Calculation Of Parameters Research Articles

Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks

The exploitation of the spatial and spectral characteristics of hyperspectral remote sensing images (HRSIs) for the high-precision classification of earth observation targets is crucial. Convolutional neural networks (CNNs) have good classification performance and are widely used neural networks. Herein, a morphological processing (MP)-based HRSI classification method and a 3D–2D CNN are proposed to improve HRSI classification accuracy. Principal component analysis is performed to reduce the dimensionality of the HRSI cube, and MP is implemented to extract the spectral–spatial features of the low-dimensional HRSI cube. The extracted features are concatenated with the low-dimensional HRSI cube, and the designed 3D–2D CNN framework completes the classification task. Residual connections and an attention mechanism are added to the CNN structure to prevent gradient vanishing, and the scale of the control parameters of the model structure is optimized to guarantee the model’s feature extraction ability. The CNN structure uses multiscale convolution, involving depthwise separable convolution, which can effectively reduce the amount of parameter calculation. Two classic datasets (Indian Pines and Pavia University) and a self-made dataset (My Dataset) are used to compare the performance of this method with existing classification techniques. The proposed method effectively improved classification accuracy despite its short classification time.

The effect of PBEsol GGA and mBJ potentials on the structural, electronic, optical, elastic and thermoelectric properties of A2BAuI6 (A = K or Rb or Cs, B = Sc or Y)

Perovskites systems are leading the photovoltaic technology now a days. For the consistent renewable energy applications new materials required with the desirable properties. In this work six new materials are being predicted which may be very useful for the renewable energy applications. The full potential scheme of linearized augmented plane wave with the local orbitals is used for the calculations with the PBEsol GGA and mBJ potentials for the exchange-correlation effects. The structural and elastic parameters of A2BAuI6 (A = K or Rb or Cs, B = Sc or Y) are computed, and the responses exhibits that such compounds are stable, have a ductile nature, and are described by a high elastic anisotropy. The bandgaps were identified via the electrical band structure computations for A2BAuI6 (A = K, Rb, Cs; B = Sc or Y) compounds as 1.25 eV, 1.64 eV, 1.24 eV, 1.62 eV, 1.25 eV and 2.04 eV with TB-mBJ + SOC approach. The calculated compounds have much dispersion in their bands and minimal carrier effective mass. Lattice thermal conductivity (KL) is computed via Slack's equation for all computed compounds are 0.29 × 1014 W/mK, 0.31 × 1014 W/mK, 0.29 × 1014 W/mK, 0.31 × 1014 W/mK, 0.39 × 1014 W/mK and 0.29 × 1014 W/mK, indicating a promising future for thermoelectric uses. The calculation of thermoelectric parameters, including the power factor, Seebeck coefficient, and figure of merit, serves another prospective purpose and confirms the potential high use of these materials in thermoelectric devices. Likewise, acceptable quality characteristics like long diffusion length, tunable band-gap, high mobility, am-bipolar charge transport, and high absorption reinforce these compounds which make them even more suitable for photovoltaic applications.

Prediction and evaluation of key parameters in coalbed methane pre-extraction based on transformer and inversion model

Accurate parameter prediction in the coalbed methane (CBM) pre-extraction process is crucial for formulating effective control measures and preventing CBM-related accidents. Traditional prediction methods rely on feature extraction or complex physical model parameter calculations, which require extensive manual intervention and have limited practical applicability. Additionally, simple neural network methods are prone to overfitting and gradient vanishing when handling parameters, and they lack the capability to dynamically monitor gas pressure during extraction, leading to inefficient and blind extraction operations. This study proposes a CBM pre-extraction parameter and completion time prediction method based on the Transformer model. By integrating autoregressive models and wavelet denoising techniques, the approach effectively captures temporal features and long-term dependencies in CBM data. Experimental results demonstrate that the proposed model outperforms traditional methods in short-, medium-, and long-term predictions, with a median R2 value of 0.99072, and 76% of the training results exceeding 0.9. Furthermore, a CBM pressure inversion model was developed, combining dimensional analysis and physical similarity principles with the Transformer model, enabling the dynamic detection of high- and low-pressure regions in coal seams. In single borehole compliance time predictions, the median compliance time for the first stage is 4 days, with an average of 49 days and a maximum of 277 days, providing adjustment guidance for boreholes with extended compliance times. The proposed model significantly improves prediction accuracy and stability, offering critical support for developing scientifically sustainable pre-extraction plans and advancing intelligent CBM management.

