Welding
robots
are
widely
used
in
the
automotive
welding
process,
but
automated
process
data
of
is
huge
and
there
problems
such
as
weak
relationships
lack
effective
management,
so
this
paper
proposes
a
knowledge
graph
construction
method
for
process.
Firstly,
we
analyze
data,
use
top-down
to
construct
ontology
build
on
Protégé
form
conceptual
framework
Secondly,
rule-based
extraction
transformation
algorithm
proposed
automatically
extract
entities,
attributes
from
according
mapping
rules
between
model
structured
data.
Finally,
Neo4j
store
entity
their
complete
graph.
The
provides
new
way
organization
representation
support
intelligent
research
application
Journal of Biogeography,
Год журнала:
2023,
Номер
unknown
Опубликована: Окт. 3, 2023
Abstract
Biotic
interactions
are
widely
recognised
as
the
backbone
of
ecological
communities,
but
how
best
to
study
them
is
a
subject
intense
debate,
especially
at
macro‐ecological
scales.
While
some
researchers
claim
that
biotic
need
be
observed
directly,
others
use
proxies
and
statistical
approaches
infer
them.
Despite
this
ambiguity,
studying
predicting
influence
on
biogeographic
patterns
thriving
area
research
with
crucial
implications
for
conservation.
Three
distinct
currently
being
explored.
The
first
approach
involves
empirical
observation
measurement
interactions'
effects
species
demography
in
laboratory
or
field
settings.
these
findings
contribute
theory
understanding
species'
demographies,
they
can
challenging
generalise
larger
scale.
second
centers
inferring
associations
from
co‐occurrences
space
time.
goal
distinguish
environmental
distributions.
third
constructs
extensive
potential
interaction
networks,
known
metanetworks,
by
leveraging
existing
knowledge
about
ecology
interactions.
This
analyses
local
realisations
networks
using
occurrence
data
allows
large
distributions
multi‐taxa
assemblages.
In
piece,
we
appraise
three
approaches,
highlighting
their
respective
strengths
limitations.
Instead
seeing
conflicting,
advocate
integration
enhance
our
expand
applications
emerging
biogeography.
shows
promise
ecosystem
management
Anthropocene
era.
Lecture notes in computer science,
Год журнала:
2023,
Номер
unknown, С. 152 - 175
Опубликована: Янв. 1, 2023
Abstract
The
Relational
to
RDF
Mapping
Language
(R2RML)
became
a
W3C
Recommendation
decade
ago.
Despite
its
wide
adoption,
potential
applicability
beyond
relational
databases
was
swiftly
explored.
As
result,
several
extensions
and
new
mapping
languages
were
proposed
tackle
the
limitations
that
surfaced
as
R2RML
applied
in
real-world
use
cases.
Over
years,
one
of
these
languages,
(RML),
has
gathered
large
community
contributors,
users,
compliant
tools.
So
far,
there
been
no
well-defined
set
features
for
language,
nor
consensus-marking
ontology.
Consequently,
it
become
challenging
non-experts
fully
comprehend
utilize
full
range
language’s
capabilities.
After
three
years
work,
Community
Group
on
Knowledge
Graph
Construction
proposes
specification
RML.
This
paper
presents
modular
RML
ontology
accompanying
SHACL
shapes
complement
specification.
We
discuss
motivations
challenges
emerged
when
extending
R2RML,
methodology
we
followed
design
while
ensuring
backward
compatibility
with
novel
which
increase
expressiveness.
consolidates
RML,
empowers
practitioners
define
rules
constructing
graphs
previously
unattainable,
allows
developers
implement
systems
adherence
[R2]RML.
Resource
type
:
Ontology/
License
CC
BY
4.0
International
DOI
10.5281/zenodo.7918478
/
URL
http://w3id.org/rml/portal/
Plants,
Год журнала:
2025,
Номер
14(5), С. 786 - 786
Опубликована: Март 4, 2025
A
novel
eggplant
disease
detection
method
based
on
multimodal
data
fusion
and
attention
mechanisms
is
proposed
in
this
study,
aimed
at
improving
both
the
accuracy
robustness
of
detection.
The
integrates
image
sensor
data,
optimizing
features
through
an
embedded
mechanism,
which
enhances
model’s
ability
to
focus
disease-related
features.
Experimental
results
demonstrate
that
excels
across
various
evaluation
metrics,
achieving
a
precision
0.94,
recall
0.90,
0.92,
mAP@75
0.91,
indicating
excellent
classification
object
localization
capability.
Further
experiments,
ablation
studies,
evaluated
impact
different
loss
functions
model
performance,
all
showed
superior
performance
for
approach.
combined
with
mechanism
effectively
model,
making
it
highly
suitable
complex
identification
tasks
demonstrating
significant
potential
widespread
application.
