Programming of thermoelectric generation systems based on a heuristic composition of ant colonies

a b s t r a c t Studies related to biologically inspired optimization techniques, which are used for daily operational scheduling of thermoelectric generation systems, indicate that combinations of biologically inspired computation methods together with other optimization techniques have an important role to play in obtaining the best solutions in the shortest amount of processing time. Following this line of research, this article uses a methodology based on optimization by an ant colony to minimize the daily scheduling cost of thermoelectric units. The proposed model uses a Sensitivity Matrix (SM) based on the information provided by the Lagrange multipliers to improve the biologically inspired search process. Thus, a percentage of the individuals in the colony use this information in the evolutionary process of the colony. The results achieved through the simulations indicate that the use of the SM results in quality solutions with a reduced number of individuals

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

Validade e análise de sensibilidade dos coeficientes cinéticos do modelo matemático mecanístico proposto para predizer a cinética de hidrólise de lactose por β-galactosidase de células permeabilizadas de kluyveromyces lactis

Um modelo matemático mecanístico foi desenvolvido para descrever a hidrólise de lactose, levando em consideração o efeito de inibição por galactose. Foi utilizado um sistema dinâmico de equações diferenciais ordinárias da variação de lactose, glicose e galactose com o tempo. Essas equações foram resolvidas no programa Fortran 90 que gerou uma tabela de dados simulados de lactose, glicose e galactose ao longo do tempo. A análise de sensibilidade dos coeficientes do modelo foi realizada variando em + 20% os valores dos coeficientes cinéticos. As concentrações de glicose e galactose não foram influenciadas pelas variações nos coeficientes Km e Ki, sendo que um aumento no valor de Vmax em 20% observou-se um melhor ajuste do modelo com os dados experimentais. Para a validação do modelo, foram realizados testes de hidrólise em solução tampão fosfato contendo 5% de lactose e em leite desnatado com teor de lactose de 4,8%. O modelo representou satisfatoriamente o processo de hidrólise em solução tampão fosfato com 5% de lactose. Para esta solução a variação entre o valor medido e o predito de glicose foi de 4%, quando 75% de hidrólise foi atingida, alterando o valor de Vmax em 30%. No caso da hidrólise da lactose do leite, verificou-se uma variação entre as concentrações de glicose medida e predita de 13% quando 85% da hidrólise foi atingida. Os dados simulados pelo modelo apresentaram boa correlação com os dados experimentais originados da hidrólise de lactose em tampão fosfato contendo 5% de lactose e em leite desnatado

The high fermentative metabolism of Kluyveromyces marxianus UFV-3 relies on the increased expression of key lactose metabolic enzymes

The aim of this work was to obtain insights about the factors that determine the lactose fermentative metabolism of Kluyveromyces marxianus UFV-3. K. marxianus UFV-3 and Kluyveromyces lactis JA6 were cultured in a minimal medium containing different lactose concentrations (ranging from 0.25 to 64 mmol l−1) under aerobic and hypoxic conditions to evaluate their growth kinetics, gene expression and enzymatic activity. The increase in lactose concentration and the decrease in oxygen level favoured ethanol yield for both yeasts but in K. marxianus UFV-3 the effect was more pronounced. Under hypoxic conditions, the activities of β-galactosidase and pyruvate decarboxylase from K. marxianus UFV-3 were significantly higher than those in K. lactis JA6. The expression of the LAC4 (β-galactosidase), RAG6 (pyruvate decarboxylase), GAL7 (galactose-1-phosphate uridylyltransferase) and GAL10 (epimerase) genes in K. marxianus UFV-3 was higher under hypoxic conditions than under aerobic conditions. The high expression of genes of the Leloir pathway, LAC4 and RAG6, associated with the high activity of β-galactosidase and pyruvate decarboxylase contribute to the high fermentative flux in K. marxianus UFV-3. These data on the fermentative metabolism of K. marxianus UFV-3 will be useful for optimising the conversion of cheese whey lactose to ethanol