Triply Bifurcations and Metric Universalities in 1D Trimodal Maps

The famous Feigenbaum’s constants such as scaling factor and convergence rate can be measured and recomputed in unimodal maps, one of the methods is by the n-tupling bifurcations to chaos and parameter calculation algorithm which are called star transformation and word-lifting technique respectively. In the paper, we presented a kind of simple triply star transformation rule and the corresponding proof of admissible three super-stable kneading sequences (TSSKS), a few of TSSKS to chaos by triply bifurcations and the metric universalities are investigated.

AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices

AbstractRock mass classification is fundamental for evaluating rock mass quality, essential for stability analysis and geotechnical design. Traditional classification methods are limited by joint observation technology, which typically gathers joint information from one-dimensional or two-dimensional perspectives, failing to comprehensively capture three-dimensional joint occurrences. This often necessitates empirical formulas for joint distribution, resulting in less precise joint parameter calculations. This paper reviews 44 seminal articles on rock engineering classification in construction and subterranean projects, tracing the evolution from foundational methods like Rock Quality Designation, Rock Mass Rating, Q-system, Basic Quality, and Hydropower Classification to contemporary techniques. It highlights the transformative impact of data science, particularly artificial intelligence, on rock engineering. The analysis reveals 73 distinct algorithms used 162 times in literature, with Support Vector Machines Support, Vector Regression, K-means clustering, K-Nearest Neighbors, Artificial Neural Networks and Random Forest being the most successful. This paper examines each method's advantage and limitations, discussing the challenges of algorithm deployment in the scientific community. The findings underscore the integration of machine learning and meta-heuristic optimization methods in rock engineering classification, offering valuable insights for future research and applications.

Optimization of Nano-DAP Fertilization for Improvement in Growth and Yield of Finger Millet (Eleusine coracana (L.) Gaertn.)

Aim: Optimization of nano-DAP fertilization for improvement in growth, yield and to maximize the production of finger millet. Study Design: Randomized Block Design (RBD). Place and Duration of Study: This investigation was conducted in Kharif 2021 and 2022, with variety VL-352 at the Research and Extension Centre, Gaja, College of Forestry, Ranichauri (Tehri Garhwal), Uttarakhand, India. Methodology: This experiment comprised of ten treatments viz., T1- NK 100% (Control), T2- NPK 100%, T3- 75%P + 100%N + 100%K, T4- 50%P + 100%N + 100%K, T5- 75%P + 100%N + 100%K + Seed Treatment (ST) + Foliar Spray (FS) (2mL), T6- 75%P + 100%N + 100%K + ST + FS(4mL), T7- 50%P + 100%N + 100%K + ST + FS(2mL), T8- 50%P + 100%N + 100%K + ST + FS (4mL), T9- 50%P + 100%N + 100%K + ST + FS(2mL) + FS(2mL), T10- 50%P + 100%N + 100%K + ST + FS (4mL) + FS(4mL) and was laid out in RBD with three replications. Measurements and calculations of plant growth parameters, yield attributes, and economic considerations were made. Result: The plant growth and yield attributing parameters were recorded significantly higher in the treatment T10- 50%P +100%N + 100%K + ST + FS (4mL) + FS(4mL) while significantly lower yield was recorded in T1- NK 100% (Control). Conclusion: The use of 50% P through DAP and 100% NK along with seed treatment by nano DAP @5mL/kg seed as well as two foliar sprays of nano-DAP @4 mL/L of water, first at 30-35 days after germination and second at 45 days after seed germination gave significantly higher plant growth, seed yield and seed quality of finger millet.

Tunnel Cross-Section Deformation Monitoring Based on Mobile Laser Scanning Point Cloud.

Mobile laser scanning (MLS) has emerged as a pivotal tool for accurately collecting tunnel point cloud data and enabling the detection of tunnel deformation. This study introduces a novel approach for the precise monitoring of tunnel cross-section deformation, a critical factor in assessing stability and lining safety. The MLS system used in this study is the Self-mobile Intelligent Laser Scanning System (SILSS) for data acquisition. A comparison with corresponding data acquired by Leica P16 demonstrates that the data collected by SILSS are accurate. The methodology developed utilizes ellipticity parameters and deformation analysis indices based on the ellipse-fitting analysis of circular shield tunnel deformation. A key innovation is the robust denoising of data using the Random Sample Consensus (RANSAC) method, ensuring accurate ellipse fitting and extraction of tunnel lining. Subsequently, an algorithm segmented the tunnel cross-section lining into individual shield tunnels, enabling the calculation of ellipticity parameters for shield tunnels, which are the objects for deformation analysis. The experimental results underscore the novelty and effectiveness of this approach in monitoring deformation across different indices. The method proves to be a reliable tool for assessing tunnel health, providing a detailed evaluation of the cross-section's condition through statistical and graphical visualization. This study significantly advances shield tunnel monitoring, offering a practical and precise methodology for tunnel deformation analysis based on MLS point cloud data.

