Energy rehabilitation studies of a large group of historical buildings: a case study

In this paper, energy rehabilitation studies of a large group of historical buildings are assessed. A general methodology and some particular constraints are discussed. For a case study including 65 buildings in one of Lisbon’s historical centres, the methodology used, the proposed energy-efficient measures and the results in terms of heating energy savings and summer thermal convert are presented and discussed

Deriving phytoplankton size classes from satellite data: Validation along a trophic gradient in the eastern Atlantic Ocean

In recent years, the global distribution of phytoplankton functional types (PFT) and phytoplankton size classes (PSC) has been determined by remote sensing. Many of these methods rely on interpretation of phytoplankton size or type from pigment data, but independent validation has been difficult due to lack of appropriate in situ data on cell size. This work uses in situ data (photosynthetic pigments concentration and cell abundances) from the north-east Atlantic, along a trophic gradient, sampled from 2005 to 2010, as well as Atlantic Meridional Transect (AMT) data for the same region, to test a previously developed conceptual model, which calculates the fractional contributions of pico-, nano- and micro-plankton to total phytoplankton chlorophyll biomass (Brewin et al., 2010). The application of the model proved to be successful, as shown by low mean absolute error between data and model fit. However, regional values obtained for the model parameters had some effect on the relative distribution of size classes as a function of chlorophyll-a, compared with the results according to the original model. The regional parameterisation yielded a dominance of micro-plankton contribution for chlorophyll-a concentrations greater than 0.5 mg m− 3, rather than from 1.3 mg m− 3 in the original model. Intracellular chlorophyll-a (Chla) per cell, for each size class, was computed from the cell enumeration results (microscope counts and flow cytometry) and the chlorophyll-a concentration for that size class given by the model. The median intracellular chlorophyll-a values computed were 0.004, 0.224 and 26.78 pg Chla cell− 1 for pico-, nano-, and micro-plankton respectively. This is generally consistent with the literature, thereby providing an indirect validation of the method based on pigments to assign size classes. Using a satellite-derived composite image of chlorophyll-a for the study area, a map of cell abundance was generated based on the computed intracellular chlorophyll-a for each size-class, thus extending the remote-sensing method for mapping size classes of phytoplankton from chlorophyll-a concentration to mapping cell numbers in each class. The map reveals the ubiquitous presence of pico-plankton, and shows that all size classes are more abundant in more productive areas

Linfoma Cutâneo Primário de Grandes Células B, Tipo Perna: Relato de Caso com Apresentação Rara

Primary cutaneous lymphomas are defined as a heterogeneous group of malignant lymphoproliferative neoplasms that attack the skin, without extracutaneous involvement at the moment of the diagnosis. The subgroup "primary cutaneous lymphoma of great cells B, leg type (PCLBCL, LT)" generally attacks one or both lower limbs, however in 10% - 15 % of the cases other areas of the skin may be affected. We present a rare case of PCLBCL, LT with a nodular lesion located in the cervical region to empathize an atypical presentation. Considering its quick growth and high proliferative rate, it is of great importance to know all its possible clinical presentations for a precocious diagnosis and efficient treatment.Linfomas cutâneos primários são definidos como um grupo heterogéneo de neoplasias malignas linfoproliferativas que acometem a pele, sem evidência de envolvimento extracutâneo no momento do diagnóstico. O subgrupo “linfoma cutâneo primário de grandes células B, tipo perna (PCLBCL, LT)” geralmente acomete um ou ambos membros inferiores, porém 10% - 15% dos casos apresentam lesões em outros locais. Apresenta-se um caso raro de PCLBCL, LT com lesão nodular na região cervical posterior, enfatizando sua apresentação atípica. Considerando o crescimento rápido da lesão e o alto índice proliferativo, é importante conhecer todas as possíveis apresentações clínicas para assim realizar o diagnóstico precoce e instituir o tratamento efetivo

Spatial and temporal variability of biogenic isoprene emissions from a temperate estuary

