Slope Factor Research Articles

THE ANALYSIS LANDSLIDES POTENTIAL DUE TO CHANGES OF LAND-USE IN MIU WATERSHED

This study aims to determine the magnitude of land-use change in the Miu watershed and to determine the relation between slope and soil type to erosions. The method used to predict the rate of erosion is the Universal Soil Loss Equation (USLE) in 2005, 2010, and 2016 with the help of the Geographic Information System (GIS). This study uses data on rainfall, slope, soil type, land use, and conservation and crop management factors which are then entered into the USLE equation. From this equation, the actual erosion rate in 2005 was 2,634,104.25 tons / year, 2010 was 3,285,025.34 tons / year, and 2016 was 1,856,140.79 tons / year. The average actual erosion in 2005, 2010, and 2016 was 2,591,756.79 tons/year, which is classified as minor erosion. The landslide-prone area based on the lowest slope of 5.50% is equivalent to an area of ​​36 km2, for the medium it is 34.81% equivalent to an area of ​​228 km2, and a height of 59.69% covering an area of ​​391 km2 where very steep slopes dominate this river basin. The effect of significant land-use change affects the rate of erosion when there is a high intensity of rain on a steep slope so the potential for erosions in the area.

Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions. To prevent this disastrous problem, researchers have used several approaches for landslide susceptibility modeling, for the purpose of preparing accurate maps marking landslide prone areas. Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network (ANN) method. However, the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms, which are scarcely applied in landslide mapping. In the current study, nine hybrid metaheuristic algorithms, genetic algorithm (GA)-ANN, evolutionary strategy (ES)-ANN, ant colony optimization (ACO)-ANN, particle swarm optimization (PSO)-ANN, biogeography based optimization (BBO)-ANN, gravitational search algorithm (GHA)-ANN, particle swarm optimization and gravitational search algorithm (PSOGSA)-ANN, grey wolves optimization (GWO)-ANN, and probability based incremental learning (PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers’ Sahel, Algeria. The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images, field surveys, and six conditioning factors (lithology, elevation, slope, land cover, distance to stream, and distance to road). Initially, a gamma test was used to decrease the input variable numbers. Furthermore, the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN techniques and their performance was assessed through seven statistical indicators. The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model, which yielded higher performance in predicting landslide susceptibility compared to the other models. Sensitivity analysis using the step-by-step technique was done afterward, which revealed that the distance to the stream is the most influential factor on landslide susceptibility, followed by the slope factor which ranked second. Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility. Based on these findings, an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards.

Assessment of the temporal variability and health risk of atmospheric particle-phase polycyclic aromatic hydrocarbons in a northeastern city in China.

In this study, we examined the sources and temporal variability of 16 polycyclic aromatic hydrocarbons (PAHs) found in fine particulate matter (PM2.5) in a typical industrial city in northern China. We also evaluated the incremental lifetime cancer risk (ILCR) from the inhalation of these PAHs. Atmospheric PM2.5 samples were collected for 7 consecutive days each month from 2014 to 2019, and the 16 PAHs were measured using multiplex gas chromatography-tandem mass spectrometry. The carcinogenic risk of PAH exposure was assessed using the inhalation unit risk (IUR) and cancer slope factor (CSF) methods. The annual average concentrations of PM2.5 for each year from 2014 to 2019 were 102.87±55.25, 86.92±60.43, 69.17±37.74, 58.20±59.15, 56.01±34.52, and 52.54±58.15 µg m-3, and the annual average ΣPAH concentrations were 56.03±81.09, 47.99±79.30, 40.41±57.31, 33.57±51.79, 43.23±74.80, and 25.20±50.91 ng m-3, respectively. Source identification, using diagnostic ratio analysis, indicated that the major PAH sources were coal/biomass combustion, fuel combustion, and traffic emissions. A health risk assessment showed that the ILCR from PAH inhalation decreased throughout the study period and varied with age. The IUR and CSF methods both showed that the adult ILCR exceeded 1.0×10-6. These findings demonstrate the importance of addressing the carcinogenic risk of PM2.5-bound PAHs, particularly in adults.

