Mining Phase Research Articles

The Environmental Stake of Bitcoin Mining: Present and Future Challenges

The environmental impact of Bitcoin mining has raised severe concerns considering the expected growth of 30% by 2030. This study aimed to develop a Life Cycle Assessment model to determine the carbon dioxide equivalent emissions associated with Bitcoin mining, considering material requirements and energy demand. By applying the impact assessment method IPCC 2021 GWP (100 years), the GHG emissions associated with electricity consumption were estimated at 51.7 Mt CO2 eq/year in 2022 and calculated by modelling real national mixes referring to the geographical area where mining takes place, allowing for the determination of the environmental impacts in a site-specific way. The estimated impacts were then adjusted to future energy projections (2030 and 2050), by modelling electricity mixes coherently with the spatial distribution of mining activities, the related national targeted goals, the increasing demand for electricity for hashrate and the capability of the systems to recover the heat generated in the mining phase. Further projections for 2030, based on two extrapolated energy consumption models, were also determined. The outcomes reveal that, in relation to the considered scenarios and their associated assumptions, breakeven points where the increase in energy consumption associated with mining nullifies the increase in the renewable energy share within the energy mix exist. The amount of amine-based sorbents hypothetically needed to capture the total CO2 equivalent emitted directly and indirectly for Bitcoin mining reaches up to almost 12 Bt. Further developments of the present work would rely on more reliable data related to future energy projections and the geographical distribution of miners, as well as an extension of the environmental categories analyzed. The Life Cycle Assessment methodology represents a valid tool to support policies and decision makers.

Promoting Adaptive Apparel on Instagram: The Role of People with Disabilities as Users, Influencers, and Advocates

This study aimed to understand how social media is used as a marketing and promotion tool for fashion brands within the adaptive apparel marketplace and further explores how disability advocates and influencers use social media to promote adaptive apparel. To achieve this goal, the study used a qualitative methodology of semi-structured interviews and a computational methodology of collocation analysis to analyze social media posts from users with hashtags related to adaptive apparel. The result of the study revealed three themes that supported social capital theory, communities of practice, and virtual communities of practice theories – Inclusion, Representation, and Education. Further, the data mining phase provided valuable insights into the significant event and the broader community of people with disabilities. This study showed how fashion brands, disability organizations, advocates, and people with disabilities use social media to market and promote adaptive apparel.

Safety and high-recovery mechanisms and application research for initial mining of thick-coal-seam with complex structure and thick-hard roof

Thick-coal-seam with complex structures and thick-hard roof in the initial mining phase pose various challenges, including a long weighting interval, strong rock pressure, poor top coal caving performance (TCCP), and significant coal loss. These problems directly affect the safety and efficiency of the mining operations. This study employs the principles of elastic thin plates and ellipsoidal bodies to unravel the formation mechanism of strong rock pressure in thick-hard roof and the influence of parting on the TCCP. In addition, a hydraulic fracturing technique is proposed for safe-efficient recovery during the initial mining phase. The reliability of this technique is verified through numerical simulations and field experiments. The research findings reveal the following. (1) The primary causes of strong rock pressure in the mining face are attributed to a long weighting interval and wide collapse range of the main roof, and the weighting interval is primarily influenced by the thickness and tensile strength of the main roof. (2)The key factor affecting the TCCP is the cantilever beam structure formed by the fracture of the thick-hard parting, as it intersects the ellipsoidal body during coal caving. The simultaneous fracturing of both the top coal and roof can reduce the weighting interval on the working face. This process effectively decreased the strength of the thick-hard parting within the top coal, while simultaneously enhancing its load strength and eliminating the cantilever structure resulting from the parting fracture. It not only reduces the first weighting intensity but also promotes the early and proper coal release, thereby enhancing the TCCP and ensuring safe-efficient mining during the initial mining phase. (3) Aiming at the difference in the strengths of the top coal and roof, a graded hydraulic fracturing technique and system were proposed. Fracturing boreholes with a diameter of 60 mm, spacing of 10 m, and height of 20.85 m, which can economically and effectively ensure the fracturing results. Field applications have demonstrated that fractured coal and rocks in fracturing areas exhibit well-developed fractures. During the initial mining phase, the weighting interval in the working face was reduced by 20 m, resulting in a decrease in the overburden pressure and 26.9% reduction in the lumpiness of the top coal. Additionally, the recovery rate increased by 31.19%.

Dimensions of hospital resilience emphasized during the COVID-19 pandemic response: A systematic review.

