The effect of terrain factors on landslide features along forest road

The objective of this study was to investigate the effects of physiographic features such as slope, altitude, aspect and soil on landslides dimensions and distribution in Pahnehkola forest, north of Iran. 30 landslides were selected for detailed observation, with their occurrences recorded by global positioning system (GPS) along the surveyed forest road. Then, landslides were mapped in Arc view and subsequently digitized into a geographic information system (GIS). Results indicate that the landslide area at a distance of 80 to 100 m from road edge was significantly more than that of other distances. The landslide dimensions increased with increasing slope angle. The mean of landslide area and mean of landslide volume on the Northwest aspect was significantly more than that on other aspects (P<0.01). The mean of landslide dimensions in altitude class of 400 to 650 m was significantly less than that in altitude class of 150 to 400 m (P<0.01). The mean of landslide dimensions increased significantly with increasing soil liquid and plastic limit. The logistic regression modeling indicate that independent variables including aspect, liquid limit, plastic limit and soil moisture, significantly influence the landslides area. The majority of landslides were situated along roads and on faults, and shallow landslides were more frequent along roads compared to those on faults.Key words: Landslide, forest road, physiographic features, GPS, Pahnehkola forest

Microstructural response and wear behaviour of Ti-6Al-4V impregnated with Ni/Al2O3 + TiO2 nanostructured coating using an electric arc

YesTitanium alloys are known for their excellent corrosion resistance; however, low surface hardness results in poor wear resistance, which limits its potential application. This study employs a novel two-step process to embed a hard Ni coating containing a mixture of nanosized particles (Al2O3 and TiO2) into the surface of the Ti-6Al-4V alloy using an electric arc produced during the inert tungsten gas welding process. The surface of the sample was evaluated using Vickers Microhardness, Scanning electron microscopy, Energy dispersive spectroscopy and pin-on-plate wear testing. Microstructural analysis showed that impregnating the titanium surface with Ni/(Al2O3 and TiO2) nanomaterials resulted in the formation of a hard martensitic structure to a depth of approximately 2 mm below the surface. The changes observed are driven by modification of the surface chemistry and the presence of nickel, causing grain size reduction, solid solution strengthening and dispersion strengthening of the treated layer by the nanoparticles. The hardness of the treated layer increased by more than 180% when 40 nm Al2O3 and 30 nm TiO2 particles were embedded into the surface. Similarly, the wear resistance of the treated surface improved by 100%

Gully erosion susceptibility mapping using multivariate adaptive regression splines-replications and sample size scenarios

Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with 5, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features

Evaluation of multi-hazard map produced using MaxEnt machine learning technique

Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research

Micro-alloying and surface texturing of Ti-6Al-4V alloy by embedding nanoparticles using gas tungsten arcwelding

YesTitanium alloy Ti-6Al-4V is known for both its excellent mechanical properties and its low surface hardness. This study explores a two-step process for depositing a hard nanocrystalline coating onto the surface of the Ti-alloy, followed by surface melting, which embeds hard nanoparticles into a thin surface layer of the alloy. The treated surface layer was studied using X-ray diffraction, scanning electron microscopy, and Vicker's micro-hardness testing. The results of the study show that the surface of the Ti-6Al-4V alloy can be successfully hardened by embedding nanosized Al2O3 particles into the surface using gas tungsten arc welding to melt the surface of the material. Surface melting the Ti-6Al-4V alloy with a 50A welding current produced the maximum microhardness of 701 HV0.2kg. The micro-hardness of the treated surface layer decreased with the increasing size of the nanoparticles, while the roughness of the surface increased with the increasing welding current. The heat input into the surface during the surface melting process resulted in the formation of various intermetallic compounds capable of further increasing the hardness of the Ti-6Al-4V surface

A comprehensive comparative investigation on solar heating and cooling technologies from a thermo-economic viewpoint—A dynamic simulation

© 2020 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. The yearly thermo-economic performance is dynamically investigated for three solar heating and cooling systems: solar heating and absorption cooling (SHAC), solar heating and ejector cooling (SHEC), and heating and solar vapor compression cooling (HSVC). First, the effects of important design parameters on the thermo-economic performance of the systems to supply the heating and cooling loads of the building are evaluated. The systems are parametrically analyzed with the weather conditions of Tehran, Iran. The results show that the life cycle costs (LCC) of the SHAC and HSVC systems are alike and much lower than those of the SHEC system. The HSVC system exhibits the best performance from exergetic and solar fraction viewpoints. The comparative analysis shows that the energy efficiencies of the SHAC and SHEC systems are higher in colder climatic conditions. However, the collector efficiency of the HSVC system declines in colder climates, mainly due to the lower solar intensities relative to in hotter climates. Further, the solar fraction of the SHAC system is higher than the SHEC technology under all climatic conditions. Moreover, higher values of solar fractions are obtained under colder weather conditions for the SHEC and HSVC systems. The best economic performance is observed for the SHAC and HSVC technologies, having significantly lower LCCs than the SHEC system. These lower LCCs under colder climatic conditions are due to the lower cost of supplying the heating load compared to the cooling load. Furthermore, all systems exhibit enhanced exergetic performance in colder weather conditions. The yearly thermo-economic performance is dynamically investigated for three solar heating and cooling systems: SHAC, SHEC, and HSVC. In addition, the effects of important design parameters on the thermo-economic performance of the systems to supply the heating and cooling loads of the building are evaluated

