Manutenção preventiva ou manutenção corretiva em linhas aéreas de média tensão? Utilização do data envelopment analysis como auxiliar de gestão dos ativos técnicos de manutenção

O uso de Data Envelopment Analysis (DEA) no setor de distribuição de energia elétrica tem dado origem à publicação de vários artigos científicos. No geral, estes artigos focam-se na comparação da eficiência das empresas de distribuição de eletricidade. Na generalidade dos artigos, o tratamento da informação tem sido predominantemente descritivo e classificatório, sem focar no processo de transformação. Em contraste, o trabalho que se apresenta aqui pretende mostrar as potencialidades do DEA na análise de variáveis do processo de transformação e procura explorar o seu potencial para a identificação dos programas e intervenções que contribuem para a melhoria efetiva no processo de distribuição de eletricidade. É nossa convicção que as avaliações de natureza formativa, com fins de aprendizagem, são mais eficazes do que os estudos sumativos porque contribuem para uma melhor compreensão das estruturas e processos, sendo portanto mais adequadas para contribuir para a melhoria do desempenho. Neste trabalho, apresenta-se uma questão importante no contexto da análise DEA: a de investigar, se as diferenças de eficiência são devidas a um programa específico de gestão ou às características de conceção. Para o efeito, o estudo recorre a dois métodos diferentes para realizar este tipo de análise. Em primeiro lugar, aplicamos a estatística de rank de Mann-Whitney aos scores do DEA, a fim de avaliar a significância estatística das diferenças observadas entre um programa de tratamento e o programa de controlo. Em segundo lugar, procedemos a uma análise dinâmica com o Índice de Produtividade de Malmquist, a fim de estudar o impacto da introdução de uma nova tecnologia num grupo de unidades. O estudo de caso desenvolvido centra-se na avaliação do desempenho de linhas de média tensão afetas a uma das regiões de serviço de uma empresa de distribuição de energia elétrica, regulada pelo Sistema Público de Distribuição de Energia em Portugal (ERSE). Os resultados do estudo de caso mostram que a aplicação do DEA tem um grande potencial para contribuir para a melhoria dos processos e deve ser explorado noutros contextos

Changes in Vessel Traffic Disrupt Tidal Flats and Saltmarshes in the Tagus Estuary, Portugal

The Tagus Estuary is one of the largest in Europe with 320 km2 , and it has been, for centuries, a gateway to Lisbon. This study focuses on the Moita-Montijo Bay and on the recent dynamics of its tidal flats and saltmarshes. Aerial photographs, orthomosaics, and very high-resolution satellite imagery were used to analyze landcover and shoreline changes. Tidal flats have shown expansion from 1977 to 1995, but since then, contraction dominated, with a change of position of the tidal flat edge of−2.8 m/year in the north bank and−4.2 m/year in the south bank of the Montijo channel. Most contraction occurred along the route of the fast transport catamarans that started operating in 1995, while in the sector without catamaran navigation, expansion was observed. Saltmarshes have been suffering contraction since 1958, with increased rates after 1995 (−0.38 to−0.44 m/year), especially along the catamaran route (−0.57 to−1.27 m/year). The analysis of the wake generated by different vessel types shows a wake increase with the catamarans, in agreement with the increase in contraction along the Montijo channel. Inside abandoned salt pans, saltmarshes expanded. Since 1995, major changes are also observed along the tidal flat margin, with the formation of coarse lag deposits of coarse sands and shells. Given the contraction increase associated with catamaran traffic and the resulting degradation of the tidal flat and the saltmarshes, it is important to introduce measures for containing contraction.info:eu-repo/semantics/acceptedVersio

Fish eyes and brain as primary targets for mercury accumulation : a new insight on environmental risk assessment