Development of a Diamond Composite in the NI-CO-FE3P-SN-WC System Via Free Sintering for Use in Multi-Find Diamond Pearls

Objective: The main objective of this study is to manufacture a diamond composite with a metallic matrix based on Ni-Co-Fe3P-Sn-WC, using the Free Sintering process through cold uniaxial compaction, intended for the production of pearls for diamond wire saws, used in cutting ornamental stones. Theoretical Framework: The multi-wire technology represents an advancement in cutting ornamental stone blocks, allowing the simultaneous production of multiple slabs. Powder metallurgy is the predominant technique used in the manufacturing of pearls for diamond wire saws. Through testing, including scanning electron microscopy (SEM) and shear tests, it is possible to evaluate the alloy's strength, diamond crystal adhesion, and failure behavior of the alloy. Methodology: The adopted methodology involves the processing and characterization of pearls for diamond wire saws, following the techniques of powder metallurgy. Initially, parametric calculations were performed using Excel, providing a basis for subsequent analyses. Diamond-coated pearls were produced with different proportions of constituent elements, and uniaxial compressive strength tests and microstructural characterization were conducted to evaluate the results. Results and Discussion: In the adhesion tests to the metal tube, the pearls produced with the NICOFE alloy showed similar performance to the commercial pearls "A" and "B," with satisfactory direct shear strength values. However, some point defects were observed, such as porosity in the transition zone, creating a weaker bond, negatively impacting the final strength of the composite. Analysis of the commercial samples indicated that diamond abrasion was not related to the composition of the sintered alloy. Research Implications: This research suggests significant economic and technological implications, such as the development and nationalization of technology for the ornamental stone industry. The use of the NICOFE alloy could strengthen the local industry by providing a viable and more accessible alternative for the production of diamond pearls. Originality/Value: The NICOFE blend presents technical advantages for manufacturing diamond wire pearls, highlighting the environmental benefits from reduced cobalt use and economic advantages by offering more accessible materials for the ornamental stone industry.

ИЗМЕНЕНИЯ ПРОДОЛЖИТЕЛЬНОСТИ БЕЗЛЕДНОГО ПЕРИОДА ПО ДАННЫМ БЕРЕГОВЫХ НАБЛЮДЕНИЙ В КАРСКОМ МОРЕ

Based on observations for the timing of ice events at 12 coastal stations in the Kara Sea, the longterm means and trends are calculated for start dates, end dates and duration of ice-free period (IFP) for 1979–2020. The obtained results were compared with the calculations for the same IFP parameters based on passive microwave remote sensing data. An increase in the duration of the ice-free period is observed at 9 of the 12 stations considered. This happens both due to the shift of the start dates of the IFP (observed at 8 out of 12 stations) to earlier dates, and due to the shift of the end dates of the IFP to later dates (noted at 11 out of 12 stations). A comparison of long-term means and trends of the start, end dates and duration of the IFP between satellite data and coastal observations confirms that remotes sensing microwave data (sea ice concentration) can be used to analyze the interannual variability of IFP characteristics in the coastal zone of the Kara Sea. It is shown, that the results obtained using the proposed modified threshold method (MPM) are in a better agreement with the coastal observation data than the widely used threshold 15 % method.

