The United Nations General Assembly established the Sustainable Development Goals in 2015 to achieve an equitable and sustainable future for all by 2030. This study aims to model the relationship between government revenue per capita, quality of governance and the targets of several of these goals, including the coverage of the critical determinants of health; water, sanitation, healthcare, and education. We used government revenue because the policies and practices of international and multinational organisations - including corporations and banks - are more likely to influence revenue rather than government spending in countries in which they are engaged. Also, government revenue reflects a government's ability to spend across all sectors rather than just health or education. An unbalanced non-linear panel data model was employed, and annual data on 217 countries over the period 1960-2000 was used. The coverage of the Sustainable Development Goal variables was expressed as percentages and measures of the quality of governance included in the model. A linear relationship between revenue and the determinants of health would not be appropriate; therefore, we employ a logistic function. A standard panel logistic function would impose the same shape “S” curve on all countries, which is inappropriate. Therefore, we augment the parameters of the logistic function with measures of the quality of governance in each country, which allows each country to have a different “S” shape as the quality of its governance varies. Our study found that increased government revenue is associated with increased progress towards the Sustainable Development Goals. An improvement in the quality of governance could amplify this effect. This modelling and its accompanying visualisations can predict the potential of an increase in government revenue in an individual country regarding progress towards the Sustainable Development Goals.

In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.

Studies were performed on two species of fish from family Chaenichthyidae and on seven species of family Nototheniidae. Statistical analysis of the subcutaneous and trunk muscle fibres diameter from the trunk and tail area allowed to state that the thickness of these fibres is inversely proportional to the metabolism level of fish. The inter-species similarities were found, but they were not found within families, if their representatives differed in metabolic level.