considering the constraints posed by the LM, this study investigates whether there is a possibility of transforming the landscape by single modifying the LS from more to less fire prone. To better understand landscape–fire relationships, the individual and interactive effects of the LS and LM on burned areas were also analyzed. Even in the more fire-prone LM types, a 40% proportion of agricultural uses in the landscape results in an effective reduction in the burned area.

Climate and land-use changes have been contributing to the increase in the occurrence of extreme wildfires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the influence of land use/cover on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini Index) between 1984 and 2019. This typology was then combined with carbon stock data and different landscapes to explore relationships between landscape types and two important ecosystem services: wildfire reduction and carbon stock. Multivariate analyses were performed on these data and the results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt plantations and highly hazardous fire regimes, which in turn hold the highest carbon stocks. Shrubland and mixed landscapes were associated with low carbon stocks and less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agroforestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes, however, this good wildfire performance is achieved at the cost of the poorest carbon stock, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

Robotic competitions are an excellent way to promote innovative solutions for the current industries’ challenges and entrepreneurial spirit, acquire technical and transversal skills through active teaching, and promote this area to the public. In other words, since robotics is a multidisciplinary field, its competitions address several knowledge topics, especially in the STEM (Science, Technology, Engineering, and Mathematics) category, that are shared among the students and researchers, driving further technology and science. A new competition encompassed in the Portuguese Robotics Open was created according to the Industry 4.0 concept in the production chain. In this competition, RobotAtFactory 4.0, a shop floor, is used to mimic a fully automated industrial logistics warehouse and the challenges it brings. Autonomous Mobile Robots (AMRs) must be used to operate without supervision and perform the tasks that the warehouse requests. There are different types of boxes which dictate their partial and definitive destinations. In this reasoning, AMRs should identify each and transport them to their destinations. This paper describes an approach to the indoor localization system for the competition based on the Extended Kalman Filter (EKF) and ArUco markers. Different innovation methods for the obtained observations were tested and compared in the EKF. A real robot was designed and assembled to act as a test bed for the localization system’s validation. Thus, the approach was validated in the real scenario using a factory floor with the official specifications provided by the competition organization.

Hexaploid triticale results from crosses between durum wheat and rye. Despite its high agronomic potential, triticale is mainly used for livestock feed. Triticale surpasses their parental species in adaptability and tolerance to abiotic and biotic stresses, being able to grow in acidic soils where a high amount of iron (Fe) and zinc (Zn) is typical. On the other hand, high amounts of these essential trace elements can be cytotoxic to bread wheat. The cytotoxicity induced by seed priming with a high concentration of Fe and Zn impaired root cell division and induced nucleolar changes in bread wheat. Such cytogenetic approaches were expedited and successfully determined cytotoxic and suited micronutrient dosages for wheat nutripriming. With this study, we intended to analyse the hexaploid triticale cv ‘Douro’ root mitotic cell cycle and nucleolar activity after seed priming performed with aqueous solutions of iron (Fe) and/or zinc (Zn), containing a concentration that was previously considered cytotoxic, to bread wheat and to infer the higher tolerance of triticale to these treatments. The overall cytogenetic data allowed us to conclude that the Fe + Zn treatment enhanced the root mitotic index (MI), mitosis regularity and nucleolar activity of ‘Douro’ relative to the control and the individual treatments performed with Fe or Zn alone. The Fe + Zn treatment might suit triticale biofortification through seed priming.

Landscape patterns and composition were identified as key drivers of fire risk and fire regimes. However, few studies have focused on effective policymaking aimed at encouraging landowners to diversify the landscape and make it more fire-resilient. We propose a new framework to support the design of wildfire mitigation policies aimed at promoting low-risk fire regimes based on land use/land cover choices by landowners. Using the parishes of a fire-prone region in central Portugal as analysis units, a two-step modelling approach is proposed, coupling an agent-based model that simulates land use/land cover choice and a logistic model that predicts fire regimes from a set of biophysical variables reported as important fire regime drivers in the literature. The cost-effectiveness of different policy options aimed at promoting low-risk fire regimes at the parish level is assessed. Our results are in line with those of previous studies defending the importance of promoting landscape heterogeneity by reducing forest concentration and increasing agricultural or shrubland areas as a measure to reduce the risk of wildfire. Results also suggest the usefulness of the framework as a policy simulation tool, allowing policymakers to investigate how annual payments supporting agricultural or shrubland areas, depending on the policy mix, can be very cost-effective in removing a substantial number of parishes from high-risk fire regimes.

