Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI’s capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

Bioactive materials are those that cause a number of interactions at the biomaterial-living tissue inter-face that result in the evolution of a mechanically strong association between them. For this reason, an implantable material’s bioactive behavior is highly advantageous. Silicate glasses are encouraged to be used as bioactive glasses due to their great biocompatibility and beneficial biological effects. The sol-gel method is the most effective for preparing silicate glasses because it increases the material’s bioactivity by creating pores. Glass densities are altered by the internal network connectivity between network formers and network modifiers. The increase in the composition of alkali or alkaline oxides reduces the number of bridging oxygens and increases the number of non-bridging oxygens by retaining the overall charge neutrality between the alkali or alkaline cation and oxygen anion. Higher drying temperatures increase pore densities, while the melt-quenching approach encourages the creation of higher density glasses. Band assignments for the BAG structure can be explained in detail using Fourier Transform Infrared (FTIR) and Raman spectroscopic investigations. Raman spectroscopy makes it simple to measure the concentration of the non-bridging oxygens in the silica matrix.

Total factor productivity (TFP) is essential for disentangling the determinants of economic growth, productivity, and the standard of living. Understanding the variations in TFP, however, is greatly challenging because of the many assumptions that comprise the theoretical growth framework. In this paper, we aim to explore the determinants of TFP growth for countries at different stages of information and communication technology (ICT) development. To address the endogenous nature of the associated growth variables, we implement a three-stage-least (3SLS) square panel regression to improve the efficiency and asymptomatic accuracy of the estimators. We find that transmission channels, such as financial openness and trade globalization, have contributed substantially to growth in both advanced and developing countries. However, we also discover that greater financial openness can undermine a country’s TFP growth if the financial system is not sufficiently developed. When time horizons are decomposed into pre-ICT development and post-ICT development periods, a significant crowding-out effect is observed between ICT investment and financial openness in the pre-period, implying that the allocation of resources is critical for countries in the developing stage. Trade and finance policies that are adopted by advanced and developed countries might not be ideal for underdeveloped countries. Discretion in choosing adequate policies regarding financial integration and trade liberalization is advised for these emerging countries.

The ultimate objective of the study was to investigate the effects of being landlocked on the living standards in Sub-Saharan African (SSA) countries from 1991 to 2019. Adopting the two-step estimation technique of System GMM (generalized method of moments), the study found that being landlocked has a negative and significant effect on the living standards in SSA countries when using GDP per capita as the living standard measure. Moreover, the historical living standard experiences of SSA countries have a positive and significant influence on the current living standard level. In addition, the population growth rate has a positive and significant effect on the living standards in SSA countries. On the other hand, the official exchange rate, broad money as a percentage of GDP, and inflation have a negative and significant effect on the living standards in SSA countries. Generally, the estimated result reveals the existence of a significant variation in the living standards in landlocked and coastal SSA countries. This study suggests that regional integration between landlocked and transit countries should be improved to minimize entry costs and increase access to global markets for landlocked countries. We argue that this study is of interest to landlocked and coastal countries to increase trade integration and promote the development of both groups, and it will contribute to the scarce empirical evidence.