Powerful Mining Research Articles

Influence of roof confined water drainage on stress distribution in coal seam

Coal and gas outbursts, along with rock bursts, are common dynamic disasters in coal mining, threatening the safety and green production of coal mines. The distribution of coal seam stress is an external factor contributing to coal mine power disasters. The drainage of confined water in coal seam roof is the primary factor of coal seam stress changes. Using theoretical analysis and numerical simulations, the influence of roof confined water on coal seam stress by looking at the law of stress and stress conservation transfer in the drainage layer is investigated. Our results show that: (1) after drainage, there is an increase in the local stress within the coal seam, with a stress concentration factor of 1.35. The influence range of theoretically calculated stress is consistent with the measured results. (2) the maximum value of drainage boundary stress correlated positively with the angle of the water-rich abnormal area, with a linear fitting of R2 = 0.98. These findings hold significant theoretical and practical implications for preventing and controlling dynamic disasters during the mining of water-rich coal seams.

Exploration of Data Mining Techniques in Business Decision-Making

This research examines the use of data mining techniques in business decision-making. By analyzing various data mining methods such as classification, clustering, and association, the study demonstrates how data mining can enhance operational efficiency and marketing strategies. The literature review provides insights into the practical applications and benefits of data mining across different industries. The research highlights the potential of data mining to uncover hidden patterns and trends in large datasets, which can be used to make more informed and timely business decisions. Additionally, the study identifies key challenges in implementing data mining, such as data integration, selecting appropriate algorithms, and interpreting results. The findings are expected to offer practical guidance for companies aiming to leverage data mining in their operations. By understanding the advantages and applications of data mining, businesses can improve their decision-making processes, optimize resource allocation, and develop more effective strategies. This research serves as a valuable resource for organizations looking to harness the power of data mining to gain a competitive edge in the market.

Blockchain Double Spending With Low Mining Power and Network Delays

Blockchain Double Spending With Low Mining Power and Network Delays

Exploring the power of data mining for uncovering traditional medicinal plant knowledge: A case study in Shahrbabak, Iran.

The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants' mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70-30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants.

Analysis of mechanical response mechanism and energy evolution characteristics of saturated coal with a single pre-existing hole

It is essential to elucidate the mechanical mechanism of water injection and large-diameter drilling pressure relief measures on coal rock deformation and failure. This helps coal mines adopt more precise and effective dynamic disaster prevention and control measures. To obtain the mechanical response mechanism and energy evolution characteristics of saturated coal with a single pre-existing hole, uniaxial compression tests was carried out, and the deformation and failure process of coal samples were monitored by high-speed camera and acoustic emission monitoring systems. It is found that water saturation, single pre-existing hole measures, and their coupling effect resulted in a deterioration in the coal sample's strength by approximately 23.49%, 9.47%, and 47.95% respectively. The final failure mode of coal samples with different measures is shear failure. The law of fracture development and expansion and energy accumulation and release in different coal samples is roughly the same, the speed of energy accumulation and release in different stages is different. Water saturation and single pre-existing hole measures can affect the impact risk of coal samples, and water saturation can significantly reduce the impact tendency of coal samples, and reduce the impact energy index of complete coal samples by 56.59%. The impact tendency index of natural coal sample is slightly increased by single pre-existing hole measure, and the impact energy index of intact coal sample is reduced by 46.78% by the coupling of the two measures. These findings can provide technical support for coal mine power disaster prevention and control.

Experimental Study on Surface Segregation Law of Three-Dimensional Accumulation Body of Sand and Stone Particles by Binary Mechanism

