Unveiling nature's treasures: actinobacteria from Meghalaya's mining sites as sources of bioactive compounds.

Coal and sillimanite mining sites present unique ecological niches favoring the growth of actinobacteria, a group of Gram-positive bacteria known for producing a wide array of bioactive compounds. Isolating these bacteria from such environments could unveil novel compounds with potential biotechnological applications. This study involved the isolation of actinobacteria from two mining sites in Meghalaya, India. The dominant genera from both sites were Streptomyces, Amycolatopsis, Nocardia, and Streptosporangium. Metabolic pathway prediction from 16S rRNA gene revealed several pathways beneficial for plant growth. Exploration of biosynthetic genes indicated a prevalence of the type-II polyketide synthase gene. Sequencing the ketosynthase-alpha domain of the gene led to predictions of various bioactive secondary metabolites. Around 44% of the isolates demonstrated antimicrobial properties, with some also displaying plant growth-promoting traits. Amycolatopsis SD-15 exhibited promising results in planta when tested on tomato plants. These findings highlight the potential of actinobacteria from Meghalaya's mining sites across medical, agricultural, and industrial domains.

Actinomycetota Amycolatopsis nalaikhensis sp. nov. and Amycolatopsis carbonis sp. nov., two novel actinobacteria with antimicrobial activity isolated from a coal mining site in Mongolia.

Two-novel filamentous actinobacteria designated strains 2-2T and 2-15T were isolated from soil of a coal mining site in Mongolia, and their taxonomic positions were determined using a polyphasic approach. Phylogenetic analyses based on 16S rRNA gene sequences showed that each of the strains formed a distinct clade within the genus Amycolatopsis. The 16S rRNA gene sequence similarity analysis showed that both strains were mostly related to Amycolatopsis rhabdoformis NCIMB 14900T with 99.0 and 99.4% sequence similarity, respectively. The genome-based comparison indicated that strain 2-2T shared the highest digital DNA-DNA hybridization value of 35.6% and average nucleotide identity value of 86.9% with Amycolatopsis pretoriensis DSM 44654T, and strain 2-15T shared the corresponding values of 36.5 and 87.9% with A. rhabdoformis NCIMB 14900T, all of which being well below the thresholds for species delineation. The chemotaxonomic properties of both strains were typical of the genus Amycolatopsis. In silico prediction of chemotaxonomic markers was also carried out, and the results were consistent with the chemotaxonomic profiles of the genus. Genome mining for secondary metabolite production in strains 2-2T and 2-15T revealed the presence of 29 and 24 biosynthetic gene clusters involved in the production of polyketide synthase, non-ribosomal peptide synthetase, ribosomally synthesized and post-translationally modified peptides, lanthipeptide, terpenes, siderophore, and a number of other unknown type compounds. Both strains showed broad antifungal activity against several filamentous fungi and also antibacterial activity against methicillin-resistant Staphylococcus aureus and Acinetobacter baumannii. The phenotypic, biochemical, and chemotaxonomic properties indicated that both strains could be clearly distinguished from other species of Amycolatopsis, and thus the names Amycolatopsis nalaikhensis sp. nov. (type strain, 2-2T=KCTC 29695T=JCM 30462T) and Amycolatopsis carbonis (type strain, 2-15T=KCTC 39525T=JCM 30563T) are proposed accordingly.

Amycolatopsis mongoliensis sp. nov., a novel actinobacterium with antifungal activity isolated from a coal mining site in Mongolia.

