Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers

Gully erosion is an erosive process that contributes considerably to the shape of the earth’s surface and is a major contributor to land degradation and soil loss. This study applied a methodology for mapping gully erosion susceptibility using only topographic related attributes derived from a medium-resolution digital elevation model (DEM) and a hybrid analytical hierarchy process (AHP) and the technique for an order of preference by similarity to ideal solutions (TOPSIS) and compare the results with naïve Bayes (NB) and support vector machine learning (SVM) algorithms. A transboundary sub-basin in an arid area of southern Iraq was selected as a case study. The performance of the developed models was compared using the receiver operating characteristic curve (ROC). Results showed that the areas under the ROC were 0.933, 0.936, and 0.955 for AHP-TOPSIS, NB, and SVM with radial basis function, respectively, which indicated that the performance of simply derived AHP-TOPSIS model is similar to sophisticated NB and SVM models. Findings indicated that a medium resolution DEM and AHP-TOPSIS are a promising tool for mapping of gully erosion susceptibility

Modeling of groundwater potential using cloud computing platform: A case study from nineveh plain, Northern Iraq

Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Aver-aged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to esti-mate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential

Complete genome sequence of Mycobacterium vaccae type strain ATCC 25954

Item does not contain fulltextMycobacterium vaccae is a rapidly growing, nontuberculous Mycobacterium species that is generally not considered a human pathogen and is of major pharmaceutical interest as an immunotherapeutic agent. We report here the annotated genome sequence of the M. vaccae type strain, ATCC 25954.1 november 201

Complete genome sequence of Mycobacterium vaccae type strain ATCC 25954

Item does not contain fulltextMycobacterium vaccae is a rapidly growing, nontuberculous Mycobacterium species that is generally not considered a human pathogen and is of major pharmaceutical interest as an immunotherapeutic agent. We report here the annotated genome sequence of the M. vaccae type strain, ATCC 25954.1 november 201

Defining remission in childhood-onset lupus:PReS-endorsed consensus definitions by an international task force

Objective: To derive childhood-onset SLE (cSLE) specific remission definitions for future treat-to-target (T2T) trials, observational studies, and clinical practice. Methods: The cSLE International T2T Task Force conducted Delphi surveys exploring paediatric perspectives on adult-onset SLE remission targets. A modified nominal group technique was used to discuss, refine, and agree on the cSLE remission target criteria.Results: The Task Force proposed two definitions of remission: ‘cSLE clinical remission on steroids (cCR)’ and ‘cSLE clinical remission off steroids (cCR-0)’. The common criteria are: (1) Clinical-SLEDAI-2 K = 0; (2) PGA score &lt; 0.5 (0–3 scale); (4) stable antimalarials, immunosuppressive, and biologic therapy (changes due to side-effects, adherence, weight, or when building up to target dose allowed). Criterion (3) in cCR is the prednisolone dose ≤0.1 mg/kg/day (maximum 5 mg/day), whereas in cCR-0 it is zero. Conclusions: cSLE definitions of remission have been proposed, maintaining sufficient alignment with the adult-SLE definition to facilitate life-course research.</p

Combined Discrete-Continuum Analysis for Ballasted Rail Tracks

A study on the load-deformation behaviour of railway ballast aggregates subjected to cyclic loadings using a combined discrete-continuum modelling approach is presented. Discrete ballast particles are simulated in the DEM and the continuum-based subgrade is simulated by the FDM. Interface elements are generated to transmit contact forces and displacements between the two domains (i.e. discrete and continuum) whereby the DEM exchanges contact forces to the FDM, and then the FDM transfers the displacement back to the DEM. Distributions of contact forces, coordination number, stress contours on the subgrade and corresponding number of broken bonds (representing ballast breakage) are analysed

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work