Inverse-distance Weighting method is a simple, easy and understandable interpolation in many branches of earth sciences, and it is embedded in the mining software related to estimation, Efforts to enhance the accuracy and precision of this method can be applied to a wider and reliable interpolation process. In this paper, spatial structure of different elements from analysis of rock samples associated with a porphyry copper deposit is studied using variogram. A criterion based on variogram parameters is suggested for each element to calculate the distance power. In order to validate the method, inverse-distance Weighting interpolation of the different elements and different values for the inverse-distance power is implemented (common values and calculated power), error percent and root mean square error of interpolation is calculated and analyzed. interpolation is coded in MATLAB environment and the results for different elements are demonstrated and analyzed. Based on the results, the slope of the linear part spherical variogram is measured of the amount of inverse-distance power (a) so that the values for the elements with relatively continuous spatial structure equal common values of a (1, 2 and 3) and Inverse distance Weighting method is applicable for these elements.

Studying the marine accidents of Arvand channel and estuary during last five years, this research aims to locate the geographic place of risky points. Then through an annual and monthly chronological analysis and by application of time and place management, it tries to identify the eventful months in order to maximize the success of marine search and rescue measures. On the other hand, the chronological study of the mentioned accidents indicates that to what extent the accomplished attempts could have reduced or limited marine losses. As well, this study indicates that how marine rescue stations and the place of strategic search and rescue equipment suit the geographic place of eventful places. All accidents have been summarized into vessels' marine accidents, borderline aggressions and crews' illness and injuries. Then the factors of fatal and financial losses and damages have been identified based on which Weighting is done. The weights of all accidents have been determined prior to chronological and spatial analysis. ARC VIEW GIS program has been applied for spatial analysis. The accidents position and their relevant intensity as the relevant specification have been entered into the program. The spatial analysis in this program has been done via Inverse Distance Weighting (IDW) method. The focus map of various accidents has been extracted. Place analyses in this research led to identification of 6 dangerous areas in view point of vessels accidents, 4 hazardous areas for human accidents and one highly risky area in view point of aggression to vessels. The annual time analysis show that during last 5 years, only the number of border aggressions has decreased while there is no obvious decreasing trend in vessels and human accidents. As well, the monthly time analysis shows 3 hazardous time sections in vessels accidents. The occurrence of accidents relevant to border aggressions is focused in the first quarter of each year. Management of rescue equipments - in two aspects of place and time - in hazardous areas as well as suggestion of having marine equipment in hazardous places is of the obtained results of place and time analyses in this research.

Context: Alpha-1-antitrypsin (A1AT) is the most abundant liver-derived, highly polymorphic, glycoprotein in plasma. Hereditary deficiency of alpha-1-antitrypsin in plasma (A1ATD) is a consequence of accumulation of polymers of A1AT mutants in endoplasmic reticulum of hepatocytes and other A1AT-producing cells. One of the clinical manifestations of A1ATD is liver disease in childhood and cirrhosis and/or hepatocellular carcinoma (HCC) in adulthood. Epidemiology and pathophysiology of liver failure in early childhood caused by A1ATD are well known, but the association with hepatocellular carcinoma is not clarified. The aim of this article is to review different aspects of association between A1AT variants and hepatocellular carcinoma, with emphasis on the epidemiology and molecular pathogenesis. The significance of A1AT as a biomarker in the diagnosis of HCC is also discussed.Evidence Acquisitions: Search for relevant articles were performed through Pub Med, HighWire, and Science Direct using the keywords “alpha-1-antitrypsin”, “liver diseases”, “hepatocellular carcinoma”, “SERPINA1”. Articles published until 2011 were reviewed.Results: Epidemiology studies revealed that severe A1ATD is a significant risk factor for cirrhosis and HCC unrelated to the presence of HBV or HCV infections. However, predisposition to HCC in moderate A1ATD is rare, and probably happens in combination with HBV and/or HCV infections or other unknown risk factors. It is assumed that accumulation of polymers of A1ATD variants in endoplasmic reticulum of hepatocytes leads to damage of hepatocytes by gain-of-function mechanism. Also, increased level of A1AT was recognized as diagnostic and prognostic marker of HCC.Conclusions: Clarification of a carcinogenic role for A1ATD and identification of proinflammatory or some still unknown factors that lead to increased susceptibility to HCC associated with A1ATD may contribute to a better understanding of hepatic carcinogenesis and to the development of new drugs.

