Studying Relationships between the Fractal Dimension of the Drainage Basins and Some of Their Geomorphological Characteristics

Complex nonlinear dynamic systems are ubiquitous in the landscapes and phenomena studied by earth sciences in general and by geomorphology in particular. Many natural landscape features have an aspect such as fractals. In the many geomorphologic phenomena such as river networks and coast lines this is visible. In recent years fractal geometry offers as an option for modeling river geometry and physical processes of rivers. The fractal dimension is a statistical quantity that indicates how a fractal scales with the shrink, the space occupied. This theory has the mathematical basis but also applied in geomorphology and also shown great success. Physical behavior of many natural processes as well as using fractal geometry is predictable relations. Behavior of complex natural phenomena as rivers has always been of interest to researchers. In this study using data basic maps, drainage networks map and Digital Elevation Model of the ground was prepared. Then applying the rules Horton-Strahler river network, fractal dimensions were calculated to examine the relationship between fractal dimension and some rivers geomorphic features were investigated. Results showed fractal dimension of watersheds have meaningful relations with factors such as shape form, area, bifurcation ratio and length ratio in the watersheds.

Facies Analysis and Depositional Environment of the Oligocene-Miocene Asmari Formation, Bandar Abbas Hinterland, Iran

The Asmari Formation is a thick carbonate sequence of the Oligocene-Miocene in the Zagros Basin, southwest of Iran. This formation is located in Bandar Abbas and Coastal Fars regions on the following two sections: Anguro anticline (west-northwest of Bandar Abbas) and Gavbast anticline (southwest of Lar County). The Asmari Formation has diameters of 68 and 26 m in the Anguro and Gavbast sections, respectively. This formation is composed of limestone, dolomitic limestone and an altered form of marl. Based on the results of petrographic analyses, 7 facies were identified in the Anguro and Gavbast sections in the study region. The facies were deposited on the following 3 belts: tidal flat (MF 1 - 3), lagoon (MF 4 - 5) and open marine (MF 6, 7). According to evidence such as the gradual change of microfacies, the lack of main reef barriers, and the lack of slumping and sliding features, the Asmari Formation was formed in a marine environment of carbonate homoclinal ramp type. This environment is composed of the following two subenvironments: the inner ramp and the middle ramp. The comparison of the facies identified in the Anguro and Gavbast sections indicates that Gavbast section is mainly composed of lagoon facies. Moreover, the Anguro section demonstrates more facies diversity than Gavbast section.

Estimation of Species Richness of Permian Foraminifera in Non-Parametric Methods and Investigation of Its Change Trend in Central Alborz, Western Tethys

Species richness of foraminifera assemblages in the Permian succession, contains Dorud, Ruteh and Nessen Formations, in Central Alborz—North of Iran, was estimated and studied based on lithostratigraphy and microbiostratigraphy of Permian. We used four non-parametric estimators to investigate the species richness: Chao 2, Jackknife 1, Jackknife 2 and bootstrap. These methods estimates the species richness based on the presence/absence data of each taxon identified in the samples. We use the submenu of quadrat richness in “Past” [1] software to estimate richness in regional chronostratigraphic stages.The results show that the estimated diversity of foraminiferal assemblages with the exception of late Yakhtashian, increased constantly from Asselian to Murgabian with the highest diversity of foraminifera seen in the Murgabian. The main decrease in foraminiferal species richness happened during the Midian which corresponds to the kamura cooling event.

Geochemical, Sedimentological and Mineralogical Characterization of Surficial Sediments in Eynak Marsh (North of Iran)

A multidisciplinary study of the sedimentology, geochemistry and mineralogy has been conducted to understand the linkage between marsh and alluvial sediments and also their potential sources in Eynak marsh, North of Iran. The influence of the upstream potential sources on recent sediment geochemistry has been discussed based on geochemical, sedimentological and mineralogical results. A spatial grain size distribution study was carried out to investigate the hydrodynamic and deposition system of the marsh. So, the surficial sediment sampling was carried out to describe the sedimentological parameters and elemental geochemistry of sediments in Eynak marsh. Mineralogical complexes are mainly made up of felsic minerals such as quartz, calcite, feldspar, pyrite, mica, and clay minerals (in very low values) indicated by high amounts of Al, Ca, and Ni. As expected, the mineralogy of sediments is controlled mainly by the rock formations. Also sediment textures are controlled by the hydrodynamic condition in the marsh. So its distribution has been influenced by distance from the entrance sediments to Eynak marsh. The results showed that there are no enrichments related to fine grain sediment distributions. An association of Al with the trace elements such as Sc, Y, La, Ce, and Zr indicates that their distributions are mainly controlled by the felsic rocks in the upstream. On the other side, due to the waste water entrance to the marsh, Ni and Pb concentration could be under the effects of anthropogenic activities around the marsh. Results represented high values for Mn concentration (min 462, max 1784 and average 1037 ppm) and it showed a significant correlation with Ca, Sr, and Mg. A redox habitat and constantly calm hydrodynamic circumstance in the study area, likely cause high concentration of Ca, Sr, and Mg, and Mn. And they are representing negative correlations with some elements such as Al, Be, Fe, K, and Na.

Depression diagnosis by deep learning using EEG signals:A systematic review

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life,in particular public and private health.This mental disorder is usually characterized by considerable changes in feelings,routines,or thoughts.With respect to the fact that early diagnosis of this illness would be of the critical importance in effective treatment,some developments have occurred in the purpose of depression detection.EEG signals reflect the working status of the human brain which are considered the most proper tools for a depression diagnosis.Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them.Owing to this significant characteristic of deep learning,recently,these methods have intensely utilized in the diverse research fields,specifically medicine and healthcare.Thereby,in this article,we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data.Regarding the adopted search method,we have finally evaluated 22 articles between 2016 and 2021.This article which is organized according to the systematic literature review(SLR)method,provides complete summaries of all exploited studies and compares the noticeable aspects of them.Moreover,some statistical analyses have been performed to gain a depth perception of the general ideas of the latest pieces of research in this area.A pattern of a five-step procedure has also been established by which almost all reviewed articles have fulfilled the goal of depression detection.Finally,open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions have been discussed.