Search results for: Deep approach metacognitive methods

Authors: G. E. Nadirova, B. Zh. Aktaulova

Abstract:

The main question of the research is - how did the Kazakhs manage to keep their religious thinking in the period of active propaganda of Soviet atheism, for seventy years of struggle against religion with the involvement of the scientific worldview as the primary means of proving the absence of the divine nature and materiality of the world?

Our hypothesis is that In case of Kazakhstan the conservative female religious consciousness seems to have been a factor that helped to preserve the “everyday” religiousness of Kazakhs, which was far from deep theological contents of Islam, but able to revive in a short time after the decennia of proclaimed atheism.

Keywords: Woman, Religious thinking, Kazakhstan, Soviet ideology, Rituals, Family.

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Authors: Hashibah Hamid

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The issue of classifying objects into one of predefined groups when the measured variables are mixed with different types of variables has been part of interest among statisticians in many years. Some methods for dealing with such situation have been introduced that include parametric, semi-parametric and nonparametric approaches. This paper attempts to discuss on a problem in classifying a data when the number of measured mixed variables is larger than the size of the sample. A propose idea that integrates a dimensionality reduction technique via principal component analysis and a discriminant function based on the location model is discussed. The study aims in offering practitioners another potential tool in a classification problem that is possible to be considered when the observed variables are mixed and too large.

Keywords: classification, location model, mixed variables, principal component analysis.

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Authors: G. Mesfin, M. Shuhaimi

Abstract:

This paper proposes a new optimization techniques for the optimization a gas processing plant uncertain feed and product flows. The problem is first formulated using a continuous linear deterministic approach. Subsequently, the single and joint chance constraint models for steady state process with timedependent uncertainties have been developed. The solution approach is based on converting the probabilistic problems into their equivalent deterministic form and solved at different confidence levels Case study for a real plant operation has been used to effectively implement the proposed model. The optimization results indicate that prior decision has to be made for in-operating plant under uncertain feed and product flows by satisfying all the constraints at 95% confidence level for single chance constrained and 85% confidence level for joint chance constrained optimizations cases.

Keywords: Butane, Feed composition, LPG, Productspecification, Propane.

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Authors: Andreas Theissler, Ian Dear

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The one-class support vector machine “support vector data description” (SVDD) is an ideal approach for anomaly or outlier detection. However, for the applicability of SVDD in real-world applications, the ease of use is crucial. The results of SVDD are massively determined by the choice of the regularisation parameter C and the kernel parameter  of the widely used RBF kernel. While for two-class SVMs the parameters can be tuned using cross-validation based on the confusion matrix, for a one-class SVM this is not possible, because only true positives and false negatives can occur during training. This paper proposes an approach to find the optimal set of parameters for SVDD solely based on a training set from one class and without any user parameterisation. Results on artificial and real data sets are presented, underpinning the usefulness of the approach.

Keywords: Support vector data description, anomaly detection, one-class classification, parameter tuning.

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Authors: Xinde Li, Xinhan Huang, Min Wang

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In this paper, we propose the pre-processor based on the Evidence Supporting Measure of Similarity (ESMS) filter and also propose the unified fusion approach (UFA) based on the general fusion machine coupled with ESMS filter, which improve the correctness and precision of information fusion in any fields of application. Here we mainly apply the new approach to Simultaneous Localization And Mapping (SLAM) of Pioneer II mobile robots. A simulation experiment was performed, where an autonomous virtual mobile robot with sonar sensors evolves in a virtual world map with obstacles. By comparing the result of building map according to the general fusion machine (here DSmT-based fusing machine and PCR5-based conflict redistributor considereded) coupling with ESMS filter and without ESMS filter, it shows the benefit of the selection of the sources as a prerequisite for improvement of the information fusion, and also testifies the superiority of the UFA in dealing with SLAM.

Keywords: DSmT, ESMS filter, SLAM, UFA

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

Abstract:

The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: Dimensional affect prediction, Output-associative RVM, Multivariate regression.

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Authors: Yingying Ding

Abstract:

This study presents a methodology of specialized translation with the objective of helping teachers to improve the strategies in teaching translation. In order to allow students to acquire skills to translate specialized texts, they need to become familiar with the semantic and syntactic features of source texts and target texts. The aim of our study is to use a corpus-based approach in the teaching of specialized translation between Chinese and Italian. This study proposes to construct a specialized Chinese - Italian comparable corpus that consists of 50 economic contracts from the domain of food. With the help of AntConc, we propose to compile a comparable corpus in for translation teaching purposes. This paper attempts to provide insight into how teachers could benefit from comparable corpus in the teaching of specialized translation from Italian into Chinese and through some examples of passive sentences how students could learn to apply different strategies for translating appropriately the voice.