A novel filamentous actinobacterium designated strain 4-36T showing broad-spectrum antifungal activity was isolated from a coal mining site in Mongolia, and its taxonomic position was determined using polyphasic approach. Optimum growth occurred at 30 °C, pH 7.5 and in the absence of NaCl. Aerial and substrate mycelia were abundantly formed on agar media. The colour of aerial mycelium was white and diffusible pigments were not formed. Phylogenetic analyses based on 16S rRNA gene sequence showed that strain 4-36T formed a distinct clade within the genus Amycolatopsis. The 16S rRNA gene sequence similarity showed that the strain was mostly related to Amycolatopsis lexingtonensis DSM 44544T and Amycolatopsis rifamycinica DSM 46095T with 99.3 % sequence similarity. However, the highest digital DNA-DNA hybridization value to closest species was 44.1 %, and the highest average nucleotide identity value was 90.2 %, both of which were well below the species delineation thresholds. Chemotaxonomic properties were typical of the genus Amycolatopsis, as the major fatty acids were C15 : 0, iso-C16 : 0 and C16 : 0, the cell-wall diamino acid was meso-diaminopimelic acid, the quinone was MK-9(H4), and the main polar lipids were diphosphatidylglycerol, phosphatidylmethanolamine and phosphatidylethanolamine. The in silico prediction of chemotaxonomic markers was also carried out by phylogenetic analysis. The genome mining for biosynthetic gene clusters of secondary metabolites in strain 4-36T revealed the presence of 34 gene clusters involved in the production of polyketide synthase, nonribosomal peptide synthetase, ribosomally synthesized and post-translationally modified peptide, lanthipeptide, terpenes, siderophore and many other unknown clusters. Strain 4-36T showed broad antifungal activity against several filamentous fungi. The phenotypic, biochemical and chemotaxonomic properties indicated that the strain could be clearly distinguished from other species of Amycolatopsis, and thus the name Amycolatopsis mongoliensis sp. nov. is proposed accordingly (type strain, 4-36T=KCTC 39526T=JCM 30565T).

Machine Learning Demonstrates Dominance of Physical Characteristics over Particle Composition in Coal Dust Toxicity.

Mine dust has been linked to the development of pneumoconiotic diseases such as silicosis and coal workers' pneumoconiosis. Currently, it is understood that the physicochemical and mineralogical characteristics drive the toxic nature of dust particles; however, it remains unclear which parameter(s) account for the differential toxicity of coal dust. This study aims to address this issue by demonstrating the use of the partial least squares regression (PLSR) machine learning approach to compare the influence of D50 sub 10 µm coal particle characteristics against markers of cellular damage. The resulting analysis of 72 particle characteristics against cytotoxicity and lipid peroxidation reflects the power of PLSR as a tool to elucidate complex particle-cell relationships. By comparing the relative influence of each characteristic within the model, the results reflect that physical characteristics such as shape and particle roughness may have a greater impact on cytotoxicity and lipid peroxidation than composition-based parameters. These results present the first multivariate assessment of a broad-spectrum data set of coal dust characteristics using latent structures to assess the relative influence of particle characteristics on cellular damage.

Characteristics of China's coal mine methane emission sources at national and provincial levels.

Methane (CH4) is the second most abundant greenhouse gas. China is the largest CH4 emitter in the world, with coal mine methane (CMM) being one of the main anthropogenic contributions. Thus, there is an urgent need for comprehensive estimates and strategies for reducing CMM emissions in China. However, the development of effective strategies is currently challenged by a lack of information on temporal variations in the contributions of different CMM sources and the absence of provincial spatial analysis. Here, considering five sources and utilization, we build a comprehensive inventory of China's CMM emissions from 1980 to 2022 and quantify the contributions of individual sources to the overall CMM emissions at the national and provincial levels. Our results highlight a significant shift in the source contributions of CMM emissions, with the largest contributor, underground mining, decreasing from 89% in 1980 to 69% in 2022. Underground abandoned coal mines, which were ignored or underestimated in past inventories, have become the second source of CMM emissions since 1999. From 2011 to 2022, we identified Shanxi, Guizhou, and Shaanxi as the three largest CMM-emitting provinces, while the Emissions Database for Global Atmospheric Research (EDGAR) v8 overestimated emissions from Inner Mongolia, ranking it third. Notably, we observed a substantial decrease (exceeding 1 Mt) in CMM emissions in Sichuan, Henan, Liaoning, and Hunan between 2011 and 2022, which was not captured by EDGAR v8. To develop targeted CMM emission reduction strategies at the provincial level, we classified 31 provinces into four groups based on their CMM emission structures. In 2022, the number of provinces with CMM emissions mainly from abandoned coal mines has exceeded that of provinces with mainly underground mines, which requires attention. This study reveals the characteristics of the source of CMM emissions in China and provides emission reduction directions for four groups of provinces.

Ventilation heterogeneity is increased in adults exposed to coal mine fire-related PM2.5.

