Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Deconvolution01:20

Deconvolution

592
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
592
Mixing Concrete01:30

Mixing Concrete

391
Concrete mixing ensures a homogenous blend where aggregates are well-coated with cement paste. Concrete mixing is typically done using two main types of mixers: batch and continuous. Batch mixers handle one batch at a time, thoroughly combining materials before discharging and receiving the next batch. In contrast, continuous mixers receive a steady flow of ingredients, mixing them consistently and discharging without interruption. Within batch mixers, tilting drum mixers mix with internal...
391
Mixing Time01:19

Mixing Time

481
The concept of mixing time is significant in producing a uniform concrete mix with the required strength. The mixing period starts once all components are in the mixer. Initially, the mixer is charged with 10% of the water, followed by the consistent addition of solids and then 80% of the water. The remaining water is added later, within the first quarter of the mixing period. The minimum mixing time varies according to the mixer's capacity; for example, mixers with up to 1 cubic yard...
481
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

277
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
277
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

570
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
570
Ready Mixed Concrete01:26

Ready Mixed Concrete

373
Ready-mixed concrete, also known as pre-mixed concrete, is prepared in a centralized plant and then transported in trucks to construction sites where it is ready for placement. This type of concrete is categorized into central-mixed, truck-mixed (or transit-mixed), and shrink-mixed. Central-mixed concrete is entirely prepared at a plant and moved to the site in agitator trucks that rotate at a speed of 2 to 6 rpm. Truck-mixed concrete, on the other hand, has the ingredients batched at the plant...
373

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Economic evaluation and intercropping indices of maize-common bean system under variable fertilization in Southwestern Ethiopia.

Scientific reports·2026
Same author

Microbial and small zooplankton communities predict density of baleen whales in the southern California Current Ecosystem.

PloS one·2026
Same author

A rare tropical storm event drives partial nursery evacuation by juvenile white sharks, followed by rapid aggregation reformation.

Movement ecology·2026
Same author

Enhanced grain quality of malt barley (Hordeum distichon L.) in response to mixed use of organic compost and mineral nitrogen rates.

PloS one·2026
Same author

Canopy Microclimate and Leaf Traits Shape Interspecies Variation in Photosynthetic Temperature Responses of Evergreen Tropical Trees in the Congo Basin.

Global change biology·2026
Same author

Congo Basin Carbon Cycle Responses to Global Change.

Global change biology·2026

Related Experiment Video

Updated: Feb 5, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment.

William H Blake1, Pascal Boeckx2, Brian C Stock3

  • 1School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK. william.blake@plymouth.ac.uk.

Scientific Reports
|September 1, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces Deconvolutional-MixSIAR (D-MIXSIAR), a new Bayesian mixing model for sediment source apportionment. D-MIXSIAR accurately identifies sediment sources in complex river basins, improving soil and sediment management.

More Related Videos

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K
Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
05:31

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

16.9K

Related Experiment Videos

Last Updated: Feb 5, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K
Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
05:31

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

16.9K

Area of Science:

  • Environmental Science
  • Hydrology
  • Geomorphology

Background:

  • Human-environment interactions in watersheds are increasingly complex.
  • Accurate sediment source apportionment is crucial for sustainable soil and sediment management.
  • Existing Bayesian mixing models struggle with the hierarchical structure of river basins.

Purpose of the Study:

  • To introduce Deconvolutional-MixSIAR (D-MIXSIAR), a novel Bayesian mixing model.
  • To account for the hierarchical structure of river basins in sediment source apportionment.
  • To improve the accuracy and efficiency of sediment source analysis in complex watersheds.

Main Methods:

  • Developed and applied Deconvolutional-MixSIAR (D-MIXSIAR) to two distinct watersheds (England and Nepal).
  • Stratified source data by sub-watershed and deconvoluted apportionment data along the stream-river network.
  • Utilized geochemical fingerprints and compound-specific stable isotope markers for source identification.

Main Results:

  • D-MIXSIAR provided more distinct sediment source signatures compared to pooled-MixSIAR.
  • The model accurately identified contributions from pasture and cultivated topsoil, unlike conventional methods.
  • D-MIXSIAR reduced uncertainty by 25-50% and decreased model run times.

Conclusions:

  • D-MIXSIAR offers a significant advancement in sediment source apportionment for complex river basins.
  • The model's hierarchical approach aligns better with field observations and local knowledge.
  • This tool supports sustainable management of soil and sediment in dynamic environmental systems.