Matching images and sentences demands a fine understanding of both modalities. In this article, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image/text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss on heterogeneous features (i.e., text and image features) is less effective, because it is hard to find appropriate triplets at the beginning. So the naive way of using the ranking loss may compromise the network from learning inter-modal relationship. To address this problem, we propose the instance loss, which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image/text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this article constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language-based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available.

Direct flame fuel cells (DFFCs) have been investigated as an alternative means of combustion based power generation devices, but current challenges for this technology have included low fuel utilization and efficiency. In order to overcome these obstacles a new micro-tubular flame-assisted fuel cell (mT-FFC) concept is developed in this work and its performance is assessed at different equivalence ratios and temperatures. The concept is based on fuel-rich combustion exhaust, with the combustion equivalence ratio controlled and the exhaust flowing through the fuel cell for complete electrochemical energy conversion. The results were compared to a hydrogen baseline with the same electron potential as the fuel-rich exhaust. The mT-FFC concept offers significant advantages including high fuel utilization and greater performance stability compared to DFFCs.

The performance of yttria-stabilized zirconia (YSZ)–samaria-doped ceria (SDC) dual layer electrolyte anode-supported solid oxide fuel cell (AS-SOFC) was investigated. Tape-casting, lamination, and co-sintering of the NiO–YSZ anode followed by wet powder spraying of the SDC buffer layer and BSCF cathode was proposed for fabrication of these cells as an effective means of reducing the number of sintering stages required. The AS-SOFC showed a significant fuel cell performance of ∼1.9 W cm−2 at 800 °C. The fuel cell performance varies significantly with the sintering temperature of the SDC buffer layer. An optimal buffer layer sintering temperature of 1350 °C occurs due to a balance between the YSZ–SDC contact and densification at low sintering temperature and reactions between YSZ and SDC at high sintering temperatures. At high sintering temperatures, the reactions between YSZ and SDC have a detrimental effect on the fuel cell performance resulting in no power at a sintering temperature of 1500 °C.

Interest in measurement of children's executive functions has shown a major increase over the past two decades. The present study evaluates two new apps (EYT and eFun) for measuring executive functions in children. The results of this study show that children (aged 5-8) enjoy executive function assessment in the form of games on an iPad. However, only one executive function, EYT working memory, showed significant positive correlations with several types of grades (e.g., English and maths) in primary school students. New, self-assessed, child-friendly executive function measurement tools have the potential to provide future possibilities for teachers to integrate information on cognitive ability into student learning plans.

No abstract

Combustion based power generation has been accomplished for many years through a number of heat engine systems. Recently, a move towards small scale power generation and micro combustion as well as development in fuel cell research has created new means of power generation that combine solid oxide fuel cells with open flames and combustion exhaust. Instead of relying upon the heat of combustion, these solid oxide fuel cell systems rely on reforming of the fuel via combustion to generate syngas for electrochemical power generation. Procedures were developed to assess the combustion by-products under a wide range of conditions. While theoretical and computational procedures have been developed for assessing fuel-rich combustion exhaust in these applications, experimental techniques have also emerged. The experimental procedures often rely upon a gas chromatograph or mass spectrometer analysis of the flame and exhaust to assess the combustion process as a fuel reformer and means of heat generation. The experimental techniques developed in these areas have been applied anew for the development of the micro-tubular flame-assisted fuel cell. The protocol discussed in this work builds on past techniques to specify a procedure for characterizing fuel-rich combustion exhaust and developing a model fuel-rich combustion exhaust for use in flame-assisted fuel cell testing. The development of the procedure and its applications and limitations are discussed.

Advances in Information Technology (IT) and computer science have without a doubt had a significant impact on our daily lives. The past few decades have witnessed the advancement of IT enabled processes in generating actionable insights in various fields, encouraging research based applications of modern Data Science methods. Among many other fields, education research has also been adopting different analytical approaches to advance the state of education systems. Moreover, developments in software engineering and web-based applications have made collection of education data possible at large scales. This systematic review aims to explore the 21st century’s state of the art applications of text mining methods used in the field of education. We analyse the metadata of all publications that use text mining or natural language processing in educational settings to report on the key themes of application of text mining methods in educational studies providing an overview of the current state of the art and the future directions for research and applications.

Similar to the original direct flame fuel cell the flame-assisted fuel cell, which has a solid oxide fuel cell (SOFC) operating in combustion exhaust, can potentially simplify the fuel cell system and has applications in micro-Combined Heat and Power. Development and testing of a 9 microtubular flame-assisted fuel cell stack is demonstrated in this work. Two different systems are investigated having 1) fixed fuel flow rate and varying air flow rate and 2) fixed total flow rate of air and fuel for the micro-Combined Heat and Power burners. The micro-tubular flame-assisted fuel cell stack achieves a significant performance of 237 mW cm -2 in model methane combustion exhaust at 0.5 V and 790⁰C with a lanthanum strontium manganite based cathode. Electrochemical impedance spectroscopy reveals that the fuel cell ohmic losses are unaltered by variations in the exhaust species concentrations while the polarization losses increase with decreasing first-stage combustion fuel-air equivalence ratio. Variations in the combustion exhaust temperature effects both the ohmic and polarization losses.