Hospitals must maintain their effective operations during and after disasters. Due to the current increase in disasters, hospital resilience has drawn scholarly attention. This study aimed to review studies on the changes in the definition of hospital resilience after COVID-19, build a conceptual framework for careful measurement, and identify the main dimensions of hospital resilience emphasized during the COVID-19 pandemic. The initial phase of this study was a systematic review of articles published before the COVID-19 pandemic to extract the hospital resilience-related dimensions for the second phase. The second phase involved text-mining articles published both before and after the emergence of COVID-19. In the systematic review phase, 12 databases were searched from 2006 to January 2020, including Scopus, Web of Science, MEDLINE through PubMed, Embase, ERIC, ProQuest, the Cochrane Library, Emerald, Springer, Science Direct/ELSEVIER, Google Scholar, and SID (for Persian language papers). Then, after COVID-19, articles published in these databases between January 2020 and May 2022 were evaluated using text mining. During the systematic phase, 17 out of 1530 papers published before COVID-19 were synthesized to collect components of hospital disaster resilience. These identified components were the inputs for the text-mining phase. The text mining on pre-COVID papers resulted in six clusters, with the highest weight (0.65) belonging to general resilience and disaster preparedness, while in the post-COVID text mining phase, including 70 papers, 8 clusters have been identified, with the highest weight cluster (0.78) focusing on the mental and psychological aspects of resilience among healthcare workers. Following the COVID pandemic, scholarly attention has shifted to the more personal dimensions of hospital resilience, including psychological resiliency. It seems necessary for policymakers to focus more on the individual and psychological resilience of hospital staff.

Social risks assessment of the supply chain of an aluminium semi-finished profile for window

Abstract Purpose Aluminium is among the most energy-intensive industries in the world and is produced from mining operations in bauxite mines. The effects on the environment of the entire aluminium supply chain, especially mining, also entail social risks. This article aims to understand the potential social risks along the supply chain of aluminium by focusing on a semi-finished frame for windows through the use of the Product Social Impact Life Cycle Assessment (PSILCA) database. Method Social Life Cycle Assessment (S-LCA) was implemented by following the Social Life Cycle Assessment of Products And Organizations 2020, to analyse the potential social risks and opportunities associated with the aluminium sector. Specifically, the PSILCA database, designed for S-LCA, was used to evaluate the background processes of the aluminium supply chain of an Italian company specialized in surface treatment of aluminium semi-finished products used in the production of doors and windows. Results The primary social risks manifest in the background processes. Comparing these findings with the literature review, it is straightforward to attribute them to the mining phase of bauxite, which constitutes the initial stage in aluminium production and occurs in various regions globally. Specifically, the highest social risk is associated with “Corruption in the public sector”, likely linked to Italy. Additionally, the study reveals a positive impact in terms of “Contribution to economic development” as also revealed by the literature review. Conclusions An analysis of the potential social risks within the aluminium supply chain was provided, addressing the research gap between the utilization of S-LCA methodology and its application within the sector. Additionally, the PSILCA database was employed for investigating the background processes in the case study. However, a social performance assessment using primary data would be required to enhance the representation of the evaluated production system.

Do Australian 19th Century Gold Discoveries have Implications for Interpreting Early Gold Mining Elsewhere?

Eight discovery histories of well-documented Australian goldfields indicate the bonanza recoveries available to “first-movers” into previously unmined areas. These have implications for how we interpret goldfields elsewhere that have been repeatedly re-worked since Antiquity and where earliest exploitation has left no clear record. Australian gold was first mined after European settlement in the 19th century, and the manual techniques in the initial stages were not substantially better than available to Bronze Age miners. Dry blowing was used extensively in both arid and seasonally dry regions, often as an initial step followed by transport to a water source. The ease of gold recovery from placers conferred a huge competitive advantage over lode gold, which required crushing and grinding. Hence, early production was generally dominated by free gold recovered from proximal settings including non-alluvial placers (residual, eluvial, colluvial, and hillslope areas). Earliest returns were often prodigious: 5 to 50 grams of gold or more per miner per day in the discovery phase, and 1-5 grams while unworked ground remained. Commonly these high returns were prolonged by “rolling rushes” in which multiple nearby gold occurrences were discovered. Nevertheless, production waned rapidly (within 2-15 years) unless major lodes or large distal placers were discovered. Early returns are not a good predictor of overall productivity: more than 90 percent of gold was recovered in the initial burst of activity on some fields. Colluvial and proximal alluvial placer mining overwhelmingly dominated early production in most Australian goldfields but leave minimal archaeological evidence. That was probably the case elsewhere in the world, where gold was first mined in the Bronze Age or earlier, and we should heed this when interpreting their earliest mining phases.