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

Introdução: Os jovens adultos tendem a ser considerados como uma população com um bom estado de saúde. No entanto, nos últimos tempos, a transição da adolescência para a idade adulta tem sido foco de atenção para implementar estratégias de promoção da saúde e prevenção da doença. O objetivo deste estudo foi perceber o estado da saúde de uma população jovem adulta a frequentar o ensino universitário e de que forma a pandemia por COVID‐19 teve impacto nas diferentes dimensões do quotidiano, nomeadamente no bem‐estar e na saúde mental. Métodos: Foi aplicado um questionário que foi difundido via eletrónica por jovens do ensino universitário e foram obtidas 149 respostas. Resultados: Os alunos demonstraram alguns sentimentos de baixa realização pessoal (28%), sentimentos de solidão (26%), sentimentos de depressão (42%), falta de confiança em si próprio (17%) e alguns sentem que os seus níveis de energia são baixos ou extremamente baixos (26%). O contexto pandémico trouxe vários desafios aos jovens adultos que sentiram dificuldade em fazer novas amizades (94%), a perceção de sentimentos de nervosismo, ansiedade ou tensão durante o confinamento foi reportada por 51% dos alunos e a incapacidade em controlar as suas preocupações ou emoções foi reportada por 43%. Conclusão: Vários estudos relatam alterações mentais que podem desenvolver‐se ou agravar‐se após eventos traumáticos como uma pandemia, estando os resultados do nosso estudo em linha com essa evidência. Sentimentos de nervosismo, tensão e ansiedade, assim como dificuldade em controlar as emoções, surgiram em associação à pandemia, enfatizando a necessidade de desenvolver estratégias de saúde que deem respostas a estas necessidades emergentes.

A presente pesquisa avaliou a partida de um Reator UASB no tratamento de efluente doméstico por meio de parâmetros físico-químicos e operacionais. A partida empregou inoculação de 10 m³ de lodo anaeróbio como semeadura. Durante 12 semanas de avaliação observou-se estabilidade em relação aos parâmetros: pH, Temperatura, Alcalinidade Total (AT) e Ácidos Graxos Voláteis (AGV). Todavia, houve baixas eficiências de remoção para Sólidos Suspensos Totais (SST), Demanda Química de Oxigênio (DQO) e Demanda Bioquímica de Oxigênio (DBO), de 36,8%, 56,13% e 42,04%, respectivamente, justificadas pelas variações de vazão afluente à ETE, e pelas características do lodo ocasionando perda de sólidos com o efluente. Assim, apesar da inoculação, a partida não foi concluída, constatada, contudo expressiva menor variabilidade nas eficiências para DQO e DBO nas últimas semanas, apontando tendência do reator ao alcance do estado estacionário.

Fire regimes in Mediterranean countries have been shifting in recent decades, including changes in wildfire size and frequency. We sought to describe changes in fire regimes across two periods (1975–1995 and 1996–2018) in a fire-prone region of central Portugal, explore the relationships between these regimes and territorial features, and check whether these associations persisted across periods. Two independent indicators of fire regimes were determined at parish level: fire incidence and burn concentration. Most parishes presented higher values of both indicators in the second period. Higher values of fire incidence were associated with lower population densities, lower proportions of farmland areas and higher proportions of natural vegetation. Higher levels of burn concentration were associated with smaller areas of farmland and natural vegetation. These associations differed across periods, reflecting contrasting climatic and socio-economic contexts. Keeping 40% of a parish territory covered by farmland was effective to buffer the increased wildfire risks associated with different management and climate contexts. The effectiveness of higher population densities in keeping fire incidence low decreased in the last decades. The results can improve the knowledge on the temporal evolution of fire regimes and their conditioning factors, providing contributions for spatial planning and forest/wildfire management policies.

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.