Effect of heat-treatment on the thermal and mechanical stability of Ni/Al2O3 nanocrystalline coatings

YesHeat-treatment is a frequently used technique for modifying the physical and chemical properties of materials. In this study, the effect of heat-treatment on the mechanical properties, thermal stability and surface morphology of two types of electrodeposited coatings (pure-Ni and Ni/Al2O3) were investigated. The XRD analyses showed that the crystal structure of the as-deposited coating changes from slightly amorphous to crystalline as the heat-treatment temperature increases. The heat-treatment of both the pure-Ni and the Ni/Al2O3 coating caused an increase of the grain size within the coatings. However, the unreinforced Ni coating experienced a faster growth rate than the Ni/Al2O3 coating, which resulted in a larger average grain size. The temperature-driven changes to the microstructure of the coatings caused a reduction in the hardness and wear resistance of the coatings. The presence of nanoparticles within the Ni/Al2O3 coating can successfully extend the operational temperature range of the coating to 473 K by pinning grain boundaries

The natural organosulfur compound dipropyltetrasulfide prevents HOCL-induced systemic sclerosis in the mouse

PublishedArticleIntroduction: The aim of this study was to test the naturally occurring organosulfur compound dipropyltetrasulfide (DPTTS) found in plants, which has antibiotic and anti-cancer properties, as a treatment of HOCl-induced systemic sclerosis in the mouse. Methods: The pro-oxidative, anti-proliferative and cytotoxic effects of DPTTS were evaluated ex vivo on fibroblasts from normal and HOCl-mice. In vivo, the anti-fibrotic and immunomodulating properties of DPTTS were evaluated in the skin and lungs of HOCl-mice. Results: H2O2 production was higher in fibroblasts derived from HOCl-mice than in normal fibroblasts (P<0.05). DPTTS did not increase H2O2 production in normal fibroblasts, but DPTTS dose-dependently increased H2O2 production in HOCl-fibroblasts (P<0.001 with 40μM DPTTS). Because H2O2 reached a lethal threshold in cells from HOCl-mice, the anti-proliferative, cytotoxic and pro-apoptotic effects of DPTTS were significantly higher in HOCl-fibroblasts than for normal fibroblasts. In vivo, DPTTS decreased dermal thickness (P<0.001), collagen content in skin (P<0.01) and lungs (P<0.05), SMA (P<0.01) and pSMAD2/3 (P<0.01) expression in skin, formation of advanced oxidation protein products and anti-DNA topoisomerase-1 antibodies in serum (P<0.05) versus untreated HOCl- mice. Moreover, in HOCl-mice, DPTTS reduced splenic B cell counts (P<0.01), the proliferative rates of B-splenocytes stimulated by lipopolysaccharide (P<0.05) and T-splenocytes stimulated by anti-CD3/CD28 mAb (P<0.001). Ex vivo, it also reduced the production of IL-4 and IL-13 by activated T cells (P<0.05 in both cases). Conclusions: The natural organosulfur compound DPTTS prevents skin and lung fibrosis in the mouse through the selective killing of diseased fibroblasts and its immunomodulating properties. DPTTS may be a potential treatment of Systemic sclerosis.This work was supported by European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 215009 RedCat for financial support. The authors are grateful to Ms Agnes for her excellent typing of the manuscript

Asymptotic expansions of the solutions of the Cauchy problem for nonlinear parabolic equations

Let $u$ be a solution of the Cauchy problem for the nonlinear parabolic equation $$\partial_t u=\Delta u+F(x,t,u,\nabla u) \quad in \quad{\bf R}^N\times(0,\infty), \quad u(x,0)=\varphi(x)\quad in \quad{\bf R}^N,$$ and assume that the solution $u$ behaves like the Gauss kernel as $t\to\infty$. In this paper, under suitable assumptions of the reaction term $F$ and the initial function $\varphi$, we establish the method of obtaining higher order asymptotic expansions of the solution $u$ as $t\to\infty$. This paper is a generalization of our previous paper, and our arguments are applicable to the large class of nonlinear parabolic equations

Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas