Estudo e optimização de materiais para aplicações termo-magnéticas

Mestrado em FísicaO presente trabalho pretende estudar e optimizar a aplicação de materiais magnéticos para dispositivos termomagnéticos. Assim, estudámos as séries Ni2MnGa1-xBix (com x de 0 a 0.05), PrNi5-xCox (com x entre 1.95 e 3.00) e RNi2 (com R=Tb;Nd;Gd). Para a série Ni2MnGa1-xBix estudámos a influência da substituição de bismuto numa tentativa de aproximar, e possivelmente juntar, as temperaturas de transição estrutural e magnética, TM e TC, juntamente com o estudo da anisotropia magnética desta liga. Os nosso resultados de facto mostram a aproximação de TM e TC, provocado pelo aumento do parâmetro de rede a e do aumento da concentração electrónica das amostras dopadas, embora a dopagem estudada não tenha sido suficiente para juntar as duas temperaturas. Encontrámos um máximo de variação de entropia magnética de 3.8 J/kg.K para a amostra pura e de 2.2 J/kg.K para a amostra com o máximo de dopagem (0.05). Na serie PrNi5-xCox, devido á competição entre as energias de anisotropia das sub-redes de Co e Pr esta série apresenta uma reorientação de spin a baixa temperatura (~140K). Esta série apresenta um rico diagrama de fases magnéticas, que foi construído no presente trabalho e alguns efeitos ratificados baseando-se na teoria de percolação. Também encontramos um largo pico de entropia magnética associado ao processo de reorientação de spin e à transição magnética. No que toca á serie RNi2 o nosso estudo apenas se limitou a produção, análise da homogeneidade e cálculo de variação de entropia magnética das amostras, para posterior estudo pelo Dr. Pedro J. von Ranke (Universidade Estadual do Rio de Janeiro) utilizando um modelo Hamiltoniano teórico para simular as propriedade magnetocaloricas da serie RNi2. As amostras TbNi2 e GdNi2 mostraram ser monofásicas embora a de NdNi2 não. O modelo teórico utilizado mostrou ser muito eficiente a prever o comportamento magnético das amostras. ABSTRACT: The present work intends to study and optimize the application of magnetic materials for thermal-magnetic devices. As such we have studied the series Ni2MnGa1-xBix (with x from 0 to 0.05), PrNi5-xCox (with x in the interval 1.95 to 3.00) and RNi2 (com R=Tb;Nd;Gd). For the Ni2MnGa1-xBix series we studied the influence of the bismuth substitution in an attempt to o make the magnetic and structural transition temperatures, TC and TM, come closer and possible merge, in addition, we also study the magnetic anisotropy of this alloy. Our results have in fact increased TM and decreased TC, caused by the increase of the lattice parameter a and the increase of the electron concentration of the alloyed samples, although the studied alloying was not enough to merge the two temperatures. We found a maximum magnetic entropy change of 3.8 J/kg.K for the pure sample and 2.2 J/kg.K for the sample with the most alloying (0.05). In the PrNi5-xCox series, due to the competition between the anisotropy energies of both the Co and Pr sub lattices this series has a spin reorientation phenomenon at low temperature (~140 K). This series presents a rich magnetic phase diagram that was constructed in the present work and some effects ratified based on the percolation theory. We also found a large magnetic entropy change peak due to the spin-reorientation process and the magnetic transition. As far as the RNi2 series goes, our study was only limited to the production, homogeneity analyses and magnetic entropy variation calculation, for further study by Dr. Pedro J. von Ranke (State University of Rio de Janeiro) using a theoretical Hamiltonian model to simulate the magnetic properties of the RNi2 series. The TbNi2 and the GdNi2 samples have shown to be single phase while the NdNi2 does not. The theoretical model has shown to be very effective in predicting the magnetic behavior of the samples

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

A database of freshwater fish species of the Amazon Basin

The Amazon Basin is an unquestionable biodiversity hotspot, containing the highest freshwater biodiversity on earth and facing off a recent increase in anthropogenic threats. The current knowledge on the spatial distribution of the freshwater fish species is greatly deficient in this basin, preventing a comprehensive understanding of this hyper-diverse ecosystem as a whole. Filling this gap was the priority of a transnational collaborative project, i.e. the AmazonFish project - https://www.amazon-fish.com/. Relying on the outputs of this project, we provide the most complete fish species distribution records covering the whole Amazon drainage. The database, including 2,406 validated freshwater native fish species, 232,936 georeferenced records, results from an extensive survey of species distribution including 590 different sources (e.g. published articles, grey literature, online biodiversity databases and scientific collections from museums and universities worldwide) and field expeditions conducted during the project. This database, delivered at both georeferenced localities (21,500 localities) and sub-drainages grains (144 units), represents a highly valuable source of information for further studies on freshwater fish biodiversity, biogeography and conservation

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Perfil dos alunos à saída da escolaridade obrigatória

O Perfil dos Alunos à Saída da Escolaridade Obrigatória, homologado pelo Despachon.º 6478/2017, 26 de julho, afirma-se como referencial para as decisões a adotar por decisorese atores educativos ao nível dos estabelecimentos de educação e ensino e dos organismosresponsáveis pelas políticas educativas, constituindo-se como matriz comum para todas asescolas e ofertas educativas no âmbito da escolaridade obrigatória, designadamente ao nívelcurricular, no planeamento, na realização e na avaliação interna e externa do ensino e daaprendizagem