Seed hydropriming or nutripriming has been used for wheat biofortification. Previously, the untreated S1 offspring of bread wheat S0 seeds hydro- and nutriprimed with FeSO4.7H2O and/or ZnSO4.7H2O showed improved yield relative to the offspring of untreated S0 seeds. We hypothesize that such improvement would have its origin in the higher quality of S1 seeds resulting from plants whose seeds were primed. In this work, we characterised biochemically the whole-wheat flour of unprimed S1 offspring whose S0 seeds were hydro- and nutriprimed with Fe and/or Zn and compared it to the offspring of untreated S0 seeds (control). We identified and quantified 16 free amino acids and five soluble sugars per offspring using high-performance liquid chromatography and the Association of Official Analytical Chemists (AOAC) methods. The most abundant amino acids were glutamic acid and glutamine, proline, and glycine, presenting their highest contents in the offspring of seeds nutriprimed with 8 ppm Zn (0.351 mmol∙g−1), 8 ppm Fe + 8 ppm Zn (0.199 mmol∙g−1), and (0.135 mmol∙g−1), respectively. The highest contents of glucose (1.91 mg∙g−1 sample), ash (24.90 g∙kg−1 dry matter, DM), and crude protein (209.70 g∙kg−1 DM) were presented by the offspring resulting from 4 ppm Fe + 4 ppm Zn, 8 ppm Zn, and 8 ppm Fe + 8 ppm Zn, respectively. The highest total starch content (630.10 g∙kg−1 DM) was detected in the offspring of seeds soaked in 8 ppm Fe. The nutritional value of the flour of the S1 offspring resulting from nutripriming was significantly higher than the control. Overall, the novelty of our research is that seed priming can improve the quality of the wheat grain and flour, at least till the first offspring, without the need to repeat the presowing treatment. Beyond the study of subsequent generations, the unravelling of transgenerational mechanisms underlying the biochemical improvement of the offspring is approached.

Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil & Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach’s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.

Climate change will increase the frequency of drought, heat waves, and wildfires. We intended to analyse how fire recurrence and/or induced water stress can affect seed germination and root cell division in Pinus pinaster Aiton. Seeds from stands with no prior fire history and from post-fire regeneration (in areas burnt once, twice, and thrice) in northern Portugal were germinated in distilled water (control) and polyethylene glycol (PEG) to simulate water stress for four weeks, followed by a recovery period. Roots were analysed cytogenetically. The germination index of the Pinus pinaster seeds was not statistically influenced by the induction of osmotic stress, nor by the fire recurrence of the stands. The mean germination time (MGT) was 10–29 days and 1–36 days for the stress and recovery periods, respectively, and increased with PEG concentration. The 20% PEG treatment inhibited root growth after germination. The 10% PEG treatment induced a high frequency of cytogenetic anomalies, mostly in the sites which experienced fire exposure. While fire recurrence did not affect the germination rate, it seemed to reduce the water stress response, negatively impacting cell division and impair root growth.

Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost.

This article focuses on a sensorial glove prototype capable of acquiring hand motion and estimating its pose. The presented solution features twelve inertial measurement units (IMUs) to track hand orientation. The sensors are attached to a glove to decrease the project cost. The system also focuses on sensor fusion algorithms for the IMUs and further implementations, presenting the algebraic quaternion algorithm (AQUA), used because of its modularity and intuitive implementation. An adaptation of a human hand model is proposed, explaining its advantages and its limitations. Considering that the calibration is a very important process in gyroscope performance, the online and offline calibration data was analyzed, pointing out its challenges and improvements. To better visualize the model and sensors a simulation was conducted in Unity.

In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources.

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.

O artigo em forma de ensaio analisa os 11 segmentos do filme 11’09’’01, produzido por Alain Brigand em 2002. Esses segmentos foram feitos por 11 diretores de países diferentes. Todos eles tiveram liberdade para criar curtas-metragens alusivos ao ataque sofrido pelas torres gêmeas do Trade World Center em 11 de setembro de 2001. Havia, no entanto, uma exigência: cada segmento deveria ter onze minutos, nove segundos e um quadro. Neste ensaio esses segmentos são comentados a partir de uma noção específica de espiritualidade corpórea. Essa perspectiva antropológica, nomeada corpoética, entende a espiritualidade como pnêumica – uma respiração revestida pelos signos simbólicos, traduzindo desejos; uma respiração segunda, culturalmente reinventada. Não redutível à religião, a pnêumica corpoética é uma experiência e uma experimentação que parte da materialidade somática, passa por sua historicidade sócio-psíquica e se constitui como inventividade simbólica carregada de sentido/sentimento existencial mais pleno possível. De cada segmento do 11’09’’01, uma cena é escolhida para análise e comentário. Essa cena é reproduzida como objeto empírico para o exercício corpoético do enxergar neurofisiológico, do olhar epistêmico e do ver pnêumico. Nesse último se encontra o foco principal deste ensaio sobre uma pnêumica como espiritualidade corpoética.