BACKGROUND AND OBJECTIVES: The Hazelwood Health Study was set up to study long-term health effects of a mine fire that blanketed residents of the Latrobe Valley with smoke for 45 days in 2014. The Respiratory Stream specifically assessed the impact of fine particulate matter <2.5 µm diameter (PM2.5) exposure from mine fire smoke on lung health. The multiple breath nitrogen washout (MBW) test assesses ventilation heterogeneity, which may detect sub-clinical airways dysfunction not identified using standard tests such as spirometry. This analysis assessed the association of PM2.5 exposure with measures of ventilation heterogeneity. METHODS: Exposed (Morwell) and unexposed (Sale) participants were recruited 3.5-4 years after the fire from those who had participated in an Adult Survey. MBW was performed to measure lung clearance index (LCI), functional residual capacity (FRC), acinar (Sacin) and conductive (Scond) ventilation heterogeneity. PM2.5 exposure was estimated with emission and chemical transport models. Multivariable linear regression models were fitted controlling for confounders. RESULTS: We recruited 519 participants. MBW tests were conducted on 504 participants with 479 acceptable test results (40% male; 313 exposed, 166 unexposed). Exposure to mine fire-related PM2.5 was associated with increasing Scond (ß = 1.57/kL, 95%CI: 0.20-2.95, p = 0.025), which was comparable to the estimated effect on Scond of 4.7 years of aging. No other MBW outcomes were statistically different. CONCLUSION: Increasing exposure to PM2.5 was associated with increased ventilation heterogeneity in the conductive region of the lungs 4 years after the event.

Associations of coal mine dust exposure with arterial stiffness and atherosclerotic cardiovascular disease risk in chinese coal miners.

OBJECTIVE: Whether coal mine dust exposure increases cardiovascular diseases (CVDs) risk was rarely explored. Our objective was to examine the association between coal mine dust exposure and cardiovascular risk. METHODS: We estimated cumulative coal mine dust exposure (CDE) for 1327 coal miners by combining data on workplace dust concentrations and work history. We used brachial-ankle pulse wave velocity (baPWV, a representative indicator of arterial stiffness) and ten-year atherosclerotic cardiovascular disease (ASCVD) risk to assess potential CVD risk, exploring their associations with CDE. RESULTS: Positive dose-response relationships of CDE with baPWV and ten-year ASCVD risk were observed after adjusting for covariates. Specifically, each 1 standard deviation (SD) increase in CDE was related to a 0.27 m/s (95% CI: 0.21, 0.34) increase in baPWV and a 1.29 (95% CI: 1.14, 1.46) elevation in OR (odds ratio) of risk of abnormal baPWV. Moreover, each 1 SD increase in CDE was associated with a 0.74% (95% CI: 0.63%, 0.85%) increase in scores of ten-year ASCVD and a 1.91 (95% CI: 1.62, 2.26) increase in OR of risk of ten-year ASCVD. When compared with groups unexposed to coal mine dust, significant increase in the risk of arterial stiffness and ten-year ASCVD in the highest CDE groups were detected. CONCLUSION: The study suggested that cumulative exposure to coal mine dust was associated with elevated arterial stiffness and ten-year ASCVD risk in a dose-response manner. These findings contribute valuable insights for cardiovascular risk associated with coal mine dust.

Resting diffusing capacity and severity of radiographic disease predict gas exchange abnormalities with exercise in former US coal miners.

BACKGROUND: The US Department of Labor (DOL) does not fund diffusing capacity (DLCO) or metabolic measurements from cardiopulmonary exercise testing (CPET) for coal miners' disability evaluations. Although exercise arterial blood gas testing is covered, many miners are unable to perform maximal tests, and sampling at peak exercise can be challenging. We explored the relationship between resting DLCO, radiographic disease severity, and CPET abnormalities in former US coal miners. METHODS: We analyzed data from miners evaluated between 2005 and 2015. Multivariable linear and logistic regression analyses were used to examine relationships between percent predicted (pp) forced expiratory volume in 1 s (FEV1pp), DLCOpp, VO2maxpp, A-a oxygen gradient (A-a)pp, dead space fraction (Vd/Vt), disabling oxygen tension (PO2), and radiographic findings of pneumoconiosis. RESULTS: Data from 2015 male coal miners was analyzed. Mean tenure was 28 years (SD 8.6). Thirty-twopercent had an abnormal A-a gradient (>150 pp), 20% had elevated Vd/Vt (>0.33), and 34% a VO2max < 60 pp. DLCOpp strongly predicted a disabling PO2, with an odds ratio (OR) of 2.33 [2.09-2.60], compared to 1.18 [1.08-1.29] for FEV1. Each increase in subcategory of small opacity (simple) pneumoconiosis increased the odds of a disabling PO2 by 42% [1.29-1.57], controlling for age, body mass index, pack-years of tobacco smoke exposure, and years of coal mine employment. CONCLUSIONS: DLCO is the best resting pulmonary function test predictor of CPET abnormalities. Radiographic severity of pneumoconiosis was also associated with CPET abnormalities. These findings support funding DLCO testing for impairment and suggest the term "small opacity" should replace "simple" pneumoconiosis to reflect significant associations with impairment.

