Mining Problem Research Articles

Laws Concerning Mineral and Coal Mining on Community Welfare from an Environmental Ethics Perspective

This research aims to (1) identify, explain how the legal politics of establishing the revision of Law Number 4 of 2009 to become Law Number 3 of 2020 (2) analyze and provide views on the juridical implications of Law Number 3 of 2020 on community welfare from an environmental ethics perspective. The research method used is based on qualitative research with a normative juridical type of research, by collecting data using library study type techniques, legal documentation, and additional interviews in Jatam and PT Selo Argodedali. As a result of the research and analysis that has been carried out, conclusions are obtained, namely; (1) differences of opinion between mining companies who believe that the revision of Law 3/2020 provides answers to mining problems. Meanwhile, the community believes that Law 3/2020 provides losses due to mining. The 2020 Mining and Coal Law is considered unfavorable to all parties, so legal politics in the formation of the law is a statement of the wishes of the state authorities regarding the law that will apply and the direction that will be developed. In fact, this law should provide welfare for the community in accordance with the mandate of Article 33 paragraph (3) of the 1945 Constitution. (2) The 2020 Minerba Law is considered not to contain articles that uphold aspects of environmental ethics because it prioritizes exploitation and depleting nature for the interests and needs of life. without thinking about nature.

Layout and Parameter Analysis of the Cooling System with Mine Water as Cold Source in Linglong Gold Mine

As the heat hazard problem in the deep mine becomes more prominent, it is difficult to alleviate the high-temperature problem by increasing the air supply and adjusting the ventilation mode alone. In order to solve the issue of heat damage in the Linglong Gold Mine, a cooling system based on water source heat pump technology and utilizing the return air system to exhaust heat was constructed, and each system was rationally arranged in conjunction with the mine’s actual conditions. The reasonable critical threshold of cooling system parameters is then determined by numerical simulation and field application verification. The results indicate that the closed cycle adopted by the cooling system can effectively solve the problem of groundwater shortage. The heat can be discharged directly to the return air shaft to prevent the impact of secondary heat hazards. The temperature near the working face can be reduced from 35°C to below 28°C. This system effectively resolves the issues of water shortage, heat release, and cold transfer in the water source heat pump technology and provides a reference for the application of other mines.

Problems of demining and the influence of military actions on the activities of business structures and the agricultural complex of Ukraine

The paper is devoted to the analysis of mining problems of a large part of the country's territory. It highlights the impact of military operations on the agro-industrial complex and the activities of business structures. The dynamics of losses due to mining and conducting active hostilities are given. In addition, the problems of the process of demining territories in Ukraine and directions for their solution are highlighted in detail, in particular through the financing mechanism of environmental state targeted programs. According to the research of the Ukrainian Club of Agrarian Business, about 2 million hectares of fields remain in those areas that have been cleared of mines. This leads to economic costs. Thus, Ukraine loses about 800 million dollars every year due to idleness. It is also worth emphasizing the amount of damage to agricultural production and the agro-industrial complex caused by the war. According to analytical estimates of the National Bank of Ukraine, during the war, the economy loses about 50% of "unproduced" GDP. According to experts' estimates, Ukraine loses about 50 billion hryvnias every week, not including losses from destruction. The IMF estimates Ukraine's losses due to the invasion of the occupiers at 35% of GDP. A significant number of business structures of enterprises found themselves in areas where active hostilities are taking place and were forced to evacuate to safe regions or stopped working altogether. A promising task in this direction is the approval of new environmental state target programs in order to demine agricultural territories and use them for the purposes of agricultural production and the implementation of activities of business structures. This mechanism provides for various implementation tools, sources and methods of financing. The mechanism considered in the paper provides for the active cooperation of state customers, investors of state target programs and the public in the implementation of environmental state target programs.