Selenium (Se) biofortification by seed treatments has been poorly explored in wheat due to the difficulties in establishing seed treatments without compromising plant productive traits. We investigated the effects of Se seed soaking as a pre-sowing treatment in bread wheat. Five soaking periods and six Se concentrations were assessed on germination and seedling traits and compared to unsoaked seeds. Twelve hours of soaking was found beneficial for most tested Se concentrations. Then, we evaluated the effects of untreated, 0, 2.5 and 25 mM Se in 12 h seed soaking treatments along the wheat crop cycle under water-deficit (WD) and well-watered (WW) conditions in a pot experiment. Our results evidenced that 12 h of 2.5 mM Se soaking did not affect the germination percentage, and speed-up seedling emergence resulted in a considerable Se seed uptake. These plants also displayed enhanced antioxidant capacity and vegetative biomass accumulation, especially under WD. The treatment with 25 mM of Se negatively affected aerial biomass, suggesting potential toxicity. Physiological responses of Se-treated plants remained unchanged, as well as grain traits. Altogether, we propose that 12 h soaking with 2.5 mM Se is a promissory pre-sowing approach to enrich bread wheat grain and straw, particularly under water-limited environments.

As alterações posturais e dores na coluna vertebral em crianças e adolescentes são apontadas como multicausais. Um dos fatores mais destacados em relatos científicos refere-se aos hábitos relacionados às atividades escolares. Este estudo teve como objetivo apresentar estudos acerca da postura de crianças e adolescentes em fase escolar e sua relação com os hábitos nessa fase da vida. Foi realizada uma revisão de literatura por meio de buscas em periódicos nacionais e internacionais indexados nas bases de dados Scielo, Lilacs e Pubmed, bem como livros didáticos, sem limitação de ano para a busca. Os descritores utilizados foram: coluna vertebral, escolares, infância e adolescência, mochila escolar, hábitos posturais, no idioma português, espanhol e inglês. A maior parte dos estudos avaliados apresentaram dor e diversas alterações posturais em crianças em fase escolar, sendo que, alguns destes, conseguiram relacionar o uso de mochila com peso acima do ideal, com variáveis de saúde. . ABSTRACT. Postural changes and spinal pain in children and adolescents are indicated as multicausal. One of the most prominent factors in scientific reports relates to habits related to school activities. This study aimed to present studies about the posture of children and adolescents in the school phase and its relationship with habits in this phase of life. A review of the literature was made through searches in national and international journals indexed in the Scielo, Lilacs and Pubmed databases, as well as textbooks, with no year limitation for the search. The descriptors used were: spine, school, childhood and adolescence, school backpack, posture habits, in Portuguese, Spanish and English. Most of the studies evaluated presented pain and several postural changes in school children, and some of them were able to relate the use of a backpack with weight above the ideal, with health variables.

The genus Arrabidaea, consisting of ~170 species, belongs to the family Bignoniaceae, distributed around the Neotropics and temperate zone. The center of diversity of the family is in Brazil, where 56 genera and about 340 species exist. Most species of the genus Arrabidaea are traditionally utilized as diuretics and antiseptics, as well as for treating intestinal colic, diarrhea, kidney stones, rheumatoid arthritis, wounds, and enterocolitis. The genus is chemically diverse with different substance classes; most of them are triterpenes, phenolic acids, and flavonoids, and they exhibit valuable pharmacological properties, such as antitumor, antioxidant, leishmanicidal, trypanocidal, anti-inflammatory, and healing properties. This review presents information on the chemical constituents isolated from seven Arrabidaea species, and the pharmacological activities of the extracts, fractions and pure substances isolated since 1994, obtained from electronic databases. The various constituents present in the different species of this genus demonstrate a wide pharmacological potential for the development of new therapeutic agents, however its potential has been underestimated.