Cryptocurrency blockchain and its carbon footprint: Anticipating future challenges

This study investigates the environmental impact of cryptocurrency blockchain technology, specifically focusing on the adoption of Bitcoin and Ethereum. Utilizing instrumental regression analysis, it examines the consensus mechanisms—Proof of Work (PoW) and Proof of Stake (PoS)—and their association with carbon footprint. The findings reveal a clear correlation between the adoption of PoW-based cryptocurrencies like Bitcoin and carbon emissions. Notably, PoW-based cryptocurrencies generate approximately 0.86 metric tons of carbon emissions per transaction, which is significantly higher than PoS-based transactions like Ethereum. Concerns also arise regarding the sustainability of Bitcoin mining after the fourth halving period and potential shortages in PoW-based mining resources. As mining rewards decrease, the challenge of timely block creation intensifies. By projecting future BTC mining phases, the study anticipates diminishing rewards in each halving period, potentially leaving around 2500 BTC units available for mining by 2120. This suggests that achieving the full 21 million BTC may prove practically unattainable within the current blockchain framework. Furthermore, the research highlights the speculative and risky nature of cryptocurrencies lacking regulatory oversight, which tends to attract irresponsible investors. It stresses the importance of developing a nuanced understanding of the environmental implications of PoW cryptocurrencies and advocating for responsible innovation. Additionally, the study underscores the necessity of considering the monetary policy implications and raising awareness among investors to ensure responsible decision-making.

IoT device type identification using training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IoT security

IoT networks can be defined as groups of physically connected things and devices that can connect to the Internet and exchange data with one another. Since enabling an increasing number of internets of things devices to connect with their networks, organizations have become more vulnerable to safety issues and attacks. A major drawback of previous research is that it can find out prior seen types only, also any new device types are considered anomalous. In this manuscript, IoT device type detection utilizing Training deep quantum neural networks optimized with a Chimp optimization algorithm for enhancing IOT security (IOT-DTI-TDQNN-COA-ES) is proposed. The proposed method entails three phases namely data collection, feature extraction and detection. For Data collection phase, real network traffic dataset from different IoT device types are collected. For feature mining phase, the internet traffic features are extracted through automated building extraction (ABE) method. IoT device type identification phase, Training deep quantum neural networks (TDQNN) optimized with Chimp optimization algorithm (COA) is utilized to detect the category of IoT devices as known and unknown device. IoT network is implemented in Python. Then the simulation performance of the proposed IOT-DTI-TDQNN-COA-ES method attains higher accuracy as26.82% and 23.48% respectively, when compared with the existing methods.

Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study.

The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition. We used GEO database data, including 74 non-endometriosis and 74 varying-degree EMs patients. Our approach involved identifying significant gene modules, exploring gene intersections, identifying core genes, and screening for potential EMs biomarkers using weighted gene co-expression network analysis (WGCNA) and various machine learning approaches. We also performed gene set enrichment analysis (GSEA) to understand relevant pathways. This comprehensive approach helps investigate EMs genetics and potential biomarkers. Nine genes were identified at the intersection, suggesting their involvement in EMs. GSEA linked DEGs to pathways like complement and coagulation cascades, DNA replication, chemokines, apical plasma membrane processes, and diseases such as Hepatitis B, Human T-cell leukemia virus 1 infection, and COVID-19. Five feature genes (FOS, CFD, CCNA1, CA4, CST1) were selected by machine learning for an effective EMs diagnostic nomogram. GSEA indicated their roles in mismatch repair, cell cycle regulation, complement and coagulation cascades, and IL-17 inflammation. Notable differences in immune cell proportions (CD4 T cells, CD8 T cells, DCs, macrophages) were observed between normal and disease groups, suggesting immune involvement. This study suggests the potential involvement of phase separation-related genes in the pathogenesis of endometriosis (EMs) and identifies promising biomarkers for diagnosis. These findings have implications for further research and the development of new therapeutic strategies for EMs.