Keywords: Corpus-based approach, translation teaching, specialized translation.

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Authors: Mohsen Shirani, Micaela Demichela

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During the last decades, a number of food crises such as Bovine Spongiform Encephalopathy (BSE), Mad-Cow disease, Dioxin in chicken food, Food-and-Mouth Disease (FMD), have certainly inflected the reliability of the food industry. Consequently, the trend in applying different scientific methods of risk assessment in food safety has obtained more attentions in the academic and practice. However, lack of practical approach considering entire food supply chain is tangible in the academic literature. In this regard, this paper aims to apply risk assessment tool (FMEA) with integration of Human Factor along the entire supply chain of food production and test the method in a case study of Diary production, and analyze its results.

Keywords: Food Risk Assessment, FMEA, Human Factor.

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Authors: Vijaya K. Srivastava, Davide Spinello

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This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: Constrained integer problems, enumerative search algorithm, Heuristic algorithm, tunneling algorithm.

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Authors: Shiwei Deng, Yang Bao

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In this paper, we present a model-based regression test suite reducing approach that uses EFSM model dependence analysis and probability-driven greedy algorithm to reduce software regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. The EFSM dependence analysis is performed for each elementary modification to reduce the regression test suite, and then the probability-driven greedy algorithm is adopted to select the minimum set of test cases from the reduced regression test suite that cover all interaction patterns. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Keywords: Dependence analysis, EFSM model, greedy algorithm, regression test.

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Authors: Cornelia-Eugenia Munteanu

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From a psychological perspective, psychopathology is the area of clinical psychology that has at its core psychological assessment and psychotherapy. In day-to-day clinical practice, psychodiagnosis and psychotherapy are used independently, according to their intended purpose and their specific methods of application. The paper explores how the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and Mini Mental State Examination-2 (MMSE-2) psychological tools contribute to enhancing the effectiveness of cognitive behavioral psychotherapy (CBT). This combined approach, psychotherapy in conjunction with assessment of personality and cognitive functions, is illustrated by two cases, a severe depressive episode with psychotic symptoms and a mixed anxiety-depressive disorder. The order in which CBT, MMPI-2, and MMSE-2 were used in the diagnostic and therapeutic process was determined by the particularities of each case. In the first case, the sequence started with psychotherapy, followed by the administration of blue form MMSE-2, MMPI-2, and red form MMSE-2. In the second case, the cognitive screening with blue form MMSE-2 led to a personality assessment using MMPI-2, followed by red form MMSE-2; reapplication of the MMPI-2 due to the invalidation of the first profile, and finally, psychotherapy. The MMPI-2 protocols gathered useful information that directed the steps of therapeutic intervention: a detailed symptom picture of potentially self-destructive thoughts and behaviors otherwise undetected during the interview. The memory loss and poor concentration were confirmed by MMSE-2 cognitive screening. This combined approach, psychotherapy with psychological assessment, aligns with the trend of adaptation of the psychological services to the everyday life of contemporary man and paves the way for deepening and developing the field.

Keywords: Assessment, cognitive behavioral psychotherapy, MMPI-2, MMSE-2, psychopathology.

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Authors: H. Abderrezek, M. N. Harmas

Abstract:

DC-DC converters are widely used as reliable power source for many industrial and military applications, computers and electronic devices. Several control methods were developed for DC-DC converters control mostly with asymptotic convergence. Synergetic control (SC) is a proven robust control approach and will be used here in a so called terminal scheme to achieve finite time convergence. Lyapounov synthesis is adopted to assure controlled system stability. Furthermore particle swarm optimization (PSO) algorithm, based on an integral time absolute of error (ITAE) criterion will be used to optimize controller parameters. Simulation of terminal synergetic control of a DC-DC converter is carried out for different operating conditions and results are compared to classic synergetic control performance, that which demonstrate the effectiveness and feasibility of the proposed control method.

Keywords: DC-DC converter, PSO, finite time, terminal, synergetic control.

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Authors: B. Farhadinia

Abstract:

In this article, it is considered a class of optimal control problems constrained by differential and integral constraints are called canonical form. A modified measure theoretical approach is introduced to solve this class of optimal control problems.

Keywords: control problem, Canonical form, Measure theory.

