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Light Acquisition02:16

Light Acquisition

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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.
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Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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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...
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Prediction Intervals01:03

Prediction Intervals

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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. 
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Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Updated: Jun 30, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Attention-Based Models for Multivariate Time Series Forecasting: Multi-step Solar Irradiation Prediction.

Sadman Sakib1, Mahin K Mahadi1, Samiur R Abir1

  • 1Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, 1704, Bangladesh.

Heliyon
|March 18, 2024
PubMed
Summary

Accurate solar irradiance forecasting in Bangladesh is crucial for managing solar power. Attention-based models, particularly the Temporal Fusion Transformer (TFT), significantly improve prediction accuracy for photovoltaic systems.

Keywords:
Attention-based modelsMultivariate time series forecastingSequence modelsSolar irradianceTemporal Fusion TransformerTransformer

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Time Series Forecasting

Background:

  • Bangladesh's subtropical climate offers high solar panel efficiency.
  • Accurate solar irradiance forecasting is vital for grid-connected photovoltaic systems to manage power variability.
  • Existing forecasting models struggle with the long-term sequential dependencies inherent in solar irradiation data.

Purpose of the Study:

  • To develop and evaluate an attention-based model framework for multivariate solar irradiance time series forecasting.
  • To assess the performance of Attention-based encoder-decoder, Transformer, and Temporal Fusion Transformer (TFT) models.
  • To compare these advanced models against traditional forecasting methods.

Main Methods:

  • Utilized 30-minute resolution solar irradiance data from two locations in Bangladesh.
  • Trained and tested Attention-based encoder-decoder, Transformer, and Temporal Fusion Transformer (TFT) models.
  • Evaluated model performance for predicting solar irradiance 24 steps ahead.

Main Results:

  • Attention mechanisms significantly enhanced prediction accuracy.
  • The Temporal Fusion Transformer (TFT) demonstrated superior precision and robustness compared to other models.
  • TFT achieved a Mean Squared Error (MSE) of 0.151, Mean Absolute Error (MAE) of 0.212, and R-squared (R²) of 0.815.

Conclusions:

  • Attention-based models, especially TFT, are highly effective for solar irradiance forecasting.
  • TFT offers substantial improvements over benchmark and sequential models, reducing MSE by up to 47.9% and MAE by up to 22.3%.
  • The capability of attention models to capture long-distance dependencies boosts predictive power for solar energy applications.