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

What is Climate?

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.

Global Climate Change

Global Climate Change

Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

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.

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

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

Sort by

Same author

Differential expression of the C-Myc gene in intestinal-type and diffuse-type gastric cancer and its clinical significance.

BMC gastroenterology·2026

Same author

Mechano-activation of osteocytic connexin hemichannels prime microenvironment in suppressing breast cancer bone metastasis.

Cancer letters·2026

Same author

[Mechanism of moxibustion at the governor vessel for regulating autophagy against Alzheimer's disease via lncRNA-RP4-mediated Wnt/β-catenin pathway].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2026

Same author

Immunosuppression, resistance burden, and qSOFA on short-term prognosis and difficult clearance in hospitalized patients with Salmonella infection: a single-center retrospective cohort study.

BMC infectious diseases·2026

Same author

Assessing Intraurban Variation in Heat-Attributable Health Burden to Inform Local Interventions.

GeoHealth·2026

Same author

pH-responsive CDs-based nanoplatform for chemo-resistant esophageal cancer treatment via downregulation of HIF-1α related pathway.

Journal of nanobiotechnology·2026

Search research articles

Related Experiment Video

Updated: Aug 18, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Predicting European cities' climate mitigation performance using machine learning.

Angel Hsu^1,2,3, Xuewei Wang^4,5,6, Jonas Tan⁷

¹Department of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, USA. angel.hsu@unc.edu.

Nature Communications

|December 5, 2022

Summary

European cities in climate initiatives likely reduced carbon dioxide (CO2) emissions since 2001. Cities reporting emissions data showed greater reductions, highlighting data

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Related Experiment Videos

Last Updated: Aug 18, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Environmental science
Urban planning
Climate change mitigation

Background:

Cities are increasingly recognized as key players in global climate action.
A significant challenge in assessing urban climate performance is the scarcity of reliable emissions data.
Evaluating city-level mitigation efforts requires robust and scalable methodologies.

Purpose of the Study:

To develop and apply a machine learning approach for evaluating climate mitigation performance in European cities.
To assess trends in carbon dioxide (CO2) emissions for local administrative areas across Europe from 2001 to 2018.
To investigate the relationship between participation in climate initiatives, data reporting, and emissions reduction performance.

Main Methods:

Utilized a machine learning model integrating publicly available environmental and socio-economic data with self-reported emissions.
Applied the model to nearly all local administrative areas in Europe for the period 2001-2018.
Predicted annual CO2 emissions to analyze city-scale mitigation trends.

Main Results:

European cities engaged in transnational climate initiatives demonstrated a likely decrease in CO2 emissions since 2001.
Over half of these cities appear to have met their 2020 emissions reduction targets.
Cities that reported emissions data exhibited greater emissions reductions compared to those that did not.

Conclusions:

The developed machine learning approach offers a scalable and replicable method for assessing city-level climate mitigation performance.
Data reporting by cities is positively correlated with successful emissions reductions.
Further research and data transparency are crucial for accurately tracking and enhancing urban climate action.