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Authors: Masood Karamoozian, Zhang Hong

Abstract:

The necessity and complexity of the decision-making process and the interference of the various factors to make decisions and consider all the relevant factors in a problem are very obvious nowadays. Hence, researchers show their interest in multi-criteria decision-making methods. In this research, the Analytical Hierarchy Process (AHP), Simple Additive Weighting (SAW), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods of multi-criteria decision-making have been used to solve the problem of optimal construction systems. Systems being evaluated in this problem include; Light Steel Frames (LSF), a case study of designs by Zhang Hong studio in the Southeast University of Nanjing, Insulating Concrete Form (ICF), Ordinary Construction System (OCS), and Precast Concrete System (PRCS) as another case study designs in Zhang Hong studio in the Southeast University of Nanjing. Crowdsourcing was done by using a questionnaire at the sample level (200 people). Questionnaires were distributed among experts in university centers and conferences. According to the results of the research, the use of different methods of decision-making led to relatively the same results. In this way, with the use of all three multi-criteria decision-making methods mentioned above, the PRCS was in the first rank, and the LSF system ranked second. Also, the PRCS, in terms of performance standards and economics, was ranked first, and the LSF system was allocated the first rank in terms of environmental standards.

Keywords: Multi-criteria decision making, AHP, SAW, TOPSIS.

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Authors: E. Yelgecen Tigrel, O. Kokalan

Abstract:

People at workplace always face with stress and feel it in their lives. There are many factors that create stress and mobbing is one of them. Mobbing is a psychological terror, conducted systematically toward an individual by others at the same workplace. Mobbing started to become a famous subject last years in U.S and Europe. In Turkey, it is a new concept not because it does not occur, because of human nature that does not allow confessing it. Mobbing is being ignored by people, organizations and also government in our country. The focus of this study will be mobbing in Turkey by examining the workplace mobbing among Turkish academicians. There are other studies about mobbing in Turkey but none of them studied academy. Because mobbing methods change according to sectors and occupations, it is important to analyze each sector to understand the methods used in mobbing and the reactions of victims to these actions. The concept is analyzed in detail before focusing on mobbing at universities. This paper will be unique because there is no information about this specific subject in Turkish literature. In this paper, both qualitative and quantitative methods will be used to describe the mobbing at Turkish academic environment.

Keywords: Mobbing, Turkish academic environment, workplace problems

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Authors: Kentaro Miyoko, Yoshihiko Mori, Yugo Ikeda, Yoshihiro Nishino, Yong-Bok Choi, Hiromi Okada

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Optical burst switching (OBS) has been proposed to realize the next generation Internet based on the wavelength division multiplexing (WDM) network technologies. In the OBS, the burst contention is one of the major problems. The deflection routing has been designed for resolving the problem. However, the deflection routing becomes difficult to prevent from the burst contentions as the network load becomes high. In this paper, we introduce a flow rate control methods to reduce burst contentions. We propose new flow rate control methods based on the leaky bucket algorithm and deflection routing, i.e. separate leaky bucket deflection method, and dynamic leaky bucket deflection method. In proposed methods, edge nodes which generate data bursts carry out the flow rate control protocols. In order to verify the effectiveness of the flow rate control in OBS networks, we show that the proposed methods improve the network utilization and reduce the burst loss probability through computer simulations.

Keywords: Optical burst switching, OBS, flow rate control.

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Authors: Carine Moussaed, Stephen Wornom, Bruno Koobus, Maria Vittoria Salvetti, Alain Dervieux,

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The effects of dynamic subgrid scale (SGS) models are investigated in variational multiscale (VMS) LES simulations of bluff body flows. The spatial discretization is based on a mixed finite element/finite volume formulation on unstructured grids. In the VMS approach used in this work, the separation between the largest and the smallest resolved scales is obtained through a variational projection operator and a finite volume cell agglomeration. The dynamic version of Smagorinsky and WALE SGS models are used to account for the effects of the unresolved scales. In the VMS approach, these effects are only modeled in the smallest resolved scales. The dynamic VMS-LES approach is applied to the simulation of the flow around a circular cylinder at Reynolds numbers 3900 and 20000 and to the flow around a square cylinder at Reynolds numbers 22000 and 175000. It is observed as in previous studies that the dynamic SGS procedure has a smaller impact on the results within the VMS approach than in LES. But improvements are demonstrated for important feature like recirculating part of the flow. The global prediction is improved for a small computational extra cost.

Keywords: variational multiscale LES, dynamic SGS model, unstructured grids, circular cylinder, square cylinder.

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Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.

