Mining Technology Research Articles

Experiment in resilient city: An evaluation of China’s demonstration city of safe development policies

The drive of resilient city explores a new path for urban governance in the context of risk society, and China’s demonstration city of safe development (DCSD) policies are the indigenous practice of resilient city idea. This paper used text mining technology and PMC-index model to establish an evaluation system for DCSD policies. Then eight representative sample DCSD policies were assessed. The results show that the average PMC-index scores 5.38 and reaches a great consistency grade. Nine model indicators indicate that the Chinese government has a clear policy focus on the efforts of DCSD, prefers to use compulsory type policy tools, and fully mobilizes the public to participate in safe city development jointly. Meanwhile, structural imbalance in policy instruments is a prominent disadvantage. The research establishes an evaluation system for DCSD policies, and provides a new perspective for the explorations of resilient cities worldwide. The extensive applicability of the policy evaluation model needs to be studied in depth in the future.

Comparison of Logistic Regression, Random Forest and Adaboost Algorithms for Diabetes Mellitus Classification

Diabetes mellitus is a chronic disease that affects the way the body regulates sugar (glucose). High blood sugar levels can lead to health complications including heart problems, eye disorders, nerve damage, kidney and blood vessel disorders. It is important for early detection of diabetes by utilizing data mining technology. Data mining has various classification models that can be used to detect diabetes, including logistic regression, random forest and adaboost. The comparison of the three algorithms aims to find out which algorithm is most appropriate in the classification of diabetes. From the results obtained, the random forest algorithm has the best performance in the classification of diabetes mellitus compared to other algorithms.

SSHAA: A Python Package Index for visualizing features of SSH attacks with text mining in classification

ABSTRACT With the proliferation of the Internet, the number of cyber-attacks has increased worldwide. To prevent cyber-attacks, network administrators need to use analytical tools to disclose six new critical statistics such as Internet Protocol (IP) authenticity and classification, IP ownership, frequency of attacks, time of day, day of the week, and country of attack. There are four SSH attack analysis tools available, but existing tools lack visualization and cannot provide the necessary attack statistics. This paper proposes SSHAA, the first open source PyPI analysis tool that meets the requirement. Text mining technology is used for extracting data from SSH log files, classifying IPs with identified country names and calculating a variety of statistics. PyPI allows SSHAA to run on Windows, MacOS, and Linux operating systems. This paper highlights how to debut a PyPI application using SSHAA which will be the world’s first tutorial in the security journals for maximum software dissemination. According to PePy, SSHAA has been downloaded 7668 times worldwide. The substantial number of downloads signifies the efficacy of the proposed tool. The proposed system offers ten functional statistics for SSH attack analysis. The six new functional statistics to information security are significant.

Prescription Data Mining and Network Pharmacology Study of 1152 Patients with Rectal Prolapse Using Traditional Chinese Medicine.

In recent years, the incidence of rectal prolapse has increased significantly due to the sedentary lifestyle and irregular eating habits of modern life. However, there is a lack of clinical studies on the treatment of rectal prolapse with traditional Chinese medicine (TCM) with a large sample size. Therefore, this study investigated the characteristics of rectal prolapse treatment formulas and then studied the network pharmacology of their core therapeutic drugs, which can help to provide a reference for the treatment and postoperative care of rectal prolapse patients. This study aimed to explore the prescription characteristics and the mechanism of action of core drugs in the treatment of rectal prolapse in Chinese medicine through data mining and bioinformatics techniques. We collected the diagnosis and treatment information of patients with rectal prolapse from January 2014 to September 2021 in the electronic case database of Nanjing Hospital of TCM, mined the patient information and prescription features using R, screened the active ingredients of the core pairs of drugs and disease drug intersection targets using TCMSP and GnenCard databases, and constructed a Protein-protein interaction (PPI) network using STRING and Cytoscape, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the intersecting targets were performed using Metascape and R. We found that prolapse is easy to occur in people over 50 years old, preferably in autumn and winter. Commonly used therapeutic Chinese medicines include Glycyrrhiza glabra, Radix angelicae sinensis, Radix astragali, Atractylodes macrocephala, and Pericarpium citri reticulatae, which are mostly deficiency tonic medicines, warm in nature, and belong to spleen meridian. The core therapeutic medicinal pair was "Bupleuri radix-Cimicifugae rhizoma". There were 190 common targets of Bupleuri radix and Cimicifugae rhizoma, and 71 intersection targets of the drug pair and prolapse. The main components of the core drugs for the treatment of prolapse may be quercetin, kaempferol, Stigmasterol, etc, and the core targets may be CASP3, AKT1, HIF1A, etc. The total number of GO entries for the intersection targets of "Bupleuri radix-Cimicifugae rhizoma" and diseases was 3495, among which the molecular functions accounted for the largest proportion, mainly Pathways in cancer, IL-18 signaling pathway, etc. KEGG enriched pathway analysis yielded 168 results, and the major pathways were pathways in cancer, lipid and atherosclerosis, IL-17 signaling pathway, etc. Conclusion: This study adopted real-world research methodology and used data mining and bioinformatics technology to mine the medication law of rectal prolapse and its core drug action mechanism from the clinical information of Chinese medicine.