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face.

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model's computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model's computational requirements and volume while maintaining high accuracy.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Observer-Based Adaptive Sliding Mode Compensation Position-Tracking Control for Drilling Tool Attitude Adjustment.

This study develops an adaptive sliding mode control approach for a drilling tool attitude adjustment system, aiming at solving the problems of model uncertainties and insufficient ability of disturbance suppression during the regulation behavior. To further improve the performance of the position-tracking loop in terms of response time, tracking accuracy, and robustness, a state observer based on an improved radial basis function is designed to approximate the model uncertainties, a valve dead-zone compensate controller is used to reduce control deviation, an adaptive sliding mode controller is designed to improve the position-tracking precision and attenuate sliding mode chattering. Finally, simulation and experimental results are carried out to verify the observability of the model uncertainties and position-tracking errors of the drilling tool attitude adjustment system, which can effectively improve the position-tracking performance and robustness of the drilling tool attitude adjustment system.

Experimental Study on Emergency Psychophysiological and Behavioral Reactions to Coal Mining Accidents.

Effective emergency responses are crucial for preventing coal mine accidents and mitigating injuries. This paper aims to investigate the characteristics of emergency psychophysiological reactions to coal mine accidents and to explore the potential of key indicators for identifying emergency behavioral patterns. Initially, virtual reality technology facilitated a simulation experiment for emergency escape during coal mine accidents. Subsequently, the characteristics of emergency reactions were analyzed through correlation analysis, hypothesis testing, and analysis of variance. The significant changes in physiological indicators were then taken as input features and fed into the three classifiers of machine learning algorithms. These classifications ultimately led to the identification of behavioral patterns, including agility, defensiveness, panic, and rigidity, that individuals may exhibit during a coal mine accident emergency. The study results revealed an intricate relationship between the mental activities induced by accident stimuli and the resulting physiological changes and behavioral performances. During the virtual reality simulation of a coal mine accident, subjects were observed to experience significant physiological changes in electrodermal activity, heart rate variability, electromyogram, respiration, and skin temperature. The random forest classification model, based on SCR + RANGE + IBI + SDNN + LF/HF, outperformed all other models, achieving accuracies of up to 92%. These findings hold promising implications for early warning systems targeting abnormal psychophysiological and behavioral reactions to emergency accidents, potentially serving as a life-saving measure in perilous situations and fostering the sustainable growth of the coal mining industry.

Evaluation of PVC and PTFE filters for direct-on-filter crystalline silica quantification by FTIR.

Direct-on-Filter (DoF) analysis of respirable crystalline silica (RCS) by Fourier Transform Infrared (FTIR) spectroscopy is a useful tool for assessing exposure risks. With the RCS exposure limits becoming lower, it is important to characterize and reduce measurement uncertainties. This study systematically evaluated two filter types (i.e., polyvinyl chloride [PVC] and polytetrafluoroethylene [PTFE]) for RCS measurements by DoF FTIR spectroscopy, including the filter-to-filter and day-to-day variability of blank filter FTIR reference spectra, particle deposition patterns, filtration efficiencies, and pressure drops. For PVC filters sampled at a flow rate of 2.5 L/min for 8 h, the RCS limit of detection (LOD) was 7.4 µg/m3 when a designated laboratory reference filter was used to correct the absorption by the filter media. When the spectrum of the pre-sample filter (blank filter before dust sampling) was used for correction, the LOD could be up to 5.9 µg/m3. The PVC absorption increased linearly with reference filter mass, providing a means to correct the absorption differences between the pre-sample and reference filters. For PTFE, the LODs were 12 and 1.2 µg/m3 when a designated laboratory blank or the pre-sample filter spectrum was used for blank correction, respectively, indicating that using the pre-sample blank spectrum will reduce RCS quantification uncertainty. Both filter types exhibited a consistent radially symmetric deposition pattern when particles were collected using 3-piece cassettes, indicating that RCS can be quantified from a single measurement at the filter center. The most penetrating aerodynamic diameters were around 0.1 µm with filtration efficiencies ≥ 98.8% across the measured particle size range with low-pressure drops (0.2-0.3 kPa) at a flow rate of 2.5 L/min. This study concludes that either the PVC or the PTFE filters are suitable for RCS analysis by DoF FTIR, but proper methods are needed to account for the variability of blank absorption among different filters.

