Ion-acoustic shock waves in a plasma with weakly relativistic warm ions, thermal positrons and a background electron nonextensivity

A weakly nonlinear analysis is carried out to derive a Korteweg–de Vries-Burgers-like equation for small, but finite amplitude, ion-acoustic waves in a dissipative plasma consisting of weakly relativistic ions, thermal positrons and nonextensive electrons. The travelling wave solution has been acquired by employing the tangent hyperbolic method. Our results show that in a such plasma, ion-acoustic shock waves, the strength and steepness of which are significantly modified by relativistic, nonextensive and dissipative effects, may exist. Interestingly, we found that because of ion kinematic viscosity, an initial solitonic profile develops into a shock wave. This later evolves towards a monotonic profile (dissipation-dominant case) as the electrons deviate from their Maxwellian equilibrium. Our investigation may help to understand the dissipative structures that may occur in high-energy astrophysical plasmas.     

Correction to: Geochemical Prospectivity Mapping Through a Feature Extraction–Selection Classification Scheme

Natural Resources Research - The original version of this article unfortunately contained a mistake in the second part of Table 3. The data in last four rows of Table 3, i.e.,...     

A context-aware personalized travel recommendation system based on geotagged social media data mining

The proliferation of digital cameras and the growing practice of online photo sharing using social media sites such as Flickr have resulted in huge volumes of geotagged photos available on the Web. Based on users' traveling preferences elicited from their travel experiences exposed on social media sites by sharing geotagged photos, we propose a new method for recommending tourist locations that are relevant to users (i.e., personalization) in the given context (i.e., context awareness). We obtain user-specific travel preferences from his/her travel history in one city and use these to recommend tourist locations in another city. Our technique is illustrated on a sample of publicly available Flickr dataset containing photos taken in various cities of China. Results show that our context-aware personalized method is able to predict tourists' preferences in a new or unknown city more precisely and generate better recommendations compared to other state-of-the-art landmark recommendation methods.     

GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran)

Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster–Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.     

Predicting the Building Stone Cutting Rate Based on Rock Properties and Device Pullback Amperage in Quarries Using M5P Model Tree

One of the key parameters that affect the selection of equipment and the cost estimation of dimension stone quarries is the rock cutting rate or production rate. In this study, the M5P tree algorithm is used to determine the relationship between the hard rock sawability and its factors especially the physical and mechanical characteristics of rock. To achieve the research goal, a variety of eleven types of hard dimension stone were selected and nine major physical and mechanical characteristics of rock including uniaxial compressive strength, Young’s modulus, Brazilian tensile strength, equivalent quarts content, grain size, Mohs hardness, point load test, density and P-wave velocity of these samples were evaluated. The cutting rate of diamond wire for all of the Workpiece was measured at different pullback amperage with a fully instrumented cutting platform in laboratory. All operational parameters of cutting process were entirely controlled. Thus, a database containing 99 datasets was provided and it has been used for analyses. The obtained results from the pruned and unpruned tree models showed a significant relationship between cutting rate and its factors. In the end, the results of M5P tree method were compared with statistical analyses (i.e., linear and nonlinear regression). The coefficient of determination be equal with 0.92, 0.86, 0.77 and 0.63 for unpruned tree, pruned tree, linear and nonlinear regression method respectively. This comparison showed that the both method of M5P tree technique have a better performance in predicting the cutting rate rather than the statistical regression methods.     

Soil loss and displacement by heavy equipment in forest road subgrading projects

Forest soil is an important component of the natural environment, and is a primary medium for many biological activities. In this study, soil loss and displacement by excavator and bulldozer （heavy equipments） were measured on cut and fills slopes of forest roads located in Mazandaran province, lran. The volumes of soil losses were estimated by prismoidal analyses of cut and fill slopes deformation between two time treatments （under subgrading and two years later） in slope classes of 30-50% and 50-70%. Weights of soil losses were calculated by multiplying the volumes of soil losses （cm^3） to the general bulk density （1.3g/cm^3）. Soil displaced area by heavy equipment was evaluated according to earth working width. Results indicated that heavy equipment has significant effect on deformation of cut slope gradient and fill slope length （p〈0.0001）. During the two-year period, the cut （p〈0.0002） and fill （p〈0.0001） slope gradients were significantly deformed in different slope classes. The average soil loss by excavator and bulldozer were 160.35 t/ha·yr and 429.09 t/ha·yr, respectively. Moreover, the soil displaced area during the subgrading process by bulldozer was greater than excavator in both two slope classes （p〈0.05）. Soil loss and displacement in forest roads can be rednced by applying powerful excavators in subgrading project, especially in steep terrains.     

A practical approach for seismic risk assessment of underground structures: A case study of Iranian subway tunnels

Active geological and young faulted zones have made Iran’s territory one of the most seismological active areas in the world according to recent historical earthquakes. Some of the deadliest earthquakes such as Gilan 1990 and Kermanshah 2018 caused tens of thousands fatalities. If such violent earthquakes affect strategical structures of a country, indirect losses would be more concerning than direct losses. Nowadays there is no doubt about the vital role of tunnels and underground structures in urban areas. These facilities serve as nonstop functional structures for human transportation, water and sewage systems and underground pedestrian ways. Any external hazard subjected to underground spaces, such as earthquake could directly affect passenger’s lives and significantly decrease whole system reliability of public transportation. Commonly two earthquake levels of intensities, Maximum Design Earthquake (MDE) and Operating Design Earthquake (ODE) were used in seismic design of underground structures. However, uncertain nature of earthquakes in terms of frequency content, duration of strong ground motion, and level of intensity indicate that only the two levels of earthquake (ODE and MDE) cannot cover the all range of possible seismic responses of structures during a probable earthquake. It is important to evaluate the behavior of tunnel under a wide range of earthquake intensities. For this purpose, a practical risk-based approach which is obtained using the total probability rule was used. This study illustrates a framework for evaluation seismic stability of tunnels. Urban railway tunnels of Tehran, Shiraz, Ahwaz, Mashhad, Isfahan and Tabriz were considered as study cases. Nominal value of seismic risk for three main damage states, including minor, moderate and major were calculated.     

Snow water equivalent prediction using Bayesian data assimilation methods

Using the U.S. National Weather Service’s SNOW-17 model, this study compares common sequential data assimilation methods, the ensemble Kalman filter (EnKF), the ensemble square root filter (EnSRF), and four variants of the particle filter (PF), to predict seasonal snow water equivalent (SWE) within a small watershed near Lake Tahoe, California. In addition to SWE estimation, the various data assimilation methods are used to estimate five of the most sensitive parameters of SNOW-17 by allowing them to evolve with the dynamical system. Unlike Kalman filters, particle filters do not require Gaussian assumptions for the posterior distribution of the state variables. However, the likelihood function used to scale particle weights is often assumed to be Gaussian. This study evaluates the use of an empirical cumulative distribution function (ECDF) based on the Kaplan–Meier survival probability method to compute particle weights. These weights are then used in different particle filter resampling schemes. Detailed analyses are conducted for synthetic and real data assimilation and an assessment of the procedures is made. The results suggest that the particle filter, especially the empirical likelihood variant, is superior to the ensemble Kalman filter based methods for predicting model states, as well as model parameters.