SIMULTANEOUS EVALUATION OF CRITERIA AND ALTERNATIVES FOR MINING METHOD SELECTION (CASE STUDIES: GOL-E-GOHAR NO. 3 IRON ORE AND CHAHAR-GONBAD COPPER ORE)

Selecting a mining method is an essential step in the initial phases of mining. The selection of an inappropriate mining method can have consequences, such as the potential loss of a portion of the ore deposit. Sometimes, changing the mining method can result in high costs that render the entire project economically unfeasible. For the past two decades, qualitative patterns and numerical scoring have been replaced by multi-criteria decision-making methods. In this study, the Simultaneous Evaluation of Criteria and Alternatives (SECA) multi-criteria decision analysis method was introduced for the first time in the selection of mining methods. The SECA was used to select the mining method in two ore bodies: Gol-E-Gohar No. 3 Iron ore and Chahar-Gonbad Copper ore. The final ranking compared with the results obtained from the fuzzy TOPSIS decision-making method. The satisfactory results indicated that the open-pit method was selected as the appropriate approach for both cases. The SECA method has lower computational complexity because it does not require the use of weight vectors as inputs. However, it does require a precise decision matrix. This method can be considered the foundation of artificial intelligence for selecting a mining method.

Kegiatan Sosialisasi Keselamatan Dan Kesehatan Kerja Pada Tambang Batu Gamping di PT. Diamond Alfa Propertindo Kabupaten Buton Tengah

PT. Diamond Alfa Propertindo is a company that produces gravel stones ranging from 0.5 cm to 3 cm in size, located in Southeast Sulawesi, with a land area of 4,905 hectares. PT. Diamond Alfa Propertindo has a production target of 25,000 tons per month, with an average production of 22,000 tons per month. The company employs open-pit mining using the Quarry method, with mining locations divided into blocks A1, A2, A3, currently in the mining phase. The extraction of limestone involves drilling and blasting activities to meet production targets, posing high risks of workplace accidents, occupational diseases, fires, explosions, landslides, and environmental pollution. These risks arise from workers' non-compliance with safety regulations, unsafe working conditions, inadequate facilities and infrastructure, or poor management, as well as unsafe environmental conditions. The implementation of Occupational Health and Safety (K3) is the responsibility of every mining employee, from the highest leadership level to field executives or operators. This is conveyed through a combination of methods such as lectures, discussions, and demonstrations. The community service activities aim to ensure that the limestone mining activities of PT. Diamond Alfa Propertindo in Buton Tengah are carried out with an understanding of the importance of occupational health and safety in creating a comfortable and safe working environment for mining workers.

Краткосрочное планирование открытых горных работ в горно-геологической информационной системе MINEFRAME

One of the levels in planning of surface mining operations is short-term planning during the mining phase of the deposit reserves. Algorithms and software tools for the module of automated planning of surface mining operations have been developed as part of the task to enhance the efficiency of short-term planning in the MINEFRAME mining and geological information system. This module allows to make a comparative analysis of the selected structure of complex mechanization, as well as to optimize the operation of the in-pit transport based on simulation modeling of technological processes in surface mining. The module for planning of surface mining operations increases efficiency, timeliness and accuracy of planning based on input data and limitations that characterize the operation of the enterprise. Selection of the optimal structure for complex mechanization is made based on the analysis of the received reporting data of each planning scenario according to the performance indicators of the machinery and equipment. This approach fully corresponds to the trends of digital transformation declared by the government of the Russian Federation and represents a new stage of automation and informatization of economic activities and transition to digital technologies.

Using CO2 Fracturing to Weaken Roofs During the Initial Phase of Longwall Coal Mining

Using CO2 Fracturing to Weaken Roofs During the Initial Phase of Longwall Coal Mining

Experimental investigation on stress distribution and migration of the overburden during the mining process in deep coal seam mining

Coal mining has a significant impact on the movement of the overburden, leading to potential safety hazards in the working face. In this paper, a similarity simulation experiment was conducted to investigate the migration of overburden during the mining process of a specific working face in the Liuzhuang Coal Mine located in southern China. Sand and gravel were used to simulate the geological environment of each rock stratum. The deformation of the stratum was monitored using strain gauges, the fracture and displacement changes of the overburden stratum were recorded using cameras, and the characteristics of roof collapse was monitored using infrared thermal imager. The experimental model fully simulated the situation of the working face, and the actual working face size was obtained by enlarging the model by 100 times. The experiment found that during the initial stage of mining, there was no significant subsidence of the roof. In the course of the advancement of the working face, the primary roof intermittently fractured behind the working face, with subsequent propagation of upper cracks in an upward direction. The overburden rock layer above the goaf experienced continuous compaction, leading to the gradual closure of the separation layer. Simultaneously, new cracks constantly emerged in front of the working face, resulting in the progressive stabilization of the height of the crack zone. The stress measurements at each point exhibit a pattern of initial increased, followed by decrease, and ultimately stabilization. By considering the stress variation law of the overburden rock, the stress changes in key layers of the bedrock during mining could be categorized into four zones: the stress stable zone, stress increasing zone, stress reducing zone, and compaction stable zone. During the initial phase of coal seam mining, the presence of rock layers provided support, resulting in minimal subsidence of the overburden rock. However, as the mining operation progressed, the disturbance force and collapse of the overburden rock leaded to further downward subsidence. When the working face reached the stop line, the collapsed overburden rock gradually consolidates, resulting in a deceleration of energy release and the formation of a pressure relief zone. Consequently, the overburden rock above the working face underwent a slight additional subsidence, reaching its maximum level. A short cantilever rock beam structure was formed in the experiment. This study will provide valuable reference for future coal mining and serve as a vital theoretical basis for roof control in deep coal seam mining.

