Spectra of quasiperiodic oscillations of galactic X-ray sources–dynamical regimes from a simple model

We suggest that the dynamical regime(s) underlying quasi-periodic oscillations observed in the spectra of bright galactic-bulge X-ray sources are nonlinear with a mixed phase space. The important feature of such regimes is that they are generic among nonlinear Hamiltonian and nearly Hamiltonian systems of more than two degrees of freedom. We give a simple example of such chaotic (deterministic) systems whose spectra share a number of features with those observed for quasiperiodic oscillations of such sources.     

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.     

Applying disk-based discontinuous deformation analysis (DDA) to simulate Donghekou landslide triggered by the Wenchuan earthquake

In this paper, for the first time, disk-based discontinuous deformation analysis (DDA) is applied to simulate a real landslide triggered by an earthquake. For this purpose, the kinematic behaviour of the Donghekou landslide triggered by the Wenchuan earthquake is simulated and the results obtained using disk-based DDA are compared with actual data. The comparisons show that there is a good agreement between the results obtained using disk-based DDA and observed data. The simulation results provided an understanding of the failure behaviour and temporal evolution of the landslide. This study shows that disk-based DDA is a practical numerical tool that can be used to simulate the post-failure behaviour of landslides triggered by an earthquake.     

Using inverse modeling and hierarchical cluster analysis for hydrochemical characterization of springs and Talkhab River in Tang-Bijar oilfield,Iran

Hierarchical cluster analysis (HCA) and inverse modeling (PH REdox EQuilibrium (in C language) (PHREEQC)) were simultaneously useful approaches in interpreting surface water hydrochemistry within Talkhab River in the Tang-Bijar oilfield, Iran, where large uncertainties exist in the understanding of the water quality system. Q-mode HCA applied to the data revealed three major surface water associations distinguished on the basis of the major causes of variation in the hydrochemistry. The three water groups were classified as upstream waters (group 1: Ca–SO₄), intermediate waters (group 2: Ca–SO₄–Cl), and downstream waters (group 3: Na–Cl). Geochemical reaction models were constructed using PHREEQC to establish the reactions associated with the different mineral phases through inverse modeling. The hydrochemical compositions of the water groups and the mass balance calculations indicate that the dominant processes and reactions responsible for the hydrochemical evolution in the system are (1) dissolution of evaporites, (2) precipitation of carbonate minerals, (3) silicate weathering reactions, (4) limited mixing with saline water, and (5) ion exchange.     

Hydrogeological assessment of the Tabarteh fault zone by physicochemical analysis and multivariate statistical methods

Hydrogeologically, faults may impede, conduit, exert no influence, or may play a combination of these roles on groundwater flow. The object of this paper is to study the hydrogeological role of the Tabarteh fault, which is located on the border of Zagros and Central Iran tectonic zones in an alluvial aquifer. The recorded data of water table levels, chemical parameters, and discharge rate of wells, in addition to geological maps and geophysical results, were collected and evaluated. The outcrop of travertine in limited areas and the emergence of a few small springs within the alluvium show a barrier role of the fault in the groundwater flow. The spatial analysis of chemical components, head time series, and groundwater flow direction assessment demonstrated that the fault acts as both a barrier and a non-barrier in different sections. The multivariate statistical methods of cluster and discriminant analyses also confirm the dual role of the fault.     

Spatial and temporal trends of dust storms across desert regions of Iran

Dust storms are among natural and anthropogenic hazards for socioeconomic resources, especially in desert regions. In recent years, dust storms have become a serious problem, especially in desert regions of Iran. This study investigates temporal and spatial variation of dust storm frequency in desert regions of Iran. The number of dusty days (NDD) are collected from 22 stations across the region. The statistical analysis of NDD time series is carried out to show both spatial and seasonal pattern of dust storm occurrence in the region. The regional map of statistical characteristics indicates a north to south increasing dust storm frequency. The spatial map also reveals higher year-to-year variation in south eastern Iran. The seasonality of NDD shows the highest frequency for summer followed by the spring and autumn seasons. The popular Mann–Kendall and the bootstrap MK test to consider serial correlation are then applied for Trend assessment. Results showed both negative (across the north and northwestern regions) and positive trend (across south and south eastern regions) in the annual and seasonal NDD time series. This north-to-south gradient in the spatial and temporal frequency NDD may arise from harsh dry and gusty winds as well as intense land use change in the south eastern territories of Iran. However, more careful and detailed studies are required to connect environmental conditions to change in NDD frequency.     

Prediction of Enhanced Soil–Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming

Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σ_n) and anchorage length (L) and the least effective parameters are average particle size (D₅₀) and anchor base area (A_b).