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Fulltext Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Nafees A, Javed MF, Khan S, Nazir K, Farooq F, Aslam F, et al.

Materials (Basel), 2021 Dec 08;14(24).
PMID: 34947124 DOI: 10.3390/ma14247531

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases' features to promote the usage of green concrete.
Association between lung function and dyspnoea and its variation in the multinational Burden of Obstructive Lung Disease (BOLD) study

Müller A, Wouters EF, Koul P, Welte T, Harrabi I, Rashid A, et al.

Pulmonology, 2024 Apr 13.
PMID: 38614859 DOI: 10.1016/j.pulmoe.2024.03.005

BACKGROUND: Dyspnoea is a common symptom of respiratory disease. However, data on its prevalence in general populations and its association with lung function are limited and are mainly from high-income countries. The aims of this study were to estimate the prevalence of dyspnoea across several world regions, and to investigate the association of dyspnoea with lung function.
METHODS: Dyspnoea was assessed, and lung function measured in 25,806 adult participants of the multinational Burden of Obstructive Lung Disease study. Dyspnoea was defined as ≥2 on the modified Medical Research Council (mMRC) dyspnoea scale. The prevalence of dyspnoea was estimated for each of the study sites and compared across countries and world regions. Multivariable logistic regression was used to assess the association of dyspnoea with lung function in each site. Results were then pooled using random-effects meta-analysis.
RESULTS: The prevalence of dyspnoea varied widely across sites without a clear geographical pattern. The mean prevalence of dyspnoea was 13.7 % (SD=8.2 %), ranging from 0 % in Mysore (India) to 28.8 % in Nampicuan-Talugtug (Philippines). Dyspnoea was strongly associated with both spirometry restriction (FVC
Fulltext Prevalence and Population Attributable Risk for Chronic Airflow Obstruction in a Large Multinational Study

Burney P, Patel J, Minelli C, Gnatiuc L, Amaral AFS, Kocabaş A, et al.

Am J Respir Crit Care Med, 2020 Nov 10.
PMID: 33171069 DOI: 10.1164/rccm.202005-1990OC

Rationale: The Global Burden of Disease programme identified smoking, and ambient and household air pollution as the main drivers of death and disability from Chronic Obstructive Pulmonary Disease (COPD). Objective: To estimate the attributable risk of chronic airflow obstruction (CAO), a quantifiable characteristic of COPD, due to several risk factors. Methods: The Burden of Obstructive Lung Disease study is a cross-sectional study of adults, aged≥40, in a globally distributed sample of 41 urban and rural sites. Based on data from 28,459 participants, we estimated the prevalence of CAO, defined as a post-bronchodilator one-second forced expiratory volume to forced vital capacity ratio < lower limit of normal, and the relative risks associated with different risk factors. Local RR were estimated using a Bayesian hierarchical model borrowing information from across sites. From these RR and the prevalence of risk factors, we estimated local Population Attributable Risks (PAR). Measurements and Main Results: Mean prevalence of CAO was 11.2% in men and 8.6% in women. Mean PAR for smoking was 5.1% in men and 2.2% in women. The next most influential risk factors were poor education levels, working in a dusty job for ≥10 years, low body mass index (BMI), and a history of tuberculosis. The risk of CAO attributable to the different risk factors varied across sites. Conclusions: While smoking remains the most important risk factor for CAO, in some areas poor education, low BMI and passive smoking are of greater importance. Dusty occupations and tuberculosis are important risk factors at some sites.

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