Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

Mobile Devices Interface Adaptivity Using Ontologies

Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.

Settlement modeling in central core rockfill dams by new approaches

One of the most important reasons for the serious damage of embankment dams is their impermissible settlement.Therefore,it can be stated that the prediction of settlement of a dam is of paramount importance.This study aims to apply intelligent methods to predict settlement after constructing central core rockfill dams.Attempts were made in this research to prepare models for predicting settlement of these dams using the information of 35 different central core rockfill dams all over the world and Adaptive Neuro-Fuzzy Interface System(ANFIS) and Gene Expression Programming(GEP) methods.Parameters such as height of dam(H) and compressibility index(Ci) were considered as the input parameters.Finally,a form was designed using visual basic software for predicting dam settlement.With respect to the accuracy of the results obtained from the intelligent methods,they can be recommended for predicting settlement after constructing central core rockfill dams for the future plans.

Magnesium-based alloys with adapted interfaces for bone implants and tissue engineering

Magnesium and its alloys are one of the most used materials for bone implants and tissue engineering.They are characterized by numerous advantages such as biodegradability,high biocompatibility and mechanical properties with values close to the human bone.Unfortunately,the implant surface must be adequately tuned,or Mg-based alloys must be alloyed with other chemical elements due to their increased corrosion effect in physiological media.This article reviews the clinical challenges related to bone repair and regeneration,classifying bone defects and presenting some of the most used and modern therapies for bone injuries,such as Ilizarov or Masquelet techniques or stem cell treatments.The implant interface challenges are related to new bone formation and fracture healing,implant degradation and hydrogen release.A detailed analysis of mechanical properties during implant degradation is extensively described based on different literature studies that included in vitro and in vivo tests correlated with material properties’characterization.Mg-based trauma implants such as plates and screws,intramedullary nails,Herbert screws,spine cages,rings for joint treatment and regenerative scaffolds are presented,taking into consideration their manufacturing technology,the implant geometrical dimensions and shape,the type of in vivo or in vitro studies and fracture localization.Modern technologies that modify or adapt the Mg-based implant interfaces are described by presenting the main surface microstructural modifications,physical deposition and chemical conversion coatings.The last part of the article provides some recommendations from a translational perspective,identifies the challenges associated with Mg-based implants and presents some future opportunities.This review outlines the available literature on trauma and regenerative bone implants and describes the main techniques used to control the alloy corrosion rate and the cellular environment of the implant.

Investigations on the relationship among the porosity,permeability and pore throat size of transition zone samples in carbonate reservoirs using multiple regression analysis,artificial neural network and adaptive neuro-fuzzy interface system

Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years.The most commonly used methods targeted towards regression technique to understand the correlation between pore throat radii,porosity and permeability are Winland and Pittman equation approaches.While these methods are very common among petrophysicists,they do not give a good prediction in certain cases.Consequently,this paper investigates the relationship among porosity,permeability,and pore throat radii using three methods such as multiple regression analysis,artificial neural network(ANN),and adaptive neuro-fuzzy inference system(ANFIS)for application in transition zone permeability modeling.Firstly,a comprehensive mercury injection capillary pressure(MICP)test was conducted using 228 transition zone carbonate core samples from a field located in the Middle-East region.Multiple regression analysis was later performed to estimate the permeability using pore throat and porosity measurement.For the ANN,a two-layer feed-forward neural network with sigmoid hidden neurons and a linear output neuron was used.The technique involves training,validation,and testing of input/output data.However,for the ANFIS method,a hybrid optimization consisting of least-square and backpropagation gradient descent methods with a subtractive clustering technique was used.The ANFIS combines both the artificial neural network and fuzzy logic inference system(FIS)for the training,validation,and testing of input/output data.The results show that the best correlation for the multiple regression technique is achieved for pore throat radii with 35%mercury saturation(R35).However,for both the ANN and ANFIS techniques,pore throat radii with 55%mercury saturation(R55)gives the best result.Both the ANN and ANFIS are later found to be more effective and efficient and thus recommended as compared with the multiple regression technique commonly used in the industry.