birddog

Detecting Technological and Scientific Trajectories


Professor Roney Fraga Souza | Faculty of Economics - UFMT - Brazil | Presenter

Master Student Luis Felipe Rodrigues | Faculty of Economics - UFMT - Brazil

Professor José Maria Silveira | Institute of Economics - Unicamp - Brazil

Who are we?

Roney Fraga Souza

Faculty of Economics

Associate Professor

Federal University of Mato Grosso

Cuiabá - Mato Grosso - Brazil

roneyfraga.com

http://lattes.cnpq.br/6380212729787758

https://github.com/roneyfraga

https://scholar.google.com.br

www.researchgate.net

Luis Felipe de Souza Rodrigues

Master Student

Faculty of Economics

Federal University of Mato Grosso

Cuiabá - Mato Grosso - Brazil

http://lattes.cnpq.br/5404832707334563

José Maria Ferreira Jardim da Silveira

Titular Professor

Institute of Economics

University of Campinas

Campinas - São Paulo - Brazil

http://lattes.cnpq.br/4984859173592703

www.researchgate.net

https://scholar.google.com.br

How important is technology to society?

See Roser (2023) Technology over the long run: zoom out to see how dramatically the world can change within a lifetime. Our World in Data.[1]

See Roser (2023) Technology over the long run: zoom out to see how dramatically the world can change within a lifetime. Our World in Data.[1]

  • Perspective problem:
  • Earth’s distance from the Sun: approximately 149.6 million km.
  • If the Earth were the size of a tennis ball, the Sun (11.7 meters in diameter) would be approximately 7.19 km away.

Technological change was extremely slow in the past and fast now.

Technology will continue to change the world, that’s a certainty.

Can we predict the future?


Unlike chess, the economy is a complex system!

  • With
  • Interconnection and Interdependence
  • Self-organization
  • Non-linearity
  • Feedback
  • Emergent Properties

Don’t be pessimistic, there is Synchronization.







Schumpterian Creative Destruction


Schumpeter investigated the anatomy of the capitalist engine.[2]


Books

  • The Theory of Economic Development (1911)
  • Business Cycles (1939)
  • Capitalism, Socialism and Democracy (1942)
    • Chapter 7: The process of creative destruction


Creative Destruction vs Creative Accumulation

Dosi’s Paradigms and Trajectories

Technological Paradigm[3]

Guides technological change by defining important problems and how to solve them, much like a scientific paradigm directs research.

Technological Trajectories[3]

Cumulative progress to refine and optimize solutions in line with the paradigm’s prescriptions.


  • Discontinuity: Radial Inventions/Innovations tend to overcome Scientific/Technological Paradigms.

  • Continuity: Changes in the same trajectory.

  • Paradigms and trajectories are choices, made in a multidimensional way (economic, institutional and social aspects).


Source: Zhang 2023[4] A holistic method for radical concept generation based on technological evolution.


How to identify scientific and technological trajectories?















Source: https://images.app.goo.gl/Stm3TC2PHkdSNLQx9

Main Path Analisys

  • Step 1: calculate citation weights:

  • Search path link count (SPLC);[5]

  • Search path node pair (SPNP);[5]

  • Node pair projection count (NPPC).[5]

  • Search path count (SPC)[6]

  • Step 2: finding the most meaningful path:

  • Local search[5]

  • Global search

  • Key-route search[7]

  • Step 1: the SPC value for the link (B, D) is 5 because five paths (B-D-F-H-K, B-D-F-I-L, B-D-F-I-M-N, B-D-I-L and B-D-I-M-N) pass through it.
  • Step 2: global search (B, N) is in red, it is the path with the greatest weights (5 + 3 + 2 + 2 + 3 = 15).
Code 
library(igraph)
library(tibble)

igraph::graph_from_data_frame(
  tibble::tribble(
    ~from, ~to,
    "A", "C",
    "B", "C",
    "B", "D",
    "B", "J",
    "C", "E",
    "C", "H",
    "D", "F",
    "D", "I",
    "J", "M",
    "E", "G",
    "F", "H",
    "F", "I",
    "G", "H",
    "I", "L",
    "I", "M",
    "H", "K",
    "M", "N"
  ),
  directed = TRUE) ->
  net

# https://stackoverflow.com/questions/67792685/main-path-analysis-in-citation-network-using-igraph-in-r

spc <- function(g) {
  linegraph <- make_line_graph(g)
  source_edges <- V(linegraph)[degree(linegraph, mode = "in") == 0]
  sink_edges <- V(linegraph)[degree(linegraph, mode = "out") == 0]
  tabulate(
    unlist(
      lapply(
        source_edges,
        all_simple_paths,
        graph = linegraph,
        to = sink_edges,
        mode = "out"
      )
    )
  )
}

main_search <- function(g) {
  linegraph <- make_line_graph(g)
  V(linegraph)$spc <- spc(g)
  source_edges <- V(linegraph)[degree(linegraph, mode = "in") == 0]
  sink_edges <- V(linegraph)[degree(linegraph, mode = "out") == 0]
  paths <- unlist(
    lapply(
      source_edges,
      all_simple_paths,
      graph = linegraph,
      to = sink_edges,
      mode = "out"
    ),
    recursive = FALSE
  )
  path_lengths <- unlist(lapply(paths, function(x) sum(x$spc)))
  vertex_attr(linegraph, "main_path") <- 0
  vertex_attr(
    linegraph,
    "main_path",
    paths[[which(path_lengths == max(path_lengths))[[1]]]]
  ) <- 1
  V(linegraph)$main_path
}

plot(
  net,
  vertex.label = V(net)$name, # Rótulos dos vértices
  vertex.color = "lightblue", # Cor dos vértices
  vertex.size = 25, # Tamanho dos vértices
  vertex.label.color = "black", # Cor do texto dos vértices
  vertex.label.cex = 3, # Tamanho da fonte do texto
  edge.arrow.size = 2, # Tamanho da seta
  edge.width = 8,
  # edge.color = ifelse(edge_labels == 1, "red", "gray"), # Caminho principal em vermelho
  # edge.width = ifelse(edge_labels == 1, 4, 2), # Espessura do caminho principal
  edge.label = spc(net),
  edge.label.cex = 5,
  edge.label.color = 'brown',
  layout = layout_as_tree(net, root = "B") # Layout em árvore com o nó A como raiz
)

edge_labels <- main_search(net)

plot(
  net,
  vertex.label = V(net)$name, # Rótulos dos vértices
  vertex.color = "lightblue", # Cor dos vértices
  vertex.size = 25, # Tamanho dos vértices
  vertex.label.color = "black", # Cor do texto dos vértices
  vertex.label.cex = 3, # Tamanho da fonte do texto
  edge.arrow.size = 2, # Tamanho da seta
  edge.width = 8,
  edge.color = ifelse(edge_labels == 1, "red", "gray"), # Caminho principal em vermelho
  edge.width = ifelse(edge_labels == 1, 4, 2), # Espessura do caminho principal
  # edge.label = spc(net),
  # edge.label.cex = 3,
  layout = layout_as_tree(net, root = "B") # Layout em árvore com o nó A como raiz
)
Figure 1: Step 1
Figure 2: Step 2
  • Norman P. Hummon
  • University of Pittsburgh, USA
  • more info here


  • Patrick Doreian
  • University of Pittsburgh, USA

Source: Hummon e Dereian (1989)[5] Connectivity in a citation network: The development of DNA theory. Social Networks.


  • University of Ljubljana, Slovenia


Batagelj (2003)[6] proposes efficient algorithms to make the method possible for large databases.

  • Search path link count (SPLC)[5]
  • Search path node pair (SPNP)[5]
  • Node pair projection count (NPPC)[5] \(N^2\)


And added another way of calculating citation weights:

  • Search path count (SPC)[6]

Source: Batagelj 2003 Efficient Algorithms for Citation Network Analysis.

First major application of the method, 3371 patents.[8]


  • Maastricht University, Netherlands

  • Key-route search[9]

  • More than one route.

  • National Taiwan University of Science and Technology, Taiwan

    • Identifies not only the single main path, but also derived paths emanating from technological junctions in the main path.[10]

  • KAIST Electrical Engineering, Republic of Korea

  • Topological Skeleton

The Topological Skeleton algorithm was developed using the SPLC algorithm to identify the most significant trajectories in the network structure.[11]

  • Unicamp, Brazil

Methodological advances

  • Searching for paths using a stochastic approach.[12]

  • Cross-sectional weight counting methods:[13]

  • SPAD

  • SPGD

  • SPHD

  • Method to identify the importance of intermediate publications.[14]

  • Method based on the persistence of knowledge along trajectories.[15]

Softwares

  • pajek
  • Windows dependent
  • Depends on external software to manipulate data




Softwares

  • Jiang 2019[16]

  • software em java

  • scientist software

  • limited documentation

  • PyMAP - mpa_splc

  • command line

  • graphical interface

  • limited documentation

  • extension of Gephi

  • lost source code

  • dependent on a version of Java

  • limited documentation

Emergence Literature

The study by Rotolo 2015[17] - What is an emerging technology? - generated the conceptual basis needed to consolidate emergence studies as a field of knowledge. The author argues that emergence studies are concentrated in the area of Science and Technology Studies.

