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

Biological Treatment of Effluent and Waste Water01:30

Biological Treatment of Effluent and Waste Water

Biological wastewater treatment relies on the metabolic activity of microorganisms to remove pollutants from sewage. In modern treatment systems, this process is organized into sequential stages that progressively reduce solid material, dissolved organic matter, and microbial contamination. Each stage plays a distinct role in improving water quality and preparing the effluent for safe discharge or reuse.Primary and Secondary TreatmentPrimary treatment is a physical process that removes large...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Microbial Wastewater Treatment01:30

Microbial Wastewater Treatment

Microbial communities in aquatic ecosystems play a key role in the natural breakdown of contaminants introduced through domestic and industrial effluents. Acting as biological catalysts, these microbes change and mineralize a wide range of organic and inorganic pollutants under different redox conditions.In oxygen-rich surface waters, aerobic heterotrophs lead organic matter breakdown, using oxygen as the terminal electron acceptor to efficiently oxidize substrates to carbon dioxide and water.
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Recycling Endosomes and Transcytosis00:58

Recycling Endosomes and Transcytosis

The recycling endosome, also known as the endosomal recycling compartment (ERC), is a part of the slow-recycling process of the endocytic pathway. Molecules internalized through receptor-mediated endocytosis are either degraded in the lysosomes or are recycled to the plasma membrane through the fast- or slow-recycling route.
The recycling endosome is not a single organelle but an extensively tubulated network of recycling pathways. It functions in storing molecules or transporting them across...

You might also read

Related Articles

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

Sort by
Same author

Antimicrobial resistance and resistance genes in Salmonella strains isolated from broiler chickens along the slaughtering process in China.

International journal of food microbiology·2017
Same author

Estrogen receptor alpha and beta regulate actin polymerization and spatial memory through an SRC-1/mTORC2-dependent pathway in the hippocampus of female mice.

The Journal of steroid biochemistry and molecular biology·2017
Same author

<i>STK11</i> rs2075604 Polymorphism Is Associated with Metformin Efficacy in Chinese Type 2 Diabetes Mellitus.

International journal of endocrinology·2017
Same author

Canopy Vegetation Indices from <i>In situ</i> Hyperspectral Data to Assess Plant Water Status of Winter Wheat under Powdery Mildew Stress.

Frontiers in plant science·2017
Same author

Caregiving burden and depression in paid caregivers of hospitalized patients: a pilot study in China.

BMC public health·2017
Same author

Insight into immunocytes infiltrations in polymorphous light eruption.

Biotechnology advances·2017

Related Experiment Video

Updated: Jun 8, 2026

Measuring Biomethane Potential of Food Scrap Waste Anaerobically Co&#45;Digested with Waste&#45;Activated Sludge Using Respirometry
06:11

Measuring Biomethane Potential of Food Scrap Waste Anaerobically Co-Digested with Waste-Activated Sludge Using Respirometry

Published on: April 26, 2024

An inexact reverse logistics model for municipal solid waste management systems.

Yi Mei Zhang1, Guo He Huang, Li He

  • 1Environmental Systems Engineering Program, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, Canada. yimei.zhang@iseis.org

Journal of Environmental Management
|October 15, 2010
PubMed
Summary

This study introduces an inexact reverse logistics model (IRWM) for municipal solid waste management, incorporating interval parameters to handle uncertainties. The model aids waste managers in strategic planning and operational execution for efficient waste management systems.

More Related Videos

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer
10:22

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer

Published on: November 30, 2020

Scalable Step-by-Step Approach of Sustainable Bioplastic Production from Food Waste
08:14

Scalable Step-by-Step Approach of Sustainable Bioplastic Production from Food Waste

Published on: July 18, 2025

Related Experiment Videos

Last Updated: Jun 8, 2026

Measuring Biomethane Potential of Food Scrap Waste Anaerobically Co&#45;Digested with Waste&#45;Activated Sludge Using Respirometry
06:11

Measuring Biomethane Potential of Food Scrap Waste Anaerobically Co-Digested with Waste-Activated Sludge Using Respirometry

Published on: April 26, 2024

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer
10:22

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer

Published on: November 30, 2020

Scalable Step-by-Step Approach of Sustainable Bioplastic Production from Food Waste
08:14

Scalable Step-by-Step Approach of Sustainable Bioplastic Production from Food Waste

Published on: July 18, 2025

Area of Science:

  • Operations Research
  • Environmental Management
  • Supply Chain Management

Background:

  • Municipal solid waste management faces significant dynamic and uncertain characteristics.
  • Effective reverse logistics are crucial for optimizing waste management systems.
  • Existing models often struggle to adequately address uncertainties in waste management.

Purpose of the Study:

  • To propose an inexact reverse logistics model (IRWM) for municipal solid waste management systems.
  • To incorporate interval parameters to quantify uncertainties in optimization processes.
  • To facilitate strategic planning and operational execution in waste management.

Main Methods:

  • Developed an inexact reverse logistics model (IRWM) for municipal solid waste management.
  • Utilized interval parameters to represent uncertainties in all model parameters.
  • Employed piecewise interval programming to solve the model, addressing Min-Min functions in objectives and constraints.

Main Results:

  • The IRWM effectively reflects the dynamic and uncertain nature of municipal solid waste management.
  • The model facilitates the generation of effective management plans for waste.
  • Analysis of two scenarios with varying landfill and waste-to-energy (WTE) costs demonstrated the model's applicability.

Conclusions:

  • The proposed IRWM provides a robust framework for managing uncertainties in municipal solid waste logistics.
  • The model can be enhanced by integrating stochastic or fuzzy parameters for more comprehensive analysis.
  • Future work could focus on developing multi-waste, multi-echelon, and multi-uncertainty models for waste management networks.