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Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy.

The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the Expectation-Maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm.

Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system.

The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.

The purpose of this paper is to conduct a survey on research in fabric and cloth simulation using mass spring model. Also in this paper some of the common methods in process of fabric simulation in mass spring model are discussed and compared.

This paper reviews and compares presented mesh types in mass spring model, forces applied on model, super elastic effect and ways to settle the super elasticity problem, numerical integration methods for solving equations, collision detection and its response. Some of common methods in fabric simulation are compared to each other. And by using examples of fabric simulation, advantages and limitations of each technique are mentioned.

Mass spring method is a fast and flexible technique with high ability to simulate fabric behavior in real time with different environmental conditions. Mass spring model has more accuracy than geometrical models and also it is faster than other physical modeling.

In the edge of digital, fabric simulation technology has been considered into many fields. 3D fabric simulation is complex and its implementation requires knowledge in different fields such as textile engineering, computer engineering and mechanical engineering. Several methods have been presented for fabric simulation such as physical and geometrical models. Mass spring model, the typical physically based method, is one of the methods for fabric simulation which widely considered by researchers.

This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often encounter some teaching problems. These are frequently related to the fact that the teacher cannot clearly know the learning status of students, such as their degree of learning concentration and capacity to absorb knowledge. In order to deal with this situation, this study uses a learning concentration detection system (LCDS), combining sensor technology and an artificial intelligence method, to better understand the learning concentration of students in a learning environment.

The proposed system uses sensing technology to collect information about the learning behavior of the students, analyzes their concentration levels, and applies an artificial intelligence method to combine this information for use by the teacher. This system includes a pressure detection sensor and facial detection sensor to detect facial expressions, eye activities and body movements. The system utilizes an artificial bee colony (ABC) algorithm to optimize the system performance to help teachers immediately understand the degree of concentration and learning status of their students. Based on this, instructors can give appropriate guidance to several unfocused students at the same time.

The fitness value and computation time were used to evaluate the LCDS. Comparing the results of the proposed ABC algorithm with those from the random search method, the algorithm was found to obtain better solutions. The experimental results demonstrate that the ABC algorithm can quickly obtain near optimal solutions within a reasonable time.

A learning concentration detection method of integrating context-aware technologies and an ABC algorithm is presented in this paper. Using this learning concentration detection method, teachers can keep abreast of their students' learning status in a teaching environment and thus provide more appropriate instruction.

Throughout human history and around the world, co-sleeping was the context for human evolutionary development. Currently, most of the world’s peoples continue to practice co-sleeping with infants, but there is increasing pressure on families in the West not to co-sleep. Research from anthropology, family studies, medicine, pediatrics, psychology, and public health is reviewed through the lens of a developmental theory to place co-sleeping within a developmental, theoretical context for understanding it. Viewing co-sleeping as a family choice and a normative, human developmental context changes how experts may provide advice and support to families choosing co-sleeping, especially in families making the transition to parenthood. During this transition, many decisions are made by parents “intuitively” (Ball, Hooker, & Kelly, 1999), making understanding the developmental consequences of some of those choices even more important. In Western culture, families are making “intuitive” decisions that research has shown to be beneficial, but families are not receiving complete messages about benefits and risks of co-sleeping. Co-sleeping can be an important choice for families as they make the life-changing transition to parenthood, if individualized messages about safe infant sleep practices (directed toward their individual family circumstances) are shared with them.

This study aims to examine members’ perceptions of interactivity in brand communities on social networking sites in the Super Basketball League (SBL) context in Taiwan.

The proposed model was empirically evaluated using survey data collected from 332 followers of the SBL teams’ Facebook pages on their perceptions of brand communities. Structural equation modeling was used to examine the relationships in the research model.

The results suggest significant relationships between perceived interactivity and community benefits, including special treatment, social influence, sense of membership and the notion that community satisfaction has a strong and positive effect on brand loyalty. Both social influence and a sense of membership positively affect community satisfaction. However, special treatment negatively affects community satisfaction. Perceived interactivity positively affects a sense of membership and social influence, which, in turn, positively affect community satisfaction.

This study examines the effects of members’ perceived interactivity and community benefits. The results significantly advance the understanding of the antecedents of members’ loyalty to specific brands. The study offers insights into practical ways of improving community satisfaction and brand loyalty by running brand communities on social networking sites. The findings also augment the theory of brand management.