Rotolo 2015[17]

  1. radical novelty;
  2. relatively rapid growth;
  3. coherence;
  4. prominent impact; and
  5. uncertainty and ambiguity.

Carley 2017[18]

  1. novelty;
  2. persistence;
  3. community; and
  4. growth.

Fonte: Rotolo (2015) What is an emerging technology?

  • SPRU (Science Policy Research Unit) University of Sussex Business School, United Kingdom

  • Search

  • emerging research topics

  • emerging topics

  • research fronts

  • emerging trends

  • emerging technologies

  • emerging research fields

  • Three stages of development

  • the emergency stage

  • the exploration stage

  • the development stage

  • Beijing University of Technology, China

  • technological forecasting
  • Coates 2001[19]
  • Daim 2006[20]
  • Porter 2011[21]
  • Amparo 2012[22]
  • Robinson 2013[23]
  • Lee 2021[24]
  • mapping science/technology
  • Klavans 2008[25]
  • Rafols 2010[26]
  • Teixeira 2011[27]
  • Amparo 2012[22]
  • Boyack 2013[28]
  • Deorsola 2013[29]
  • Jeong 2021[30]
  • roadmap
  • Jeong 2021[30]
  • Kostoff 2001[31]
  • research fronts/topics/fields/domains/trends/landscape/structure
  • Jo 2007[32]
  • Shibata 2008[33]
  • Kajikawa 2008[34]
  • Tseng 2009[35]
  • Upham 2009[36]
  • Shibata 2011[37]
  • Fujita 2012[38]
  • Lamirel 2012[39]
  • Huang 2013[40]
  • Maltseva 2020[41]
  • Qian 2021[42]
  • Kontostathis 2004[43]
  • Morris 2005[44]
  • Daim 2006[20]
  • LucioArias 2007[45]
  • Shibata 2008[33]
  • Lee 2008[46]
  • Bettencourt 2008[47]
  • Takeda 2008[48]
  • Upham 2009[36]
  • Cozzens 2010[49]
  • Schiebel 2010[50]
  • Ohniwa 2010[51]
  • Glanzel 2011[52]
  • Shibata 2011[37]
  • Alexander 2012[53]
  • Clausen 2012[54]
  • Arora 2012[55]
  • Robinson 2013[23]
  • Liu 2013[56]
  • Jaric 2014[57]
  • Small 2014[58]
  • Furukawa 2015[59]
  • Rotolo 2015[17]
  • Carley 2017[18]
  • Garner 2017[60]
  • Carley 2018[61]
  • Teran 2018[62]
  • Ranaei 2019[63]
  • Porter 2019[64]
  • Wang 2019[65]
  • Kwon 2019[66]
  • Burmaoglu 2019[67]
  • Porter 2020[68]
  • Choi 2021[69]
  • Jang 2021[70]

Souza et al. – birddog

Collect data


Downloaded on Web of Science plataform.

12,689 results from Web of Science Core Collection for:

"Sugarcane" AND ("Straw" OR "Bagasse" OR "Filter cake" OR "Press mud" OR "pressmud cake" OR "molasses" OR "vinasse" OR "dried yeast" OR "fusel oil")

https://www.webofscience.com/wos/woscc/summary/0fa06733-b4aa-4348-854d-a799cdad2c68-a711a88c/relevance/1

Downloaded in 2023-09-27.

Growth

Code 
library(bibliometrix)
library(birddog)
library(igraph)
library(ggraph)
library(tidygraph)
library(tidyverse)
library(fs)
library(plotly)
library(RColorBrewer)
library(ggHoriPlot)
library(ggthemes)
library(ggplot2)
source('code/sniff_group_trajectory_2d.R')
source('code/sniff_group_trajectory_3d.R')

# arquivos <- fs::dir_ls('bibs', regexp = '.bib$')

# tictoc::tic()
# M <- bibliometrix::convert2df(file = arquivos, dbsource = "wos", format = "bibtex")
# tictoc::toc()

rio::import('rawfiles/M-sugarcane.rds') |>
  dplyr::select(SR, PY) |>
  tibble::as_tibble() ->
  pub

periodo1 <- 2000:2022

pub |>
  dplyr::count(PY, sort = T, name = 'Papers') |>
  dplyr::filter(PY %in% periodo1) |>
  dplyr::arrange(PY) |>
  dplyr::mutate(trend = 1:dplyr::n()) ->
  d

d$lnp <- log(d$Papers)

# linear model
m1 <- lm(lnp ~ trend, data = d)
# summary(m1)

beta0 <- m1$coefficients[[1]]
beta1 <- m1$coefficients[[2]]

# no linear model
m2 <- nls(Papers ~ b0 * exp(b1 * (PY - min(periodo1))), start = list(b0 = beta0, b1 = beta1), data = d)

# summary(m2)
# sjPlot::tab_model(m2, digits = 3)

d$predicted <- coef(m2)[1] * exp(coef(m2)[2] * (d$PY - min(periodo1)))

d |>
  dplyr::mutate(Year = PY) |>
  dplyr::mutate(predicted = round(predicted, 0)) ->
  d

periodo2 <- min(periodo1):(max(periodo1) + 4)
predicted <- tibble::tibble(PY = periodo2, Predicted = round(coef(m2)[1] * exp(coef(m2)[2] * (periodo2 - min(periodo2))), 0))

pub |>
  dplyr::count(PY, sort = T, name = 'Papers') |>
  dplyr::filter(PY %in% periodo1) |>
  dplyr::full_join(predicted, by = 'PY') |>
  dplyr::arrange(PY) |>
  dplyr::mutate(Papers = ifelse(PY > max(periodo1), NA, Papers)) |>
  dplyr::rename(Year = PY) ->
  d2

pub |>
  dplyr::mutate(PY = as.numeric(PY)) |>
  dplyr::count(PY, sort = F, name = 'Papers') |>
  dplyr::arrange(PY) |>
  dplyr::filter(PY %in% 1980:1989) |>
  # dplyr::filter(PY %in% periodo1) |>
  dplyr::rename(Year = PY) |>
  dplyr::full_join(d2) ->
  d3

# why not?
#highcharter::hchart(d3, "column", highcharter::hcaes(x = Year, y = Papers), name = "Publications", showInLegend = TRUE) |>
#  highcharter::hc_add_series(d2, "line", highcharter::hcaes(x = Year, y = Predicted), name = "Predicted", showInLegend = TRUE) |>
#  highcharter::hc_add_theme(highcharter::hc_theme_google()) |>
#  highcharter::hc_navigator(enabled = FALSE)  |>
#  highcharter::hc_exporting(enabled = TRUE, filename = 'topic_growth_rate')
#
# why not?
#plot_ly() |>
# add_bars(data = d3, x = ~Year, y = ~Papers, name = "Publications") |>
# add_lines(data = d2, x = ~Year, y = ~Predicted, name = "Predicted") |>
# layout(
#   title = "Publications and Predictions",
#   xaxis = list(title = "Year"),
#   yaxis = list(title = "Count"),
#   legend = list(x = 0.1, y = 0.9)
# )

ggplot(data = d3, aes(x = Year, y = Papers)) +
  geom_bar(stat="identity") +
  geom_line(aes(x = Year, y = Predicted), color = 'red', size = 1) +
  geom_point(aes(x = Year, y = Predicted), size = 2, color = 'darkred') +
  geom_vline(xintercept = 2022, color = 'blue', size = 0.65, linetype='dashed') +
  scale_x_continuous(expand = c(0.01, 0), limits = c(1980, 2026), breaks = seq(1980, 2026, 2)) +
  scale_y_continuous(expand = c(0.01, 0), limits = c(0, 2100), breaks = seq(0, 2100, 200)) +
  xlab('Year') +
  ylab('Publications') +
  theme_classic() +
  theme(
    text = element_text(size = 14),
    panel.spacing.y = unit(0, "lines"),
    strip.text.y = element_text(size = 10, angle = 0, hjust = 0),
    legend.position = 'none',
    panel.border = element_blank(),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)
  ) +
  annotate("text", x = 1982, y = 1890, label = "- Observed values in gray bars", color = 'darkgray', size = 5, hjust = 0) +
  annotate("text", x = 1982, y = 1770, label = "- Predicted values in red line", color = 'red', size = 5, hjust = 0) ->
  gp

# see demo(plotmath)
# alternative to use latex code
expressao1 <- 'y[0] == b[0] %*% italic(e) ^ b[1](t ~~ - ~~ t0)'
expressao2 <- paste0('y[', min(periodo1), ']', ' == ', round(coef(m2)[1], 3), ' %*% italic(e) ^ ', round(coef(m2)[2], 3), '(t ~~ - ~~ t[', max(periodo1), '])')