Systematic Review of GIS and Remote Sensing Applications for Assessing the Socioeconomic Impacts of Mining

As the mining industry expands, a comprehensive understanding of its socioeconomic risks and benefits is urgently needed. This paper systematically reviews 71 studies (1996–2021) that utilized spatially integrated approaches to evaluate socioeconomic mining impacts. The number of studies that utilize geographic information systems and remote sensing to study mining impacts increased from 2014 onwards. A framework was used to classify the mining impacts studied in the literature and all eight framework categories – Environment, Land, People, Community, Culture, Livelihoods, Infrastructure and Housing – were captured by the literature though Culture was least studied. Coal mining, active mining phase, Landsat data and classic remote sensing algorithms were most highlighted. Future research should focus on advancing geospatial technology like artificial intelligence (AI) to better capture intangible socioeconomic impacts, under-researched minerals and long-term mine lifecycle components. Spatially referenced social data can improve stakeholder involvement and support spatially explicit planning to ensure sustainability.

First molecular data on the human roundworm Ascaris lumbricoides species complex from the Bronze and Iron Age in Hallstatt, Austria

Palaeoparasitological studies can provide valuable information on the emergence, distribution, and elimination of parasites during a particular time in the past. In the prehistoric salt mines of Hallstatt, located in the Austrian Alps, human faeces have been conserved in salt. The aim of this study was to recover ancient DNA of intestinal parasites from these coprolites. Altogether, 35 coprolites from the Hallstatt salt mines, dating back to the Bronze Age mining phase (1158–1063 BCE) and the Iron Age mining phase (750–662 BCE), respectively, were analysed by microscopy and molecular methods. In 91% of the coprolite samples, eggs of soil-transmitted helminths (STH), namely of Trichuris and/or Ascaris were detected by light microscopy. The Ascaris eggs were exceptionally well preserved. For further analysis, DNA was extracted from the palaeofaecal samples and species-specific primers targeting different genes were designed. While amplification of Trichuris DNA remained unsuccessful, sequence data of A. lumbricoides species complex were successfully obtained from 16 coprolites from three different genes, the mitochondrial cytochrome c oxidase subunit 1 gene (cox1), the mitochondrial cytochrome B gene (cytB) and the mitochondrial NADH dehydrogenase subunit 1 gene (nadh1). Importantly, these included two Ascaris sequences from a coprolite from the Bronze Age, which to the best of our knowledge are the first molecular data of this genus from this period.

Lexicon-based content analysis of BIM logs for diverse BIM log mining use cases

This study conducted a lexicon-based content analysis of building information modeling (BIM) logs from four major BIM authoring tools and four custom-developed BIM loggers to understand whether the BIM logs satisfy the information requirements for various BIM log mining use cases, as well as to assess their potential for future development and research. First, through a critical review of previous studies, 19 different ways of using BIM logs were identified, including authoring and collaborative pattern discovery, authoring process modeling, collaboration pattern analysis, command predictions, and team optimization; however, most of the uses concerned process discovery. The analysis also revealed that BIM log mining has mainly been used for the design phase, with a few examples of being used for the construction phase. For BIM log mining, various techniques ranging from simple frequency analyses via social network analyses to advanced pattern discovery were deployed. In terms of BIM log sources, native BIM logs from Revit were dominantly used almost in all studies, aside from a few studies that used custom-developed BIM logs. The content analysis of BIM logs showed that the contents of native BIM logs provided by major BIM authoring tools varied, but commonly lacked model-element-specific information; this limitation prevents in-depth analyses of BIM processes. Overall, the current disproportionate focus on process discovery in the design phase of BIM log mining suggests that the application of BIM log mining is still in its early stages and holds significant potential for other project phases if adding model-element-specific information is incorporated.

