EMPRESA JÚNIOR E O PROCESSO DE ENSINO-APRENDIZAGEM PRÁTICA EM ADMINISTRAÇÃO: O CASO DA CRIAÇÃO DA SEM FRONTEIRAS CONSULTORIA JÚNIOR DA UNIVERSIDADE FEDERAL DA FRONTEIRA SUL

A Empresa Júnior é uma associação civil sem fins lucrativos gerida somente por discentes sob a supervisão de docentes. Neste caso específico, prestará consultoria na área de Administração. Os objetivos para o alcance da sua criação incluem o embasamento teórico para a verificação das estruturas e funcionamento de Empresas Juniores já existentes, para a elaboração do estatuto, definição do regimento, das normas e estrutura para o desenvolvimento das atividades, possibilitando assim, a implantação da Empresa Júnior na Universidade Federal da Fronteira Sul (UFFS). A pesquisa iniciou com o contato aos órgãos gestores do Movimento Júnior no Brasil e em Santa Catarina, buscando referencial para a implantação da Empresa Júnior na UFFS. Por fim, caberá ao grupo, juntamente com os professores orientadores, selecionar os alunos que comporão a Empresa Júnior e finalizar sua implantação. Perante a universidade, além de a Empresa Júnior estimular os estudantes a praticarem as teorias, estará contribuindo para o desenvolvimento da economia local, divulgando os cursos da instituição e valorizando os seus docentes

Enhancing Spin Transfer Torque in Magnetic Tunnel Junction Devices: Exploring the Influence of Capping Layer Materials and Thickness on Device Characteristics

We have developed and optimized two categories of spin transfer torque magnetic tunnel junctions (STT-MTJs) that exhibit a high tunnel magnetoresistance (TMR) ratio, low critical current, high outputpower in the micro watt range, and auto-oscillation behavior. These characteristics demonstrate the potential of STT-MTJs for low-power, high-speed, and reliable spintronic applications, including magnetic memory, logic, and signal processing. The only distinguishing factor between the two categories, denoted as A-MTJs and B-MTJs, is the composition of their free layers, 2 CoFeB/0.21 Ta/6 CoFeSiB for A-MTJs and 2 CoFeB/0.21 Ta/7 NiFe for B-MTJs. Our study reveals that B-MTJs exhibit lower critical currents for auto-oscillation than A-MTJs. We found that both stacks have comparable saturation magnetization and anisotropy field, suggesting that the difference in auto-oscillation behavior is due to the higher damping of A-MTJs compared to B-MTJs. To verify this hypothesis, we employed the all-optical time-resolved magneto-optical Kerr effect (TRMOKE) technique, which confirmed that STT-MTJs with lower damping exhibited auto-oscillation at lower critical current values. Additionally, our study aimed to optimize the STT-MTJ performance by investigating the impact of the capping layer on the device's response to electronic and optical stimuli

RF signal classification in hardware with an RF spintronic neural network

Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications. Here we show how to leverage the intrinsic dynamics of spintronic nanodevices called magnetic tunnel junctions to process multiple analogue RF inputs in parallel and perform synaptic operations. Furthermore, we achieve classification of RF signals with experimental data from magnetic tunnel junctions as neurons and synapses, with the same accuracy as an equivalent software neural network. These results are a key step for embedded radiofrequency artificial intelligence.Comment: 8 pages, 5 figure

Unbiased Random Number Generation using Injection-Locked Spin-Torque Nano-Oscillators

Unbiased sources of true randomness are critical for the successful deployment of stochastic unconventional computing schemes and encryption applications in hardware. Leveraging nanoscale thermal magnetization fluctuations provides an efficient and almost cost-free means of generating truly random bitstreams, distinguishing them from predictable pseudo-random sequences. However, existing approaches that aim to achieve randomness often suffer from bias, leading to significant deviations from equal fractions of 0 and 1 in the bitstreams and compromising their inherent unpredictability. This study presents a hardware approach that capitalizes on the intrinsic balance of phase noise in an oscillator injection locked at twice its natural frequency, leveraging the stability of this naturally balanced physical system. We demonstrate the successful generation of unbiased and truly random bitstreams through extensive experimentation. Our numerical simulations exhibit excellent agreement with the experimental results, confirming the robustness and viability of our approach.Comment: 13 pages, 8 figure

Multilayer spintronic neural networks with radio-frequency connections

Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided that they implement state-of-the art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radio frequency (RF) signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly-separable RF inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of the-art identification of drones from their RF transmissions, without digitization, and consuming only a few milliwatts, which is a gain of more than four orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks

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

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

Obtenção de nanocompósitos de polipropileno com grafite utilizando líquidos iônicos

O crescente interesse tecnológico em nanocompósitos poliméricos com propriedades elétricas conduziu a utilização de materiais à base de carbono, como as nanolâminas de grafite, em sua obtenção. Polipropileno (PP) é uma das poliolefinas de maior importância comercial devido a sua baixa densidade, facilidade de processamento e boas propriedades mecânicas. O emprego de nanolâminas de grafite nessa matriz polimérica visa o sinergismo entre as características do PP, e a condutividade elétrica e excelentes propriedades térmicas e mecânicas desta nanocarga. Entretanto, a dificuldade de dispersão desta nanocarga na matriz de PP e a baixa interação entre as fases torna a obtenção de nanocompósitos com grafite um desafio. Neste contexto, o líquido iônico (LI) hexafluorofosfato de 1-n-decil-3-metilimidazólio foi utilizado neste trabalho com o objetivo de superar estas dificuldades. Os nanocompósitos foram obtidos por incorporação de 2% em massa de grafite na matriz polimérica, através de intercalação no estado fundido. A separação das lamelas do grafite foi feita através de esfoliação, em banho de ultrassom, utilizando como solventes acetonitrila, tetrahidrofurano e N,N-dimetilformamida, a fim de avaliar a influência do solvente na separação das lamelas. Além disso, foram preparados grafites esfoliados com adição do LI durante a ultrassonificação. Os nanocompósitos foram caracterizados por análises termogravimétricas, calorimetria diferencial de varredura, ensaios de tração, análises dinâmico-mecânicas, microscopia eletrônica de transmissão e varredura. Os resultados indicaram que a adição de LI, associado ao grafite, resultou em aumento da rigidez e da estabilidade térmica dos nanocompósitos, possivelmente devido ao aumento da interação entre as fases. A utilização de THF, como solvente na esfoliação em ultrassom, resultou em melhor dispersão das cargas no PP promovendo aumento da temperatura de transição vítrea (Tg). E quando este solvente foi associado ao LI, no processo de esfoliação, foi observada maior adesão das cargas na matriz polimérica. A ultrassonificação demostrou ser um método eficiente para separação das lamelas, como observado pelas análises de difração de raios-X. Além disso, provavelmente este método foi necessário para incorporação do LI nas lamelas, pois a mistura direta do LI e do grafite em PP, no estado fundido, resultou em redução da estabilidade térmica deste polímero. O emprego do ultrassom de ponteira associado ao banho de ultrassom, no processo de esfoliação, resultou em maior separação das lamelas e maior redução do tamanho dos tactóides do grafite