Fish eyes and brain are highly susceptible to environmental Hg exposure but this issue is still scarcely investigated, mainly regarding methylmercury (MeHg) accumulation. Yet, Hg levels in fish lens have not been previously examined under field conditions. Total Hg (tHg), MeHg and inorganic Hg (iHg) levels were assessed in the brain, eye wall and lens of the golden grey mullet (Liza aurata) from an Hg contaminated area, both in winter and summer, together with water and sediment levels. Sampling was performed at Aveiro lagoon (Portugal) where a confined area (LAR) is severely contaminated by Hg. Fish brain, eye wall and lens accumulated higher levels of tHg, MeHg and iHg at LAR than the reference site, reflecting faithfully environmental spatial differences. The brain and eye wall responded also to the winter-summer changes found in water and sediment, accumulating higher levels of MeHg (and tHg) in winter. Contrarily, lens was unable to reflect seasonal changes, probably due to its composition and structural stability over time. The three neurosensory structures accumulated preferentially MeHg than iHg (MeHg was higher than 77% of tHg). Lens exhibited a higher retention capacity of MeHg (mean around 1 µg g(-1) at LAR), accumulating higher levels than the other two tissues. Interestingly, MeHg and iHg levels were significantly correlated for the brain and eye wall but poorly associated within the two analysed eye components. The high levels of MeHg found in the brain, eye wall and lens could compromise their functions and this needs further research.Patricia Pereira (SFRH/BPD/69563/2010) and Joana Raimundo (SFRH/BPD/91498/2012) benefit from Post-doctoral grants supported by "Fundacao para a Ciencia e a Tecnologia" (FCT). This work as been supported by the Research project financed by FCT PTDC/AAG-REC/2488/2012 (NEUTOXMER - Neurotoxicity of mercury in fish and association with morphofunctional brain alterations and behavior shifts), as well as by the Centre for Environmental and Marine Studies (CESAM). Authors are also grateful to Sofia Guilherme for the support in sampling campaigns

Vegetation shadow casts impact remotely sensed reflectance from permafrost thaw ponds in the subarctic forest-tundra zone

Thermokarst lakes and ponds are a common landscape feature resulting from permafrost thaw, but their intense greenhouse gas emissions are still poorly constrained as a feedback mechanism for global warming because of their diversity, abundance, and remoteness. Thermokarst waterbodies may be small and optically diverse, posing specifc challenges for optical remote sensing regarding detection, classifcation, and monitoring. This is especially relevant when accounting for external factors that afect water refectance, such as scattering and vegetation shadow casts. In this study, we evaluated the efects of shadowing across optically diverse waterbodies located in the forest–tundra zone of northern Canada. We used ultra-high spatial resolution multispectral data and digital surface models obtained from unmanned aerial systems for modeling and analyzing shadow efects on water refectance at Earth Observation satellite overpass time. Our results show that shadowing causes variations in refectance, reducing the usable area of remotely sensed pixels for waterbody analysis in small lakes and ponds. The efects were greater on brighter and turbid inorganic thermokarst lakes embedded in post-glacial silt–clay marine deposits and littoral sands, where the mean refectance decrease was from -51 to -70%, depending on the wavelength. These efects were also dependent on lake shape and vegetation height and were amplifed in the cold season due to low solar elevations. Remote sensing will increasingly play a key role in assessing thermokarst lake responses and feedbacks to global change, and this study shows the magnitude and sources of optical variations caused by shading that need to be considered in future analyses.info:eu-repo/semantics/publishedVersio

Are male soccer players accumulating sufficient load across varying microcycle structures? Examining the load, wellness and training/match ratios of a professional team

Professional soccer involves varying numbers of training sessions and matches each week, which can influence load distribution. Understanding the exact distribution may allow appropriate load periodisation and planning for players. Thus, this study aimed to (i) compare accumulated load and wellness between weeks with different numbers of training sessions and (ii) compare training/match ratio (TMr) of external and internal load between weeks with different numbers of training sessions. Ten players with a minimum of 45 minutes of weekly match-play were analysed over 16 weeks. The microcycle structures consisted of three (3dW), four (4dW), five (5dW) and six (6dW) training sessions plus match-day per week. The following measures were used for analysis: duration, fatigue, quality of sleep, muscle soreness, stress, mood, rating of perceived exertion (RPE), session-RPE (s-RPE), high-speed running distance (HSR), sprint distance (SPD), number of accelerations (ACC) and decelerations (DEC). Accumulated wellness/load were calculated by adding all training and match sessions, while TMr was calculated by dividing accumulated load by match data. The main results showed that accumulated wellness and load were significantly different, with moderate to very large effect sizes, except regarding mood, duration, s-RPE, SPD during 5dW vs. 6dW and s-RPE, HSR, SPD, ACC and DEC during 3dW vs. 4dW (all p > 0.05). Moreover, 6dW was significantly higher than 4dW regarding TMr of duration (p < 0.05, moderate effect size), RPE, HSR and SPD (all p < 0.05 with very large effect sizes) and for 3dW of HSR and ACC (p < 0.05 with very large effect sizes). This study showed that 5dW and 6dW had higher training measures than 3dW or 4dW. Additionally, higher wellness was presented in the microcycles with higher training frequencies. These findings suggest that physical load and wellness were not adjusted according to the number of training sessions within a microcycle.info:eu-repo/semantics/publishedVersio