Effects of groundwater level changes on soil characteristics and vegetation response in arid and semiarid coal mining areas.

Coal mining in arid and semiarid regions often leads to numerous ecological and environmental problems, such as aquifer depletion, lake shrinkage, vegetation degradation, and surface desertification. The drainage from coal mining activities is a major driving force in the evolution of the groundwater-soil-vegetation system. In order to explore the effect of groundwater level fluctuation on soil properties and the response mechanism of surface vegetation in coal mining areas, this study is based on hydrogeological and ecological vegetation investigations in the Bojianghaizi Basin, and soil and vegetation samples are collected in the areas with different groundwater levels, and soil and vegetation indexes are analyzed with the aid of methods such as numerical statistics, linear regression, and correlation analysis with the aid of the Origin software. The results show that there is a significant negative correlation between groundwater table (GWT) and soil water content (SWC), soil conductivity, soil organic matter (SOM), soil available nitrogen (SAN), and soil available potassium (SAK). Mining activities have led to the destruction of the soil structure, greatly reducing its ability to retain water and fertilizer. The contents of SWC, SOM, and SAN in the mining area are significantly reduced, which are at least 49.73%, 47.56% and 59.90% lower than those around the mining area. On the northern and southern sides of the lake, serious soil salinization exists in the lakeshore zone where the depth to the water table is <0.5 m, and the water required for the growth of vegetation here mainly comes from the groundwater, so there are only a few water-loving and saline-resistant plants; when the depth to the water table is 0.5-7 m, the growth of surface vegetation is influenced by the double impacts of the water table and atmospheric precipitation with a high degree of species richness; when the depth to the water table is >7 m, the surface vegetation is only dependent on the limited atmospheric precipitation for water. When the depth of groundwater is >7 m, the surface vegetation only relies on limited atmospheric precipitation for water, and drought-tolerant plants mainly grow in these areas. This study not only provides a scientific basis for the sustainable development and environmental protection of similar mines in the world, but also has important significance in guiding the ecological management and rational utilization of water resources in coal mine areas. What is more, This study provides valuable insights into sustainable water resource management in arid and semi-arid regions, crucial for mitigating the ecological impacts of coal mining activities.

[Neural network analysis of mechanization's impact on coal miner's occupational health].

In order to clarify the transmission mechanism of the impact of mechanization on the occupational health of miners and to provide empirical evidence for the development of new quality productivity in the coal industry that balances health and efficiency. In August 2022, we selected a typical coal mine, constructed a comprehensive evaluation index of miners' occupational health through a questionnaire survey based on the fully connected neural network model. A Bayesian model was used to verify the influence of mechanization level on miners' occupational health. We found that: the predicted probability of occupational diseases could be used as a comprehensive indicator of the level of occupational health, providing a basis for early intervention and prevention of occupational diseases. Mechanization could directly promote the improvement of miners' occupational health level, and also indirectly affect occupational health level by influencing hazards level and work intensity. The indirect effect of mechanization on work intensity was positive, and the indirect effect of mechanization on hazards level was positive. Presented the "inverted U-shaped" process in the mechanization breakthrough semi-mechanized level would realize the economies of scale of health protection, its impact on the prevention and control of occupational hazards would turn from negative to positive.

Insights into relationships between polycyclic aromatic hydrocarbon concentration, bacterial communities and organic matter composition in coal gangue site.