Classification of Majene Regency Landslide Prone Areas Using Geographic Information System and Storie Index

On January 14, 2021, several villages in Majene Regency suffered landslides triggered by a M6.2 earthquake. The study’s aim is to use the Index Storie model approach, remote sensing data, and Geographic Information Systems (GIS) to map the distribution of landslide-prone areas as a mitigation basis in Majene Regency to reduce the potential for future landslide disasters. The level of landslide potential was determined based on slope conditions, soil types, rainfall, land use types, and potential earthquake risk. In general, morphological conditions in Majene Regency are dominated by slopes that are still covered in forests and receive relatively low rainfall, resulting in low landslide potential if seismic potential factors are not taken into account. Based on the results of an analysis that considers slope factors and potential seismic risks, the results of a map that illustrates the risk of landslide are quite high in several areas, are Malunda District, Ulumanda District, and Tubo Sendana District.

Glacier geometry and flow speed determine how Arctic marine-terminating glaciers respond to lubricated beds

Abstract. Basal conditions directly control the glacier sliding rate and the dynamic discharge of ice. Recent glacier destabilization events indicate that some marine-terminating glaciers quickly respond to lubricated beds with increased flow speed, but the underlying physics, especially how this vulnerability relates to glacier geometry and flow characteristics, remains unclear. This paper presents a 1D physical framework for glacier dynamic vulnerability assuming sudden basal lubrication as an initial perturbation. In this new model, two quantities determine the scale and the areal extent of the subsequent thinning and acceleration after the bed is lubricated: Péclet number (Pe) and the product of glacier speed and thickness gradient (dubbed J0 in this study). To validate the model, this paper calculates Pe and J0 using multi-sourced data from 1996 to 1998 for outlet glaciers in the Greenland ice sheet and Austfonna ice cap, Svalbard, and compares the results with the glacier speed change during 1996/1998–2018. Glaciers with lower Pe and J0 are more likely to accelerate during this 20-year span than those with higher Pe and J0, which matches the model prediction. A combined factor of ice thickness, surface slope, and initial flow speed physically determines how much and how fast glaciers respond to lubricated beds in terms of speed, elevation, and terminus change.

Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey

In recent years, the number of wildfires has increased all over the world. Therefore, mapping wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire management decisions. This study aims to generate Machine Learning (ML) based wildfire susceptibility maps for Adana and Mersin provinces, which are located in the Mediterranean Region of Turkey. To generate a wildfire inventory, this study uses active fire pixels derived from MODIS monthly MCD14ML composites. Furthermore, as a sub aim, the performance of seven ML approaches, namely, stand-alone Logistic Regression (LR), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and ensemble algorithms, namely Random Forest (RF), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and AdaBoost (AB), was evaluated based on wildfire susceptibility mapping. The capabilities of the corresponding ML methods were assessed using thirteen wildfire conditioning factors, which can be grouped into four main categories: topographical, meteorological, vegetation, and anthropogenic factors. The Information Gain (IG) approach was used to assess their importance scores. A multicollinearity analysis was also performed to assess the relationship between conditioning factors. To compare the predictive performances of ML algorithms, five performance metrics, namely average accuracy, precision, recall, F1 score, and area under the curve, were used. To test the significance of the generated wildfire susceptibility maps and to detect similarities and differences among the output of these ML algorithms, McNemar's test was implemented. In the end, the ML-based models were locally interpreted using the Shapley Additive exPlanations (SHAP) technique. The AUC values of seven methods varied from 0.817 to 0.879, and the accuracy scores ranged between 0.734 and 0.812. The results showed that the RF model provided the best results considering all performance metrics. The accuracy score and AUC values of the RF model were equal to 0.812 and 0.879, respectively. On the other hand, stand-alone algorithms (LDA, SVM, and LR) represented lower performance than tree-based ensemble methods. Both the IG and SHAP analyses showed that elevation, temperature, and slope factors were the most contributing factors. The RF model classifier found that 7.20% of the study area has very high wildfire susceptibility, and the majority of the wildfire samples (68.84%) correspond to the very high susceptible areas in the RF model. The outcomes of this study are likely to provide decision-makers with a better understanding of wildfires in the Eastern Mediterranean Region of Turkey.

