Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Density current occurs when a fluid flowing through another fluid with different density. Lot of research can be found in the literature about density current, but in all of them one of the fluids is stationary. Since no study can be found about density current when fresh water is flowing in the same direction of density current, therefore in this research attempts are made to study this especial case with the aid of experimental flume. The experiments were conducted in a flume with 35 cm wide, 8.8 m long and 70 cm height. The dense fluid was salt water with discharges of 0.5, 1 and 1.5 L/s and the discharge of fresh water were equal to 2, 3 and 4 L/s. The concentrations of dense fluid were 10, 15 and 20 g/L, and the bed slop of the flume was 0, 1 and 2%. The results showed that the head velocityis a function of the velocity of fresh water and therefore the relative head velocity is selected for further analyses. Finally, an equation is suggested to find relative head velocity.

The probability density function is a basic concept in statistics, which can be used to study all behaviors of a random variable; several nonparametric methods have been used to estimate the probability density function. One of a nonparametric procedure used to estimate densities is the kernel method. In this paper, to reduce bias of kernel density estimation, methods such as usual kernel (UK), geometric extrapolated usual kernel (GEUK), a bias reduction kernel (BRK) and a geometric extrapolated bias reduction kernel (GEBRK) are introduced. Theoretical properties, including the selection of smoothness parameter and the accuracy of resultant estimators are studied. Accordingly, the mean integrated squared error of GEBRK method achieves a faster convergence rate when kernels are symmetric. ....

Considering soil compaction problem in sugarcane fields due to using heavy harvester and haulout equipment under unsuitable moisture conditions, this research aims to assess soil compaction in sugarcane fields located in Da’bal Khazaei Plantation unit of Sugarcane Development and By-product Company, Ahvaz. Undisturbed soil samples from the furrow (wheel tracks) were collected for measuring soil water content and bulk density. Considering the changes in soil texture of sugarcane fields, for expressing the degree of soil compactness, in addition to soil bulk density (BD), relative bulk density (BD divided by reference BD) was also determined. The change in soil mechanical resistance with depth was determined by a cone penetrometer. Results showed that most of soil BD values measured in the sugarcane fields were in the range of small root development scale (high limitation). Comparing the calculated RBD values with optimum value (0.85), it was observed that most of the values were higher than the optimum values recommended for root growth. This shows excessive soil compaction in the sugarcane fields. The values of cone indices measured in soil profiles indicated that most of the values were higher than either limiting (2 MPa) or critical (3 MPa) values for root growth. Therefore, for improving soil physical fertility and achieving sustainability in crop production, management of farm machinery traffic in sugarcane fields, especially at the harvest time, needs to be reconsidered.

Buildings and bridges on loose to medium dense sands are often built on piles foundation. If sand layer is saturated, liquefaction is likely to occur. In recent earthquakes, liquefaction and its effects have been reported as one of the main reasons for failure of piles and pile-supported structures [1]. When pile foundations are exposed to intense dynamic transverse loads during earthquakes, soil–structure interaction (SSI) plays an important role in allocating the response of pile foundations to lateral excitation [2]. Recent observations after major earthquakes have shown that extensive damages and destructions are still likely to be happened to pile foundations. This problem is significant particularly for pile foundations in loose saturated cohesionless deposits which are vulnerable to liquefaction and lateral spreading during seismic loading. Design procedures that have been developed for evaluating pile behavior under earthquake loading, have many uncertainties to be used for cases involving liquefaction. The performance of piles in liquefied soil layers is much more complex than that of non-liquefying soil layer as a result of the diminishing of stiffness and shear strength of the surrounding soil over time due to the increase of pore water pressure [3].By increasing the density of the soil the probability of liquefaction is reduced liquefaction. Soil compaction increases the soil relative density, modulus of elasticity, the angle of internal friction, and SPT-N of the soil. With regard to the relationships in soil mechanics science increase of relative density causes the bearing capacity of foundation to be increased, improves the soil properties, reduces the inappropriate subsidence, risk of liquefaction, and stabilizes embankments. High relative density is accounted suitable for geotechnical activities and provides the ideal conditions at least for static designs.

