Liaoning Technical University

The KBr pellet press method for detecting the infrared spectrum of coal is one of the commonly used methods for analyzing the types and content of functional groups in coal. However, KBr crystalline water or moisture has a significant impact on the peak position, peak shape, and peak area of the organic O–H based stretching vibration wave in coal. In this paper, the theoretical characteristics of infrared spectra of phenols and alcohols have been simulated and analyzed using the Gaussian 16 series of programs. Four infrared spectral analysis techniques, in situ infrared, KBr pellet press, dry KBr pellet press, and paste methods, have been used to detect the infrared spectra of coal. The results show that the stretching vibration peaks of free O–H radicals without hydrogen bonding are located between 3700 and 3600 cm–1. After the O–H form hydrogen bonds with each other, the O–H stretching vibration frequency moves toward the low frequency direction, and the lower the wavenumber, the more O–H content. The conventional KBr gasket manufacturing process will absorb moisture in the air to interfere with the hydroxyl absorption peak of coal, and the experimental process requires absolute drying. The relative content of hydroxyl in coal can be compared and analyzed based on the peak position, peak shape, and peak area of the hydroxyl stretching vibration wave. Quantitative analysis of hydroxyl groups in coal also requires combination of elemental analysis and X-ray photoelectron spectroscopy.

Microbes play key roles in various biogeochemical processes, including carbon (C) and nitrogen (N) cycling. However, changes of microbial community at the functional gene level by livestock grazing, which is a global land-use activity, remain unclear. Here we use a functional gene array, GeoChip 4.0, to examine the effects of free livestock grazing on the microbial community at an experimental site of Tibet, a region known to be very sensitive to anthropogenic perturbation and global warming. Our results showed that grazing changed microbial community functional structure, in addition to aboveground vegetation and soil geochemical properties. Further statistical tests showed that microbial community functional structures were closely correlated with environmental variables, and variations in microbial community functional structures were mainly controlled by aboveground vegetation, soil C/N ratio, and NH4 (+) -N. In-depth examination of N cycling genes showed that abundances of N mineralization and nitrification genes were increased at grazed sites, but denitrification and N-reduction genes were decreased, suggesting that functional potentials of relevant bioprocesses were changed. Meanwhile, abundances of genes involved in methane cycling, C fixation, and degradation were decreased, which might be caused by vegetation removal and hence decrease in litter accumulation at grazed sites. In contrast, abundances of virulence, stress, and antibiotics resistance genes were increased because of the presence of livestock. In conclusion, these results indicated that soil microbial community functional structure was very sensitive to the impact of livestock grazing and revealed microbial functional potentials in regulating soil N and C cycling, supporting the necessity to include microbial components in evaluating the consequence of land-use and/or climate changes.

Coal and gas outburst is an extremely complex dynamic disaster in coal mine production process which will damage casualties and equipment facilities, and disorder the ventilation system by suddenly ejecting a great amount of coal and gas into roadway or working face. This paper analyzed the interaction among the three essential elements of coal and gas outburst dynamic system. A stress-seepage-damage coupling model was established which can be used to simulate the evolution of the dynamical system, and then the size scale of coal and gas outburst dynamical system was investigated. Results show that the dynamical system is consisted of three essential elements, coal-gas medium (material basis), geology dynamic environment (internal motivation) and mining disturbance (external motivation). On the case of C13 coal seam in Panyi Mine, the dynamical system exists in the range of 8–12 m in front of advancing face. The size scale will be larger where there are large geologic structures. This research plays an important guiding role for developing measures of coal and gas outburst prediction and prevention. Keywords: Coal and gas outburst, Dynamic system, Coal-gas medium, Geology dynamic environment, Mining disturbance, Stress-seepage-damage coupling model

The power Bonferroni mean (PBM) operator can relieve the influence of unreasonable aggregation values and also capture the interrelationship among the input arguments, which is an important generalization of power average operator and Bonferroni mean operator, and Pythagorean fuzzy set is an effective mathematical method to handle imprecise and uncertain information. In this paper, we extend PBM operator to integrate Pythagorean fuzzy numbers (PFNs) based on the interaction operational laws of PFNs, and propose Pythagorean fuzzy interaction PBM operator and weighted Pythagorean fuzzy interaction PBM operator. These new Pythagorean fuzzy interaction PBM operators can capture the interactions between the membership and nonmembership function of PFNs and retain the main merits of the PBM operator. Then, we analyze some desirable properties and particular cases of the presented operators. Further, a new multiple attribute decision making method based on the proposed method has been presented. Finally, a numerical example concerning the evaluation of online payment service providers is provided to illustrate the validity and merits of the new method by comparing it with the existing methods.

Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS) imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN) architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%.