# ggplot2::ggsave('images/field-growth-rate.png', units = 'cm', width = 11, height = 6, dpi = 300, scale = 2)
gp +
  annotate("text", x = 1995, y = 1670, label = expressao1, parse = T, color = 'red', size = 5, hjust = 0) +
  annotate("text", x = 1995, y = 1560, label = expressao2, parse = T, color = 'red', size = 5, hjust = 0)
Figure 3: Surgarcane growth
Code 
tibble::tribble(
  ~Measure,   ~Sugarcane, ~WoS,
  "Annual growth rate (%)",  '15', '6.6',
  "Doubling-time", '5y', '10y+9m'
) |>
  gt::gt() |>
  gt::tab_header(title = "Growth Rate", subtitle = gt::md("Sugarcane *vs* WoS")) |>
  gt::cols_label(
    Measure = gt::md(" "),
    Sugarcane = gt::md("Sugarcane"),
    WoS = gt::md("WoS")
  )
Table 1: Sugarcane growth vs WoS growth
Growth Rate
Sugarcane vs WoS
Sugarcane WoS
Annual growth rate (%) 15 6.6
Doubling-time 5y 10y+9m

Field analysis

Code 
plot_horizon_field <- function(field) {
  top_n_sc_py |>
    dplyr::mutate(SC2 = janitor::make_clean_names(SC, allow_dupes = T)) |>
    dplyr::filter(SC2 == field) |>
    ggplot() +
    geom_horizon(aes(PY, papers), origin = "min", horizonscale = 5) +
    scale_fill_hcl(palette = "BluGrn", reverse = T) +
    theme_few() +
    scale_x_continuous(limits = c(1980, 2022), breaks = seq(1980, 2022, 5), expand = c(0, 0.4)) +
    scale_y_continuous(expand = c(0, 0)) +
    theme(
      panel.spacing.y = unit(0, "lines"),
      strip.text.y = element_text(size = 11, angle = 0, hjust = 0),
      legend.position = "none",
      axis.text.y = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks.y = element_blank(),
      axis.title.x = element_blank(),
      axis.ticks.x = element_blank(),
      panel.border = element_blank(),
      axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 50)
    )
}

rio::import("tables/top_n_sc_py.rds") -> top_n_sc_py
rio::import("tables/tab_field_analysis.rds") -> tab_field_analysis

tab_field_analysis |>
  gt::gt() |>
  gt::tab_header(title = "Fields Attributes") |>
  gt::cols_label(
    name2 = gt::md("Field"),
    papers = gt::md("Papers"),
    average_age = gt::md("Average age"),
    growth_rate_percentage_year = gt::md("Growth rate"),
    doubling_time = gt::md("Doubling time"),
    SC = gt::md("Horizon plot")
  ) |>
  gt::text_transform(
    locations = gt::cells_body(columns = "SC"),
    fn = function(column) {
      purrr::map(column, plot_horizon_field) |>
        gt::ggplot_image(aspect_ratio = 3, height = 100)
    }
  ) |>
  gt::tab_source_note(source_note = gt::md("**Source**: Web of Science. Data extracted, organized and estimated by the authors.")) |>
  gt::tab_footnote(footnote = "Average publication year. Example, 2015+6m means that the articles were published on average in the year of 2015 plus 6 months. Time span, 2010 until 2022.", locations = gt::cells_column_labels(columns = average_age)) |>
  gt::tab_footnote(footnote = "y = years, m = months. Calculated by ln(2)/b1 where b1 is the econometric model coefficient.", locations = gt::cells_column_labels(columns = doubling_time)) |>
  gt::tab_footnote(footnote = "Publications between 1980 and 2022. Chart type horizon plot.", locations = gt::cells_column_labels(columns = SC)) |>
  gt::tab_options(table.font.size = "14pt")
Figure 4: WoS Field Analysis
Fields Attributes
Field Papers Average age1 Growth rate Doubling time2 Horizon plot3
AGRICULTURE 2812 2014+9m 6.2 12y+6m
CHEMISTRY 1410 2017+6m 11.0 7y+7m
BIOTEC A M 1283 2015+1m 7.3 10y+10m
ENGINEERING 1225 2016+11m 10.5 7y
SCI TEC O T 1067 2018+10m 14.6 5y+1m
ENERGY FUELS 908 2018+11m 21.4 4y+7m
ENV SCI ECO 864 2018+9m 18.5 4y+1m
MAT SCIENCE 815 2017+4m 9.3 8y+10m
BIOCH M B 620 2015+2m 9.2 8y+10m
THERMODYNAMICS 238 2016+11m 6.4 11y+1m
Source: Web of Science. Data extracted, organized and estimated by the authors.
1 Average publication year. Example, 2015+6m means that the articles were published on average in the year of 2015 plus 6 months. Time span, 2010 until 2022.
2 y = years, m = months. Calculated by ln(2)/b1 where b1 is the econometric model coefficient.
3 Publications between 1980 and 2022. Chart type horizon plot.

Citation network

  • Total documents 12689
Figure 5: Citation Networks
Citation Networks

Source: Shibata (2009)[71]

  • Through Bibliographic Coupling the Giant component holds 12374 papers
  • 97.5% of all documents
Code 
# tictoc::tic()
# net <- birddog::sniff_network(M, type = 'bibliographic coupling', database_source = 'wos')
# tictoc::toc()

cgn <- function(x) {gsub('^.*_', '', x)}

M <- rio::import('rawfiles/M-sugarcane.rds')
net <- rio::import('rawfiles/net-coupling.rds')

comp <- birddog::sniff_components(net)

net2 <- comp$network

comp$components |>
  dplyr::rename(documents = quantity_publications) |>
  dplyr::mutate(average_age = round(average_age, 1)) |>
  dplyr::slice(1:4) |>
  gt::gt() |>
  gt::tab_header(title = "Components") |>
  gt::cols_label(
    component = gt::md("Component"),
    documents = gt::md("Documents"),
    average_age = gt::md("Average Age")
  ) |>
  gt::tab_options(table.font.size = "16pt")
Table 2: Network components
Components
Component Documents Average Age
component01 12374 2016.9
component02 3 2008.7
component03 2 2012.5
component04 2 1993.5

Building groups

Group’s detected by citations similarity and maximizing modularity as Louvain Algorithm[72].

Code 
gru <- birddog::sniff_groups(
  net2,
  min_group_size = 50,
  keep_component = "component01",
  cluster_component = "component01",
  algorithm = "louvain"
)


Groups Attributes
Group Papers Average age Growth rate (%) Doubling time (y + m)
g01 2815 2016+7m 13.1 6y+7m
g02 2701 2017+5m 12.2 6y
g03 2217 2017+11m 18.0 4y+2m
g04 1734 2016+10m 12.5 6y+11m
g05 1594 2016+1m 8.9 8y+1m
g06 856 2018+1m 29.4 3y+8m
g07 438 2012+2m 9.9 7y+4m

Contents

Group Terms NLP extracted Description
g01 sugarcane bagasse (1034); ethanol production (670); sugarcane molasses (592); degrees c (437); sugarcane straw (416); sugarcane vinasse (399) Trends in flex-fuel cars in Brazil
g02 sugarcane bagasse (2480); degrees c (1240); enzymatic hydrolysis (1220); lignocellulosic biomass (840); ethanol production (777); bioethanol production (424) Utilizing agro-industrial residues, as xylitol and ethanol
g03 sugarcane bagasse (2786); degrees c (1026); adsorption capacity (497); surface area (398); aqueous solution (311); contact time (267) Carbon derived from agricultural by-products, specifically sugarcane bagasse
g04 sugarcane bagasse (2178); degrees c (479); mechanical properties (395); thermal stability (351); tensile strength (233); electron microscopy (221) Properties and potential applications of sugarcane bagasse and its by-products
g05 sugarcane bagasse (1860); degrees c (826); enzymatic hydrolysis (528); state fermentation (398); ethanol production (323); carbon source (236) Fermentation processes, both solid-state and submerged, using lignocellulosic residues
g06 sugarcane bagasse (1070); bagasse ash (1001); sugarcane bagasse ash (710); compressive strength (477); degrees c (225); mechanical properties (214) By-products from the sugar-alcohol industry to construction materials: bricks, concrete, and cement
g07 sugarcane bagasse (424); dry matter (227); crude protein (99); detergent fiber (89); sugarcane molasses (83); body weight (83) Animal nutrition: fermentation, digestibility, chemical treatment, and feed production

Geographic distribution

Published documents by country.

Hubs

  • Measures the prestige of each document

  • \(K_i\) = All network citations

  • \(k_i\) = Group citations

  • \(Z_i\) = The within-group degree \(z_i\) measures how ‘well-connected’ article \(i\) is to other articles in the group [\(z_i \geq 2.5\) Hub]

  • \(P_i\) = Measures how ‘well-distributed’ the links of article \(i\) are among different groups. [higher \(=\) citations better distributed between groups]

  • zone = \(R5\) provincial hubs; \(R6\) connector hubs; \(R7\) kinless hubs

\[ z_i = {k_i - \bar k_{s_i} \over \sigma_{k_{s_i}}} \qquad(1)\]

\[ P_i = 1 - \sum_{s=1}^{S} \left({k_{s_i} \over K_i} \right)^2 \qquad(2)\]

Source: Guimera and Amaral (2005)[73] Functional cartography of complex metabolic networks. Nature.

Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g01') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
DIAS MOS, 2012, BIORESOUR TECHNOL 249 143 93 8.93 0.51 R6
LEAL MRLV, 2013, BIOMASS BIOENERG 216 136 106 10.23 0.38 R6
HOFSETZ K, 2012, BIOMASS BIOENERG 175 120 59 5.53 0.71 R6
DIAS MOS, 2011, BIORESOUR TECHNOL 170 87 65 6.13 0.42 R6
SINDHU R, 2016, RENEW ENERGY 144 82 37 3.32 0.72 R6
DANTAS GA, 2013, RENEW SUST ENERG REV 113 75 51 4.73 0.51 R6
JANKE L, 2015, INT J MOL SCI 102 59 48 4.43 0.32 R6
SEABRA JEA, 2010, BIOMASS BIOENERG 116 59 43 3.92 0.44 R6
FURLAN FF, 2013, BIOTECHNOL BIOFUELS 88 55 36 3.22 0.52 R6
DIAS MOS, 2013, APPL ENERGY 116 54 36 3.22 0.50 R6
JUNQUEIRA TL, 2017, BIOTECHNOL BIOFUELS 87 52 42 3.82 0.32 R6
TAPIA CARPIO LG, 2017, RENEW ENERGY 65 48 29 2.52 0.57 R6
LOPES SILVA DA, 2014, RENEW SUST ENERG REV 79 47 29 2.52 0.57 R6
BOTHA T, 2006, ENERGY POLICY 118 45 34 3.02 0.41 R6
FURLAN FF, 2012, COMPUT CHEM ENG 76 42 32 2.82 0.37 R6
CONTRERAS AM, 2009, J CLEAN PROD 134 38 30 2.62 0.36 R6
CHRISTOFOLETTI CA, 2013, WASTE MANAGE 331 212 205 20.14 0.06 R5
MACEDO IC, 2008, BIOMASS BIOENERG 581 154 144 14.04 0.12 R5
MORAES BS, 2015, RENEW SUST ENERG REV 259 152 149 14.54 0.04 R5
MORAES BS, 2014, APPL ENERGY 189 117 116 11.23 0.02 R5
NUNES CARVALHO JL, 2017, GCB BIOENERGY 146 103 91 8.73 0.22 R5
FUESS LT, 2014, J ENVIRON MANAGE 132 94 93 8.93 0.02 R5
NUNES FERRAZ JUNIOR AD, 2016, RENEW ENERGY 108 81 81 7.73 0.00 R5
FUESS LT, 2017, APPL ENERGY 124 71 70 6.63 0.03 R5
BORDONAL RO, 2018, AGRON SUSTAIN DEV 229 68 65 6.13 0.09 R5
DO CARMO JB, 2013, GCB BIOENERGY 141 68 64 6.03 0.11 R5
PARSAEE M, 2019, BIOMASS BIOENERG 125 67 64 6.03 0.09 R5
FUESS LT, 2018, SCI TOTAL ENVIRON 71 59 58 5.43 0.03 R5
SEABRA JEA, 2011, BIOFUELS BIOPROD BIOREFINING 175 58 56 5.23 0.07 R5
SEABRA JEA, 2011, ENERGY POLICY 116 58 54 5.03 0.13 R5
DIAS MOS, 2011, ENERGY 143 58 53 4.93 0.16 R5
DE OLIVEIRA BG, 2013, GEODERMA 86 56 55 5.13 0.04 R5
NUNES FERRAZ JUNIOR AD, 2014, INT J HYDROG ENERGY 98 52 51 4.73 0.04 R5
HOARAU J, 2018, J WATER PROCESS ENG 79 52 50 4.63 0.07 R5
LUO L, 2009, RENEW SUST ENERG REV 217 48 40 3.62 0.29 R5
RODRIGUES REIS CE, 2017, FRONT ENERGY RES 83 45 42 3.82 0.13 R5
CASTIONI GA, 2018, SOIL TILLAGE RES 62 44 44 4.02 0.00 R5
NUNES FERRAZ JUNIOR AD, 2015, ANAEROBE 66 41 39 3.52 0.09 R5
CANELLAS LP, 2003, REV BRAS CIENC SOLO 110 40 40 3.62 0.00 R5
FUESS LT, 2016, INT J HYDROG ENERGY 70 40 40 3.62 0.00 R5
SANTANA H, 2017, BIORESOUR TECHNOL 80 40 39 3.52 0.05 R5
FUESS LT, 2018, RENEW ENERGY 53 39 38 3.42 0.05 R5
LAZARO CZ, 2014, INT J HYDROG ENERGY 65 39 34 3.02 0.23 R5
SANTOS SC, 2014, INT J HYDROG ENERGY 51 38 37 3.32 0.05 R5
BORDONAL RO, 2018, GEODERMA 46 37 37 3.32 0.00 R5
DE RESENDE AS, 2006, PLANT SOIL 100 37 37 3.32 0.00 R5
FUESS LT, 2017, J ENVIRON SCI HEALTH PART A-TOXIC/HAZARD SUBST ENVIRON ENG 49 37 36 3.22 0.05 R5
LISBOA IP, 2018, IND CROP PROD 47 37 35 3.12 0.10 R5
BERNAL AP, 2017, J CLEAN PROD 69 36 36 3.22 0.00 R5
LEME RM, 2017, ENERGY 66 36 36 3.22 0.00 R5
WALTER A, 2010, ENERGY 74 36 30 2.62 0.30 R5
DEL NERY V, 2018, BIOMASS BIOENERG 45 34 34 3.02 0.00 R5
FUESS LT, 2017, CHEM ENG RES DES 49 34 34 3.02 0.00 R5
NUNES FERRAZ JUNIOR AD, 2015, BIORESOUR TECHNOL 70 34 33 2.92 0.06 R5
SIQUEIRA LM, 2013, J ENVIRON SCI HEALTH PART A-TOXIC/HAZARD SUBST ENVIRON ENG 39 34 33 2.92 0.06 R5
PALACIOS-BERECHE R, 2013, ENERGY 70 34 29 2.52 0.25 R5
FUESS LT, 2019, BIORESOUR TECHNOL 56 33 33 2.92 0.00 R5
MAZINE KIYUNA LS, 2017, BIORESOUR TECHNOL 65 33 33 2.92 0.00 R5
FUESS LT, 2018, BIORESOUR TECHNOL 73 33 31 2.72 0.12 R5
ISABEL MARQUES SS, 2013, APPL BIOCHEM BIOTECHNOL 56 32 31 2.72 0.06 R5
SATIRO LS, 2017, GEODERMA REG 41 32 31 2.72 0.06 R5
FUESS LT, 2018, PROCESS SAF ENVIRON PROTECT 40 31 31 2.72 0.00 R5
DOS REIS CM, 2015, INT J HYDROG ENERGY 51 29 29 2.52 0.00 R5
JANKE L, 2016, BIORESOUR TECHNOL 46 29 29 2.52 0.00 R5
SANTOS SC, 2014, BIORESOUR TECHNOL 50 29 29 2.52 0.00 R5
Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g02') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
PANDEY A, 2000, BIORESOUR TECHNOL 800 439 37 3.66 0.45 R6
CARDONA CA, 2010, BIORESOUR TECHNOL 484 267 125 13.58 0.71 R6
CANILHA L, 2012, J BIOMED BIOTECHNOL 302 160 89 9.52 0.62 R6
SARKAR N, 2012, RENEW ENERGY 1007 152 107 11.55 0.48 R6
ROCHA GJM, 2012, IND CROP PROD 202 148 74 7.83 0.67 R6
ZHAO X, 2009, APPL MICROBIOL BIOTECHNOL 795 136 109 11.78 0.34 R6
DE MORAES ROCHA GJ, 2011, BIOMASS BIOENERG 179 134 85 9.07 0.57 R6
RABELO SC, 2011, BIORESOUR TECHNOL 244 129 56 5.80 0.72 R6
SAHA BC, 2003, J IND MICROBIOL BIOTECHNOL 1354 109 67 7.04 0.55 R6
DE MORAES ROCHA GJ, 2015, IND CROP PROD 136 109 44 4.45 0.75 R6
SUN S, 2016, BIORESOUR TECHNOL 593 102 79 8.40 0.37 R6
LAVARACK BP, 2002, BIOMASS BIOENERG 334 97 73 7.72 0.41 R6
MARTIN C, 2007, ENZYME MICROB TECHNOL 190 96 63 6.59 0.52 R6
ZHAO X, 2012, BIOFUELS BIOPROD BIOREFINING 605 94 70 7.38 0.42 R6
BEHERA S, 2014, RENEW SUST ENERG REV 557 90 67 7.04 0.42 R6
PATTRA S, 2008, INT J HYDROG ENERGY 250 84 36 3.55 0.68 R6
