Search

This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines. To reduce numerical simulation time and optimize ventilation design, several back propagation neural network (BPNN) models optimized by honey badger algorithm (HBA) with four chaos mapping (CM) functions (i.e., Chebyshev (Che) map, Circle (Cir) map, Logistic (Log) map, and Piecewise (Pie) map) are developed to predict the migration time. 125 simulations by the computational fluid dynamics (CFD) method are used to train and test the developed models. The determination coefficient (R²), the variance accounted for (VAF), the Willmott's index (WI), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the sum of squares error (SSE) are utilized to evaluate the model performance. The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R² (0.9945), WI (0.9986), VAF (99.4811%), and the lowest values of RMSE (15.7600), MAPE (0.0343) and SSE (6209.4), respectively. The wind velocity in roadway (Wv) is the most important feature for predicting the migration time of toxic fumes. Furthermore, the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design. [ABSTRACT FROM AUTHOR]

The technical challenges associated with deep underground space activities have become increasingly significant. Among these challenges, one major concern is the assessment of rockburst risks and the instability of rock masses. Extensive research has been conducted by numerous scholars to mitigate the risks and prevent occurrences of rockburst through various assessment methods. Rockburst incidents commonly occur during the excavation of hard rock in underground environments, posing severe threats to personnel safety, equipment integrity, and operational continuity. Thus, it is crucial to systematically document real cases of rockburst, allowing for a comprehensive understanding of the underlying mechanisms and triggering conditions. This understanding will contribute to the advancement of rockburst prediction and prevention methods. Proper selection of an appropriate rockburst assessment method is a fundamental aspect in underground operations. However, there is a limited number of studies that summarize and compare different prediction and prevention methods of rockburst. This paper aims to address this gap by analyzing global trends using CiteSpace software since 1990. It discusses rockburst classification and characteristics, comprehensively reviews research findings related to rockburst prediction, including empirical, simulation, mathematical modeling, and microseismic monitoring methods. Additionally, the paper presents a compilation of current rockburst prevention measures. Notably, the paper emphasizes the significance of control strategies, which provide key insights into the effective utilization of stored energy within rock. Finally, the paper concludes by suggesting six directions for implementing intelligent management techniques to mitigate hazards during underground operations and reduce the probability of rockburst incidents. [ABSTRACT FROM AUTHOR]

The intention of this paper is to evaluate the sensitivity of the Storm Water Management Model (SWMM) output to its input parameters. A global parameter sensitivity analysis is conducted in order to determine which parameters mostly affect the model simulation results. Two different methods of sensitivity analysis are applied in this study. The first one is the partial rank correlation coefficient (PRCC) which measures nonlinear but monotonic relationships between model inputs and outputs. The second one is based on the mutual information which provides a general measure of the strength of the non-monotonic association between two variables. Both methods are based on the Latin Hypercube Sampling (LHS) of the parameter space, and thus the same datasets can be used to obtain both measures of sensitivity. The utility of the PRCC and the mutual information analysis methods are illustrated by analyzing a complex SWMM model. The sensitivity analysis revealed that only a few key input variables are contributing significantly to the model outputs; PRCCs and mutual information are calculated and used to determine and rank the importance of these key parameters. This study shows that the partial rank correlation coefficient and mutual information analysis can be considered effective methods for assessing the sensitivity of the SWMM model to the uncertainty in its input parameters. [ABSTRACT FROM AUTHOR]

Current formulas for calculating scour depth below of a free over fall are mostly deterministic in nature and do not adequately consider the uncertainties of various scouring parameters. A reliability-based assessment of scour, taking into account uncertainties of parameters and coefficients involved, should be performed. This paper studies the reliability of a dam foundation under the threat of scour. A model for calculating the reliability of scour and estimating the probability of failure of the dam foundation subjected to scour is presented. The Maximum Entropy Method is applied to construct the probability density function (PDF) of the performance function subject to the moment constraints. Monte Carlo simulation (MCS) is applied for uncertainty analysis. An example is considered, and there liability of its scour is computed, the influence of various random variables on the probability failure is analyzed. [ABSTRACT FROM AUTHOR]

• A novel scheme with low time complexity for combining modulation format identification (MFI) and optical performance monitoring (OPM) using convolutional-neural-network-based and equally weighted multi-task learning is proposed. • This method relies on the distinguishable features of the signal's（QPSK, QAM8, QAM16, QAM64)constellation maps. • During the training process, the memory occupation of the computer on the non-training program should be removed. • We verifies the high preformance and robustness of the proposed EW-MTL. A novel scheme for combining modulation format identification (MFI) and optical performance monitoring (OPM) in elastic optical networks using convolutional-neural-network-based and equally weighted multi-task learning is proposed. In this scheme, shared features of optical signal-to-noise ratio (OSNR) and modulation formats (10 GBaud QPSK, QAM8, QAM16, QAM64) are extracted from constellation maps and used for multi-label classification. Simulation results demonstrate that the accuracies of MFI and OSNR can reach 100% stably and continuously. Moreover, we evaluated the robustness of this model in a system with fixed physical parameters and concluded that it is tolerant to chromatic dispersion and transmission distance. This technology predicts an image in ∼0.51 ms. Compared with the general multi-task learning (MTL) scheme, the proposed approach massively saves execution time (∼107 h) by removing weight selection. It is time-efficient and intelligent for OPM equipment that will perform multiple monitoring tasks in next-generation optical networks. [ABSTRACT FROM AUTHOR]

Background: Excess body weight measured as body mass index (BMI) has a positive association with risk of common cancers. However, previous meta-analyses related to BMI and liver cancer had inconsistent results. The purpose of the current study is to establish a nonlinear dose-response relationship between BMI and incidence risk of liver cancer. Methods: A systematic literature search for relevant articles published from 1966 to November 2011 was conducted in PUBMED and EMBASE digital databases. Using the reference lists of identified papers manually searched additional articles. Restricted cubic splines and generalized least-squares regression methods were used to model a potential curvilinear relationship and to make a dose-response meta-analysis. Stratified analysis, sensitivity analysis and assessment of bias were performed in our meta-analysis. Results: 8 articles including 1,779,471 cohort individuals were brought into meta-analysis. A non-linear dose-response association between BMI and risk of liver cancer was visually significant (P for nonlinearity<0.001), besides, the point value of BMI also enhanced the results quantitatively, where relative risks were 1.02 (95%CI = 1.02-1.03), 1.35 (95%CI = 1.24-1.47) and 2.22-fold (95%CI = 1.74-2.83) when BMI was at the point of 25, 30 and 35 kg/m² compared with reference (the median value of the lowest category), respectively. The ethnicity of the population was found as the main source of heterogeneity. In subsequent stratified analysis, no evidence of heterogeneity was showed in Asian and White populations (P for heterogeneity>0.1), and all value of BMI still presented significantly increased risk of cancer. Conclusions: The findings from meta-analysis provided that excess BMI had significant increased association with risk of liver cancer, although the biological mechanisms underlying the obesity-cancer link still need to be clarified. [ABSTRACT FROM AUTHOR]