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Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides. [ABSTRACT FROM AUTHOR]

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans. [ABSTRACT FROM AUTHOR]

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas. [ABSTRACT FROM AUTHOR]

Following our interest in new diterpene glycosides with better taste profiles than that of Rebaudioside M, we have recently isolated and characterized Rebaudioside IX--a novel steviol glycoside -- from a commercially-supplied extract of Stevia rebaudiana Bertoni. This molecule contains a hexasaccharide group attached at C-13 of the central diterpene core, and contains three additional glucose units when compared with Rebaudioside M. Here we report the complete structure elucidation -- based on extensive Nuclear Magnetic Resonance (NMR) analysis (¹H, 13C, Correlation Spectroscopy (COSY), Heteronuclear Single Quantum Coherence-Distortionless Enhancement Polarization Transfer (HSQC-DEPT), Heteronuclear Multiple Bond Correlation (HMBC), 1D Total Correlation Spectroscopy (TOCSY), Nuclear Overhauser Effect Spectroscopy (NOESY)) and mass spectral data -- of this novel diterpene glycoside with nine sugar moieties and containing a relatively rare 1→6 α-linked glycoside. A steviol glycoside bearing nine glucose units is unprecedented in the literature, and could have an impact on the natural sweetener catalog. [ABSTRACT FROM AUTHOR]

Four new minor diterpene glycosides with a rare α-glucosyl linkage were isolated from a cyclodextrin glycosyltransferase glucosylated stevia extract containing more than 98% steviol glycosides. The new compounds were identified as 13-[(2-O-β-Dglucopyranosyl- 3-O-(4-O-α-D-glucopyranosyl)-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(4-O-α-D-glucopyranosyl-β-D-glucopyranosyl) ester] (1), 13-[(2-O-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(4-O-(4- O-(4-O-α-D-glucopyranosyl)-α-D-glucopyranosyl)-α-D-glucopyranosyl)-β-D-glucopyranosyl ester] (2), 13-[(2-O-β-D-glucopyranosyl-3-O-(4-O-(4-O-(4-O-α-D-glucopyranosyl)-α-Dglucopyranosyl)- α-D-glucopyranosyl)-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur- 16-en-19-oic acid β-D-glucopyranosyl ester (3), and 13-[(2-O-β-D-glucopyranosyl-3-O-(4-O- (4-O-(4-O-α-D-glucopyranosyl)-α-D-glucopyranosyl)-α-D-glucopyranosyl)-β-D-glucopyranosyl- β-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(4-O-α-D-glucopyranosyl-β-Dglucopyranosyl) ester] (4) on the basis of extensive NMR and mass spectral (MS) data as well as hydrolysis studies. [ABSTRACT FROM AUTHOR]

To supply the increasing demand of natural high potency sweeteners to reduce the calories in food and beverages, we have looked to steviol glycosides. In this work we report the bioconversion of rebaudioside A to rebaudioside I using a glucosyltransferase enzyme. This bioconversion reaction adds one sugar unit with a 1→3 linkage. We utilized 1D and 2D NMR spectroscopy (¹H, 13C, COSY, HSQC-DEPT, HMBC, 1D TOCSY and NOESY) and mass spectral data to fully characterize rebaudioside I. [ABSTRACT FROM AUTHOR]

A minor product, rebaudioside M2 (2), from the bioconversion reaction of rebaudioside A (4) to rebaudioside D (3), was isolated and the complete structure of the novel steviol glycoside was determined. Rebaudioside M2 (2) is considered an isomer of rebaudioside M (1) and contains a relatively rare 1→6 sugar linkage. It was isolated and characterized with NMR (1H, 13C, COSY, HSQC-DEPT, HMBC, 1D-TOCSY, and NOESY) and mass spectral data. Additionally, we emphasize the importance of 1D and 2D NMR techniques when identifying complex steviol glycosides. Numerous NMR spectroscopy studies of rebaudioside M (1), rebaudioside D (3), and mixture of 1 and 3 led to the discovery that SG17 which was previously reported in literature, is a mixture of rebaudioside D (3), rebaudioside M (1), and possibly other related steviol glycosides. [ABSTRACT FROM AUTHOR]

Continuous phytochemical studies of the crude extract of Luo Han Guo (Siraitia grosvenorii) furnished three additional new cucurbitane triterpene glycosides, namely 11-deoxymogroside V, 11-deoxyisomogroside V, and 11-deoxymogroside VI. The structures of all the isolated compounds were characterized on the basis of extensive NMR and mass spectral data as well as hydrolysis studies. The complete 1H- and 13C-NMR spectral assignments of the three unknown compounds are reported for the first time based on COSY, TOCSY, HSQC, and HMBC spectroscopic data. [ABSTRACT FROM AUTHOR]