ZHAO XB, 2008, J CHEM TECHNOL BIOTECHNOL 160 83 47 4.79 0.62 R6
DEL RIO JC, 2015, BIOMASS BIOENERG 182 81 51 5.24 0.57 R6
ALVES LA, 1998, APPL BIOCHEM BIOTECHNOL 132 78 49 5.02 0.56 R6
CANILHA L, 2011, J IND MICROBIOL BIOTECHNOL 112 78 48 4.90 0.58 R6
KRISHNAN C, 2010, BIOTECHNOL BIOENG 125 71 44 4.45 0.57 R6
OLIVEIRA FMV, 2013, BIORESOUR TECHNOL 125 71 40 4.00 0.63 R6
ROCHA GJM, 2012, BIORESOUR TECHNOL 98 69 39 3.89 0.62 R6
YU Q, 2013, BIORESOUR TECHNOL 115 68 55 5.69 0.34 R6
CARRASCO C, 2010, ENZYME MICROB TECHNOL 105 67 42 4.23 0.56 R6
ZHAO X, 2009, ENZYME MICROB TECHNOL 116 67 40 4.00 0.59 R6
MARTÍN C, 2002, APPL BIOCHEM BIOTECHNOL 129 66 51 5.24 0.39 R6
KARP SG, 2013, BRAZ ARCH BIOL TECHNOL 95 66 33 3.21 0.68 R6
MARTÍN C, 2002, ENZYME MICROB TECHNOL 215 64 45 4.57 0.46 R6
CHENG KK, 2008, BIOCHEM ENG J 158 63 39 3.89 0.55 R6
RAMOS LP, 2003, QUIM NOVA 397 62 48 4.90 0.38 R6
PEREIRA SC, 2015, BIOTECHNOL BIOFUELS 121 62 32 3.10 0.66 R6
HASSAN SS, 2018, BIORESOUR TECHNOL 416 61 48 4.90 0.36 R6
ZABED H, 2016, RENEW SUST ENERG REV 422 61 40 4.00 0.54 R6
GUPTA A, 2015, RENEW SUST ENERG REV 458 61 37 3.66 0.58 R6
MESA L, 2011, CHEM ENG J 141 60 42 4.23 0.49 R6
NEVES PV, 2016, BIORESOUR TECHNOL 90 60 38 3.78 0.56 R6
KAAR WE, 1998, BIOMASS BIOENERG 114 60 33 3.21 0.61 R6
LIMA MA, 2014, BIOTECHNOL BIOFUELS 80 60 31 2.99 0.66 R6
MASARIN F, 2011, BIOTECHNOL BIOFUELS 119 60 27 2.54 0.72 R6
ZHANG Z, 2012, BIORESOUR TECHNOL 90 58 37 3.66 0.54 R6
RABELO SC, 2011, BIOMASS BIOENERG 107 58 33 3.21 0.57 R6
ASGHER M, 2013, IND CROP PROD 150 57 39 3.89 0.50 R6
DE CARVALHO DM, 2016, IND CROP PROD 98 55 29 2.76 0.65 R6
DE SOUZA MORETTI MM, 2014, APPL ENERGY 109 54 30 2.87 0.62 R6
NOVO LP, 2011, BIORESOUR TECHNOL 98 53 41 4.11 0.38 R6
TRAVAINI R, 2013, BIORESOUR TECHNOL 110 53 38 3.78 0.45 R6
BOUSSARSAR H, 2009, BIORESOUR TECHNOL 115 53 29 2.76 0.64 R6
RIBAS BATALHA LA, 2015, BIORESOUR TECHNOL 92 52 36 3.55 0.49 R6
GAO Y, 2013, BIORESOUR TECHNOL 75 51 36 3.55 0.47 R6
BATISTA G, 2019, BIORESOUR TECHNOL 105 51 30 2.87 0.61 R6
ZHU Z, 2016, BIOMASS BIOENERG 99 51 29 2.76 0.62 R6
ZHANG HONGDAN ZH, 2013, BIORESOUR TECHNOL 89 50 37 3.66 0.43 R6
BRIENZO M, 2017, RENEW ENERGY 71 48 30 2.87 0.57 R6
TRAVAINI R, 2016, BIORESOUR TECHNOL-a 160 45 32 3.10 0.44 R6
CHANDEL AK, 2018, BIORESOUR TECHNOL 278 44 35 3.44 0.35 R6
SANTO ME, 2018, IND CROP PROD 72 44 35 3.44 0.35 R6
SINGH J, 2015, CARBOHYDR POLYM 320 44 35 3.44 0.35 R6
XU F, 2005, ANAL CHIM ACTA 136 44 27 2.54 0.58 R6
ZOGHLAMI A, 2019, FRONT CHEM 300 44 27 2.54 0.56 R6
ZHUANG X, 2016, BIORESOUR TECHNOL 184 43 34 3.33 0.36 R6
SANTUCCI BS, 2015, BIOENERGY RES 66 43 31 2.99 0.45 R6
MENG X, 2014, CURR OPIN BIOTECHNOL 300 42 33 3.21 0.37 R6
MOGHADDAM L, 2014, BIOMASS BIOENERG 66 42 31 2.99 0.44 R6
GEDDES CC, 2011, BIORESOUR TECHNOL 86 40 30 2.87 0.42 R6
ROCHA GJM, 2015, IND CROP PROD 56 39 32 3.10 0.32 R6
RASTOGI M, 2017, RENEW SUST ENERG REV 245 39 28 2.65 0.45 R6
EVANGELINA VALLEJOS M, 2012, GREEN CHEM 69 38 28 2.65 0.42 R6
NAKANISHI SC, 2017, BIOTECHNOL BIOENG 63 38 27 2.54 0.47 R6
AGUILAR-REYNOSA A, 2017, ENERGY CONV MANAG 201 37 30 2.87 0.32 R6
PRASAD S, 2007, RESOUR CONSERV RECYCL 427 37 30 2.87 0.33 R6
SUN FF, 2015, BIORESOUR TECHNOL 89 33 27 2.54 0.31 R6
ZHANG Z, 2013, BIORESOUR TECHNOL 58 39 33 3.21 0.28 R5
MOUSAVIOUN P, 2010, IND CROP PROD 164 37 32 3.10 0.25 R5
YU Q, 2013, BIORESOUR TECHNOL-a 58 36 33 3.21 0.16 R5
RUIZ HA, 2020, BIORESOUR TECHNOL 186 33 27 2.54 0.30 R5
EVANGELINA VALLEJOS M, 2016, IND CROP PROD 58 32 27 2.54 0.28 R5
Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g03') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
KARNITZ O, 2007, BIORESOUR TECHNOL 335 103 85 13.96 0.30 R6
VARMA AK, 2017, IND CROP PROD 168 84 68 11.08 0.33 R6
TSAI WT, 2006, J ANAL APPL PYROLYSIS 318 68 56 9.05 0.31 R6
MUNIR S, 2009, BIORESOUR TECHNOL 446 51 38 6.00 0.43 R6
GARCÌA-PÈREZ M, 2002, J ANAL APPL PYROLYSIS 161 45 37 5.83 0.31 R6
ZANDERSONS J, 1999, BIOMASS BIOENERG 71 44 25 3.80 0.60 R6
ANUKAM A, 2016, RENEW SUST ENERG REV 93 43 32 4.99 0.42 R6
CHEN WH, 2012, BIORESOUR TECHNOL 180 43 28 4.31 0.53 R6
AMIN NK, 2008, DESALINATION 364 40 32 4.99 0.34 R6
MOTHE CG, 2009, J THERM ANAL CALORIM 144 40 23 3.46 0.57 R6
DING W, 2014, CHEMOSPHERE 261 38 31 4.82 0.33 R6
SARKER TC, 2017, CLEAN TECHNOL ENVIRON POLICY 52 38 31 4.82 0.33 R6
GARCÌA-PÈREZ M, 2001, FUEL 153 37 30 4.65 0.33 R6
DAS P, 2004, BIOMASS BIOENERG 188 37 24 3.63 0.52 R6
FOO KY, 2013, BIORESOUR TECHNOL 82 35 28 4.31 0.35 R6
RUEDA-ORDONEZ YJ, 2015, BIORESOUR TECHNOL 80 32 26 3.97 0.33 R6
DAVID GF, 2018, J ANAL APPL PYROLYSIS 45 32 23 3.46 0.46 R6
ZHANG Z, 2011, CHEM ENG J 172 32 23 3.46 0.46 R6
ZHANG Z, 2013, IND CROP PROD 147 29 24 3.63 0.31 R6
ALVES GURGEL LV, 2009, WATER RES 178 29 23 3.46 0.35 R6
ULLAH I, 2013, ECOL ENG 178 28 22 3.29 0.37 R6
DO CARMO RAMOS SN, 2016, IND CROP PROD 91 27 22 3.29 0.32 R6
KARNITZ JUNIOR O, 2010, CARBOHYDR POLYM 76 24 18 2.62 0.41 R6
NASSAR MM, 1996, J APPL POLYM SCI 66 24 18 2.62 0.42 R6
GUO Y, 2020, CHEM ENG J 226 22 18 2.62 0.32 R6
ALVES GURGEL LV, 2008, CARBOHYDR POLYM 167 71 62 10.07 0.22 R5