Coal mining usually brought polycyclic aromatic hydrocarbons (PAHs) contamination. Relationships between the concentration of PAHs, bacterial communities and soil environmental factors were important for bioremediation of PAHs in soil. Total 4 kinds of soil samples with different concentrations of PAHs were selected from 7 typical coal gangue（CG） sites in Huainan, Anhui Province. The relationships between microorganisms, dissolved organic matter (DOM) composition and PAHs concentration were systematically analyzed in this work. Total 11 kinds of PAHs were enriched in the soil surface layer. That was attributed to the strong binding of soil organic matter (SOM) to PAHs. PAHs contamination reduced the diversity of soil microbial. The abundance of PAHs-degrading genera such as Arthrobacter decreased with the increasing concentration of PAHs. Mycobacterium increased with the increasing concentration of PAHs in all samples. The microbial activities decreased with increasing concentration of PAHs. The increasing contents of LWM-PAHs and DOM were beneficial to improve the activities of soil microbial. The increasing DOM aromaticity was beneficial to improve the bioavailability of PAHs according to the correlation analysis between PAHs content and DOM structural parameters. The obtained results provide a basis for better understanding the contamination characteristics and microbial communities of coal gangue PAH-contaminated sites.

Impacts of high-quality coal mine drainage recycling for replenishment of aquatic ecosystems in arid regions of China: Bacterial community responses.

Coal mine water is usually recycled as supplementary water for aquatic ecosystems in arid and semiarid mining regions of China. To ensure ecosystem health, the coal mine water is rigorously treated using several processes, including reverse osmosis, to meet surface water quality standards. However, the potential environmental impacts of this management pattern on the ecological function of receiving water bodies are unclear. In this study, we built several microcosm water ecosystems to simulate the receiving water bodies. High-quality treated coal mine drainage was mixed into the model water bodies at different concentrations, and the sediment bacterial community response and functional changes were systematically investigated. The results showed that the high-quality coal mine drainage could still shape bacterial taxonomic diversity, community composition and structure, with a concentration threshold of approximately 50%. Moreover, both the Mantel test and the structural equation model indicated that the salinity fluctuation caused by the receiving of coal mine drainage was the primary factor shaping the bacterial communities. 10 core taxa in the molecular ecological network influenced by coal mine drainage were identified, with the most critical taxa being patescibacteria and g_Geothermobacter. Furthermore, the pathway of carbohydrate metabolism as well as signaling molecules and interactions was up-regulated, whereas amino acid metabolism showed the opposite trend. All results suggested that the complex physical-chemical and biochemical processes in water ecosystems may be affected by the coal mine drainage. The bacterial community response and underlying functional changes may accelerate internal nutrient cycling, which may have a potential impact on algal bloom outbreaks.

Study on the Recognition of Coal Miners' Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision.

The hoist cage is used to lift miners in a coal mine's auxiliary shaft. Monitoring miners' unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners' statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light.

3D LiDAR Point Cloud Registration Based on IMU Preintegration in Coal Mine Roadways.

Point cloud registration is the basis of real-time environment perception for robots using 3D LiDAR and is also the key to robust simultaneous localization and mapping (SLAM) for robots. Because LiDAR point clouds are characterized by local sparseness and motion distortion, the point cloud features of coal mine roadway environments show a weak texture and degradation. Therefore, for these environments, the traditional point cloud registration method to register directly will lead to problems, such as a decline in registration accuracy, z-axis drift, and map ghosting. To solve the above problems, we propose a point cloud registration method based on IMU preintegration with the sensor characteristics of LiDAR and IMU. The system framework of this method mainly consists of four modules: IMU preintegration, point cloud preprocessing, point cloud frame matching and point cloud registration. First, IMU sensor data are introduced, and IMU linear interpolation is used to correct the motion distortion in LiDAR scanning, and the IMU preintegration error function is constructed. Second, the point cloud segmentation is performed using the ground segmentation method of RANSAC to provide additional ground constraints for the z-axis displacement and to remove the unstable flawed points from the point cloud. On this basis, the LiDAR point cloud registration error function is constructed by extracting the feature corner points and feature plane points. Finally, the Gaussian Newton solution is used to optimize the constraint relationship between the LiDAR odometry frames to minimize the error function, complete the LiDAR point cloud registration and better estimate the position and pose of the mobile robot. The experimental results show that compared with the traditional point cloud registration method, the proposed method has a higher point cloud registration accuracy, success rate and computational efficiency. The LiDAR odometry constructed using this method can better reflect the authenticity of the robot trajectory and has higher trajectory accuracy and smaller absolute position and pose error.

A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment.

Coal is an important resource that is closely related to people's lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.