Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis

Arifuzzaman, Md; Gazder, Uneb; Alam, Shah; Şirin, Okan; Mamun, A. A.

doi:10.1155/2019/3183050

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…[ 17a,b ] As identified, the δ h had the highest positive impact and δ d had the lowest positive impact on CED. [ 18 ] Therefore, it is imperative to put much more emphasis on Parameter δ h to predict the adhesive property of the peptoid type.…”

Section: Resultsmentioning

confidence: 99%

Sequence‐Defined Peptoids with OH and COOH Groups As Binders to Reduce Cracks of Si Nanoparticles of Lithium‐Ion Batteries

Zhang

Luo

et al. 2020

Advanced Science

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Silicone (Si) is one type of anode materials with intriguingly high theoretical capacity. However, the severe volume change associated with the repeated lithiation and delithiation processes hampers the mechanical/electrical integrity of Si anodes and hence reduces the battery's cycle‐life. To address this issue, sequence‐defined peptoids are designed and fabricated with two tailored functional groups, “OH” and “COOH”, as cross‐linkable polymeric binders for Si anodes of LIBs. Experimental results show that both the capacity and stability of such peptoids‐bound Si anodes can be significantly improved due to the decreased cracks of Si nanoparticles. Particularly, the 15‐mer peptoid binder in Si anode can result in a much higher reversible capacity (ca. 3110 mAh g−1) after 500 cycles at 1.0 A g−1 compared to other reported binders in literature. According to the density functional theory (DFT) calculations, it is the functional groups presented on the side chains of peptoids that facilitate the formation of Si−O binding efficiency and robustness, and then maintain the integrity of the Si anode. The sequence‐designed polymers can act as a new platform for understanding the interactions between binders and Si anode materials, and promote the realization of high‐performance batteries.

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Section: Resultsmentioning

confidence: 99%

Sequence‐Defined Peptoids with OH and COOH Groups As Binders to Reduce Cracks of Si Nanoparticles of Lithium‐Ion Batteries

Zhang

Luo

et al. 2020

Advanced Science

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“…It creates a set of logical rules, the response to which determines the split in the dataset. The advantages of CART algorithm include fast processing of data and easy interpretation of the algorithm [ 31 – 33 ].…”

Section: Methodsmentioning

confidence: 99%

Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India

2021

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Background Machine learning (ML) algorithms have been successfully employed for prediction of outcomes in clinical research. In this study, we have explored the application of ML-based algorithms to predict cause of death (CoD) from verbal autopsy records available through the Million Death Study (MDS). Methods From MDS, 18826 unique childhood deaths at ages 1–59 months during the time period 2004–13 were selected for generating the prediction models of which over 70% of deaths were caused by six infectious diseases (pneumonia, diarrhoeal diseases, malaria, fever of unknown origin, meningitis/encephalitis, and measles). Six popular ML-based algorithms such as support vector machine, gradient boosting modeling, C5.0, artificial neural network, k-nearest neighbor, classification and regression tree were used for building the CoD prediction models. Results SVM algorithm was the best performer with a prediction accuracy of over 0.8. The highest accuracy was found for diarrhoeal diseases (accuracy = 0.97) and the lowest was for meningitis/encephalitis (accuracy = 0.80). The top signs/symptoms for classification of these CoDs were also extracted for each of the diseases. A combination of signs/symptoms presented by the deceased individual can effectively lead to the CoD diagnosis. Conclusions Overall, this study affirms that verbal autopsy tools are efficient in CoD diagnosis and that automated classification parameters captured through ML could be added to verbal autopsies to improve classification of causes of death.

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“…Describing as multidimensional nanomaterial having at least one dimension between 1 and 100 nm. With characteristic that presents nanoparticle with different conventional material due to higher proportions between surfaces, volumes and sheet of nanometer dimension [56].…”

Section: Types Of Multidimensional Nanomaterials In Asphaltsmentioning

confidence: 99%

Resistance to Aging of Asphalt Modified with Multidimensional Nanomaterials: A literary Review

Pérez

Horna²,

Pastor³

2022

Period. Polytech. Civil Eng.

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Multidimensional nanomaterials, is a resource that has been formed over time becoming a revolutionary technology with a transcendent character providing the use in asphalt construction procedures, benefiting in the aspects of resistance in the environments to the field of civil engineering, in the same way it is a way in the advance for a better conservation of horizontal works with a favorable impact on the materials used, helping to preserve the care of the environment. In such a way that, in this article, a systematic review is reflected, around the exhaustive research in the databases such as Scopus and Science Direct, compiling a sum of 85 articles, which are indexed in journals from 2017 to 2021, carried out correspondingly on the different investigations of the management of multidimensional nanomaterials in asphalts. The objective of this literary review is to transmit various research by authors such as: the impact on the life cycle of asphalt, the performance it produces, the profitability and the advantage of the teaching that multidimensional nanomaterials have left, to later show results that show a growth in yields in asphalts through the application of multidimensional nanomaterials, So that it presents greater efficiency in the resistance to aging of the asphalt it is concluded that the nanomaterials reduce the environmental impact caused by the field of the constructions, improving the anti-aging capacity of the asphalt.

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Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis

Cited by 17 publications

References 43 publications

Sequence‐Defined Peptoids with OH and COOH Groups As Binders to Reduce Cracks of Si Nanoparticles of Lithium‐Ion Batteries

Sequence‐Defined Peptoids with OH and COOH Groups As Binders to Reduce Cracks of Si Nanoparticles of Lithium‐Ion Batteries

Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India

Resistance to Aging of Asphalt Modified with Multidimensional Nanomaterials: A literary Review

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