NGAH WSW, 2008, BIORESOUR TECHNOL 1388 63 60 9.73 0.09 R5
HOMAGAI PL, 2010, BIORESOUR TECHNOL 158 58 50 8.03 0.25 R5
KARNITZ JUNIOR O, 2009, CARBOHYDR POLYM 194 53 49 7.87 0.14 R5
GUIMARAES GUSMAO KA, 2012, DYES PIGMENT 131 49 44 7.02 0.19 R5
WHITE JE, 2011, J ANAL APPL PYROLYSIS 711 44 40 6.34 0.17 R5
ANGELES MARTIN-LARA M, 2010, DESALINATION 87 44 39 6.17 0.21 R5
TAO HC, 2015, BIORESOUR TECHNOL 122 39 37 5.83 0.10 R5
RAMAJO-ESCALERA B, 2006, THERMOCHIM ACTA 86 36 30 4.65 0.29 R5
DOS SANTOS VCG, 2011, WATER AIR SOIL POLLUT 63 33 28 4.31 0.27 R5
PEHLIVAN E, 2013, FOOD CHEM 71 32 29 4.48 0.17 R5
HAFSHEJANI LD, 2016, ECOL ENG 155 31 31 4.82 0.00 R5
CREAMER AE, 2014, CHEM ENG J 252 31 29 4.48 0.12 R5
BRANDAO PC, 2010, J HAZARD MATER 85 31 28 4.31 0.18 R5
GARG U, 2008, J HAZARD MATER 252 30 25 3.80 0.29 R5
LU JJ, 2015, APPL ENERGY 190 28 25 3.80 0.20 R5
ALOMA I, 2012, J TAIWAN INST CHEM ENG 155 27 23 3.46 0.27 R5
VECINO MANTILLA S, 2014, J ANAL APPL PYROLYSIS 52 26 23 3.46 0.21 R5
KHORAMZADEH E, 2013, J TAIWAN INST CHEM ENG 110 26 22 3.29 0.28 R5
DEWANGAN A, 2016, FUEL 116 25 22 3.29 0.22 R5
LEE Y, 2013, BIORESOUR TECHNOL 378 25 22 3.29 0.21 R5
BACH QV, 2016, RENEW SUST ENERG REV 268 24 21 3.12 0.22 R5
PEREIRA FV, 2010, J HAZARD MATER 105 24 21 3.12 0.23 R5
YU JX, 2013, APPL SURF SCI 86 24 21 3.12 0.23 R5
GUIMARAES GUSMAO KA, 2013, J ENVIRON MANAGE 119 23 22 3.29 0.08 R5
HAN J, 2019, IND CROP PROD 126 23 22 3.29 0.08 R5
FIDELES RA, 2018, J COLLOID INTERFACE SCI 77 23 21 3.12 0.16 R5
KRISHNAN KA, 2011, BIORESOUR TECHNOL 158 23 21 3.12 0.16 R5
YU J, 2015, RES CHEM INTERMED 44 23 20 2.95 0.23 R5
MONTOYA JI, 2015, J ANAL APPL PYROLYSIS 67 22 21 3.12 0.09 R5
DOMINGUES RR, 2017, PLOS ONE 313 22 20 2.95 0.17 R5
GRANADOS DA, 2017, ENERGY 59 22 20 2.95 0.17 R5
CHAO HP, 2014, J IND ENG CHEM 96 21 20 2.95 0.09 R5
DO CARMO RAMOS SN, 2015, IND CROP PROD 84 21 19 2.79 0.18 R5
CRONJE KJ, 2011, DESALINATION 151 20 18 2.62 0.18 R5
ESFANDIAR N, 2014, J IND ENG CHEM 75 20 18 2.62 0.18 R5
DOS SANTOS VCG, 2010, WATER SCI TECHNOL 42 19 18 2.62 0.10 R5
Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g04') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
REZENDE CA, 2011, BIOTECHNOL BIOFUELS 351 235 65 7.15 0.76 R7
CHANDEL AK, 2012, J CHEM TECHNOL BIOTECHNOL 235 142 34 3.59 0.78 R7
SUN JX, 2004, POLYM DEGRAD STABIL 517 182 142 16.02 0.38 R6
SUN JX, 2004, CARBOHYDR POLYM 252 128 61 6.69 0.68 R6
LOH YR, 2013, RESOUR CONSERV RECYCL 181 113 61 6.69 0.64 R6
CERQUEIRA DA, 2007, CARBOHYDR POLYM 106 70 47 5.08 0.52 R6
SHAIKH HM, 2009, CARBOHYDR POLYM 115 66 47 5.08 0.46 R6
LIU CF, 2007, CARBOHYDR RES 114 51 42 4.51 0.30 R6
GUIMARAES JL, 2009, IND CROP PROD 259 47 27 2.78 0.61 R6
MULINARI DR, 2009, COMPOS SCI TECHNOL 136 46 38 4.05 0.31 R6
SENE L, 2002, BIORESOUR TECHNOL 78 42 29 3.01 0.47 R6
MANDAL A, 2011, CARBOHYDR POLYM 675 203 188 21.31 0.14 R5
LI J, 2012, CARBOHYDR POLYM 338 122 114 12.79 0.13 R5
SAELEE K, 2016, IND CROP PROD 125 62 54 5.89 0.24 R5
TEIXEIRA EM, 2011, IND CROP PROD 220 61 55 6.00 0.18 R5
DE OLIVEIRA FB, 2016, IND CROP PROD 126 59 50 5.43 0.27 R5
MULINARI DR, 2009, CARBOHYDR POLYM 101 47 39 4.16 0.28 R5
VILAY V, 2008, COMPOS SCI TECHNOL 273 42 36 3.82 0.26 R5
SARAIVA MORAIS JP, 2013, CARBOHYDR POLYM 370 41 38 4.05 0.14 R5
BHATTACHARYA D, 2008, CARBOHYDR POLYM 180 40 37 3.93 0.14 R5
LUZ SM, 2007, COMPOS PT A-APPL SCI MANUF 107 38 35 3.70 0.15 R5
FERREIRA FV, 2018, APPL SURF SCI 102 38 33 3.47 0.24 R5
PHANTHONG P, 2018, CARBON RESOURCES CONVERS 456 38 33 3.47 0.24 R5
FENG YH, 2018, IND CROP PROD 97 35 31 3.24 0.21 R5
LUZ SM, 2008, COMPOS PT A-APPL SCI MANUF 154 33 32 3.36 0.06 R5
WULANDARI WT, 2016, 10TH JOINT CONFERENCE ON CHEMISTRY 191 33 31 3.24 0.12 R5
LIU CF, 2006, POLYM DEGRAD STABIL 65 32 27 2.78 0.28 R5
LAM NT, 2017, SUGAR TECH 40 31 29 3.01 0.12 R5
SLAVUTSKY AM, 2014, CARBOHYDR POLYM 194 31 29 3.01 0.12 R5
CERQUEIRA EF, 2011, 11TH INTERNATIONAL CONFERENCE ON THE MECHANICAL BEHAVIOR OF MATERIALS (ICM11) 92 30 27 2.78 0.18 R5
LIU CF, 2007, CARBOHYDR POLYM 104 30 26 2.66 0.24 R5
VERCELHEZE AES, 2012, CARBOHYDR POLYM 76 27 26 2.66 0.07 R5
MANDAL A, 2014, J IND ENG CHEM 153 25 25 2.55 0.00 R5
Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g05') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
CHANDEL AK, 2013, BIOTECHNOL BIOFUELS-a 67 108 38 9.05 0.65 R6
BINOD P, 2012, RENEW ENERGY 263 94 29 6.78 0.68 R6
CHANDEL AK, 2007, BIORESOUR TECHNOL 300 94 17 3.74 0.45 R6
CHANDEL AK, 2014, BIOTECHNOL BIOFUELS 125 82 14 2.98 0.73 R6
HERNANDEZ-SALAS JM, 2009, BIORESOUR TECHNOL 134 70 13 2.73 0.66 R6
VELMURUGAN R, 2012, BIORESOUR TECHNOL 122 65 13 2.73 0.59 R6
SINDHU R, 2011, BIORESOUR TECHNOL 148 61 25 5.76 0.64 R6
RABELO SC, 2014, FUEL 83 60 13 2.73 0.44 R6
VELMURUGAN R, 2011, BIORESOUR TECHNOL 116 59 18 3.99 0.69 R6
RAMOS LP, 2015, BIORESOUR TECHNOL 80 53 13 2.73 0.57 R6
BRIENZO M, 2010, APPL BIOCHEM BIOTECHNOL 104 51 19 4.25 0.69 R6
CHANDEL AK, 2013, BIOTECHNOL BIOFUELS 12 51 14 2.98 0.73 R6
MAEDA RN, 2011, PROCESS BIOCHEM 128 50 27 6.27 0.63 R6
