Discrete Fractional Order Modeling of Plant Capture Carbon Dioxide Dynamical Analysis with Neural Networking
離散分数次モデリングによる植物の二酸化炭素吸収動的解析とニューラルネットワーク (AI 翻訳)
Aziz Khan, Hadeel Bin Amer, Thabet Abdeljawad, Rajermani Thinakaran
🤖 gxceed AI 要約
日本語
植物のCO2吸収能力の違いが大気中のCO2濃度に与える影響を非線形数学モデルで解析。離散数値反復法とニューラルネットワーク（LMアルゴリズム）を用いて、植物の成長率と収穫率がCO2濃度に与える影響を評価。高い吸収能力を持つ植物はCO2削減に効果的であり、収穫率の増加はCO2濃度上昇と関連することが示された。
English
A nonlinear discrete mathematical model with neural networks analyzes how plant CO2 absorption capacities affect atmospheric CO2 levels. Using DNIM-LM algorithm, the study shows that higher absorption capacity leads to faster CO2 reduction, while increased harvesting rates raise CO2 concentration. The findings inform carbon sequestration and ecosystem management policies.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の森林管理やカーボンクレジット制度において、植物のCO2吸収能力をモデル化する本手法は、自然を活用した炭素除去（NBS）の政策立案に示唆を与える。
In the global GX context
This paper provides a quantitative framework for modeling natural carbon sinks, relevant to global carbon accounting and nature-based solutions (NBS) under frameworks like the Paris Agreement and corporate net-zero targets.
👥 読者別の含意
🔬研究者:Provides a novel discrete fractional modeling approach combined with neural networks for analyzing CO2 sequestration dynamics.
🏛政策担当者:Highlights the importance of plant growth rates and harvesting in carbon sequestration, informing land-use and carbon credit policies.
📄 Abstract（原文）
A nonlinear mathematical model is suggested to examine the role of varying abilities of plants in absorbing atmospheric Carbon Dioxide (CO2) and its impact on the ecosystem. Various plant species show distinct abilities to absorb and capture CO2, which can affect the overall reduction in atmospheric CO2 quantities. The study objectives to consider how differences in plant capacities for CO2 absorption influence atmospheric CO2 levels. The focus is on identification the impact of plant growth rates and reaping rates on CO2 concentration. The model is formulated using a difference operator to facilitate numerical exploration. It employs the Discrete Numerical Iterative Method (DNIM) joint with neural networks, particularly the Levenberg-Marquardt (LM) algorithm, known as DNIM-LM. The model's performance, training status, error distribution, regression, and suitability are calculated using artificial intelligence procedures. The dataset is split into 70% for training, 15% for authentication, and 15% for testing. The analysis shows that plants with higher CO2 absorption capacities attain faster reductions in atmospheric CO2 quantities as their growth rate increases. On the other hand, an increase in the harvesting rate coefficient is related to an increase in CO2 concentration. The study determines that variations in plant absorption capacities expressively influence the dynamic contrast of atmospheric CO2 reduction in the environment. This finding highlights the importance of plant growth rates and harvesting performs in managing CO2 levels, present insights into ecosystem management and carbon sequestration policies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.37256/cm.7320269264first seen 2026-05-17 06:36:47 · last seen 2026-05-20 05:13:48
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