The contemporary debate on globalization and gender equality has a strong impact on economic growth. The present study analyzes the impacts of globalization and gender parity on economic growth in the Organization of Islamic Cooperation (OIC) 47 member countries for the period (1991-2017), using System GMM panel data technique. The results of system GMM have also been empirically estimated by making two groups (viz., low-income and high-income OIC member countries from the World Bank data classification, 2019) to examine the robustness of globalization and gender parity on economic growth. The results reveal that there is a negative impact of globalization on economic growth in the overall sample of OIC countries. When estimated by decomposing low-income countries and high-income countries, globalization has a significantly positive impact on economic growth in the case of high-income OIC countries, whereas globalization slashes GDP in the case of low-income OIC countries. The study finds that there is a positive impact of gender parity (ratio of female to male labor force work participation) on economic growth. Moreover, foreign remittances, government expenditures, capital formation, and human capital are also becoming the causes of a significant increase in economic growth in OIC member countries.
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.