SINDHU R, 2010, APPL BIOCHEM BIOTECHNOL 86 49 17 3.74 0.67 R6
FORTES GOTTSCHALK LM, 2010, BIOCHEM ENG J 142 48 37 8.80 0.39 R6
CUNHA FM, 2012, BIORESOUR TECHNOL 92 41 31 7.28 0.40 R6
BRAGATTO J, 2013, IND CROP PROD 59 40 19 4.25 0.55 R6
MICHELIN M, 2016, BIORESOUR TECHNOL 71 38 15 3.23 0.51 R6
YOON LW, 2011, J CHEM TECHNOL BIOTECHNOL 77 35 13 2.73 0.62 R6
BORIN GP, 2015, PLOS ONE 77 32 25 5.76 0.36 R6
FLORENCIO C, 2016, ENZYME MICROB TECHNOL 54 27 21 4.75 0.36 R6
KOVACS K, 2009, BIOTECHNOL BIOFUELS 93 27 20 4.50 0.40 R6
MAITAN-ALFENAS GP, 2015, BIORESOUR TECHNOL 38 27 19 4.25 0.44 R6
GONCALVES TA, 2012, BIORESOUR TECHNOL 69 25 19 4.25 0.38 R6
MARQUES NP, 2018, IND CROP PROD 64 25 15 3.23 0.58 R6
DE CASSIA PEREIRA J, 2015, J APPL MICROBIOL 69 24 17 3.74 0.45 R6
MORETTI MMS, 2012, BRAZ J MICROBIOL 79 24 16 3.49 0.50 R6
VISSER EM, 2015, BIOTECHNOL BIOFUELS 44 24 14 2.98 0.58 R6
AIELLO C, 1996, BIORESOUR TECHNOL 45 23 13 2.73 0.62 R6
THOMAS L, 2013, BIOCHEM ENG J 298 22 15 3.23 0.49 R6
GOLDBECK R, 2016, J MOL CATAL B-ENZYM 32 22 14 2.98 0.52 R6
MARX IJ, 2013, BIOTECHNOL BIOFUELS 63 21 16 3.49 0.39 R6
PINTO BRAGA CM, 2014, BIORESOUR TECHNOL 46 20 13 2.73 0.51 R6
RODRIGUEZ-ZUNIGA UF, 2014, APPL BIOCHEM BIOTECHNOL 26 19 13 2.73 0.48 R6
DE CASTRO AM, 2010, J IND MICROBIOL BIOTECHNOL 80 17 13 2.73 0.38 R6
RIBEIRO DA, 2012, PLOS ONE 53 17 13 2.73 0.38 R6
DELABONA PS, 2013, BIORESOUR TECHNOL 65 40 35 8.29 0.22 R5
DELABONA PS, 2012, BIOMASS BIOENERG 91 38 33 7.79 0.24 R5
BANSAL N, 2012, WASTE MANAGE 184 29 25 5.76 0.24 R5
FALKOSKI DL, 2013, BIORESOUR TECHNOL 54 29 25 5.76 0.24 R5
DE SOUZA WR, 2011, BIOTECHNOL BIOFUELS 87 26 24 5.51 0.14 R5
VISSER EM, 2013, BIORESOUR TECHNOL 49 22 19 4.25 0.24 R5
CASCIATORI FP, 2016, CHEM ENG J 39 18 17 3.74 0.10 R5
RAGHUWANSHI S, 2014, FUEL 62 16 15 3.23 0.12 R5
DA SILVA R, 2005, BRAZ J MICROBIOL 79 15 13 2.73 0.24 R5
GOLDBECK R, 2014, APPL MICROBIOL BIOTECHNOL 33 15 13 2.73 0.23 R5
PITOL LO, 2016, CHEM ENG J 54 15 13 2.73 0.24 R5
DE SOUZA MOREIRA LR, 2013, FUNGAL GENET BIOL 40 14 13 2.73 0.13 R5
Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g06') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
GANESAN K, 2007, CEM CONCR COMPOS 392 190 187 14.02 0.03 R5
BAHURUDEEN A, 2015, CEM CONCR COMPOS 158 115 114 8.41 0.02 R5
BAHURUDEEN A, 2015, CEM CONCR COMPOS-a 187 115 113 8.34 0.03 R5
SALES A, 2010, WASTE MANAGE 171 111 102 7.49 0.15 R5
FARIA KCP, 2012, J ENVIRON MANAGE 179 104 99 7.26 0.09 R5
KAZMI SMS, 2016, CONSTR BUILD MATER 164 94 92 6.72 0.04 R5
PAYÁ J, 2002, J CHEM TECHNOL BIOTECHNOL 142 80 68 4.88 0.27 R5
XU Q, 2019, MATERIALS 99 67 57 4.04 0.26 R5
KAZMI SMS, 2016, J BUILD ENG 101 62 62 4.42 0.00 R5
TEIXEIRA SR, 2008, J AM CERAM SOC 88 57 50 3.50 0.22 R5
ARENAS-PIEDRAHITA JC, 2016, CONSTR BUILD MATER 69 55 53 3.73 0.07 R5
AKRAM T, 2009, CONSTR BUILD MATER 138 53 53 3.73 0.00 R5
BAHURUDEEN A, 2014, CONSTR BUILD MATER 70 50 48 3.35 0.08 R5
ARIF E, 2016, CONSTR BUILD MATER 72 48 46 3.19 0.08 R5
KAZMI SMS, 2017, CONSTR BUILD MATER 73 46 46 3.19 0.00 R5
RAJASEKAR A, 2018, CONSTR BUILD MATER 71 46 45 3.12 0.04 R5
SOUZA AE, 2011, J ENVIRON MANAGE 75 46 40 2.73 0.24 R5
SUA-IAM G, 2013, J CLEAN PROD 107 45 45 3.12 0.00 R5
ZAREEI SA, 2018, CONSTR BUILD MATER 86 44 44 3.04 0.00 R5
DEEPIKA S, 2017, J MATER CIV ENG 54 43 42 2.88 0.05 R5
ALAVEZ-RAMIREZ R, 2012, CONSTR BUILD MATER 103 43 39 2.65 0.17 R5
DUC-HIEN LE DHL, 2018, CONSTR BUILD MATER 54 39 39 2.65 0.00 R5
MORETTI JP, 2018, CONSTR BUILD MATER 70 39 37 2.50 0.10 R5
BAHURUDEEN A, 2016, J MATER CIV ENG 56 38 38 2.58 0.00 R5
Code 
rio::import('rawfiles/hubs-coupling.rds') |>
  tibble::as_tibble() |>
  dplyr::filter(zone != 'noHub') |>
  dplyr::filter(group == 'component01_g07') |>
  dplyr::arrange(dplyr::desc(zone)) |>
  dplyr::select(- group) |>
  dplyr::mutate(Zi = round(Zi, 2), Pi = round(Pi, 2)) |>
  dplyr::left_join(M |> dplyr::select(SR, url = DI) |> tibble::as_tibble(), by = 'SR') |>
  dplyr::mutate(SR = paste0('<a href="https://doi.org/', url, '">', SR, '</a>')) |>
  dplyr::select(- url) |>
  knitr::kable(format = "html", escape = FALSE)
SR TC Ki ki Zi Pi zone
ZADRAZIL F, 1995, BIORESOUR TECHNOL 85 29 6 3.60 0.68 R6
RAMOS NP, 2016, PESQUI AGROPECU BRAS 9 14 6 3.60 0.49 R6
GUNUN N, 2016, TROP ANIM HEALTH PROD 19 12 5 2.91 0.72 R6
INACIO JG, 2017, REV BRAS ZOOTECN 8 10 8 4.97 0.32 R6
LEME PR, 2003, REV BRAS ZOOTECN 25 10 8 4.97 0.34 R6
HARRIS B, 1983, J DAIRY SCI 33 9 6 3.60 0.44 R6
FREITAS WR, 2018, PESQUI AGROPECU BRAS 6 9 5 2.91 0.59 R6
OLIVEIRA CASALI A, 2008, REV BRAS ZOOTECN 385 8 6 3.60 0.38 R6
PONTES DE ALMEIDA GA, 2018, ASIAN AUSTRALAS J ANIM SCI 15 8 5 2.91 0.53 R6
PIRES AJV, 2004, REV BRAS ZOOTECN 14 7 5 2.91 0.45 R6
BULLE MLD, 2002, REV BRAS ZOOTECN 20 9 9 5.66 0.00 R5
SARMENTO P, 1999, REV BRAS ZOOTECN 11 8 7 4.29 0.22 R5
MARQUES RS, 2016, J ANIM SCI 22 7 7 4.29 0.00 R5
CÂNDIDO MJD, 1999, REV BRAS ZOOTECN 14 7 6 3.60 0.24 R5
CHEN X, 2017, ANIM SCI J 22 6 6 3.60 0.00 R5
VIEIRA PIRES AJ, 2006, REV BRAS ZOOTECN 23 6 5 2.91 0.28 R5
MORIEL P, 2020, J ANIM SCI 10 5 5 2.91 0.00 R5