Resource utilisation of hazardous aluminium dross and coal gangue for fibre-reinforced cemented foam backfill

The resource utilisation of hazardous aluminium dross and coal gangue for fibre-reinforced cemented foam backfill can enhance the stability of filling stopes and address environmental issues. Herein, aluminium dross was first co-calcined with quicklime at 800°C, 950°C and 1100°C to prepare modified foaming agents, and their phase transformations during calcination were analysed. Next, the calcined mixes, cement, coal gangue and fibres were employed to prepare the samples. The expansion ratio and hydration evolution—as well as the strength, deformation and heavy metal–leaching behaviours—of the fibre-reinforced cemented foam backfill were comprehensively investigated. Experimental results reveal that partial elemental Al in raw aluminium dross becomes active in the C12A7 and CA2 forms, which enhances the hydration reactivity of the calcined mixes. The efficient denitrification achieved at 1100°C is because of the coupled effects of quicklime dispersion and high-temperature calcination. The grindability of the calcined mixes initially improves and subsequently worsens with increasing calcination temperature, which is related to the Al2O3 crystallinity. Active Al in the calcined mixes is conducive to hydration at an early age, leading to the generation of larger quantities of Hc/Mc and the AH3 gel and a smaller amount of portlandite. Owing to the coupled effects of the porous structure and fibre reinforcement, the fibre-reinforced cemented foam backfill exhibits good mechanical performance and large plastic deformation. Furthermore, the M1-10, M2-10 and M2-30 samples possessing an expansion ratio of 4%–10 %, a compressive strength of >3 MPa and a good ability to stabilise heavy metals are excellent choices for the practical application of foam backfill technology in mining engineering.

Laboratory Tests of Large-Scale Block Models on the Load Transfer and Failure Mechanisms of Rock Masses Subjected to Anchor Loading

This paper presents a laboratory testing rig designed to investigate the failure mechanisms and load transfer of rock masses under rock anchor loading. The study revealed that joint patterns significantly affect the strength and capacity of the rock mass, with existing design approaches based on apex angles of 90∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} or 60∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} found to be potentially overly conservative. Joint orientation and spacing should be included in the anchor design to obtain a more precise design criterion. In all the studied patterns, increasing the horizontal and vertical stress increased the block models’ capacity, and load arching did not occur in the model with an unfavourable joint orientation. This study also revealed that the failure surface followed the joints in all tests and that the block model capacity was significantly higher than estimated with the design principle based on the weight of the overlying rock. The findings of this work have important implications for designing rock anchoring systems in mining and civil engineering demonstrate that joint orientation and block size significantly affect the rock mass capacity. This study confirms the need to consider the influence of in situ stresses on rock mass capacity when designing of rock anchors. Overall, this work guides the development of more precise design criteria for rock anchors, contributing to more efficient and effective rock engineering practices.

Experimental investigation on dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales.

The dynamic subsidence disaster caused by underground mining of coal resources is a complex spatiotemporal process, which is a common disaster in mining areas. The backfilling strip mining technology is a green and sustainable coal mining method, which has been commonly used to reduce the subsidence disaster of the overlying strata and protect surface buildings. The transient deformation is the main reason of surface buildings damage; therefore, in this study, the similar material model was used to research dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales, and the optical image method was employed to monitor and obtain the movement data of the overlying strata automatically. The data analysis shows that there is a time-scale effect in mining subsidence. The deformation of the overlying strata increases instantaneously at a certain time under the monitoring of small time scale, and this phenomenon gradually disappears as time scales increase. According to the subsidence velocity of small time scale, the subsidence state of the overlying strata can be further divided into the abrupt subsidence state and the gentle subsidence state. This is really significant for promoting the development of the backfilling strip mining technology and preventing the damage of surface buildings.

