AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN

Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings

Effect of mechanical treatment on properties of cellulose nanofibrils produced from bleached hardwood and softwood pulps

Bleached hardwood and softwood South African kraft pulps were passed through a commercially available micro grinder for varying number of passes and the properties of the resultant pulps were assessed periodically using microscopy, Fourier transform infrared spectroscopy (FTIR), X-ray crystallography (XRD) and Thermogravimetric analysis (TGA). The ultrastructural analysis of the pulp fibres revealed that after 120 passes both hardwood and softwood bleached fibres showed the presence of cellulose nanofibres (CNFs). The FTIR analysis showed no modification to the cellulose structure and side groups upon treatment with the supermasscolloider (SMC). Both hardwood and softwood pulp fibres showed a decline in crystallinity after SMC treatment. For the hardwood pulps there were no major differences between the untreated pulps and those passed through the SMC. In the case of the softwood pulps, the SMC treatment resulted in more thermally stable CNFs compared with the untreated bleached pulps. This was observed at several levels of treatment (40, 120 and 200 passes). After 200 passes both the hardwood and softwood kraft pulp fibres produced CNFs with an average width of 11 nm and lengths with several micrometers

Adrenal involvement in MEN1. Analysis of 715 cases from the Groupe d'etude des Tumeurs Endocrines database.

Objective Limited data regarding adrenal involvement in multiple endocrine neoplasia type 1 (MEN1) is available. We describe the characteristics of MEN1-associated adrenal lesions in a large cohort to provide a rationale for their management. Methods Analysis of records from 715 MEN1 patients from a multicentre database between 1956 and 2008. Adrenal lesions were compared with those from a multicentre cohort of 144 patients with adrenal sporadic incidentalomas. Results Adrenal enlargement was reported in 20.4% (146/715) of patients. Adrenal tumours (>10 mm in size) accounted for 58.1% of these cases (10.1% of the whole patient cohort). Tumours were bilateral and >40 mm in size in 12.5 and 19.4% of cases respectively. Hormonal hypersecretion was restricted to patients with tumours and occurred in 15.3% of them. Compared with incidentalomas, MEN1-related tumours exhibited more cases of primary hyperaldosteronism, fewer pheochromocytomas and more adrenocortical carcinomas (ACCs; 13.8 vs 1.3%). Ten ACCs occurred in eight patients. Interestingly, ACCs occurred after several years of follow-up of small adrenal tumours in two of the eight affected patients. Nine of the ten ACCs were classified as stage I or II according to the European Network for the Study of Adrenal Tumors. No evident genotype/phenotype correlation was found for the occurrence of adrenal lesions, endocrine hypersecretion or ACC. Conclusions Adrenal pathology in MEN1 differs from that observed in sporadic incidentalomas. In the absence of relevant symptoms, endocrine biology can be restricted to patients with adrenal tumours and should focus on steroid secretion including the aldosterone-renin system. MEN1 is a high-risk condition for the occurrence of ACCs. It should be considered regardless of the size of the tumour