The approach presented is promising as a simple and efficient method for first-tier, marker-assisted screening of environment-specific B.t. germplasm effective in controlling a single target pest.
Usability is one of the most relevant quality aspects in Web applications. A Web application is usable if it provides a friendly, direct and easy to understand interface. Many Usability Inspection Methods (UIMs) have been proposed as a cost effective way to enhance usability. However, many companies are not aware of these UIMs and consequently, are not using them. A secondary study can identify, evaluate and interpret all data that is relevant to the current knowledge available regarding UIMs that have been used to evaluate Web applications in the past few decades. Therefore, we have extended a systematic mapping study about Usability Evaluation Methods by analyzing 26 of its research papers from which we extracted and categorized UIMs. We provide practitioners and researches with the rationale to understand both the strengths and weaknesses of the emerging UIMs for the Web. Furthermore, we have summarized the relevant information of the UIMs, which suggested new ideas or theoretical basis regarding usability inspection in the Web domain. In addition, we present a new UIM and a tool for Web usability inspection starting from the results shown in this paper.
Abstract. This article presents the results of a Systematic Review of Literature on Recommendation of Learning Objects based on Learning Styles. We selected 49 publications for information extraction. The results show that the most widely used learning style model is Felder and Silverman, highlighting the use of Learning Object Metadata (LOM) as the metadata standard and the specification and management of metadata used as a support mechanism for the recommendation. In the virtual environment aspect, the study presented a plurality of environments, evidencing a discreet application of Moodle. As well as enabling discussions that may guide new research.Resumo. Este artigo apresenta os resultados de uma Revisão Sistemática da Literatura sobre Recomendação de Objetos de Aprendizagem baseados em Modelos de Estilos de Aprendizagem. Foram selecionadas 49 publicações para extração das informações. Os resultados mostraram que o modelo de estilo de aprendizagem mais utilizado é de Felder e Silverman, destacando o emprego do Learning Object Metadata (LOM) como padrão de metadados e a especificação e o gerenciamento de metadados empregados como mecanismo de apoio para recomendação. No aspecto de ambiente virtual, o estudo apresentou uma pluralidade de ambientes, evidenciando uma discreta aplicação do Moodle. Além de possibilitar discussões que podem nortear novas pesquisas.
IntroduçãoCom o avanço das tecnologias da informação e comunicação, estamos vivenciando uma sociedade cada vez mais conectada, viabilizando o acesso às informações, a interação social e a educação a distância, entre outros. Esse impacto tecnológico pode ser visto na mudança de comportamento, no consumo de informações e também no contexto educacional. Os estudantes atuais estão cada vez mais familiarizados com as tecnologias digitais, configurando-se como uma geração que estabelece novas relações com o conhecimento [Bacich et al. 2015]. Com isso, surge a motivação para o emprego da
Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using fixed model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this paper, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the log-distance model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analysis executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% better than a fixed-parameters model from the literature.
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