The HNCO ring in the Sgr B2 region

The large-scale structure associated with the 2′N HNCO peak in Sgr B2 [Minh, Y.C., Haikala, L., Hjalmarson, Å., Irvine, W.M., 1998. ApJ 498, 261 (Paper I)] has been investigated. A ring-like morphology of the HNCO emission has been found; this structure may be colliding with the Principal Cloud of Sgr B2. This “HNCO Ring” appears to be centered at (l,b) = (0.7°,−0.07°), with a radius of 5 pc and a total mass of 1.0 × 10⁵ to 1.6 × 10⁶ M. The expansion velocity of the Ring is estimated to be 30–40 km s⁻¹, which gives an expansion time scale of 1.5 × 10⁵ year. The morphology suggests that collision between the Ring and the Principal Cloud may be triggering the massive star formation in the Sgr B2 cloud sequentially, with the latest star formation taking place at the 2′N position. The chemistry related to HNCO is not certain yet, but if it forms mainly via reaction with the evaporated OCN⁻ from icy grain mantles, the observed enhancement of the HNCO abundance can be understood as resulting from shocks associated with the collision between the Principal Cloud and the expanding HNCO Ring.     

Observations of interstellar HOCO+: abundance enhancements toward the galactic center

A survey of the 4(04)-3(03) and 1(01)-0(00) transitions of HOCO+ has been made toward several molecular clouds. The HOCO+ molecule was not observed in any sources except Sgr B2 and Sgr A. The 5(05)-4(04) and 4(14)-3(13) transitions were also detected toward Sgr B2. The results indicate that gas phase CO2 is not a major carbon reservoir in typical molecular clouds. In Sgr B2, the HOCO+ antenna temperature exhibits a peak approximately 2' north of the Sgr B2 central position (Sgr B2[M]) and the 4(04)-3(03) line emission is extended over a approximately 10' x 10' region. The column density of HOCO+ at the northern peak in Sgr B2 is approximately 3 x 10(14) cm-2, and the fractional abundance relative to H2 > or = 3 x 10(-10), which is about 2 orders of magnitude greater than recent predictions of quiescent cloud ion-molecule chemistry.     

Parameter identification for lined tunnels in a viscoplastic medium

This paper is dedicated to the identification of constitutive parameters of elasto‐viscoplastic constitutive law from measurements performed on deep underground cavities (typically tunnels). This inverse problem is solved by the minimization of a cost functional of least‐squares type. The exact gradient is computed by the direct differentiation method and the descent is done using the Levenberg–Marquardt algorithm. The method is presented for lined or unlined structures and is applied for an elastoviscoplastic constitutive law of the Perzyna class. Several identification problems are presented in one and two dimensions for different tunnel geometries. The used measurements have been obtained by a preliminary numerical simulation and perturbed with a white noise. The identified responses match the measurements. We also discuss the usage of the sensitivity analysis of the system, provided by the direct differentiation method, for the optimization of in situ monitoring. The sensitivity distribution in space and time assess the location of the measurements points as well as the time of observation needed for reliable identification. Copyright © 2002 John Wiley & Sons, Ltd.     

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province,Vietnam

Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In...     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique

Natural Resources Research - In this paper, we used artificial intelligence (AI) techniques to investigate the relation between the rock size distribution (RSD) and blasting parameters for rock...     

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA) based on Deep Belief Network(DBN) with Back Propagation(BP) algorithm optimized by the Genetic Algorithm(GA).For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE) technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy, root mean square error(RMSE), and area under the receiver operatic characteristic curve(AUC) were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC = 0.989) and prediction accuracy(AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR(0.885), LMT(0.934), BLR(0.936), ADT(0.976), NBT(0.974), REPTree(0.811), ANFIS-BAT(0.944), ANFIS-CA(0.921), ANFIS-IWO(0.939), ANFIS-ICA(0.947), and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.