[1] Isoprene is important for its atmospheric impacts and the ecophysiological benefits it affords to emitting organisms; however, isoprene emissions from marine systems remain vastly understudied compared to terrestrial systems. This study investigates for the first time drivers of isoprene production in a temperate estuary, and the role this production may play in enabling organisms to tolerate the inherently wide range of environmental conditions. Intertidal sediment cores as well as high and low tide water samples were collected from four sites along the Colne Estuary, UK, every six weeks over a year. Isoprene concentrations in the water were significantly higher at low than high tide, and decreased toward the mouth of the estuary; sediment production showed no spatial variability. Diel isoprene concentration increased with light availability and decreased with tidal height; nighttime production was 79% lower than daytime production. Seasonal isoprene production and water concentrations were highest for the warmest months, with production strongly correlated with light (r2 = 0.800) and temperature (r2 = 0.752). Intertidal microphytobenthic communities were found to be the primary source of isoprene, with tidal action acting as a concentrating factor for isoprene entering the water column. Using these data we estimated an annual production rate for this estuary of 681 μmol m−2 y−1. This value falls at the upper end of other marine estimates and highlights the potentially significant role of estuaries as isoprene sources. The control of estuarine isoprene production by environmental processes identified here further suggests that such emissions may be altered by future environmental change

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development

This is the final version. Available from Frontiers Media via the DOI in this record.To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.ESA SEOM SY-4Sci Synergy projectNAS

Programa de Monitorização dos Ecossistemas Terrestre e Estuarino na Envolvente à CTRSU de S. João da Talha

A monitorização ambiental desempenha um papel fundamental no contexto da avaliação de impacto ambiental, permitindo acompanhar a evolução dos ecossistemas e inventariar e descrever as possíveis alterações decorrentes da implementação do projecto. A monitorização biológica dos ecossistemas terrestre e estuarino da envolvente à CTRSU teve como objectivo, no seu primeiro ano de trabalho, a criação de uma situação de referência que permitisse a comparação com os dados a obter nos anos seguintes e já durante a fase de exploração do empreendimento. Neste contexto procurou-se estabelecer o programa de recolha de dados que melhor permitisse equacionar os efeitos sobre o ecossistema em vários descritores que vêm sendo avaliados desde 1998: flora epífita, flora vascular e aves (ambiente terrestre); fitoplâncton, zooplâncton, algas macrófitas, vegetação halófita, macroinvertebrados e ictiofauna (ambiente estuarino). No presente trabalho apenas serão apresentados os resultados de um número reduzido de componentes (flora epifítica, aves, fitoplâncton, zooplâncton, macroinvertebrados e ictiofauna).info:eu-repo/semantics/publishedVersio

Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas

The recent expansion of wild Pacific oysters already had negative repercussions on sites in Europe and has raised further concerns over their potential harmful impact on the balance of biomes within protected areas. Monitoring their colonisation, especially at early stages, has become an urgent ecological issue. Current efforts to monitor wild Pacific oysters rely on “walk-over” surveys that are highly laborious and often limited to specific areas of easy access. Remotely Piloted Aircraft Systems (RPAS), commonly known as drones, can provide an effective tool for surveying complex terrains and detect Pacific oysters. This study provides a novel workflow for automated detection, counting and mapping of individual Pacific oysters to estimate their density per square meter by using Convolutional Neural Networks (CNNs) applied to drone imagery. Drone photos were collected at low tides and altitudes of approximately 10 m across a variety of cases of rocky shore and mudflats scenarios. Using object detection, we compared how different Convolutional Neural Networks (CNNs) architectures including YOLOv5s, YOLOv5m, TPH-YOLOv5 and FR-CNN performed in the detection of Pacific oysters over the surveyed areas. We report the precision of our model at 88% with a difference in performance of 1% across the two sites. The workflow presented in this work proposes the use of grid maps to visualize the density of Pacific oysters per square meter towards ecological management and the creation of time series to identify trends