238 research outputs found
Geometric post-Newtonian description of spin-half particles in curved spacetime
Einstein Equivalence Principle (EEP) requires all matter components to universally couple to gravity via a single common geometry: that of spacetime. This relates quantum theory with geometry as soon as interactions with gravity are considered. In this work, I study the geometric theory of coupling a spin-1/2 particle to gravity in a twofold expansion scheme: First with respect to the distance based on Fermi normal coordinates around a preferred worldline (e.g., that of a clock in the laboratory), second with respect to 1/c (post-Newtonian expansion). I consider the one-particle sector of a massive spinor field in QFT, here described effectively by a classical field. The formal expansion in powers of 1/c yields a systematic and complete generation of GR corrections for quantum systems. I find new terms that were overlooked in the literature at order 1/c and extended the level of approximation to the next order. These findings are significant for a consistent inclusion of gravity corrections in the description of quantum experiments of corresponding sensitivities, and also for testing aspects of GR, like the EEP, in the quantum realm
Application of Artificial Intelligence algorithms to support decision-making in agriculture activities
Deep Learning has been successfully applied to image recognition, speech recognition, and
natural language processing in recent years. Therefore, there has been an incentive to apply
it in other fields as well. The field of agriculture is one of the most important in which the
application of artificial intelligence algorithms, and particularly, of deep learning needs to
be explored, as it has a direct impact on human well-being. In particular, there is a need
to explore how deep learning models for decision-making can be used as a tool for optimal
planting, land use, yield improvement, production/disease/pest control, and other activities.
The vast amount of data received from sensors in smart farms makes it possible to use deep
learning as a model for decision-making in this field. In agriculture, no two environments are
exactly alike, which makes testing, validating, and successfully implementing such technologies
much more complex than in most other sectors. Recent scientific developments in the
field of deep learning, applied to agriculture, are reviewed and some challenges and potential
solutions using deep learning algorithms in agriculture are discussed. Higher performance
in terms of accuracy and lower inference time can be achieved, and the models can be made
useful in real-world applications. Finally, some opportunities for future research in this area
are suggested. The ability of artificial neural networks, specifically Long Short-Term Memory
(LSTM) and Bidirectional LSTM (BLSTM), to model daily reference evapotranspiration
and soil water content is investigated. The application of these techniques to predict these
parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was
selected. Bayesian optimization was used to determine the hyperparameters, such as learning
rate, decay, batch size, and dropout size. The model achieved mean square error (MSE)
values ranging from 0.07 to 0.27 (mm d–1)² for ETo (Reference Evapotranspiration) and
0.014 to 0.056 (m³m–3)² for SWC (Soil Water Content), with R2 values ranging from 0.96
to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate
potential performance improvement. Performance dropped in all datasets due to the
complexity of the model. The performance of the models was also compared with CNN, traditional
machine learning algorithms Support Vector Regression, and Random Forest. LSTM
achieved the best performance. Finally, the impact of the loss function on the performance
of the proposed models was investigated. The model with the mean square error (MSE) as
loss function performed better than the model with other loss functions. Afterwards, the
capabilities of these models and their extension, BLSTM and Bidirectional Gated Recurrent
Units (BGRU) to predict end-of-season yields are investigated. The models use historical
data, including climate data, irrigation scheduling, and soil water content, to estimate endof-
season yield. The application of this technique was tested for tomato and potato yields at a
site in Portugal. The BLSTM network outperformed the GRU, the LSTM, and the BGRU networks
on the validation dataset. The model was able to capture the nonlinear relationship