Dynamic Testing and Finite Element Model Adjustment of the Ancient Wooden Structure Under Traffic Excitation

In situ dynamic testing is conducted to study the dynamic characteristics of the wooden structure of the North House main hall. The velocity response signals on the measurement points are obtained and analyzed using the self-interaction spectral method and stochastic subspace method, yielding natural frequencies, mode shapes, and damping ratios. This study reveals that the natural frequencies and damping ratios are highly consistent between the two methods. Therefore, to eliminate errors, the average of the results from both modal identification methods is taken as the final measured modal parameters of the structure. The natural frequencies of the first and second order in the X direction were 2.097 Hz and 3.845 Hz and in the Y direction were 3.955 Hz and 5.701 Hz. The modal frequency in the Y direction of the structure exceeds that in the X direction. Concurrently, a three-dimensional finite element model was established using ANSYS 2021R1, considering the semi-rigid properties of mortise–tenon connections, and validated based on in situ dynamic testing. The sensitivity analysis indicates adjustments to parameters such as beam–column elastic modulus, tenon–mortise joint stiffness, and roof mass for finite element model refinement. Modal parameter calculations from the corrected finite element model closely approximate the measured modal results, with maximum errors of 9.41% for the first two frequencies, both within 10% of the measured resonant frequencies. The adjusted finite element model closely matches the experimental results, serving as a benchmark model for the wooden structure of North House main hall. The validation confirms the rationality of the benchmark finite element model, providing valuable insights into ancient timber structures along transportation routes.

An improved prediction method for BDS-3 SISA parameters and the preliminary performance evaluation

Abstract The ephemeris accuracy parameters broadcast in the ephemeris messages are commonly utilized as an alarm threshold for the signal-in-space ranging errors (SISRE) of the Global Navigation Satellite System (GNSS). Accurately predicting the orbit and clock errors of broadcast ephemeris, and converting the prediction errors into broadcast ephemeris accuracy parameters using a robust model, is important for the integrity service of GNSS. The BeiDou Navigation Satellite System (BDS-3) broadcasts four ephemeris accuracy parameters, namely SIS A oe , SIS A ocb , SIS A oc1 , and SIS A oc2 , to represent the standard deviations of the satellite orbit and clock prediction errors in the broadcast ephemeris. The probability that the broadcast ephemeris SISRE exceeds 4.42 times the signal-in-space accuracy (SISA) without triggering an alarm in time is expected to be less than 1 × 10−5. In addition to accurately enveloping the broadcast ephemeris SISRE, SISA should also reduce the over-envelopment margin over SISRE to accurately express the system service accuracy. Thus, an accurate prediction of orbit and clock errors in the broadcast ephemeris is crucial for calculating the SISA. An orbit and clock error prediction method based on the Long Short-Term Memory (LSTM) neural network is proposed in this study. This method uses historical satellite orbit and clock errors, derived from the difference between precise and broadcast ephemeris, to train an LSTM neural network. The trained LSTM model is then used to predict the orbit and clock errors for the next 24 h. The predicted errors are used to calculate the corresponding SISA for the broadcast ephemeris. The results indicate that the 24 h accuracies of the predicted orbit and clock errors are less than 0.06 m and 0.25 m, respectively. The calculated SISA can completely envelop the actual broadcast ephemeris SISRE, achieving 100% coverage while also decreasing the over-envelopment margin by 80%. Furthermore, the horizontal protection level and vertical protection level calculated using the corresponding SISA are reduced by approximately 14%, meeting the integrity requirements set by the International Civil Aviation Organization for Approach with Vertical Guidance I. The proposed method enhances the timeliness of SISA parameter calculation and the integrity and availability of the BDS-3 service, serving as a crucial reference value for safety critical applications such as civil aviation services.

Fault in Converter Interfaced Micro Grid Using Detection and Identification of Hybrid Technique

ABSTRACTThis paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments.

Experimental and Numerical Studies of the Temperature Field in a Dielectrophoretic Cell Separation Device Subject to Joule Heating.

Technologies for rapid and high-throughput separation of rare cells from large populations of other types of cells have recently attracted much attention in the field of bioengineering. Among the various cell separation technologies proposed in the past, dielectrophoresis has shown particular promise because of its preciseness of manipulation and noninvasiveness to cells. However, one drawback of dielectrophoresis devices is that their application of high voltage generates Joule heat that exposes the cells within the device to high temperatures. To further explore this problem, this study investigated the temperature field in a previously developed cell separation device in detail. The temperature rise at the bottom of the microfluidic channel in the device was measured using a micro-LIF method. Moreover, the thermofluidic behavior of the cell separation device was numerically investigated by adopting a heat generation model that takes the electric-field-dependent heat generation term into account in the energy equation. Under the operating conditions of the previously developed cell separation device, the experimentally obtained temperature rise in the device was approximately 20 °C, and the numerical simulation results generally agreed well. Next, parametric calculations were performed with changes in the flow rate of the cell sample solution and the solution conductivity, and a temperature increase of more than 40 °C was predicted. The results demonstrated that an increase in temperature within the cell separation device may have a significant impact on the physiological functions of the cells, depending on the operating conditions of the device.

