Measuring the relative efficiency of cultural-historical museums in Tehran: DEA approach

Heritage institutions, as one of the main aspects of culture, play an important role in economic prospect of culture by promoting tourism industry. Turning to the interpretation of culture in economic terms, the goal of this paper is to adopt a practical evaluation tool in order to assess the efficiency of heritage institutions. This study evaluates the technical efficiency of a regional system of museums in Tehran, the capital of Iran, using Data Envelopment Analysis approach. The findings may prove useful for management of these institutions in economic point of view, as well as for those responsible for public resource allocation policies in the area of cultural heritage.     

Neural network adaptive control design for robot manipulators under velocity constraints

This paper studies the neural adaptive control design for robotic systems with uncertain dynamics under the existence of velocity constraints and input saturation. The control objective is achieved by choosing a control Lyapunov function using joint error variables that are restricted to linear growth and furthermore by introducing a secant type barrier Lyapunov function for constraining the joint rate variables. The former is exploited to bind the forward propagation of the position errors, and the latter is utilized to impose hard bounds on the velocity. Effective input saturation is expressed, and neural networks are employed to tackle the uncertainty problem in the system dynamics. Feasibility conditions are formulated, and the optimal design parameters are obtained by solving the constrained optimization problem. We prove that under the proposed method, semi-global uniform ultimate boundedness of the closed-loop system can be guaranteed. Tracking errors meanwhile converge to small neighborhoods of the origin, and violations of predefined velocity constraints are avoided. Finally, numerical simulations are performed to verify the effectiveness of the theoretical developments.     

Position control of Stewart manipulator using a new extended adaptive fuzzy sliding mode controller and observer (E-AFSMCO)

Previously proposed adaptive fuzzy sliding mode control (AFSMC) and adaptive fuzzy sliding mode observer (AFSMO) methods are mixed and extended for the case of affine systems in which the input gain matrix is state-dependent, non-diagonal and non-positive definite. The proposed Extended AFSMCO (E-AFSMCO) method is then applied for position control of a Stewart Manipulator (SM), whose parameters are strongly state-dependent and complex and not suitable for practical control purposes. A robust observer-based control method which can work with a simplified model of the plant, and at the same time can preserve the stability and performance of the overall complex system is of great need. In this study, the SM dynamic model is simplified by removing the dynamic effects of the legs and the neglected terms are considered as un-modeled dynamics, for which the upper bound of the uncertainty is progressively estimated using the proposed adaptation rules. The final controller is comprised of a fuzzy controller in parallel with a robust switching controller. The second Lyapunov theorem is used to prove the closed-loop asymptotic stability. The proposed E-AFSMCO method is verified numerically and experimentally, depicting the effectiveness of the method for real-time industrial applications.     

Electromagnetic transients in nonuniform untransposed three-phase transmission lines

In this paper a mathematical analysis is given, based on the modal technique, for transient calculations in nonuniform transmission lines. The untransposition and earth effects are included. A general formula for the voltage transform is deduced. Sequential pole switching is considered. The proposed method is applied to a typical 500 kV transmission line. The results obtained in this study are compared with similar results calculated by other investigators.     

A review on 3D printing functional brain model

Modern neuroscience increasingly relies on 3D models to study neural circuitry, nerve regeneration, and neural disease. Several different biofabrication approaches have been explored to create 3D neural tissue model structures. Among them, 3D bioprinting has shown to have great potential to emerge as a high-throughput/high precision biofabrication strategy that can address the growing need for 3D neural models. Here, we have reviewed the design principles for neural tissue engineering. The main challenge to adapt printing technologies for biofabrication of neural tissue models is the development of neural bioink, i.e., a biomaterial with printability and gelation properties and also suitable for neural tissue culture. This review shines light on a vast range of biomaterials as well as the fundamentals of 3D neural tissue printing. Also, advances in 3D bioprinting technologies are reviewed especially for bioprinted neural models. Finally, the techniques used to evaluate the fabricated 2D and 3D neural models are discussed and compared in terms of feasibility and functionality.     

Teachers’ Perceptions of the Use of Computer Assisted Language Learning to Develop Children’s Reading Skills in English as a Second Language in the United Arab Emirates

This study investigated ESL teachers’ perceptions regarding the use of Computer Assisted Language Learning (CALL) in teaching reading to children. A random sample of 145 teachers participated in the study by completing a survey developed by the researchers. To explore the situation in depth, 16 teachers were later interviewed. Results indicated that the main purpose of using CALL with children is to help them develop reading strategies and increase their motivation. The most common strategies teachers reported using were computers to: enhance children's reading skills, support children's reading, listen to stories read aloud, recognize letter/sound relationships, and identify letters and beginning sounds of words. Teachers reported that CALL has some benefits, such as providing opportunities for active interaction and differentiated instruction. The main barriers of using CALL are the lack of resources and suitable software and hardware. The study ended with recommendations for further research and instructional improvements.     

The Perceptions of Students in Secondary School in Regard to Evolution-Based Teaching: Acceptance and Evolution Learning Experiences—The Kingdom of Saudi Arabia

Research in Science Education - The original version of this article unfortunately contained a mistake. The author would like to add the below text as acknowledgement.     

PERSON: Personalized information retrieval evaluation based on citation networks

Despite the importance of personalization in information retrieval, there is a big lack of standard datasets and methodologies for evaluating personalized information retrieval (PIR) systems, due to the costly process of producing such datasets. Subsequently, a group of evaluation frameworks (EFs) have been proposed that use surrogates of the PIR evaluation problem, instead of addressing it directly, to make PIR evaluation more feasible. We call this group of EFs, indirect evaluation frameworks. Indirect frameworks are designed to be more flexible than the classic (direct) ones and much cheaper to be employed. However, since there are many different settings and methods for PIR, e.g., social-network-based vs. profile-based PIR, and each needs some special kind of data to do the personalization based on, not all the evaluation frameworks are applicable to all the PIR methods. In this paper, we first review and categorize the frameworks that have already been introduced for evaluating PIR. We further propose a novel indirect EF based on citation networks (called PERSON), which allows repeatable, large-scale, and low-cost PIR experiments. It is also more information-rich compared to the existing EFs and can be employed in many different scenarios. The fundamental idea behind PERSON is that in each document (paper) d, the cited documents are generally related to d from the perspective of d’s author(s). To investigate the effectiveness of the proposed EF, we use a large collection of scientific papers. We conduct several sets of experiments and demonstrate that PERSON is a reliable and valid EF. In the experiments, we show that PERSON is consistent with the traditional Cranfield-based evaluation in comparing non-personalized IR methods. In addition, we show that PERSON can correctly capture the improvements made by personalization. We also demonstrate that its results are highly correlated with those of another salient EF. Our experiments on some issues about the validity of PERSON also show its validity. It is also shown that PERSON is robust w.r.t. its parameter settings.