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Carbon Dioxide

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AI-driven predictive models for sustainability.

Journal of environmental management
This research presents an AI-driven, explainable energy management model that aligns with Net Zero sustainability objectives by optimizing energy consumption, enhancing predictive accuracy, and ensuring transparency. The model integrates machine lear...

Noninvasive estimation of PaCO from volumetric capnography in animals with injured lungs: an Artificial Intelligence approach.

Journal of clinical monitoring and computing
To investigate the feasibility of non-invasively estimating the arterial partial pressure of carbon dioxide (PaCO) using a computational Adaptive Neuro-Fuzzy Inference System (ANFIS) model fed by noninvasive volumetric capnography (VCap) parameters. ...

Effective carbon footprint assessment strategy in fly ash geopolymer concrete based on adaptive boosting learning techniques.

Environmental research
In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO-FP) of fly ash geopolymer...

An examination of daily CO emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models.

Environmental science and pollution research international
Human-induced global warming, primarily attributed to the rise in atmospheric CO, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO emissions, which are crucial for setting long-term emission mi...

Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

ACS sensors
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low c...

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO Emissions.

Environmental science & technology
Satellite data provides essential insights into the spatiotemporal distribution of CO concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and...

Climate Sustainability through AI-Crypto Synergies and Energy Transition in the Digital Landscape to Cut 0.7 GtCOe by 2030.

Environmental science & technology
The rapid expansion of artificial intelligence (AI)-enabled systems and cryptocurrency mining poses significant challenges to climate sustainability due to energy-intensive operations relying on fossil-powered grids. This work investigates the strate...

Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO.

Scientific reports
Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO) plays an essential role in the related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and ...

Determination of 5-fluorouracil anticancer drug solubility in supercritical COusing semi-empirical and machine learning models.

Scientific reports
In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO. This important rol...

Investigating the causal relationship between electricity pricing policy and CO emission: An application of machine learning-driven metalearners.

Journal of environmental management
Investigating the causal relationship between electricity pricing policies and CO emissions is vital for crafting effective climate strategies, as it reveals how pricing mechanisms can inadvertently influence environmental outcomes. So, the paper uti...