Současný stav odkrytí fosiliferních karbonátů v Suchém Dole (Český masiv, lužická oblast, krkonošsko-jizerské krystalinikum)

The collection of fossils in the abandoned limestone (? Lower Cambrian) quarry in Suchý Důl close to Dolní Albeřice (eastern margin of the Krkonoše Mts.) became practically impossible after the end of mining and modification of the pit for private recreational purposes. However, in the last phase of mining (2016), hundreds of blocks of these limestones with a weight of 200 to 500 kg were used to reinforce the banks of the Lysečiny and Albeřice brooks and the retention reservoir on the Lysečiny brook. After several years of natural weathering of the surfaces, the primary and secondary textures in the limestones are very well preserved and distinct when viewed with the naked eye or a hand lens. In the summer of 2022, I examined about 10 m2 of mostly oolitic limestone surfaces with a hand lens and found several structures which were probably of organic origin. The total area of studiable limestone is estimated at 100–250 m2. Due to weathering of the surfaces, the possibility of collection of fossils culminates in the current years and will become negligible within 5–10 years. Only cross-sections through the potential biogenic structures were observed. They have an ovoid, elliptical, or triangular shape. This could indicate their affiliation to the Archaeocyatha group, two individuals of which have been already found in the quarry. The comparison with the previous findings suggests that the findings depicted in Fig. 1A–1D can represent preserved fragments of Archaeocyatha. A little crest of an irregularly sinusoid shape in Fig. 1E shows preservation similar to the primary textures, i.e. ooids. This suggests its organogenic origin. The size and course of the crest indicates that it could represent, for example, a cross-section through a trilobite cephalon. For comparison, a usual way of weathering of veins of secondary origin in limestone block is shown in Fig. 1F. In addition to their sharp contact, they are tightly folded, which is typical of deformed rocks. On the other hand, during the field work in 2022, any deformed, flattened or elongated ooids (as known, e.g., from the Ordovician ferrite ooids of the Barrandian area) were observed. It is suggested that during deformation, the ooids (primarily build of magnesium carbonate) “floated” in the ductile matrix (primarily calcite) similar to, for example, raisins in leavened dough. Therefore, the deformation, judging from the shape of the ooids, is small or negligible; in reality, however, the shells of the vast majority of organisms were completely destroyed, “mixed up”. Acceptance of this idea then explains why the Albeřice carbonates are so poor in fossils, although the oolitic facies gives the impression of a rock that is not deformed or recrystallized at all and the carbonates point to a revived, climatically favourable, rather shallow marine environment. However, the results of a field research in August 2022 show that the search for fossils in the limestones around Albeřice is currently (and will be for at most 10 years) in a promising state, due to the use of the Albeřice blocks as a flood control measure in the villages and their gradual selective weathering. A very fragmentary preservation of fossils can be expected.

A novel missing value imputation relying on K-means clustering and kernel-based weighting using grey relation (KWGI)

Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t-test show that the proposed method performs significantly better than all the other compared methods.

Retracement Ground Pressure Appearance and Control of the Working Face under the Overlying Residual Pillar: A Case Study

On the working face below shallow and close coal seams, there are residual pillars. The mine’s ability to operate safely is constrained by the coal pillars’ vulnerability to sudden instability and powerful ground pressure disasters during withdrawal. This paper uses the 31,106 working face of the Huoluowan coal mine as its research backdrop and employs field observation, theoretical analysis, and numerical simulation to examine the strong dynamic load mechanism of the overlying coal pillars. According to the analysis, the residual pillar’s stress diffusion angle is 29 degrees after mining the working face above it, which has an impact on the main roof’s stability above the working face’s retracement roadway. The main roof is impacted by the excavation disturbance and the remaining pillars during the working face’s final mining phase, displaying a complex stress superposition state. The retracement roadway is significantly deformed as a result of the plastic zone of the surrounding rock changing from small-scale damage to extensive damage. The proposed “hydraulic roof cutting + reinforcement support” prevention technology is based on the prevention idea of weakening important rock strata, changing the stress transmission path, and strengthening adjacent rock. Field testing shows how hydraulic fracturing reinforces the roof structure, lessens the heavy dynamic load on the supporting pillars of overlying residual coal, reduces rock deformation in the retracement roadway, and ensures the stability of the working face during withdrawal. The study’s findings are significant for the secure removal of a working face under similar circumstances.