∼2 μm broadband luminescence in Tm3+/Ho3+/Er3+-doped tellurite glass

Improving the broadband luminescent properties in ∼2 μm band has always been a serious challenge. This paper proposed a Tm3+, Ho3+ and Er3+ doped combination in tellurite glass, which was synthesized through melt-quenching and characterized by a series of physical and spectral tests. Firstly, tellurite glass of Tm3+–Ho3+ co-doping produced a ∼2 μm broadband luminescence ranging from 1570 to 2200 nm with FWHM (full width at half maximum) of 379 nm under 808 nm pumping. This broadband luminescence originated from 3F4 to 3H6 level transition of Tm3+ and 5I7 to 5I8 level transition of Ho3+. Furthermore, after adding an appropriate amount of Er3+, the luminescent intensity was improved by 116 %, mainly attributed to the direct or indirect energy transfers from Er3+ to Tm3+ and Ho3+ ions. Calculations of Judd-Ofelt spectroscopic parameters and gain cross-sections supported the results obtained from Tm3+/Ho3+/Er3+ doped tellurite glass in ∼2 μm band. In addition, DSC (differential scanning calorimetry) curves exhibited excellent thermal stability, XRD (X-ray diffraction) patterns and Raman spectra disclosed the non-crystalline and network structural units of the synthesized tellurite glasses. The findings in this work demonstrate that tellurite glass with Tm3+, Ho3+ and Er3+ combination is an efficient strategy which can be applied in ∼2 μm band ultra-short pulse lasers and broadband amplifiers.

Research on intelligent cutting control technology of transverse moving machine for large cross-section roadheader

With the continuous development of the coal mine industry in our country, the cross-section of the roadway is getting larger and larger. The widely used EBZ series roadheader cannot complete the roadway cutting operation with a large cross-section at one time, so it usually needs to move the equipment left and right to complete all its cutting tasks. This paper studies the intelligent control technology of large cross-section roadway cutting, which mainly solves the positioning and attitude determination technology of roadheader in the large cross-section, and offers an accurate shape-cutting control technology and a path planning technology using a lateral moving machine. This paper proposes to combine the signals of laser sensor and fiber-optic inertial navigation system to form a combined positioning and attitude determination system, so as to make use of the advantages and avoid the disadvantages of their respective systems, and improve the accuracy of roadheader positioning and attitude determination. A swing kinematics model of the cutting arm is established, based on which the precise motion control of the first roadway section cutting and the second roadway section cutting can be realized. A key parameter calculation model of roadheader lateral moving machine is put forward to calculate the operation parameters for roadheader lateral moving machine path planning. Finally, the experimental research on intelligent cutting control technology of large cross-section roadheader is carried out in 12307 intelligent heading face of Wangjialing Coal Mine. The results show that the positioning error of the combined positioning and attitude determination system proposed in this paper is between 50-90 mm in the X-axis direction and between 25-45 mm in the Y-axis direction. The average attitude measurement error of heading angle is between 0.5 and 1.5°. This makes the technology appropriate for engineering use. The studied cutting control system achieves a cutting error of 25-50 mm for the first section, and a cutting error of 40-90 mm for the complete section. The complete section cutting error meets the ±100 mm cutting error requirement required in engineering. The intelligent cutting control technology of the lateral moving machine of the large-section tunnel boring machine studied in this paper can realize accurate cutting of the large-section tunnel of 5.6 meters. The research results of this paper provide a reference for the intelligent construction of heading faces.

Presentation of representative shape parameters of porous media based on mathematical computation on images

The shape parameters of porous media are really important to describe the petrophysical and geomechanical parameters of reservoirs. In addition, shape analysis can provide essential information about the origin, transport, deposition history of particles, etc. Because macroscopic properties (such as permeability, diffusion, etc.) are influenced by microscopic properties (such as pore size, grain shape, etc.). So far, various studies have been conducted to calculate some shape parameters, however, there is no comprehensive study that first includes most of the geometric parameters of the shape and then presents the representative shape parameters. Therefore, the aim of this study is to present representative shape parameters of porous media based on mathematical computation on images. The shape parameters studied included roundness, circularity, angularity, convexity, compactness, sphericity, aspect ratio, elongation, eccentricity, shape factor, and regularity. Calculation of shape parameters is done digitally using the developed program. The necessary data for the desired analysis are obtained from real sandstone samples. To develop this research, first, the mentioned shape parameters are calculated using the developed program for all rock samples. Then, by establishing a relationship between the mentioned parameters, the representative parameters that can show the shape of the porous medium are expressed. The results of this study show that the representative shape parameters of the porous medium are compactness, shape factor-round, and aspect ratio, which are good indicators for the shape of the porous medium.