between irrigation amount, climate data, and soil water content and predict yield with an
MSE of 0.017 to 0.039 kg/ha. The performance of the BLSTM in the test was compared with
the most commonly used deep learning method called CNN, and machine learning methods
including a Multi-Layer Perceptrons model and Random Forest regression. The BLSTM out-performed the other models with a R2-score between 0.97 and 0.99. The results show that
analyzing agricultural data with the LSTM model improves the performance of the model in
terms of accuracy. The CNN model achieved the second-best performance. Therefore, the
deep learning model has a remarkable ability to predict the yield at the end of the season. Additionally,
a Deep Q-Network was trained for irrigation scheduling. The agent was trained to
schedule irrigation for a tomato field in Portugal. Two LSTM models trained previously were
used as the agent environment. One predicts the total water in the soil profile on the next
day. The other one was employed to estimate the yield based on the environmental condition
during a season and then measure the net return. The agent uses this information to decide
the following irrigation amount. LSTM and CNN networks were used to estimate the Q-table
during training. Unlike the LSTM model, the ANN and the CNN could not estimate the Qtable,
and the agent’s reward decreased during training. The comparison of the performance
of the model was done with fixed-base irrigation and threshold-based irrigation. The trained
model increased productivity by 11% and decreased water consumption by 20% to 30% compared
to the fixed method. Also, an on-policy model, Advantage Actor–Critic (A2C), was
implemented to compare irrigation scheduling with Deep Q-Network for the same tomato
crop. The results show that the on-policy model A2C reduced water consumption by 20%
compared to Deep Q-Network with a slight change in the net reward. These models can be
developed to be applied to other cultures with high importance in Portugal, such as fruit,
cereals, and grapevines, which also have large water requirements. The models developed
along this thesis can be re-evaluated and trained with historical data from other cultures with
high production in Portugal, such as fruits, cereals, and grapes, which also have high water
demand, to create a decision support and recommendation system that tells farmers when
and how much to irrigate. This system helps farmers avoid wasting water without reducing
productivity. This thesis aims to contribute to the future steps in the development of precision
agriculture and agricultural robotics. The models developed in this thesis are relevant to
support decision-making in agricultural activities, aimed at optimizing resources, reducing
time and costs, and maximizing production.Nos últimos anos, a técnica de aprendizagem profunda (Deep Learning) foi aplicada com
sucesso ao reconhecimento de imagem, reconhecimento de fala e processamento de linguagem
natural. Assim, tem havido um incen tivo para aplicá-la também em outros sectores.
O sector agrícola é um dos mais importantes, em que a aplicação de algoritmos de inteligência
artificial e, em particular, de deep learning, precisa ser explorada, pois tem impacto direto
no bem-estar humano. Em particular, há uma necessidade de explorar como os modelos de
aprendizagem profunda para a tomada de decisão podem ser usados como uma ferramenta
para cultivo ou plantação ideal, uso da terra, melhoria da produtividade, controlo de produção,
de doenças, de pragas e outras atividades. A grande quantidade de dados recebidos
de sensores em explorações agrícolas inteligentes (smart farms) possibilita o uso de deep
learning como modelo para tomada de decisão nesse campo. Na agricultura, não há dois
ambientes iguais, o que torna o teste, a validação e a implementação bem-sucedida dessas
tecnologias muito mais complexas do que na maioria dos outros setores. Desenvolvimentos
científicos recentes no campo da aprendizagem profunda aplicada à agricultura, são revistos
e alguns desafios e potenciais soluções usando algoritmos de aprendizagem profunda na agricultura
são discutidos. Maior desempenho em termos de precisão e menor tempo de inferência
pode ser alcançado, e os modelos podem ser úteis em aplicações do mundo real. Por fim,
são sugeridas algumas oportunidades para futuras pesquisas nesta área. A capacidade de redes
neuronais artificiais, especificamente Long Short-Term Memory (LSTM) e LSTM Bidirecional
(BLSTM), para modelar a evapotranspiração de referência diária e o conteúdo de água
