Ferramenta de modelação desenvolvida em ambiente web para apoio à gestão de albufeiras

A utilização das tecnologias de informação e comunicação no caso específico da gestão de recursos hídricos, em que a incerteza associada aos processos hidrológicos determina um grau de dificuldade acrescido, revela-se essencial. Desta forma, o desenvolvimento de ferramentas que permitam a consulta de dados de redes de monitorização hidrológica, de previsões meteorológicas e de resultados de simulação em ambientes de múltiplas plataformas, fixas ou móveis, é uma das tarefas fundamentais para a incorporação das tecnologias de informação nos processos correntes de gestão de recursos hídricos. No presente trabalho apresenta-se uma ferramenta, desenvolvida para ambiente web, que permite consultar informação e operar modelos hidráulicos de simulação de barragens e respetivas albufeiras. A interface é adaptável a cada caso de estudo e poderá ser desenvolvida com diferentes tipos de software de modelação hidrológica e hidrodinâmica, considerando as necessidades estabelecidas pelo conjunto dos seus utilizadores. A partir da interface é possível estabelecer as condições de fronteira definidas para cada modelo, aplicar o modelo e visualizar os resultados de simulações dinâmicas. A aplicabilidade da ferramenta desenvolvida é demonstrada em exemplos de implementação em diversas albufeiras situadas no rio Guadiana (Empreendimento de Fins Múltiplos de Alqueva, Portugal

Estudo das correntes oceânicas na região envolvente da Ilha Terceira no Arquipélago dos Açores

Este trabalho tem como principal objectivo o estudo das correntes oceânicas na região envolvente da Ilha Terceira no Arquipélago dos Açores. Pretende-se identificar os factores que mais influenciam as correntes na sua magnitude e direcção na região envolvente da ilha. Para tal, foram analisados trabalhos realizados anteriormente e foi desenvolvido um modelo hidrodinâmico para modelação das correntes. Os cenários simulados foram definidos, de forma a reproduzir situações próximas da realidade para a zona em estudo, fazendo variar nas fronteiras, a altura de maré, a velocidade da corrente e os ventos actuantes no domínio, considerando-se cenários quer em regime permanente, quer em regime variado

Hazard assessment of storm events in the central region of the Portuguese coast

The environmental and socio-economic importance of the coastal areas is widely recognized, but these areas face today severe weaknesses and high-risk situations. The increased demand and human occupation of the coast has greatly contributed to increase such weaknesses. Today, throughout the world, in all countries with coastlines, episodes of waves overtopping and coastal flooding are frequent. These episodes are usually responsible for property losses and often put human lives at risk. The floods are caused by coastal storms due to the action of very strong winds. The propagation of these storms towards the coast induces high water levels. Consequently, carrying out series of storm scenarios and analyzing their impacts through numerical modelling is of prime interest to the coastal decision-makers. A contribution to the preservation and sustainability of the coastal zone constitutes the main aim of this work. Firstly, historical storm tracks and intensities are characterized for the Portuguese coast, in terms of probability of occurrence. Secondly, several storm events with high potential of occurrence are generated using the specific tool DelftDashboard. The hydrodynamic model Delft3D is then used to simulate their effects on currents and on the coastal water levels. Two spatial domains are considered: a large domain encompassing the Iberian coastal zone and a smaller domain for the central region of the Portuguese coast (between cities of Aveiro and Figueira da Foz); this one with a more refined grid. Based on statistical data and by numerical modelling, a review of the impact of coastal storms to different locations within the study area is performed

Storm surge assessment methodology based on numerical modelling

Coastal zones face severe weaknesses and high-risk situations due to coastal threats like erosion and storms and due to an increasing intensive occupation. Tropical storms events can contribute to the occurrence of these situations, by causing storm surges with high water levels and, consequently, episodes of waves overtopping and coastal flooding. This work aims to describe a methodology to estimate the storm surge occurrences in the Portuguese coastal zone, recurring to historical tropical storms data that occurred in the vicinity of Portugal and to numerical modeling of its characteristics. Delft3D software together with DelfDashboard tools were applied for the numerical modelling. An automatic generation procedure of storms was implemented based on the few available historical storms data characteristics. Obtained results allows to characterize storm surges along the Portuguese coast, identifying the most vulnerable areas and, consequently contributing for its proper planning and management.info:eu-repo/semantics/publishedVersio