Farming systems under contrasting management practices can contribute differently to the delivery of bundles of ecosystem services (ES) in agricultural landscapes. Low intensity farming systems, such as High Nature Value farmlands, are expected to deliver a wider range of ES, whereas landscapes under more intensive management are expected to deliver mainly provisioning services. Understanding the management practices associated with desirable outcomes in terms of biodiversity and ES in agricultural landscapes is needed. Our research aimed to understand the links between the delivery of ES bundles associated with agricultural landscapes, and their socio-ecological drivers, using a region in northern Portugal as a case study. Based on publicly available data on ecosystems services and drivers, we analyzed ES associations, delineated ES bundles, and investigated their relationship with socio-ecological drivers. Overall, our results suggested spatial trade-offs between landscapes delivering provisioning services of high economic value, and landscapes delivering a more balanced set of multiple ES. Bundle analysis highlighted an association between higher landscape multifunctionality and higher values of landscape complexity, higher number of farmers, and farm sizes. Our results reflected the complexity of social and ecological factors operating at the landscape level, pinpointed landscapes with higher multifunctionality and disclosed the conditions underlying their occurrence. The results also highlighted the importance of low-intensity farming systems, namely those supporting High Nature Value farmlands, for the delivery of a wider range of ES at the landscape scale.

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Forest management at the landscape level is a requirement for reducing wildfire hazard. In contexts where non-industrial private forest ownership prevails, the collaboration among multiple owners has been proposed as the way forward to reach consistent fuel management at that level. The current literature has been focused on identifying the factors that lead to collaboration among owners. In this study we explored other ways to reach landscape-level management in addition to the collaborative way, such as those that may be promoted through land renting or selling. Different contexts and owner types may require different solutions. Thus, we explicitly asked which alternative would be chosen by a given forest owner, from the following set: keeping individual management, entering a multi-owner collaborative arrangement where they delegate management, renting to a pulp company; or selling the land. In a context of small-scale ownership and high recurrence of wildfires in Portugal, a face-to-face survey was carried out to a sample of landowners. Our results suggest that there is not an a priori generalized unwillingness of owners to delegate management, rent or sell the land and thus they seem prone to align themselves with policy strategies to promote management at the landscape level. Multinomial logit regression modelling allowed us to explain and predict owners’ choices among the aforementioned set of alternative management options. We found that choosing multi-ownership collaboration, as opposed to keeping current individual management, is associated with passive management under harsher conditions, by non-residents without bonding capital. The identified factors of owners’ choices show the limited scope of tenancy and land-market mechanisms to promote landscape-level management. The best policy option was found to depend on the owner profiles prevailing in the target area. This suggests that studying the existing context and owner types is required to design effective policies.

Food illness is a serious health threat and has significant economic consequences for people in both the developing and developed world. Salmonella genus is one of the most common pathogens and a major cause of foodborne illness in humans worldwide. Nowadays, the application of mathematical models and functions to describe the microorganism growth kinetics provides a new behavioral vision of the interaction between microorganisms and the environment. Lately the studies on the subject have been gathering interest in the elaboration and application of mathematical modeling and equations over the last years to be used in biotechnological and industrial process, therefore being a most useful tool, with the intent of reducing time and expenses associated with the conventional tests. The purpose of the present study was to compare the Baranyi and Roberts (1994) model with quadratic function generated from data experimentally obtained of the Salmonella typhimurium growth in vitro. It was observed that the quadratic function had a better fitting to describe the kinetics growth of Salmonella typhimurium, this function being a low cost, efficient and easily applied tool.

In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires higher computing power; thus, the most advanced BMS algorithms reported in the literature are developed and verified by laboratory experiments using PC-based software. The objective of this paper is to describe the design of an autonomous and versatile embedded system based on an 8-bit microcontroller, where a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) algorithm for State of Charge (SOC) estimation is implemented; the developed prototype meets most of the constraints for BMSs reported in the literature, with an energy efficiency of 94% and an error of SOC accuracy that varies between 2% and 8% based on low-cost components.