do solo é investigada. A aplicação destas técnicas para prever estes parâmetros foi testada em
três locais em Portugal. Um BLSTM de camada única com 512 nós foi selecionado. A otimização
bayesiana foi usada para determinar os hiperparâmetros, como taxa de aprendizagem,
decaimento, tamanho do lote e tamanho do ”dropout”. O modelo alcançou os valores de erro
quadrático médio na faixa de 0,014 a 0,056 e R2 variando de 0,96 a 0,98. Um modelo de
Rede Neural Convolucional (CNN – Convolutional Neural Network) foi adicionado ao LSTM
para investigar uma potencial melhoria de desempenho. O desempenho decresceu em todos
os conjuntos de dados devido à complexidade do modelo. O desempenho dos modelos
também foi comparado com CNN, algoritmos tradicionais de aprendizagem máquina Support
Vector Regression e Random Forest. O LSTM obteve o melhor desempenho. Por fim,
investigou-se o impacto da função de perda no desempenho dos modelos propostos. O modelo
com o erro quadrático médio (MSE) como função de perda teve um desempenho melhor
do que o modelo com outras funções de perda. Em seguida, são investigadas as capacidades
desses modelos e sua extensão, BLSTM e Bidirectional Gated Recurrent Units (BGRU) para
prever os rendimentos da produção no final da campanha agrícola. Os modelos usam dados
históricos, incluindo dados climáticos, calendário de rega e teor de água do solo, para estimar
a produtividade no final da campanha. A aplicação desta técnica foi testada para os rendimentos
de tomate e batata em um local em Portugal. A rede BLSTM superou as redes GRU,
LSTM e BGRU no conjunto de dados de validação. O modelo foi capaz de captar a relação não
linear entre dotação de rega, dados climáticos e teor de água do solo e prever a produtividade com um MSE variando de 0,07 a 0,27 (mm d–1)² para ETo (Evapotranspiração de Referência)
e de 0,014 a 0,056 (m³m–3)² para SWC (Conteúdo de Água do Solo), com valores de R2
variando de 0,96 a 0,98. O desempenho do BLSTM no teste foi comparado com o método de
aprendizagem profunda CNN, e métodos de aprendizagem máquina, incluindo um modelo
Multi-Layer Perceptrons e regressão Random Forest. O BLSTM superou os outros modelos
com um R2 entre 97% e 99%. Os resultados mostram que a análise de dados agrícolas
com o modelo LSTM melhora o desempenho do modelo em termos de precisão. O modelo
CNN obteve o segundo melhor desempenho. Portanto, o modelo de aprendizagem profunda
tem uma capacidade notável de prever a produtividade no final da campanha. Além disso,
uma Deep Q-Network foi treinada para programação de irrigação para a cultura do tomate.
O agente foi treinado para programar a irrigação de uma plantação de tomate em Portugal.
Dois modelos LSTM treinados anteriormente foram usados como ambiente de agente. Um
prevê a água total no perfil do solo no dia seguinte. O outro foi empregue para estimar a produtividade
com base nas condições ambientais durante uma o ciclo biológico e então medir
o retorno líquido. O agente usa essas informações para decidir a quantidade de irrigação.
As redes LSTM e CNN foram usadas para estimar a Q-table durante o treino. Ao contrário
do modelo LSTM, a RNA e a CNN não conseguiram estimar a tabela Q, e a recompensa do
agente diminuiu durante o treino. A comparação de desempenho do modelo foi realizada
entre a irrigação com base fixa e a irrigação com base em um limiar. A aplicação das doses
de rega preconizadas pelo modelo aumentou a produtividade em 11% e diminuiu o consumo
de água em 20% a 30% em relação ao método fixo. Além disso, um modelo dentro da táctica,
Advantage Actor–Critic (A2C), é foi implementado para comparar a programação de
irrigação com o Deep Q-Network para a mesma cultura de tomate. Os resultados mostram
que o modelo de táctica A2C reduziu o consumo de água consumo em 20% comparado ao
Deep Q-Network com uma pequena mudança na recompensa líquida. Estes modelos podem
ser desenvolvidos para serem aplicados a outras culturas com elevada produção em Portugal,
como a fruta, cereais e vinha, que também têm grandes necessidades hídricas. Os modelos
desenvolvidos ao longo desta tese podem ser reavaliados e treinados com dados históricos
de outras culturas com elevada importância em Portugal, tais como frutas, cereais e uvas,
que também têm elevados consumos de água. Assim, poderão ser desenvolvidos sistemas
de apoio à decisão e de recomendação aos agricultores de quando e quanto irrigar. Estes
sistemas poderão ajudar os agricultores a evitar o desperdício de água sem reduzir a produtividade.
