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

Light Acquisition02:16

Light Acquisition

8.7K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.7K
Light as Energy01:35

Light as Energy

83.3K
The energy required to carry out photosynthesis is light— typically electromagnetic radiation from the sun. The range of all possible wavelengths is known as the electromagnetic spectrum.
Photons
A photon is a discrete electromagnetic particle or bundle of energy. Photons are characterized by their frequency, wavelength, and amplitude, similar to the properties of a wave. Waves with higher frequencies transmit more energy and have shorter wavelengths than longer wavelengths that transmit...
83.3K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.9K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.9K
Photoreceptors and Plant Responses to Light02:00

Photoreceptors and Plant Responses to Light

26.3K
Light plays a significant role in regulating the growth and development of plants. In addition to providing energy for photosynthesis, light provides other important cues to regulate a range of developmental and physiological responses in plants.
26.3K
Flame Photometry: Overview01:02

Flame Photometry: Overview

889
Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
889
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

6.8K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Chromosome-level genomes of scleractinian corals: gene prediction and functional annotation.

Scientific data·2026
Same author

Genomic and genetic insights into speciation and pigment pattern diversification in <i>Danio</i> fishes.

bioRxiv : the preprint server for biology·2025
Same author

Investigating Simulated Cellular Interactions on Nanostructured Surfaces with Antibacterial Properties: Insights from Force Curve Simulations.

Nanomaterials (Basel, Switzerland)·2025
Same author

Real-world analysis of acamprosate use in patients with cirrhosis and alcohol-associated hepatitis.

BMJ open gastroenterology·2025
Same author

An assessment of bacterial transmission via rebound tonometry: An <i>in vitro</i> pilot study.

Open veterinary journal·2024
Same author

Chromosome-scale genome assembly and de novo annotation of Alopecurus aequalis.

Scientific data·2024
Same journal

Influence of localized roadway surface obstacles on vehicular emissions under real-world urban driving conditions.

Frontiers in big data·2026
Same journal

Adaptive class-aware feature selection for high-dimensional and imbalanced multi-class network intrusion detection.

Frontiers in big data·2026
Same journal

Deep learning model to predict COPD hospital admissions based on meteorological data: a medical meteorological forecast.

Frontiers in big data·2026
Same journal

Where diverse populations gather: transit accessibility and the spatial structure of social mixing.

Frontiers in big data·2026
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
See all related articles

Related Experiment Video

Updated: Oct 14, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

6.0K

A Data-Driven Personalized Lighting Recommender System.

Atousa Zarindast1, Jonathan Wood1

  • 1Department of Civil and Environment Engineering, Iowa State University, Ames, IA, United States.

Frontiers in Big Data
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a smart lighting recommender system using machine learning. It predicts user preferences for lighting routines and colors by clustering similar users, improving recommendation accuracy.

Keywords:
color predictionlightingpersonalizationroutine recommendersmart recommendation system

More Related Videos

Building a Simple and Versatile Illumination System for Optogenetic Experiments
06:41

Building a Simple and Versatile Illumination System for Optogenetic Experiments

Published on: January 12, 2021

4.1K
Blue-hazard-free Candlelight OLED
10:18

Blue-hazard-free Candlelight OLED

Published on: March 19, 2017

9.6K

Related Experiment Videos

Last Updated: Oct 14, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

6.0K
Building a Simple and Versatile Illumination System for Optogenetic Experiments
06:41

Building a Simple and Versatile Illumination System for Optogenetic Experiments

Published on: January 12, 2021

4.1K
Blue-hazard-free Candlelight OLED
10:18

Blue-hazard-free Candlelight OLED

Published on: March 19, 2017

9.6K

Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Smart Home Technology

Background:

  • Recommender systems are crucial for personalized user experiences.
  • Smart lighting systems generate extensive user interaction data.
  • Understanding user preferences is key to effective smart home automation.

Purpose of the Study:

  • To develop an automated recommender system for smart lighting routines and color schemes.
  • To leverage historical data and machine learning for personalized lighting recommendations.
  • To enhance user experience in home-based smart lighting environments.

Main Methods:

  • Utilized unsupervised learning for smart lighting routine recommendations.
  • Analyzed user logs, geographical data, and temporal/usage information for preference prediction.
  • Employed user clustering based on geographical and usage patterns.
  • Built and trained predictive models within user clusters and aggregated results.

Main Results:

  • Clustering users based on similar characteristics improved prediction accuracy.
  • The system effectively predicted preferred light colors by analyzing user data.
  • Recommendations were enhanced by leveraging data from similar users, even without explicit preference knowledge.

Conclusions:

  • Machine learning models incorporating similar user data significantly boost recommender system performance.
  • Personalized smart lighting recommendations can be achieved through data analysis and user clustering.
  • This approach offers a robust method for developing intelligent automated systems for smart homes.