AIMC Topic: Glass

Clear Filters Showing 1 to 10 of 22 articles

Single replica spin-glass phase detection using field variation and machine learning.

PloS one
The Sherrington-Kirkpatrick (SK) spin-glass model exhibits well-studied phase transitions that are mostly established using replica-based methods. Regardless of the method used for detection, the intrinsic phase of a system exists whether or not repl...

Dual Embedding: A Fine-Tuned Language Model Approach for Accurate Polymer Glass Transition Temperature Prediction.

Journal of chemical information and modeling
Recent years have witnessed major advances in polymer informatics, yet accurately predicting polymer properties, such as the glass transition temperature (), remains a challenge. Language models like BERT have been leveraged to derive embeddings from...

Data-Driven Modeling and Design of Sustainable High Tg Polymers.

International journal of molecular sciences
This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combine...

GLASSR-Net: Glass Substrate Spectral Restoration Neural Network for Fourier Transform Infrared Microspectroscopy in the Fingerprint Region.

Analytical chemistry
Fourier transform infrared (FTIR) microspectroscopy has emerged as a pivotal pathological tool, offering informative spectral biomarkers for numerous diseases. However, the dependency on specialized infrared (IR) substrates limits effective and wides...

An explainable machine learning system for efficient use of waste glasses in durable concrete to maximise carbon credits towards net zero emissions.

Waste management (New York, N.Y.)
Recycling waste glass (WG) can be time-consuming, costly, and impractical. However, its incorporation into concrete significantly reduces environmental impact and carbon emissions. This paper introduces machine learning (ML) to civil engineering to o...

Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method.

PloS one
The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with opti...

The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized Y glass microspheres SIRT: a preliminary machine learning study.

European journal of nuclear medicine and molecular imaging
BACKGROUND: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes...

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data.

Macromolecular rapid communications
Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes ...

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

PloS one
Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP ...

AI and the transformation of industrial work: Hybrid intelligence vs double-black box effect.

Applied ergonomics
It is uncertain how the application of artificial intelligence (AI) technology transforms industrial work. We address this question from the perspective of cognitive systems, which, in this case, includes considerations of AI and process transparency...