Asteroid Mining research based on the Global Equity Evaluation Model

With the aggravation of the imbalance in world development, people pay more and more attention to global equity. In general, global equity means that the fruits of development are shared by all mankind, and the development gap between countries is reasonable, so that people of all countries can enjoy stability and happiness. Based on the principle of global equity, this paper further studies the problem of asteroid mining. First, we established a global equity evaluation model (two-level) model, which calculates development indicators from economic, scientific and technological, cultural, social and other aspects, and uses analytic hierarchical process (AHP) and entropy weight method (EWM) to determine the dimensions and weight of indicators. We then describe our vision for asteroid mining. Based on the premise that asteroid mining is managed by a unified department, an asteroid mining allocation model (three-level) is established, using the ARIMA model to predict the data, using AHP and EWM to define the weights to reflect the impact of mining on global equity. In the end, we discuss the strengths and weaknesses of our models, leaving room for further studies.

Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network

Data classification in the real world is often faced with the challenge of data imbalance, where there is asignificant difference in the number of instances among different classes. Dealing with imbalanced data isrecognized as a challenging problem in data mining, as it involves identifying minority-class data with ahigh number of errors. Therefore, the selection of unique and appropriate features for classifying data withsmaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presenceof imbalanced medical data in many real-world problems, the processing of such data has gained attentionfrom researchers. The objective of this research is to propose a method for classifying imbalanced medicaldata. In this paper, the hypothyroidism dataset from the UCI repository is used. In the feature selection stage,a support vector machine algorithm is used as a cost function, and the wrapper algorithm is employed asa search strategy to achieve an optimal subset of features. The proposed method achieves high accuracy,reaching 99.6% accuracy for data classification through the optimization of a neural network using learningautomata.

Combined PS-InSAR Technology and High-Resolution Optical Remote Sensing for Identifying Illegal Underground Mining in the Suburb of Yangquan City, Shanxi Province, China

Illegal mining is one of the biggest problems in many coal mines. With the rapid development of the economy, driven by huge economic benefits, some outlaws illegally exploit mineral resources without a mining license, which is destructive and a potential safety hazard. In order to avoid inspection by law enforcement officials, some outlaws, regardless of the cost or risk, privately and surreptitiously excavate coal mines in self-built houses. The coal resources they excavate are shallow coal resources. Because surface buildings can maintain strong and stable radar scattering characteristics over a long time series, in this study, we combined PS-InSAR technology and high-resolution optical remote sensing to extract the subsidence information of surface buildings corresponding to PS point sets and analyzed their spatiotemporal characteristics. Finally, we developed a fast and accurate method for detecting suspected illegal mining sites from building subsidence information over a larger area. We also carried out a case study using Shandi Village, a suburb of Yangquan City, Shanxi Province, China, as our research object. QuickBird-2, WorldView-2 data, and 20 PALSAR scenes were selected for the experimental research, and two illegal mining sites were detected from 29 December 2006 to 9 January 2011. By comparing our results with previous investigation data, it was found that the accuracy rate reached 40% in local areas, and the detection rate reached 66.67%. In addition, the mining periods were basically consistent. This research shows that our method is feasible and has certain engineering applicability and practical value.

Nonlinear neural-like P model for time series classification

Gated spiking neural P (GSNP) model is a novel recurrent model proposed recently, which is viewed as a variant of recurrent neural network. Time series classification (TSC) is one of the most challenging problems in data mining. In this work, based on the GSNP model, a nonlinear neural-like P (NNP) model for TSC is proposed. NNP model introduces the bidirectional mechanism and attention mechanism in deep learning, and builds nonlinear neuron modules together with GSNP to realize end-to-end learning. The neuron modules excavate the hidden features of time series to distinguish data and realize effective classification. Through evaluating on UCR Time Series Classification Archive, the classification performance of NNP model is significantly better than that of GSNP and other classification models. The NNP model can compete with advanced models on TSC task.

