With the purpose of strengthening the knowledge and prevention of landslide disasters, this work develops a methodology that integrates geomorphological mapping with the elaboration of landslide susceptibility maps using geographic information systems (GIS) and the multiple logistic regression method (MLR). In Mexico, some isolated works have been carried out with GIS to evaluate slope stability. However, to date, no practical and standardized method has been developed to integrate geomorphological maps with landslide inventories using GIS. This paper shows the analysis carried out to develop a multitemporal landslide inventory together with the morphometric analysis and mapping technique for the El Estado River basin where, selected as the study area, is located on the southwestern slope of the Citlaltepetl or Pico de Orizaba volcano. The geological and geomorphological factors in combination with the high seasonal precipitation, the high degree of weathering and the steep slopes predispose its surfaces to landslides. To assess landslide susceptibility, a landslide inventory map was prepared using aerial photographs, followed by geomorphometric mapping (altimetry, slopes and geomorphology) and field work. With this information, landslide susceptibility was modeled using multiple logistic regression (MLR) within a GIS platform and the landslide susceptibility map was obtained.

Leaf litter decomposition and carbon release patterns in five homegarden tree species of Kumaun Himalaya viz. Ficus palmata, Ficus auriculata, Ficus hispida, Grewia optiva and Celtis austalaris were investigated. The study was carried out for 210 days by using litter bag technique. In the current investigation, the duration needed for desertion of the original biomass of diverse leaf litter varied from 150 to 210 days and specifies a varying pattern of decomposition and carbon release among the species. Grewia optiva took the longest time to decompose (210 days) while Ficus hispida decomposed more quickly than rest of the species (150 days). The relative decomposition rate (RDR) was reported highest in Ficus hispida (0.009-0.02 g-1d-1) and lowest in Grewia optiva (0.008-0.004 g-1d-1). Carbon (%) in remaining litter was in the order: Ficus auriculata (24.4 %) >Ficus hispida (24.3%) > Celtis austaralis (19.8%) > Ficus palmata (19.7%) > Grewia optiva (19%). The relationship between percentage weight loss and time elapsed showed the significant negative correlation with carbon release pattern in all the species. Releasing nutrients into the soil through the decomposition of homegarden tree residuals is a crucial ecological function that also regulates the nutrient recycling in homegarden agroforestry practices.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.

Objective: To describe magnetic resonance imaging (MRI) findings of the brain in patients younger than 65 years who were studied by transcranial Doppler (TCD) with microbubble contrast, with a history of cryptogenic cerebrovascular accident (CVA) and suspected patent foramen ovale (PFO).

Materials and methods: This retrospective cross-sectional study included patients of both sexes, younger than 65 years of age.

Results: Our sample (n = 47.47% male and 53% female, mean age is 42 years) presented high-intensity transient signals (HITS) positive in 61.7% and HITS-negative in 38.3%. In HITS-positive patients, lesions at the level of the subcortical U-brains, single or multiple with bilaterally symmetrical distribution, predominated. In patients with moderate HITS, lesions in the vascular territory of the posterior circulation predominated.

Conclusion: In patients younger than 65 years with cryptogenic stroke and subcortical, single or multiple U-shaped lesions with bilateral and symmetrical distribution, a PFO should be considered as a possible cause of these lesions.

Facing the digital economy era, considerable attention is paid to the importance of understanding the fundamental impact on the information and development of blended teaching methods regarding the higher education. For this reason, the purpose of this study is to answer the challenges brought by the digital economy era, identify the effective teaching methods which would be used in English Correspondence course in the era of digital economy, aiming to form the patterns of learning, provide high motivation, strength and knowledge, and most importantly contribute to the complex competences of future working. For further research, it is expected to be able to prove that using the blended teaching methods will effectively improve students’ communication skills and learning efficiency, enhance students’ learning experience and critical thinking skills.