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The purpose of this paper is to investigate the effect of user’s learning style (including accommodators, divergers, convergers, and assimilators) on user’s satisfaction on the web-based learning system and their learning effectiveness.

This experimental research used the college students from a technology institute in Taiwan as the subject sources. By using the Kolb’s learning style model, the students are classified as four types of learners: convergers, divergers, assimilators, and accommodators. The authors analyzed the relationships among the different learning styles with their effectiveness of learning and satisfaction of using the web-based learning system. The mediation effect of gender is also presented.

This research indicates that: first, the satisfaction of the web-based learning system has significant influence on the learning performance of learners; second, different learning styles learners have no significant effect to the satisfaction on using the web-based learning system; third, learning effectiveness has significant difference among different learning style learners on the web-based learning system; the learning effectiveness of accommodators and divergers was significantly higher than the assimilators; fourth, different learning styles learners show significant difference in gender proportion. In addition to accommodators, whose proportion of women is higher than men, the other three learning styles’ proportions in men are higher than women.

This study was grounded in the Kolb’s learning style theory. The authors provide implications for academic studies in e-learning research stream that aimed at understanding the role of learning style as well as gender differences in the asynchronous web-based learning system.

Results from this study provided the implications for students, educators, and e-learning system designers. The design of teaching materials as well as functions of e-learning systems should take learners’ learning style into consideration to ensure the best learning outcome.

This study examined the students’ learning style as well as gender differences in the asynchronous web-based learning system. An experiment was conducted to ensure the data were collected in a controlled environment, thus, offer the value that most of the prior study lacks.

This paper aims to report a stretchable piezoresistive strain sensor array that can detect various static and dynamic stimuli, including bending, normal force, shear stress and certain range of temperature variation, through sandwiching an array of conductive blocks, made of multiwalled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS) composite. The strain sensor array induces localized resistance changes at different external mechanical forces, which can be potentially implemented as electronic skin.

The working principle is the piezoresistivity of the strain sensor array is based on the tunnelling resistance connection between the fillers and reformation of the percolating path when the PDMS and MWCNT composite deforms. When an external compression stimulus is exerted, the MWCNT inter-filler distance at the conductive block array reduces, resulting in the reduction of the resistance. The resistance between the conductive blocks in the array, on the other hand, increases when the strain sensor is exposed to an external stretching force. The methodology was as follows: Numerical simulation has been performed to study the pressure distribution across the sensor. This method applies two thin layers of conductive elastomer composite across a 2 × 3 conductive block array, where the former is to detect the stretchable force, whereas the latter is to detect the compression force. The fabrication of the strain sensor consists of two main stages: fabricating the conducting block array (detect compression force) and depositing two thin conductive layers (detect stretchable force).

Characterizations have been performed at the sensor pressure response: static and dynamic configuration, strain sensing and temperature sensing. Both pressure and strain sensing are studied in terms of the temporal response. The temporal response shows rapid resistance changes and returns to its original value after the external load is removed. The electrical conductivity of the prototype correlates to the temperature by showing negative temperature coefficient material behaviour with the sensitivity of −0.105 MΩ/°C.

The conductive sensor array can potentially be implemented as electronic skin due to its reaction with mechanical stimuli: compression and stretchable pressure force, strain sensing and temperature sensing.

This prototype enables various static and dynamic stimulus detections, including bending, normal force, shear stress and certain range of temperature variation, through sandwiching an array of conductive blocks, made of MWCNT and PDMS composite. Conventional design might need to integrate different microfeatures to perform the similar task, especially for dynamic force sensing.

Traditionally, an armor-grade composite is based on a two-dimensional (2D) architecture of its fiber reinforcements. However, various experimental investigations have shown that armor-grade composites based on 2D-reinforcement architectures tend to display inferior through-the-thickness mechanical properties, compromising their ballistic performance. To overcome this problem, armor-grade composites based on three-dimensional (3D) fiber-reinforcement architectures have recently been investigated experimentally. The paper aims to discuss these issues.

In the present work, continuum-level material models are derived, parameterized and validated for armor-grade composite materials, having four (two 2D and two 3D) prototypical reinforcement architectures based on oriented ultra-high molecular-weight polyethylene fibers. To properly and accurately account for the effect of the reinforcement architecture, the appropriate unit cells (within which the constituent materials and their morphologies are represented explicitly) are constructed and subjected to a series of virtual mechanical tests (VMTs). The results obtained are used within a post-processing analysis to derive and parameterize the corresponding homogenized-material models. One of these models (specifically, the one for 0°/90° cross-collimated fiber architecture) was directly validated by comparing its predictions with the experimental counterparts. The other models are validated by examining their physical soundness and details of their predictions. Lastly, the models are integrated as user-material subroutines, and linked with a commercial finite-element package, in order to carry out a transient non-linear dynamics analysis of ballistic transverse impact of armor-grade composite-material panels with different reinforcement architectures.

The results obtained clearly revealed the role the reinforcement architecture plays in the overall ballistic limit of the armor panel, as well as in its structural and damage/failure response.

To the authors’ knowledge, the present work is the first reported attempt to assess, computationally, the utility and effectiveness of 3D fiber-reinforcement architectures for ballistic-impact applications.

Based on previous information systems/educational technology success models, the purpose of this paper is to establish a comprehensive, multidimensional model for assessing blog-based learning systems success.

Data collected from 240 blog-based learning systems users in the context of higher education were tested against the model using the structural equation modelling approach.

The results indicate the interrelationships between six system success variables: system quality, content quality, context and linkage quality, user satisfaction, system use, and learning performance. In particular, this study confirms that quality attributes positively affect user satisfaction, which in turn positively influences learning performance directly or indirectly through the mediation of system use.

This study is a pioneering effort to develop and validate a blog-based learning systems success model.

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross-sectional and panel data models, and in this chapter we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point-identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a “non-standard” exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.