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Miraculin is a glycoprotein present in miracle fruit berries (Richadella dulcifica or Synsepalum dulcificum) known to have taste modifying properties: conversion of sour into sweet taste. So far, research has shown that the effects occur when consuming miracle fruit powder or miracle fruit berries prior to consumption of a sour drink or food product. However, limited research has examined the effects of miraculin consumed within a food or drink product and if there might be any taste‐modifying effects on other food and drinks consumed subsequently. This research looks at miraculin, from freeze‐dried miracle fruit powder, served within model soft drink formulations at concentrations ethically approved for consumption in soft drink beverages (50 and 80 ppm miracle fruit powder). Three experiments were carried out. The first investigated how long any taste‐modifying effects would last following consumption and whether the effects were dose‐dependent. Experiment 2 looked at the effect of repeated exposure on perception of sweet, sour, and bitter tastes, whilst experiment 3 explored whether the receptor binding of miraculin in miracle fruit powder would affect sweetness perception of other sweeteners. All results showed minimal effects of using freeze‐dried miracle fruit powder within a model beverage at the 50 and 80 ppm levels suggesting that future water‐based beverages produced containing low levels of miraculin protein will have little or no taste‐modifying effects on foods and beverages subsequently consumed. Practical Applications: Miraculin, derived from miracle fruit berries, acts as a taste modifier and can change food or drink from tasting sour to tasting sweet. This property is known to occur if either a fresh or freeze‐dried powder of miraculin is ingested before food or drink. Miraculin has potential as an ingredient in water‐based beverages to enhance sweetness, reduce the undesirable aftertaste associated with some high‐potency sweeteners, and to add mouthfeel. However, until now, it was unknown whether, by adding miraculin to a beverage at levels considered safe for human consumption, the taste of other foods consumed with or after the beverage would be affected. Here, we show that a low level of miraculin protein within a water‐based beverage has little to no taste‐modifying effects on products that are subsequently consumed. Our study therefore opens new avenues for using miraculin as a taste modifier. [ABSTRACT FROM AUTHOR]

Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were used for the development of flash flood susceptibility maps for hilly road section (115 km length) of National Highway (NH)-6 in Hoa Binh province, Vietnam. In the proposed models, 88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors. The performance of the models was evaluated using standard statistical measures including Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC) and Root Mean Square Error (RMSE). The results revealed that all the models performed well (AUC > 0.80) in predicting flash flood susceptibility zones, but the performance of the DL model is the best (AUC: 0.972, RMSE: 0.352). Therefore, the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area. [ABSTRACT FROM AUTHOR]

This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model's performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC = 0.839, Accuracy = 76.55% and Kappa Index = 0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC = 0.815) and Prediction Rate (AUC = 0.826) revealed the achievement of high-quality results. [ABSTRACT FROM AUTHOR]

Landslides are considered to be a significant risk to life and property all over the world in general and in Vietnam in particular. Spatial prediction of landslides is required to reduce the landslides risk and to plan the development of hilly areas. In this regard, the accurate landslide susceptibility maps are very useful tool for decision-makers to identify areas where new landslides are likely to occur for planning timely adequate remedial measures. For the development of landslide susceptibility maps, seven hybrid models were developed namely AdaBoost-LMT (ABLMT), bagging-LMT (BLMT), cascade generalization-LMT (CGLMT), dagging-LMT (DLMT), MultiBoostAB-LMT (MBLMT), rotation forest-LMT (RFLMT) and random sub-space-LMT (RSSLMT) with logistic model trees (LMTs) as a base classifier. The model's performance and validation were assessed through various statistical indices, such as sensitivity (SST), specificity (SPF), accuracy (ACC), area under ROC curve (AUC), RMSE and k index. The results show that all these models are performing well for the prediction of landslide susceptibility in the study area, but the performance of the RSSLMT model is the best (AUC: 0.816). In this study, open-source data has been used for the development of landslide susceptibility maps Along National Highway-6, passing through Hoa Binh province, Vietnam. These approaches can be applied also in other hilly regions of the world which are susceptible to landslides for better landslides prevention and management. [ABSTRACT FROM AUTHOR]