13,189 research outputs found
Avaliação dos impactes ambientais de sistemas de produção agrícola alternativos no Baixo Mondego
O objectivo principal deste trabalho centra-se em avaliar os impactes ambientais de dois sistemas de produção agrícola na cultura do milho – Sementeira Directa (SD) e Modo de Produção Biológico (MPB) - na região do Baixo Mondego, Portugal.
Durante o estudo, um programa de computador AMBITEC-AGRO - sistema da avaliação do impacto ambiental da tecnologia agropecuária foi utilizado após a adaptação do mesmo à realidade Portuguesa. Um inquérito foi preparado e apresentado aos produtores que aplicavam a(s) tecnologia(s) afim de obter informações sobre o impacte das mesmas quer na parcela ou na região.
Os resultados foram recolhidos e inseridos posteriormente no programa afim de proceder à avaliação dos impactes ambientais.
Os resultados principais mostram que ambos os sistemas de produção indicam um impacte positivo, com +2.22 para a SD e +2.07 para MPB numa escala de -15 a +15. O software utilizado para avaliação do impacte é de fácil aplicação e pode ser extremamente útil na eco-certificação futura das explorações agrícolas, fornecendo uma ferramenta para avaliar a sua sustentabilidade
The role of Dark Matter interaction in galaxy clusters
We consider a toy model to analyze the consequences of dark matter
interaction with a dark energy background on the overall rotation of galaxy
clusters and the misalignment between their dark matter and baryon
distributions when compared to {\Lambda}CDM predictions. The interaction
parameters are found via a genetic algorithm search. The results obtained
suggest that interaction is a basic phenomenon whose effects are detectable
even in simple models of galactic dynamics.Comment: RevTeX 4.1, 5 pages, 3 figure
X-ray method to study temperature-dependent stripe domains in MnAs/GaAs(001)
MnAs films grown on GaAs (001) exhibit a progressive transition between
hexagonal (ferromagnetic) and orthorhombic (paramagnetic) phases at wide
temperature range instead of abrupt transition during the first-order phase
transition. The coexistence of two phases is favored by the anisotropic strain
arising from the constraint on the MnAs films imposed by the substrate. This
phase coexistence occurs in ordered arrangement alternating periodic terrace
steps. We present here a method to study the surface morphology throughout this
transition by means of specular and diffuse scattering of soft x-rays, tuning
the photon energy at the Mn 2p resonance. The results show the long-range
arrangement of the periodic stripe-like structure during the phase coexistence
and its period remains constant, in agreement with previous results using other
techniques.Comment: 4 pages, 4 figures, submitted to Applied Physics Letter
The Influence of Neural Networks on Hydropower Plant Management in Agriculture: Addressing Challenges and Exploring Untapped Opportunities
Hydropower plants are crucial for stable renewable energy and serve as vital
water sources for sustainable agriculture. However, it is essential to assess
the current water management practices associated with hydropower plant
management software. A key concern is the potential conflict between
electricity generation and agricultural water needs. Prioritising water for
electricity generation can reduce irrigation availability in agriculture during
crucial periods like droughts, impacting crop yields and regional food
security. Coordination between electricity and agricultural water allocation is
necessary to ensure optimal and environmentally sound practices. Neural
networks have become valuable tools for hydropower plant management, but their
black-box nature raises concerns about transparency in decision making.
Additionally, current approaches often do not take advantage of their potential
to create a system that effectively balances water allocation.
This work is a call for attention and highlights the potential risks of
deploying neural network-based hydropower plant management software without
proper scrutiny and control. To address these concerns, we propose the adoption
of the Agriculture Conscious Hydropower Plant Management framework, aiming to
maximise electricity production while prioritising stable irrigation for
agriculture. We also advocate reevaluating government-imposed minimum water
guidelines for irrigation to ensure flexibility and effective water allocation.
Additionally, we suggest a set of regulatory measures to promote model
transparency and robustness, certifying software that makes conscious and
intelligent water allocation decisions, ultimately safeguarding agriculture
from undue strain during droughts
A Self-Adaptive Penalty Method for Integrating Prior Knowledge Constraints into Neural ODEs
The continuous dynamics of natural systems has been effectively modelled
using Neural Ordinary Differential Equations (Neural ODEs). However, for
accurate and meaningful predictions, it is crucial that the models follow the
underlying rules or laws that govern these systems. In this work, we propose a
self-adaptive penalty algorithm for Neural ODEs to enable modelling of
constrained natural systems. The proposed self-adaptive penalty function can
dynamically adjust the penalty parameters. The explicit introduction of prior
knowledge helps to increase the interpretability of Neural ODE -based models.
We validate the proposed approach by modelling three natural systems with prior
knowledge constraints: population growth, chemical reaction evolution, and
damped harmonic oscillator motion. The numerical experiments and a comparison
with other penalty Neural ODE approaches and \emph{vanilla} Neural ODE,
demonstrate the effectiveness of the proposed self-adaptive penalty algorithm
for Neural ODEs in modelling constrained natural systems. Moreover, the
self-adaptive penalty approach provides more accurate and robust models with
reliable and meaningful predictions
Growth-Driven Percolations: The Dynamics of Community Formation in Neuronal Systems
The quintessential property of neuronal systems is their intensive patterns
of selective synaptic connections. The current work describes a physics-based
approach to neuronal shape modeling and synthesis and its consideration for the
simulation of neuronal development and the formation of neuronal communities.
Starting from images of real neurons, geometrical measurements are obtained and
used to construct probabilistic models which can be subsequently sampled in
order to produce morphologically realistic neuronal cells. Such cells are
progressively grown while monitoring their connections along time, which are
analysed in terms of percolation concepts. However, unlike traditional
percolation, the critical point is verified along the growth stages, not the
density of cells, which remains constant throughout the neuronal growth
dynamics. It is shown, through simulations, that growing beta cells tend to
reach percolation sooner than the alpha counterparts with the same diameter.
Also, the percolation becomes more abrupt for higher densities of cells, being
markedly sharper for the beta cells.Comment: 8 pages, 10 figure
O tucumã (Astrocaryum vulgare Mart.) principais características e potencialidade agroindustrial.
bitstream/item/40987/1/Boletim-Pesquisa-75-CPATU.pd
The fracturation influence on the exploitation of ornamental rocks: the case of the Pedras Salgadas granite (Vila Real, North of Portugal)
[Abstract] The exploitation of ornamental granite in Pedras Salgadas region is limited by the regional fault pattern, which is formed by three sets of principal faults. Based upon the joint spacings measurements, the medium was computed for each quarry and for the massif. The medium exploitaion yield of the quarries was also estimated
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