Search Results (374)

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. Four main geological indicators were identified by examining the attributes of these factors and their association to outburst intensity. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K Nearest Neighbors (KNN), Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision support tool for mine executives to prevent and control outburst incidents. Full article

The intrinsically low stiffness of titanium alloy thin-walled components causes residual stresses to readily accumulate during high-speed micro-milling, leading to deformation and hindering machining precision. To clarify the residual-stress formation mechanism and enable deformation control, this study first proposes a surface residual stress characterization model based on an exponentially decaying sinusoidal function, with model parameters efficiently identified via an improved particle swarm optimization algorithm, allowing rapid characterization of stress distributions under different process conditions. A response surface model constructed using a central composite design is then employed to reveal the coupled effects of machining parameters on residual stress and top-surface deformation. On this basis, a GA-BP neural network–based prediction framework is developed to improve the accuracy of residual stress and deformation prediction, while the AGE-MOEA2 multi-objective evolutionary algorithm is used to optimize micro-milling parameters for the simultaneous minimization of residual stress and deformation via Pareto-optimal solutions. Validation experiments on thin-wall micro-milling confirm that the optimized parameters significantly reduce peak residual stress and suppress top-surface deformation. The proposed modeling and optimization strategy provides an effective reference for high-precision machining of titanium alloy thin-walled components. Full article

Thorough analysis of regional carbon emissions is essential, given their substantial contribution to overall carbon emissions, as it underpins the formulation of targeted low-carbon development strategies. This study investigates whether Hubei Province can achieve carbon peaking by 2030 and explores feasible emission reduction pathways. To address the limitations of existing regional carbon emission studies—particularly the direct use of decomposition factors in prediction models and the lack of logical separation between mechanism analysis and forecasting—a hybrid analytical-predictive framework is proposed. Specifically, the logarithmic mean Divisia index (LMDI) method is first employed to decompose historical carbon emissions and identify the driving forces, while the STIRPAT model combined with the Lasso regression is subsequently used to screen key influencing factors for emission prediction, thereby avoiding the direct use of decomposition factors in forecasting. Based on the selected factors, a genetic algorithm–optimized backpropagation neural network (GA-BP) is developed to predict carbon emissions in Hubei Province from 2024 to 2035. The predictive performance of the GA-BP model is validated using three statistical indicators (R², MAPE, and RMSE) and compared with Extreme Learning Machine (ELM), Support Vector Regression (SVR), and conventional BP models. Furthermore, six development scenarios are designed in accordance with provincial policy objectives to assess the feasibility of carbon peaking. The results indicate the following: (1) Based on the results of the LMDI decomposition, Lasso–STIRPAT analysis, and model sensitivity analysis, per capita GDP is identified as the primary driving factor of carbon emissions in Hubei Province. (2) The GA-BP model demonstrates superior predictive accuracy compared with benchmark models and (3) carbon peaking by 2030 can only be achieved under Scenario 6, highlighting the necessity of coordinated structural and technological interventions. Based on these findings, targeted policy recommendations for carbon emission reduction are proposed. Full article

Objective: Growth traits are important economic characteristics in livestock. Genetic polymorphism has great influences on the improvement of goat growth traits. As an important member of the myogenic regulatory factor (MRFs) family, MyoG gene polymorphisms can alter the growth characteristics in goats. In this study, we aimed to investigate the regulation mechanism of the MyoG gene promoter region from the perspective of single nucleotide polymorphisms (SNPs) and transcription factors. Methods: Genomic DNA sequencing was carried out to detect SNPs in the −1000 bp upstream to 300 bp downstream of the MyoG gene promoter region in 224 Guizhou White goats (Capra hircus), and the genetic parameters of novel SNPs were calculated. The association between SNPs and growth traits, comprising body weight, body length, body height, chest circumference and cannon circumference, were analyzed using one-way ANOVA by IBM SPSS 23.0 software according to the general linear model. Transcription factor binding sites in the promoter region of the MyoG gene before and after mutation were predicted using bioinformatics software programs. Results: Four SNPs, including g.–709C>T, g.–461G>T, g.–377G>T and g.–249G>A, were identified in the 1 246 bp promoter region of the MyoG gene in Guizhou White goats. Based on χ² test, the g.–709C>T and g.–461G>T loci were consistent with Hardy–Weinberg equilibrium, while two other SNPs were deviated from Hardy–Weinberg equilibrium in Guizhou White goats. Association analysis revealed that the body weight of those with the CT genotype at the g.–709C>T locus was greater than of those with the CC and TT genotypes in Guizhou White goats (p < 0.05). At the g.–461G>T locus, the body weight of individuals with the GG genotype was significantly higher than that of those with GT genotype (p < 0.01). The body length of individuals with the GG genotype formed by the g.–249G>A locus was significantly higher than that of those with the GA genotype (p < 0.01). Online software programs found that four SNPs within the promoter region of the MyoG gene changed some transcription factor binding sites. Conclusions: Mutations of the MyoG gene promoter region may have a significant regulatory effect on the growth traits of Guizhou White goats. The small sample size may be one of the limitations for this study; nevertheless, these findings could provide a theoretical basis for further exploring the relationship between the four SNPs studied and the growth traits in Guizhou White goats, as well as the promoter function of the MyoG gene. Full article