Tunneling in soft grounds causes to changes in displacements and subsequently in-situ stresses around ground. These displacements may damage structural assets. Thus, estimating the magnitude and shape of settlement curve is necessary. There are several empirical and analytical methods for predicting settlement. For example, Peck’s empirical method is well known method for predicting settlement due to tunneling. Tunneling process is done by imposing volume loss in tunnel. Then, soil displacement is measured by using image processing technique and that data is fitted to Gaussian curve. By conducting tests in loose and dense sands, it is concluded that by increasing relative density of the soil, the magnitude of settlement decreases and the settlement trough width will be increased. Also soil volume loss is not the same as the tunnel volume loss.Introduction: Many researchers investigated settlement due to tunneling but there is a lack of research about the effect of relative density on settlement. Marshall et al. (2012) by conducting centrifuge tests in high density sandy soil, showed that settlement trough is affected by tunnel size, tunnel depth and tunnel volume loss. Zhou et al. (2014) by performing several tests in loose, medium and dense sand, examined the effect of relative density on settlement and showed that by decreasing the relative density the magnitude of settlement increases and settlement trough width will be decreased. In this paper by using 1g physical modeling (Figure 1) which is designed in Sahand University of Technology, the effect of relative density on settlement has been studied.Material and methods: Simulation of tunnel volume loss is carried out by using two different diameter tubes as a shield and lining (Figure 2), while pulling out the larger tube volume loss is imposed. Also by changing tube diameter different volume losses have been applied. Measuring of soil displacements is achieved by image processing technique. For this purpose, different photos are taken from the whole process of the test by digital camera and by using Geo PIV, settlement of ground is determined.Results and discussions: Experiments were conducted in loose and dense silica sands and the measured data have been fitted to Gaussian curve. The result showed that Peck equation fitted well to surface and sub-surface settlement data. As shown in Figures 3 and 4, contour of displacement curve versus normalized tunnel depth and distance versus normalized tunnel diameter indicate that in dense sands most of the displacement occurred in the region which placed in distance of 1.25 times tunnel diameter and in loose sands in the region of 0.6 times tunnel diameter. Thus, settlement trough width in loose sands is narrower. Also by measuring soil volume loss in loose and dense sands at different levels (Figure5) it is concluded that in loose sands due to less dilation, more volume loss is transferred to higher levels.Conclusion: The following main conclusion can be drawn: 1. Gaussian curve predicts well surface and subsurface transverse settlements but selection of its parameters requires more accuracy that may result in inaccurate prediction.2. Settlement curve in loose sands is narrower than dense sands.3. Displacement and soil volume loss in loose sand are more than dense sand.

The aim of this study was to investigate catch composition and catch per unit effort of invertebrates in shrimp trawl in different areas and depths. This research was carried out in Hormozgan coastal waters (Bandar Abbas and Hormuz Island) at depths of less than 10 meters, 10-20 meters and over 20 meters in shrimp fishing season in 2015. 40 hauls that duration of each one was approximately 2 to 2. 5 hours with a total catch of 7623 kg were done. The catch composition included 1068 kg of commercial shrimp and 6555 kg of by-catch that included invertebrates and fish which were 2386. 8 and 4168. 2 kg, respectively. The results showed that the catch composition consisted of 14% shrimp (1068 kg), 31% by-catch invertebrates (6555 kg) and 55% by-catch fish (4168. 2 kg). There were significant differences among the CPUE in the different areas and depths as well as the interaction of area and depth on CPUE of the total catch, target species (Shrimp), Oratosquilla oratoria, Portunus pelagicus, Crustacea (Small crab), Sepia pharaonis, Anthozoa, Demospongiae, Rhizostomatidae (Jelly fish), Asteroidea and Octopoda (P<0. 01). The highest percentage of catch occurrence belonged to Oratosquilla oratoria, Rhizostomatidae and Sepia pharaonis and the Demospongiae was at the least.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

One of the most popular additive manufacturing techniques is called laser powder bed fusion. Several parameters are involved in this method and four of the most important factors are laser power, scanning speed, infill pattern angle, and hatch space. Change in this parameter has a direct effect on defects and fabricated parts quality. Post-processing treatment such as heat treatment was cared out in order to improve part property and applications. Built time and costs reduce significantly by suitable choice of process parameters and post-processing treatments. In this article genetic algorithm cared out to highest relative density and lowest surface roughness and best value of each parameter presented. The results showed that the best output could achieve by using 102-105 Watt of laser power, 623-630 mm.s-1 scan speed, 73-76 µm of hatch space, and 638-640 °C of heat treatment temperature.