Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques.

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area

Functional electrical stimulation for foot drop in multiple sclerosis: a systematic review and meta-analysis of the effect on gait speed

Objective: To review the efficacy of functional electrical stimulation (FES) used for foot drop in people with multiple sclerosis (pwMS) on gait speed in short and long walking performance tests. Data sources: Five databases (Cochrane Library, CINAHL, Embase, MEDLINE, Pubmed) and reference lists were searched. Study selection: Studies of both observational and experimental design where gait speed data in pwMS could be extracted were included. Data extraction: Data were independently extracted and recorded. Methodological quality was assessed using the Effective Public Health Practice Project (EPHPP) tool. Data synthesis: Nineteen studies (described in 20 articles) recruiting 490 pwMS were identified and rated moderate or weak, with none gaining a strong rating. All studies rated weak for blinding. Initial and ongoing orthotic and therapeutic effects were assessed with regards to the impact of FES on gait speed in short and long walking tests. Meta-analyses of the short walk tests revealed a significant initial orthotic effect (t = 2.14, p = 0.016) with a mean increase in gait speed of 0.05 meters per second (m/s) and ongoing orthotic effect (t = 2.81, p = 0.003) with a mean increase of 0.08m/s. There were no initial or ongoing effect on gait speed in long walk tests and no therapeutic effect on gait speed in either short or long walk tests. Conclusions: FES used for foot drop has a positive initial and ongoing effect on gait speed in short walking tests. Further fully-powered randomized controlled trials comparing FES with alternative treatments are required

Evolutionary-stable strategies with increasing and decreasing marginal utilities in the Ausubel auction

Congress on Evolutionary Computation. Vancouver, BC, 16-21 July 2006A genetic algorithm has been developed to solve bidding strategies in a dynamic multi-unit auction: the Ausubel auction, with independent private values and without dropout information. The genetic algorithm aims to maximize each bidder’s payoff. To this end two experimental environments have been tested with decreasing and increasing marginal utilities. The bidding strategies are analyzed, along with their effects on revenue and efficiency. With decreasing marginal utilities the computational experiments yield to sincere bidding as the evolutionary-stable strategy, which is also the weakly dominant strategy and the ex post perfect equilibrium. Nevertheless, with increasing marginal utilities there is no theory model developed in order to find the equilibrium. Therefore, the challenge of this work is to study the auction outcome where theoretical predictions are unknown. The genetic algorithm finds bidding sincerely as the evolutionary-stable strategy with increasing marginal utilities

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms

Horizontal well placement determination within a reservoir is a significant and difficult step in the reservoir development process. Determining the optimal well location is a complex problem involving many factors including geological considerations, reservoir and fluid properties, economic costs, lateral direction, and technical ability. The most thorough approach to this problem is that of an exhaustive search, in which a simulation is run for every conceivable well position in the reservoir. Although thorough and accurate, this approach is typically not used in real world applications due to the time constraints from the excessive number of simulations. This project suggests the use of a genetic algorithm applied to the horizontal well placement problem in a gas reservoir to reduce the required number of simulations. This research aims to first determine if well placement optimization is even necessary in a gas reservoir, and if so, to determine the benefit of optimization. Performance of the genetic algorithm was analyzed through five different case scenarios, one involving a vertical well and four involving horizontal wells. The genetic algorithm approach is used to evaluate the effect of well placement in heterogeneous and anisotropic reservoirs on reservoir recovery. The wells are constrained by surface gas rate and bottom-hole pressure for each case. This project's main new contribution is its application of using genetic algorithms to study the effect of well placement optimization in gas reservoirs. Two fundamental questions have been answered in this research. First, does well placement in a gas reservoir affect the reservoir performance? If so, what is an efficient method to find the optimal well location based on reservoir performance? The research provides evidence that well placement optimization is an important criterion during the reservoir development phase of a horizontal-well project in gas reservoirs, but it is less significant to vertical wells in a homogeneous reservoir. It is also shown that genetic algorithms are an extremely efficient and robust tool to find the optimal location