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Authors: Pasayev, A., C. Askerov, R. Sadiqov, C. Ardil

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In contrast to existing of calculation of temperature field of a profile part a blade with convective cooling which are not taking into account multi connective in a broad sense of this term, we develop mathematical models and highly effective combination (BIEM AND FDM) numerical methods from the point of view of a realization on the PC. The theoretical substantiation of these methods is proved by the appropriate theorems.

Keywords: multi coherent systems, method of the boundary integrated equations, singular operators, gas turbines

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Authors: Saleh Abu Dabous, Khaled Hamad, Rami Al-Ruzouq

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Bridge condition assessment and rating provide essential information needed for bridge management. This paper reviews bridge inspection and condition rating practices and introduces a defect-based urgency index. The index is estimated at the element-level based on the extent and severity of the different defects typical to the bridge element. The urgency index approach has the following advantages: (1) It facilitates judgment submission, i.e. instead of rating the bridge element with a specific linguistic overall expression (which can be subjective and used differently by different people), the approach is based on assessing the defects; (2) It captures multiple defects that can be present within a deteriorated element; and (3) It reflects how critical the element is through quantifying critical defects and their severity. The approach can be further developed and validated. It is expected to be useful for practical purposes as an early-warning system for critical bridge elements.

Keywords: Condition rating, deterioration, inspection, maintenance.

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Authors: Mana Tarjoman, Shaghayegh Zarei

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Using efficient classification methods is necessary for automatic fingerprint recognition system. This paper introduces a new structural approach to fingerprint classification by using the directional image of fingerprints to increase the number of subclasses. In this method, the directional image of fingerprints is segmented into regions consisting of pixels with the same direction. Afterwards the relational graph to the segmented image is constructed and according to it, the super graph including prominent information of this graph is formed. Ultimately we apply a matching technique to compare obtained graph with the model graphs in order to classify fingerprints by using cost function. Increasing the number of subclasses with acceptable accuracy in classification and faster processing in fingerprints recognition, makes this system superior.

Keywords: Classification, Directional image, Fingerprint, Graph, Super graph.

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Authors: Rima Grati, Khouloud Boukadi, Hanene Ben-Abdallah

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Due to the dynamic nature of the Cloud, continuous monitoring of QoS requirements is necessary to manage the Cloud computing environment. The process of QoS monitoring and SLA violation detection consists of: collecting low and high level information pertinent to the service, analyzing the collected information, and taking corrective actions when SLA violations are detected. In this paper, we detail the architecture and the implementation of the first step of this process. More specifically, we propose an event-based approach to obtain run time information of services developed as BPEL processes. By catching particular events (i.e., the low level information), our approach recognizes the run-time execution path of a monitored service and uses the BPEL execution patterns to compute QoS of the composite service (i.e., the high level information).

Keywords: Monitoring of Web service composition, Cloud environment, Run-time extraction of execution path of BPEL.

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Authors: Ashly Joseph, Jithu Paulose

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In an era where misleading information may quickly circulate on digital news channels, it is crucial to have efficient and trustworthy methods to detect and reduce the impact of misinformation. This research proposes an innovative framework that combines Natural Language Processing (NLP), Reinforcement Learning (RL), and Blockchain technologies to precisely detect and minimize the spread of false information in news articles on social media. The framework starts by gathering a variety of news items from different social media sites and performing preprocessing on the data to ensure its quality and uniformity. NLP methods are utilized to extract complete linguistic and semantic characteristics, effectively capturing the subtleties and contextual aspects of the language used. These features are utilized as input for a RL model. This model acquires the most effective tactics for detecting and mitigating the impact of false material by modeling the intricate dynamics of user engagements and incentives on social media platforms. The integration of blockchain technology establishes a decentralized and transparent method for storing and verifying the accuracy of information. The Blockchain component guarantees the unchangeability and safety of verified news records, while encouraging user engagement for detecting and fighting false information through an incentive system based on tokens. The suggested framework seeks to provide a thorough and resilient solution to the problems presented by misinformation in social media articles.

Keywords: Natural Language Processing, Reinforcement Learning, Blockchain, fake news mitigation, misinformation detection.