Seasonal temperature reconstructions provide a critical approach for reconciling discrepancies between paleoclimate model simulations and proxy records. However, cold-season temperature variations remain poorly constrained due to the scarcity of robust cold-season temperature proxies. This study provides critical insights into lake ice-covered season temperature dynamics in Northeast China, a region where cold-season climate variability has remained poorly constrained in paleoclimate reconstructions. We collected total organic carbon sequences from seven closed lakes in Northeast China over the last 10,000 years to evaluate the lake ice cover duration as a proxy for lake ice-covered season temperature during the early–middle Holocene. Our results show that the lake ice cover duration decreased from ~8 ka BP, reaching a minimum at around 4 ka BP. This pattern is linked to ice-covered season temperature changes, with warmer ice-covered seasons leading to shorter ice cover durations and increased lake productivity, which were driven by orbital forcing (seasonal insolation changes) and greenhouse gas concentrations. Orbital forcing played a dominant role in winter warming between 8 and 4 ka BP, while greenhouse gas also contributed, but to a lesser extent. Full article

With the improvement scale of grid connection renewable power, accurately forecasting and effectively coordinating systems with various energy sources has become much more important for power system scheduling and operation. Considering the uncertain characteristics of renewable energy and CHP units, this paper proposes an optimal dispatch method with multi-prediction models and an improved solving method by series correction and parallel coupling analysis. Firstly, multiple-model stationary time series are obtained by EMD (empirical mode decomposition) of the prediction results from multiple models. Then, series decomposition is updated by the UKF (unscented Kalman filter). Using the least-squares method, the parallel coupling of the correction results is solved. A complex optimal scheduling model with multiple renewable energy sources and CHP units is proposed and solved with the help of the improved GA and PSO combined algorithm to avoid the algorithm falling into local optimal conditions. Simulations show that the proposed optimal dispatch model and algorithm are able to consider the uncertain characteristics of renewable energy and CHP units with better performance than some typical methods, such as the baseline method that combines single-model BP forecasting with conventional PSO-based dispatch. These results demonstrate that the proposed EMD–UKF-based multi-model forecasting combined with the improved GA–PSO-based dispatch framework provides an effective and practically applicable tool for enhancing the economic and low-carbon operation of multi-energy systems with high renewable penetration. Full article

Highway under-bridge areas represent a valuable land resource while simultaneously constituting a sensitive ecological zone. Achieving a balance between its redevelopment and ecological preservation constitutes a critical challenge within the field of ecological engineering. Although prior research has addressed urban elevated underbridge space, investigations specifically focusing on highway underpasses remain limited. The absence of standardized criteria for assessing the suitability of these spaces has resulted in uncoordinated and fragmented utilization. In response, this study proposes a comprehensive evaluation framework that integrates multi-criteria decision-making (MCDM) methodologies with optimized backpropagation neural networks, specifically genetic-algorithm-optimized BP (GA-BP) and particle-swarm-optimization-optimized BP (PSO-BP). The model incorporates indicators spanning physical characteristics, environmental factors, safety considerations, and accessibility metrics, and is applied to an empirical dataset comprising 134 highway bridge underpasses in Fuzhou City. The results indicate that (1) both the GA-BP and PSO-BP models enhance convergence speed and classification accuracy, with the GA-BP model demonstrating superior stability and suitability for classifying underpass suitability; (2) the principal determinants of suitability include traffic accessibility, safety parameters, and spatial relationships with adjacent water bodies and agricultural lands; and (3) underpasses characterized as hub-type, single-sided road-adjacent, and cross-connection configurations exhibit greater potential for redevelopment. This investigation represents the first integration of MCDM and optimized neural network techniques in this context, offering a robust tool to support the scientific planning and ecological conservation of underbridge space environments. Full article