Trajectory 2d

Trajectory 3d

STM

Groups Topics Terms Topics Description
g01 (STM)    t01 product, sugarcane, environment, use, ethanol, energy, impact, assess, potential, base Assessment of the life cycle of co-products as a tool for sustainable production
t02 energy, process, bagasse, ethanol, product, sugarcane, generate, plant, economy, use 2nd generation ethanol production from bagasse
t03 soil, straw, sugarcane, area, remove, harvest, system, manage, carbon, increase Life cycle and burn straw
t04 anaerobe, digest, reactor, biogas, methane, cod, product, vinasse, remove, sugarcane Anaerobic digestion of vinasse and biogas generation
t05 soil, fertile, increase, organ, yield, plant, sugarcane, application, filter, cake Filter cake and its applications in fertilization
t06 molasses, product, ferment, use, sugarcane, ethanol, strain, yeast, production, yield Utilization of molasses for ethanol production
t07 use, model, sugarcane, bagasse, method, temperature, analysis, obtain, process, study Experimental analyzes of physicochemical properties of co-products
t08 bagasse, waste, compost, community, substrate, bacterium, microbio, differ, sugarcane, treatment Study on bacterial growth using sugarcane waste as a carbon source
t09 sugarcane, straw, plant, evaluation, herbicide, root, control, treatment, differ, use Utilization of herbicides and weed control
t10 extract, activity, sugarcane, compound, acid, antioxidant, phenol, molasses, fraction, use Antioxidant activity and polyphenol composition of sugarcane molasses extract
t11 acid, product, hydrogen, ferment, pretreat, lactic, use, degree, production, acet Production of hydrogen and organic acids from bagasse
t12 vinasse, water, use, concentration, treatment, sugarcane, study, organ, industry, potential Characterization of vinasse and utilization in fertigation
t13 biomass, product, culture, growth, medium, oil, cultivation, use, lipid, microalgae Yeast cultivation for lipid and microalgae production
t14 emission, sugarcane, residue, crop, burn, fertile, field, trash, biochar, harvest Impacts of gaseous emissions from burning and fertilization in the cultivation of sugarcane
t15 sugar, cane, juice, product, mill, industry, factory, molasses, process, raw Aspects related to the production of sugar
g02 (NLP) t01, t02 and t03 By frequency: sugarcane bagasse (2480); degrees c (1240); enzymatic hydrolysis (1220); lignocellulosic biomass (840); ethanol production (777); bioethanol production (424); g l (340); enzymatic saccharification (267); xylitol production (228); steam explosion (227) By tf-idf: enzymatic digestibility (179); degrees c (1240); enzymatic hydrolysis (1220); glucose yield (152); lignin removal (134); simultaneous saccharification (187); enzymatic saccharification (267); bioethanol production (424); ethanol yield (175); lignocellulosic biomass (840) t01: Xylitol production; t02: Ethanol production; t03: Valorization and modification of sugarcane bagasse for industrial, energetic, and environmental applications through chemical and biotechnological processes.
g03 (NLP) t01, t02 and t03 By frequency: sugarcane bagasse (2786); degrees c (1026); adsorption capacity (497); surface area (398); aqueous solution (311); contact time (267); mg g (227); rice husk (216); adsorption process (214); aqueous solutions (213); maximum adsorption (209) By tf-idf: bagasse biochar (113); aqueous solution (311); aqueous solutions (213); langmuir isotherm (141); sugarcane bagasse biochar (87); pyrolysis of sugarcane bagasse (81); langmuir model (113); pyrolysis process (75); isotherm models (74); batch adsorption (72) t01: Efficiency in sugarcane production; t02: Adsorbents from agricultural residues for removing contaminants in wastewater; t03: Production of biochar through the pyrolysis of sugarcane bagasse and the study of its adsorption capacity.
g04 (NLP) t1, t2 and t3 By frequency: sugarcane bagasse (2178); degrees c (479); mechanical properties (395); thermal stability (351); tensile strength (233); electron microscopy (221); cellulose nanocrystals (189); enzymatic hydrolysis (165); fourier transform (156); ray diffraction (142) By tf-idf: cellulose nanocrystals (189); cellulose nanofibers (92); mechanical properties (395); bagasse fiber (128); degree of substitution (88); nanocrystalline cellulose (56); tensile properties (49); thermal stability (351); pressure homogenization (45); food packaging (66) t1: Use of agricultural residues for industrial and environmental applications; t2: Mechanical and thermal properties of bagasse fibers for use in composite materials; t3: Extraction and characterization of cellulose, hemicellulose, nanocellulose, and lignin, as well as modification and application of these materials in various contexts including fermentation, adsorption, bioremediation, and production of biofuels and composites.
g05 (NLP) t1, t2, t3 and t4 By frequency: sugarcane bagasse (1860); degrees c (826); enzymatic hydrolysis (528); state fermentation (398); ethanol production (323); carbon source (236); lignocellulosic biomass (235); bioethanol production (214); rice straw (206); cellulase production (178) By tf-idf: cellulase production (178); degrees c (826); beta -glucosidase (145); trichoderma reesei (83); xylanase activity (81); xylanase production (112); t. reesei (35); bagasse saccharification (34); active enzymes (34); enzymatic hydrolysis (528) t1: Application of agricultural waste in the production of chemical substances (peracetic acid, hydrogen peroxide and xylose reducing enzyme); t2: Saccharification, process of converting lignocellulosic biomass, such as sugarcane bagasse, into fermentable sugars through the use of enzymes or microorganisms; t3: Obtaining enzymes (cellulase, xylanase, glucoamylases, laccases, and amylases), responsible for saccharification of materials such as bagasse and straw, used in ethanol production; t4: Fermentation processes with a specific focus on the utilization of agro-industrial residues, such as sugarcane bagasse, for the production of biofuels, xylitol, and other value-added chemicals substances.   
g06 (NLP) t1 and t2 By frequency: sugarcane bagasse (1070); bagasse ash (1001); sugarcane bagasse ash (710); compressive strength (477); degrees c (225); mechanical properties (214); water absorption (177); rice husk (167); clay bricks (133); husk ash (130) By tf-idf: bagasse ash (1001); sugarcane bagasse ash (710); compressive strength (477); clay bricks (133); cementitious materials (82); portland cement (116); cement replacement (77); pozzolanic activity (62); husk ash (130); cane bagasse ash (61) t1: Different agricultural practices and additives that can influence soil health and plant growth, particularly in the context of sugarcane. t2: Valorization of by-products from the sugar-alcohol industry through various forms of reutilization of sugarcane bagasse, primarily in the production of construction materials (brick, cement and concrete).
g07 (NLP) t1 By frequency: sugarcane bagasse (424); dry matter (227); crude protein (99); detergent fiber (89); sugarcane molasses (83); body weight (83); gas production (82); neutral detergent fiber (73); kg dm (64); chemical composition (63) By tf-idf: neutral detergent fiber (73); corn silage (62); crude protein (99); feed lot (46); matter intake (43); ruminal fermentation (43); kg dm (64); dry matter intake (42); nutritive value (61); daily gain (38) t1: Use of sugarcane bagasse and other agricultural by-products primarily in animal nutrition, considering factors such as fermentation, digestibility, chemical treatment, and feed production.

Indicators - Citations Cycle Time

  • Kayal (1999)[74]
  • Technology Cycle Time - TCT

The median age of U.S. patents cited in a specific patent. This indicator uses patent citations to indicate the age of the inventions on which a new invention is based. It is assumed that the more recent the age the more quickly one generation of inventions is being replaced with another.

measure the pace of technological or scientific progress or change.

Indicators - Entropy

Entropy by Shannon (1948)[75] quantifies the average level of uncertainty.

The normalised entropy is a scale-independent measure of uncertainty that can be used to compare the uncertainty of different groups.

Entropy measures the randomness or disorder in the group.

Conclusions

  • The proposed method, birddog, was able to effectively identify research trajectories and their attribute’s: emergence, convergence, divergence, maturity, dormancy, continuity, and discontinuity.

  • Seven research areas and their trajectories:

    • Group 01: Trends in flex-fuel cars in Brazil - mature trajectory stable since 2000s - Brazil;
    • Group 02: Utilizing agro-industrial residues (xylitol and ethanol) - incremental trajectory with convergence - China and Brazil;
    • Group 03: Carbon derived from agricultural by-products - mature trajectory based in literature from the 1990s - China, Brazil and India;
    • Group 04: Applications of sugarcane bagasse and its by-products - mature trajectory consolidated in 2006 - Brazil, China and India;
    • Group 05: Fermentation processes using lignocellulosic residues - incremental trajectory with cumulative innovations - Brazil;
    • Group 06: Construction bio-materials: bricks, concrete, and cement - fast-growing field with emergent trajectory - India;
    • Group 07: Animal nutrition: fermentation, digestibility, chemical treatment, and feed production - mature trajectory with refreshed literature - Brazil.

Promising technologies: biochar, nanocellulose, fertilizers, ethanolic extract from molasses, and biogas, as well as the use of ashes generated from burning in construction material industries, and the processing of inputs such as molasses, straw, and bagasse in ethanol production.

Scenarios: a) focuses on the use of co-products for energy and ethanol production, while b) focuses on the production of various value-added products.

References

roneyfraga.com/2025-gtm-birddog

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