Search Results (4)

Epoxy resin was conjugated with halloysite nanotubes (HNT) and different types of ZnO nanoparticles (commercial ZnO and modified ZnO-ODTES) to obtain HNT-ZnO/epoxy resin composites. These ZnO nanoparticles (ZnO NPs) were utilized with the intention to enhance the interfacial bonding between the epoxy resin and the reinforcing agent (HNT). The properties of resulted epoxy resin composites were characterized by various methods such as FTIR-ATR, TGA, DSC, TEM-EDX, and Nanoindentation analyses. The thermal properties of the epoxy resin composites were enhanced to a greater extent by the addition of HNT-ZnO nanofillers. DSC testing proved that the modification in the glass transition temperature can be due to the physical bonding between the epoxy resin and filler (HNT and/or ZnO). It was seen that the epoxy resin modified with HNT and ZnO-ODTES has the highest resistance to scratching by having a good elastic recovery as well as high values for surface hardness (~187.6 MPa) and reduced modulus (2980 MPa). These findings can pave the way for the developing of ZnO-based marine coatings with improved properties. Full article

This study aims to observe which thumb, the left or right, is used for keystrokes and examine the patterns during two-thumb key entry on a smartphone QWERTY soft keyboard. A total of 36 college students, including 18 left-handed and 18 right-handed, were recruited for testing, and they had 9.7 years of smartphone use experience on average. A smartphone application was implemented, and whether the left or right thumb was used for touch interactions was recorded for each of the 26 letter keys. As a result, it was found that there were slightly more letter keys that were statistically more often tapped by the left thumb during the two-thumb key entry on the QWERTY soft keyboard, regardless of the participant’s handedness. In addition, all the letter keys were touched statistically more often with the relatively closer one of both thumbs, except for the letter keys G and V in the center. It seemed that the distance between keys and thumbs was regarded as the most important factor influencing the thumb choice for keystrokes, followed by the habituated experience of using physical QWERTY keyboards. Full article

This research aims to examine touch performance and user-satisfaction depending on key location in a QWERTY soft keyboard during two-thumb key entry on a smartphone. Thirty-three college students who were smartphone users were recruited, and an experimental program was implemented to measure their task completion time, the number of touch errors, and user-satisfaction during key entry. The QWERTY layout was split into 15 zones to assign absolute positions for reliable statistical analysis. The results showed that the zones with significantly longer task completion times were observed more prevalently in the zones in the periphery (p < 0.0001). In addition, relatively higher subjective satisfaction ratings were found in the zones in the center area of the QWERTY layout (p < 0.0001). It seemed that both of the results were improved in the zones that participants could immediately see without moving the thumbs, before touch interaction. Meanwhile, touch error frequencies failed to show statistical significance among the zones (p = 0.3195). Full article

Due to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately: the state-of-the-art diacritics restoration methods do not tolerate other typos, but classical spellcheckers also cannot deal adequately with all the diacritics missing.In this work, we tackle both problems at once by employing the newly-developed universal ByT5 byte-level seq2seq transformer model that requires no language-specific model structures. For a comparison, we perform diacritics restoration on benchmark datasets of 12 languages, with the addition of Lithuanian. The experimental investigation proves that our approach is able to achieve results (>98%) comparable to the previous state-of-the-art, despite being trained less and on fewer data. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. Our simultaneous diacritics restoration and typos correction approach reaches >94% alpha-word accuracy on the 13 languages. It has no direct competitors and strongly outperforms classical spell-checking or dictionary-based approaches. We also demonstrate all the accuracies to further improve with more training. Taken together, this shows the great real-world application potential of our suggested methods to more data, languages, and error classes. Full article