Big Data Trade Survey Pricing, Trading, and Data Protection

In this era, Big Data is considered as the key to unlocking the growth of people's productivity in daily life. Internet usage, mobile applications and social networks, as well as the internet of things based on smart-grid and so on has an impact on the amount of data collected. The advancement of analytics data provided by machine learning and data mining technology supported by cloud computing, the resulting information will be more useful. Since the advent of Big Data, the dataset has become one type of "new money" in the digital world. Copyright protection mechanisms including digital encryption and watermarking need to be done to maintain data values. To maximize this, an effective container is needed to allow data owners and buyers to trade the data. The focus of the topic is on designing data trading platforms and schemes, supporting effective and efficient data trading, security, and maintaining the privacy of data owners.

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

Simulation of fretting wear in steel wires under variable coefficient of friction and variable wear coefficient

In this paper, the fretting wear behaviour of steel wires working in coal mining technology is studied numerically. In past studies, the fretting process of steel wires was carried out numerically considering that the coefficient of friction (COF) and wear coefficient (WC) are constant parameters. However, it has been noticed experimentally that COF increases up to a certain number of fretting cycles and then becomes constant, i.e. a steady-state stage, depending on the loading conditions. This increase in COF during the fretting process is also known as the running-up stage. The fretting wear model is modified to evaluate the influence of the varying coefficient of friction (VCOF), which is associated with the variable wear coefficient (VWC), so the influence of VWC is also considered. The subroutine UMESHMOTION used to implement the wear law is also modified to study the effect of VCOF and VWC. Therefore, in this study, the numerical results of a three-dimensional finite element (FE) model are compared, with analytical results of contact area and contact stresses, and with experimental results of peak wear depth. After validating the FE model, the wear scar, the increasing wear depth, wear volume, and the decreasing contact stress with increasing fretting cycles are determined numerically considering VCOF and VWC using cycle jump approach. The energy dissipation effect of frictional force and fretting amplitude is also studied for varying interaction properties of fretting wear models. The numerical simulations are performed by considering both elastic and plastic material properties to analyse the influence of varying interaction properties on fretting wear models at the running-up stage. The results indicate that the VWC model exhibits comparable impacts on both the elastic and plastic models. The results also show that the VWC fretting wear model leads to higher wear scar, wear volume, and wear depth values at the running-up stage as well as at the steady state stage, which are close to the experimental data.

Machine learning with SHapley additive exPlanations for evaluating mine truck productivity under real-site weather conditions at varying temporal resolutions

ABSTRACT This study built truck productivity prediction models incorporating real-site weather conditions at varying temporal resolutions. The best models were combined with SHapley Additive exPlanations to offer quantitative and qualitative analysis for input variables’ effect on the model outputs. The results showed that mining engineers can make more accurate predictions of truck productivity at the weekly resolution compared with other resolutions. The three most influential input parameters were haul distance, empty speed, and ambient temperature. Extreme weather, such as strong wind speed, heavy precipitation, and extreme relative humidity, had a certain effect on truck-shovel allocation. Meanwhile, a unified graphical user interface was developed to predict mine truck productivity.

Sociotechnical perspectives of digital technologies in sustainable mining

This paper adopts an interpretive case study approach to understand the role of digital technologies in addressing seemingly contradictory sustainability goals in mining. The sociotechnical model of information systems was used as a framework to guide the analysis of twenty-five in-depth interviews with globally dispersed digital technology experts working collaboratively at an industry-leading hi-tech mining solutions company. The sociotechnical-led thematic analysis findings highlight the trade-offs experts face in balancing narrow technological imperatives and economic outcomes with broader sustainability goals. The analysis moves beyond the technological and economic to a harmonious perspective of social, human, environmental, and technological interactions. A visual thematic map is presented to aid practitioners in designing and optimally implementing digital technologies to simultaneously address the United Nations Sustainable Development Goals while prioritising business sustainability. We conclude by drawing from the proposed sociotechnical perspectives approach for digital sustainability to provide scholars with possible pathways for future responsible information systems research.

