Damage Detection of Irregular Plates and Regular Dams by Wavelet Transform Combined Adoptive Neuro Fuzzy Inference System

This paper presents a technique for irregular plate and regular dam damage detection based on combination of wavelet with adoptive neuro fuzzy inference system (ANFIS). Many damage detection methods need response of structures (such as the displacements, stresses or mode shapes) before and after damage, but this method only requires response of structures after damage, otherwise many damage detection methods study regular plate but this method also studies irregular plate. First, the structure (irregular plate or regular dam) is modelled by using ANSYS software, the model is analysed and structure’s responses with damage are obtained by finite element approach. Second, the responses at the finite element points with regular distances are obtained by using ANFIS. The damage zone is represented as the elements with reduced elasticity modules. Then these responses of structures are analysed with 2D wavelet transform. It is shown that matrix detail coefficients of 2D wavelet transform can specified the damage zone of plates and regular dams by perturbation in the damaged area

LEE: Light‐Weight Energy‐Efficient encryption algorithm for sensor networks

Data confidentiality in wireless sensor networks is mainly achieved by RC5 and Skipjack encryption algorithms. However, both algorithms have their weaknesses, for example RC5 supports variable-bit rotations, which are computationally expensive operations and Skipjack uses a key length of 80-bits, which is subject to brute force attack. In this paper we introduce a light-weight energy- fficient encryption-algorithm (LEE) for tiny embedded devices, such as sensor network nodes. We present experimental results of LEE under real sensor nodes operating in TinyOS. We also discuss the secrecy of our algorithm by presenting a security analysis of various tests and cryptanalytic attacks

A Scientometric Analysis and Visualization of the Universal Reading Motivation Studies Based on Co-Word and Co-Author

Abstract Reading is an intellectual process that everyone for doing it, need to motivation, so that they can interrelate with the concepts of the texts that they read. Reading is the agent of wisdom and knowledge. Today is the age of information. Information is power. People who have internal motivation for reading information resources, are those who have information and change it to knowledge and knowledge is the power of readers who have internal motivation. The aim of present research is to analyze world scientific studies on reading motivation based on Scientometric and benchmarks of centrality in articles of web of science website from 1983 to 2018. This study is a descriptive, analytical, cross-sectional and Scientometric research. The statistical population under study are 267 published articles on the motivations of reading in web of science website from 1983 to 2018. In the present study, the 267 articles are divided into 6 clusters based on reading motivation studies. The USA, Germany, England and Australia have the most numerous articles on reading motivation. People like Guther, Moeller, Schefle, Gutherie and Wigfield have been the most effective in producing articles on reading motivation in youth. Regarding the Scientometric content analysis, the published articles in web of science data center were divided into 6 clusters. Numerous factors have played a role in reading motivation. Regarding the accomplished studies, these factors include external motivation like education, brain electromagnetic induction, teaching students to read accurately and voluntarily during primary school, the role of youth in promotion of reading, competition, sponsoring; and internal motivation like child psychology, having a purpose, personal and intrinsic behaviors, self-regulation, comprehension and articulation

The Upper Mantle Geoid for Lithospheric Structure and Dynamics

Geoid anomalies offer crucial information on the internal density structure of the Earth, and thus, on its constitution and dynamic state. In order to interpret geoid undulations in terms of depth, magnitude and lateral extension of density anomalies in the lithosphere and upper mantle, the effects of lower mantle density anomalies need to be removed from the full geoid (thus obtaining the residual “upper mantle geoid”). However, how to achieve this seemingly simple filtering exercise has eluded consensus for decades in the solid Earth community. While there is wide agreement regarding the causative masses of degrees &gt;10 in spherical harmonic expansions of the upper mantle geoid, those contributing to degrees &lt;7–8 remain ambiguous. Here we use spherical harmonic analysis and recent tomography and density models from joint seismic-geodynamic inversions to derive a representative upper mantle geoid, including the contributions from low harmonic degrees. We show that the upper mantle geoid contains important contributions from degrees 5 and 6 and interpret the causative masses as arising from the coupling between the long-wavelength lithospheric structure and the sublithospheric upper mantle convection pattern. Importantly, the contributions from degrees 3 &lt; l &lt; 8 do not show a simple power-law behavior (e.g., Kaula's rule), which precludes the use of standard filtering techniques in the spectral domain. Our upper mantle geoid model will be useful in studies of (a) lithospheric structure, (b) dynamic topography and mantle viscosity, (c) lithosphere-asthenosphere interactions and (d) the global stress field within the lithosphere and its associated hazards.</p

A model for the management risks related to the implementation of insurance policies

Introduction: The aim of present study was to provide a model for evaluating the insurance policies of the Social Security Organization with a network governance approach. Methods: Through a mixed method study in Tehran and Golestan provinces, in first stage 35 people were selected among the experts and university professors and in second stage, 217 managers of insurance and health were included in the study. To achieve the optimal model four factors of network design, network formation, network health management and health network participation were identified and based on administrative, judicial and political evaluation, 18 questions were considered for each indicator. The data were analyzed through SPSS and LISREL software by exploratory factor analysis and path analysis. Results: Factor analysis showed the relationship between management risk indicators and dimensions of insurance risk, liquidity, credit, market, operational, national, reputation, legal, management, insurance and health and human resource factors. There was relationship between the indicators of level of implementation of policies and dimensions of human resources, investment, technology, market and information and communication technology. Among the sub-criteria of policy implementation, areas of investment, market, technical and human resources were ranked first to forth, and the information and communication technology area was ranked last. Conclusion: The results revealed that policy evaluation indicators, except for the judicial approach in the indicator of health network formation in network governance, are approved and could be introduced appropriate criterion for assessing network governance indicators defined to evaluate the insurance policies of the Social Security Organization

A Hybrid of Artificial Neural Networks and Particle Swarm Optimization Algorithm for Inverse Modeling of Leakage in Earth Dams

A new intelligent hybrid method for inverse modeling (Parameter Identification) of leakage from the body and foundation of earth dams considering transient flow model has been presented in this paper. The main objective is to determine the permeability in different parts of the dams using observation data. An objective function which concurrently employs time series of hydraulic heads and flow rates observations has been defined to overcome the ill-posedness issue (nonuniqueness and instability of the identified parameters). A finite element model which considers all construction phases of an earth dam has been generated and then orthogonal design, back propagation artificial neural network and Particle Swarm Optimization algorithm has been used simultaneously to perform inverse modeling. The suggested method has been used for inverse modeling of seepage in Baft dam in Kerman, Iran as a case study. Permeability coefficients of different parts of the dam have been inspected for three distinct predefined cases and in all three cases excellent results have been attained. The highly fitting results confirm the applicability of the recommended procedure in the inverse modeling of real large-scale problems to find the origin of leakage channels which not only reduces the calculation cost but also raises the consistency and efficacy in such problems