Food Chemistry: Food quality and new analytical approaches

Peer Reviewe

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

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

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

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.

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HIV-1 mother-to-child transmission in Brazil (1994\u20132016): a time series modeling

HIV-1 mother-to-child transmission (HIV-1 MTCT), is an important cause of children mortality worldwide. Brazil has been traditionally praised by its HIV/Aids program, which provides free-of-charge care for people living with HIV-1. Using public epidemiology and demographic databases, we aimed at modeling HIV-1 MTCT prevalence in Brazil through the years (1994\u20132016) and elaborate a statistical model for forecasting, contributing to HIV-1 epidemiologic surveillance and healthcare decision-making. We downloaded sets of live births and mothers\u2019 data alongside HIV-1 cases notification in children one year old or less. Through time series modeling, we estimated prevalence along the years in Brazil, and observed a remarkable decrease of HIV-1 MTCT between 1994 (10 cases per 100,000 live births) and 2016 (five cases per 100,000 live births), a reduction of 50%. Using our model, we elaborated a prognosis for each Brazilian state to help HIV-1 surveillance decision making, indicating which states are in theory in risk of experiencing a rise in HIV-1 MTCT prevalence. Ten states had good (37%), nine had mild (33%), and eight had poor prognostics (30%). Stratifying the prognostics by Brazilian region, we observed that the Northeast region had more states with poor prognosis, followed by North and Midwest, Southeast and South with one state of poor prognosis each. Brazil undoubtedly advanced in the fight against HIV-1 MTCT in the past two decades. We hope our model will help indicating where HIV-1 MTCT prevalence may rise in the future and support government decision makers regarding HIV-1 surveillance and prevention