Introduction: Usually osseointegration takes between three to six months after implant placement but patients are interested to have early loading. There are no definitive criteria for measuring bone mineral density (BMD), insertion torque (IT) (final torque force) and resonance frequency analysis (RFA) (primary implant stability) to determine exact loading time based on the relationship between the above-mentioned parameters. The aim of this study was to determine the relationship between IT, RFA and BMD in screw-type implants.Materials and Methods: This clinical trial was conducted on 18 patients who were candidates for ITI implant placement. Written consent was taken and jaw bone density was determined via a digital radiography technique before surgery. After implant placement, RFA and IT were measured. Fifty-five ITI implants of the total 62 implants placed were evaluated; the implants were 12 mm long with a diameter of 4.1 mm. Data was analyzed with Pearson’s test using SPSS.15 software (a=0.05).Results: There was a significant relationship between IT, RFA and BMD. Pearson’s test showed a correlation coefficient of 0.872 to 0.789 between the three parameters, indicating a strong relationship between them. The mean bone density was 1.468±0.042 g/cm2; the mean RFA was 66.01±2.2 ISQ and the mean IT was 34.62±3.33 N/cm2.Conclusion: Based on the results of the present study there is a significant relationship between, IT, RFA and BMD (p value=0.001).

Flood can be having devastating effects on watersheds and human life, so, recognize of flood prone areas have a high influences on flood management. The aim of this study is to assessment of flood susceptibility at Talar watershed with an area of 2067 square kilometers. For this purpose some flood conditioning factor such as elevation, land slope, curvature, topographic wetness index, stream power index, average rainfall, distance to the river, hydrologic group of soil, maximum depth of soil, and land-use have been used. Flood conditioning factor have been prepared using related software and then converted to raster format. In the next step the 135 flood location in the study area were determined. These flood location were divided into two groups consisting of 93 locations for calibration and 42 locations, for validation. Then with comparatively analysis between previous floods coordinates and impressive environmental parameters on flood occurrence the probability of flooding for each class of each parameter was calculated. Faunally was obtained the flood risk map of the study area. Based on the flood risk map, the territory was divided to 5 class sensitivity, very high, high, medium, low and very little. The results have been showed that the frequency ratio method with 80% accuracy have high performance in determination of flood prone areas in Talar watershed.

Quantifying fire hazards in natural areas and their spatial patterns are essential for developing appropriate fire management strategies, especially in countries with limited historical data on past fires. In this study, a fire hazard map for the Andika region of Iran was constructed by examining the correlation of past fires with the criteria of topography, meteorology, land cover, and human factors. The locations of eight-year fire points from 2013 to 2020 of Nova satellite sensor VIIRS were received and the fire map of each was constructed using the NBR (Normalized Burn Ratio). The wildfire events distribution maps were randomly divided into 70 and 30 percent ratios for training (modeling) and testing (validation) data, respectively. Using the frequency ratio, a fire hazard map of the region was created. Four fire hazard areas ranging from very high to low were identified. The results of past fires and frequency ratio model showed that in the study area, land cover (2. 982), elevation (2. 778), and annual precipitation (2. 419) have the greatest prediction rate and influence on fire occurrence. The results also showed that a large proportion of past fires (71. 37%) were located in high and very high-risk areas. The evaluation results of the area under the curve method showed an accuracy of 71. 1% using evaluation data and 74. 4% using training data, which can be considered desirable. The small differences between the validation results using test and training data indicate an unbiased fire hazard map.

In many speech applications, the target signal is corrupted by highly correlated noise sources. Separating desired speaker signals from the mixture is one of the most challenging research topics in speech signal processing. This paper proposes a binaural system combined with a monaural incoherent post processor for speech segregation. The proposed binaural system is based on spatial localization cues: Interaural Time Differences (ITD) and Interaural Intensity Differences (IID). A target speech is separated from interfering sounds by estimating time-frequency binary and ratio masks. The binary mask is estimated using the multi-level extension of the Otsu thresholding algorithm used in image segmentation. ITD and IID are important features for mask estimation in low and high frequencies, respectively. The ratio mask is estimated using the incoherent monaural speech separation system as the post processing stage. Systematic evaluations show that the proposed system can separate the target signal with acceptance quality.

The thornless half-shrub Astragalus podolobus is one of the most valuable species in Iran rangelands providing nursing condition with important roles in soil and water conservation together with biodiversity conservation. However, land-use changes and rangeland degradation have represented real threats to this species. To determine how to manage this plant better, its potential spatial distribution was mapped using a bivariate statistical model (FR: frequency ratio) for the Maraveh Tapeh rangelands of Golestan Province, north-east Iran. A total of 115 occurrences of A. podolobus were recorded using GPS during field surveys from April to September in 2018 and 2019, then 80 data points (70%) were used in modeling, and the remaining 35 data points (30%) in evaluating the model. A number of 8 digital layers potentially affecting the habitat suitability of the plant were selected as independent variables, including: distance from roads and rivers, elevation, plan curvature, precipitation, slope percentage, aspect and temperature. The results showed that the variables temperature, precipitation, and distance from the roads have larger effects on the presence of A. podolobus. The habitat suitability map produced using the FR model provides a powerful tool for development of conservation plans for the degraded habitats of the species. The analytical framework used in this study could be applied to other arid and semi-arid environments to determine habitat suitability of A. podolobus and stabilize this species, and to introduce new adaptive rules for rangeland management.

