27 research outputs found
Structural Evolution of the Brazilian Airport Network
The aviation sector is profitable, but sensitive to economic fluctuations,
geopolitical constraints and governmental regulations. As for other means of
transportation, the relation between origin and destination results in a
complex map of routes, which can be complemented by information associated to
the routes themselves, for instance, frequency, traffic load or distance. The
theory of networks provides a natural framework to investigate dynamics on the
resulting structure. Here, we investigate the structure and evolution of the
Brazilian Airport Network (BAN) for several quantities: routes, connections,
passengers and cargo. Some structural features are in accordance with previous
results of other airport networks. The analysis of the evolution of the BAN
shows that its structure is dynamic, with changes in the relative relevance of
some airports and routes. The results indicate that the connections converge to
specific routes. The network shrinks at the route level but grows in number of
passengers and amount of cargo, which more than doubled during the period
studied.Comment: 10 pages, 8 figures, 2 tables. The analysis moved from an
airport-based network to a city-based network. The conclusions were
unaffecte
Size dependent word frequencies and translational invariance of books
It is shown that a real novel shares many characteristic features with a null
model in which the words are randomly distributed throughout the text. Such a
common feature is a certain translational invariance of the text. Another is
that the functional form of the word-frequency distribution of a novel depends
on the length of the text in the same way as the null model. This means that an
approximate power-law tail ascribed to the data will have an exponent which
changes with the size of the text-section which is analyzed. A further
consequence is that a novel cannot be described by text-evolution models like
the Simon model. The size-transformation of a novel is found to be well
described by a specific Random Book Transformation. This size transformation in
addition enables a more precise determination of the functional form of the
word-frequency distribution. The implications of the results are discussed.Comment: 10 pages, 2 appendices (6 pages), 5 figure
The meta book and size-dependent properties of written language
Evidence is given for a systematic text-length dependence of the power-law
index gamma of a single book. The estimated gamma values are consistent with a
monotonic decrease from 2 to 1 with increasing length of a text. A direct
connection to an extended Heap's law is explored. The infinite book limit is,
as a consequence, proposed to be given by gamma = 1 instead of the value
gamma=2 expected if the Zipf's law was ubiquitously applicable. In addition we
explore the idea that the systematic text-length dependence can be described by
a meta book concept, which is an abstract representation reflecting the
word-frequency structure of a text. According to this concept the
word-frequency distribution of a text, with a certain length written by a
single author, has the same characteristics as a text of the same length pulled
out from an imaginary complete infinite corpus written by the same author.Comment: 7 pages, 6 figures, 1 tabl
Bad news travels fast! | Notícia ruim corre depressa!
Many proverbs are created through everyday experience. Although many of them are readily understood by ordinary people, the more detailed view generates many questions and doubts related to their credibility. Motivated by one of these proverbs, in the present paper, we analyse propagation of news in the network of electronic contacts (e-mails). More specifically, we propose transmission protocols intended to reproduce properties of real systems. These protocols are simulated in a real e-mail network and in the random network proposed by p. Erdos and a. Rényi prize. The results suggest that news spreads faster in the random network. The hubs in the real network tend to attract the news, in prejudice to the less connected nodes
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
Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
The success of new scientific areas can be assessed by their potential for
contributing to new theoretical approaches and in applications to real-world
problems. Complex networks have fared extremely well in both of these aspects,
with their sound theoretical basis developed over the years and with a variety
of applications. In this survey, we analyze the applications of complex
networks to real-world problems and data, with emphasis in representation,
analysis and modeling, after an introduction to the main concepts and models. A
diversity of phenomena are surveyed, which may be classified into no less than
22 areas, providing a clear indication of the impact of the field of complex
networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions
are welcome
Canagliflozin and renal outcomes in type 2 diabetes and nephropathy
BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years
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