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This paper aims to discuss the evaluation of the compressive and splitting tensile strength of concrete mixes containing different proportions of up to 20 per cent glass aggregate. Portions of sand in concretes with and without admixtures were replaced with measurements of glass aggregates.

“Glascrete” is a term used for concrete in which crushed glass is used as a substitute for all or part of the aggregates. Glass can be recycled many times without changing its properties, making it an ideal material in concrete. Overall, 144 cubes and 144 cylinders of glascretes were prepared with different admixtures and subjected to compressive and splitting tensile strength test.

A comparison with a 21-day control mix indicated that glass aggregates are replacing sand in concrete ranging from 5 to 20 per cent by volume, resulting in 3.8-10.6 per cent and 3.9-16.4 per cent fall in compressive and tensile strength, respectively. However, the use of mineral admixture improved the properties of the mixes at 3, 7, 14 and 21 days.

Cities worldwide are congested, and even those with the best waste-management system would have issues with waste disposal after the year 2030. Consequently, waste management is a current issue for cities all over the world.

This study aims to evaluate the physical properties of mortar mixes that contain different volumes of waste glass as substitutes for fine aggregate with or without additives. Mineral additives are used to improve the mechanical properties of glascrete mixes in addition to its chemical resistance by absorbing the OH⁻ ions responsible for the possible alkali-silica reaction (ASR). It also reduces the adverse effects of mix-dimensional stability. Water-reducing admixtures are used to reduce the impact of the ASR by minimizing the amount of moisture in concrete, in effect decreasing the possible expansion of any produced gel. In this research, compressive and splitting tensile strength of concrete mortar containing waste glass of limited substitutions is evaluated.

The purpose of this paper is to establish the relationship between tax fairness, isomorphic forces, strategic responses and tax compliance in Ugandan small and medium enterprises (SMEs).

This is a correlational and cross-sectional study using two respondent types, the demand (represented by the tax collecting body respondents) and supply (represented by SME respondents) sides of tax compliance, to examine perceived tax compliance in Uganda’s SMEs.

Tax fairness, isomorphic forces and strategic responses have a predictive force on tax compliance. Significant mediation effects of tax fairness and also strategic responses are found. The two respondent types perceive the study variables differently – providing an understanding of why the tax compliance puzzle has remained a burgeoning concern. For example, the tax-collecting body respondents perceived more tax fairness than SME respondents, suggesting that perceived tax fairness depends on whose “lenses” you look through.

Rather than focussing only on the importance of the rational analytical deliberation of tax fairness by taxpayers in influencing their tax compliance, the current paper shows that in addition, isomorphic forces and strategic responses establish the basis for understanding taxpayers’ compliance.

The methodology that enlists two respondent types, i.e. the supply side of tax compliance and the demand side of tax compliance, probably offers a unique way of deriving better results than previous studies.

This paper aims to develop a simple model for estimating sustainable development goals index using the capabilities of artificial neural networks.

Sustainable development has three pillars, including social, economic and environmental pillars. Three clusters corresponding to the three pillars were created by extracting sub-indices of three 2018 global reports and performing cluster analysis on the correlation matrix of sub-indices. By setting the sustainable development goals index as the target variable and selecting one indicator from each cluster as input variables, 20 artificial neural networks were run 30 times.

Artificial neural networks with seven nodes in one hidden layer can estimate sustainable development goals index by using just three inputs, including ecosystem vitality, human capital and gross national income per capita. There is an excellent similarity (>95%) between the results of the artificial neural network and the sustainable development goals index.

Instead of calculating 232 indicators for determining the value of sustainable development goals index, it is possible to use only three sub-indices, but missing 5% of precision, by using the proposed artificial neural network model.

The study provides additional information on the estimating of sustainable development and proposes a new simple method for estimating the sustainable development goals index. It just uses three sub-indices, which can be retrieved from three global reports.