AIMC Topic: Nitrogen

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Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale.

PloS one
Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug d...

An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems.

Food chemistry
Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics ...

Data fusion of near-infrared and Raman spectroscopy: An innovative tool for non-destructive prediction of the TVB-N content of salmon samples.

Food research international (Ottawa, Ont.)
Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings...

Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar.

Bioresource technology
N-doped porous biochar is a promising carbon material for supercapacitor electrodes due to its developed pore structure and high chemical activity which greatly affect the capacitive performance. Predicting the capacitance and exploring the most infl...

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.

Journal of hazardous materials
Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated exce...

Effect of different targets of goal-directed fluid therapy on intraoperative hypotension and fluid infusion in robot-assisted laparoscopic gynecological surgery: a randomized non-inferiority trial.

Journal of robotic surgery
Carotid corrected flow time (FTc) and tidal volume challenge pulse pressure variation (VtPPV) are useful clinical parameters for assessing volume status and fluid responsiveness in robot-assisted surgery, but their usefulness as goal-directed fluid t...

Deep learning-assisted flavonoid-based fluorescent sensor array for the nondestructive detection of meat freshness.

Food chemistry
Gas sensors containing indicators have been widely used in meat freshness testing. However, concerns about the toxicity of indicators have prevented their commercialization. Here, we prepared three fluorescent sensors by complexing each flavonoid (fi...

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven NO mitigation effect in constructed wetlands.

Journal of environmental management
Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also e...

Prediction of Aureococcus anophageffens using machine learning and deep learning.

Marine pollution bulletin
The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in China, leading to pronounced ecological degradation and substantial economic losses. ...

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.

Environmental science and pollution research international
Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-...