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The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.

An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.

The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.

The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.

The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

In response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) LiDAR.

Leveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot’s adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this paper.

The experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.

This paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

The purpose of this paper is to present a case study the application of lean six sigma combined with mining transportation overall vehicle effectiveness (MTOVE) to improve mining transportation performance. MTOVE is a newly developed model to measure the overall effectiveness of mining transportation.

The method used is case study combines the MTOVE and LSS methodologies. Data were collected from the hauling operation during a three-month period. Various lean six-sigma tools, such as the Pareto chart, ANOVA, two sample t-tests, one sample t-test, cause-and-effect analysis and time study, have been used.

The case study resulted in improvement of vehicle overall effectiveness; a 35% increase in MTOVE value, a 17% improvement in productivity and a 9% increment in truck utilization. Statistical tests confirmed the significance of reducing the mean and variation in the hauling process cycle time, which led to productivity improvement.

This study provides practitioners with additional quantitative evidence of the potential benefits of LSS methods in the coal mining industry.

This paper practically and unquestionably has contributed to the LSS body of knowledge focused on the mining sector, which is recently still far behind the manufacturing sector. The study has demonstrated that some challenges in the mining environment can be solved through the effective implementation of LSS tools. Hence, this paper could be used as a reference for both researchers and practitioners.

The study contributes in the field of LSS spread in mining industries using a case study. This study shows practical evidence of improving overall vehicle effectiveness using LSS. Practitioners can refer to this study to understand the benefits of LSS in mining sector. Since the mining industry should also adopt the LSS principle into the mining business process due to its ability to improve business performance (Valente et al., 2020; Tupamahu et al., 2019; Zanon et al., 2021).

There has been little scientific study of the LSS implementation in the mining industry. This research provides detailed evidence of LSS implementation in the mining sector. The main contribution is the implementation framework, which shows the combination of newly developed indicators (MTOVE and LSS) to enhance hauling operation effectiveness. This paper demonstrates how LSS tools and methods can be applied in the mining transportation industry.

The paper explores the roles of involved organizations in the designation of Batu Arang in Gombak, Selangor, Malaysia, as the Coal Mining and Geological Heritage Site, the heritage significance selected as the elements of its designation and the challenges faced by the involved stakeholders during the designation process.

The primary research material is derived from the documentation review on the final draft of the Special Area Plan (RKK) of Batu Arang and the final draft of the Geopark Management Plan, field investigation on the heritage significances and interviews with the involved organizations: the Selayang Municipal Council (MPS), the State of Selangor Malay Custom and Heritage Corporation (PADAT) and the Village Community Management Council (MPKK) of Batu Arang.

This paper found that Batu Arang has the potential to be developed as an important heritage mining site and heritage tourism destination; however, many of the heritage significances are being demolished, invaded and abandoned due to human aggression or effects of nature. In addition, it reveals the roles of each involved organization, issues that occurred and challenges faced by the organizations during the designation process as a heritage site, namely in terms of management, property ownership and promotion.

The paper outlines that a heritage mining site like Batu Arang should be considered an important heritage as well as other heritage sites in Malaysia, and issues and challenges faced during the designation process should be discussed critically to ensure that these heritage significances will not be neglected and abandoned.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

This study examines the evidence of the impact of climate change on the financial performance of basic materials companies in Vietnam.

The research sample includes eighty-two basic materials companies listed on the Vietnamese stock market from 2003 to 2022. This study used one-way and two-way fixed-effects feasible generalized least squares (FGLS) estimation methods.

Climate change, measured through variables including changes in temperature, average rainfall, greenhouse gas emissions and rising sea levels, has a negative impact on the financial performance of companies in this industry. The study also found that, with rising temperatures, the financial performance of steel manufacturing companies decreased less than that of coal mining and forestry companies, but increasing greenhouse gases and rising sea levels reduced the financial performance of steel companies. We did not find evidence of any difference in the impact of climate change on the financial performance of basic materials companies before and after the UN Climate Change Conference (COP 21). This is a new finding, which is consistent with empirical studies in Vietnam and different from previous studies in that it provides new evidence on the impact of climate change on the financial performance of basic materials companies in the Vietnamese market and cross-checks the impact of climate change by sector and over time.

To the best of our knowledge, this is one of the first articles on climate change and the financial performance of basic materials companies.

This research is intended to assess the nickel smelter industry’s investment competitiveness in Indonesia and identify ways to improve its competitive advantage for the nation.

This research uses a sequential mixed-methods approach, expanding on a first qualitative phase with a second quantitative phase. Interviews are used in the qualitative phase to identify the underlying causes of issues and potential solutions to increase the competitiveness of the nickel smelter industry, while a system dynamics (SD) model is used to conduct the quantitative phase. This study uses the idea of a country’s competitive advantage from Porter’s diamond model (PDM). The model was tested and validated using SD simulation resulting in a new policy scenario, which was evaluated in metallurgy expert conferences and high policymaker discussion forums.

The results reveal the complexity of the nickel smelter industry in Indonesia and conclude that the integrated export duty beneficence policy is the most effective way to boost competitiveness. This policy gives a significant improvement both in the number of smelters and state revenue compared to the current policy. The industry’s investment competitiveness is enhanced by the six factors of the diamond model, with the first three factors being integrated strategy, limited export of excess production and export duty beneficence, while the remaining factors are metal price fluctuation, domestic demand and mineral supply which are related to mining conditions uncertainty.

The research creates a SD model to support Indonesia’s competitive advantage in the smelter industry. Despite limitations like interpretations and distorted semantic analysis, it provides a useful framework for exploring complex industry themes, excluding social factors due to limited data and knowledge requirements.

The findings of this research offer a framework for policymaking by the government to enhance the competitiveness of investments in Indonesia’s nickel smelter industry.

This study delves into Indonesia’s nickel industry competitiveness using PDM. Using a more detailed SD model with quantitative analysis, it goes beyond strategy development to provide a comprehensive approach to the nickel smelter industry.

The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.

The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.

The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.

This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.