Background and Objectives: Landslides are significant natural geologic hazard around the world. Expansion of urban and man-made structures into potentially hazardous areas leads to extensive damage to infrastructure and occasionally results in loss of life every year. Identification of factor affecting existing of landslide as well as its zonation in the given watershed is one of the basic tools for landslide control and selection of appropriate and effective solution as well. Thus, a research study with objective of recognizing factor affecting landslide and determination of lands with hypothential to its occurrence was conducted to prepare landslide zonation map for the Sorkhoon watershed using Dempster-shafer and frequency ratio models.Materials and Methods: To reach this goal, after preparing of Landslide inventory map using field survey and aerial photo interpretation, data layers of distance from stream, distance from faults, elevation, slope, aspect, Topography wetness index (TWI), distance from roads, land use, lithology and Stream Power index (SPI) as Factors affecting landslides were selected and after applying Dempster-shafer and frequency ratio methods the final Landslide Hazard zoning was prepared. For calculating of weight of affective factors, was used the analytic hierarchy process in the software of expert choice. To validation of used methods the ROC curve was used.Results: The main factors that caused the landslides in this area based on field observations and Expertise opinions include lithology, distance from roads and slope, respectively, with scores (0.181, 0.163, 0.145) and vis-à-vis factors of elevation, slope and topography wetness index (TWI) respectively with scores (0.018, 0.036, 0.054) have the lowest impact on landslides. According to the results, frequency ratio models have obtained higher AUC (0.927) as compared to the Dempster-shafer (0.858) that shows the high correlation between hazard map and distribution map landslide inventory map and better evaluation of frequency ratio toward Dempster-shafer model.Conclusion: The results of the validation showed that the frequency ratio model has higher efficiency and accuracy toward Dempster-shafer model for preparing of zonation map. Based on the results of the frequency ratio model 21128200 square meters (7.05 percentage) of the region located in the very low risk class, 67, 144, 500 square meters (20.45 percentage) of the area located in the low risk class, 90, 113, 400 square meters (27.45 percentage) located in the moderate Risk class, 91733400 square meters (27.94 percentage) of the area located in the high class and finally 56.160000 square meters (17.11 percentage) of the area located in the very high risk class.

Climatic factors and human activities can cause mass movements. Due to the risk of these movements to human communities, one has to control them. In order to control mass movements, different aspects of the movements should be studied in detail. The aim of this research is to evaluate the efficiency of a statistical model - logistic regression - and a probabilistic model - frequency ratio - in rockfall hazard mapping in the Salavatabad saddle in eastern Sanandaj, Iran. The study of 34 sensitive slopes and 34 stable slopes in the study area using field works, local interviews and the literature review showed that 8 factors including slope gradient, slope aspect, slope curvature, elevation above the sea, and distance from road, distance from faults, lithology and landuse are the most effective factors for occurring rockfalls. The relationships of rockfall and these 8 factors were studied by the logistic regression model and the probabilistic frequency ratio model and the generated rockfall susceptibility maps were compared. The results showed that both models work reasonably well for rockfall hazard studies; however, the logistic regression model has an accuracy of 85.09 percent of the Area under ROC Curve (AUC) in prediction of sensitive areas compared to the accuracy of 76.74 percent by the probabilistic frequency ratio model. Therefore, the logistic regression model can be used for rockfall hazard studies in the study area.

Introduction and Objective: The severity and frequency of floods, depends on watershed characteristics, including shape, slope, drainage density and terrain parameters. Also, hydrological characteristics such as rainfall, storage, interception, infiltration, and anthropogenic activities can intensify or reduce the flood losses. Determining flood-prone and flood-prone areas is a prerequisite for flood management planning and damage. Material and Methods: This study was conducted to assess the flood susceptibility of Khiavachai sub-watersheds located in the west of Ardabil province with an area of 12,680 hectares using frequency ratio method. Parameters of elevation, slope, land curvature, topographic wetness index, distance from rivers, direction, drainage density, hydrological soil group, geology and land use were used to assess flood sensitivity and prioritize sub-watersheds. The spatial map of selected parameters was prepared in raster format. Then, according to the historical flood events, the spatial location of 107 flood points in the area was determined. The observed flood event points were randomly divided into groups consisting of 74 points (70%) for calibration and 33 points (30%) for validation. Then, by comparative analysis between the occurrence of previous floods and environmental parameters affecting the occurrence of floods, the impact weight of each class of parameters was obtained. Finally, a flood probability map of the study area was obtained. Results: According to the flood potential map, flood susceptibility of Khiavachai watershed was shown as five classes with very high, high, medium, low and very low levels. The results showed that the sub-watersheds (1 and 2) adjacent to the outlet of the basin have the highest flood sensitivity. In other words, flooding of sub-watersheds located downstream of Khiavachai watershed is due to runoff accumulation and high flood prone due to the impact of other variables of slope and rainfed agricultural land use. Conclusion: Protecting the river and carrying out various watershed management operations to convey the flow in these areas can reduce the potential damage caused by floods. The proposed flood sensitivity map can be the basis for prioritizing flood areas as well as control measures for flood management. Therefore, watershed management measures to control floods in this area are more important.