The extensive area of goaf makes high-temperature points highly concealed, and prolonged heating can easily trigger spontaneous coal combustion. Traditional temperature monitoring methods are limited in spatial coverage and thus fail to detect high-temperature points in a timely manner. To address this issue, this study proposes an integrated analytical method combining numerical simulation and intelligent inversion, with Taihe Coal Mine as the research object. First, A coupled flow–temperature–gas field model of the goaf was established in COMSOL Multiphysics 6.3 to simulate working-face CO concentration distributions corresponding to high-temperature points at different locations, thereby constructing a comprehensive dataset. Then, a BP neural network prediction model improved by the dung beetle optimization algorithm (DBO-BP) was trained to infer the spatial location of high-temperature points based on CO concentration distributions. Finally, a geometric prediction method was introduced to guide precise drilling within the predicted high-risk areas for field verification. The results demonstrate that the proposed DBO-BP model can effectively trace the locations of high-temperature points from CO concentration data. When combined with the geometric prediction method, it provides an efficient and reliable technical solution for the early prevention of spontaneous coal combustion in goaf. Full article

In recent years, with the deepening of mining and tunnel excavation operations, the incidence of rock burst has also increased, prompting people to attracting increasing attention to microseismic monitoring technology. The location algorithm of microseismic events is the core of microseismic monitoring. In this study, a hybrid optimization algorithm, BP-GA-GN, which combines genetic algorithm (GA), BP neural network (BP) and Gauss-Newton method (GN), is introduced. The BP-GA-GN algorithm optimizes the initial weights and thresholds of the BP neural network through GA to avoid local optimum. The BP neural network is used to learn the nonlinear mapping between the sensor arrival time difference and the source position. Combined with the physical model constraints of GN, fine convergence is performed. We prove the robustness of the BP-GA-GN algorithm through a large number of numerical simulations. Compared with the traditional single algorithm, the algorithm shows excellent performance. Subsequently, the high precision and high efficiency of the method are further highlighted in the field data test of mine environment and tunnel environment. The average errors are 0.42 m and 2.54 m, respectively, rendering it a valuable tool for real-time microseismic monitoring. This study overcomes the limitations of traditional positioning methods. The algorithm can achieve high-speed training and high precision, thus significantly improving the early warning effect of rockburst risk. Full article

This study focuses on an integrated three-level multi-port liner ship vessel routing and scheduling optimization problem. Specifically, the three-level multi-port network consists of hub ports, feeder ports, and cargo source points, which provide the demands’ loading/unloading at each port. Considering vessel-specific constraints such as speed, capacity, and cost, we formulate the multi-port liner ship routing and scheduling optimization problem as a mixed integer linear programming model with the objective of minimizing total voyage cost and operating time. First, we employ machine learning models to forecast the short-term demand at different ports as the input. There are multiple feasible routes generated and allowed to be elected. Second, to ensure both computational efficiency and solution quality, we devise and compare genetic algorithm (GA), simulated annealing (SA), Gurobi and the branch-and-price (B&P) algorithm to optimize scheduling plans. Experimental results demonstrate that the proposed predict-then-optimization framework effectively addresses the complexity of multi-port scheduling and routing problems, achieving a reduction in total transportation cost by 0.81% to 8.08% and a decrease in computation time by 16.86% to 24.7% compared to baseline methods, particularly with the SA + B&P hybrid approach. This leads to overall efficiency and cost-saving ocean vessel operations. Full article

TBM construction projects require substantial investment, making the accurate and rational prediction of TBM advance speed essential for cost control and timely project completion. To address this, the study develops a precise predictive model for TBM advance speed by integrating the BP neural network model with the genetic algorithm. Initially, raw TBM data were processed to remove non-operational records and anomalous values recorded during construction, resulting in a refined database of TBM operational parameters. The surrounding rock conditions were classified based on FPI and TPI, two key indices reflecting rock mass excavatability and rock-breaking efficiency. Using the K-means clustering algorithm, the dataset was segmented into three distinct groups. Seven tunneling parameters were selected as input features for the neural network model. Subsequently, three GA-BP neural network models were developed for different rock mass categories, with key parameters optimized for enhanced performance. Prediction results demonstrate that the GA-BP neural network exhibits superior accuracy and generalization capability. Compared to a conventional BP neural network, the GA-BP model reduces prediction errors by more than 10%. Full article