Present mobile devices, transportation tools, and renewable energy technologies are more dependent on newly developed battery chemistries than ever before. Intrinsic properties, such as safety, high energy density, and cheapness, are the main objectives of rechargeable batteries that have driven their overall technological progress over the past several decades. Unfortunately, it is extremely hard to achieve all these merits simultaneously at present. Alternatively, exploration of the most suitable batteries to meet the specific requirements of an individual application tends to be a more reasonable and easier choice now and in the near future. Based on this concept, here, a range of promising alternatives to lithium-sulfur batteries that are constructed with non-Li metal anodes (e.g., Na, K, Mg, Ca, and Al) and sulfur cathodes are discussed. The systems governed by these new chemistries offer high versatility in meeting the specific requirements of various applications, which is directly linked with the broad choice in battery chemistries, materials, and systems. Herein, the operating principles, materials, and remaining issues for each targeted battery characteristics are comprehensively reviewed. By doing so, it is hoped that their design strategies are illustrated and light is shed on the future exploration of new metal-sulfur batteries and advanced materials.

Purpose – The purpose of this paper is to investigate service quality of e-commerce Websites in online platform and their contribution on e-business promotion. Design/methodology/approach – The online survey was performed on a survey portal provided by Nepal Telecom in Nepal. Findings – The findings of this study suggest that information quality and online service quality were the key determinants for user satisfaction and sustainability of e-commerce technology. Research limitations/implications – Research opportunities of web services and e-commerce area are fruitful and important for both academics and practitioners. Practical implications – The findings on online service quality of e-commerce technology will be useful for current management practice such as making business policies and strategies and sharing information to managers and organization leaders. This study can be used for e-commerce Website operators wishing to enhance the competitiveness of their Websites in the highly competitive online market. Originality/value – E-commerce is considered an excellent alternative for individuals and companies to reach new customers. Service quality delivery through Internet is an essential strategy to success, more important than price and web presence. The e-commerce Website has been identified as having a significant impact on business activities in solving the geographical problem. A number of performance problems have been observed for e-commerce Websites, and much work has gone into characterizing the performance of web-servers and Internet applications.

Micro-organisms play critical roles in many important biogeochemical processes in the Earth's biosphere. However, understanding and characterizing the functional capacity of microbial communities are still difficult due to the extremely diverse and often uncultivable nature of most micro-organisms. In this study, we developed a new functional gene array, GeoChip 4, for analysing the functional diversity, composition, structure, metabolic potential/activity and dynamics of microbial communities. GeoChip 4 contained approximately 82 000 probes covering 141 995 coding sequences from 410 functional gene families related to microbial carbon (C), nitrogen (N), sulphur (S), and phosphorus (P) cycling, energy metabolism, antibiotic resistance, metal resistance/reduction, organic remediation, stress responses, bacteriophage and virulence. A total of 173 archaeal, 4138 bacterial, 404 eukaryotic and 252 viral strains were targeted, providing the ability to analyse targeted functional gene families of micro-organisms included in all four domains. Experimental assessment using different amounts of DNA suggested that as little as 500 ng environmental DNA was required for good hybridization, and the signal intensities detected were well correlated with the DNA amount used. GeoChip 4 was then applied to study the effect of long-term warming on soil microbial communities at a Central Oklahoma site, with results indicating that microbial communities respond to long-term warming by enriching carbon degradation, nutrient cycling (nitrogen and phosphorous) and stress response gene families. To the best of our knowledge, GeoChip 4 is the most comprehensive functional gene array for microbial community analysis.

This study proposes a new optimal hybrid renewable energy system (HRES) arrangement, including a photovoltaic system, wind turbine, and fuel cell, for electrifying a remote area in Turkey. The study is based on considering system cost and reliability. To deliver an optimal configuration, system sizing has been designed based on an Amended version of the DragonFly optimizer. The achievements of the method have been then compared with some other published methods, including Particle Swarm Optimizer (PSO)-based algorithm and Firefly (FA)-based method. Simulation results show that the proposed method with 1,888,827.5 USD provides the minimum Net Present Cost value among the others. The main idea is to assess the objective function by lessening the Net Present Cost (NPC) by confirming based on the loss of power supply probability (LPSP). Final simulations indicated that the proposed approach provides lower NPC and LCOE toward the others.

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>F</mml:mi></mml:math>-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

In view of the fugitive dusts caused by wind disturbance and material handling in coal bunkers, surface plants, and open-air coal stocking yards of coal businesses, the solidifying dust suppressant based on modified chitosan is synthesized and prepared through the chemical modification of –NH 2 with chitosan as a raw material and –NH 2 was replaced by –CH 2 CH(OH)CH 2 N + (CH 3 )CI − through the technique of Fourier transform infrared spectroscopy. According to viscosity experiment results, the viscosity of the modified solidifying dust suppressant increased significantly. The coal particles suppressed by the dust suppressant as observed with a 50,000X scanning electron microscope were coagulated together, which indicated very good cohesion effect. In addition, wind erosion resistance experiment was conducted to analyze the wind erosion rate of coal powders before and after sprayed with the suppressant at different wind speeds, which indicated that the dust suppressant can effectively prevent fugitive dusts at a wind speed of 17 m/s.