No Significant Changes in Addictive and Problematic Behaviors During the COVID-19 Pandemic and Related Lockdowns: A Three-Wave Longitudinal Study.

IntroductionThe COVID-19 outbreak and related lockdowns brought substantial changes in people’s lives and led to concerns about possible increases of addictive behaviors at the initial stages of the pandemic. To examine these concerns, the aim of the present study was to assess longitudinal changes in addictive and problematic behaviors (i.e., problematic social media use, Internet gaming disorder, gambling disorder, problematic pornography use, and compulsive sexual behavior disorder) over time during the COVID-19 pandemic.MethodsThree waves of data collection took place in different stages of the COVID-19 outbreak in Hungary in a general population, from the first wave of lockdowns to the second and third waves of restrictions (May, 2020; NT1 = 1747; June–August, 2020; NT2 = 656; January, 2021; NT3 = 411). Latent growth curve models were calculated to assess the potential changes in addictive and problematic behaviors over time.ResultsLatent growth curve models showed that the sample varied in their initial scores, but there were no significant changes over time in any of the examined behaviors, except for compulsive sexual behavior disorder, which demonstrated a small but significant increase (i.e., positive and significant slope factor). However, the rate of this change was negligible. Overall, there were no noteworthy changes over time regarding any of the examined addictive and problematic behaviors.ConclusionContrary to initial concerns, no substantial changes over time were observed regarding the examined addictive behaviors during the COVID-19 pandemic and related lockdowns. These findings indicate that those who had no previous problem with these addictive behaviors, might have not developed a problem, and those who had problem with either of the behaviors previously, might have not experienced a significant increase in their symptoms.

Probabilistic risk assessment of heavy metals in urban farmland soils of a typical oasis city in northwest China

Previous studies lacked quantitative evaluation studies of the probability of ecology and human health risks from soil heavy metals. This study assessed heavy metal risk level by collecting topsoil samples from a typical oasis city (Wuwei) in northwest China and then quantitatively evaluating the ecological risk from heavy metals by incorporating the uncertainty of health risk model parameters into the risk assessment. This study found that anthropogenic activities have influenced the accumulation of heavy metals in the study area and that the risk of contamination of soil heavy metals was characterized as light to moderate contamination and low ecological risk. On this basis, the species sensitivity distribution curves of heavy metals were constructed using species acute toxicity data, the predicted no effect concentrations of heavy metals were derived, and a probabilistic ecological risk evaluation was conducted. The results show that the current soil environmental quality standards in China are not effective in protecting species diversity. In addition, the probability of ecological risk for Cr, Ni and As in the study area was 63.3%, 23.8% and 7.1%, however, traditional pollution assessment methods underestimate the hazard of Cr. Monte Carlo simulations have shown that the probability of the carcinogenic risk of Cr (adults: 79.4%; children: 94.5%) and As (adults: 78.9%; children: 94.0%) is high, the probability of the total carcinogenic risk exceeding 1E-06 is 99.0%, the probability of the non-carcinogenic risk is low, and the slope factor and reference dose can significantly affect the evaluation of human health risks.

Landslide hazard zonation along Phulmawi village andTlungvel village road, Thingsulthliah Rural Development Block, Aizawl District, Mizoram, India

Landslides are the most common hazard in the state of Mizoram. Rapid development and the uncontrolled interaction with the nature are the reason for l and slide in Mizoram. Landslide vulnerability of Mizoram is already high due to lithology, land use and land cover, geomorphology and slope factor. The extensive mining activities along the highways for road construction and building materials also cause one of major landslide along the highways in the state of Mizoram. The present study investigates the Landslide Hazard Zones along the national highway 54 between Phulmawi village and Tlungvel village. The National Highway Number 54 is the most important road connecting Aizawl city and Northern, Southern and Eastern part of the State of Mizoram. Thematic layers such as slope morphometry, geomorphology, Lithology and land use / land cover were created using Remote Sensing and Geographic Information System (GIS) techniques. On the different groups of thematic layers, a weight age rating system dependent on the relative importance of various causative factors is used. In the GIS setting, the classes were given the corresponding rating value as attribute information. An ordinal ranking of 0 to 10 was allocated to each class within a thematic layer. Landslide hazard zonation map generated could be utilized as a reliable database for mitigation measures and planning for secured growth of the village.