Optoelectronic and thermodynamic properties of the Yttrium filled skutterudite YFe4P12

In this work, we sought to understand the structural, electronic, optical, and thermodynamic properties of the compound YFe4P12 using the full-potential and linear augmented plane-wave method (FP-LAPW) based on density functional theory (DFT), our calculation of the lattice parameter of this compound YFe4P12 is reasonably in good agreement with the experimental results. Calculations carried out on the electronic band structure showed that the YFe4P12 compound is classified as a direct-gap Half- half-metallic in the minority spins. Moreover, optical properties, such as the optical absorption coefficient, real and imaginary parts of the dielectric function, optical conductivity, refractive index, and optical reflectivity have been studied. The effects of pressure and temperature on the lattice parameter, heat capacities, coefficients of thermal expansion, entropy, and Debye temperature were explored using the quasi-harmonic Debye model.

Mechanical fatigue in microtubules

Mechanical failure of biological nanostructures due to sustained force application has been studied in great detail. In contrast, fatigue failure arising from repeated application of subcritical stresses has received little attention despite its prominent role in engineering and potentially biology. Here, paclitaxel-stabilized microtubules are up to 256 times bent into sinusoidal shapes of varying wavelength and the frequency of breaking events are observed. These experiments allow the calculation of fatigue life parameters for microtubules. Repeated buckling due to 12.5% compression–equal to the compression level experienced by microtubules in contracting cardiomyocytes – results in failure after in average 5 million cycles, whereas at 20.0% compression failure occurs after in average one thousand cycles. The fatigue strength (Basquin) exponent B is estimated as − 0.054±0.009.

Multiscale monitoring and analysis of complex rupture and source mechanisms of mining-related seismicity on fault networks

Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards. To bridge gaps in understanding these intricate processes, this study employed a multi-local seismic monitoring network, integrating both in-mine and local instruments at overlapping length scales. We specifically focused on a damaging local magnitude (ML) 2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province, China. Moment tensor (MT) inversion revealed a complex cascading rupture mechanism: an initial moment magnitude (Mw) 2.2 normal fault slip along the DF60 fault in an ESE–WNW direction, transitioning to a Mw 3.0 event as the FD24 and DF60 faults unclamped. The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations, providing valuable insights into the driving mechanism of these seismic sequences. The in-mine network, constrained by its low dynamic changes, captured only the nucleation phase of the DF60 fault. Furthermore, standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture. To enhance safety and risk management in mining environments, we examined the implications of source reactivation within the cluster area post-stress-adjustment. This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity. The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.

Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model

In order to better analyze and predict energy-related carbon emissions in East China to address climate change, this paper enhances the predictive capabilities of grey models in spatial joint prediction by creating the reverse-accumulation spatial discrete grey model RSDGM (1,1,m) and accumulation spatial discrete grey breakpoint model RSDGBM (1,1,m,t), which took the impact of system shocks into consideration. The efficiency of the models is confirmed by calculating the energy-related carbon emissions in East China from 2010 to 2022. Future emissions are predicted, and the spatial spillover effect of emissions in East China is discussed. The conclusions are as follows: (1) The RSDGM (1,1,m) theoretically avoids errors in background values and parameter calculations, reducing computational complexity. Empirically, the model exhibits high performance and reflects the priority of new information in spatial joint analysis. (2) The RSDGBM (1,1,m,t) captures the impact of shocks on system development, improving the reliability of carbon emissions prediction. (3) Jiangsu and Shandong are positively affected by spatial factors in terms of carbon emissions, while Shanghai and Zhejiang are negatively affected. (4) It is estimated that carbon emissions in East China will increase by approximately 23.8% in 2030 compared to the level in 2022, with the levels in Zhejiang and Fujian expected to increase by 45.2% and 39.7%, respectively; additionally, the level in Shanghai is projected to decrease. Overall, East China still faces significant pressure to reduce emissions.