With increase in cities' population and development of urbane life, passing buried pipelines near the ground's surface is inevitable in urban areas, roads, subways and highways. In this paper, the results of laboratory tests on exible pipe with a 160 mm diameter, placed in reinforced-trench by a geogrid layer and an EPS geofoam block subjected to repeated load are investigated. The pipe diameter change, strain and pressure acting over the pipe were measured throughout the loading, unloading and reloading. The parameters inspected in the tests included the thickness (30, 60 and 100 mm) and width (160 and 240 mm) of EPS block and burial depth of pipe (1. 2 and 1. 5 times the pipe diameter). Based on the results, the values of the pipe diameter change and pipe strain swiftly increases during the early cycles of loadings and followed the stability trend with the gathering of load cycles. In the geogrid-reinforced system, the change in pipe diameter and in pipe's strain at the end of the loading cycles showed 19% and 20% reduction, respectively, as related to the unreinforced system. According to the results, the minimum pipe diameter change and pipe's strain were acquired by using EPS block with maximum width, thickness, and 30 kg/m3 density over the pipe in addition to a geogrid layer representing values of, respectively, 0. 26 and 0. 3 times of the others acquired in the reinforced trench with a geogrid layer.

This study investigates how the market reacts to a negative revision of management earnings forecast (bad news) and late announcement of this news. The study spans the period of 2004 (AHS) to 2011.  The linear multivariate regressions have been used to test the hypotheses. We found that market reacts negatively to bad news but late news are followed by a positive market reaction. Also, there is no difference in market reaction to late and on time bad news. But late announcement of good news is followed by a positive differential reaction, as compared to on-time announcement of it.

Aim of this paper is cash flow effect & EPS for dividend forecasting. According to the results, there is between EPS and dividend meaningful relation. But there isn’t operating cash flow and dividend meaningful relation.

Strength measurement of rock requires testing that must be carried out on test specimens with particular sizes in order to fulfill testing standards or suggested methods. Often, the coring process breaks up the weaker core pieces, and they are too small to be used in either index tests or conventional strength tests such as point load index (Is) and Brazilian tensile strength (BTS). One of the index tests to indirectly determine the rock strength is the block punch index (BPI) test, which requires flat disc specimens without special treatment. This study aimed to evaluate the applicability of the BPI test for predicting the uniaxial compressive strength (UCS), BTS and IS of the sandstones by empirical equations. Also, we have compared the performance of the BPI and IS for predicting the UCS and BTS. It was experimentally shown that BPI is a reliable method for predicting the UCS, BTS and Is of the sandstones under study. Moreover, the results indicate that BPI could be utilized with same importance as Is for predicting the UCS, while predicting the BTS by Is appears to be more reliable than BPI.

With regard to the non-linear nature of real-life data, their clusters' shapes are non-convex and unfortunately, some clustering methods cannot identify non-convex clusters and this is a challenge. Density-based clustering methods could be a solution to this problem. Among all methods of this type, the DBSCAN algorithm can cluster data with different shapes, sizes, and densities and also identify noise points. However, owing to the use of static input parameters-the neighbourhood radius ($Eps$) and the minimum value for cluster formation ($MinPts$)- this algorithm has some problems such as the difficulty in accurately determining these parameters in high-dimensional data sets and not recognizing clusters with different densities. Accordingly, this paper presents a density clustering algorithm, which requires minimal input parameters and one of its main parameters is $Eps$, which is automatically calculated based on the $k$-nearest neighbours of points and its value is different for each cluster. To evaluate the effectiveness of the proposed algorithm, some experiments were conducted. The obtained results showed the effectiveness and efficiency of the presented algorithm regarding the correct identification of clusters with the desired shape, size, and density. In addition, the proposed algorithm was found effective in estimating the number of clusters in most of the data sets considered in this study.