Road extraction is one of the most significant tasks for modern transportation systems. This task is normally difficult due to complex backgrounds such as rural roads that have heterogeneous appearances with large intraclass and low interclass variations and urban roads that are covered by vehicles, pedestrians and the shadows of surrounding trees or buildings. In this paper, we propose a novel method for extracting roads from optical satellite images using a refined deep residual convolutional neural network (RDRCNN) with a postprocessing stage. RDRCNN consists of a residual connected unit (RCU) and a dilated perception unit (DPU). The RDRCNN structure is symmetric to generate the outputs of the same size. A math morphology and a tensor voting algorithm are used to improve RDRCNN performance during postprocessing. Experiments are conducted on two datasets of high-resolution images to demonstrate the performance of the proposed network architectures, and the results of the proposed architectures are compared with those of other network architectures. The results demonstrate the effective performance of the proposed method for extracting roads from a complex scene.

Abstract We report laboratory experiments to investigate the dynamic failure characteristics of outburst‐prone coal using a split Hopkinson pressure bar (SHPB). For comparison, two groups of experiments are completed on contrasting coals—the first outburst‐prone and the second outburst‐resistant. The dynamic mechanical properties, failure processes, and energy dissipation of both outburst‐prone and outburst‐resistant coals are comparatively analyzed according to the obtained dynamic compressive and tensile stress‐strain curves. Results show that the dynamic stress‐strain response of both outburst‐prone and outburst‐resistant coal specimens comprises stages of compression, linear elastic deformation, then microfracture evolution, followed by unstable fracture propagation culminating in rapid unloading. The mechanical properties of both outburst‐prone and outburst‐resistant coal specimens exhibit similar features: The uniaxial compressive strength and indirect tensile strength increase linearly with the applied strain rate, and the peak strain increases nonlinearly with the strain rate, whereas the elastic modulus does not exhibit any clear strain rate dependency. Differences in the dynamic failure characteristics between outburst‐prone and outburst‐resistant coals also exist. The hardening effect of strain rate on outburst‐prone coal is more apparent than on outburst‐resistant coal, which is reflected in the dynamic increase factor at the same strain rate. However, the dynamic strength of outburst‐prone coals is still lower than that of outburst‐resistant coals due to its low quasi‐static strength. The dissipated energy of outburst‐prone coal is smaller than that of outburst‐resistant coal. Therefore, the outburst‐prone coal, characterized by low strength, high deformability, and small energy dissipation when dynamically loaded to failure, is more favorably disposed to the triggering and propagation of gas outbursts.

ABSTRACT To mitigate the dust dispersion pollution in the open‐pit coal mines, this study experimentally develops a novel environmentally friendly coal dust suppressant. The experiment uses naturally biodegradable soybean protein isolate (SPI) as the main material and utilizes anion surfactant sodium dodecyl sulfate (SDS) to modify SPI. In the carboxymethylcellulose sodium and sodium silicate and other auxiliary agents, this process produces gives rise to the SDS‐SPI coal dust suppressant. Experimental characterization of the developed dust suppressant reveals that the viscosity of the 5% dust suppressant solution can reach 24.6 mPa s. Correspondingly, the compressive strength reaches 0.48 MPa, and the dust suppression efficiency can reach 93.47% with the presence of force 9 wind. Furthermore, this study uses Fourier‐transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) to analyze the dust suppression mechanism of the developed dust suppressant. It is observed that a layer of compact hardened shell is formed at the surface of the coal powder treated with the newly developed dust suppressant. Also, there exists a strong cementing effect among dust particles, yielding a decent cementing performance. Therefore, the present dust suppressant can effectively suppress dust dispersion in the open‐pit coal mines, allowing a mitigation of the environmental pollution. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47354.

