Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

Distribution Reliability and Automation

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...

Distributed Loads

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

You might also read

Related Articles

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

Sort by

Same author

Generating roots of cubic polynomials by Cardano's approach on correspondence analysis.

Heliyon·2020

Same author

Estimation of the Basic Reproductive Ratio for Dengue Fever at the Take-Off Period of Dengue Infection.

Computational and mathematical methods in medicine·2015

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

Dynamic items delivery network: prediction and clustering.

Mokhammad R Yudhanegara¹, Sapto W Indratno¹, Rr Kurnia N Sari¹

¹Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia.

|May 24, 2021

Summary

This summary is machine-generated.

This study introduces a new delivery model that accounts for road density changes. By using clustering and predictive distribution, it optimizes delivery zones for reduced costs.

Keywords:

Dynamic network Mathematics Predictive distribution Spectral

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Nov 4, 2025

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Operations Research
Logistics Management
Data Science

Background:

Delivery companies aim to minimize costs using mathematical models with constraints like zones, vehicles, and routes.
Existing models often overlook dynamic road density, leading to suboptimal delivery strategies and increased costs.

Purpose of the Study:

To develop an optimized delivery model that incorporates dynamic road density.
To improve delivery cost minimization by adapting to real-time network changes.

Main Methods:

Zone division using clustering algorithms.
Prediction of dynamic network changes using predictive distribution.
Integration of these methods into a comprehensive delivery cost model.

Main Results:

Demonstrated a method to dynamically adjust delivery zones based on predicted road density.
Showcased how incorporating road density improves delivery model optimality.

Conclusions:

Optimized delivery models require dynamic adaptation to factors like road density.
Clustering and predictive distribution are effective for creating suitable delivery zones and strategies.
The proposed model enhances efficiency and reduces delivery costs in dynamic environments.