This paper presents a legged-wheeled hybrid robotic vehicle that uses a combination of rigid and non-rigid joints, allowing it to be more impact-tolerant. The robot has four legs, each one with three degrees of freedom. Each leg has two non-rigid rotational joints with completely passive components for damping and accumulation of kinetic energy, one rigid rotational joint, and a driving wheel. Each leg uses three independent DC motors—one for each joint, as well as a fourth one for driving the wheel. The four legs have the same position configuration, except for the upper hip joint. The vehicle was designed to be modular, low-cost, and its parts to be interchangeable. Beyond this, the vehicle has multiple operation modes, including a low-power mode. Across this article, the design, modeling, and control stages are presented, as well as the communication strategy. A prototype platform was built to serve as a test bed, which is described throughout the article. The mechanical design and applied hardware for each leg have been improved, and these changes are described. The mechanical and hardware structure of the complete robot is also presented, as well as the software and communication approaches. Moreover, a realistic simulation is introduced, along with the obtained results.

Clogging tendency has been analyzed in sandy soil soakaways at pilot scale receiving either septic tank effluent (SUM1) or UASB effluent (SUM2) and also at laboratory scale (SUMB1 and SUMB2), in relation to Chemical Oxygen Demand (COD) and accumulated Suspended Solids (SS) concentrations. Clogging was also estimated, by modeling the predicted time for infiltration hydraulic failure. The laboratory results obtained with SUMB1 and SUMB2 confirmed the results obtained for SUM1 and SUM2, showing that soakaways built in sandy soils which receive effluent from septic tanks treating predominantly domestic wastewater tended to clog 58% faster than those receiving UASB effluent. The good correlation observed between the decrease in average infiltration rate and the time of operation suggests that the UASB reactor is a promising technological alternative to septic tanks as a pre‑treatment prior to effluent soil disposal for on‑site decentralized wastewater treatment systems.

A resistência aos antibióticos é uma ameaça à saúde publica, em parte, devida ao uso indiscriminado de antibióticos. As infecções do tracto urinário são as segundas mais frequentes na comunidade, sendo a Escherichia coli o microrganismo mais frequente. Para uma correcta terapêutica empírica é fundamental realizar a monitorização do perfil de sensibilidade aos antibióticos. Esse constitui o objectivo deste trabalho. Realizámos um estudo do tipo observacional, descritivo e retrospectivo, no qual foram avaliados os resultados laboratoriais das uroculturas efectuadas entre 1/7/2015 e 30/6/2017 em vários postos de colheita do Laboratório Unilabs, na nossa área de influência. Com os dados fornecidos, foi avaliada a sensibilidade aos antibióticos dos agentes isolados, de forma global e por sexo. Obtivemos 6584 uroculturas, das quais 14,4% positivas; destas, 74,3% eram referentes ao sexo feminino. A estirpe predominante foi a E.coli (61,03%), seguida de K. pneumoniae (7,81%) e P. mirabilis (6,23%). De forma global, os agentes isolados foram sensíveis à cefixima em 92,5%, à fosfomicina em 89,9%, à nitrofurantoína em 82,9%, e à amoxicilina/ácido clavulânico em 82,9%. Os dados obtidos são concordantes com outros estudos e com as normas de orientação clinica da DGS confirmando a adequação destas recomendações à nossa população. Este trabalho representa uma importante contribuição para o conhecimento do nosso contexto epidemiológico, permite uma abordagem terapêutica das ITU’s dirigida e fundamentada e é decisiva para a redução das resistências aos antibióticos.

Abstract Purpose: To evaluate the contribution of ursodeoxycholic acid (UDCA) in the first 12 months after Roux-en-Y gastric bypass in the prevention of gallstone formation. Methods: A community-based clinical trial was conducted. A total of 137 patients were included in the study; 69 were treated with UDCA, starting 30 days after the surgery, at a dose of 150 mg twice daily (300 mg/day) over a period of 5 consecutive months (GROUP A), and 68 were control patients (GROUP B). The patients were followed-up, and ultrasonography was performed to determine the presence of gallstones at various times during follow-up. Demographic, anthropometric and comorbid indicators were obtained. The data were subjected to normality tests and evaluated using appropriate tests. Results: Patients did not differ in their baseline characteristics. Of the 69 patients who used UDCA, only one patient developed cholelithiasis (1%), whereas 18 controls (26%) formed gallstones (OR = 24.4, p