Ambiguity Resolution (AR) is regarded as an effective technique for enhancing positioning accuracy and reducing convergence time in Precise Point Positioning (PPP). However, the Wide-Lane Fractional Cycle Bias (WL FCB) and Narrow-Lane Fractional Cycle Bias (NL FCB) needed for AR are generated from network solutions based on numerous globally distributed stations, leading to considerable computational load and processing time. A prediction model for FCB is proposed using the Genetic Algorithm Optimized Backpropagation Neural Network (GA-BPNN), and high-precision predictions of WL and NL FCB for Day of Year (DOY) 321 in 2023 are successfully achieved. Comparisons with iGMAS products show that predicted WL FCB deviations are within 0.01 cycles, and predicted NL FCB over 12 h deviates within 0.1 cycles (excluding satellite C20). The performance of three PPP schemes, Float, Fixed (based on FCB from iGMAS), and BP-Fixed (based on FCB predicted by GA-BPNN), is compared through experiments. For GPS + BDS-3, the accuracies of the BP-Fixed scheme are 0.0034 m, 0.0039 m, and 0.0100 m in the east, north, and up directions, respectively. The ambiguity fixed rates reach 98.62% for BP-Fixed. These outcomes confirm that the positioning performance using the predicted FCB of GA-BPNN is highly consistent with that using FCB products. Full article

Highways constructed on stratified foundations with thick soft soil interlayers in the Yellow River floodplain of Shandong Province have experienced long-term settlement. However, accurately predicting subgrade settlement caused by the secondary consolidation of soft soils remains a major engineering challenge. In this study, PLAXIS 3D numerical simulation was combined with a neural network model to predict the long-term temporal and spatial settlement behavior of highway subgrades. The results show that the soft soil creep (SSC) constitutive model better represents the consolidation process of the soft soil interlayer than the soft soil (SS) model. A decrease in permeability will prolong the dissipation time of excess pore water pressure and the settlement stabilization time, leading to an increase in the proportion of post-construction settlement in the total settlement. The final settlement increases linearly with the thickness of the soft soil interlayer and embankment height, while it decreases following a power-law function with increasing interlayer burial depth. By comprehensively considering the combined effects of multiple factors, a genetic algorithm–optimized backpropagation neural network (GA-BP) model was developed. The testing dataset achieved a root mean square error (RMSE) of 0.01488 m, a mean absolute percentage error (MAPE) of 7.0562%, and a coefficient of determination (R²) of 0.9706, demonstrating the model’s ability to achieve intelligent full-period and full-section settlement prediction for subgrades with soft soil interlayers. Overall, this study developed an intelligent framework for predicting long-term settlement in subgrades with soft soil interlayers, offering practical guidance for evaluation and timely settlement control. Full article

Accurate determination of soil and contact parameters is crucial for tillage machinery design; however, the interactions among soil, tools, and roots in citrus orchards covered with green manure remain insufficiently defined. This study, therefore, combined physical experiments with DEM simulations to characterize these interactions. Using significance analysis and response surface methodology (RSM), the effects of major factors on angle of repose (AoR) and initial slip angle (ISA) at varying soil depths were evaluated, enabling precise calibration of both external (soil–machinery) and internal (particle–particle) parameters. Subsequently, a GA-BP optimization model was constructed to enhance calibration accuracy, yielding optimal values for the soil-to-soil rolling friction coefficient (γ = 0.125–0.136), soil-to-65Mn static friction coefficient (μ′ = 0.431 − 0.540), and soil surface energy (JKR = 0.952 − 1.091 J·m⁻²). Shear tests using the bonding V2 model were conducted to calibrate the Bonding parameters of green manure stems and roots, while pull-out tests and simulations were used to validate the root–soil parameters. Direct shear tests confirmed the model’s reliability, with errors in internal friction angle and cohesion below 10%. These findings may contribute to improving DEM simulation accuracy for soil improvement under green manure coverage and support the optimization of soil tillage in citrus orchards. Full article