The mining industry significantly impacts the three pillars of sustainable development: the economy, the environment, and society. Therefore, it is essential to incorporate sustainability principles into operational practices. Organizations can accomplish this through knowledge management activities and diverse knowledge resources. A study of 300 employees from two of the largest mining corporations in South Kalimantan, Indonesia, found that four out of five elements of knowledge management—green knowledge acquisition, green knowledge storage, green knowledge application, and green knowledge creation—have a direct impact on the sustainability of businesses. The calculation was determined using Structural Equation Modelling (SEM). However, the study also found that the influence of collectivist cultural norms inhibits the direct effect of green knowledge sharing on corporate sustainable development. The finding suggests that companies operating in collectivist cultures may need to take additional measures to encourage knowledge sharing, such as rewarding employees for sharing their expertise on green initiatives, supportive organizational culture, clear expectations, and opportunities for social interaction.

Data mining technology is a product of the development of the new era. Unlike other similar technologies, data mining technology is mainly committed to solving various application problems, and the main means of solving problems are to use big data technology and machine learning algorithms. Simply put, data mining technology is like panning for gold in the sand, searching for useful information among massive amounts of information. Data mining technology is widely applied in various fields, such as scientific research and business, and also has its shadow in the education industry. Currently, major universities are applying data mining technology to teaching quality evaluation. This article first explains the impact of data mining technology on the education industry, and then specifically discusses the application of data mining technology in the evaluation of teaching quality in universities.

The mining sector faces a complex dilemma as an economic development agent through social upliftment in places where mining corporations operate. Resource extraction is destructive and non-renewable, making it dirty and unsustainable. To ensure corporate sustainability, this paper examines the effects of knowledge management (KM), organizational learning (OL), and innovation capability (IC) on Indonesian coal mining’s organizational performance (OP). We used factor and path analysis to examine the relationships between the above constructs. After forming a conceptual model, principal component analysis validated the factor structure of a collection of observed variables. Path analysis examined the theories. The hypothesized framework was confirmed, indicating a positive association between constructs. However, due to mining industry peculiarities, IC does not affect organizational performance (OP). This study supports the importance of utilizing people and their relevant skills to improve operational performance. The findings have implications for managers of coal mining enterprises, as they suggest that KM and OL are critical drivers of OP. Managers should focus on creating an environment that facilitates knowledge sharing and learning, as this will help improve their organizations’ performance.

Under the background of the development of the network information age, the current Internet industry has obtained more development opportunities, but it has also brought corresponding challenges in the process of wide application. In the development and construction of modernization, society pays more attention to the supervision and determination of the characteristics of online public opinion. From the perspective of the current characteristics of network public opinion, because social information is more extensive and involves many fields, network public opinion has a high degree of complexity and diffusion. Therefore, it is necessary to strengthen the analysis and application of relevant data mining systems in order to achieve efficient management of network public opinion. The key to the disadvantage of the traditional excavation of public opinion communication characteristics lies in the lag of the excavation process, and it is difficult to deal with malignant public opinion in a timely and effective manner. Therefore, in order to truly solve the lagging problem of public opinion data dissemination feature mining technology, it is necessary to strengthen the application of artificial intelligence technology in it.

The influence of mining activity on the environment on the environment belongs to the most negative industrial influences. Mine subsidence on the surface can be a result of many deep underground mining activities. The present study offers the theory to the specific case of the deformation vectors solution in a case of disruption of the data homogeneity of the geodetic network structure in the monitoring station during periodical measurements in mine subsidence. The theory was developed for the mine subsidence at the abandoned magnesite mine of Košice-Bankov near the city of Košice in East Slovakia. The outputs from the deformation survey were implemented into geographical information system (GIS) applications to a process of gradual reclamation of whole mining landscape in the magnesite mine vicinity. After completion of the mining operations and liquidation of the mine company, it was necessary to determine the exact edges of the mine subsidence of Košice-Bankov with the zones of residual ground motion in order to implement a comprehensive reclamation of the devastated mining landscape. Requirement of knowledge about stability of the former mine subsidence was necessary for starting the reclamation work. Outputs from the present specific solutions of the deformation vectors confirmed the multi-year stability of the mine subsidence in the area of interest. Some numerical and graphical results from the deformation vectors survey in the abandoned magnesite mine of Košice-Bankov are presented. The obtained results were transformed into GIS for the needs of the municipality of Košice City to the implementation of the reclamation activities in the mining territory of Košice-Bankov.

The rare earth mining area in South China is the main production base of ionic rare earth in the world, which has brought inestimable economic value to the local area and even the whole nation. However, due to the lack of mining technology and excessive pursuit for economic profits, a series of environmental problems have arisen, which is a great threat to the ecosystem of the mining area. Taking Lingbei rare earth mining area in Ganzhou as an example, this paper discriminated and analyzed such aspects as the ecological source, ecological corridor and ecological nodes of the mining area based on the landscape ecological security pattern theory and the minimum cumulative resistance model (MCR) method, and constructed a landscape ecological security pattern of the mining area during the 2009, 2013 and 2018. The results show that: i) The patch area of the ecological source of rare earth mining area is small, mainly concentrated in the east and west sides of the mining area. ii) During the selected year, the ecological source area, ecological corridors, radiation channels and the number of ecological nodes in the rare earth mining area are increasing, indicating that the landscape ecological security of the rare earth mining area has been improved to some extent, but it remains necessary for relevant departments to make a optimized planning to further reconstruct the ecological security pattern of the rare earth mining area.