GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

The main objective of this study is to propose and verify a novel ensemble methodology that could improve prediction performances of landslide susceptibility models. The proposed methodology is based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost. According to current literature, these methods have been rarely used for the modeling of rainfall-induced landslides. The corridor of the National Road 32 (Vietnam) was selected as a case study. In the first stage, the landslide inventory map with 262 landslide polygons that occurred during the last 20 years was constructed and then was randomly partitioned into a ratio of 70/30 for training and validating the models. Second, ten landslide conditioning factors were prepared such as slope, aspect, relief amplitude, topographic wetness index, topographic shape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall. The model performance was assessed and compared using the receiver operating characteristic and statistical evaluation measures. Overall, the FT with Bagging model has the highest prediction capability (AUC = 0.917), followed by the FT with MultiBoost model (AUC = 0.910), the FT model (AUC = 0.898), and the FT with AdaBoost model (AUC = 0.882). Compared with those derived from popular methods such as J48 decision trees and artificial neural networks, the performance of the FT with Bagging model is better. Therefore, it can be concluded that the FT with Bagging is promising and could be used as an alternative in landslide susceptibility assessment. The result in this study is useful for land use planning and decision making in landslide prone areas

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Neutronics study on small power ADS loaded with recycled inert matrix fuel for transuranic elements transmutation using Serpent code

A small power ADS design using thorium oxide and diluent matrix reprocessed fuel is proposed for a high transmutation rate, small reactivity swing, and strong safety features. Two fuel matrices (CERCER and CERMET) and different recycled fuel compositions recovered from UO2 spent fuels with 45 GWd/tU and 60 GWd/tU burnup were investigated to determine the suitable fuel for the ADS. It was found that the transmutation of each isotope depends on TRU initial loading amount. After examining the cores, the results show that CERCER fueled ADS has a negative coolant void reactivity (CVR) and a smaller radiotoxicity at discharge compared to that of CERMET core. It implies that CERCER fuel has enhanced safety features and more flavor in terms of radiotoxicity management. To increase fuel utilization and core operation efficiency, a simple assembly shuffling pattern for the CERCER fueled ADS is also proposed. Eigenvalue and burnup calculations were conducted using Serpent 2 with ENDF/B-VII.0 library in both kcode and external source modes, and it indicates that the results of transmutation analyses obtained by kcode only is reliable to discuss the transmutation potential of ADS. Burnup calculation with the fixed-source mode is essential to be used for more practical results of the transmutation by ADS

A tree-based intelligence ensemble approach for spatial prediction of potential groundwater

The objective of this research is to propose and confirm a new machine learning approach of Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), and Bagging (Bag) ensembles for potential groundwater mapping and assessing role of influencing factors. The Yasuj-Dena area (Iran) is selected as a case study. For this regard, a Yasuj-Dena database was established with 362 springs locations and 12 groundwater-influencing factors (slope, aspect, elevation, stream power index (SPI), length of slope (LS), topographic wetness index (TWI), topographic position index (TPI), land use, lithology, distance from fault, distance from river, and rainfall). The database was employed to train and validate the proposed groundwater models. The area under the curve (AUC) and statistical metrics were employed to check and confirm the quality of the models. The result shows that the BFTree-Bag model (AUC = 0.810, kappa = 0.495) has the highest prediction performance, followed by the BFTree-MB model (AUC = 0.785, kappa = 0.477), and the BFTree-MB model (AUC = 0.745, kappa = 0.422). Compared to the benchmark of Random Forests, the BFTree-Bag model performs better; therefore, we conclude that the BFtree-Bag is a new tool should be used for modeling of groundwater potential