Dye-sensitized solar cells (DSCs) offer the possibilities to design solar cells with a large flexibility in shape, color, and transparency. DSC research groups have been established around the worl ...

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Dye-Sensitized Solar Cells

Social Network Analysis

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

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Educational Data Mining: A Review of the State of the Art

Although augmented reality (AR) has gained much research attention in recent years, the term AR was given different meanings by varying researchers. In this article, we first provide an overview of definitions, taxonomies, and technologies of AR. We argue that viewing AR as a concept rather than a type of technology would be more productive for educators, researchers, and designers. Then we identify certain features and affordances of AR systems and applications. Yet, these compelling features may not be unique to AR applications and can be found in other technological systems or learning environments (e.g., ubiquitous and mobile learning environments). The instructional approach adopted by an AR system and the alignment among technology design, instructional approach, and learning experiences may be more important. Thus, we classify three categories of instructional approaches that emphasize the ''roles,'' ''tasks,'' and ''locations,'' and discuss what and how different categories of AR approaches may help students learn. While AR offers new learning opportunities, it also creates new challenges for educators. We outline technological, pedagogical, learning issues related to the implementation of AR in education. For example, students in AR environments may be cognitively overloaded by the large amount of information they encounter, the multiple technological devices they are required to use, and the complex tasks they have to complete. This article provides possible solutions for some of the challenges and suggests topics and issues for future research.

Current status, opportunities and challenges of augmented reality in education

This study investigates the effect of electromigration upon the interfacial reactions between the promising lead-free solders, Sn–Cu and Sn–Ag, with Ni substrate. Sandwich-type reaction couples, Sn–0.7 wt% Cu/Ni/Sn–0.7 wt% Cu and Sn–3.5 wt% Ag/Ni/Sn–3.5 wt% Ag, were reacted at 160, 180, and 200 °C for various lengths of time with and without the passage of electric currents. Without passage of electric currents through the couples, only one intermetallic compound Ni3Sn4 with ∼7 at. % Cu solubility was found at both interfaces of the Sn–0.7 wt% Cu/Ni couples. With the passage of an electric current of 500 A/cm2 density, the Cu6Sn5 phase was formed at the solder/Ni interface besides the Ni3Sn4 phase. Similar to those without the passage of electric currents, only the Ni3Sn4 phase was found at the Ni/solder interface. Directions of movement of electrons, Sn, and Cu atoms are the same at the solder/Ni interface, and the growth rates of the intermetallic layers were enhanced. At the Ni/solder interface, the el...

Electromigration effect upon the Sn–0.7 wt% Cu/Ni and Sn–3.5 wt% Ag/Ni interfacial reactions

Chemical deposition of platinum on metallic sheets as counterelectrodes for dye-sensitized solar cells

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning fields. Most past researches for web-based learning focused on the issues of adaptive presentation, adaptive navigation support, curriculum sequencing, and intelligent analysis of student's solutions. These systems commonly neglect to consider whether learner can understand the learning courseware and generate misconception or not. To neglect learner's learning misconception will lead to obviously reducing learning performance, thus generating learning difficult. In order to discover common learning misconceptions of learners, this study employs the association rule to mine the learner profile for diagnosing learners' common learning misconceptions during learning processes. In this paper, the association rules that occurring misconception A implies occurring misconception B can be discovered utilizing the proposed association rule learning diagnosis approach. Meanwhile, this study applies the discovered association rules of the common learning misconceptions to tune courseware structure through modifying the difficulty parameters of courseware in the courseware database so that learning pathway is appropriately tuned. Besides, this paper also presents a remedy learning approach based on the discovered common learning misconceptions to promote learning performance. Experiment results indicate that applying the proposed learning diagnosis approach can correctly discover learners' common learning misconceptions according to learner profile and help learners to learn more effectively.

Mining learner profile utilizing association rule for web-based learning diagnosis

With the rapid growth of wireless techniques, the mobile learning by the handheld devices has been gradually considered as a novel learning form. Developing context-aware ubiquitous learning environment can bring the most desired learning services for learners. Accordingly, this study proposed a personalized context-aware ubiquitous english vocabulary learning system which can exploit appropriate context based on learner's location, leisure learning time, and individual abilities to adapt learning contents toward learners for promoting the learning interests and performance.

Personalized Context-Aware Ubiquitous Learning System for Supporting Effectively English Vocabulary Learning

Multi-objective optimization is an essential and challenging topic in the domains of engineering and computation because real-world problems usually include several conflicting objectives. Current trends in the research of solving multi-objective problems (MOPs) require that the adopted optimization method provides an approximation of the Pareto set such that the user can understand the tradeoff between objectives and therefore make the final decision. Recently, an efficient framework, called MOEA/D, combining decomposition techniques in mathematics and optimization methods in evolutionary computation was proposed. MOEA/D decomposes a MOP to a set of single-objective problems (SOPs) with neighborhood relationship and approximates the Pareto set by solving these SOPs. In this paper, we attempt to enhance MOEA/D by proposing two mechanisms. To fully employ the information obtained from neighbors, we introduce a guided mutation operator to replace the differential evolution operator. Moreover, a update mechanism utilizing a priority queue is proposed for performance improvement when the SOPs obtained by decomposition are not uniformly distributed on the Pareto font. Different combinations of these approaches are compared based on the test problem instances proposed for the CEC 2009 competition. The set of problem instances include unconstrained and constrained MOPs with variable linkages. Experimental results are presented in the paper, and observations and discussion are also provided.

Chih-Ming Chen

Papers

Electromigration effect upon the Sn–0.7 wt% Cu/Ni and Sn–3.5 wt% Ag/Ni interfacial reactions

Chemical deposition of platinum on metallic sheets as counterelectrodes for dye-sensitized solar cells

Mining learner profile utilizing association rule for web-based learning diagnosis

Personalized Context-Aware Ubiquitous Learning System for Supporting Effectively English Vocabulary Learning

Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization