AIMC Topic: Temperature

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Climate and traits drive bark decomposition patterns at global scale.

Nature communications
Tree bark represents a large global carbon stock, comprising 2-20 % of woody biomass, and plays a distinct role in carbon and nutrient cycling. It is poorly understood how different abiotic and biotic drivers contribute to bark decomposition globally...

Direct-Ink-Writing Multifunctional Flexible Robotic Electronic Skin.

ACS applied materials & interfaces
Electronic skin for robotics often suffers from limited scalability, high fabrication costs, and inflexible designs, restricting its ability to support multimodal sensing and customization. To address these challenges, we propose a robotic e-skin fab...

Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars.

PloS one
This study presents a statistical approach to accurately predict the effective temperatures of pre-main sequence stars, which are necessary for determining stellar ages using the isochrone methodology and cutting-age starspots-dependent models. By tr...

A novel prediction approach of three-dimensional thermal fatigue cracks in thermal compression bonding electrodes based on digital twin.

PloS one
Thermal compression bonding (TCB) electrodes that initiate thermal fatigue cracks compromise reliability and takt time in electronic manufacturing, and accurate prediction of three-dimensional (3D) electrode cracks is a prerequisite for crack mitigat...

Deconvoluting Biophysical Factors that Influence Long-Term Aggregation Rates of High-Concentration Monoclonal Antibody Formulations.

Molecular pharmaceutics
Efficient determination of developable protein drug candidates and stable solution conditions is a key challenge in industrial drug development. Protein aggregation is difficult to predict and can lead to challenges in manufacturing, storage, and pat...

Unveiling and interpreting the relationships among multi-pollutant emission factors in municipal solid waste incineration by machine learning.

Waste management (New York, N.Y.)
Effective control of key parameters is critical for regulating pollutant emissions in municipal solid waste incineration (MSWI), but existing research on these parameters remains limited and lacks comprehensiveness. This study used over 600,000 indus...

Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing.

Scientific reports
Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study de...

Computational analysis on the influence of pressure and temperature on drug solubility in supercritical CO with machine learning and optimizer.

Scientific reports
Machine learning models can be applied for estimation of continuous manufacturing parameters in pharmaceutical processing of oral-solid formulations. Development of Quality by Design (QbD) has motivated the pharmaceutical sector to move towards conti...

From fixed points to optimum regions: AI-NSGA-II framework for high-recovery, low-energy brackish water RO.

Water research
Escalating global freshwater scarcity demands more energy-efficient and sustainable brackish water reverse osmosis (BWRO) desalination. This study demonstrates how integrating high-fidelity Artificial Neural Network (ANN) surrogates with a robust Non...

Modeling and forecasting vibrio vulnificus concentration of long-range dependence on marine environmental conditions.

Water research
Vibrio vulnificus (vvh) is an epidemiologically significant bacterium that naturally occurs in coastal waters under favorable environmental conditions and causes one of the highest mortality rates among known foodborne pathogens. Little is currently ...