Three-dimensional mineral prospectivity mapping based on natural language processing and random forests: A case study of the Xiyu diamond deposit, China

The advent of the big data era has gradually brought about a new demand for the integration of geological text big data in three-dimensional (3D) mineral prospectivity mapping (3DMPM). Here, we report a novel workflow suitable for the application of natural language processing in 3DMPM. Our study area is the Xiyu kimberlite-type diamond deposit in Shandong, China, which has significant prospecting potential in its deep portion. In this study, exploration criteria served as a bridge connecting geological text big data and 3DMPM, and exploration criteria for the Xiyu diamond deposit were constructed employing text mining technologies. A comparative evaluation of conventional and text-mining-based exploration criteria was also conducted. Considering the text-mining-based deposit exploration criteria, we reconstructed 3D spatial anomalies and quantitatively analyzed ore-controlling factors. A random forests classification model, created using the deposit exploration criteria obtained through text mining, was applied to prospecting predictions in the Xiyu deposit, with superior results being obtained. The prediction results confirmed the applicability of the workflow and demonstrated its capacity for effective dual coupling of geological data and knowledge.

Research on the Value and Language Features of Chinese Language and Literature Texts Based on Text Mining Technology

The linguistic characteristics of a literary work are the way of thinking embodied in the author’s use of language. From the textual value of Chinese language literature, this paper analyzes the spiritual connotation of Chinese language literature from two dimensions: reading and education. Based on the web crawler technology, we obtain the text data of Chinese language literature from three writers, Bajin, Yu Zheng and Qiong Yao, preprocess the data through data cleaning, Chinese word segmentation, de-duplication, etc., and extract the feature values of the text by using the TF-IDF algorithm. Then the text documents are mapped onto vectors using the VSM model, and the parameters of the LDA topic model are estimated by the Gibbs sampling algorithm in order to better obtain the topic changes of the Chinese language literature texts. This paper carries out linguistic feature verification from the lexical and similarity features of Chinese language literary texts. It is found that the difference in lexical density between Ba Jin’s Cold Night and Resting Garden is only 2.1 percentage points, and the frequency of the verb “to say” is 1,213 times and 735 times respectively. The average sentence lengths of Yu Zheng and Qiong Yao fluctuate within the range of [18.49,34.27], and Qiong Yao’s works have a higher thematic concentration than Zheng Zheng’s works. Analyzing the linguistic features of Chinese language literary texts based on text mining techniques helps to understand the authors’ language usage methods and helps to promote innovative expression paths in literary texts.

Nonlinear evolution characteristics and seepage mechanical model of fluids in broken rock mass based on the bifurcation theory

With the deep extension of coal mining in China, fault water inrush has become one of the major disasters threatening the safety production of coal mine. Based on the control equations of steady state and non-Darcy seepage in fractured rock mass, the multi-parameter nonlinear dynamic seepage equations of fractured rock mass are established in this paper. Based on the nonlinear dynamics theory, the function of the state variable in the system is derived, and the influence of the gradual change of non-Darcy flow factors on the structural stability of seepage system is studied. The research achievements show that there are three branches in the equilibrium state of the seepage system. Specifically, the stability of the equilibrium state changes abruptly near the limit parameter. The seepage dynamic system of fractured rock mass has the delayed bifurcation, and the coal mine disaster such as fault water inrush occurs easily at the bifurcation point. The research results are of great significance to enrich the theory of fault water inrush in coal mine, and to reveal the disastrous mechanism of fault water inrush and guide its prevention and control technology in coal mine, which can provide the theoretical reference for predicting the water seepage stability in fractured rock mass.

Heart Stroke Prediction Using Machine Learning Algorithms

A Stroke is a disease when there is insufficient blood supply to the brain, which causes cell death. It is currently the world’s biggest cause of death. Upon examining the affected individuals, a number of risk variables that are thought to be connected to the cause of stroke have been identified. Numerous studies have been conducted to predict and categorize stroke disorders using the risk variables. The majority of the models are built using machine learning and data mining technologies. In this work, we have employed four machine learning algorithms to identify the type of stroke that may have happened based on medical report data and an individual’s physical condition. We have gathered a sizable amount of hospital entries. This study employs many methodologies, including decision trees, Naive Bayes, ANN algorithm, and Random Forest algorithm. Thus, the aim of this study is to evaluate the mentioned algorithms and determine which one does the task more accurately. After completing all of the evaluations, we can conclude that the Random Forest method has the highest accuracy of all the algorithms with 99%.