Abstract Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data (real data) has been extremely costly owing to the amount of human effort and expertise required. Here, we develop a novel transfer learning strategy to address problems of small or insufficient data. This strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining procedure. For a specific task of grain instance image segmentation, this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the “image style” information in real images. The results show that the model trained with the acquired synthetic data and only 35% of the real data can already achieve competitive segmentation performance of a model trained on all of the real data. Because the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data, our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

Fusing a low-spatial-resolution hyperspectral data with a high-spatial-resolution (HSR) multispectral data has been recognized as an economical approach for obtaining HSR hyperspectral data, which is important to accurate identification and classification of the underlying materials. A natural and promising fusion criterion, called coupled nonnegative matrix factorization (CNMF), has been reported that can yield high-quality fused data. However, the CNMF criterion amounts to an ill-posed inverse problem, and hence, advisable regularization can be considered for further upgrading its fusion performance. Besides the commonly used sparsity-promoting regularization, we also incorporate the well-known sum-of-squared-distances regularizer, which serves as a convex surrogate of the volume of the simplex of materials’ spectral signature vectors (i.e., endmembers), into the CNMF criterion, thereby leading to a convex formulation of the fusion problem. Then, thanks to the biconvexity of the problem nature, we decouple it into two convex subproblems, which are then, respectively, solved by two carefully designed alternating direction method of multipliers (ADMM) algorithms. Closed-form expressions for all the ADMM iterates are derived via convex optimization theories (e.g., Karush–Kuhn–Tucker conditions), and furthermore, some matrix structures are employed to obtain alternative expressions with much lower computational complexities, thus suitable for practical applications. Some experimental results are provided to demonstrate the superior fusion performance of the proposed algorithm over state-of-the-art methods.

With the urgent requirement for high-performance rechargeable Li-S batteries, besides various carbon materials and metal compounds, lots of conducting polymers have been developed and used as components in Li-S batteries. In this review, the synthesis of polyaniline (PANI), polypyrrole (PPy) and polythiophene (PTh) is introduced briefly. Then, the application progress of the three conducting polymers is summarized according to the function in Li-S batteries, including coating layers, conductive hosts, sulfur-containing compounds, separator modifier/functional interlayer, binder and current collector. Finally, according to the current problems of conducting polymers, some practical strategies and potential research directions are put forward. We expect that this review will provide novel design ideas to develop conducting polymer-containing high-performance Li-S batteries.

Soil organic carbon (SOC) has a large impact on soil quality and global climate change. It is therefore important to be able to predict SOC accurately to promote sustainable soil management. Although the synthetic aperture radar (SAR) has many advantages and has been widely used in soil science research, it has rarely been used in previous SOC mapping studies based on remote sensing images. The purpose of this study was to investigate the ability of multi-temporal Sentinel-1A data in SOC prediction, by comparing the predictive performance of random forest (RF) and boosted regression tree (BRT) models in the Heihe River Basin in northwestern China. A set of 162 topsoil (0–20 cm) samples were taken and 15 environmental variables were obtained including land use, topography, climate, and remote sensing images (optical and SAR data). Using a cross-validation procedure to evaluate the performance of the models, three statistical indices were calculated. Overall, both RF and BRT models effectively predicted SOC content, exhibiting similar performance and producing similar spatial distribution patterns of SOC. The results showed that the addition of multi-temporal Sentinel-1A images improved prediction accuracy, with the root mean squared error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) improving by 9.0%, 8.3% and 13.5%, respectively. Furthermore, the combination of all environmental variables had the best prediction performance explaining 75% of SOC variation. The most important environmental variables explaining SOC variation were precipitation, elevation, and temperature. The multi-temporal Sentinel-1A data in RF and BRT models explained 9% and 7%, respectively. The results from our case study highlight the usefulness of multi-temporal Sentinel-1 data in SOC mapping.

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs.

Carbon dioxide (CO2)-enhanced coalbed methane recovery (CO2-ECBM) is a critical way to increase methane production and reduce greenhouse gas (CO2 and CH4) emissions. As captured CO2 is continuously injected in the coal seams, a low cost of CO2 sequestration and high efficiency of CH4 recovery can be achieved via the flooding and replacing effects driven by the injected CO2 flow. Scientific insights into the complex process of CO2-ECBM in experiments, modelings, and technological developments need to be made to propose appropriate countermeasures. This review first highlights the progress of CO2-ECBM under laboratory conditions, e.g., the binary gas competitive adsorption and gas displacement experiments in the macroscale and porous structure tests using technologies of nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and computed tomography (CT) in the microscale. Then, the advances of mathematical models for changing in coal permeability and porosity during CO2-ECBM are reviewed, accompanying with the multi-field and multi-phase coupling responses of competitive sorption, diffusion, gas–water seepage, heat transfer, and solid deformation. Furthermore, the field pilot tests of CO2-ECBM in various countries and regions are also covered to reveal the key technical challenges confronted with the development of CO2-ECBM technology. The perspectives in experiments, models, and field pilots of CO2-ECBM are made, which include but are not limited to the following: conducting a core CH4/CO2 flooding test under in situ conditions, modeling CO2-ECBM with real fractures/faults and coal failure, developing a new method for gas migration and leakage monitoring in the field, and enacting relevant standards, laws, and regulations to promote CO2-ECBM.