A Novel Hunger Games Search Optimization-Based Artificial Neural Network for Predicting Ground Vibration Intensity Induced by Mine Blasting

Innovation efforts in developing soft computing models (SCMs) of researchers and scholars are significant in recent years, especially for problems in the mining industry. So far, many SCMs have been proposed and applied to practical engineering to predict ground vibration intensity (BIGV) induced by mine blasting with high accuracy and reliability. These models significantly contributed to mitigate the adverse effects of blasting operations in mines. Despite the fact that many SCMs have been introduced with promising results, but ambitious goals of researchers are still novel SCMs with the accuracy improved. They aim to prevent the damages caused by blasting operations to the surrounding environment. This study, therefore, proposed a novel SCM based on a robust meta-heuristic algorithm, namely Hunger Games Search (HGS) and artificial neural network (ANN), abbreviated as HGS–ANN model, for predicting BIGV. Three benchmark models based on three other meta-heuristic algorithms (i.e., particle swarm optimization (PSO), firefly algorithm (FFA), and grasshopper optimization algorithm (GOA)) and ANN, named as PSO–ANN, FFA–ANN, and GOA–ANN, were also examined to have a comprehensive evaluation of the HGS–ANN model. A set of data with 252 blasting operations was collected to evaluate the effects of BIGV through the mentioned models. The data were then preprocessed and normalized before splitting into individual parts for training and validating the models. In the training phase, the HGS algorithm with the optimal parameters was fine-tuned to train the ANN model to optimize the ANN model's weights. Based on the statistical criteria, the HGS–ANN model showed its best performance with an MAE of 1.153, RMSE of 1.761, R² of 0.922, and MAPE of 0.156, followed by the GOA–ANN, FFA–ANN and PSO–ANN models with the lower performances (i.e., MAE?=?1.186, 1.528, 1.505; RMSE?=?1.772, 2.085, 2.153; R²?=?0.921, 0.899, 0.893; MAPE?=?0.231, 0.215, 0.225, respectively). Based on the outstanding performance, the HGS–ANN model should be applied broadly and across a swath of open-pit mines to predict BIGV, aiming to optimize blast patterns and reduce the environmental effects.

     

Estimation of Ground Vibration Intensity Induced by Mine Blasting using a State-of-the-Art Hybrid Autoencoder Neural Network and Support Vector Regression Model

Natural Resources Research - In surface mining, blasting is an indispensable method for fragmenting rock masses. Nevertheless, it can inherently induce many side effects like ground vibrations. At...     

Predicting Blast-induced Ground Vibration in Quarries Using Adaptive Fuzzy Inference Neural Network and Moth–Flame Optimization

Natural Resources Research - Blasting is a first preparatory stage that plays a fundamental role in the subsequent operations of an open pit mine. However, its adverse effects can seriously affect...     

Vertical fault systems in the tectonosphere geoelectrical section in petroliferous domains of Sakhalin Island (Russia) and Gulf of Tonkin (Vietnam): Evidence from magnetotelluric sounding

The transverse current anomalies evident from magnetotelluric sounding (MTS) data in the transition zone from the Asian continent to the Pacific Ocean and a geoelectrical model suitable for explaining these effects are discussed. Using simulation results and new MTS data on Sakhalin and northern Vietnam, ultra-deep fluid–fault system (UDFFS) models are constructed, comprising steeply dipping low-resistivity and high-resistivity linear bodies which penetrate through the whole section of the lithosphere, continuing deep into the asthenosphere. The structure of these systems reflects the development of deep and ultra-deep fault zones, along which mantle-originated fluids may migrate through sediments to reach the surface. This allows the anisotropic–conductive fractured structures adjacent to these faults to be considered as being most favorable for hydrocarbon accumulations.     

SWAT application to estimate design runoff curve number for South Korean conditions

Runoff estimations based on the standard USDA–NRCS curve number (CN) table without calibration have a tendency to give inaccurate results when the CN values are applied in South Korea which has many high slope watersheds and that has a continental monsoon climate. Particularly for the design flood estimation, accurately calibrated CN values are required because the estimated peak flow is very sensitive to the selection of CN. However, the lack of flood data makes it difficult to calibrate and assign runoff CNs to Korean watersheds. Even if sufficient data are available to estimate CN values, it is also difficult to obtain the direct flows by separating base flows from total runoff hydrographs due to the temporal irregularity of rainfall events and the resulting complex pattern of runoff. Therefore, an alternative method for estimating CNs needs to be developed to overcome these issues. The purpose of this study is to present a method for estimating runoff CNs using the soil and water assessment tool (SWAT) model which can take into account watershed heterogeneities such as climate conditions, land use and soil types. The proposed CN estimation method uses the simulated flow data by SWAT instead of using measured flow data. This method has advantages in estimating CN values spatially for each subbasin division considering watershed characteristics. The use of daily data can reduce the sensitivity to the abnormality that is commonly involved in flow data with a small time scale. The SWAT‐based CN estimation method, combined with the asymptotic CN method, was applied to the Chungju dam watershed in South Korea. A regression equation was then developed from this approach, which was used to estimate CN values that decrease exponentially as rainfall amounts increase and that converge to 60·6 and 79·4 without and with considering subsurface lateral flow, respectively. Furthermore, the CN values for the antecedent moisture conditions were determined using the probabilistic approach. The CN associated with the 50% probability for the Chungju dam watershed is 87·8 which can be taken to be representative of antecedent moisture condition (AMC) II. The CN_I and CN_III associated with 90% and 10% probabilities are 78·9 and 94·1, respectively. The estimated CN_II = 87·8 differs markedly from the geographic information system (GIS)‐based CN 65·0, which implies that the standard USDA–NRCS CN method should be calibrated to the studied area of interest. Copyright © 2010 John Wiley & Sons, Ltd.     