Introduction LiDAR (Light Detection and Ranging) employs pulse models which penetrates vegetation cover easilyand provides the possibility of retrieving data related to Digital Terrain Model (DTM). Pulses sent by the Lidar sensorhitdifferent geographical features on the surfaceground and scatter inall directions. Distance to the object is determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance traveled by the small portion of pulses backscattered. Most LiDAR receivers at least record the first and last backscattered pulses. The first backscattered pulses are used to produce Digital Surface Models (DSMs) and the last ones are used to produce DTMs. Despite the fact that these data can provide a valuable source for DTM generation, the volume of vegetation (vegetation density) in forest areas reducesthe accuracyof DTMs. Onthe other hand, ground surveying of forest areas is rather expensive and time consuming, especially in largerforests. Aerial images are also used as a source for DTM generation, but this approach requires a 60– 80% overlap between images which along with canopy height reduce the potential of this method for DTM generation. Also, low spatial resolution of satellite images collected from forest areas increases errors in DTM generation to a large degree. The present study investigates the accuracy and precision of DTMsproduced from LiDAR data in a forest area. Furthermore, the effect of different methods of filtering and DTM interpolation was explored. Different methods of DTM generation were also closely analyzed and evaluated. Materials & Methods The case study area is located in Doroodforests, a part of Zagros forests, in the southeastern regions of Lorestan province in Iran (48° 51’ 19’ ’ E to 48° 54’ 31’ ’ E and 33° 19’ 21’ ’ N to 33° 21’ 15’ ’ N). Minimum and maximum altitude above sea level were 1143 and 2413m, respectively. The study area covers 100 hectares of mountains with an average slope of 38%. Approximately 50% of the area is covered by forests in which Brant’ s oak (Quercusbrantii Lindley) is the most frequent species. LiDAR data were collected by the National Cartographic Center of Iran (NCC) in 2012 using a Laser scanner system (Litermapper 5600) fixed on an aircraft flying at an average altitude of 1000m. LiDAR data consisted of the first and last returns (backscattered pulses), distance and their intensity value. Collected data had an irregular structure and included an average of more than four points per square meter. A DTM was produced using a two-step filtering. First, a morphological filter removed most of non-ground points, and then a slope-based filter detected remaining points. Inforest areas with rough-surface, DTM was producedthrough processing ofLiDAR data with statistical methods likekriging and inverse distance Weighting (IDW). These methods apply third and fourth power to detect and remove non-ground points. To assess the accuracy of DTMs produced by different approached, 5 percent of the LiDAR point cloudswererandomly separated as the test data. Amongst these data sets, 62 points with a suitable dispersion were selected and measured using a GPS-RTK. An error matrix, along with accuracy indices (including correlation and Root Mean Square Error (RMSE)) were calculated based on these data. Results & Discussion Results indicated that 44-degree slope is the best threshold for isolation of non-ground points and inverse distance Weighting (IDW) is the best third power interpolation method with the highest correlation (0. 9986) and the lowest RMSE (0. 204 meter). Amongst the filtering methods, slope-based filter used for separation of ground and non-ground points had the best performance. Since this filter combines two parameters of slope and radius, it can remove cloud points related to the vegetation cover and results in high efficiency for steep forest areas. Slope-based filter is suitable for processing of near-surface vegetation, whilst statistical filter is well-suited for vegetation cover consisting of tall trees. Conclusion The present study proposed and investigated different scenarios for the production offorest areas’ DTM using LiDAR data and two interpolation methods. These algorithms were practicallyassessed using LiDAR data collected from Dorood forest areas. The best scenario was slope-based filter with inverse distance Weighting (IDW) interpolation. Based on accurate assessment, this approach can produce reliable DTM in forest areas.

Regarding to importance of Juniperus sites, it is important to estimation of tree density and spatial structure of them using Geostatic. This study was conducted in Golestan province, Chaharbagh with area of 60 ha. We refined a 15×20 grid and 300 plots (45×45 m2) were measured. The number of trees with diameter at breast height (DBH) <7. 5 cm were considered. The results of the variogram analysis showed that the tree density parameter had a vigorous spatial structure. First, the spherical model was fitted on the variogram. Then, the estimations and mapping were done using Kriging and IDW approaches. Cross validation results of approaches showed that the estimation of tree density was unbiased and these methods can be used for mapping spatial distribution of tree density with acceptable precision. The Kriging method with RMSE of 6. 8 tree. ha-1 and 8. 7% had the better performance in comparison with IDW with RMSE of 7. 5 tree. ha-1 and 9. 5%. So, the Kriging method is useable in ChaharBagh for tree density mapping.

Let G = (V (G), E(G)) be a simple, finite and undirected graph of order n. A k-vertex Weighting of a graph G is a mapping w: V (G) → {1, . . ., k}. A k-vertex Weighting induces an edge labeling fw: E(G) → N such that fw(uv) = w(u) + w(v). Such a labeling is called an edge-coloring k-vertex Weighting if fw(e) ̸ = fw(e′ ) for any two adjacent edges e and e′ . Denote by μ ′ (G) the minimum k for G to admit an edge-coloring k-vertex Weighting. In this paper, we determine μ ′ (G) for some classes of graphs.