Degradation of rebaudioside M, a minor sweet component of Stevia rebaudiana Bertoni, under conditions that simulated extreme pH and temperature conditions has been studied. Thus, rebaudioside M was treated with 0.1 M phosphoric acid solution (pH 2.0) and 80 °C temperature for 24 h. Experimental results indicated that rebaudioside M under low pH and higher temperature yielded three minor degradation compounds, whose structural characterization was performed on the basis of 1D (¹H-, 13C-) & 2D (COSY, HSQC, HMBC) NMR, HRMS, MS/MS spectral data as well as enzymatic and acid hydrolysis studies. [ABSTRACT FROM AUTHOR]

Two additional novel minor diterpene glycosides were isolated from the commercial extract of the leaves of Stevia rebaudiana Bertoni. The structures of the new compounds were identified as 13-{β-D-glucopyranosyl-(1→2)-O-[β-D-glucopyranosyl- (1→3)-β-D-glucopyranosyl-oxy} ent-kaur-16-en-19-oic acid {β-D-xylopyranosyl-(1→2)- O-[β-D-glucopyranosyl-(1→3)]-O-β-D-glucupyranosyl-ester} (1), and 13-{β-D-6-deoxyglucopyranosyl-( 1→2)-O-[β-D-glucopyranosyl-(1→3)-β-D-glucopyranosyl-oxy} ent-kaur- 16-en-19-oic acid {β-D-glucopyranosyl-(1→2)-O-[β-D-glucopyranosyl-(1→3)-β-D-glucopyranosyl- ester} (2), on the basis of extensive 1D (1H- and 13C-) 2D NMR (COSY, HSQC and HMBC) and MS spectroscopic data as well as chemical studies. [ABSTRACT FROM AUTHOR]

Catalytic hydrogenation of the exocyclic double bond present between C16 and C17 carbons of the four ent-kaurane diterpene glycosides namely rebaudioside A, rebaudioside B, rebaudioside C, and rebaudioside D isolated from Stevia rebaudiana has been carried out using Pt/C, Pd(OH)2, Rh/C, Raney Ni, PtO2, and 5% Pd/BaCO3 to their corresponding dihydro derivatives with 17a and 17ß methyl group isomers. Reactions were performed using the above-mentioned catalysts with the solvents methanol, water, and ethanol/water (8:2) under various conditions. Synthesis of reduced steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR spectral data, including a comparison with reported spectral data. [ABSTRACT FROM AUTHOR]

Catalytic hydrogenation of rebaudioside B, rebaudioside C, and rebaudioside D; the three ent-kaurane diterpene glycosides isolated from Stevia rebaudiana was carried out using Pd(OH)2. Reduction of steviol glycosides was performed using straightforward synthetic chemistry with the catalyst Pd(OH)2 and structures of the corresponding dihydro derivatives were characterized on the basis of 1D and 2D nuclear magnetic resonance (NMR) spectral data indicating that all are novel compounds being reported for the first time. Also, the taste properties of all reduced compounds were evaluated against their corresponding original steviol glycosides and sucrose. [ABSTRACT FROM AUTHOR]

Catalytic hydrogenation of the three ent-kaurane diterpene glycosides isolated from Stevia rebaudiana, namely rubusoside, stevioside, and rebaudioside-A has been carried out using Pd(OH)2 and their corresponding dihydro derivatives have been isolated as the products. Synthesis of reduced steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR spectral data and chemical studies. Also, we report herewith the sensory evaluation of all the reduced compounds against their corresponding original steviol glycosides and sucrose for the sweetness property of these molecules. [ABSTRACT FROM AUTHOR]

The structure activity relationship between the C16-C17 methylene double bond on the aglycone of steviol glycosides and the corresponding impact on their sweet taste has been reported here for the first time. It has been observed that converting stevioside and rebaudioside A to their corresponding ketones by switching the doubly bonded methylene on C-17 for a ketone group actually removes the sweet taste properties of these molecules completely. Regenerating the original molecules tends to restore the sweet taste of both the steviol glycosides. Thus this C16-C17 methylene double bond in rebaudioside A and stevioside can be regarded as a pharmacophore essential for the sweetness property of these molecules. [ABSTRACT FROM AUTHOR]

Steviol glycosidesrebaudioside (reb) A, C and D have low aqueous solubilities. To improve their aqueous solubilities, inclusion complex of steviol glycosides, reb A, C and D and gamma cyclodextrin were prepared by freeze drying method and further characterized by means of differential scanning calorimetry, Fourier transform infrared spectroscopy and Raman spectroscopy. The effect of gamma cyclodextrin on chemical shifts of the steviol glycosides was also studied in proton NMR experiments as well as in solid state 13C CP/MAS NMR experiments. These results indicated that the steviol glycosides were clearly in inclusion complex formation with the gamma cyclodextrin which also results in solubility enhancement of these steviol glycosides. Phase solubility studies showed that amounts of soluble reb A, C and D increased with increasing amounts of gamma cyclodextrin indicating formation of 1:1 stoichiometric and higher order inclusion complexes. [ABSTRACT FROM AUTHOR]