Density functional theory study of CH4 dissociation and C[sbnd]C coupling on W-terminated WC(0001) surface

To explore the catalytic activity of tungsten monocarbide (WC), the methane (CH4) dehydrogenation and further CC coupling process on WC surface have been studied systematically by performing density functional theory (DFT) calculations. The scaling relationship of the adsorption energies of CHx intermediates on W-terminated WC(0001) surface has been summarized together with those cases on pure metal surfaces. By the way, we also studied the thermal stabilities of low Miller index surfaces of WC to obtain the Wulff construction of WC nanocrystal. The W-terminated WC(0001), W-terminated WC(112̄1), and WC-terminated WC(112̄0) facets constitute more than 96% of the total exposed surface in the WC Wulff construction. The van der Waals (vdW) interaction between adsorbate and W-terminated WC(0001) surface is corrected by the BEEF-vdW functional. Our calculations show that W-terminated WC(0001) surface is the most favorably exposed surface with a lower surface energy. Based on the data collected from literature, the linear relationship between the CHx adsorption energies and the x in CHx on metal (e.g., Fe, Co, Ni, Cu, Pt, Ir, Rh, Ru, Pd, Ag, and Au) surfaces has been analyzed. More interestingly, we found that the Co(111), Ni(111), and W-terminated WC(0001) surfaces possess very close slope factors in such a linear relationship, suggesting the similar catalytic property of methane dehydrogenation on these surfaces. More importantly, the rate-limiting step of CH4 dissociation on W-terminated WC(0001) surface corresponds to CH∗→ C∗+H∗. The energy barrier [i.e., 1.46 eV (1.63 eV) with (without) zero point energy (ZPE) correction] for this reaction step is slightly larger than the one on Ni(111) surface and smaller than the one on Cu(111) surface. Furthermore, we have studied the CC coupling through the CH∗ intermediates on W-terminated WC(0001) surface and found that the formation of C2H2 is kinetically favorable. Our results provides useful information for the development of tungsten-carbide-based catalyst materials.

Hydrogen storage performance of LaNi3.95Al0.75Co0.3 alloy with different preparation methods

Rare-earth AB5-type La–Ni–Al hydrogen storage alloys are widely studied due to their extensive application potentials in hydrogen isotope storage, hydrogen isotope isolation and hydrogen compressors, etc. Good hydriding/dehydriding kinetics, easily activation, high reversibility are important factors for their practical application. However, their overall hydrogen storage performance, especially plateau pressure and hydrogen absorption/desorption durability need to be further optimized. In this study, the microstructures and the hydrogen storage properties of as-cast, annealed, and melt-spun LaNi3.95Al0.75Co0.3 alloys were investigated. The experimental results of XRD and SEM showed that all alloys contained a pure CaCu5 type hexagonal structure LaNi4Al phase. The cell volume increased in an order of annealed ​> ​melt-spun ​> ​as-cast, resulting in a lower hydrogen absorption/desorption plateau pressure and a more stable hydride phase. The hydrogen storage capacity of three alloys was almost the same. The slope factor of the annealed and melt-spun alloys is smaller than the as-cast alloy, indicating that heat-treatment process can make the alloys more uniform. For the cycle stability of the alloys, the hydrogen absorption rate of the annealed alloy and melt-spun alloy was much faster than that of the as-cast alloy after 500 cycles. The melt-spun alloy showed high pulverization resistance during hydrogen absorption/desorption, and exhibited an excellent cycling retention of 99% after 500 cycles, suggesting that melt-spinning process can enhance the cycle stability and improve the cycle life of the alloy.