PENGELOLAAN PESISIR DAN KELAUTAN DALAM STUDI KASUS DAMPAK REKLAMASI PANTAI DAN TAMBANG PASIR TERHADAP EKOSISTEM LAUT DAN MASYARAKAT PESISIR

Environmental problems in Indonesia are very diverse, including those related to coastal and marine management. The main problem in coastal and marine management is that beach reclamation and sand mining activities are likely to have an impact on the environment, especially marine ecosystems and coastal communities. The existing problem of sand mining and beach reclamation has had a negative impact on coastal ecosystems and communities around the coast. The purpose of this study is to describe (1) Coastal and Marine Management in a Case Study of the Impact of Beach Reclamation and Sand Mining on Marine Ecosystems and Coastal Communities. (2) Knowing the management efforts that can be carried out in minimizing the negative impacts of sand mining and beach reclamation activities. The research location was carried out in coastal areas in Indonesia through observations from case studies of previous journals. This observation was carried out from September 2021 to December 2021. The research methodology is based on previous literature studies. The type of research used in this research is Literary Study or Library Research. The data source comes from Google Scholar and the data collection method is a qualitative method in the form of literature review (document analysis). The results of this study indicate that the existence of an overview of environmental management in the presence of mine reclamation activities has an impact on the damage caused by reclamation. The problems of sand mining and beach reclamation have had a negative impact on coastal ecosystems and communities around the coast. It also contains efforts that can be made in overcoming and minimizing environmental problems.

Virality is not enough when the audience misses the point: A Content Analysis of YouTube Comments on Environmental Documentary “Sexy Killers”

During the Indonesian presidential election campaign, a documentary on coal mining problems in Indonesia titled “ Sexy Killers” became viral and was highly engaged on YouTube in early 2019, attracting 36 million views and above 100,000 comments. The documentary also received mainstream media coverage and had such a far-reaching appeal that it swayed many voters into not voting in the election. Showing the impact of coal mining on Indonesia’s environment and its people, the documentary exposed networks of business owners and politicians who had stakes in the coal mining company. This research thus delves into understanding the public discourse, especially the high number of YouTube comments on the documentary. The analysis revealed that despite the documentary’s widespread popularity, the discussion within the comments did not focus on the environmental problems (72.9%). Most of the comments discussed the political campaign and presidential candidates (41.3%), instead of discussing the environmental impact of coal mining in Indonesia (13.9%). The research is valuable in highlighting the centrality of political debates dwarfing real environmental issues in Indonesia. Environment activists use documentary movies as one of their advocacy strategies, but it must be followed by audience engagement strategy to assure the discussions are still relevant to increase audience awareness on the issue. Keywords: Social media, environment, text analysis, YouTube, Indonesia.

Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator

Cross-domain graph few-shot learning attempts to address the prevalent data scarcity issue in graph mining problems. However, the utilization of cross-domain data induces another intractable domain shift issue which severely degrades the generalization ability of cross-domain graph few-shot learning models. The combat with the domain shift issue is hindered due to the coarse utilization of source domains and the ignorance of accessible prompts. To address these challenges, in this paper, we design a novel Cross-domain Task Coordinator to leverage a small set of labeled target domain data as prompt tasks, then model the association and discover the relevance between meta-tasks from the source domain and the prompt tasks. Based on the discovered relevance, our model achieves adaptive task selection and enables the optimization of a graph learner using the selected fine-grained meta-tasks. Extensive experiments conducted on molecular property prediction benchmarks validate the effectiveness of our proposed method by comparing it with state-of-the-art baselines.

GEOPHYSICAL STUDIES OF ROCK DISTORTION IN MINING OPERATIONS IN COMPLEX GEOLOGICAL CONDITIONS

Abstract. Geophysical methods of rock mass survey are one of the most effective ways of solving various problems in mining and are widely used in mining, gas and oil industries, as well as in science. They allow carrying out prospecting and evaluation works remotely, to reveal structural heterogeneities, cavities, contact zones of different media in rock mass with rather high accuracy. At the stage of designing a mining enterprise and making major decisions, it is necessary to have reliable information about the tectonic structure of the undermined and adjacent massif and parameters of its geodynamic activity. For this purpose, special studies of the tectonic structure of the rock massif must be carried out and the parameters of modern geodynamic movements must be determined. The most complete information about tectonic disturbances, identification of gliding surfaces, zones of macrofracturing are provided by geophysical methods of rock mass survey.In the article analysis of study of structural-tectonic structure of ore areas, separation and specification of ore-controlling structures, detection and depth mapping of ore-controlling faults, volume mapping of intrusive massifs based on previously conducted geophysical research in Zhezkazgan syncline area carried out. Geological and geophysical conditions of the central ore field (presence of rich bodies with high electric conductivity, considerable thicknesses of ore bodies, etc.) were favorable for application of various geophysical methods to search and trace rich ore bodies occurring at low depths.Moreover, possibilities of application of seismic works are considered at the solution of problems of ore geology in difficult mining-geological conditions of Kazakhstan.

Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection

Feature selection, which aims to improve the classification accuracy and reduce the size of the selected feature subset, is an important but challenging optimization problem in data mining. Particle swarm optimization (PSO) has shown promising performance in tackling feature selection problems, but still faces challenges in dealing with large-scale feature selection in Big Data environment because of the large search space. Hence, this paper proposes a bi-directional feature fixation (BDFF) framework for PSO and provides a novel idea to reduce the search space in large-scale feature selection. BDFF uses two opposite search directions to guide particles to adequately search for feature subsets with different sizes. Based on the two different search directions, BDFF can fix the selection states of some features and then focus on the others when updating particles, thus narrowing the large search space. Besides, a self-adaptive strategy is designed to help the swarm concentrate on a more promising direction for search in different stages of evolution and achieve a balance between exploration and exploitation. Experimental results on 12 widely-used public datasets show that BDFF can improve the performance of PSO on large-scale feature selection and obtain smaller feature subsets with higher classification accuracy.

Mining Frequent Infix Patterns from Concurrency-Aware Process Execution Variants

Event logs, as considered in process mining, document a large number of individual process executions. Moreover, each process execution consists of various executed activities. To cope with the vast amount of process executions in event logs, the concept of variants exists that group process executions with identical ordering relations among their executed activities. Variants are an integral concept of process mining and help process analysts explore, filter, and manage large amounts of event data. In this paper, we consider concurrency-aware variants that allow activities within a process execution to be partially ordered---the execution of individual activities can overlap in time. However, the number of variants is often vast, making it challenging for process analysts to explore event data. Therefore, we present a novel approach to frequent pattern mining from concurrency-aware variants. We show that mining frequent patterns from concurrency-aware variants can be reduced to the frequent subtree mining problem. Further, we compare our proposed algorithm to a state-of-the-art frequent subtree mining algorithm exhibiting improved performance on real-life event logs.

Disease Prediction Using Machine Learning Algorithms

bstract: The objective of this project is to develop a machine learning model that can predict the disease of a patient based on their symptoms. While data mining has been successfully applied in many areas, such as market analysis and e-commerce, the medical field still lacks powerful analytical tools to uncover hidden relationships and trends in data. Medical data contains a wealth of information, but this knowledge is often not effectively utilized. Machine learning is a field of study that involves developing algorithms that can improve automatically through experience and data. These algorithms use training data to build a model that can make predictions or decisions without being explicitly programmed. In this project, techniques such as association rule mining, classification, and clustering will be used to explore various general health problems. Classification is a crucial problem in data mining, and decision trees are a popular classifier used to create class models. The ID3 Decision Tree algorithm is commonly used for information classification. However, this algorithm can be inaccurate, so techniques such as entropy-based cross-validation and partitioning will be used to improve the accuracy of the model. Finally, the results will be compared to determine the best model. Introduction I would like to begin by highlighting the indispensability of computers in our lives. Computers are integral components in virtually every aspect of our lives today, comprising various hardware and software components. Software, which is a collection of programs designed to perform specific tasks, is an essential component of computer systems. However, software development is a complex process that involves a team of professionals, as denoted by the term "project." The term "project" is an acronym for Planning, Resource, Operating, Joint effort, Engineering, Cooperation, and Technique. Planning involves conceptualizing and identifying the necessary steps to accomplish the project. Resource refers to addressing the financial aspects and acquiring the resources required for the project. Operating entails the systematic procedure for carrying out the project tasks. Joint effort relates to the collaborative effort of individuals working towards achieving the project goals. Engineering signifies the importance of having well-educated professionals in the project team to produce optimal results. Co-operation is essential for the success and timely completion of the project. Finally, technique denotes the importance of utilizing suitable methodologies to achieve project objectives. To conclude, software development is a crucial process that requires a project-based approach that involves planning, resource acquisition, operating procedures, joint effort, engineering, cooperation, and technique. This approach ensures successful completion of software development projects.