Esta tese visa contribuir para os passos futuros na evolução da agricultura de
precisão e da robótica agrícola. Os modelos desenvolvidos ao longo desta tese são relevantes
para apoiar a tomada de decisões em atividades agrícolas, direcionadas à otimização de recursos,
redução de tempo e custos, e maximização da produção.Centro-01-0145-FEDER000017-EMaDeS-Energy,
Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020),
within the Regional Operational Program of the Center (CENTRO 2020) and the EU through
the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia
(FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).
It was also supported by the R&D Project BioDAgro – Sistema operacional inteligente de
informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by
Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST
- Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical
Engineering of the University of Beira Interior, Covilhã, Portugal
Effects of matricaria chamomilla extract on motor coordination impairment induced in rat and determination antioxidant properties of chamomile
Objective
To evaluate the effect of ethanolic extract of chamomile on balance and motor learning in rats receiving scopolamine and intact rats.
Methods
Fourty-two rats were divided into 6 groups (n = 7). Control group received distilled water. Rats in Group 2 were given 1 mg/kg scopolamine. Groups 3 and 4 received chamomile extract 200 mg/kg and 500 mg/kg, respectively, and scopolamine simultaneously for 20 days. Intact groups (Groups 5 and 6) only received chamomile extract 200 mg/kg and 500 mg/kg, respectively. Motor coordination of rats was assessed with rotarod apparatus.
Results
According to the obtained results, compared with the control group, scopolamine significantly decreased time spent on rotarod performance (P < 0.001). Compared with scopolamine group, the strength and staying on rotarod apparatus in Group 3 significantly increased (P < 0.05). The results of this research showed that intact groups that received only chamomile extract at doses of 200 mg/kg and 500 mg/kg significantly increased time spent on rotarod, compared with scopolamine group (P < 0.001).
Conclusions
The results of this study indicated the high antioxidant property and protective effect of chamomile extract on motor coordination in the groups that received scopolamine
Geometric post-Newtonian description of massive spin-half particles in curved spacetime
We consider the Dirac equation coupled to an external electromagnetic field
in curved four-dimensional spacetime with a given timelike worldline
representing a classical clock. We use generalised Fermi normal coordinates in
a tubular neighbourhood of and expand the Dirac equation up to, and
including, the second order in the dimensionless parameter given by the ratio
of the geodesic distance to the radii defined by spacetime curvature, linear
acceleration of , and angular velocity of rotation of the employed
spatial reference frame along . With respect to the time measured by
the clock , we compute the Dirac Hamiltonian to that order. On top of
this `weak-gravity' expansion we then perform a post-Newtonian expansion up to,
and including, the second order of , corresponding to a `slow-velocity'
expansion with respect to . As a result of these combined expansions we
give the weak-gravity post-Newtonian expression for the Pauli Hamiltonian of a
spin-half particle in an external electromagnetic field. This extends and
partially corrects recent results from the literature, which we discuss and
compare in some detail.Comment: 21+2+13 pages (main text, references, appendix). v2: corrected author
spelling in arXiv metadat
Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method
In recent years, deep learning algorithms have been successfully applied in the development
of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases,
weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as
lack of natural resources and climate change, an efficient decision support system for irrigation
is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation
scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM
(BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The
application of these techniques to predict these parameters was tested for three sites in Portugal.
A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine
the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved
the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to
0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential
performance improvement. Performance dropped in all datasets due to the complexity of the
model. The performance of the models was also compared with CNN, traditional machine learning
algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance.
Finally, the impact of the loss function on the performance of the proposed models was investigated.
The model with the mean square error as loss function performed better than the model with other
loss functions.Project Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).info:eu-repo/semantics/publishedVersio
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