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Authors: Jeries J. Abou-Hanna

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Several techniques exist for determining stress-intensity factors in linear elastic fracture mechanics analysis. These techniques are based on analytical, numerical, and empirical approaches that have been well documented in literature and engineering handbooks. However, not all techniques share the same merit. In addition to overly-conservative results, the numerical methods that require extensive computational effort, and those requiring copious user parameters hinder practicing engineers from efficiently evaluating stress-intensity factors. This paper investigates the prospects of reducing the complexity and required variables to determine stress-intensity factors through the utilization of the stress gradient and a weighting function. The heart of this work resides in the understanding that fracture emanating from stress concentration locations cannot be explained by a single maximum stress value approach, but requires use of a critical volume in which the crack exists. In order to understand the effectiveness of this technique, this study investigated components of different notch geometry and varying levels of stress gradients. Two forms of weighting functions were employed to determine stress-intensity factors and results were compared to analytical exact methods. The results indicated that the “exponential” weighting function was superior to the “absolute” weighting function. An error band +/- 10% was met for cases ranging from a steep stress gradient in a sharp v-notch to the less severe stress transitions of a large circular notch. The incorporation of the proposed method has shown to be a worthwhile consideration.

Keywords: Fracture mechanics, finite element method, stress intensity factor, stress gradient.

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Authors: Hadeel Salim Alkutubi

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The objective of this study is to introduce estimators to the parameters and survival function for Weibull distribution using three different methods, Maximum Likelihood estimation, Standard Bayes estimation and Modified Bayes estimation. We will then compared the three methods using simulation study to find the best one base on MPE and MSE.

Keywords: Maximum Likelihood estimation , Bayes estimation, Jeffery prior information, Simulation study

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Authors: Ren Jian, Yang Shulin

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In this paper, the difference between the Alternating Direction Method (ADM) and the Non-Splitting Method (NSM) is investigated, while both methods applied to the simulations for 2-D multimaterial radiation diffusion issues. Although the ADM have the same accuracy orders with the NSM on the uniform meshes, the accuracy of ADM will decrease on the distorted meshes or the boundary of domain. Numerical experiments are carried out to confirm the theoretical predication.

Keywords: Alternating Direction Method, Non-SplittingMethod, Radiation Diffusion.

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Authors: Mahmood Niroobakhsh

Abstract:

This article deals with special structuralism approaches to explain a certain kind of social problem. Widespread presence of poverty is a reminder of deep-rooted unresolved problems of social relations. The expected role from an individual for the social system recognizes poverty derived from an interrelated social structure. By the time, enabled to act on his role in the course of social interaction, reintegration of the poor in society may take place. Poverty and housing type are reflections of the underlying social structure, primarily structure’s elements, systemic interrelations, and the overall strength or weakness of that structure. Poverty varies based on social structure in that the stronger structures are less likely to produce poverty.

Keywords: Absolute poverty, relative poverty, social structure, urban poverty.

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Authors: Sachin Kumar, Vasilis Sotiris, Michael Pecht

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With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysis. These methods have been used to detect deviations in system performance from normal operation and for critical parameter isolation in multivariate environments. The study evaluates the detection capability of each approach on a set of test data with known faults against a baseline set of data representative of such “healthy" systems.

Keywords: Mahalanobis distance, Principle components, Projection pursuit, Health assessment, Anomaly.

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Authors: Mahmoud Zarrini, R. N. Pralhad

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We estimate snow velocity and snow drift density on hilly terrain under the assumption that the drifting snow mass can be represented using a micro-continuum approach (i.e. using a nonclassical mechanics approach assuming a class of fluids for which basic equations of mass, momentum and energy have been derived). In our model, the theory of coupled stress fluids proposed by Stokes [1] has been employed for the computation of flow parameters. Analyses of bulk drift velocity, drift density, drift transport and mass transport of snow particles have been carried out and computations made, considering various parametric effects. Results are compared with those of classical mechanics (logarithmic wind profile). The results indicate that particle size affects the flow characteristics significantly.

Keywords: Snow velocity, snow drift density, mass transport of snow particles, snow avalanche.

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Authors: Oke Oktavianty, Tadayuki Kyoutani, Shigeyuki Haruyama, Junji Kaneko, Ken Kaminishi

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Modelica has many advantages and it is very useful in modeling and simulation especially for the multi-domain with a complex technical system. However, the big obstacle for a beginner is to understand the basic concept and to build a new system model for a real system. In order to understand how to solve the simple circuit model by hand translation and to get a better understanding of how modelica works, we provide a detailed explanation about solver ordering system in horizontal and vertical sorting and make some proposals for improvement. In this study, some difficulties in using modelica software with the original concept and the comparison with Finite Element Method (FEM) approach is discussed. We also present our textual modeling approach using FEM concept for acausal and causal model construction. Furthermore, simulation results are provided that demonstrate the comparison between using textual modeling with original coding in modelica and FEM concept.

Keywords: FEM, acausal model, modelica, horizontal and vertical sorting.

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