Monitoring the Beaufort Sea coast using very high resolution remote sensing

Arctic permafrost coasts are major carbon (Schuur et al., 2015) and mercury pools (Schuster et al., 2018). They represent about 34% of the Earth’s coastline, with long sections affected by high erosion rates (Fritz et al, 2017), increasingly threatening coastal communities. Year-round reduction in Arctic sea ice is forecasted and by the end of the 21st century, models indicate a decrease in sea ice area from 43 to 94% in September and from 8 to 34% in February (IPCC, 2014). An increase of the sea-ice free season leads to a longer exposure of coasts to wave action. Further, climate warming is also expected to modify the contribution of terrestrial erosion (Fritz et al., 2015, Ramage et al., 2018, Irrgang et al., 2018). Within the project EU Horizon2020 project NUNATARYUK, we are updating the mapping of the Arctic coast, with the Canadian Beaufort coast as a case-study. The surveying methodology includes: i. a high resolution update of the coastline mapping and change rates using Pleiades (CNES) satellite acquisitions from 2018, ii. a survey using RTK-UAV aerial imagery of long-term monitoring sites from the Canada-US border to King Point, and iii. the experimental use of TerraSAR-X staring spotlight scenes at key sites to monitor intraseasonal dynamics of cliff edge retreat. This research is funded by the EC H2020 Project NUNATARYUK. Support on remote sensing imagery access by the WMO Polar Space Task Group

Application of Machine Learning Technics for Evaluation of the Soils Capability to Irrigation

Este trabalho consiste no desenvolvimento e validação de modelos de Machine Learning para a otimização de um sistema de rega de precisão utilizando algoritmos de classificação. A finalidade é atribuir a cada solo, localizado a sul do concelho do Fundão, Portugal, uma classe de aptidão para o regadio, classes essas que identificam as zonas regáveis, não regáveis bem como as que precisam de intervenção para serem regadas. Os dados dos casos de estudo foram anteriormente recolhidos por uma aluna de Mestrado da Escola Superior Agrária do IPCB (Portugal), onde incluíam vários condicionalismos (características dos solos que podem condicionar a aptidão para o regadio). A análise exploratória dos dados permitiu utilizar apenas os valores dos resultados relativamente às características dos solos que podem condicionar a aptidão para o regadio rejeitando assim todo o cálculo efetuado para a obtenção dos mesmos. Desta forma os dados do caso de estudo foram enriquecidos com esta informação para a aplicação nos algoritmos de Machine Learning. Em geral, o facto de retirar estas características que não revelavam impacto no estudo ajudaram a melhorar os modelos de classificação bem como a sua precisão. Diferentes algoritmos de Machine Learning foram desenvolvidos, testados e validados, tais como, Support Vetor Machine, kNN, Árvore de Decisão, Naive Bayes e Regressão Logística, para otimizar um sistema de rega de precisão de modo a atribuir uma a classe de aptidão de rega a novos solos introduzidos. A comparação dos modelos demonstrou que o método Naive Bayes é o que apresenta uma melhor precisão na altura de gerar uma classe de previsão.This work consists of the development and validation of Machine Learning models for the optimization of a precision irrigation system using classification algorithms. The purpose is to assign to each soil, located in the south of the municipality of Fundão, Portugal, an class of capability to irrigation, classes that identify the irrigable and non-irrigated areas as well as those that need intervention to be irrigated. Data from the case studies were previously collected by a Master's student at the Escola Superior Agrária – IPCB (Portugal), which included several constraints (characteristics of soils that may affect the suitability for irrigation). The exploratory analysis of the data allowed us to use only the values of the results regarding the characteristics of the soils that may affect the suitability for irrigation, thus rejecting all the calculation made to obtain them. In this way, the case study data were enriched with this information for application in Machine Learning algorithms. In general, removing these features that had no impact on the study helped to improve the classification models as well as their accuracy. Different Machine Learning algorithms were developed, tested, and validated, such as Support Vector Machine, kNN, Decision Tree, Naive Bayes and Logistic Regression, to optimize a precision irrigation system in order to assign an irrigation suitability class. to new introduced soils. The comparison of the models showed that the Naive Bayes method is the one that presents the best precision when generating a prediction class.info:eu-repo/semantics/publishedVersio