Sand and stone materials are one of the important raw materials for infrastructure construction. In recent years, natural sand resources have decreased rapidly. In order to protect rivers and DAMS and ecological balance, local governments have increased the control power of natural sand mining, and replacing natural sand with mechanically broken parent rock formation mechanism sand has become an inevitable trend of the industry development. During the crushing process of parent rock, sand particles of different particle size range are produced, and particle segregation occurs in the process of stacking in the yard, which brings great trouble to the calculation of particle grading and material sample detection, and hinders the large-scale application of machine-made sand. In this paper, three-dimensional stacking tests of irregular particles were carried out to identify the particle distribution on the surface of the stack by digital image technology, analyze the relationship between particle size composition of particle mixture and the particle distribution of the accumulation body, and study the distribution law and segregation characteristics of the accumulation body. The experimental results show that the mixture with different coarse particle content will produce the corresponding distribution on the surface of the accumulation body after the accumulation. On the surface of the accretion body, the content of coarse particles is vertically distributed along the height of the accretion body. The content of coarse particles in the lower part of the accretion body is greater than that in the upper part of the accretion body. The overall segregation coefficient on the surface of the pile decreases with the increase of coarse particle content, and the variation is the largest in the lower part of the pile surface with the initial coarse particle content. Based on the test results in this paper, it provides a basis for formulating the sampling and inspection method of machine-made sand, ensuring the quality of machine-made sand and improving the utilization rate of machine-made sand.

Обеспечение безопасности труда горнорабочих совершенствованием электрических сетей при подземной добыче руд

In recent years, more attention has been paid to the increase in energy consumption by mining enterprises operating mines due to the need to replace the installed electric equipment and implement automated process management systems in mining and transport areas of the mine, which ultimately ensures safe and faultless operation of power supply system at various voltage levels in both individual subdivisions and the entire mine. During the experimental studies and solving problems, safe conditions for maintaining electric installations of the mine and maintenance personnel will be ensured. Weaknesses of the grid insulation and its elements have been detected; stable values for the resistance of motor insulation, switching equipment, laid cables, and the relevant probability distribution densities have been provided. This enables the replacement of individual unstable sectors of the power grid with new cables and equipment, which will significantly improve the safety of mining operations in the conditions of a high-altitude mine. It has been established that the motors used as actuators for scraper winches and fans must be replaced with motors of similar power equipped with organosilicon insulation or filling of frontal parts with enamel paint. The conducted studies enable the development of a comprehensive inspection methodology of the mine power supply system’s insulation elements and its application for further studies at other mines.

MediTrade

MediTrade represents a paradigm shift in healthcare provision through its data-driven approach and personalized care solutions. By harnessing the power of data mining and machine learning, MediTrade offers a comprehensive platform that caters to individual health needs. Through precise disease prediction algorithms and accurate prescription management, MediTrade ensures that users receive tailored healthcare recommendations based on their unique symptoms and medical history. This not only improves the accuracy of diagnoses but also enhances the overall patient experience by providing timely interventions and proactive health management tools. Furthermore, MediTrade integrates advanced technologies to streamline medicine procurement and prescription refills, making healthcare more accessible and convenient for users. The platform's user-friendly interface and intuitive design facilitate seamless navigation, empowering individuals to take control of their health journey. By bridging the gap between patients and healthcare professionals, MediTrade fosters a collaborative healthcare ecosystem that prioritizes personalized care, preventive measures, and holistic well-being Key Words: Social Media, COVID, Customer, E-Pharmacies

Gaslighting Australia: The Instrumental Power of Australia's Mining and Energy Industries

Australia has been harshly criticised for inaction on climate change. Previous Australian governments adopted an adversarial stance to counter criticism, notably at the United Nations Framework Convention on Climate Change (UNFCCC) 26th Conference of the Parties (COP26) in 2021. How had Australia come to be in this position? We demonstrate it was not through inaction, but proactive government support for the interests of the mining and energy industries. Applying a three faces of power framework, we identify the relations these industries have with government as the reason why support was provided. Crucially, we show that it is not their structural power due to controlling underlying economic relations that explains their power. It is their ability to instrumentally command public subsidies and policy support. The discourse around economic benefits, propounded by both government and the industries themselves, served as a “smokescreen” to hide this reality. Far from being too big to fail or indispensable, we conclude that it is more accurate to say that instrumental power was successfully deployed to produce policy protections and “rivers of gold” in public funding.