Transnational labour migration and the politics of care in the Southeast Asian family

Recent increases in female labour migration in and from Asia have triggered a surge of interest in how the absence of the mother and wife for extended periods of time affects the left-behind family, particularly children, in labour-sending countries. While migration studies in the region have shown that the extended family, especially female relatives, is often called on for support in childcare during the mother’s absence it is not yet clear how childcare arrangements are made. Drawing on in-depth interviews with non-parent carers of left-behind children in Indonesia and Vietnam, the paper aims to unveil complexities and nuances around care in the context of transnational labour migration. In so doing it draws attention to the enduring influence of social norms on the organisation of family life when women are increasingly drawn into the global labour market. By contrasting a predominantly patrilineal East Asian family structure in Vietnam with what is often understood as a bilateral South-East Asian family structure in Indonesia, the paper seeks to provide interesting comparative insights into the adaptive strategies that the transnational family pursues in order to cope with the reproductive vacuum left behind by the migrant mother.     

Environmental reconnaissance of the Shivee-Ovoo coalmine area, Mongolia

An exploratory investigation was conducted to assess the present conditions and evaluate the potential impacts of mining activities on soil and water environments at the Shivee-Ovoo coalmine area in Mongolia. Water quality analysis was conducted on samples from mine dewatering boreholes, a tailings dam, and drinking water sources around the mine area. In drinking water supplies of the area, concentrations of Mg, Fe, and F exceeded the levels set in Mongolian and World Health Organization guidelines. Water type and the principal components analysis indicated that water from the mine area and from public water-supply wells originated from the same aquifer. However, the water quality differed in shallow wells and deep wells because of bedrock differences and geochemical weathering processes, rather than climate effects such as from evaporation. The discharged water poses medium to high salinity hazard for use in irrigation, suggesting the need to manage its use in sustainable agriculture or projects to prevent desertification. The particle size distribution and fractal dimension values of soils revealed the dominance of fine to medium sands, which have strong potential for desertification. X-ray photoelectron spectroscopy analysis showed that the soils currently pose no apparent threat for acid rock drainage. Consequently, the open-pit mining of the study area shows no obvious environmental impact at present in its vicinity. However, for sustainable development and expansion of the mine, environmental changes should be continuously monitored, with consideration of possible measures for waste recycling.     

Large-Scale Variation of Solar Wind Electron Properties from Quasi-Thermal Noise Spectroscopy: Ulysses Measurements

The transport of energy in space plasmas, especially in the solar wind, is far from being understood. Measuring the temperature of the electrons and their non-thermal properties is essential to understand the transport properties in collisionless plasmas. Quasi-thermal noise spectroscopy is a reliable tool for measuring the electron temperature accurately since it is less sensitive to the spacecraft perturbations than particle detectors. We apply this method to Ulysses radio data obtained during the first pole-to-pole fast latitude scan in the high-speed solar wind, using a kappa function to describe the electron velocity distribution. We deduce the variations with heliocentric distance between 1.5 and 2.3 AU in the fast solar wind at high latitude in terms of three fitting parameters: the electron density varies as n _e??R ^?1.96±0.08, the electron temperature as T _e??R ^?0.53±0.15, and the kappa index of the distribution remains constant at ??=2.0±0.2. These observations agree with the predictions of the exospheric theory.     

Self-Interacting Dark Matter in the SU(3)C⊗ SU(3)L ⊗ U(1)N models

We present the gauge models based on SU(3)_C⊗ SU(3)_L ⊗ U(1)_N (3-3-1) group which contain particles satisfying conditions for dark matter. There are two such models: one with exotic positive charged lepton and a variant with right-handed neutrinos. The scalar self-interacting dark matters are stable without imposing of new symmetry and should be weak-interacting. PACS Nos: 95.35.+d, 12.60.Fr, 14.80.Cp