Effects of Imazethapyr on Soybean Root Growth and Soil Microbial Communities in Sloped Fields

The herbicide imazethapyr was previously recommended for controlling weeds in soybean fields. However, the effects of imazethapyr on soil microbial communities and their relationship with crop root growth in sloped soils remain unclear. In this study, a field experiment was conducted on a sloped field to explore the effects of imazethapyr on crop root growth, microbial communities, microbial co-occurrence networks, and the interactions between microbes and crop root growth. The field experiment included two factors: slope and imazethapyr. The slope factor included three different slope gradients: 5° (S1), 10° (S2), and 15° (S3). The imazethapyr factor included two treatments: with (I1) and without (I0) imazethapyr. Thus, six total combinations of slope and imazethapyr treatments were tested in this study: S1I1, S2I1, S3I1, S1I0, S2I0, and S3I0. The results show that, compared to the I0 treatments, the I1 treatments significantly increased the soybean root length, surface area, and volume by 11.7~26.5 m, 171.7~324.2 cm2, and 1.8~3.1 cm3, respectively, across all the slopes. The Proteobacteria, Actinobacteriota, and Bacteroidota bacterial phyla and Ascomycota and Basidiomycota fungal phyla were found to be the top phyla represented bacterial and fungal communities. These five phyla were scattered in co-occurrence networks of bacterial and fungal communities, suggesting these phyla play critical roles in enhancing the stability of co-occurrence networks. Compared to the I0 treatments, the I1 treatments increased nodes from Proteobacteria, Actinobacteriota, and Bacteroidota phyla by 6.4%, 9.1%, and 11.2%, respectively, in the bacterial co-occurrence network. Similarly, in the fungal co-occurrence network, the I1 treatments improved nodes from Ascomycota and Basidiomycota phyla by 1.8% and 5.8%, respectively. Compared to the I0 treatments, the I1 treatments increased positive relations by 8.3% and 3.2%, respectively, in the bacterial and fungal co-occurrence networks. Moreover, the I1 treatments increased the relative abundance of root-promoting biomarkers and suppressed root-limiting biomarkers. However, the application of imazethapyr reduced the diversity and richness of bacterial and fungal communities in general. Furthermore, the nodes and links of bacterial co-occurrence networks in the I0 treatments were 9.2% and 78.8% higher than these in the I1 treatments. Similarly, the I1 treatments also decreased 17.9% of fungal community links compared to the I0 treatments. Our data also show that compared to the I0 treatments, the I1 treatments decreased almost all gene families encoding nitrogen and carbon cycling pathways. In conclusion, the application of imazethapyr increased soybean root growth by increasing root-promoting biomarkers and improved the stability and cooperation of co-occurrence networks of bacterial and fungal communities. However, the application of imazethapyr had some negative impacts on microbial communities, such as reducing the diversity of bacterial and fungal communities and nitrogen and carbon cycling pathways.

Hydrogen absorption-desorption characteristic of (Ti0.85Zr0.15)1.1Cr1-xMoxMn based alloys with C14 Laves phase

The microstructure and hydrogen absorption-desorption characteristic of (Ti0.85Zr0.15)1.1Cr1-xMoxMn (x ​= ​0.05, 0.1, 0.15, 0.2 ​at.%) alloys were investigated. The results showed that the corresponding alloys were determined as a single phase of C14-type Laves structure. With the increase of Mo content, the maximum and reversible hydrogen absorption capacity decreased, the slope factor Hf increased. Among the studied alloys, (Ti0.85Zr0.15)1.1Cr0.95Mo0.05Mn had the best overall properties for practical application of hydrogen storage materials. The maximum and reversible hydrogen storage capacity were 1.76 ​wt% and 1.09 ​wt%, the slope factor Hf was 0.51, and its dissociation enthalpy (ΔHd) and entropy change (ΔSd) were 23.1 ​kJ ​mol−1H2, 93.8J ​K−1mol−1H2 at 303K, respectively. By studying the dissociation pressures of the synthesized metal hydrides, it was found that Mo had a special effect on the dissociation pressure of Ti–Zr–Cr–Mo–Mn alloys. Among the four alloys, (Ti0.85Zr0.15)1.1Cr0.95Mo0.05Mn alloy had the largest hydrogen absorption capacity and the fastest hydrogen desorption rate, which can meet the commercialization demand of hydrogen fuel cell hydrogen supply system.

Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning

Maintaining the stability of highway soft rock slopes is of critical importance for ensuring the safety of road networks. Although much research has been carried out to assess the stability of individual soft rock slope, the goal of efficient and effective risk management focusing on multiple highway soft rock slopes has not been fully achieved due to the many complex factors involved and the interactions among these factors. In the present study, a machine learning algorithm based on a fuzzy neural network (FNN) and a comprehensive evaluation method based on the FNN is developed, in order to identify and issue early warnings regarding the risks induced by soft rock slopes along highways, in an efficient and effective way. Using a large amount of collected soft rock slope information as training and validation data, an FNN-based risk identification model is first developed to identify the risk level of individual soft rock slope based on the meteorological conditions, topographical and geomorphological factors, geotechnical properties, and the measured horizontal displacement. An FNN-based comprehensive evaluation method is then developed, in order to quantify the risk level of a soft rock slope group according to the slope, road and external factors. The results show that the risk level identification accuracy obtained based on validation of the FNN model was higher than 90%, and the model showed a good training effect. On this basis, we further made early warnings of the risks of soft rock slope groups. The proposed early-warning model can quickly and accurately evaluate the risk posed by multiple soft rock slopes to a highway, thereby ensuring the safety of the highway.

Exploratory Analysis on the Spatial Distribution and Influencing Factors of Beitang Landscape in the Shangzhuang Basin

Beitang landscape is a production system and land use pattern that ancient people created to adapt to droughts and floods during a long traditional farming culture. It has a critical reference meaning for water resource use and water systems protection in modern cities. Taking the Shangzhuang Basin (China) as an example, this study used multi-source data, such as remote sensing images, Beitang vector dataset, land-use dataset, elevation, slope, river, road, and field survey, to investigate the spatial distribution and influencing factors Beitang landscape. Results showed that in a typical small watershed basin, an area of ponds accounted for 1.0%, about 12 ponds per square kilometer—the average area of ponds is 814 m2, of which the vast majority is less than 1000 m2. The study found that the spatial distribution of Beitang in the Shangzhuang Basin has cluster characteristics, influenced by elevation, slope, aspect, river, roads, villages, farmland, woodland, and other factors, all of which have closely related to the natural environment development and human activities. The upstream, middle, and downstream of three Beitang landscapes were coordinated to support the Beitang landscape system in the small watershed of the basin. Findings provided a model for protecting and utilizing natural water systems in rural areas during the construction of sponge cities.

The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises

I examine how the level of uncertainty affects the sensitivity of the U.S Treasury yield curve to monetary policy surprises. My analysis is based on a nonlinear dynamic Nelson-Siegel model of bond yields with regime shifts that depend on the level of macroeconomic uncertainty. The dynamics of the entire yield curve are captured by three latent factors, level, slope, and curvature, with exogenous monetary policy surprises affecting the yield curve through the factors. I find that uncertainty and the zero lower bound both play important roles in the yield curves reactions to monetary policy surprises. Specifically, during the ZLB period there is a significant increase in the sensitivity of the yield curve when uncertainty is high, with the differences being primarily in terms of the level and slope factors.

Application of a Comprehensive Rock Slope Stability Assessment Approach for Selected Malaysian Granitic Rock Slopes

In Malaysia, rock slope stability analysis has been largely confined to kinematic analysis with rock mass rating systems as assessment tools for stability analysis. While this method addresses the fundamental issues of rock slope stability including identifying potential failure modes, an information gap still exists between geologists and engineers in designing proper mitigation measures for rock slopes. This paper aims to address this issue by incorporating several methodologies, including kinematic analysis, slope mass rating and the Barton-Bandis criterion for the limit equilibrium method. Four rock slopes with potential instabilities namely KSA, KSB, LHA, and LHB were studied. KSA and KSB were located near Kajang, Selangor while LHA and LHB were located near Rawang, Selangor. Each slope exhibits multiple potential failures, with attention given on sliding-type failures in planar or wedge form. A slope mass rating value was assigned to each potential failure based on rock mass ratingbasic and the slope mass rating based on readjustments for discontinuity orientation and excavation method. Factor of safety from limit equilibrium method show potentially unstable blocks and failed blocks (Factor of Safety <1.00) with confirmation on site. Water filling of discontinuity apertures plays an important role in destabilizing rock blocks, especially in wet conditions experienced in Malaysia’s tropical climate. Several geometries are identified as potentially unstable due to low slope mass rating (Class V) and factor of safety of <1.2, such as planar J5 and wedge J2*J5 at KSA, wedge forming with sets J3, J4 and fault plane at KSB, planar J2 at LHA, and wedge J3*J4 at LHB. Stabilization structures such as rock bolts can be better designed with the determined factor of safety values coupled with relevant geological and geotechnical inputs. In this comprehensive rock slope stability assessment approach, limit equilibrium method serves as a useful method in analyzing rock slope stability to complement kinematic analysis and stability ratings often used in Malaysia.