A randomized blockchain consensus algorithm for enhancing security in health insurance

Health insurance fraud is a significant problem affecting insurance providers and policyholders. To address the rising problem of fraudulent activities in the health insurance sector, this paper proposes a pioneering blockchain-based system aimed at increasing transparency and security. Utilizing a hybrid Blockchain architecture, the system incorporates a consensus algorithm influenced by practical byzantine fault tolerance (PBFT) and proof of activity (PoA) to ensure reliability and efficiency in distributing mining power. Developed using Python, extensive testing confirms the system's performance and security metrics. Results show that a block size containing one transaction is 1.63 KB, with 1.2 KB for data and 0.43 KB for identification and hashing. Operational tests demonstrate that a single participant can upload 850 transactions to the transaction pool, with validation completed in just 7.49 seconds. Block appending time for these transactions is a swift 10 seconds. Notably, the system exhibits resilience against data tampering, detecting unauthorized changes within 881.3 milliseconds across 10,000 blocks and identifying irregularities in the transaction pool within 8.78 seconds. Additionally, to enhance data privacy, patient information is accessible only through a unique QR code, providing an extra layer of security; this research represents a significant advancement in combatting fraud and safeguarding data privacy.

Assessing the efficiency of measures to enhance electric power quality in variable-frequency drive for scraper conveyors

The intensive implementation of variable-frequency drive machines and installations in underground mining processes necessitates addressing several issues, with a primary focus on ensuring the quality of electric power. Elevating the energy resource of mining machines and enhancing the energy efficiency of mining operations requires maintaining the rated indicators of electric power quality in mine power distribution systems. Achieving this involves assessing the level and composition of higher harmonic components in voltage and current within power circuits equipped with variable-frequency drives (VFD). Key objectives encompass the development of a simulation model based on the equivalent diagram of the power distribution system substitution with a scraper conveyor VFD to scrutinize the spectral composition of higher harmonic components in the power circuits of the mine power distribution system (MPDS). Additionally, the study involves analyzing the impact of harmonic filters (HFs), reactors, and sine filters on the quality of electric power in the VFD system of a scraper conveyor. Further analysis extends to the spectral composition of higher harmonic components in circuits related to insulation leakage and metering circuits of the residual-current device. Practical recommendations for improving electric power quality in the VFD system of a scraper conveyor are then developed based on the research findings. The established model of a variable-frequency drive system for scraper conveyors facilitates the assessment of the effectiveness of electric power quality improvement measures. The harmonic composition of voltage and current in the mine power distribution system is determined under maximum distortion conditions and in the presence of HFs, reactors, and sine filters. Research methods are chosen to unveil the spectral composition of voltage and current in symmetrical and single-phase modes of insulation leakage, as well as in metering circuits of residual-current devices (RCDs). It is noted that the harmonic composition of leakage voltage and current is primarily influenced by the parameters of the output voltage modulated by the autonomous frequency converter inverter. Considering the high level of harmonic components in voltage and current, adjustments to RCD settings, capacitive current compensator, and the protective shunting unit are recommended for electrical safety. The study emphasizes the importance of scientifically substantiating the rated indicators of higher harmonic components for leakage circuits and further exploring the physiological effects of higher current harmonics on the human body. The feasibility of installing a harmonic filter (HF) directly on the low-voltage supply section of a scraper conveyor should be technically justified. Interestingly, the presence of HFs, reactors, and sine filters does not significantly impact the harmonic composition or the magnitudes of coefficients of the harmonic components in the phase voltage of the system concerning ground and leakage currents through insulation. However, higher harmonic components induced in leakage current circuits may pose a potential hazard, leading to a violation of magnetic compatibility and posing risks in case of contact with live parts of electrical equipment.

Исследование системы электропотребления угольной шахты

The article is devoted to the study of information sources of the power consumption system of a coal mine. All parameters and the state of structural elements of the power supply network can serve as sources of information. The process of transmitting information about these functional changes is an information system of the mine distribution network, which can serve as an information base for monitoring and controlling the process of mine power consumption. Other sources of information are daily schedules of electrical load for power supply centers, energy-intensive installations and the mine as a whole. Obtaining quantitative characteristics of information sources of the coal mine power consumption system is the main task of the conducted research

A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier

Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study's problem formulation offers a common framework for addressing similar challenges across various domains.

Low Grade Coal Quality With Up Graded Brown Coal Technology

The aim of this research is to increase the calorific value of coal. In this way, coal which has a calorific value of 3500 kCal/kg can be increased to 6500-7000 kCal/kg. Apart from that, by carrying out this up grading, a number of volatile meters will disappear as a result of the heating and pressure process. The knock-on impact is an increase in the economic value of coal and thus the selling price will be more competitive. After the data was collected, the data was analyzed using descriptive methods. The descriptive method is used to describe the upgraded brown coal (UBC) process. Findings that can be obtained in this research include: 1) Potential company profits can be increased, 2) Increased local government income through taxes, 3) Many mining areas can be exploited, 4) Reduction of unemployment by expanding employment opportunities in the mining sector, 5) Reducing environmental damage by mining power by mining wisely. 6) Reducing environmental pollution due to the combustion process. 7) With this process, investment in the coal mining sector can be increased. This research concludes that the economic value of low quality coal will increase and the profits that can be obtained will be better.

Study of leakage current in underground mine power network: a case study in mining in Vietnam

Purpose. To determine DC leakage current in mine power network with long DC power transmission. Methodology. Nowadays, the increase in capacity and working depth leads to the use of DC power transmission, which has many benefits both economically and technically in mining. However, the appearance of DC power transmission changes the structure of the network. In the underground mine power network, there will be electrical networks with industrial frequency 50 Hz, DC power networks, and power networks after variable frequency inverters. The correlation of these network parameters complicates leakage protection in the mine power network. For DC power transmission in mining, the DC network parts have a large length, so during the working process, electricity leakage in these parts of the network often occurs. Leakage current in a DC network depends not only on DC network parameters but also on AC network parameters. The article uses analytical methods and simulation methods on Matlab/Simulink software to determine leakage currents in underground mine power networks with DC transmission when there is a change in power network parameters. Findings. The research results show that the leakage current value of the DC network is greatly affected when the insulation parameters of the electrical network change, not only in the DC power network but also in the AC network before and after the inverter. This causes the unreliable operation of the leakage protection device in this DC transmission network. Originality. Calculation model and simulation of DC leakage current in underground mine power networks with long DC transmission in mining in Vietnam Practical value. The research results are the basis for calculating and selecting leakage protection equipment for the purpose of improving safety in underground mining in Vietnam.

Data Mining for Literary Trends: A Big Data Approach

In the rapidly evolving landscape of digital humanities, the exploration of literary trends through the lens of big data and data mining methodologies. Traditional approaches to literary analysis have grappled with the sheer volume of textual data, hindering comprehensive examinations across diverse genres and historical periods. Recognizing the transformative potential of big data, these limitations and provide a scalable framework for the nuanced exploration of literary landscapes. On harnessing the power of data mining to uncover overarching trends, stylometric nuances, and thematic evolutions within expansive bodies of literature. Drawing from digital libraries, online platforms, and literary archives, our dataset spans a wide array of genres, authors, and historical epochs. The systematic methodology involves rigorous data pre-processing to ensure quality and consistency, coupled with the application of carefully selected data mining algorithms to extract meaningful patterns. Illustrative case studies form a pivotal part of our investigation, demonstrating the versatility and depth of insights achievable through our big data approach. The interpretative aspects of the findings, unravelling implications for literary studies and criticism. Ethical considerations, including biases in algorithms and responsible data usage, are addressed, underscoring the ethical dimensions of our research. The transformative power of data mining in uncovering literary trends on a scale previously unimaginable. By synthesizing computational methodologies with the richnessof literary expression, our study contributes to the burgeoning field of digital humanities, offering valuable insights into the evolution of language and storytelling.

Construction of agricultural product consumer group portrait and analysis of precision marketing strategies based on K-means cluster analysis

<p>In today’s technologically advanced landscape, where data flows in unparalleled volumes, the power of data mining stands out as a transformative force. Its capabilities extend beyond mere analysis; data mining can be harnessed to create intricate and detailed profiles of various consumer groups. This is particularly pertinent to the agricultural sector, which has long grappled with challenges of reaching its consumers effectively. The quest to refine and elevate marketing strategies for agricultural products amidst this data deluge led us to a methodical approach. We initiated this by sourcing and cleansing customer analysis datasets from Baidu AI, a leading platform in the realm of artificial intelligence and data analytics. Such a foundational step ensured that the data underpinning our analysis was robust and free of inconsistencies. The subsequent analytical journey, comprising rigorous data exploration and the utilization of the <em>K</em>-means clustering method, allowed us to dissect and segment the vast data pool. Through this, we crafted a comprehensive consumer profile that is tailor-made for agricultural product consumption. Such segmentation offers invaluable insights, paving the way for marketers and producers to understand their audience’s nuances. Our research findings highlight the remarkable prowess of the <em>K</em>-means clustering technique. When underpinned by sophisticated intelligent algorithms, it doesn’t just cluster data; it offers a pathway to identify distinct customer segments, shed light on core product offerings that resonate with them, and sculpt effective marketing strategies. By integrating these insights with modern media channels, we can craft marketing narratives that echo the desires and needs of the target demographic. Such a precision-driven approach ensures a symbiotic relationship where products align seamlessly with customer preferences. Beyond meeting immediate needs, this alignment has broader implications. It allows enterprises to tap into the vast opportunities presented by the digital era, positioning them on a trajectory of sustained growth and relevance in an ever-evolving market landscape.</p>

BMP: An Evaluation System for Bitcoin Mining Pools

With the in-depth research and cross-domain applications of blockchain in various industries, blockchain mining attracts great attention. A comprehensive and in-depth evaluation of mining pool is essential to the Bitcoin network supervision as well as the reduction of carbon emission. However, due to the difficulty of obtaining mining pool internal data, the current evaluation is limited to a single mining power indicator. In this article, we build a four-layer mining pool evaluation model, named BMP, based on the analytic hierarchy process (AHP). The model contains three first-level indicators and nine second-level indicators, covering various aspects of the performance of mining pool. Miner address extraction provides basic data for the calculation of these indicators. By utilizing the model proposed, we evaluated and ranked eight mainstream mining pools, which broadcasted 71.69% of the blocks during the period January 1, 2021-July 31, 2021. Through comparative analysis of evaluation result, we find that the 65% decline of mining power in June did not pose a threat to the stability of Bitcoin system. In addition, we also revealed the reasons for various fluctuations.

Why Fixed Costs Matter for Proof-of-Work–Based Cryptocurrencies

We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4901 .

EVaDe: Efficient and Lightweight Mirai Variants Detection via Approximate Largest Submatrix Search

The Mirai botnet, notorious for launching significant Distributed Denial of Service (DDoS) attacks and crippling portions of internet services in late 2016, has emerged as a significant threat. Its threat is magnified by the open-source nature of the original Mirai code, which enables a propagation and evolution rate that surpasses traditional malware and frequently defies common sense. As the primary targets of Mirai attacks, Internet of Things (IoT) devices must promptly adapt to the evolving variations of the Mirai threat scenario. In practice, however, IoT devices are frequently constrained by insufficient security detection resources. Therefore, there is an urgent need for a lightweight framework capable of handling Mirai variants and dynamically updating its rule set in order to effectively counter the threat. In response to these challenges, we present Efficient and lightweight Mirai Variants Detection (EVaDe), a novel, lightweight framework for detecting Mirai. EVaDe unleashes the power of sample function mining to efficiently automate the generation of detection rules, requiring limited hardware resources while maintaining effectiveness against Mirai and its numerous variants. In addition, to improve the efficacy of rule generation, we propose a sophisticated algorithm designed to optimize the maximum submatrix problem, thereby facilitating the efficient and rapid extraction of malicious rules from the sample group. We validated the experiments on actual IoT devices with significantly compressed performance overheads. An average sample detection time of 5 ms to make sure the system can be deployed in real production. According to the result, the approach has an average detection rate of 95% for Mirai and its variants, which beats every other well-known piece of commercial antivirus software on the market by 3% to 56%.