Dynamic Optimization of Role Concepts for Role-Based Access Control Using Evolutionary Algorithms

To ensure the security of current information systems, role-based access control (RBAC) is a widely used concept. For this purpose, based on an initial assignment of permissions to users, permissions are grouped to roles, which are then assigned to users. The corresponding (NP-complete) optimization problem, the so-called role mining problem (RMP), aims at finding a minimal set of roles and a corresponding assignment of those roles to users. Previously, the RMP has been considered as a static optimization problem. However, the application of RBAC in real business use cases requires the inclusion of dynamically occurring events that reflect changes in the business environment of companies, as well as events that result from direct user interaction with the role mining process. Therefore, in this paper, we provide a comprehensive overview and classification of the most relevant events for role mining and present methods for integrating them into the framework of an evolutionary role mining algorithm. The functional performance of these methods as well as the overall performance gain of dynamic role mining compared to the static approach is then examined in a series of experiments.

Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors

Decarbonization of the mining industry on the basis of closing the energy generation, on the basis of cogeneration of coal mine methane, and on the internal consumption of the mine is a promising direction in ensuring sustainable development. Known problems of deep underground mining do not allow for realizing the potential of man-made gas reservoirs due to the deterioration of the conditions of development of reserves of georesources. The aim of the work was to improve recommendations for the substantiation of drilling parameters for undermined drainage boreholes for increasing methane production from unconventional coal-gas collectors. The authors’ approach innovation lies in the possibility of using the established patterns of better natural stability of undermined boreholes to optimize them as spatial orientation parameters in an existing drilling passport for the improvement of methane extraction productivity. For this purpose, smoothing (LOESS) of the experimental data of two similar types of wells was used; then deterministic interpolation methods in combination with a three-dimensional representation of the response function in “gnuplot” were used. As a result, it was found that the increase in the inclination angle from 40° to 60° leads to a significant transformation of the model of the studied process, accompanied by a decline in the dynamics of methane emission and a decrease in the distance of the productive work zone of this type of well from 13 to 5 m before the roof landing, which then is replaced by a sharp increase in the productive work zone up to 35 m ahead of the longwall face. This allows under specific conditions for recommending increasing the productivity of methane capex from technogenic disturbed coal-gas reservoir replacement of wells with a smaller angle of rise to the transition to a more frequent grid of clusters from wells #4.

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Induced seismicity has been a serious problem for many coal mines in the Upper Silesian Coal Basin in Poland for many decades. The occurring mining tremors of the rock mass generate seismic vibrations that cause concern to the local population and in some rare cases lead to partial damage to buildings on the surface. The estimation of peak ground acceleration values caused by high energy mining seismic tremors is an important part of seismic hazard assessment in mining areas. A specially designed bootstrapping procedure has been applied to estimate the ground motion prediction model and makes it possible to calculate the confidence intervals of these peak ground acceleration values with no assumptions about the statistical distribution of the recorded seismic data. Monte Carlo sampling with the replacement for 132 seismic records measured for mining seismic tremors exceeding 150 mm/s2 have been performed to estimate the mean peak ground acceleration values and the corresponding upper limits of 95% confidence intervals. The specially designed bootstrap procedure and obtained ground motion prediction model reflect much better the observed PGA values and therefore provide more accurate PGA estimators compared to the GMPE model from multiple regression analysis. The bootstrap analysis of recorded peak ground acceleration values of high-energy mining tremors provides significant information on the level of seismic hazard on the surface infrastructure. A new tool has been proposed that allows for more reliable determination of PGA estimators and identification in the areas in coal mines that are prone to high-energy seismic activity.