Abstract
Natural
history
collections
play
a
crucial
role
in
our
understanding
of
biodiversity,
informing
research,
management,
and
policy
areas
such
as
biosecurity,
conservation,
climate
change,
food
security.
However,
the
growing
volume
specimens
associated
data
presents
significant
challenges
for
curation
management.
By
leveraging
human–AI
collaborations,
we
aim
to
transform
way
biological
are
curated
managed,
realizing
their
full
potential
addressing
global
challenges.
In
this
article,
discuss
vision
improving
management
using
collaboration.
We
explore
rationale
behind
approach,
faced
general
problems,
benefits
that
could
be
derived
from
incorporating
AI-based
assistants
collection
teams.
Finally,
examine
future
possibilities
collaborations
between
human
digital
curators
collection-based
research.
Soil
and
soil-biodiversity
protection
are
increasingly
important
issues
in
environmental
science
policies,
requiring
the
availability
of
high-quality
empirical
data
on
soil
biodiversity.
Here
we
present
a
publicly
available
warehouse
for
domain,
Edaphobase
2.0,
which
provides
comprehensive
toolset
storing
re-using
international
sets,
following
FAIR
(Findable,
Accessible,
Interoperable,
Reusable)
principles.
A
major
strength
is
possibility
annotating
biodiversity
with
exhaustive
geographical,
methodological
metadata,
allowing
wide
range
applications
analyses.
The
system
harmonises
integrates
heterogeneous
from
diverse
sources
into
standardised
formats,
can
be
searched
together
using
numerous
filter
possibilities,
offers
exploration
analysis
tools.
features
strict
transparency
policy,
quality
control,
DOIs
provided
individual
sets.
database
currently
contains
>450,000
records
>35,0000
sites
accessed
nearly
14,000
times/year.
curated
by
2.0
greatly
aid
researchers,
conservationists
decision
makers
understanding
protecting
Comptes Rendus Biologies,
Год журнала:
2024,
Номер
347(G1), С. 223 - 247
Опубликована: Ноя. 25, 2024
Recent
climate
and
land
use
change,
pollution
have
led
to
concerning
alterations
in
biodiversity
ecosystem
functions,
jeopardizing
nature's
contributions
people.
Mountainous
regions
are
not
immune
these
threats,
experiencing
the
impacts
of
global
warming,
increased
recreational
activities,
changes
agricultural
practices.
Leveraging
natural
elevational
gradients
mountain
environments,
ORCHAMP
program
was
established
2016
as
a
comprehensive
initiative
monitor,
understand,
predict
repercussions
environmental
on
associated
functions
French
Alps
Pyrenees.Beyond
its
monitoring
role,
has
catalyzed
development
tools
for
data
integration,
statistical
analyses,
visualization,
AI-based
automated
processing
predictions.
Through
combination
traditional
sampling
methods
(e.g.,
botanical
surveys)
cutting-edge
technologies
(remote-sensing,
DNA,
video,
acoustic
sensors),
offers
holistic
approach
understanding
how
faces
changes.
By
showcasing
examples
key
results,
this
paper
provides
an
overview
ORCHAMP's
advancements
outlines
potential
future
directions.
The
broad
inclusion
diverse
techniques
treatments
positions
pioneering
effort,
paving
way
long-term
insights
into
dynamics—a
crucial
step
toward
effective
conservation
strategies.
Les
récents
changements
en
matière
de
climat
et
d'utilisation
des
sols,
ainsi
que
la
pollution,
ont
entraîné
altérations
préoccupantes
biodiversité
fonctions
écosystèmes,
mettant
péril
les
nature
aux
populations.
régions
montagneuses
ne
sont
pas
à
l'abri
ces
menaces,
subissant
effets
du
réchauffement
climatique,
l'augmentation
activités
récréatives
dans
pratiques
agricoles.
Tirant
parti
d'altitude
naturels
environnements
montagne,
le
programme
été
créé
tant
qu'initiative
globale
visant
surveiller,
comprendre
prédire
répercussions
environnementaux
sur
écosystémiques
associées
Alpes
Pyrénées
françaises.Au-delà
son
rôle
surveillance,
promu
développement
d'outils
pour
l'intégration
données,
analyses
statistiques,
visualisation
traitement
automatisé
données
prédictions
basées
l'IA.
Grâce
une
combinaison
méthodes
d'échantillonnage
traditionnelles
(par
exemple,
relevés
botaniques)
pointe
(télédétection,
ADN
environnemental,
pièges
photos
capteurs
acoustiques),
offre
approche
holistique
comment
fait
face
environnementaux.
En
présentant
exemples
résultats
clés,
cet
article
donne
vue
d'ensemble
avancées
d'ORCHAMP
esquisse
orientations
futures
potentielles.
diverses
surveillance
figure
pionnier
ouvre
voie
compréhension
long
terme
dynamique
biodiversité,
étape
essentielle
mise
place
stratégies
efficaces.