Upconversion emission and second harmonic generation of the YVO4: Tm3+, Er3+, Yb3+ inverse opals

The YVO4:Tm3+, Er3+, Yb3+ nanomaterials were fabricated with a simple chemical method. The X-ray diffraction, scanning electron microscopy and transmission electron microscopy characterizations confirm that the YVO4:Tm3+, Er3+, Yb3+ nanomaterials have good structure with the polycrystalline. With the excitation of the 975 nm laser, the white color emissions were observed in the YVO4:Tm3+, Er3+, Yb3+ inverse opals with the polystyrene (PS) size of 450 nm and the composition of Y:Tm:Er:Yb = 77.5:0.5:2:20, in which the blue emission is the second harmonic generation (SHG) signal of the incident laser. The inverse opals have great effect on the upconversion emission and the SHG signal. With the inverse opal structures the blue SHG signal increases significantly for the inverse opals with the PS size of 250 and 450 nm, in which the SHG signal of the inverse opal with the PS size of 450 nm is stronger and is more suitable for the white emission due to the narrow width of the SHG signal. The power dependencies confirm that the upconversion emissions have the slope factor of 2–3, which means that the upconversion processes are the three-photon processes.

The effects of terrain factors on the drainage area threshold: comparison of principal component analysis and correlation analysis.

How and to which extent terrain factors affecting the drainage area threshold (DAT) are disputable. This paper uses principal component analysis (PCA) and correlation analysis to study the influence degree of terrain factors on DAT. Firstly, 22 watersheds, locating in the severe soil erosion region (SSER) of Loess Plateau of China, are picked out as the example areas. The purpose of the mean change point method (MCP) to detect the relationship between DAT and gully density (GD) is to get a reasonable DAT. Secondly, nine terrain factors are calculated, and their statistical values are compared and put in the matrix to clear the different effects on DAT. Finally, the effects of statistical eigenvalues of terrain factors on DAT are compared with PCA and the correlation analysis. According to the PCA, the nine terrain factors are summarized into three principal components, which are slope, height variation, and relief factor. By calculating the score weighted by each factor coefficient matrix and eigenvalue, the result states that slope (S), terrain curvatures (K), and surface roughness (SR) are the factors that have great influence on DAT. Meanwhile, the results of correlation analysis indicate that S, SR, and K have exerted a great influence on the DAT, and the significance level was above 0.05. Both the results of PCA and correlation analysis make clear that the slope is the most direct and influential factor affecting DAT, while other factors are more or less related to slope directly and indirectly. The result implies that the vertical variation of terrain has a strong correlation with the slope, and also has a great influence on DAT. This research not only would be of great significance to recognize the mechanism of gully development, but also able to provide a scientific reference for soil and water conservation in the Loess Plateau.

Attainment of low subthreshold slope in planar inversion-channel InGaAs MOSFET with in situ deposited Al2O3/Y2O3 as a gate dielectric

We have demonstrated a record low 85 mV dec−1 subthreshold slope (SS) at 300 K among the planar inversion-channel InGaAs metal-oxide-semiconductor field-effect transistors (MOSFETs). Our MOSFETs using in situ deposited Al2O3/Y2O3 as a gate dielectric were fabricated with a self-aligned inversion-channel gate-first process. The temperature-dependent transfer characteristics showed a linear reduction of SS versus temperature, with the attainment of an SS of 22 mV dec−1 at 77 K; the value is comparable to that of the state-of-the-art InGaAs FinFET. The slope factor of SS with temperature (m) is 1.33, which is lower than those reported in the planar InGaAs MOSFETs.