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events.

Experimental investigation of cohesive soil erosion and suspension caused by a Coandă-effect-based polymetallic-nodule collector

Coandă-effect-based collection stands out as the foremost technology in polymetallic-nodule mining due to the absence of direct contact between the collector and the ocean floor. Yet, this collection method disturbs the ocean floor, and minimizing such disturbance is crucial from an environmental viewpoint. To this end, a solid understanding of the interplay between the collector and the sediment bed is required. Therefore, we carried out a series of small-scale experiments, where a collector drives over a subaqueous clayey bed. These experiments provide the very first quantitative data on cohesive sediment erosion caused by a moving Coandă-effect-based collector, as well as on turbidity currents generated behind the collector head. This paper discusses the observations and findings derived from these experiments. Our findings reveal a logarithmic relationship between erosion depth and the flow impinging force applied on the clayey bed. An increased flow rate in the collection duct results in a slower turbidity current generated behind the collector head. This study enhances the ability to forecast sediment erosion caused by Coandă-effect-based collectors, offering the possibility to optimize the collector operational conditions and minimize the magnitude of the resulting sediment plumes.

Influence of pre-existing crack on rockburst characteristics in coal-rock combination: A laboratory investigation

The existing cracks in the roof rock are critical to stability of the coal-rock combination structure during mining activities, which is vital for safe exploitation in coal mines. However, the physical mechanisms by which pre-existing crack in the roof rock induces rockburst in coal mines are still poorly understood. In this study, to investigate the rockburst characteristics in an underground mine, triaxial compressive experiments on coal-rock combinations containing a pre-existing crack in the roof rock were carried out. Both mechanical and failure behaviours of composite specimen containing a pre-existing crack were analysed. The testing results showed that a pre-existing crack in roof significantly affects mechanical properties of combinations. The average peak stress and average elastic modulus of specimen exhibit an M-shaped change trend when dip angle is in a range of 0°–90°. The average peak strain increases before decreasing with increase in dip angle. In addition, the roof rock is more likely to fail when it contains a pre-existing crack. Both the initial failure location and failure pattern of combinations are highly related to angle of pre-existing crack. A novel rockburst model considering in-situ stress and pre-existing crack was proposed to determine the physical mechanism of rockburst. The findings are useful to understand characteristics and physical mechanism of rockburst in mine engineering.

Experimental Study on Strength Enhancement and Porosity Variation of 3D-Printed Gypsum Rocks: Insights on Vacuum Infiltration Post-Processing

Three-dimensional printing (3DP) technology has shown great potential in rock mechanics and mining engineering due to its ability to create complex and customized objects with high precision and accuracy. At present, an emerging research focus is improving the mechanical properties of 3D-printed samples, which originally has low strength and stiffness, to match those of natural rocks. The objective of this study was to investigate the effectiveness of different post-treatments on the strength enhancement of 3D-printed gypsum samples. To achieve this goal, 3D-printed gypsum samples were subjected to different post-treatments including dipped infiltration treatment and vacuum infiltration treatment using different infiltrants: water, saltwater, ColorBond, and StrengthMax. Subsequently, each sample was subjected to ultrasonic wave velocity testing and uniaxial compression experiments to characterize their mechanical properties, CT scans to investigate their microstructural characteristics. Additionally, X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM) tests were conducted to explore the underlying reasons for changes in macroscopic strength. Finally, the physical characteristics and mechanical properties of untreated and post-processed 3D-printed gypsum samples were compared with natural rocks. The results showed that the strength of samples treated with water and saltwater was much lower than that of those treated with ColorBond and StrengthMax, while the porosity was the opposite. In water-treated and saltwater-treated samples, water or saltwater treatment can alter particle characteristics, but weak adhesive bonding and numerous pores result in low mechanical strength. Samples treated with Colorbond or StrengthMax exhibit improved strength due to effective gap filling and cohesive structure formation, with StrengthMax-treated samples showing higher strength despite having more pores than Colorbond-treated ones. Moreover, the physical and mechanical properties of these treated samples matched a wider range of natural rock types compared to the untreated samples.