Synthesis of two ent-kaurane diterpene glycosides, steviol 19-O-β-D-glucopyranosiduronic acid (steviol glucuronide, 5), and 13-hydroxy ent-kaur-16-en-19-oic acid-≈-D-glucopyranosyl ester (7) has been achieved from a common starting material, steviol, using phase transfer catalyst. Also, synthesis of an additional 17-nor-ent-kaurane glycoside, namely 13-methyl-16-oxo-17-nor-ent-kauran-19-oic acid-≈-D-glucopyranosyl ester (10) was performed using the starting material isosteviol and similar synthetic methodology. Synthesis of all three steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR as well as mass spectral (MS) data. [ABSTRACT FROM AUTHOR]

Three novel diterpene glycosides were isolated for the first time from the commercial extract of the leaves of Stevia rebaudiana, along with several known steviol glycosides, namely stevioside, rebaudiosides A-F, rubusoside and dulcoside A. The new compounds were identified as 13-[(2-O-β-D-glucopyranosyl-3-O-β-D-glucopyranosyl-β-D-glucopyranosyl) oxy] ent-kaur-15-en-19-oic acid (1), 13-[(2-O-β-D-glucopyranosyl-3-O-β- D-glucopyranosyl-β-D-glucopyranosyl)oxy]-16β-hydroxy-ent-kauran-19-oic acid (2) and 13-methyl-16-oxo-17-nor-ent-kauran-19-oic acid-β-D-glucopyranosyl ester (3) on the basis of extensive 2D NMR and MS spectroscopic data as well as chemical studies. [ABSTRACT FROM AUTHOR]

From the commercial extract of the leaves of Stevia rebaudiana, a new diterpene glycoside was isolated besides the known steviol glycosides including stevioside, rebaudiosides A-F, rubusoside and dulcoside A. The new compound was identified as 13- [(2-O-β-D-glucopyranosyl-3-O-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur-16- en-19-oic acid-(2-O-α-L-rhamnopyranosyl-β-D-glucopyranosyl) ester (1) on the basis of extensive spectroscopic (NMR and MS) and chemical studies. [ABSTRACT FROM AUTHOR]

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world. [ABSTRACT FROM AUTHOR]

Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of the models also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) is superior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, the proposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which can be used for the suitable designing of civil engineering structures. [ABSTRACT FROM AUTHOR]

Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world. [ABSTRACT FROM AUTHOR]

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management. [ABSTRACT FROM AUTHOR]

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development. [ABSTRACT FROM AUTHOR]

The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas. [ABSTRACT FROM AUTHOR]

This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different methods such as Area under the Receiver Operating Characteristics (ROC) curve (AUC), Mean Square Error (MSE), Root Mean Square Error (RMSE). Results indicate that the RFBFDT (AUC = 0.826, MSE = 0.189, and RMSE = 0.434) is the best method in comparison to other hybrid models, namely PSOANFIS (AUC = 0.76, MSE = 0.225, and RMSE = 0.474) and PSOANN (AUC = 0.72, MSE = 0.312, and RMSE = 0.558). Thus, it is reasonably concluded that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling. [ABSTRACT FROM AUTHOR]

Catalytic hydrogenation of the exocyclic double bond present between C16 and C17 carbons of the four ent-kaurane diterpene glycosides namely rebaudioside A, rebaudioside B, rebaudioside C, and rebaudioside D isolated from Stevia rebaudiana has been carried out using Pt/C, Pd(OH)2, Rh/C, Raney Ni, PtO2, and 5% Pd/BaCO3 to their corresponding dihydro derivatives with 17α and 17β methyl group isomers. Reactions were performed using the above-mentioned catalysts with the solvents methanol, water, and ethanol/water (8:2) under various conditions. Synthesis of reduced steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR spectral data, including a comparison with reported spectral data.

The structure activity relationship between the C₁₆-C₁₇ methylene double bond on the aglycone of steviol glycosides and the corresponding impact on their sweet taste has been reported here for the first time. It has been observed that converting stevioside and rebaudioside A to their corresponding ketones by switching the doubly bonded methylene on C-17 for a ketone group actually removes the sweet taste properties of these molecules completely. Regenerating the original molecules tends to restore the sweet taste of both the steviol glycosides. Thus this C₁₆-C₁₇ methylene double bond in rebaudioside A and stevioside can be regarded as a pharmacophore essential for the sweetness property of these molecules.