The pro-resolving lipid mediator Maresin 1 ameliorates pain responses and neuroinflammation in the spared nerve injury-induced neuropathic pain: A study in male and female mice

Specialized pro-resolving mediators (SPMs) have recently emerged as promising therapeutic approaches for neuropathic pain (NP). We evaluated the effects of oral treatment with the SPM Maresin 1 (MaR1) on behavioral pain responses and spinal neuroinflammation in male and female C57BL/6J mice with spared nerve injury (SNI)-induced NP. MaR1, or vehicle, was administered once daily, on post-surgical days 3 to 5, by voluntary oral intake. Sensory-discriminative and affective-motivational components of pain were evaluated with von Frey and place escape/avoidance paradigm (PEAP) tests, respectively. Spinal microglial and astrocytic activation were assessed by immunofluorescence, and the spinal concentration of cytokines IL-1 & beta;, IL-6, IL-10, and macrophage colony-stimulating factor (M-CSF) were evaluated by multiplex immunoassay. MaR1 treatment reduced SNI-induced mechanical hypersensitivity on days 7 and 11 in both male and female mice, and appeared to ameliorate the affective component of pain in males on day 11. No definitive conclusions could be drawn about the impact of MaR1 on the affective-motivational aspects of pain in female mice, since repeated suprathreshold mechanical stimulation of the affected paw in the dark compartment did not increase the preference of vehicle-treated SNI females for the light side, during the PEAP test session (a fundamental assumption for PAEP's validity). MaR1 treatment also reduced ipsilateral spinal microglial and astrocytic activation in both sexes and marginally increased M-CSF in males, while not affecting cytokines IL-1 & beta;, IL-6 and IL-10 in either sex. In summary, our study has shown that oral treatment with MaR1 (i) produces antinociception even in an already installed peripheral NP mouse model, and (ii) this antinociception may extend for several days beyond the treatment time-frame. These therapeutic effects are associated with attenuated microglial and astrocytic activation in both sexes, and possibly involve modulation of M-CSF action in males.& nbsp;This work was supported by University of Porto/Faculty of Medicine (https://sigarra.up.pt/fmup) and ESF - European Social Fund (https://ec. europa.eu/esf/home.jsp), through NORTE2020 - North Portugal Regional Operational Programme [NORTE-08-5369-FSE-000011-Doctoral Programmes - LTS' PhD fellowship], and by Fundacao Grunenthal Portugal (https://www. fundacaogrunenthal.pt), Bolsa Jovens Investigadores em Dor 2018 - LTS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes

Hazard assessment of storm events for the battery, New York

The environmental and socio-economic importance of coastal areas is widely recognized, but at present these areas face severe weaknesses and high-risk situations. The increased demand and growing human occupation of coastal zones have greatly contributed to exacerbating such weaknesses. Today, throughout the world, in all countries with coastal regions, episodes of waves overtopping and coastal flooding are frequent. These episodes are usually responsible for property losses and often put human lives at risk. The floods are caused by coastal storms primarily due to the action of very strong winds. The propagation of these storms towards the coast induces high water levels. It is expected that climate change phenomena will contribute to the intensification of coastal storms. In this context, an estimation of coastal flooding hazards is of paramount importance for the planning and management of coastal zones. Consequently, carrying out a series of storm scenarios and analyzing their impacts through numerical modeling is of prime interest to coastal decision-makers. Firstly, throughout this work, historical storm tracks and intensities are characterized for the northeastern region of United States coast, in terms of probability of occurrence. Secondly, several storm events with high potential of occurrence are generated using a specific tool of DelftDashboard interface for Delft3D software. Hydrodynamic models are then used to generate ensemble simulations to assess storms' effects on coastal water levels. For the United States’ northeastern coast, a highly refined regional domain is considered surrounding the area of The Battery, New York, situated in New York Harbor. Based on statistical data of numerical modeling results, a review of the impact of coastal storms to different locations within the study area is performed

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings