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

Electron Orbital Model01:18

Electron Orbital Model

71.8K
Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
71.8K
What are Estimates?01:06

What are Estimates?

8.2K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.2K
Electron Carriers01:24

Electron Carriers

91.5K
Electron carriers can be thought of as electron shuttles. These compounds can easily accept electrons (i.e., be reduced) or lose them (i.e., be oxidized). They play an essential role in energy production because cellular respiration is contingent on the flow of electrons.
Over the many stages of cellular respiration, glucose breaks down into carbon dioxide and water. Electron carriers pick up electrons lost by glucose in these reactions, temporarily storing and releasing them into the electron...
91.5K
One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance00:56

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

349
Clearance is a key pharmacokinetic parameter that quantifies the volume of body fluid from which a drug is entirely removed within a specific time frame. It is crucial in assessing how a drug is eliminated from the body and has critical clinical applications.
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
349
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

215
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
215
Electron Affinity03:07

Electron Affinity

43.1K
The electron affinity (EA) is the energy change for adding an electron to a gaseous atom to form an anion (negative ion).
43.1K

You might also read

Related Articles

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

Sort by
Same author

Editorial: Nutrition at the Crossroads: Food at the Intersection of Health, Environmental, Economic, and Social Sustainability-Volume II.

Frontiers in nutrition·2022
Same author

Editorial: Nutrition at the Crossroads: Food at the Intersection of Environmental, Economic, and Social Sustainability.

Frontiers in nutrition·2019
Same author

Integrating Environmental and Social Sustainability Into Performance Evaluation: A Balanced Scorecard-Based Grey-DANP Approach for the Food Industry.

Frontiers in nutrition·2018
Same author

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art.

Journal of environmental management·2009
Same author

Modeling the effects of pelleting on the logistics of distillers grains shipping.

Bioresource technology·2009

Related Experiment Video

Updated: Jan 21, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Estimation of electronic waste using optimized multivariate grey models.

Gazi Murat Duman1, Elif Kongar2, Surendra M Gupta3

  • 1Department of Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA.

Waste Management (New York, N.Y.)
|July 29, 2019
PubMed
Summary
This summary is machine-generated.

Accurate e-waste (electrical and electronic equipment waste) predictions are vital for green city initiatives. A new model using population density and income data accurately forecasts e-waste generation, improving collection and recycling efforts.

Keywords:
Electronic wasteForecastingMultivariate grey modeling with convolution integralParticle Swarm Optimization

More Related Videos

Preparing a Celadonite Electron Source and Estimating Its Brightness
09:14

Preparing a Celadonite Electron Source and Estimating Its Brightness

Published on: November 5, 2019

4.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Related Experiment Videos

Last Updated: Jan 21, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Preparing a Celadonite Electron Source and Estimating Its Brightness
09:14

Preparing a Celadonite Electron Source and Estimating Its Brightness

Published on: November 5, 2019

4.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Area of Science:

  • Environmental Science
  • Waste Management
  • Data Science

Background:

  • Rapid technological advancements and consumer demand fuel significant electrical and electronic equipment waste (WEEE) accumulation.
  • E-waste is a rapidly growing municipal solid waste stream, necessitating efficient management for sustainable urban environments.
  • Accurate e-waste estimations are critical for optimizing collection, recycling, and disposal operations.

Purpose of the Study:

  • To introduce a novel forecasting technique for predicting e-waste generation with multiple inputs and limited historical data.
  • To enhance grey forecasting models for improved accuracy in e-waste prediction.
  • To analyze the key factors influencing e-waste generation and its distribution patterns.

Main Methods:

  • Development of a nonlinear grey Bernoulli model with convolution integral (NBGMC(1,n)).
  • Optimization of the NBGMC(1,n) model using Particle Swarm Optimization (PSO).
  • Application and validation of the proposed model using Washington State e-waste data.

Main Results:

  • The NBGMC(1,n) model optimized by PSO demonstrated superior accuracy compared to alternative forecasting methods.
  • Population density was identified as the primary driver of e-waste generation, followed by household income.
  • E-waste generation in Washington State exhibits a saturated distribution pattern.

Conclusions:

  • The proposed advanced grey forecasting model provides a reliable method for e-waste prediction with limited data.
  • Understanding the impact of population density and income is crucial for effective e-waste management strategies.
  • Findings support the development of efficient reverse logistics for sustainable e-waste handling.