The pre-Linnaean <i>herbarium</i> of Paolo Boccone (1633–1704) kept in Leiden (the Netherlands) and its connections with the imprinted one in Paris

Costa, R. M. S.; Andel, Tinde van; Pavone, Pietro; Pulvirenti, S.

doi:10.1080/11263504.2018.1445132

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…(http://linnean-online.org/2045/) and probably sent to Linnaeus by C. Allioni after the publication of Flora alpina, cannot be used for typification. Actually, no evidence exists about the presence of Eritrichium nanum collections in Boccone's herbaria kept in the Civica e A. Ursino Recupero Libraries of Catania (Pulvirenti & al., 2017) and in the Naturalis Biodiversity Center in Leiden (the latter as part of the collection of Hieronymus van Beverningh; Costa & al., 2018). Although existence of this material cannot be excluded, the plant drawing in plate 107 is therefore the only suitable element to serve as lectotype at the present state of knowledge.…”

Section: ■ Typification Of the Namementioning

confidence: 99%

Typification of the Linnaean name Myosotis nana (Boraginaceae)

Galasso¹,

Selvi

2019

TAXON

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Section: ■ Typification Of the Namementioning

confidence: 99%

Typification of the Linnaean name Myosotis nana (Boraginaceae)

Galasso¹,

Selvi

2019

TAXON

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“…Forest fires are one of the important disturbance factors in the global forest ecosystem [1], and it causes various degrees of negative impacts on the ecological environment, resources, human health, economy, and so on [2][3][4][5][6][7][8]. On March 30, 2019, there was a forest fire in Muli County, Liangshan Prefecture, Sichuan Province, China.…”

Section: Introductionmentioning

confidence: 99%

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Zhong-ke

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

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“…During the last decade, a large number of mathematical models have been developed to simulate, analyse, and understand the dynamics of a population of diabetics; in a related research work, Boutayeb and Chetouani [2] and Derouich et al [3] proposed a mathematical model for the dynamics of the population of diabetes patients with or without complications using a system of ordinary di erential equations. Also, many researches have focused on this topic and other related topics [4,5,[12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Control Strategy for a Discrete Time to the Dynamics of a Population of Diabetics with Highlighting the Impact of Living Environment

Kouidere

Balatif

Ferjouchia

et al. 2019

Discrete Dynamics in Nature and Society

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Nowadays, Diabetes is one of the most common diseases, which has a huge and growing socio-economic burden affecting individuals, families, and the whole society. In this paper, we propose an optimal control approach modeling the evolution from pre-diabetes to diabetes with and without complications and the effect of living environment. We show the existence of an optimal control and then use a numerical implicit finite-difference method to monitor the size of population in each compartment.

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The pre-Linnaean herbarium of Paolo Boccone (1633–1704) kept in Leiden (the Netherlands) and its connections with the imprinted one in Paris

Cited by 20 publications

References 21 publications

Typification of the Linnaean name Myosotis nana (Boraginaceae)

Typification of the Linnaean name Myosotis nana (Boraginaceae)

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Optimal Control Strategy for a Discrete Time to the Dynamics of a Population of Diabetics with Highlighting the Impact of Living Environment

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