AIMC Topic: Carbon
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Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.
PloS one
Mar 25, 2025
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their develo...
Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: An advanced approach toward sustainable and carbon-neutral wastewater treatment.
Chemosphere
Mar 17, 2025
Integrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitori...
Machine learning-based biological process optimization for low molecular weight welan gum production.
International journal of biological macromolecules
Mar 16, 2025
This study focuses on optimizing the fermentation process for the production of low molecular weight welan gum (LMW-WG) using Sphingomonas sp. ATCC 31555 with glycerol as the sole carbon source. A series of single-factor experiments were conducted to...
Enhanced nitrogen prediction and mechanistic process analysis in high-salinity wastewater treatment using interpretable machine learning approach.
Bioresource technology
Mar 11, 2025
This study introduces an interpretable machine learning framework to predict nitrogen removal in membrane bioreactor (MBR) treating high-salinity wastewater. By integrating Shapley additive explanations (SHAP) with Categorical Boosting (CatBoost), we...
A fuzzy robust optimization model for dual objective forward and reverse logistics networks considering carbon emissions.
PloS one
Mar 11, 2025
The inherent unpredictability within the low-carbon integrated supply chain logistics network complicates its management. This paper endeavours to address the challenge of designing a low-carbon logistics network within a context of uncertainty and w...
Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.
The Science of the total environment
Mar 7, 2025
The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity a...
Dissolved organic carbon estimation in lakes: Improving machine learning with data augmentation on fusion of multi-sensor remote sensing observations.
Water research
Feb 21, 2025
This paper presents a novel approach for estimating Dissolved Organic Carbon (DOC) concentrations in lakes considering both carbon sources and sink operators. Despite the critical role of DOC, the combined application of machine learning, as a robust...
Driving factors of TOC concentrations in four different types of estuaries (canal, urban, agricultural, and natural estuaries) identified by machine learning technique.
Marine pollution bulletin
Feb 21, 2025
Mangroves are among the most significant organic carbon sinks on Earth. However, the drivers of mangrove carbon remain poorly understood due to the lack of data on organic carbon across different types of estuaries. In this study, boosted regression ...
Carbon source dosage intelligent determination using a multi-feature sensitive back propagation neural network model.
Journal of environmental management
Feb 15, 2025
The carbon reduction concept drives the development of low-carbon and sustainable wastewater treatment plant (WWTP) operation technologies. In the denitrification stage of WWTPs in China, there are widespread problems of uneconomical dosage consumpti...
Simultaneous detection of trace protein biomarkers from a single drop of blood using AI-enhanced smartphone-based digital microscopy.
Biosensors & bioelectronics
Feb 13, 2025
The detection of early-stage diseases is often impeded by the low concentrations of protein biomarkers, necessitating sophisticated and costly technologies. In response, we have developed an advanced cyber-physical system that integrates blood plasma...
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