Global conformance checking measures using shallow representation and deep learning

Conformance checking refers to techniques that can compare normative process behavior, typically captured by process models, and observed process behavior, usually captured in an event log. Recently, machine and deep learning methods have been proposed to tackle multiple problems in process mining, mostly for predictive process monitoring purposes. In this work, we propose two different neural network-based conformance checking techniques, allowing for a fully data-driven approach. Concretely, two approaches for measuring fitness and precision are introduced, using techniques from representation learning and deep learning respectively. The first approach relies on computing the distances between process traces coming from both an event log and a process model by means of vector representations extracted from a shallow neural network trained in a self-supervised setting. The second approach uses a recurrent neural network trained on an event log and a log containing artificially created counterfactuals. The first setup of these methods is tested on a proof-of-concept artificial event log, a plethora of artificially generated process models, and 4 real-life data sets. Our approaches exhibit generally promising and interesting results and in this way provide fully data-driven alternatives to other conformance checking techniques, which are usually model-driven. Moreover, they allow for a possible combination of different purposes, when already using neural networks for process discovery or predictive process monitoring.

Distribution Law of Mine Ground Pressure via a Microseismic Sensor System

The particularity of the occurrence conditions of the ore body in Xianglushan Tungsten Mine determines the mining form of the ore body and the particularity of the ground pressure distribution after mining. A large number of mined-out areas, supporting pillars, and natural and human factors have formed a comprehensive disaster environment. This can lead to frequent disasters, great harm, serious economic losses, and the necessity of severe environmental protection operations in the mine. This study aims to establish a microseismic monitoring system according to the actual needs of the site and to reveal the law of ground pressure manifestation by analyzing the distribution characteristics of microseismic events; to analyze the occurrence stability of the goaf; further verify it laterally; and finally, demonstrate the feasibility and effectiveness of the microseismic monitoring sensor system. In view of the current ground pressure problem in Xiangxuoshan tungsten mine, the stress change characteristics during dynamic mining and filling were obtained through comparative analysis of different perspectives such as surface change, energy release, and mining loudness, and key areas were identified to improve the reliability of underground ground pressure monitoring. The results show that the process of deposit destabilization caused by ore body mining can be further analyzed by microseismic monitoring, and the combination of surface settlement, mining intensity, and energy release can verify the accuracy of stress distribution and ground pressure transfer. In turn, the general reliability of underground ground pressure hazard warning is empirically improved.

Struggles Against Mining in Brazil: Framing Disputes and Tensions in Civil Society.

This paper seeks to characterise the relationship between civil society and mining in Minas Gerais, Brazil, between 2000 and 2020 by observing the actions of three different groups in resisting the expansion of mining. The analysis points to the existence of a plurality of forms of engagement, organisation and ways of establishing relations between civil society and the state and the market. It also reveals tension between different ways of framing the mining problem by civil society, of posing this problem publicly and establishing ways to confront it. Three sets of actors are identified: (i) environmental NGOs, who are market-oriented; (ii) groups with looser ties who are more radical; and (iii) social movements aligned with the identities of a state-orientated traditional left. My analysis suggests that the divergence in framing the context by these three different groups hinders the construction of a substantive public debate on the mining issue in Brazil. The article is divided into three parts. First, it briefly outlines the process of mining expansion in Brazil, starting in the mid-2000s, highlighting its economic impact. Second, it considers the relationship between civil society articulation and deliberation. Third, it characterises the constitution of these different civil society groups who have established interactions with market and state actors that fostered this expansion.