Rational design of high‐performance epoxy/expandable microsphere foam with outstanding mechanical, thermal, and dielectric properties

Tang, Haiwen; Li, Yi; Zhu, Ziyi; Zhan, Yingqing; Li, Ying; Li, Kui; Wang, Pan; Zhong, Fei; Feng, Wei; Yang, Xulin

doi:10.1002/app.55502

Cited by 21 publications

(5 citation statements)

References 50 publications

(125 reference statements)

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“…The data curve is flatter than the normal distribution curve when kurtosis is negative and more peaked than when it is positive. Conversely, the data distribution is normal with a medium peak on zero kurtosis value [79][80][81] . A skewness value of exactly zero indicates asymmetry, whereas positive and negative values indicate that the probability distribution curve's tail is stretched to the right and left of the curve, respectively 82 .…”

Section: Methodology Source and Features Of Literature Datamentioning

confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

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The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

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Section: Methodology Source and Features Of Literature Datamentioning

confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…As the absolute value of the r decreases, the correlation between two parameters also decreases, and vice versa. Smith 102 suggests that a r exceeding 0.80 indicates a strong relationship between two parameters. This type of issue is often referred to as the multi-collinearity problem 98 .…”

Section: Methodsmentioning

confidence: 99%

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Fawad,

Alabduljabbar,

Farooq

et al. 2024

Sci Rep

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Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

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“…At this point, we may use integral inequality (16) to obtain the value of each integral on the right side of (61):…”

Section: Theorem 10 Letmentioning

confidence: 99%

“…Once more, we may obtain the following relation by using integral inequality (16) for the first two integrals on the right-hand side of (70):…”

Section: Theorem 10 Letmentioning

confidence: 99%

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A New Class of Coordinated Non-Convex Fuzzy-Number-Valued Mappings with Related Inequalities and Their Applications

Rakhmangulov,

Aljohani,

Mubaraki

et al. 2024

Axioms

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Both theoretical and applied mathematics depend heavily on integral inequalities with generalized convexity. Because of its many applications, the theory of integral inequalities is currently one of the areas of mathematics that is evolving at the fastest pace. In this paper, based on fuzzy Aumann’s integral theory, the Hermite–Hadamard’s type inequalities are introduced for a newly defined class of nonconvex functions, which is known as U·D preinvex fuzzy number-valued mappings (U·D preinvex F·N·V·Ms) on coordinates. Some Pachpatte-type inequalities are also established for the product of two U·D preinvex F·N·V·Ms, and some Hermite–Hadamard–Fejér-type inequalities are also acquired via fuzzy Aumann’s integrals. Additionally, several new generalized inequalities are also obtained for the special situations of the parameters. Additionally, some of the interesting remarks are provided to acquire the classical and new exceptional cases that can be considered as applications of the main outcomes. Lastly, a few suggested uses for these inequalities in numerical integration are made.

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Rational design of high‐performance epoxy/expandable microsphere foam with outstanding mechanical, thermal, and dielectric properties

Cited by 21 publications

References 50 publications

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

A New Class of Coordinated Non-Convex Fuzzy-Number-Valued Mappings with Related Inequalities and Their Applications

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